{ "nodes": [ { "id": "b-acoustic-metamaterials-phononic-band-gaps", "type": "bridge", "title": "Phononic crystals exhibit acoustic band gaps analogous to electronic band gaps in semiconductors, enabling acoustic metamaterials that control sound propagation through the same mathematical framework as photonic crystals and electronic band theory.\n", "status": "established", "fields": [ "acoustics", "condensed-matter-physics", "materials-science" ], "color": "blue" }, { "id": "b-phononic-crystals-acoustic-band-gap-bragg", "type": "bridge", "title": "Phononic crystals - periodic elastic composites - open complete acoustic band gaps through Bragg scattering (wavelength ~ period) and local resonance mechanisms, making solid-state photonic crystal theory directly transferable to acoustic wave control and enabling acoustic metamaterials that break the mass-density law.\n", "status": "established", "fields": [ "acoustics", "materials-science" ], "color": "blue" }, { "id": "b-grokking-criticality", "type": "bridge", "title": "The \"grokking\" generalisation transition in deep learning is a second-order phase transition governed by the same universality classes that describe magnetisation, percolation, and neural avalanches in physical systems.\n", "status": "proposed", "fields": [ "machine-learning", "statistical-physics", "information-theory", "neuroscience" ], "color": "blue" }, { "id": "b-openalex-renormalization-group-deep-learning", "type": "bridge", "title": "Deep residual networks implement a discrete renormalization group flow, where each residual block performs a coarse-graining step that preserves the relevant features while discarding irrelevant fine-grained details — the same operation that defines a renormalization group transformation in statistical physics.\n", "status": "proposed", "fields": [ "machine-learning", "statistical-physics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-complex-systems-emergence", "type": "bridge", "title": "Emergence — the appearance of macro-level properties not predictable from micro-level rules without full simulation — is the unifying concept across all scientific domains: consciousness from neurons, wetness from H₂O, markets from trades, and ant colonies from individual ant behaviour, formalised by renormalization group theory (why coarse-graining yields qualitatively new laws) and Tononi's Integrated Information Theory (Φ as a quantitative measure).\n", "status": "established", "fields": [ "physics", "biology", "neuroscience", "computer-science", "social-science", "philosophy-of-science", "complex-systems", "mathematics" ], "color": "blue" }, { "id": "b-scientific-method-epistemological-foundations", "type": "bridge", "title": "The scientific method is a cross-domain bridge in itself: Popper's falsificationism, Kuhn's paradigm shifts, Lakatos's research programmes, and Bayesian confirmation theory are competing but complementary formalisms that all fields use to distinguish knowledge from belief — and USDR bridges are explicit falsifiable predictions about structural analogies between disciplines.\n", "status": "established", "fields": [ "philosophy-of-science", "mathematics", "physics", "biology", "social-science", "all-domains" ], "color": "blue" }, { "id": "b-standard-model-unity-physics", "type": "bridge", "title": "The Standard Model SU(3)×SU(2)×U(1) is the most precisely tested scientific theory — its gauge symmetry framework unifies three fundamental forces while explicitly marking what it excludes as the frontier of all physics", "status": "established", "fields": [ "physics", "chemistry", "mathematics", "biology", "cosmology" ], "color": "blue" }, { "id": "b-cultural-group-selection-multilevel-theory", "type": "bridge", "title": "Cultural evolution drives human ultrasociality through group-level selection acting on culturally transmitted norms and institutions: multilevel selection theory (MLS) formalises this as Price equation decomposition into within-group and between-group fitness components, making evolutionary biology the quantitative framework for cultural anthropology of cooperation.\n", "status": "contested", "fields": [ "anthropology", "evolutionary-biology" ], "color": "blue" }, { "id": "b-aesthetic-complexity-information", "type": "bridge", "title": "Aesthetic preference correlates with intermediate algorithmic complexity: Birkhoff's measure M = O/C, Kolmogorov complexity, and fractal dimension operationalise the information-theoretic \"sweet spot\" between randomness and repetition, unifying aesthetics with mathematics and cognitive science.\n", "status": "proposed", "fields": [ "aesthetics", "cognitive-science", "information-theory", "mathematics", "music-cognition", "visual-neuroscience" ], "color": "blue" }, { "id": "b-mirror-neurons-aesthetic-empathy", "type": "bridge", "title": "Mirror neurons fire both when executing an action and when observing another perform it — providing the neural substrate for motor empathy, aesthetic experience, and imitation learning, with direct implications for understanding the uncanny valley, embodied simulation in art viewing, and the neural basis of social cognition.\n", "status": "proposed", "fields": [ "art-and-cognition", "neuroscience", "cognitive-science", "social-neuroscience", "aesthetics" ], "color": "blue" }, { "id": "b-cosmic-rays-mutagenesis", "type": "bridge", "title": "Galactic cosmic ray flux and gamma-ray burst irradiation of Earth's biosphere have varied systematically with the solar system's galactic position, correlating with mass extinction timing and potentially modulating the long-term pace of biological evolution through elevated mutagenesis and DNA double-strand break rates.\n", "status": "proposed", "fields": [ "astronomy", "astrobiology", "evolutionary-biology", "geophysics", "radiation-biology" ], "color": "blue" }, { "id": "b-stellar-forcing-paleoclimate", "type": "bridge", "title": "Solar variability (Milankovitch orbital cycles, total solar irradiance variations, cosmic ray flux modulation) governs Earth's climate history — the same celestial mechanics and stellar physics that determines exoplanet habitability zones controls Dansgaard-Oeschger events, glacial terminations, and the faint young Sun paradox.\n", "status": "proposed", "fields": [ "astronomy", "stellar-physics", "paleoclimatology", "orbital-mechanics", "climate-science" ], "color": "blue" }, { "id": "b-dark-matter-substructure-x-halo-merger-tree-algorithms", "type": "bridge", "title": "Cold dark matter predicts hierarchical assembly: small halos form early and later merge into larger hosts — a process represented computationally by halo merger trees built from N-body simulations using recursive linking algorithms (friends-of-friends, SUBFIND-like catalogs, merger-tree builders), drawing qualitative analogies to tree data structures in algorithms despite radically different physics and noise models.\n", "status": "established", "fields": [ "cosmology", "computational-astrophysics", "computer-science", "algorithms" ], "color": "blue" }, { "id": "b-doppler-redshift-x-option-adjusted-carry", "type": "bridge", "title": "Cosmological redshift and line-of-sight Doppler shifts ↔ option-adjusted carry and curve positioning in fixed-income markets (astronomy ↔ finance; speculative analogy)\n", "status": "proposed", "fields": [ "astronomy", "cosmology", "finance", "fixed-income" ], "color": "blue" }, { "id": "b-planetary-rings-viscous-accretion-disk", "type": "bridge", "title": "Saturn's rings and protoplanetary accretion disks obey the same viscous spreading equation: both are Keplerian disk systems where angular-momentum transport by viscosity (collisional in rings, turbulent in disks) determines radial evolution, making ring dynamics a laboratory-scale test-bed for protoplanetary disk physics.\n", "status": "established", "fields": [ "astronomy", "fluid-mechanics" ], "color": "blue" }, { "id": "b-blackhole-information-paradox", "type": "bridge", "title": "The black hole information paradox is an information-theoretic crisis: whether quantum gravity destroys von Neumann entropy is equivalent to whether the black hole acts as a quantum channel with zero capacity, and the holographic principle (AdS/CFT) resolves this by identifying bulk gravity with a boundary quantum error-correcting code.\n", "status": "proposed", "fields": [ "astronomy", "quantum-gravity", "information-theory", "quantum-error-correction" ], "color": "blue" }, { "id": "b-neural-operator-x-space-weather-data-assimilation", "type": "bridge", "title": "Neural operators for plasma dynamics bridge operator learning and space-weather data assimilation workflows.", "status": "proposed", "fields": [ "astronomy", "machine-learning", "space-physics" ], "color": "blue" }, { "id": "b-celestial-mechanics-kam-chaos", "type": "bridge", "title": "The long-term stability of planetary orbits is determined by the Kolmogorov-Arnold-Moser (KAM) theorem: quasi-periodic orbits persist on invariant tori in phase space provided the perturbation is small and the frequency ratio is sufficiently irrational (Diophantine condition), while resonant orbits are destroyed, leading to the chaotic diffusion observed in the asteroid belt and in Laskar's numerical simulations of the inner solar system.\n", "status": "established", "fields": [ "celestial-mechanics", "chaos-theory", "mathematics", "astronomy" ], "color": "blue" }, { "id": "b-exoplanet-spectral-retrieval-bayesian", "type": "bridge", "title": "Exoplanet atmospheric composition is inferred by Bayesian spectral retrieval: the posterior P(θ|d) over temperature-pressure profile and molecular abundances is sampled via nested sampling or MCMC", "status": "established", "fields": [ "astronomy", "statistics", "atmospheric-science" ], "color": "blue" }, { "id": "b-frb-random-matrix", "type": "bridge", "title": "The non-Poissonian, power-law waiting-time statistics of repeating fast radio burst sources share the eigenvalue repulsion and universality-class signatures of random matrix theory (GUE/GOE), suggesting that FRB emission physics is governed by quantum-chaotic dynamics analogous to those seen in nuclear resonances, quantum dots, and classically chaotic billiards.\n", "status": "proposed", "fields": [ "astronomy", "mathematics", "statistical-physics", "quantum-chaos" ], "color": "blue" }, { "id": "b-helioseismology-x-inverse-eigenvalue-problems", "type": "bridge", "title": "Global helioseismology infers solar interior structure by matching observed eigenfrequencies ω_nl of acoustic modes to stellar oscillation equations — structurally parallel to classical inverse Sturm–Liouville / vibrating-string eigenvalue problems asking which potentials reproduce measured spectra — placing asteroseismology inside inverse spectral geometry narratives taught in applied mathematics departments.\n", "status": "established", "fields": [ "astrophysics", "applied-mathematics" ], "color": "blue" }, { "id": "b-baryon-asymmetry-cp-violation", "type": "bridge", "title": "The cosmological matter-antimatter asymmetry (baryon-to-photon ratio eta ~ 6e-10) demands CP-violating physics beyond the Standard Model: the observed CKM CP violation is ten orders of magnitude too small, linking baryogenesis directly to the open problem of CP violation in leptonic and hadronic sectors.\n", "status": "proposed", "fields": [ "astronomy", "cosmology", "particle-physics", "nuclear-physics" ], "color": "blue" }, { "id": "b-dark-energy-vacuum-cosmological-constant", "type": "bridge", "title": "The observed cosmological constant Λ ≈ 1.11 × 10⁻⁵² m⁻² driving accelerated cosmic expansion corresponds to a vacuum energy density ρ_Λ = Λc²/(8πG) ≈ 5.4 × 10⁻¹⁰ J/m³, which is ~120 orders of magnitude smaller than the naive quantum-field-theory estimate of zero-point energies — the cosmological constant problem is the largest numerical discrepancy in physics.\n", "status": "contested", "fields": [ "cosmology", "quantum-field-theory", "particle-physics", "astronomy" ], "color": "blue" }, { "id": "b-dark-matter-phase-transition-relics", "type": "bridge", "title": "Cosmological dark matter candidates are thermal or non-thermal relics of specific early-universe phase transitions — WIMPs from electroweak freeze-out, axions from the QCD phase transition at 150 MeV, and primordial black holes from density fluctuations — connecting galactic-scale astrophysical observations to statistical mechanics of symmetry breaking in the early universe.\n", "status": "proposed", "fields": [ "astronomy", "cosmology", "particle-physics", "statistical-physics", "nuclear-physics" ], "color": "blue" }, { "id": "b-gamma-ray-burst-jets-x-relativistic-hydrodynamics", "type": "bridge", "title": "Gamma-ray burst jets are relativistic outflows whose shocks, deceleration, and afterglow breaks are modeled with relativistic hydrodynamics and blast-wave theory bridging astronomy and plasma physics.", "status": "established", "fields": [ "astrophysics", "high-energy-astrophysics", "fluid-dynamics", "relativity" ], "color": "blue" }, { "id": "b-neutron-star-matter-x-qcd-phases", "type": "bridge", "title": "Neutron star interiors probe cold ultra-dense matter whose equation of state ties nuclear theory and QCD-informed models to observable masses, radii, and tidal deformabilities.", "status": "established", "fields": [ "nuclear-physics", "astrophysics", "dense-matter", "QCD" ], "color": "blue" }, { "id": "b-red-sequence-x-galaxy-evolution", "type": "bridge", "title": "The galaxy red sequence — a tight correlation between color and luminosity for passive galaxies — encodes a long-timescale link between star-formation quenching, stellar population aging, and small scatter that bridges observational astronomy and stellar evolution physics.", "status": "established", "fields": [ "astronomy", "astrophysics", "galaxy-formation", "stellar-populations" ], "color": "blue" }, { "id": "b-stellar-structure-thermodynamics", "type": "bridge", "title": "Stars are self-gravitating thermodynamic systems with negative heat capacity — a feature unique to long-range gravitational interactions (Lynden-Bell & Wood 1968) — causing them to heat up when they lose energy, and the Lane-Emden polytrope equations describe hydrostatic equilibrium as a competition between gravitational potential and thermal pressure whose stability is governed by the virial theorem.\n", "status": "established", "fields": [ "astronomy", "statistical-physics", "thermodynamics", "astrophysics" ], "color": "blue" }, { "id": "b-tidal-locking-spin-orbit-resonance", "type": "bridge", "title": "Tidal locking is a dissipative dynamical systems problem where tidal torques drive a satellite toward spin-orbit resonance attractors, with the 1:1 resonance (synchronous rotation) being the stable fixed point for low eccentricity orbits — explained by the same dissipative mechanics that governs coupled oscillator synchronization in physics.\n", "status": "proposed", "fields": [ "astronomy", "physics", "dynamical-systems" ], "color": "blue" }, { "id": "b-virial-theorem-x-molecular-cloud-cluster-equilibrium", "type": "bridge", "title": "The virial theorem balances kinetic and gravitational potential energy in self-gravitating systems — central to molecular-cloud mass estimates from line widths and to galaxy-cluster masses inferred from galaxy velocities and X-ray gas pressure — with explicit caveats when turbulence, magnetic fields, or departures from spherical equilibrium break simple 2K+V≈0 scaling assumptions.\n", "status": "established", "fields": [ "astrophysics", "star-formation", "cosmology" ], "color": "blue" }, { "id": "b-stellar-nucleosynthesis-periodic-table", "type": "bridge", "title": "All chemical elements heavier than hydrogen and helium were forged in stars — the periodic table is a record of stellar evolution history, quantitatively explained by nuclear physics reactions in successive stellar environments.\n", "status": "established", "fields": [ "astrophysics", "chemistry", "nuclear-physics" ], "color": "blue" }, { "id": "b-accretion-disk-mhd-turbulence", "type": "bridge", "title": "Accretion disk angular momentum transport is governed by the magnetorotational instability (MRI) — a linear MHD instability in differentially rotating magnetized plasmas that drives turbulence and mediates the anomalous viscosity α required to explain observed accretion rates.\n", "status": "established", "fields": [ "astrophysics", "fluid-dynamics", "magnetohydrodynamics", "plasma-physics" ], "color": "blue" }, { "id": "b-solar-wind-alfven-wave-turbulence", "type": "bridge", "title": "The solar wind is a magnetohydrodynamic turbulent medium dominated by Alfvén wave fluctuations propagating outward from the corona, whose spectral cascade from large injection scales to dissipation at ion inertial lengths follows Kolmogorov-like scaling modified by anisotropy and Alfvénic imbalance\n", "status": "established", "fields": [ "astrophysics", "plasma-physics", "fluid-mechanics" ], "color": "blue" }, { "id": "b-black-hole-entropy-holographic", "type": "bridge", "title": "The Bekenstein-Hawking entropy S = A/4 (area, not volume) of a black hole implies the holographic principle — that the maximum information content of any 3D region is bounded by its 2D boundary area, making information theory and spacetime geometry equivalent at the Planck scale.\n", "status": "established", "fields": [ "astrophysics", "information-theory", "quantum-gravity", "theoretical-physics" ], "color": "blue" }, { "id": "b-general-relativity-differential-geometry", "type": "bridge", "title": "General relativity is differential geometry applied to physics — spacetime curvature is the Riemann curvature tensor and gravity emerges from geodesic deviation", "status": "established", "fields": [ "astrophysics", "mathematics" ], "color": "blue" }, { "id": "b-gravitational-lensing-optical-caustics", "type": "bridge", "title": "Gravitational lensing by galaxy clusters and individual galaxies produces arc patterns and caustic surfaces that are mathematically identical to optical caustics described by catastrophe theory: the Einstein ring, fold, and cusp arcs correspond to the fold, cusp, and swallowtail catastrophes of Thom's classification, unifying astrophysical lensing with the geometric optics of wavefront singularities", "status": "established", "fields": [ "astrophysics", "mathematics", "optics" ], "color": "blue" }, { "id": "b-stellar-nucleosynthesis-network-flow", "type": "bridge", "title": "Stellar nucleosynthesis proceeds through a reaction network of hundreds of isotopes connected by nuclear reactions, and the relative abundances of elements produced can be computed by solving the same maximum-flow and steady-state flux equations used in metabolic network analysis and chemical engineering yield problems", "status": "established", "fields": [ "astrophysics", "nuclear-physics", "network-science" ], "color": "blue" }, { "id": "b-cosmological-inflation-slow-roll-scalar", "type": "bridge", "title": "Cosmological inflation is driven by a slowly rolling scalar field (inflaton) in a de Sitter-like background, generating a nearly scale-invariant power spectrum of primordial density perturbations that directly tests quantum field theory in curved spacetime\n", "status": "established", "fields": [ "cosmology", "quantum-field-theory", "physics" ], "color": "blue" }, { "id": "b-neutron-star-nuclear-eos", "type": "bridge", "title": "Neutron star mass-radius relationships encode the dense matter equation of state, connecting neutron star astrophysics to nuclear symmetry energy and constraining the pressure-density relationship of matter at 2-8 times nuclear saturation density", "status": "established", "fields": [ "astrophysics", "nuclear-physics", "physics" ], "color": "blue" }, { "id": "b-neutron-star-nuclear-matter", "type": "bridge", "title": "Neutron star interiors at 2-8× nuclear saturation density are the densest observable matter in the universe — the equation of state P(ρ) bridges nuclear physics (strong force) to astrophysics (compact object structure) through the Tolman-Oppenheimer-Volkoff equation, constrained by LIGO/Virgo tidal deformability measurements.\n", "status": "established", "fields": [ "astrophysics", "nuclear-physics", "particle-physics", "gravitational-wave-astronomy", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-primordial-nucleosynthesis-reaction-networks", "type": "bridge", "title": "Primordial nucleosynthesis is a nuclear reaction network ODE: Big Bang nucleosynthesis (BBN) computes the abundances of H, D, He-3, He-4, and Li-7 from baryon-to-photon ratio η using the same coupled ODE formalism as stellar nucleosynthesis", "status": "established", "fields": [ "cosmology", "nuclear-physics", "astrophysics" ], "color": "blue" }, { "id": "b-aerosol-nucleation-cloud-formation", "type": "bridge", "title": "Atmospheric aerosol particles act as cloud condensation nuclei (CCN) by reducing the Kelvin-barrier to droplet nucleation, quantified by classical nucleation theory: droplet formation requires supersaturation S > S_crit = exp(4σ*M_w / (ρ_w*R*T*r)) where the critical radius r_crit = 2σ/(ρ_w*R*T*ln(S)) determines which particles activate as cloud droplets", "status": "established", "fields": [ "atmospheric-science", "physics", "chemistry" ], "color": "blue" }, { "id": "b-xenobiotic-metabolism-cyp450", "type": "bridge", "title": "Xenobiotic metabolism by cytochrome P450 enzymes follows Michaelis-Menten saturable kinetics v = V_max*[S]/(K_m + [S]) where each CYP isoform (CYP3A4, CYP2D6, CYP2C9) has distinct substrate specificity encoded in the active site topology, and competitive inhibition between co-administered drugs follows the Dixon equation for competitive inhibition, providing a biochemical kinetics framework for predicting drug-drug interactions", "status": "established", "fields": [ "pharmacology", "biochemistry", "chemistry" ], "color": "blue" }, { "id": "b-enzyme-allostery-monod-wyman-changeux-model", "type": "bridge", "title": "Enzyme allostery — the regulation of enzyme activity by molecules binding at sites remote from the active site — is formalized by the Monod-Wyman-Changeux (MWC) model from biophysics, which treats the enzyme as a two-state thermodynamic system whose T (tense/inactive) ↔ R (relaxed/active) equilibrium is shifted by ligand binding, explaining cooperative kinetics and sigmoidal dose-response curves.\n", "status": "established", "fields": [ "biochemistry", "biophysics", "structural-biology" ], "color": "blue" }, { "id": "b-fermentation-thermodynamic-equilibrium", "type": "bridge", "title": "Microbial fermentation pathway selection is governed by thermodynamic free energy minimisation: the Gibbs free energy change ΔG° of each metabolic reaction determines which pathways are feasible, and cells regulate NAD⁺/NADH ratios to maintain ΔG < 0 across the fermentation network even when ATP yield is suboptimal.\n", "status": "established", "fields": [ "biochemistry", "thermodynamics", "microbiology" ], "color": "blue" }, { "id": "b-hydrothermal-vent-prebiotic-networks", "type": "bridge", "title": "Hydrothermal vent geochemistry provides a natural autocatalytic reaction network with proton gradients, mineral catalysts, and thermodynamic disequilibria that can drive prebiotic chemical evolution — making alkaline vent systems the most plausible abiogenesis laboratory and connecting deep-sea geochemistry to origin of life chemistry.\n", "status": "proposed", "fields": [ "geochemistry", "astrobiology", "chemistry", "biology" ], "color": "blue" }, { "id": "b-lichen-astrobiology", "type": "bridge", "title": "Synthetic lichen-like microbial consortia engineered for biofabrication on Earth are functional analogs of the self-sustaining biosystems required for off-world resource utilisation.\n", "status": "proposed", "fields": [ "synthetic-biology", "astrobiology", "materials-science", "ecology" ], "color": "blue" }, { "id": "b-antibiotic-mechanisms-resistance", "type": "bridge", "title": "Antibiotic mechanisms and resistance bridge biology and chemistry: four mechanistic target classes (cell wall, ribosome, DNA replication, membrane), matched by four resistance mechanisms (enzymatic inactivation, efflux, target modification, bypass), drive the ESKAPE pathogen crisis killing 1.27M/year with 10M projected by 2050.\n", "status": "established", "fields": [ "biology", "chemistry", "microbiology", "biochemistry", "public-health" ], "color": "blue" }, { "id": "b-autophagy-cellular-recycling", "type": "bridge", "title": "Autophagy couples cell biology and chemistry: a double-membrane vesicle (autophagosome) delivers cytoplasmic cargo to the lysosome for enzymatic degradation and molecular recycling — a biological waste management and nutrient recovery system with precise chemical machinery.\n", "status": "established", "fields": [ "biology", "cell-biology", "chemistry", "biochemistry" ], "color": "blue" }, { "id": "b-circadian-clock-molecular-oscillator", "type": "bridge", "title": "The ~24-hour circadian clock in eukaryotes is a biochemical limit-cycle oscillator: the PER/CRY/CLOCK/BMAL1 transcription-translation feedback loop generates self-sustained oscillations described by Goodwin-type nonlinear ODEs, and the clock's period, amplitude, and entrainability are predicted by the Hopf bifurcation structure of the oscillator.\n", "status": "established", "fields": [ "chronobiology", "systems-biology", "chemistry", "nonlinear-dynamics" ], "color": "blue" }, { "id": "b-enzyme-allostery-conformational", "type": "bridge", "title": "Allosteric enzyme regulation follows the Monod-Wyman-Changeux (MWC) model — cooperative T↔R conformational equilibrium governed by the Hill equation — a mathematical framework identical to cooperative binding in hemoglobin, ion channel gating, and gene expression switch behaviour.\n", "status": "established", "fields": [ "biochemistry", "chemistry", "molecular-biology", "biophysics", "pharmacology" ], "color": "blue" }, { "id": "b-glycobiology-cell-recognition", "type": "bridge", "title": "Glycobiology and Cell Recognition — the glycocalyx sugar code, ABO blood groups, selectin-mediated leukocyte rolling, and sialic acid as influenza species barrier", "status": "established", "fields": [ "biochemistry", "cell-biology", "immunology", "virology", "glycosciences" ], "color": "blue" }, { "id": "b-lipid-bilayer-membrane-thermodynamics", "type": "bridge", "title": "Lipid bilayer phase transitions from gel to fluid follow Landau free energy theory F = a(T-T_m)phi^2 + b*phi^4, with the transition temperature T_m tunable by lipid composition and cholesterol; membrane permeability and compressibility diverge near T_m in precise analogy to critical phenomena, connecting thermodynamic phase transition physics to membrane biophysics and the Meyer-Overton anesthetic mechanism.\n", "status": "established", "fields": [ "biology", "chemistry", "biophysics", "thermodynamics", "membrane-biology" ], "color": "blue" }, { "id": "b-lipid-bilayer-membrane-transport", "type": "bridge", "title": "Saffman-Delbrück hydrodynamics and the fluid mosaic model unify soft-matter physics with biological membrane chemistry — lipid raft phase separation and ion transport are the same physics operating at the nanoscale", "status": "established", "fields": [ "biology", "chemistry" ], "color": "blue" }, { "id": "b-protein-folding-energy-landscape", "type": "bridge", "title": "Protein folding is explained by the funnel-shaped energy landscape theory: the native state is a deep, narrow free energy minimum, folding follows a downhill path through G(Q) parameterized by fraction of native contacts Q, and AlphaFold2 implicitly learns this landscape via evolutionary covariance contact predictions with near-experimental accuracy.\n", "status": "established", "fields": [ "biology", "chemistry", "biophysics", "computational-biology", "statistical-mechanics" ], "color": "blue" }, { "id": "b-rna-folding-partition-function", "type": "bridge", "title": "RNA secondary structure prediction is a statistical-mechanics partition function problem: the ensemble of all possible base-pair configurations is weighted by Boltzmann factors exp(−ΔG°/RT), and the minimum free-energy structure, base- pair probabilities, and thermodynamic accessibility are all computed from the McCaskill partition function using dynamic programming.\n", "status": "established", "fields": [ "RNA-biology", "statistical-mechanics", "biophysics", "chemistry" ], "color": "blue" }, { "id": "b-rna-world-origin-of-life", "type": "bridge", "title": "The RNA world hypothesis bridges molecular biology and prebiotic chemistry: RNA molecules can both store genetic information and catalyze chemical reactions (ribozymes), suggesting that RNA preceded both DNA and proteins as the primordial self-replicating molecule at the origin of life.\n", "status": "established", "fields": [ "biology", "molecular-biology", "chemistry", "prebiotic-chemistry", "biochemistry", "origin-of-life" ], "color": "blue" }, { "id": "b-secondary-metabolites-drug-discovery", "type": "bridge", "title": "Biological secondary metabolites — assembled by modular PKS and NRPS molecular assembly lines — account for ~50% of approved drugs; genome mining of silent biosynthetic gene clusters in soil bacteria represents the largest untapped chemical diversity on Earth and the most promising pipeline for new antibiotic classes.\n", "status": "established", "fields": [ "biology", "chemistry", "pharmacology" ], "color": "blue" }, { "id": "b-ant-colony-distributed-computation", "type": "bridge", "title": "Ant colony optimization (ACO) formalizes the pheromone trail mechanism of foraging ants as a distributed probabilistic graph 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"bridge", "title": "Regenerative medicine can harness morphogenetic field theory from developmental biology: the bioelectric and biochemical long-range signalling fields that guide embryonic patterning operate continuously in adult tissues and can be pharmacologically re-activated to instruct stem cells to reconstruct complex anatomical structures, providing a field-theoretic design language for regenerative therapies", "status": "proposed", "fields": [ "medicine", "developmental-biology", "biophysics" ], "color": "blue" }, { "id": "b-gene-networks-waddington-landscape", "type": "bridge", "title": "Developmental gene regulatory networks are dynamical systems whose stable attractors correspond to cell fates, mathematically representing Waddington's epigenetic landscape: each cell type is an attractor of the gene-expression vector field dX/dt = F(X), canalization corresponds to attractor basin depth, and transdifferentiation corresponds to noise-driven transitions between basins", "status": "established", "fields": [ "biology", "dynamical-systems", "developmental-biology" ], "color": "blue" }, { "id": "b-morphogen-gradients-diffusion", "type": "bridge", "title": "Turing's reaction-diffusion mechanism generates biological spatial patterns from two morphogens — an activator (short-range positive feedback) and an inhibitor (long-range negative feedback) — with pattern wavelength λ ∝ √(D/k) predicted exactly from diffusion and kinetic constants.\n", "status": "established", "fields": [ "developmental-biology", "mathematical-biology", "physics", "biophysics" ], "color": "blue" }, { "id": "b-topological-defects-tissue-morphogenesis", "type": "bridge", "title": "Topological defects in active nematic liquid crystals drive cell extrusion and tissue morphogenesis: +1/2 charge defects in cellular monolayers generate extensile flows that accumulate cells and trigger apoptotic extrusion, while -1/2 defects create contractile flows that deplete cells, providing a 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"ecology", "evolutionary-biology", "microbiology", "immunology", "marine-biology" ], "color": "blue" }, { "id": "b-microbiome-ecology-host-health", "type": "bridge", "title": "The human gut microbiome is a complex ecological community of ~10¹³ microorganisms governed by ecological diversity metrics (Shannon entropy, Bray-Curtis dissimilarity) and keystone-species dynamics — and its ecological state directly determines host metabolic, immunological, and neurological health via the gut-brain axis.\n", "status": "established", "fields": [ "ecology", "biology", "microbiology", "medicine", "neuroscience" ], "color": "blue" }, { "id": "b-allelopathy-chemical-ecology", "type": "bridge", "title": "Allelopathy — plant chemical warfare via secondary metabolites — is the ecological instantiation of the same coevolutionary arms race chemistry that drives herbivore detoxification enzyme diversification, and plant VOC emissions create regional aerosol-climate feedbacks connecting chemical ecology to 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"social-psychology", "ecology" ], "color": "blue" }, { "id": "b-political-ecology-environmental-justice", "type": "bridge", "title": "Political ecology links power relations and resource access to quantifiable environmental injustice — PM2.5 exposure 1.54× higher for people of color (Tessum et al. 2021) — bridging social science power analysis with ecology, epidemiology, and environmental policy.\n", "status": "established", "fields": [ "ecology", "social-science", "environmental-science", "political-science", "public-health", "economics" ], "color": "blue" }, { "id": "b-resilience-theory-adaptive-management", "type": "bridge", "title": "Holling's ecological resilience theory (1973) — ecosystems have multiple stable states with resilience = basin of attraction width, not proximity to equilibrium — provides the panarchy framework applicable to social-ecological systems, cities, and institutions, connecting the fold bifurcation mathematics of alternative stable states to social tipping 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"thermodynamics", "microbiology" ], "color": "blue" }, { "id": "b-wetland-carbon-storage-anaerobic-decomposition", "type": "bridge", "title": "Wetlands store disproportionate amounts of carbon because anaerobic conditions thermodynamically inhibit organic matter decomposition: without oxygen as the terminal electron acceptor, microbes must use energetically inferior redox couples, slowing carbon turnover and enabling peat accumulation over millennia.\n", "status": "established", "fields": [ "ecology", "biogeochemistry", "thermodynamics" ], "color": "blue" }, { "id": "b-ellsberg-paradox-ambiguity-aversion", "type": "bridge", "title": "The Ellsberg paradox demonstrates that decision-makers prefer known-probability risks over unknown-probability ambiguity (ambiguity aversion), violating Savage's subjective expected utility axioms and requiring Choquet expected utility or maxmin expected utility theories that assign non-additive capacities to ambiguous events\n", "status": "established", 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kernel, making the arrival of market orders a mutually exciting point process whose branching ratio eta = integral of kernel determines whether liquidity cascades (flash crash) or mean-reverts", "status": "established", "fields": [ "finance", "mathematics", "economics" ], "color": "blue" }, { "id": "b-kelvin-helmholtz-instability-stratified-shear-flow", "type": "bridge", "title": "The Kelvin-Helmholtz instability arises at the interface between stratified fluid layers with velocity shear, governed by the Richardson number criterion, and produces the characteristic billowing vortices seen in clouds, ocean thermocline mixing, and planetary atmospheres.\n", "status": "established", "fields": [ "fluid-mechanics", "geophysics" ], "color": "blue" }, { "id": "b-capillary-length-x-droplet-contact-line-dynamics", "type": "bridge", "title": "Capillary length (sqrt(gamma/(rho g))) as intrinsic wetting scale ↔ contact-line friction, pinning, and droplet morphology on heterogeneous solids (fluid mechanics ↔ materials science)\n", "status": "proposed", "fields": [ "fluid-mechanics", "materials-science", "soft-matter", "surface-science" ], "color": "blue" }, { "id": "b-finite-time-lyapunov-exponents-x-intracardiac-flow-mixing", "type": "bridge", "title": "Finite-time Lyapunov exponents connect Lagrangian coherent-structure analysis to intracardiac flow-mixing risk assessment.", "status": "proposed", "fields": [ "fluid-mechanics", "medicine", "dynamical-systems", "medical-imaging" ], "color": "blue" }, { "id": "b-atmospheric-blocking-rossby-waves", "type": "bridge", "title": "Atmospheric blocking - persistent high-pressure systems that redirect the jet stream for weeks - is a quasi-stationary Rossby wave resonance phenomenon: geophysical fluid mechanics explains blocking onset through wave-mean flow interaction, barotropic instability, and the Charney-DeVore multiple equilibria framework.\n", "status": "established", "fields": [ "meteorology", "fluid-mechanics" ], "color": "blue" }, { "id": "b-isotope-fractionation-kinetic-isotope-effect-transition-state", "type": "bridge", "title": "Isotope fractionation in geochemical systems is governed by the kinetic isotope effect (KIE) from physical chemistry: heavier isotopes have lower zero-point energies relative to the transition state, leading to slower reaction rates and measurable fractionation (δ¹³C, δ¹⁸O, δD) that geochemists use as proxy records of temperature, biological activity, and reaction mechanisms.\n", "status": "established", "fields": [ "geochemistry", "chemistry", "isotope-geology" ], "color": "blue" }, { "id": "b-silicate-weathering-geocarb-carbon-cycle", "type": "bridge", "title": "Silicate weathering is the dominant long-term regulator of atmospheric CO2 over geological time: the GEOCARB model formalizes this as a negative feedback where elevated CO2 warms climate, accelerating chemical weathering of Ca-Mg silicates that consumes CO2 and precipitates carbonate, controlled by reaction kinetics and thermodynamics\n", "status": "established", "fields": [ "geology", "chemistry" ], "color": "blue" }, { "id": "b-mineral-precipitation-ostwald-ripening", "type": "bridge", "title": "Mineral precipitation from supersaturated geological fluids follows Ostwald ripening dynamics — larger crystals grow at the expense of smaller ones via dissolution- reprecipitation — governed by the same Lifshitz-Slyozov-Wagner (LSW) theory used to describe coarsening in materials science, with geochemical precipitation experiments providing the most accessible natural laboratory for crystal coarsening kinetics.\n", "status": "proposed", "fields": [ "geochemistry", "materials-science", "chemistry", "statistical-mechanics" ], "color": "blue" }, { "id": "b-plate-tectonics-mantle-convection", "type": "bridge", "title": "Plate tectonics is driven by mantle convection — thermal convection in the viscous mantle (η ~ 10²¹ Pa·s) governed by the same Navier-Stokes equations as atmospheric and oceanic fluid dynamics, with subduction as a Rayleigh-Taylor instability and ridge spreading as upwelling convection cells.\n", "status": "established", "fields": [ "geology", "geophysics", "fluid-dynamics", "physics", "planetary-science" ], "color": "blue" }, { "id": "b-earthquake-soc", "type": "bridge", "title": "Earthquake magnitude-frequency statistics (Gutenberg-Richter law) and aftershock decay (Omori's law) are signatures of self-organized criticality — the Earth's crust maintains itself at a critical state through slow tectonic loading and rapid stress release.\n", "status": "established", "fields": [ "geology", "seismology", "statistical-physics", "geophysics" ], "color": "blue" }, { "id": "b-adjoint-state-seismic-inversion-x-backprop-gradient-learning", "type": "bridge", "title": "Adjoint-state seismic inversion and neural-network backpropagation share the same reverse-mode gradient calculus.", "status": "proposed", "fields": [ "geophysics", "computer-science", "inverse-problems", "optimization" ], "color": "blue" }, { "id": "b-microseismic-acoustic-emission-fracture", "type": "bridge", "title": "Microseismic monitoring in geophysics and acoustic emission testing in materials science are the same physical phenomenon at different scales: both detect stress-wave radiation from fracture propagation, and the statistical scaling laws (Gutenberg-Richter, power-law amplitude distributions) are identical, enabling cross-scale transfer of fracture mechanics models.\n", "status": "established", "fields": [ "geophysics", "materials-science" ], "color": "blue" }, { "id": "b-geomagnetic-reversal-dynamo", "type": "bridge", "title": "Geomagnetic field reversals are spontaneous symmetry-breaking events in Earth's geodynamo, described by low-dimensional MHD models where reversals correspond to chaotic transitions between two attractors of opposite magnetic polarity", "status": "established", "fields": [ "geophysics", "physics", "mathematics" ], "color": "blue" }, { "id": "b-satellite-geodesy-spherical-harmonics", "type": "bridge", "title": "Satellite geodesy and geoid modeling are applied spherical harmonic analysis on a rotating, oblate body — the same mathematical framework that describes the quantum mechanical hydrogen atom, and the eigenfunctions (spherical harmonics Y_lm) that solve the angular Laplace equation are the fundamental basis for representing any field on a sphere.\n", "status": "proposed", "fields": [ "geophysics", "mathematics", "physics" ], "color": "blue" }, { "id": "b-seismic-tomography-inverse-problems", "type": "bridge", "title": "Seismic tomography infers Earth's 3D velocity structure from P-wave travel times via the same Tikhonov-regularized linear inverse theory used in medical imaging and geophysical prospecting, with adjoint-state methods computing sensitivity kernels efficiently through forward + adjoint wavefield simulations.\n", "status": "established", "fields": [ "geophysics", "mathematics", "seismology", "inverse-problems", "computational-science" ], "color": "blue" }, { "id": "b-tectonic-stress-coulomb-failure", "type": "bridge", "title": "Tectonic stress transfer is quantified by the Coulomb failure function: ΔCFF = Δτ + μ(Δσₙ + ΔP) predicts aftershock locations with ~70% accuracy", "status": "established", "fields": [ "geophysics", "mechanics", "mathematics" ], "color": "blue" }, { "id": "b-kriging-geostatistics", "type": "bridge", "title": "Kriging / geostatistics ↔ Gaussian process regression — optimal spatial interpolation as machine learning", "status": "established", "fields": [ "geophysics", "geostatistics", "statistics", "machine-learning", "spatial-analysis" ], "color": "blue" }, { "id": "b-earthquake-early-warning-x-recursive-bayesian-source-estimation", "type": "bridge", "title": "Earthquake early warning systems fuse sparse P-wave arrivals into evolving magnitude and location estimates before destructive S-waves arrive — the operational backbone is recursive Bayesian / Kalman-style updating of seismic source parameters under latency constraints (seismology ↔ estimation theory).\n", "status": "established", "fields": [ "geophysics", "seismology", "control-engineering", "applied-mathematics" ], "color": "blue" }, { "id": "b-kalman-state-estimation-x-nwp-data-assimilation", "type": "bridge", "title": "Kalman filtering / Kalman–Bucy smoothing ↔ operational data assimilation in numerical weather prediction (estimation theory ↔ geoscience engineering)\n", "status": "established", "fields": [ "control-engineering", "geoscience", "meteorology", "applied-mathematics" ], "color": "blue" }, { "id": "b-plate-boundary-slip-x-fracture-mechanics", "type": "bridge", "title": "Lithospheric plate boundaries concentrate shear and unlock episodic slip — earthquakes — mirroring crack-tip stress intensities and fracture toughness concepts in engineering fracture mechanics where strain energy release rates govern unstable crack growth when loading exceeds critical stress intensity K_IC.\n", "status": "established", "fields": [ "geophysics", "solid-mechanics", "earthquake-engineering" ], "color": "blue" }, { "id": "b-tsunami-shallow-water-x-dispersive-soliton-bore", "type": "bridge", "title": "Long-wavelength tsunami propagation over varying depth is commonly modeled with shallow-water equations whose nonlinear and dispersive corrections predict bore formation, shock-like steepening, and — in idealized integrable limits — solitary-wave solutions resembling solitons, though real ocean tsunamis span rupture complexity, bathymetry focusing, and dissipation beyond textbook KdV universality.\n", "status": "established", "fields": [ "geophysics", "fluid-mechanics", "oceanography" ], "color": "blue" }, { "id": "b-unet-x-satellite-flood-extent-mapping", "type": "bridge", "title": "U-Net segmentation bridges biomedical pixel-wise inference and satellite flood-extent mapping under cloud and sensor noise.", "status": "proposed", "fields": [ "geoscience", "machine-learning", "remote-sensing" ], "color": "blue" }, { "id": "b-biogeochemical-box-models-x-attractor-stability", "type": "bridge", "title": "Coupled ocean–atmosphere–sediment box models of carbon, nitrogen, and phosphorus cycles can exhibit multiple stable steady states (or slow manifolds) when nonlinear uptake kinetics and burial feedbacks combine — mapping onto finite-dimensional dynamical systems attractors and bifurcation diagrams used in mathematical ecology and climate theory despite vastly slower timescales.\n", "status": "proposed", "fields": [ "biogeochemistry", "dynamical-systems" ], "color": "blue" }, { "id": "b-coastal-erosion-x-diffusive-interface", "type": "bridge", "title": "Large-scale coastline shapes and shoreline erosion fronts can be modeled using interface dynamics and diffusive / reaction–diffusion ideas familiar from mathematical physics.", "status": "established", "fields": [ "geoscience", "coastal-geomorphology", "applied-mathematics", "pattern-formation" ], "color": "blue" }, { "id": "b-ice-core-proxy-inverse-methods", "type": "bridge", "title": "Ice core paleoclimatology is an applied inverse problem: chemical and isotopic proxies (delta-18O, dust, CO2, CH4) encode past climate states in a noisy, non-linear forward model, and reconstructing the underlying temperature history requires the same Bayesian inversion, regularisation, and uncertainty quantification methods used in geophysical tomography and medical imaging.\n", "status": "proposed", "fields": [ "climate-science", "statistics", "mathematics", "geoscience" ], "color": "blue" }, { "id": "b-nitrogen-cycle-reservoirs-x-coupled-oscillator-stability", "type": "bridge", "title": "Coupled reservoir models of the global nitrogen cycle link atmosphere, land, and ocean pools through fixation, nitrification/denitrification, and export production — under perturbation their linearized Jacobian spectra resemble stability analysis of coupled damped oscillators, clarifying when anthropogenic forcing yields monotonic relaxation versus transient oscillatory nutrient anomalies.\n", "status": "proposed", "fields": [ "earth-system-science", "mathematics", "biogeochemistry" ], "color": "blue" }, { "id": "b-plate-tectonics-topology", "type": "bridge", "title": "Plate tectonic motion on Earth's surface is an exact realisation of the mathematical theory of rigid motions on a sphere: every plate motion is a rotation in SO(3) about an Euler pole, hotspot tracks are geodesics on the rotation manifold, and triple junction stability obeys the Euler characteristic constraint of the 2-sphere.\n", "status": "established", "fields": [ "geoscience", "geology", "differential-geometry", "topology", "mathematics" ], "color": "blue" }, { "id": "b-soil-aggregate-fractal-pore-stability", "type": "bridge", "title": "Soil aggregate stability and water retention are governed by fractal pore-size distributions: the mass fractal dimension D_f of soil aggregates predicts hydraulic conductivity, air-entry pressure, and resistance to disruption, unifying soil physics and fractal geometry through a single structural parameter measurable by mercury intrusion porosimetry.\n", "status": "established", "fields": [ "geoscience", "mathematics", "soil-science" ], "color": "blue" }, { "id": "b-eikonal-wavefronts-x-cardiac-activation-mapping", "type": "bridge", "title": "Eikonal wavefront equations unify seismic travel-time inversion and cardiac activation-time mapping.", "status": "proposed", "fields": [ "geoscience", "medicine", "mathematics" ], "color": "blue" }, { "id": "b-ensemble-kalman-smoothing-x-icu-latent-state-estimation", "type": "bridge", "title": "Ensemble Kalman smoothing links weather data assimilation and ICU latent-state tracking in physiological digital twins.", "status": "proposed", "fields": [ "geoscience", "medicine", "control-engineering", "bayesian-inference" ], "color": "blue" }, { "id": "b-ensemble-smoother-x-precision-oncology-state-estimation", "type": "bridge", "title": "Ensemble smoothing from geoscience data assimilation transfers to latent-state estimation in precision oncology.", "status": "proposed", "fields": [ "geoscience", "medicine", "statistics" ], "color": "blue" }, { "id": "b-earthquake-self-organized-criticality", "type": "bridge", "title": "The Gutenberg-Richter and Omori laws are empirical signatures of self-organized criticality: fault networks spontaneously evolve to the critical point of the BTW sandpile universality class, unifying earthquake statistics with statistical physics.\n", "status": "established", "fields": [ "geophysics", "seismology", "statistical-physics", "complexity-science" ], "color": "blue" }, { "id": "b-lithospheric-planform-x-rayleigh-benard-wavelength-scaling", "type": "bridge", "title": "Horizontal wavelengths of convection rolls and cellular patterns in Rayleigh-Bénard experiments scale with layer thickness and fluid parameters via Busse–Clever–Kelly stability diagrams — motivating cautious comparison to characteristic lateral scales of plate-boundary networks and mantle flow heterogeneity inferred from seismic tomography, distinct from merely stating “mantle convection exists.”\n", "status": "proposed", "fields": [ "geoscience", "fluid-mechanics", "geophysics" ], "color": "blue" }, { "id": "b-mantle-convection-rayleigh-benard", "type": "bridge", "title": "Mantle convection driving plate tectonics is a high-Rayleigh-number Rayleigh-Bénard convection system with strongly temperature-dependent viscosity: the Rayleigh number Ra ~ 10⁷–10⁸ predicts chaotic, time- dependent flow that produces the observed pattern of plate speeds, trench depths, and heat flow at mid-ocean ridges.\n", "status": "established", "fields": [ "geophysics", "fluid-mechanics", "physics" ], "color": "blue" }, { "id": "b-river-braiding-x-soc-like-morphodynamics", "type": "bridge", "title": "Braided rivers exhibit channel splitting and merging producing avalanche-like bedload fluctuations and broad scaling regimes reminiscent of self-organized criticality phenomenology — yet identifying definitive SOC universality classes for real rivers remains speculative and should be labeled as hypothesis-stage analogy pending rigorous scaling collapses on controlled morphodynamic datasets.\n", "status": "proposed", "fields": [ "geomorphology", "statistical-physics" ], "color": "blue" }, { "id": "b-glacier-dynamics-glens-law", "type": "bridge", "title": "Glacier flow obeys Glen's flow law, a power-law viscosity relation that maps glaciology onto non-Newtonian viscous fluid mechanics, enabling glaciologists to use Stokes flow equations to predict ice sheet dynamics and sea-level contributions.\n", "status": "established", "fields": [ "glaciology", "fluid-mechanics", "geophysics" ], "color": "blue" }, { "id": "b-glacier-isostasy-viscoelastic-rebound", "type": "bridge", "title": "Glacial isostatic adjustment (GIA) connects glaciology and geophysics through viscoelastic rebound: ice sheet loading depresses the Earth's crust elastically and viscously, and postglacial rebound follows viscoelastic relaxation theory with the mantle acting as a Maxwell fluid on timescales of thousands of years.\n", "status": "established", "fields": [ "glaciology", "geophysics", "geodynamics" ], "color": "blue" }, { "id": "b-glacier-calving-fracture-mechanics", "type": "bridge", "title": "Glacier calving — the detachment of icebergs from tidewater glaciers — follows the same fracture mechanics as crack propagation in brittle materials: the calving rate is controlled by a stress intensity factor at the ice-water or ice-air interface that must exceed the mode-I fracture toughness of polycrystalline ice (~0.1 MPa m^0.5)", "status": "established", "fields": [ "glaciology", "materials-science", "geophysics" ], "color": "blue" }, { "id": "b-neural-operator-surrogates-x-groundwater-inverse-modeling", "type": "bridge", "title": "Neural operator surrogates connect operator learning advances to groundwater inverse modeling at basin scale.", "status": "proposed", "fields": [ "hydrology", "computer-science" ], "color": "blue" }, { "id": "b-river-network-hacks-law-fractal", "type": "bridge", "title": "River network geometry obeys Hack's law (L ~ A^{0.6}) and Horton's laws of stream numbers and lengths because river networks are statistically self-similar (fractal) structures grown by optimal channel network (OCN) theory - an energy-minimisation principle that mathematics predicts and hydrology observes across six orders of magnitude in drainage area.\n", "status": "established", "fields": [ "hydrology", "mathematics" ], "color": "blue" }, { "id": "b-immune-regulation-feedback", "type": "bridge", "title": "The immune system is a proportional-integral (PI) feedback controller — T-regulatory cells implement integral negative feedback on effector T-cell responses, maintaining self-tolerance exactly as a PI controller eliminates steady-state error.\n", "status": "proposed", "fields": [ "immunology", "control-theory", "systems-biology", "mathematical-biology" ], "color": "blue" }, { "id": "b-foundation-model-x-tcr-antigen-specificity-transfer", "type": "bridge", "title": "Sequence foundation-model pretraining bridges protein language transfer and T-cell receptor antigen-specificity inference.", "status": "proposed", "fields": [ "immunology", "machine-learning", "bioinformatics" ], "color": "blue" }, { "id": "b-lipid-nanoparticle-mrna-delivery", "type": "bridge", "title": "mRNA therapeutics require lipid nanoparticle delivery vehicles whose self-assembly is governed by hydrophobic balance and ionizable lipid pKa — a materials science problem with immunological constraints.\n", "status": "established", "fields": [ "immunology", "materials-science", "biochemistry", "drug-delivery" ], "color": "blue" }, { "id": "b-borrelia-immune-evasion", "type": "bridge", "title": "Borrelia burgdorferi's VlsE antigenic variation and complement evasion — studied separately in microbiology and immunology — together constitute a unified immune-escape architecture with direct therapeutic implications.\n", "status": "established", "fields": [ "microbiology", "immunology", "structural-biology", "infectious-disease" ], "color": "blue" }, { "id": "b-immune-network-idiotypic", "type": "bridge", "title": "Jerne's immune network theory (1974) — antibodies recognising other antibodies (idiotypes) form a self-regulating scale-free network whose attractor dynamics implement immune memory and self-tolerance — is formally equivalent to a Hopfield associative memory network; immunological disorders correspond to network bifurcations.\n", "status": "proposed", "fields": [ "immunology", "network-science", "computational-biology", "nonlinear-dynamics", "systems-biology" ], "color": "blue" }, { "id": "b-immune-recognition-statistical-pattern-detection", "type": "bridge", "title": "The adaptive immune system solves a high-dimensional pattern detection problem using stochastic V(D)J recombination to generate a diverse receptor repertoire, thymic selection to set affinity thresholds, and clonal expansion as a Bayesian posterior update — mathematically equivalent to a noisy channel decoder for self/non-self discrimination.\n", "status": "established", "fields": [ "immunology", "physics", "information-theory", "statistical-mechanics", "mathematics" ], "color": "blue" }, { "id": "b-masked-autoencoding-x-cryo-em-denoising-priors", "type": "bridge", "title": "Masked autoencoding bridges self-supervised reconstruction and cryo-EM denoising priors for pathogen structural biology.", "status": "proposed", "fields": [ "infectious-disease", "machine-learning", "structural-biology" ], "color": "blue" }, { "id": "b-error-threshold-information", "type": "bridge", "title": "Eigen's quasispecies error threshold in molecular evolution and Shannon's channel capacity theorem in information theory are the same mathematical result — the mutation rate at which genetic information is irreversibly lost is the Shannon capacity of the replication channel.\n", "status": "proposed", "fields": [ "information-theory", "molecular-evolution", "statistical-physics", "virology" ], "color": "blue" }, { "id": "b-knowledge-overload-findability", "type": "bridge", "title": "Scientific knowledge overload is a channel-capacity problem: the rate of cross-domain insight generation is limited not by the volume of published results but by the bandwidth of the translation layer between domain vocabularies — structured cross-domain bridges function as a lossless codec reducing mutual information distance without destroying signal.\n", "status": "proposed", "fields": [ "information-theory", "epistemology", "network-science", "cognitive-science", "library-science", "science-of-science" ], "color": "blue" }, { "id": "b-belief-propagation-x-genotype-phasing-linkage-graphs", "type": "bridge", "title": "Belief propagation on factor graphs bridges probabilistic inference in computer science with haplotype phasing and genotype imputation pipelines in statistical genetics.", "status": "established", "fields": [ "information-theory", "genetics", "computer-science" ], "color": "blue" }, { "id": "b-shannon-entropy-genetic-information", "type": "bridge", "title": "DNA is a digital information storage medium whose structure, redundancy, and mutation dynamics are quantitatively captured by Shannon's information theory — the genetic code is a natural error-correcting code whose properties minimize the cost of single-nucleotide substitutions.\n", "status": "established", "fields": [ "information-theory", "molecular-biology", "genetics", "evolutionary-biology" ], "color": "blue" }, { "id": "b-language-evolution-cultural-selection", "type": "bridge", "title": "Language change obeys evolutionary dynamics — linguistic variants compete under frequency-dependent selection (prestige bias, conformity), the replicator equation governs variant frequencies, and historical linguistics is formally homologous to molecular phylogenetics.\n", "status": "established", "fields": [ "linguistics", "evolutionary-biology", "cultural-evolution", "population-genetics" ], "color": "blue" }, { "id": "b-entropy-rate-x-language-model-perplexity", "type": "bridge", "title": "Stochastic process entropy rate h limits optimal prediction bits per symbol for stationary ergodic sources — connecting to cross-entropy training objectives for language models whose perplexity exp(H) measures geometric mean uncertainty per token under the model distribution versus empirical text statistics.\n", "status": "established", "fields": [ "information-theory", "computational-linguistics", "machine-learning" ], "color": "blue" }, { "id": "b-zipf-optimal-coding", "type": "bridge", "title": "Zipf's law (word frequency proportional to 1/rank) is derivable from the principle of least effort — a communication system minimising joint speaker-listener effort converges on a power-law frequency distribution identical to Shannon's optimal coding theorem applied to natural language.\n", "status": "established", "fields": [ "linguistics", "information-theory", "cognitive-science", "statistical-physics", "complexity-science" ], "color": "blue" }, { "id": "b-formal-grammar-automata-theory", "type": "bridge", "title": "Chomsky's hierarchy of formal grammars (regular, context-free, context-sensitive, recursively enumerable) is isomorphic to a hierarchy of computational automata (finite state machines, pushdown automata, linear-bounded automata, Turing machines), and natural human language sits above context-free in the mildly context-sensitive class.\n", "status": "established", "fields": [ "linguistics", "mathematics", "computer-science", "cognitive-science", "formal-language-theory" ], "color": "blue" }, { "id": "b-greenberg-universals-implicational-hierarchies", "type": "bridge", "title": "Greenberg's linguistic universals — cross-linguistic statistical regularities in word order, morphology, and phonology — are formalized mathematically as implicational hierarchies and lattice structures: if a language has property X it tends to have property Y, forming partial orders whose structure predicts typological distributions and constrains theories of grammar.\n", "status": "established", "fields": [ "linguistics", "mathematics", "cognitive-science" ], "color": "blue" }, { "id": "b-language-contact-x-graph-interpolation", "type": "bridge", "title": "Language contact spreads features across speaker networks and geography, naturally modeled as diffusion, interpolation, and graph dynamics on spatial social graphs.", "status": "established", "fields": [ "linguistics", "dialectology", "graph-theory", "spatial-statistics" ], "color": "blue" }, { "id": "b-language-biomarker-diagnosis", "type": "bridge", "title": "Computational linguistics measures of syntactic complexity, semantic coherence, and speech-rate variability serve as non-invasive biomarkers of neural health — detecting Alzheimer's disease, depression, and psychotic-spectrum formal thought disorder years before clinical presentation.\n", "status": "proposed", "fields": [ "computational-linguistics", "clinical-neurology", "psychiatry", "natural-language-processing", "medicine" ], "color": "blue" }, { "id": "b-birdsong-syntax-formal-language-theory", "type": "bridge", "title": "Birdsong exhibits hierarchical combinatorial syntax that maps onto the Chomsky hierarchy of formal languages: simple species generate finite-state (regular) sequences while complex learners such as Bengalese finches produce context-free dependencies, providing a non-human animal test bed for formal language theory", "status": "established", "fields": [ "ornithology", "linguistics", "cognitive-science" ], "color": "blue" }, { "id": "b-linguistic-relativity-quantum-basis", "type": "bridge", "title": "Linguistic relativity (Sapir-Whorf) and quantum measurement basis choice both reveal how the observer's representational framework determines what aspects of an underdetermined reality become definite.\n", "status": "proposed", "fields": [ "linguistics", "quantum-mechanics", "philosophy-of-mind", "cognitive-science" ], "color": "blue" }, { "id": "b-coral-symbiosis-mutualism-stability-theory", "type": "bridge", "title": "Coral-zooxanthellae symbiosis is a model mutualism whose stability is analyzed using ecological mutualism theory: partner fidelity feedback, sanctions mechanisms, and the optimal foraging trade-off between carbon provision and nitrogen limitation determine when the partnership is evolutionarily stable versus prone to cheating or bleaching.\n", "status": "established", "fields": [ "marine-biology", "ecology", "evolutionary-biology" ], "color": "blue" }, { "id": "b-fish-schooling-collective-motion", "type": "bridge", "title": "Fish schooling and bird flocking are active matter phase transitions — the Vicsek model shows that self-propelled particles aligning with neighbors undergo a continuous order-disorder transition at a critical noise threshold, exhibiting long-range order in 2D forbidden by the Mermin-Wagner theorem for equilibrium systems.\n", "status": "established", "fields": [ "marine-biology", "fluid-dynamics", "statistical-physics", "active-matter-physics", "ethology" ], "color": "blue" }, { "id": "b-antifreeze-proteins-ice-crystal", "type": "bridge", "title": "Antifreeze proteins (AFPs) modify ice crystal habit and inhibit recrystallization by adsorbing to specific ice crystal planes via hydrogen-bond and hydrophobic complementarity, quantified by the Kelvin effect: AFP adsorption on a crystal surface of radius of curvature r raises the local melting point depression ΔT = 2σ*V_m / (ΔH_f * r), creating a thermal hysteresis gap between freezing and melting points", "status": "established", "fields": [ "biophysics", "materials-science", "biochemistry" ], "color": "blue" }, { "id": "b-biomineralization-crystal-growth", "type": "bridge", "title": "Biomineralization (bone, shell, tooth formation) obeys the same nucleation and crystal-growth kinetics as inorganic mineralogy — organisms exploit organic templates (proteins, polysaccharides) to control crystal habit, orientation, and polymorph selection, while Ostwald ripening, spinodal decomposition, and Lifshitz-Slyozov-Wagner kinetics govern both biological and synthetic mineral growth.\n", "status": "established", "fields": [ "materials-science", "structural-biology", "mineralogy", "biochemistry" ], "color": "blue" }, { "id": "b-biomineralization-crystal-nucleation", "type": "bridge", "title": "Organisms direct calcium carbonate, calcium phosphate, and silica crystal nucleation through organic templates and protein matrices that lower the nucleation barrier (ΔG*) — effectively tuning the classical nucleation theory landscape — to produce hierarchically structured biominerals with mechanical properties inaccessible to inorganic synthesis alone.\n", "status": "established", "fields": [ "materials-science", "biomineralization", "biology", "crystal-nucleation-theory", "structural-biology" ], "color": "blue" }, { "id": "b-gecko-adhesion-van-der-waals", "type": "bridge", "title": "Gecko adhesion arises from millions of nanoscale setae generating ~10nN van der Waals (dispersion) forces per spatula, with total adhesion (~20N) modeled by JKR contact mechanics (F = 3πwR/2), producing direction-dependent anisotropic and self-cleaning dry adhesion — connecting condensed matter physics (van der Waals interactions) to materials engineering and bio-inspired synthetic adhesives.\n", "status": "established", "fields": [ "materials-science", "biology", "physics", "nanotechnology", "biophysics" ], "color": "blue" }, { "id": "b-phase-diagrams-alloy-design", "type": "bridge", "title": "Binary and multi-component alloy phase diagrams are thermodynamic predictions of Gibbs free energy minimization — CALPHAD parameterizes G(T,x) from sublattice models, and high-entropy alloys exploit large configurational entropy ΔS_mix = −R Σxᵢ ln(xᵢ) to stabilize single-phase solid solutions.\n", "status": "established", "fields": [ "materials-science", "chemistry", "thermodynamics", "metallurgy", "computational-materials-science" ], "color": "blue" }, { "id": "b-fracture-mechanics-griffith", "type": "bridge", "title": "Griffith's fracture criterion bridges atomic surface energy (materials science) and macroscopic structural failure (engineering) by equating the elastic strain energy release rate to the cost of creating new crack surfaces.\n", "status": "established", "fields": [ "materials-science", "engineering", "physics", "mathematics" ], "color": "blue" }, { "id": "b-active-learning-x-bayesian-optimization-alloy-discovery", "type": "bridge", "title": "Active learning with Bayesian optimization bridges sample-efficient acquisition and experimental alloy discovery loops.", "status": "proposed", "fields": [ "materials-science", "machine-learning", "chemistry" ], "color": "blue" }, { "id": "b-crystallography-group-theory", "type": "bridge", "title": "The 230 space groups classifying all possible crystal symmetries are a complete enumeration of discrete subgroups of the Euclidean group in 3D; quasicrystals (Shechtman 1984) require the mathematics of aperiodic tilings, extending the connection to non-crystallographic point groups.\n", "status": "established", "fields": [ "materials-science", "mathematics", "crystallography", "condensed-matter-physics", "group-theory" ], "color": "blue" }, { "id": "b-piezoelectricity-symmetry-breaking", "type": "bridge", "title": "Piezoelectricity requires broken centrosymmetry: group-theoretic analysis of crystal point groups identifies the 20 of 32 point groups that allow the piezoelectric tensor d_{ijk} to be non-zero", "status": "established", "fields": [ "materials-science", "group-theory", "mathematics", "condensed-matter" ], "color": "blue" }, { "id": "b-preisach-hysteresis-model", "type": "bridge", "title": "The Preisach model represents any rate-independent hysteretic material as a superposition of elementary bistable switches (hysterons), mapping hysteresis loops to a weight distribution rho(alpha,beta) that can be identified from first-order reversal curves", "status": "established", "fields": [ "materials-science", "mathematics" ], "color": "blue" }, { "id": "b-topological-persistence-x-materials-microstructure-failure-forecast", "type": "bridge", "title": "Persistent homology links microstructure topology to early failure forecasting in structural materials.", "status": "proposed", "fields": [ "materials-science", "mathematics" ], "color": "blue" }, { "id": "b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling", "type": "bridge", "title": "Peridynamic nonlocal fracture mechanics offers a direct formalism for bone microdamage accumulation and remodeling triggers.", "status": "proposed", "fields": [ "materials-science", "medicine", "biomechanics" ], "color": "blue" }, { "id": "b-biofilm-mechanics-viscoelastic-polymer", "type": "bridge", "title": "Bacterial biofilms are viscoelastic materials whose mechanical properties — creep compliance, stress relaxation, and frequency-dependent storage and loss moduli — are quantitatively described by the same polymer network models (Kelvin-Voigt, Maxwell, and power-law viscoelasticity) used for synthetic hydrogels and extracellular matrix, with the crosslinked extracellular polymeric substance (EPS) network playing the role of the polymer matrix", "status": "established", "fields": [ "microbiology", "materials-science", "biophysics" ], "color": "blue" }, { "id": "b-classical-nucleation-theory", "type": "bridge", "title": "Classical nucleation theory predicts the rate of crystal formation from supersaturated solutions as J = A * exp(-Delta-G*/kT), where the nucleation barrier Delta-G* = 16*pi*gamma^3 / (3*Delta-g_v^2) balances surface energy against volumetric driving force", "status": "established", "fields": [ "materials-science", "physics" ], "color": "blue" }, { "id": "b-fracture-griffith-statistical", "type": "bridge", "title": "The Griffith fracture criterion (K_I = K_Ic at the crack tip) is the deterministic limit of a statistical-physics crack nucleation problem: the disorder-averaged fracture strength of heterogeneous materials follows a Weibull extreme-value distribution, and the brittle-to-ductile transition maps onto a depinning phase transition in the random-field Ising model universality class.\n", "status": "established", "fields": [ "materials-science", "statistical-physics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-hydrogel-polymer-network-mechanics", "type": "bridge", "title": "Hydrogel mechanical properties are quantitatively predicted by rubber elasticity and Flory-Rehner theory, where the elastic modulus G = n*k*T (n = effective crosslink density) and swelling equilibrium balances elastic energy against polymer-solvent mixing free energy", "status": "established", "fields": [ "materials-science", "polymer-physics", "physics" ], "color": "blue" }, { "id": "b-phonon-boltzmann-thermal-transport", "type": "bridge", "title": "Thermal conductivity of crystalline solids is quantitatively predicted by the phonon Boltzmann transport equation (BTE): κ = (1/3)∫C(ω)v(ω)λ(ω)dω, where acoustic phonons are the heat carriers and three-phonon Umklapp scattering is the primary resistive process, directly connecting lattice dynamics to macroscopic heat flow.\n", "status": "established", "fields": [ "condensed-matter-physics", "materials-science", "thermodynamics" ], "color": "blue" }, { "id": "b-phonons-thermal-conductivity", "type": "bridge", "title": "Phonons and thermal conductivity — quantized lattice vibrations are the primary heat carriers in non-metallic solids and govern thermoelectric efficiency and CPU thermal management", "status": "established", "fields": [ "materials-science", "physics", "condensed-matter", "engineering", "quantum-mechanics" ], "color": "blue" }, { "id": "b-bcs-superconductivity", "type": "bridge", "title": "BCS theory explains conventional superconductivity via phonon-mediated Cooper pairing — but high-Tc cuprates and iron-based superconductors violate BCS assumptions, and the pairing mechanism remains unknown.\n", "status": "established", "fields": [ "condensed-matter-physics", "quantum-mechanics", "materials-science", "solid-state-physics" ], "color": "blue" }, { "id": "b-carbon-nanotube-graphene-band-structure-zone-folding", "type": "bridge", "title": "Carbon nanotube electronic properties — metallic or semiconducting, with chirality- dependent band gaps — are derived from graphene band structure by zone-folding: wrapping the 2-D graphene Brillouin zone onto the 1-D nanotube cylinder.\n", "status": "established", "fields": [ "materials-science", "quantum-physics" ], "color": "blue" }, { "id": "b-josephson-junction-macroscopic-quantum-tunneling", "type": "bridge", "title": "The Josephson junction provides the cleanest experimental demonstration of macroscopic quantum tunneling: the phase difference across the junction is a quantum variable describing a collective degree of freedom of billions of Cooper pairs, and its tunneling through a classical energy barrier directly tests whether quantum mechanics applies to macroscopic objects.\n", "status": "proposed", "fields": [ "condensed-matter-physics", "quantum-physics", "materials-science" ], "color": "blue" }, { "id": "b-magnons-spin-wave-collective-excitations", "type": "bridge", "title": "Magnons (spin waves) are the Goldstone bosons of spontaneously broken spin-rotation symmetry in ferromagnets: their dispersion ω∝k² (ferromagnets) or ω∝k (antiferromagnets) follows from the same quantum field theory as phonons", "status": "established", "fields": [ "condensed-matter", "quantum-mechanics", "quantum-field-theory" ], "color": "blue" }, { "id": "b-quantum-dots-particle-in-a-box", "type": "bridge", "title": "Semiconductor quantum dots are physical realizations of the quantum-mechanical particle-in-a-box: three-dimensional carrier confinement in a nanometer-scale crystal shifts energy levels according to E_n = h^2 n^2 / (8 m* L^2), making emission wavelength continuously tunable by dot size through the same quantum confinement that transforms a bulk semiconductor band gap into discrete atomic-like levels", "status": "established", "fields": [ "materials-science", "quantum-physics", "nanoscience" ], "color": "blue" }, { "id": "b-alloy-strengthening-dislocation-theory", "type": "bridge", "title": "Alloy mechanical strength is governed by dislocation theory: the Taylor relation sigma_y = M*alpha*G*b*sqrt(rho) bridges materials science and solid mechanics by quantifying how dislocation density rho controls yield stress through line tension and Peierls barrier physics.\n", "status": "established", "fields": [ "materials-science", "solid-mechanics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-auxetic-materials-negative-poisson-ratio", "type": "bridge", "title": "Auxetic materials exhibit a negative Poisson's ratio (ν < 0) because their re-entrant or chiral microgeometries cause lateral expansion under axial tension, a counterintuitive behavior predicted by continuum elasticity theory and enabling programmable mechanical metamaterial design\n", "status": "established", "fields": [ "materials-science", "mechanics" ], "color": "blue" }, { "id": "b-dendrite-growth-diffusion-limited-aggregation", "type": "bridge", "title": "Dendritic crystal growth is governed by the same diffusion-limited aggregation mathematics that generates fractal clusters in statistical physics, with the Mullins-Sekerka instability controlling tip-splitting and branch morphology.\n", "status": "established", "fields": [ "materials-science", "statistical-physics" ], "color": "blue" }, { "id": "b-fisher-information-design-x-autonomous-materials-experiments", "type": "bridge", "title": "Fisher-information design connects statistical efficiency bounds to autonomous materials-experiment scheduling.", "status": "proposed", "fields": [ "materials-science", "statistics", "experimental-design", "automation" ], "color": "blue" }, { "id": "b-semiconductor-doping-fermi-level-chemical-potential", "type": "bridge", "title": "Semiconductor doping is a chemical potential engineering problem: the Fermi level is the electrochemical potential of electrons, and donor/acceptor impurities shift it by changing the electron chemical potential exactly as pH is shifted by acid/base additions, unifying solid-state physics, thermodynamics, and electrochemistry through the single concept of electron chemical potential.\n", "status": "established", "fields": [ "materials-science", "thermodynamics" ], "color": "blue" }, { "id": "b-thermoelectric-efficiency-seebeck-onsager", "type": "bridge", "title": "Thermoelectric efficiency is governed by the dimensionless figure of merit zT = S^2 sigma T / kappa, where the Seebeck coefficient S, electrical conductivity sigma, and thermal conductivity kappa are related by the Onsager reciprocal relations of irreversible thermodynamics — the same phenomenological framework that unifies thermoelectric, Peltier, and Thomson effects as off-diagonal elements of a generalized transport coefficient matrix", "status": "established", "fields": [ "materials-science", "thermodynamics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-knot-invariants-x-dna-topology", "type": "bridge", "title": "Knot Invariants x DNA Topology - topoisomerase as knot simplifier\n", "status": "proposed", "fields": [ "mathematics", "biology", "molecular-biology" ], "color": "blue" }, { "id": "b-persistence-homology-x-protein-structure", "type": "bridge", "title": "Persistent homology x Protein structure - topological data analysis of folded chains\n", "status": "proposed", "fields": [ "mathematics", "biology", "topology", "structural_biology" ], "color": "blue" }, { "id": "b-topological-data-analysis-x-cancer-genomics", "type": "bridge", "title": "Topological Data Analysis x Cancer Genomics - persistent homology of mutation landscapes\n", "status": "proposed", "fields": [ "mathematics", "biology", "bioinformatics" ], "color": "blue" }, { "id": "b-category-theory-x-functional-programming", "type": "bridge", "title": "Category theory x Functional programming - functors as type constructors\n", "status": "proposed", "fields": [ "mathematics", "computer_science", "type_theory", "logic" ], "color": "blue" }, { "id": "b-expander-graphs-x-error-correcting-codes", "type": "bridge", "title": "Expander Graphs x Error-Correcting Codes - spectral gap as code distance\n", "status": "proposed", "fields": [ "mathematics", "computer-science" ], "color": "blue" }, { "id": "b-fourier-transform-x-signal-processing", "type": "bridge", "title": "Fourier transform x Signal processing — frequency domain as dual representation\n", "status": "proposed", "fields": [ "mathematics", "computer-science", "signal-processing" ], "color": "blue" }, { "id": "b-tda-x-shape-recognition", "type": "bridge", "title": "Topological Data Analysis x Shape Recognition — Betti numbers as shape fingerprints\n", "status": "proposed", "fields": [ "mathematics", "computer_science", "data-science" ], "color": "blue" }, { "id": "b-tropical-geometry-x-neural-networks", "type": "bridge", "title": "Tropical geometry ↔ ReLU neural networks — piecewise-linear maps as tropical polynomials", "status": "proposed", "fields": [ "mathematics", "computer_science" ], "color": "blue" }, { "id": "b-island-biogeography-x-percolation", "type": "bridge", "title": "Island biogeography ↔ Percolation — species area relationship as connectivity threshold", "status": "proposed", "fields": [ "biology", "mathematics" ], "color": "blue" }, { "id": "b-lotka-volterra-x-game-theory", "type": "bridge", "title": "Lotka-Volterra x Evolutionary game theory — predator-prey as hawk-dove\n", "status": "proposed", "fields": [ "mathematics", "ecology", "evolutionary-biology" ], "color": "blue" }, { "id": "b-percolation-x-disease-spread", "type": "bridge", "title": "Percolation theory x Epidemic spreading — connectivity threshold as herd immunity\n", "status": "proposed", "fields": [ "mathematics", "biology", "epidemiology" ], "color": "blue" }, { "id": "b-auction-theory-x-mechanism-design", "type": "bridge", "title": "Auction theory x Mechanism design — revenue equivalence as envelope theorem\n", "status": "proposed", "fields": [ "mathematics", "economics", "game-theory" ], "color": "blue" }, { "id": "b-extreme-value-theory-x-risk-modeling", "type": "bridge", "title": "Extreme Value Theory x Risk Modeling — Gumbel distribution as tail statistics\n", "status": "proposed", "fields": [ "mathematics", "economics", "statistics" ], "color": "blue" }, { "id": "b-voting-theory-x-social-choice", "type": "bridge", "title": "Voting Theory x Social Choice — Arrow's impossibility as topological obstruction\n", "status": "proposed", "fields": [ "mathematics", "economics", "social-science" ], "color": "blue" }, { "id": "b-chaos-x-ergodic-theory", "type": "bridge", "title": "Chaos x Ergodic theory - sensitivity as mixing\n", "status": "proposed", "fields": [ "mathematics", "physics", "dynamical_systems", "information_theory" ], "color": "blue" }, { "id": "b-ergodic-theory-x-statistical-mechanics", "type": "bridge", "title": "Ergodic Theory x Statistical Mechanics - time average equals ensemble average\n", "status": "proposed", "fields": [ "mathematics", "physics", "statistical-mechanics" ], "color": "blue" }, { "id": "b-knot-theory-x-quantum-gravity", "type": "bridge", "title": "Knot theory x Quantum gravity - Wilson loops as topological invariants\n", "status": "proposed", "fields": [ "mathematics", "physics", "topology", "quantum_gravity" ], "color": "blue" }, { "id": "b-lie-groups-x-symmetry-conservation", "type": "bridge", "title": "Lie groups x Conservation laws 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"bridge", "title": "Protein-protein interaction networks are scale-free graphs (P(k) ∝ k^{-γ}, γ ≈ 2.5) whose hub proteins are essential (lethal when deleted), whose modules correspond to functional complexes detectable by the Louvain algorithm, and whose bridging proteins (high betweenness centrality) are preferential drug targets — directly translating graph-theoretic concepts into biological and pharmacological predictions.\n", "status": "established", "fields": [ "mathematics", "biology", "network-science", "graph-theory", "systems-biology" ], "color": "blue" }, { "id": "b-information-geometry-evolutionary-fitness", "type": "bridge", "title": "The Fisher information matrix on the space of allele frequency distributions defines the Shahshahani Riemannian metric on population-genetic state space, making Amari's natural gradient descent in statistical learning the exact formal counterpart of Fisher's fundamental theorem — the rate of mean fitness increase equals the Fisher information 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], "color": "blue" }, { "id": "b-turing-reaction-diffusion", "type": "bridge", "title": "Turing reaction-diffusion instability ↔ biological pattern formation (digits, stripes, spots)", "status": "established", "fields": [ "mathematics", "developmental-biology", "biophysics" ], "color": "blue" }, { "id": "b-voronoi-tessellation-cellular-architecture", "type": "bridge", "title": "Biological tissues self-organise into Voronoi tessellations — the same space-partitioning geometry that minimises interface energy in soap foams and maximises packing efficiency in engineered materials.\n", "status": "established", "fields": [ "mathematics", "biology", "physics" ], "color": "blue" }, { "id": "b-knot-theory-dna-topology", "type": "bridge", "title": "Knot invariants (Alexander, Jones, HOMFLY polynomials) characterize DNA knot and catenane types arising during replication and viral packaging, with topoisomerase II inhibitor chemotherapy agents exploiting the essential unknotting reaction — bridging abstract knot theory with molecular biology and pharmacology.\n", "status": "established", "fields": [ "mathematics", "chemistry", "molecular-biology", "biochemistry", "topology" ], "color": "blue" }, { "id": "b-approximation-theory-deep-learning", "type": "bridge", "title": "Universal approximation theory establishes that neural networks with sufficient depth/width can approximate any continuous function to arbitrary precision; depth separation theorems show that deep networks require exponentially fewer neurons than shallow networks for compositional functions, grounding the empirical success of deep learning in classical Sobolev approximation theory.\n", "status": "established", "fields": [ "mathematics", "approximation-theory", "computer-science", "machine-learning" ], "color": "blue" }, { "id": "b-bond-percolation-x-cyber-lateral-movement", "type": "bridge", "title": "Bond/site percolation thresholds on graphs ↔ lateral movement probability and blast-radius growth in enterprise networks (probability ↔ cybersecurity)\n", "status": "proposed", "fields": [ "mathematics", "computer-science", "cybersecurity", "network-science" ], "color": "blue" }, { "id": "b-cahn-hilliard-phase-separation-x-diffuse-interface-segmentation", "type": "bridge", "title": "Cahn-Hilliard phase-separation models and diffuse-interface image segmentation share an energy-minimization template: interfaces are penalized by smoothness and contrast terms while domains evolve toward separated phases or labeled regions.\n", "status": "proposed", "fields": [ "mathematics", "computer-science", "materials-science" ], "color": "blue" }, { "id": "b-category-theory-functional-programming", "type": "bridge", "title": "Category theory (Eilenberg & Mac Lane 1945) is the semantic foundation of functional programming: types are objects, functions are morphisms, functors are type constructors, monads are monoids in the category of endofunctors, and the Curry-Howard correspondence makes propositions = types and proofs = programs.\n", "status": "established", "fields": [ "mathematics", "computer-science", "type-theory", "functional-programming" ], "color": "blue" }, { "id": "b-complexity-proof-theory", "type": "bridge", "title": "The Cook-Levin theorem (1971) establishes SAT as NP-complete; Gödel's incompleteness theorems and Turing's halting problem both derive from diagonalization; the Curry-Howard correspondence identifies programs with proofs and types with propositions; interactive proof systems (IP=PSPACE) reveal that probabilistic verification is exponentially more powerful than deterministic checking — mathematics and computer science study the same logical limits from different directions.\n", "status": "established", "fields": [ "mathematics", "logic", "computer-science", "complexity-theory", "proof-theory", "type-theory" ], "color": "blue" }, { "id": "b-compressed-sensing-sparse-recovery", "type": "bridge", "title": "Compressed sensing (Candès-Romberg-Tao, Donoho 2006) proves that k-sparse signals in ℝⁿ can be exactly recovered from m = O(k log n/k) random linear measurements via ℓ₁ minimisation — far fewer than the n measurements required by the Shannon-Nyquist theorem — creating a mathematical foundation for sub-Nyquist sampling that has revolutionised MRI, radar, and high-dimensional statistics.\n", "status": "established", "fields": [ "mathematics", "computer-science", "statistics", "signal-processing", "applied-mathematics" ], "color": "blue" }, { "id": "b-convolution-x-convolutional-neural-nets", "type": "bridge", "title": "Discrete convolution — diagonalized by the discrete Fourier transform via the convolution theorem — is the algebraic backbone of convolutional neural networks’ local translation-equivariant layers.", "status": "established", "fields": [ "mathematics", "computer-science", "signal-processing", "machine-learning" ], "color": "blue" }, { "id": "b-cryptography-number-theory", "type": "bridge", "title": "Modern cryptography is applied number theory: RSA security rests on the hardness of integer factorization, elliptic curve cryptography on the discrete logarithm problem over finite fields, and post-quantum cryptography on the shortest vector problem in integer lattices — each translating a mathematical hardness assumption into a practical security guarantee.\n", "status": "established", "fields": [ "mathematics", "number-theory", "computer-science", "cryptography", "algebra", "complexity-theory" ], "color": "blue" }, { "id": "b-curry-howard-proofs-programs", "type": "bridge", "title": "The Curry-Howard correspondence proves that propositions in intuitionistic logic are identical to types in typed lambda calculus, and proofs of those propositions are identical to programs of those types — mathematics and computation are the same formal system viewed from two perspectives.\n", "status": "established", "fields": [ "mathematical-logic", "type-theory", "computer-science", "proof-theory", "programming-language-theory" ], "color": "blue" }, { "id": "b-elastic-net-map-x-laplace-gaussian-composite-prior", "type": "bridge", "title": "Elastic net regularization can be read as MAP estimation under a composite sparsity-and-shrinkage prior: the L1 term behaves like a Laplace prior, while the L2 term behaves like a Gaussian prior that stabilizes correlated predictors.\n", "status": "established", "fields": [ "statistics", "machine-learning", "computer-science" ], "color": "blue" }, { "id": "b-elliptic-curve-complex-torus-x-finite-field-crypto-pedagogy", "type": "bridge", "title": "Elliptic curves over ℂ form complex tori (compact genus-one Riemann surfaces) where the group law comes from analytic geometry — modern ECC uses curves over finite fields where points form finite Abelian groups with no literal torus topology; pedagogy often introduces the complex picture first for intuition, then warns that cryptographic security lives in discrete logarithms on 𝔽_q-rational points.\n", "status": "established", "fields": [ "mathematics", "computer-science", "cryptography" ], "color": "blue" }, { "id": "b-gnn-weisfeiler-lehman-isomorphism", "type": "bridge", "title": "Graph neural networks are computationally equivalent to the Weisfeiler-Lehman graph isomorphism test, linking the expressive power of GNN architectures to a classical combinatorial algorithm from 1968.\n", "status": "established", "fields": [ "machine-learning", "combinatorics", "computer-science" ], "color": "blue" }, { "id": "b-hyperbolic-geometry-x-network-embedding", "type": "bridge", "title": "Hyperbolic geometry provides exponentially more room in a ball of fixed radius than Euclidean space, making it a natural host geometry for embeddings of trees and scale-free hierarchical networks.", "status": "established", "fields": [ "mathematics", "computer-science", "network-science", "geometry" ], "color": "blue" }, { "id": "b-information-geometry-machine-learning", "type": "bridge", "title": "Information geometry (Amari) equips 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proofs — making proof assistants (Coq, Lean) and systems languages (Rust borrow checker) instances of applied type theory.\n", "status": "established", "fields": [ "mathematics", "computer-science", "logic", "type-theory", "programming-languages" ], "color": "blue" }, { "id": "b-wasserstein-gan-gradient-penalty-x-kantorovich-lipschitz-stability", "type": "bridge", "title": "Wasserstein GAN training constrains the critic to approximate a 1-Lipschitz dual potential via gradient penalties or spectral normalization — reframing practical stability as enforcing convex-analytic regularity conditions inherited from Kantorovich optimal transport duality, beyond the coarse statement “WGAN uses Earth mover’s distance.”\n", "status": "established", "fields": [ "mathematics", "computer-science", "machine-learning" ], "color": "blue" }, { "id": "b-optimal-foraging-calculus-variations", "type": "bridge", "title": "Charnov's marginal value theorem — the optimal forager leaves a patch when 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Mirrlees optimal income tax model (Nobel 1996) shows the top marginal rate should be zero; the Crémer-McLean theorem enables full surplus extraction — mechanism design is reverse game theory unifying information economics, public finance, and social choice theory.\n", "status": "established", "fields": [ "mathematics", "economics", "mechanism-design", "game-theory", "information-economics", "social-choice-theory" ], "color": "blue" }, { "id": "b-optimal-stopping-secretary-problem", "type": "bridge", "title": "The optimal stopping secretary problem — stop searching when you have seen the best so far after sampling 1/e of candidates — is a universal decision rule for search under uncertainty that bridges pure mathematics (measure theory, Wald's equation) with cognitive science (how humans search for mates, jobs, and apartments) and provides a normative benchmark for bounded rational decision making.\n", "status": "proposed", "fields": [ "mathematics", "cognitive-science", "economics", "statistics" ], "color": "blue" }, { "id": "b-preference-elicitation-x-vickrey-auction", "type": "bridge", "title": "Dominant-strategy truthful mechanisms such as the Vickrey auction and VCG payments connect preference elicitation in economics to algorithmic mechanism design in computer science.", "status": "established", "fields": [ "mechanism-design", "microeconomics", "computer-science", "game-theory" ], "color": "blue" }, { "id": "b-bode-sensitivity-integral-x-waterbed-effect", "type": "bridge", "title": "Bode’s sensitivity integral for minimum-phase plants ↔ the “waterbed effect” tradeoff in LQG/H-infinity robust control (classical control ↔ robust control theory)\n", "status": "established", "fields": [ "control-engineering", "mathematics", "robust-control" ], "color": "blue" }, { "id": "b-koopman-operator-x-data-driven-dmd", "type": "bridge", "title": "Koopman (linear evolution on observables) ↔ dynamic mode decomposition and extended DMD for nonlinear flows (operator theory ↔ data-driven fluid mechanics)\n", "status": "established", "fields": [ "mathematics", "fluid-mechanics", "dynamical-systems", "control-engineering" ], "color": "blue" }, { "id": "b-lyapunov-stability-nonlinear-control", "type": "bridge", "title": "Lyapunov's stability theory (1892) provides the mathematical framework unifying nonlinear control engineering, passivity-based design, and automated stability verification via sum-of-squares semidefinite programming.\n", "status": "established", "fields": [ "dynamical-systems-theory", "control-engineering", "optimization", "applied-mathematics" ], "color": "blue" }, { "id": "b-optimization-theory-machine-learning", "type": "bridge", "title": "Convex optimization theory (KKT conditions, strong duality, convergence rates for gradient descent) provides the mathematical foundation for machine learning training, while empirical ML discoveries — the dominance of saddle points over local minima in high dimensions and the lottery ticket hypothesis — 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manufacturing", "status": "established", "fields": [ "mathematics", "operations-research", "engineering", "industrial-engineering", "computer-science" ], "color": "blue" }, { "id": "b-robust-control-h-infinity", "type": "bridge", "title": "H∞ optimal control minimises worst-case L²-induced gain ||T_{zw}||∞ ≤ γ via Riccati equations or LMI convex optimisation; equals a minimax Nash game between controller and adversarial disturbance; achieves 10 nm precision in hard-disk heads and flutter suppression in aircraft through structured uncertainty μ-synthesis.\n", "status": "established", "fields": [ "mathematics", "engineering", "control-theory", "optimization", "game-theory" ], "color": "blue" }, { "id": "b-robust-statistics-outlier-detection", "type": "bridge", "title": "Robust statistics bridges mathematics and engineering: Huber's M-estimators, the 50% breakdown point of least trimmed squares, and RANSAC (Random Sample Consensus) provide principled methods for fitting models to corrupted data ΓÇö enabling reliable computer vision, GPS, robotics, and fraud detection.\n", "status": "established", "fields": [ "mathematics", "engineering", "statistics", "computer-vision", "data-science" ], "color": "blue" }, { "id": "b-wavelet-theory-signal-compression", "type": "bridge", "title": "Mallat's multiresolution analysis and Daubechies compactly-supported wavelets provide an O(N) fast wavelet transform achieving near-optimal signal compression, with JPEG-2000 using 9/7 biorthogonal wavelets for 40:1 compression and Donoho-Johnstone wavelet shrinkage achieving minimax-optimal denoising over Sobolev function classes.\n", "status": "established", "fields": [ "mathematics", "engineering", "signal-processing", "harmonic-analysis", "image-processing", "statistics" ], "color": "blue" }, { "id": "b-game-theory-evolution", "type": "bridge", "title": "Nash equilibrium ↔ evolutionary stable strategy: game theory and natural selection are the same optimisation", "status": "established", "fields": [ "mathematics", "game-theory", "evolutionary-biology", "machine-learning", "economics" ], "color": "blue" }, { "id": "b-kin-selection-price-equation", "type": "bridge", "title": "Kin selection and Hamilton's rule (rB > C) are derived as a special case of the Price equation G = Cov(w,z) + E[w*Δz]: the genetic relatedness r is the regression coefficient b(z_j, z_i) of partner phenotype on focal individual's genotype, benefit B equals the selection gradient on partner phenotype, and the Price equation partitions total selection into direct and indirect (inclusive fitness) components", "status": "established", "fields": [ "evolutionary-biology", "mathematics", "genetics" ], "color": "blue" }, { "id": "b-ricci-curvature-x-price-equation-covariance-analogy", "type": "bridge", "title": "Ricci curvature from Riemannian geometry characterizes how volumes of small geodesic balls initially shrink or expand compared with Euclidean expectations — distinct but loosely evocative of the covariance structure in quantitative genetics captured by the Price equation Δz̄ = Cov(w,z)/w̄ + E[wΔz]/w̄, where selection responds to trait–fitness covariance rather than to traits alone.\n", "status": "proposed", "fields": [ "differential-geometry", "evolutionary-biology", "mathematical-biology" ], "color": "blue" }, { "id": "b-black-scholes-heat-equation", "type": "bridge", "title": "The Black-Scholes option pricing PDE is the heat equation in disguise: the change of variables C(S,t) → u(x,τ) via x=ln(S/K) transforms it into ∂u/∂τ = σ²/2 · ∂²u/∂x²", "status": "established", "fields": [ "finance", "mathematics", "physics" ], "color": "blue" }, { "id": "b-random-matrix-portfolio-optimization", "type": "bridge", "title": "Random matrix theory (Marchenko-Pastur law) identifies which eigenvalues of a financial covariance matrix carry genuine correlation signal versus statistical noise, providing an objective criterion for cleaning the matrix and dramatically improving 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"computer-science", "machine-learning" ], "color": "blue" }, { "id": "b-efficient-coding-hypothesis-x-information-bottleneck-representation-learning", "type": "bridge", "title": "Efficient coding ideas in sensory neuroscience share optimization language with information-bottleneck objectives used to train compressed latent representations in machine learning.", "status": "proposed", "fields": [ "neuroscience", "computer-science", "machine-learning" ], "color": "blue" }, { "id": "b-rl-intrinsic-motivation-x-novelty-information-gain-neuroscience", "type": "bridge", "title": "Reinforcement-learning intrinsic-motivation bonuses (count-based novelty, prediction-error curiosity, information-gain proxies) parallel neuroscience hypotheses that dopamine signals relate to expected future reward **and** reducible uncertainty — careful wording avoids claiming circuit-level isomorphism between TD-learning δ errors and midbrain dopamine in every paradigm.\n", "status": "proposed", "fields": [ 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"type": "bridge", "title": "Brain-computer interfaces decode motor intentions from cortical population activity using linear decoders (Wiener filter) and Kalman state-space models — Fisher information in the neural population code sets the fundamental accuracy bound, connecting information theory to neural prosthetics engineering.\n", "status": "established", "fields": [ "neuroscience", "engineering", "neural-engineering", "information-theory", "signal-processing" ], "color": "blue" }, { "id": "b-computational-psychiatry-digital-biomarkers", "type": "bridge", "title": "Computational psychiatry uses Bayesian brain models to explain psychosis (aberrant salience — excess dopamine random salience attribution), depression (reduced positive learning rate), and OCD (stuck prior updating), while smartphone digital biomarkers provide continuous ecological monitoring that replaces episodic clinical assessment.\n", "status": "established", "fields": [ "neuroscience", "engineering", "psychiatry", 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"computational-neuroscience", "robotics" ], "color": "blue" }, { "id": "b-neuroprosthetics-adaptive-control", "type": "bridge", "title": "Neuroprosthetics closes the sensorimotor loop by decoding motor intention from neural populations via Kalman-filter and RNN decoders, delivering intracortical microstimulation sensory feedback, and using online adaptive algorithms to compensate neural drift — the Cramer-Rao bound on Fisher information in the neural code sets the fundamental decoding limit bridging neuroscience and control theory.\n", "status": "established", "fields": [ "neuroscience", "engineering", "control-theory", "biomedical-engineering", "computational-neuroscience" ], "color": "blue" }, { "id": "b-spike-coding-neuromorphic", "type": "bridge", "title": "Biological neurons communicate via discrete action potentials (spikes) at ~10 fJ/spike; neuromorphic chips (Intel Loihi, IBM TrueNorth) implement spiking neural networks in silicon at 3–4 orders of magnitude lower energy than GPU inference, bridging computational neuroscience to ultra-low-power AI hardware.\n", "status": "proposed", "fields": [ "computational-neuroscience", "electrical-engineering", "neuromorphic-computing", "machine-learning" ], "color": "blue" }, { "id": "b-glymphatic-cerebrospinal-fluid", "type": "bridge", "title": "The glymphatic system uses perivascular cerebrospinal fluid flow driven by arterial pulsatility and aquaporin-4 water channels to clear amyloid-β and tau from the brain — a fluid dynamics problem with direct Alzheimer's disease implications.\n", "status": "established", "fields": [ "neuroscience", "fluid-dynamics", "physiology", "neurology" ], "color": "blue" }, { "id": "b-neurolyme-neuroinflammation", "type": "bridge", "title": "Lyme neuroborreliosis links blood-brain barrier biology (neuroscience) to TLR-mediated cytokine signaling (immunology) through a BBB-crossing and neuroinflammation cascade that can become self-sustaining after bacterial clearance.\n", "status": 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"bridge", "title": "Spin-glass statistical mechanics ↔ associative memory capacity and phase transitions in neural networks", "status": "established", "fields": [ "statistical-physics", "neuroscience", "machine-learning" ], "color": "blue" }, { "id": "b-boltzmann-machine-x-ising-model", "type": "bridge", "title": "Boltzmann machine x Ising model — energy-based learning as statistical mechanics\n", "status": "proposed", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "blue" }, { "id": "b-cavity-method-x-belief-propagation", "type": "bridge", "title": "Cavity method ↔ Belief propagation — Bethe-Peierls approximation as message passing", "status": "proposed", "fields": [ "physics", "computer_science" ], "color": "blue" }, { "id": "b-diffusion-models-x-stochastic-processes", "type": "bridge", "title": "Diffusion Generative Models x Stochastic Differential Equations - score matching as time-reversed diffusion\n", "status": "proposed", "fields": [ "computer-science", "mathematics", "physics" ], "color": "blue" }, { "id": "b-mean-field-theory-x-neural-networks", "type": "bridge", "title": "Mean Field Theory x Deep Neural Networks - infinite-width limit as Gaussian process\n", "status": "proposed", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "blue" }, { "id": "b-quantum-error-correction-x-topological-codes", "type": "bridge", "title": "Quantum error correction x Topological codes — anyons as logical qubits\n", "status": "proposed", "fields": [ "physics", "computer-science", "quantum-information" ], "color": "blue" }, { "id": "b-quantum-walk-x-classical-random-walk", "type": "bridge", "title": "Quantum Walks x Classical Random Walks — interference as search speedup\n", "status": "proposed", "fields": [ "physics", "computer_science", "mathematics" ], "color": "blue" }, { "id": "b-renormalization-group-x-machine-learning", "type": "bridge", "title": "Renormalization Group x Machine Learning — coarse-graining as representation learning\n", "status": "proposed", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "blue" }, { "id": "b-renormalization-x-compression", "type": "bridge", "title": "Renormalization x Data Compression - irrelevant operators as redundant bits\n", "status": "proposed", "fields": [ "physics", "computer-science", "information-theory" ], "color": "blue" }, { "id": "b-reservoir-computing-x-dynamical-systems", "type": "bridge", "title": "Reservoir computing ↔ Dynamical systems — echo state networks as kernel machines", "status": "proposed", "fields": [ "computer_science", "physics" ], "color": "blue" }, { "id": "b-simulated-annealing-x-statistical-mechanics", "type": "bridge", "title": "Simulated annealing x Statistical mechanics — optimization as cooling\n", "status": "proposed", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "blue" }, { "id": "b-thermodynamics-x-information-theory", "type": 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patterns emerge from information-theoretic constraints rather than species-specific biology.\n", "status": "proposed", "fields": [ "statistical-mechanics", "macroecology", "information-theory", "biodiversity-science" ], "color": "blue" }, { "id": "b-turing-patterns-ecosystem-tipping", "type": "bridge", "title": "Turing vegetation patterns as early-warning signals for catastrophic ecosystem collapse", "status": "established", "fields": [ "mathematical-biology", "ecology", "nonlinear-dynamics", "conservation-science" ], "color": "blue" }, { "id": "b-agent-based-models-x-emergent-markets", "type": "bridge", "title": "Agent-Based Models x Market Dynamics - heterogeneous agents as interacting particles\n", "status": "proposed", "fields": [ "economics", "physics", "complex-systems" ], "color": "blue" }, { "id": "b-blackscholes-x-diffusion-equation", "type": "bridge", "title": "Black-Scholes x Heat diffusion equation — option pricing as Brownian motion\n", "status": "proposed", "fields": [ "economics", "physics", "mathematics" ], "color": "blue" }, { "id": "b-chemical-potential-utility-maximization", "type": "bridge", "title": "Chemical potential equalization at thermodynamic equilibrium is formally identical to marginal utility equalization in consumer utility maximization: both are gradient-descent conditions on the same class of strictly convex potential function, uniting thermodynamics and neoclassical economics through the mathematics of convex optimization\n", "status": "established", "fields": [ "thermodynamics", "economics", "statistical-mechanics", "mathematical-economics" ], "color": "blue" }, { "id": "b-entropy-maximization-x-income-distribution", "type": "bridge", "title": "Maximum entropy x Income distribution - Boltzmann-Gibbs distribution of wealth\n", "status": "proposed", "fields": [ "physics", "economics", "statistical_mechanics", "econophysics" ], "color": "blue" }, { "id": "b-entropy-production-market-irreversibility", "type": "bridge", "title": 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"status": "proposed", "fields": [ "physics", "economics", "statistical-mechanics", "complex-systems", "mathematics" ], "color": "blue" }, { "id": "b-lyapunov-divergence-x-bank-run-amplification", "type": "bridge", "title": "Positive Lyapunov exponents and finite-time divergence in dynamical systems ↔ feedback amplification and panic acceleration in bank-run models (dynamical systems ↔ economics; heavy caveats)\n", "status": "proposed", "fields": [ "dynamical-systems", "economics", "finance", "mathematical-modeling" ], "color": "blue" }, { "id": "b-minority-game-market-microstructure", "type": "bridge", "title": "The minority game (Challet–Zhang) is an exactly solvable model of financial market competition whose phase transition at critical ratio α_c = P/N reproduces the efficient market boundary — spin glass theory via the replica method provides the analytic solution.\n", "status": "established", "fields": [ "physics", "statistical-mechanics", "economics", "market-microstructure", "complex-systems" ], "color": "blue" }, { "id": "b-minority-game-x-market-microstructure", "type": "bridge", "title": "Minority game ↔ Market microstructure — agent heterogeneity as market efficiency", "status": "proposed", "fields": [ "economics", "physics" ], "color": "blue" }, { "id": "b-rational-inattention-x-entropy", "type": "bridge", "title": "Rational Inattention x Shannon Entropy - cognitive bandwidth as information cost\n", "status": "proposed", "fields": [ "economics", "computer-science", "information-theory" ], "color": "blue" }, { "id": "b-schawlow-townes-linewidth-x-leeson-oscillator-phase-noise", "type": "bridge", "title": "Laser cavity linewidth obeys Schawlow–Townes quantum-limited scaling tying linewidth to cavity lifetime and photon number — electronic oscillators exhibit phase-noise spectra shaped by device noise floors plus feedback-loop filtering often summarized by Leeson’s heuristic spectrum with corner frequencies — bridges quantum optics linewidth budgets with RF/microwave PLL spectral purity engineering.\n", "status": "established", "fields": [ "photonics", "electrical-engineering", "quantum-optics" ], "color": "blue" }, { "id": "b-acoustics-room-design", "type": "bridge", "title": "Sabine's reverberation formula (T₆₀ = 0.161V/A, 1900) bridges physical wave acoustics with architectural engineering, enabling quantitative concert hall design through measurable psychoacoustic correlates (IACC, early decay time) of perceived sound quality.\n", "status": "established", "fields": [ "architectural-acoustics", "wave-physics", "perceptual-psychology", "civil-engineering", "music" ], "color": "blue" }, { "id": "b-chaos-synchronization-pecora-carroll", "type": "bridge", "title": "Chaotic oscillators can be synchronized by unidirectional coupling (Pecora-Carroll synchronization) when the conditional Lyapunov exponents of the driven system are all negative, enabling secure communications, coordinated sensor networks, and biological rhythm entrainment\n", "status": "established", "fields": [ "physics", "engineering" ], "color": "blue" }, { "id": "b-compressible-shock-x-traffic-shock-wave", "type": "bridge", "title": "Compressible gas dynamics describes shocks as discontinuities satisfying Rankine–Hugoniot jump conditions across characteristics — Lighthill–Whitham macroscopic traffic models treat vehicle density similarly, yielding kinematic shock waves propagating backward through queues — sharing hyperbolic conservation-law structure despite vastly different constitutive flux-density relations.\n", "status": "established", "fields": [ "fluid-mechanics", "transportation-engineering" ], "color": "blue" }, { "id": "b-johnson-nyquist-equilibrium-fluctuations-x-rf-noise-figure-definition", "type": "bridge", "title": "Johnson–Nyquist voltage fluctuations in resistors at temperature T set the available thermal noise power kT per hertz; RF noise figure F quantifies how much a two-port exceeds that reference — thermodynamic 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"complex-systems" ], "color": "blue" }, { "id": "b-spin-glass-replica-x-factor-covariance-clustering-finance", "type": "bridge", "title": "Replica symmetry breaking in mean-field spin glasses describes hierarchical clustering of pure states in coupling disorder — a geometric picture loosely echoed when eigenstructure cleaning of financial covariance matrices exposes nested factor structure, **with heavy caveats**: empirical correlations are non-stationary, non-Gaussian, and far from thermodynamic limits used in Parisi theory.\n", "status": "proposed", "fields": [ "statistical-physics", "spin-glasses", "quantitative-finance", "random-matrix-theory" ], "color": "blue" }, { "id": "b-turbulence-financial-markets", "type": "bridge", "title": "Kolmogorov turbulence cascade ↔ multifractal volatility in financial markets", "status": "proposed", "fields": [ "statistical-physics", "fluid-dynamics", "quantitative-finance", "econophysics" ], "color": "blue" }, { "id": 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"status": "proposed", "fields": [ "physics", "mathematics" ], "color": "blue" }, { "id": "b-spin-waves-x-magnons", "type": "bridge", "title": "Spin Waves x Magnons — collective excitations as quasiparticles\n", "status": "proposed", "fields": [ "physics", "mathematics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-topological-defects-x-homotopy", "type": "bridge", "title": "Topological defects x Homotopy groups — vortices classified by pi_1\n", "status": "proposed", "fields": [ "physics", "mathematics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-black-holes-information-theory", "type": "bridge", "title": "Bekenstein-Hawking entropy S_BH = A/4l_P² (area law) and the holographic bound connect black hole thermodynamics to information theory; the Page curve and island formula (replica wormholes) resolve Hawking's information paradox by showing entanglement entropy of radiation follows a unitary Page curve via quantum extremal surfaces.\n", "status": "established", "fields": [ "physics", "mathematics", "information-theory", "quantum-gravity", "thermodynamics" ], "color": "blue" }, { "id": "b-fluid-instabilities-bifurcation", "type": "bridge", "title": "Fluid instabilities — Rayleigh-Bénard convection, Kelvin-Helmholtz, Plateau-Rayleigh — are physical realizations of mathematical bifurcations: the transition from laminar to convective flow is a pitchfork bifurcation at Ra_c = 1708, and Lorenz's three-mode truncation of the Bénard equations produced the first mathematical proof of deterministic chaos.\n", "status": "established", "fields": [ "physics", "mathematics", "fluid-dynamics", "nonlinear-dynamics" ], "color": "blue" }, { "id": "b-noether-theorem-conservation-laws", "type": "bridge", "title": "Every differentiable symmetry of the action of a physical system corresponds to a conservation law — Noether's theorem is the deepest known connection between the geometry of symmetry groups and the conservation laws of physics.\n", "status": "established", "fields": [ "theoretical-physics", "mathematics", "differential-geometry", "field-theory" ], "color": "blue" }, { "id": "b-radiocarbon-dating-exponential-decay", "type": "bridge", "title": "Radiocarbon dating applies the first-order decay law N(t) = N0 * exp(-lambda * t) with lambda = ln2 / 5,730 yr to determine the age of organic material, with Bayesian calibration correcting for past atmospheric C-14 variations using dendrochonology", "status": "established", "fields": [ "archaeology", "nuclear-physics", "mathematics" ], "color": "blue" }, { "id": "b-random-matrix-quantum-chaos", "type": "bridge", "title": "The Bohigas-Giannoni-Schmit conjecture (1984) states that energy level statistics of quantum systems with chaotic classical dynamics follow Gaussian Orthogonal Ensemble (GOE) random matrix statistics, proved for specific systems via Sieber-Richter pairs of correlated periodic orbits, unifying quantum chaos, nuclear physics, and the Riemann zeta function zeros.\n", "status": "established", "fields": [ "physics", "quantum-mechanics", "mathematics", "random-matrix-theory", "chaos-theory", "number-theory" ], "color": "blue" }, { "id": "b-renormalization-group-fixed-points", "type": "bridge", "title": "Wilson's renormalization group maps RG flow in coupling-constant space onto a dynamical system whose fixed points — classified by their eigenvalue spectrum — determine universality classes of critical phenomena, making the mathematics of continuous-group flows and fixed-point stability the exact language for the physics of second-order phase transitions independent of microscopic details.\n", "status": "established", "fields": [ "physics", "mathematics", "statistical-mechanics", "field-theory" ], "color": "blue" }, { "id": "b-renormalization-wilson-wavelets-x-wavelet-shrinkage-denoising", "type": "bridge", "title": "Wilson’s renormalization group coarse-grains microscopic fluctuations into fixed-point long-distance physics — Mallat’s 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"title": "Phase transitions near the critical point in disordered materials and the neural dynamics associated with consciousness share mathematical structure through self-organised criticality", "status": "proposed", "fields": [ "materials-science", "cognitive-science", "statistical-physics" ], "color": "blue" }, { "id": "b-poisson-counting-process-x-decay-spike-train-likelihood", "type": "bridge", "title": "Poisson counting-process models connect radioactive decay event counts and neural spike-train likelihoods: independent rare events produce exponential waiting times and count variance equal to the mean, while deviations expose refractory periods, bursting, or nonstationary rates.\n", "status": "established", "fields": [ "probability", "physics", "neuroscience" ], "color": "blue" }, { "id": "b-quantum-biology-neural-computation", "type": "bridge", "title": "Three experimentally established quantum biological phenomena — photosynthetic exciton coherence, radical-pair 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"bridge", "title": "Magnon dispersion in ferromagnets is formally identical to phase-oscillation band structure in coupled neural networks (Kuramoto model)", "status": "proposed", "fields": [ "physics", "neuroscience" ], "color": "blue" }, { "id": "b-stochastic-resonance", "type": "bridge", "title": "Stochastic resonance — the counterintuitive enhancement of weak-signal detection by adding noise — is a universal nonlinear phenomenon observed in physical bistable systems, hair-cell mechanoreceptors, cricket cercal systems, and human tactile perception, with optimal noise amplitude predicted by the same signal-to-noise ratio analysis in all cases.\n", "status": "established", "fields": [ "statistical-physics", "neuroscience", "sensory-biology", "nonlinear-dynamics" ], "color": "blue" }, { "id": "b-synchronization-circadian", "type": "bridge", "title": "Kuramoto phase locking ↔ circadian entrainment: jet lag as desynchronization crisis", "status": "established", "fields": [ 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"statistical-physics", "condensed-matter", "neuroscience", "materials-science" ], "color": "blue" }, { "id": "b-ising-social-dynamics", "type": "bridge", "title": "The Ising model of ferromagnetism describes opinion dynamics, social norm adoption, and political polarisation — social tipping points (climate action spreading, norm cascades, market crashes) are formal phase transitions in the Ising universality class, with measurable early-warning indicators derivable from statistical physics.\n", "status": "proposed", "fields": [ "statistical-physics", "social-science", "complexity-science", "political-science", "behavioural-economics" ], "color": "blue" }, { "id": "b-statistical-physics-x-social-science", "type": "bridge", "title": "Statistical Physics x Social Science — opinion dynamics as spin systems\n", "status": "proposed", "fields": [ "physics", "social-science", "statistical-mechanics" ], "color": "blue" }, { "id": "b-crowd-dynamics-social-force-model", "type": "bridge", "title": 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(price impact linear in signed flow), the Glosten-Milgrom adverse selection spread, and the square-root market impact law connect queueing theory and statistical physics to market microstructure.\n", "status": "established", "fields": [ "physics", "social-science", "economics", "mathematics" ], "color": "blue" }, { "id": "b-percolation-rumor-spreading", "type": "bridge", "title": "Rumour and misinformation spreading on social networks maps exactly onto bond percolation on the contact network via the SIR epidemic model — with the percolation threshold p_c → 0 for scale-free networks, meaning any viral meme can reach the giant component of social attention regardless of initial conditions.\n", "status": "established", "fields": [ "physics", "social-science", "network-science", "epidemiology", "information-theory" ], "color": "blue" }, { "id": "b-schelling-phase-separation", "type": "bridge", "title": "Schelling's segregation model maps onto binary-alloy phase separation — social tolerance thresholds are thermodynamic critical points", "status": "established", "fields": [ "physics", "social-science" ], "color": "blue" }, { "id": "b-statistical-mechanics-opinion", "type": "bridge", "title": "The Ising model of opinion dynamics maps social consensus formation onto ferromagnetic phase transitions (T < T_c → ordered consensus; T > T_c → disordered pluralism), while bounded-confidence models predict opinion clustering and polarization — bridging statistical mechanics with quantitative social science.\n", "status": "established", "fields": [ "physics", "social-science", "statistical-mechanics", "complexity-science", "political-science" ], "color": "blue" }, { "id": "b-urban-scaling-statistical-physics", "type": "bridge", "title": "Urban scaling laws — city GDP, patents, and crime scaling superlinearly (β ≈ 1.15) while infrastructure scales sublinearly (β ≈ 0.85) with population — emerge from statistical physics of social interaction networks with fractal road 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allowing quantum superposition of paths, achieving quadratically faster spreading (sigma ~ t vs t^1/2) and providing the computational primitive for quantum speedup in graph algorithms.\n", "status": "established", "fields": [ "quantum-computing", "probability-theory", "algorithm-theory" ], "color": "blue" }, { "id": "b-topological-quantum-computing-anyons", "type": "bridge", "title": "Topological quantum computing encodes qubits in non-Abelian anyons — quasiparticle excitations of topological phases whose braiding operations implement quantum gates by exchanging particle worldlines, with error correction guaranteed topologically because qubit states are stored in the globally degenerate ground state subspace inaccessible to local perturbations", "status": "proposed", "fields": [ "quantum-computing", "topology", "condensed-matter" ], "color": "blue" }, { "id": "b-quantum-coherence-photosynthesis", "type": "bridge", "title": "Femtosecond spectroscopy reveals long-lived quantum coherence 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"biophysics" ], "color": "blue" }, { "id": "b-exotic-matter-casimir-negative-energy", "type": "bridge", "title": "The Casimir effect demonstrates that quantum vacuum fluctuations between conducting plates produce a measurable attractive force via negative energy density — the same exotic matter with negative energy density that general relativity requires for traversable wormholes and warp drives, making the Casimir effect the only laboratory-scale demonstration of negative energy.\n", "status": "proposed", "fields": [ "quantum-physics", "cosmology", "general-relativity", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-quantum-decoherence-einselection", "type": "bridge", "title": "Quantum decoherence selects pointer states through einselection: the preferred basis that survives entanglement with the environment is determined by the system-environment interaction Hamiltonian, explaining the emergence of classical reality from quantum superpositions", "status": "established", "fields": [ "quantum-physics", "information-theory" ], "color": "blue" }, { "id": "b-quantum-error-correction-holography", "type": "bridge", "title": "Quantum error-correcting codes (stabilizer codes, surface codes) and the holographic principle in quantum gravity (AdS/CFT) are the same mathematical structure: bulk operators in AdS are encoded in boundary CFT degrees of freedom via a quantum error-correcting code, with the Ryu-Takayanagi formula (S = A/4G_N) expressing entanglement entropy as a quantum error-correction redundancy statement.\n", "status": "proposed", "fields": [ "quantum-information-theory", "quantum-gravity", "string-theory", "quantum-error-correction", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-quantum-gravity-holographic-entropy", "type": "bridge", "title": "The Ryu-Takayanagi formula equates the entanglement entropy of a boundary CFT region to the area of the minimal bulk surface divided by 4G, connecting quantum gravity geometry to quantum information theory through holography", "status": "established", "fields": [ "physics", "information-theory", "quantum-physics" ], "color": "blue" }, { "id": "b-topological-insulators-band-theory", "type": "bridge", "title": "Topological insulators are materials with insulating bulk but conducting surface states protected by time-reversal symmetry — classified by topological invariants (Z₂, Chern number) from algebraic topology applied to electronic band theory, with applications to fault-tolerant quantum computing via Majorana edge modes.\n", "status": "established", "fields": [ "quantum-physics", "condensed-matter-physics", "materials-science", "algebraic-topology", "quantum-computing" ], "color": "blue" }, { "id": "b-entanglement-tensor-network-states", "type": "bridge", "title": "Quantum entanglement structure in many-body systems is exactly captured by tensor network states (MPS, PEPS, MERA), where the entanglement entropy S ∝ area of a region is encoded as the bond dimension χ of inter-tensor contractions, providing a mathematical framework that connects quantum information geometry to condensed-matter physics", "status": "established", "fields": [ "quantum-physics", "mathematics", "condensed-matter" ], "color": "blue" }, { "id": "b-representation-theory-particles", "type": "bridge", "title": "The classification of all elementary particles follows from the representation theory of the Poincaré group (Wigner 1939) — particle spin is the label of the irreducible representation of SU(2), the Standard Model gauge group SU(3)×SU(2)×U(1) determines all allowed interactions via group representations, and every conserved quantum number corresponds to a generator of a symmetry Lie group.\n", "status": "established", "fields": [ "quantum-physics", "mathematics", "group-theory", "particle-physics", "representation-theory" ], "color": "blue" }, { "id": "b-berry-phase-x-polarization-parallel-transport-optics", "type": "bridge", "title": "Berry phase in quantum systems and Pancharatnam-Berry phase in polarization optics share a geometric parallel-transport structure: cyclic parameter changes accumulate phase from path geometry rather than local dynamical time alone.\n", "status": "established", "fields": [ "quantum-physics", "optics", "geometry" ], "color": "blue" }, { "id": "b-photon-antibunching-sub-poissonian", "type": "bridge", "title": "Photon antibunching is the quantum optical signature of sub-Poissonian statistics: the second-order coherence g⁽²⁾(0) < 1 certifies non-classical single-photon emission", "status": "established", "fields": [ "quantum-physics", "optics", "quantum-information" ], "color": "blue" }, { "id": "b-quantum-dot-blinking-renewal-process", "type": "bridge", "title": "Quantum dot fluorescence intermittency (blinking) obeys power-law on-time and off-time distributions that follow a renewal process with Levy-stable statistics, connecting single-particle quantum physics to renewal theory and anomalous diffusion through the universal power-law trap model.\n", "status": "established", "fields": [ "quantum-physics", "statistics" ], "color": "blue" }, { "id": "b-resnet-x-histopathology-domain-shift-robustness", "type": "bridge", "title": "Residual learning bridges deep optimization stability and histopathology robustness under stain and scanner domain shift.", "status": "proposed", "fields": [ "radiology", "machine-learning", "pathology" ], "color": "blue" }, { "id": "b-physics-informed-neural-operator-x-aftershock-field-evolution", "type": "bridge", "title": "Physics-informed neural operators bridge PDE-constrained learning and spatiotemporal aftershock field evolution modeling.", "status": "proposed", "fields": [ "seismology", "machine-learning", "geophysics" ], "color": "blue" }, { "id": "b-hawkes-self-excitation-x-seizure-aftershock-clustering", "type": "bridge", "title": "Hawkes self-exciting point processes unify earthquake aftershock clustering and seizure-burst event cascades.", "status": "proposed", "fields": [ "seismology", "neuroscience", "statistics", "dynamical-systems" ], "color": "blue" }, { "id": "b-earthquake-source-dislocation-theory", "type": "bridge", "title": "Earthquake source mechanics is formally equivalent to dislocation theory in solid mechanics: seismic moment tensors describe the equivalent force system of a shear crack (dislocation) on a fault plane, and radiated seismic wavefields are computed as the elastic Green's function response to dislocation propagation\n", "status": "established", "fields": [ "seismology", "solid-mechanics" ], "color": "blue" }, { "id": "b-seismology-percolation", "type": "bridge", "title": "Earthquake fault networks exhibit Gutenberg-Richter power-law magnitude-frequency distributions because fault systems self-organize to the percolation critical point, making seismic hazard a direct application of percolation criticality theory.\n", "status": "proposed", "fields": [ "seismology", "geophysics", "statistical-physics", "network-theory", "complex-systems" ], "color": "blue" }, { "id": "b-seismograph-matched-filter-cross-correlation", "type": "bridge", "title": "Seismic signal detection uses matched filtering and cross-correlation from signal processing theory: a template waveform from a known event is cross-correlated with continuous seismic recordings to detect repeating earthquakes at signal-to-noise ratios far below the detection threshold of traditional STA/LTA methods.\n", "status": "established", "fields": [ "seismology", "signal-processing", "geophysics" ], "color": "blue" }, { "id": "b-earthquake-aftershocks-omori-utsu-etas", "type": "bridge", "title": "Earthquake aftershock sequences obey the Omori-Utsu power law and are modeled by the ETAS (Epidemic Type Aftershock Sequence) point process — a self-exciting Hawkes process that maps seismicity onto the statistical physics of critical branching processes and second-order phase transitions.\n", "status": "established", "fields": [ "seismology", "statistical-physics" ], "color": "blue" }, { "id": "b-earthquake-alarm-decision-x-wald-sequential-probability-ratio-test", "type": "bridge", "title": "Earthquake early warning public alerting is not pure estimation: stakeholders face sequential decisions under latency — Wald’s sequential probability ratio test formalizes threshold policies balancing false alarms and misses, complementing recursive Bayesian magnitude tracking (seismology ↔ sequential hypothesis testing).\n", "status": "established", "fields": [ "seismology", "statistics", "decision-theory", "civil-engineering" ], "color": "blue" }, { "id": "b-phase-retrieval-x-cryoem-orientation-inference", "type": "bridge", "title": "Phase-retrieval alternating-projection methods map onto cryo-EM orientation and reconstruction inference loops.", "status": "proposed", "fields": [ "signal-processing", "structural-biology", "mathematics" ], "color": "blue" }, { "id": "b-social-ising-polarisation", "type": "bridge", "title": "Political polarisation dynamics in networked populations are mathematically equivalent to the Ising model ferromagnetic phase transition, with partisan identity as spin, echo chambers as ferromagnetic domains, and social influence strength as inverse temperature.\n", "status": "proposed", "fields": [ "political-science", "statistical-physics", "network-science", "social-science" ], "color": "blue" }, { "id": "b-behavioral-immunology-pathogen-avoidance", "type": "bridge", "title": "Schaller's behavioral immune system (BIS) — evolved disgust-based pathogen avoidance using false-positive-biased detection — predicts cross-national correlations between historical pathogen prevalence and collectivism, sexual conservatism, and xenophobia, mapping to Neyman-Pearson Type I/II error trade-offs in signal detection theory.\n", "status": "established", "fields": [ "social-science", "biology", "psychology", "evolutionary-biology", "immunology" ], "color": "blue" }, { "id": "b-chronobiology-social-jet-lag", "type": "bridge", "title": "Social jet lag bridges chronobiology and social science: the mismatch between biological clock timing (TTFL circadian mechanism, CRY1/PER3 variants) and social schedule timing (school start times, work hours) creates measurable health and performance deficits across populations.\n", "status": "established", "fields": [ "social-science", "biology", "chronobiology", "public-health", "education" ], "color": "blue" }, { "id": "b-cultural-evolution-darwinian", "type": "bridge", "title": "Cultural evolution is formally isomorphic to biological evolution — memes are replicators subject to transmission, variation, and selection; the Price equation governs both gene frequency change and cultural trait change; and replicator dynamics describe both biological fitness and cultural payoff — making evolutionary theory a universal framework for any inherited-variation- selection system.\n", "status": "established", "fields": [ "social-science", "evolutionary-biology", "cultural-anthropology", "evolutionary-game-theory" ], "color": "blue" }, { "id": "b-cultural-evolution-dual-inheritance", "type": "bridge", "title": "Boyd and Richerson's dual inheritance theory (1985) formalizes the coevolution of genes and culture using population genetics mathematics — cultural allele frequencies evolve under selection, drift, and transmission biases including conformity and prestige, with the Price equation applying equally to both genetic and cultural change.\n", "status": "established", "fields": [ "social-science", "cultural-evolution", "biology", "evolutionary-biology", "population-genetics", "anthropology" ], "color": "blue" }, { "id": "b-cultural-evolution-memetics", "type": "bridge", "title": "Cultural transmission exhibits the three conditions of Darwinian evolution — variation, heredity, and selection — making cultural change mathematically equivalent to population genetics and amenable to the same formal tools.\n", "status": "established", "fields": [ "social-science", "biology", "evolutionary-theory", "psychology" ], "color": "blue" }, { "id": "b-moral-psychology-cooperation-game-theory", "type": "bridge", "title": "Moral intuitions of fairness (third-party punishment, inequity aversion) are quantitatively predicted by evolutionarily stable strategies in iterated public-goods games with altruistic punishment: the costly punishment instinct evolved to maintain cooperation in groups where purely self-interested free-riding would otherwise dominate.\n", "status": "proposed", "fields": [ "moral-psychology", "evolutionary-biology", "game-theory", "social-science" ], "color": "blue" }, { "id": "b-social-learning-cultural-transmission", "type": "bridge", "title": "Social learning in human and animal populations follows the same population-genetic mathematics as cultural transmission: conformist bias maps to positive frequency dependence, prestige bias maps to fitness-dependent selection, and horizontal cultural transmission maps to gene flow, allowing the Price equation and selection gradient models to quantify cultural evolution", "status": "established", "fields": [ "social-science", "evolutionary-biology", "anthropology" ], "color": "blue" }, { "id": "b-stress-biology-social-determinants", "type": "bridge", "title": "The biology of chronic stress bridges social science and biology: social determinants of health (employment, neighborhood, social status) are biologically embedded via the HPA axis, cortisol dysregulation, telomere shortening, and epigenetic modification — translating social inequality into measurable molecular and cellular damage.\n", "status": "established", "fields": [ "social-science", "sociology", "biology", "endocrinology", "epidemiology", "public-health", "epigenetics" ], "color": "blue" }, { "id": "b-drug-policy-pharmacoepidemiology", "type": "bridge", "title": "Pharmacoepidemiology bridges the molecular pharmacology of opioid receptor binding and the social epidemiology of the opioid crisis — harm reduction policies (naloxone distribution, methadone maintenance) derive their evidence base from both mu-receptor pharmacokinetics and population-level randomized trial data.\n", "status": "established", "fields": [ "social-science", "chemistry", "pharmacology", "epidemiology" ], "color": "blue" }, { "id": "b-urban-ecology-ses", "type": "bridge", "title": "Urban ecosystems are novel socio-ecological assemblages governed by Ostrom's polycentric SES framework — heat islands shift phenology, intermediate disturbance maximises biodiversity, and green infrastructure delivers ecosystem services quantifiable in economic terms, making urban ecology the laboratory for coupled human-nature systems theory.\n", "status": "established", "fields": [ "social-science", "ecology", "urban-science", "environmental-science", "sustainability-science" ], "color": "blue" }, { "id": "b-hci-cognitive-load", "type": "bridge", "title": "Human-computer interaction bridges social science (cognitive psychology) and engineering: Fitts' law, Hick's law, and cognitive load theory provide quantitative design constraints translating working memory limits and motor control psychology into interface engineering specifications for software, devices, and workplaces.\n", "status": "established", "fields": [ "social-science", "cognitive-psychology", "engineering", "human-computer-interaction", "human-factors", "user-experience-design" ], "color": "blue" }, { "id": "b-human-factors-system-safety", "type": "bridge", "title": "James Reason's Swiss Cheese model and Perrow's Normal Accident Theory connect social-science analysis of human error and organizational factors to engineering system safety design, explaining why accidents occur in tightly coupled complex systems and how High Reliability Organizations prevent them through mindful organizing and Crew Resource Management.\n", "status": "established", "fields": [ "social-science", "engineering", "organizational-psychology", "systems-engineering", "safety-science" ], "color": "blue" }, { "id": "b-social-contagion-complex-threshold", "type": "bridge", "title": "The spread of social behaviours (innovation adoption, social movements, voting) requires exposure to multiple independent contacts (complex contagion) unlike disease spread (simple contagion), described by threshold models where adoption occurs when the fraction of adopting neighbours exceeds an individual-specific threshold φ — a fundamentally different dynamic than standard SIR epidemics.\n", "status": "established", "fields": [ "social-science", "epidemiology", "network-science", "sociology" ], "color": "blue" }, { "id": "b-cultural-memes-shannon-entropy", "type": "bridge", "title": "Cultural transmission of memes across social networks obeys Shannon's noisy channel theorem — meme fidelity, cultural drift, and the homogenising effects of mass media are quantitatively described by channel capacity, noise models, and the source-channel coding theorem from information theory.\n", "status": "proposed", "fields": [ "social-science", "information-theory", "cultural-evolution", "sociology", "communication-theory" ], "color": "blue" }, { "id": "b-privacy-differential-privacy", "type": "bridge", "title": "Differential privacy provides an information-theoretic guarantee — epsilon bounds the log-likelihood ratio an adversary can achieve distinguishing any individual's data — creating a mathematically precise privacy-utility tradeoff that is dual to Neyman-Pearson hypothesis testing, bridging social privacy norms to information theory and statistical decision theory.\n", "status": "established", "fields": [ "social-science", "information-theory", "statistics", "computer-science", "privacy-law" ], "color": "blue" }, { "id": "b-agent-based-modeling-emergent-institutions", "type": "bridge", "title": "Agent-based models with heterogeneous agents following local rules generate macro-level emergent institutions — Schelling segregation, Axelrod cooperation, and Sugarscape wealth distributions — unifying mathematical complexity theory with social science explanation of spontaneous institutional order.\n", "status": "established", "fields": [ "social-science", "mathematics", "complexity-science", "economics", "computational-social-science" ], "color": "blue" }, { "id": "b-algorithmic-fairness-social-choice", "type": "bridge", "title": "Formal impossibility theorems in algorithmic fairness — showing that demographic parity, equalized odds, and calibration cannot simultaneously hold when base rates differ — are mathematical analogs of Arrow's impossibility theorem in social choice theory.\n", "status": "established", "fields": [ "machine-learning", "social-science", "mathematics", "law-and-policy", "statistics" ], "color": "blue" }, { "id": "b-auction-theory-mechanism-design", "type": "bridge", "title": "Mechanism design reverses game theory — designing incentive structures so that rational self-interest produces socially optimal outcomes", "status": "established", "fields": [ "social-science", "mathematics", "economics" ], "color": "blue" }, { "id": "b-bargaining-theory-negotiation", "type": "bridge", "title": "Nash and Rubinstein bargaining theory bridges mathematics and social science: axiomatic and strategic foundations yield unique equilibrium solutions to negotiation that apply to labor negotiations, climate burden sharing, divorce settlements, and M&A deals.\n", "status": "established", "fields": [ "social-science", "economics", "mathematics", "game-theory" ], "color": "blue" }, { "id": "b-bayesian-networks-causal-reasoning", "type": "bridge", "title": "Bayesian Networks and Causal Reasoning — directed graphical models, d-separation, and Pearl's do-calculus formalise the distinction between correlation and causation", "status": "established", "fields": [ "mathematics", "social-science", "statistics", "computer-science", "epidemiology" ], "color": "blue" }, { "id": "b-condorcet-paradox-voting-cycles", "type": "bridge", "title": "The Condorcet paradox demonstrates that majority voting on three or more alternatives can produce cyclic collective preferences (A beats B, B beats C, C beats A) even when all individual preferences are transitive — a mathematical impossibility result underlying Arrow's theorem and spatial voting theory, with the median voter theorem providing the single-peaked exception.\n", "status": "established", "fields": [ "social-science", "mathematics", "political-science", "economics", "game-theory" ], "color": "blue" }, { "id": "b-network-centrality-social-influence", "type": "bridge", "title": "Network centrality measures — degree, betweenness, eigenvector, and Katz centrality — derived from spectral properties of the adjacency matrix, provide a unified mathematical framework quantifying social influence, predicting epidemiological superspreaders, economic wage inequality in oligopoly, and information diffusion in social networks.\n", "status": "established", "fields": [ "social-science", "mathematics", "network-science", "economics", "epidemiology", "sociology" ], "color": "blue" }, { "id": "b-prediction-markets-information-aggregation", "type": "bridge", "title": "Hanson's logarithmic market scoring rule is a proper scoring rule that implements Bayesian belief aggregation as a market mechanism — linking information theory to political economy", "status": "established", "fields": [ "social-science", "mathematics" ], "color": "blue" }, { "id": "b-social-capital-network-science", "type": "bridge", "title": "Granovetter's \"strength of weak ties\" and Burt's structural holes in social capital theory are precisely identified with bridge edges and high-betweenness-centrality nodes in graph theory: social capital reduces to computable network topology, and the Erdős-Rényi giant component transition predicts the critical network density for information to spread society-wide.\n", "status": "established", "fields": [ "social-science", "sociology", "graph-theory", "network-science", "economics" ], "color": "blue" }, { "id": "b-social-mobility-markov-chain-transition-matrix", "type": "bridge", "title": "Social mobility across income or occupational classes is modeled as a Markov chain with a transition matrix P_{ij} representing the probability of moving from class i to class j across generations; the Markov eigenvalue structure determines long-run mobility rates, steady-state distributions, and whether a society converges to meritocracy or reproduces inequality.\n", "status": "established", "fields": [ "sociology", "mathematics", "economics" ], "color": "blue" }, { "id": "b-structural-holes-brokerage", "type": "bridge", "title": "Burt's structural holes bridge social science and mathematics: brokers who span disconnected network clusters gain information and control advantages quantified by the constraint measure C_i ΓÇö formalizing Granovetter's weak tie strength and Coleman's social capital closure in a unified network theory.\n", "status": "established", "fields": [ "social-science", "mathematics", "network-science", "sociology", "organizational-behavior" ], "color": "blue" }, { "id": "b-voter-model-consensus", "type": "bridge", "title": "The voter model (Clifford & Sudbury 1973) — each agent copies a random neighbor's opinion — maps opinion dynamics onto random walk theory: consensus in d≤2 dimensions, persistent diversity in d>2, T∝N·lnN in 2D, and echo-chamber polarization as network-structured metastable trapping.\n", "status": "established", "fields": [ "social-science", "mathematics", "statistical-physics", "network-science" ], "color": "blue" }, { "id": "b-voting-theory-social-choice", "type": "bridge", "title": "Arrow's impossibility theorem — no voting system with ≥3 candidates satisfies Pareto efficiency, independence of irrelevant alternatives, and non-dictatorship simultaneously — and the Gibbard-Satterthwaite theorem that any reasonable voting rule 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similarity clustering is driven by choice, opportunity, or network structure.\n", "status": "proposed", "fields": [ "social-science", "network-science", "statistics", "sociology" ], "color": "blue" }, { "id": "b-homophily-structural-segregation", "type": "bridge", "title": "Homophily and structural segregation — the tendency of similar individuals to connect produces modular networks that are the mathematical basis of filter bubbles and information siloing", "status": "established", "fields": [ "social-science", "network-science", "sociology", "mathematics", "information-theory" ], "color": "blue" }, { "id": "b-social-capital-network-centrality", "type": "bridge", "title": "Bourdieu's social capital — resources available through social networks — maps precisely onto network centrality measures: betweenness centrality captures brokerage capital (Burt's structural holes), eigenvector centrality captures prestige capital, and the Gini coefficient of the degree distribution measures inequality in social capital access.\n", "status": "established", "fields": [ "sociology", "network-science", "social-science", "graph-theory", "economics" ], "color": "blue" }, { "id": "b-cascade-failures-interdependent-networks", "type": "bridge", "title": "Interdependent network theory (Buldyrev et al. 2010) shows that mutual dependencies between coupled infrastructure networks (power grid ↔ communication network) convert continuous second-order percolation transitions into abrupt first-order cascades, with direct application to the 2003 Italy blackout and financial systemic risk.\n", "status": "established", "fields": [ "social-science", "infrastructure-systems", "physics", "network-science", "percolation-theory" ], "color": "blue" }, { "id": "b-complexity-economics-far-equilibrium", "type": "bridge", "title": "Complexity economics treats markets as far-from-equilibrium dissipative systems driven by inductive agent strategies — the El Farol minority game, Schumpeterian creative destruction, and QWERTY path dependence all emerge from the same positive- feedback and self-organised criticality physics that governs phase transitions.\n", "status": "established", "fields": [ "social-science", "economics", "physics", "complexity-science" ], "color": "blue" }, { "id": "b-complexity-emergence-social-systems", "type": "bridge", "title": "Complexity and Emergence in Social Systems — self-organised criticality, power laws, and the edge of chaos describe cities, economies, and civilisations as complex adaptive systems", "status": "established", "fields": [ "physics", "social-science", "economics", "complex-systems", "network-science" ], "color": "blue" }, { "id": "b-econophysics-wealth-distribution", "type": "bridge", "title": "Pareto's power-law wealth distribution P(w>x) ∝ x^{-α} (α≈1.5) emerges from Bouchaud-Mézard multiplicative noise models analogous to Boltzmann-Gibbs statistics, while Piketty's r>g inequality reproduces the physicist's condition for unbounded 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and predicting language death rates matching Zipf power-law observations.\n", "status": "established", "fields": [ "social-science", "physics", "complexity-science", "cultural-dynamics", "computational-social-science" ], "color": "blue" }, { "id": "b-traffic-flow-fluid-dynamics", "type": "bridge", "title": "Vehicular traffic flow obeys fluid-dynamic conservation laws: the LWR model maps vehicle density to fluid density and velocity to flow velocity, traffic jams propagate as shock waves satisfying the Rankine-Hugoniot condition, and phantom traffic jams arise from the same Turing-like linear instability that creates stop-and-go waves in supply chains, pedestrian crowds, and ant trails.\n", "status": "established", "fields": [ "social-science", "physics", "fluid-dynamics", "transportation-science" ], "color": "blue" }, { "id": "b-survey-causal-inference", "type": "bridge", "title": "The potential outcomes framework (Rubin) and Pearl's do-calculus provide the statistical foundations for causal inference from survey and observational data, connecting survey methodology to formal causal graph theory", "status": "established", "fields": [ "social-science", "statistics" ], "color": "blue" }, { "id": "b-liquid-crystals-frank-elasticity", "type": "bridge", "title": "Liquid crystal orientational order is described by the Frank elastic free energy functional F=∫[K1(∇·n̂)²+K2(n̂·∇×n̂)²+K3(n̂×∇×n̂)²]dV, which maps onto the Landau theory with a vector order parameter", "status": "established", "fields": [ "soft-matter", "physics", "condensed-matter" ], "color": "blue" }, { "id": "b-granular-matter-jamming-transition", "type": "bridge", "title": "Dense granular materials undergo a jamming transition from fluid-like to solid-like behaviour analogous to a second-order phase transition in statistical physics: at packing fraction phi_c ~ 0.64 (random close packing) the contact network percolates, diverging length and time scales appear, and the system's response maps onto the critical phenomena universality class of mean-field percolation", "status": "established", "fields": [ "soft-matter", "statistical-physics", "condensed-matter-physics" ], "color": "blue" }, { "id": "b-nematic-ordering-maier-saupe-mean-field", "type": "bridge", "title": "Nematic liquid crystal ordering is a mean-field phase transition described by the Maier-Saupe theory: the order parameter S = (second Legendre polynomial of orientational angle) undergoes a weakly first-order isotropic-to-nematic transition driven by anisotropic van der Waals interactions, with all thermodynamic properties derivable from the mean-field self-consistency equation.\n", "status": "established", "fields": [ "soft-matter", "statistical-physics" ], "color": "blue" }, { "id": "b-boltzmann-shannon-entropy", "type": "bridge", "title": "Boltzmann's entropy S = k_B ln W and Shannon's entropy H = −Σ p_i log p_i are formally identical — thermodynamic entropy IS the Shannon information entropy of the macroscopic probability distribution over microstates.\n", "status": "established", "fields": [ "statistical-mechanics", "information-theory", "thermodynamics" ], "color": "blue" }, { "id": "b-stochastic-thermodynamics-fluctuation-theorems", "type": "bridge", "title": "Fluctuation theorems (Crooks, Jarzynski) connect nonequilibrium work distributions to equilibrium free energy differences, bridging stochastic thermodynamics and information theory through the mathematical identity between entropy production and relative entropy (KL divergence).\n", "status": "established", "fields": [ "statistical-physics", "information-theory", "thermodynamics" ], "color": "blue" }, { "id": "b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling", "type": "bridge", "title": "Kramers-Moyal moment expansions can transfer from stochastic physics to tumor phenotype transition models.", "status": "proposed", "fields": [ "statistical-physics", "oncology", "mathematics" ], "color": "blue" }, { "id": "b-thermodynamic-uncertainty-relation-x-estimation-precision", "type": "bridge", "title": "Thermodynamic uncertainty relations connect entropy production budgets to lower bounds on estimator variance in nonequilibrium biochemical sensing.\n", "status": "proposed", "fields": [ "statistical-physics", "statistics", "biophysics", "information-thermodynamics" ], "color": "blue" }, { "id": "b-fisher-information-evolution", "type": "bridge", "title": "R.A. Fisher's fundamental theorem of natural selection and his Fisher information matrix in statistics are the same mathematical object — the rate of increase of mean fitness equals the population's statistical Fisher information about fitness, and this identity gives evolutionary biology the full toolkit of statistical estimation theory.\n", "status": "proposed", "fields": [ "statistics", "mathematical-statistics", "evolutionary-biology", "population-genetics", "quantum-information-theory" ], "color": "blue" }, { "id": "b-deseq2-shrinkage-estimation-x-low-count-clinical-biomarker-surveillance", "type": "bridge", "title": "DESeq2-style shrinkage estimation bridges RNA-seq dispersion modeling and low-count clinical biomarker surveillance.", "status": "proposed", "fields": [ "statistics", "medicine", "epidemiology" ], "color": "blue" }, { "id": "b-elastic-net-regularization-x-polygenic-risk-model-stability", "type": "bridge", "title": "Elastic-net regularization links high-dimensional regression theory to clinically deployable polygenic risk modeling.", "status": "proposed", "fields": [ "statistics", "medicine", "genetics" ], "color": "blue" }, { "id": "b-laplace-approximation-x-clinical-trial-adaptive-enrichment", "type": "bridge", "title": "Laplace-approximation workflows can transfer from Bayesian inference to adaptive enrichment in clinical trials.", "status": "proposed", "fields": [ "statistics", "medicine", "biostatistics" ], "color": "blue" }, { "id": "b-bayesian-inference-stat-mech", "type": "bridge", "title": "The Bayesian normalizing constant (evidence) is formally identical to the statistical-mechanical partition function Z = Σ exp(-E/T); sampling from the posterior is equivalent to sampling from a Gibbs distribution; and MCMC algorithms are molecular dynamics simulations on the posterior energy landscape, making statistical physics and Bayesian inference the same mathematical theory.\n", "status": "established", "fields": [ "statistics", "Bayesian-inference", "physics", "statistical-mechanics", "machine-learning" ], "color": "blue" }, { "id": "b-optimal-transport-barycenters-x-multiomic-patient-alignment", "type": "bridge", "title": "Optimal-transport barycenters can transfer from distributional geometry to cross-cohort multiomic alignment.", "status": "proposed", "fields": [ "statistics", "systems-biology", "mathematics" ], "color": "blue" }, { "id": "b-optimal-transport-x-single-cell-developmental-lineage-mapping", "type": "bridge", "title": "Optimal transport couplings align probability geometry with developmental lineage inference in single-cell systems.", "status": "proposed", "fields": [ "statistics", "systems-biology", "genomics" ], "color": "blue" }, { "id": "b-variational-autoencoders-x-single-cell-latent-state-denoising", "type": "bridge", "title": "Variational autoencoder inference links probabilistic latent-variable modeling with single-cell state denoising.", "status": "proposed", "fields": [ "statistics", "systems-biology", "computer-science" ], "color": "blue" }, { "id": "b-markov-jump-processes-x-cell-state-switching-therapy-design", "type": "bridge", "title": "Markov jump process control can transfer from stochastic systems engineering to cell-state switching therapy design.", "status": "proposed", "fields": [ "stochastic-processes", "oncology", "control-engineering" ], "color": "blue" }, { "id": "b-synthetic-genetics-xna-alphabet", "type": "bridge", "title": "Xeno-nucleic acids (XNAs) with chemically modified backbones (HNA, CeNA, LNA, FANA, TNA) can store and propagate genetic information through in vitro evolution, demonstrating that the Watson-Crick hydrogen-bonding code is substrate-independent: Darwinian evolution does not require the ribose- phosphate backbone of natural DNA/RNA.\n", "status": "established", "fields": [ "synthetic-biology", "chemistry", "molecular-biology", "origins-of-life" ], "color": "blue" }, { "id": "b-simclr-x-multiomics-latent-alignment", "type": "bridge", "title": "Contrastive representation learning bridges SimCLR invariance objectives and multi-omics latent alignment across assay modalities.", "status": "proposed", "fields": [ "systems-biology", "machine-learning", "statistics" ], "color": "blue" }, { "id": "b-carbon-capture-entropy-cost", "type": "bridge", "title": "Direct air carbon capture is constrained by thermodynamics — actual DAC systems consume 10-20× above the minimum work set by entropy of mixing, and closing this gap requires understanding sorbent-CO₂ kinetics at the molecular level.\n", "status": "established", "fields": [ "thermodynamics", "atmospheric-chemistry", "materials-science", "chemical-engineering" ], "color": "blue" }, { "id": "b-maxwells-demon-computation", "type": "bridge", "title": "Maxwell's demon is resolved by Landauer's principle — erasing one bit of information dissipates at least kT ln 2 of energy, exactly linking Shannon information entropy to thermodynamic entropy and establishing the physical cost of logical irreversibility.\n", "status": "established", "fields": [ "thermodynamics", "computer-science", "information-theory", "statistical-mechanics" ], "color": "blue" }, { "id": "b-urban-morphology-fractal-dimension-scaling", "type": "bridge", "title": "Urban morphology — the spatial structure of cities — exhibits fractal scaling: street networks, building footprints, and population density follow power-law distributions with fractal dimensions D ≈ 1.7-1.9, and Zipf's law governs city size distributions; these are explained by growth processes analogous to diffusion-limited aggregation and preferential attachment in complex network theory.\n", "status": "established", "fields": [ "urban-science", "mathematics", "complex-systems" ], "color": "blue" }, { "id": "b-viral-evolution-quasispecies-fitness-landscape", "type": "bridge", "title": "RNA virus populations evolve as quasispecies — clouds of mutant sequences near a fitness landscape peak — a concept borrowed from the physics of spin glasses and applied to virology, explaining error catastrophe, lethal mutagenesis, and immune escape.\n", "status": "established", "fields": [ "virology", "evolutionary-biology" ], "color": "blue" }, { "id": "b-viral-quasispecies-x-nk-rugged-landscape", "type": "bridge", "title": "Viral quasispecies theory treats mutant clouds as error-prone replication distributions shifting across fitness ridges — sharing landscape metaphors with Kauffman NK models where epistatic coupling creates rugged fitness surfaces with many local optima — enabling borrowings between virology escape pathways and combinatorial optimization rhetoric used in evolutionary computation.\n", "status": "proposed", "fields": [ "virology", "evolutionary-biology" ], "color": "blue" }, { "id": "b-viral-quasispecies-error-threshold", "type": "bridge", "title": "RNA virus populations exist as quasispecies clouds near an error threshold defined by information theory: exceeding the critical mutation rate causes mutational meltdown, making the Eigen quasispecies equations a direct application of Shannon channel capacity to molecular evolution.\n", "status": "established", "fields": [ "virology", "information-theory", "evolutionary-biology" ], "color": "blue" }, { "id": "b-protein-language-model-x-viral-escape-fitness-landscape", "type": "bridge", "title": "Protein language-model priors bridge sequence representation learning and viral escape fitness landscape forecasting.", "status": "proposed", "fields": [ "virology", "machine-learning", "evolutionary-biology" ], "color": "blue" }, { "id": "b-magma-fragmentation-rheology", "type": "bridge", "title": "Explosive volcanic eruptions occur when magma fragmentation transitions from ductile to brittle as ascent rate exceeds the structural relaxation time of silicate melt, quantified by the Deborah number De = τ_relax / τ_deform comparing melt viscosity timescale to deformation rate", "status": "established", "fields": [ "volcanology", "fluid-mechanics", "physics" ], "color": "blue" }, { "id": "h-3manifold-invariants-topological-completeness", "type": "hypothesis", "title": "Quantum 3-manifold invariants (Witten-Reshetikhin-Turaev, Kontsevich integral) are not complete invariants of homeomorphism type — pairs of non-homeomorphic 3-manifolds can have identical WRT invariants at all levels r — but the totality of all quantum invariants (stable cohomology operations) conjecturally detects all exotic smooth structures, with categorification (Khovanov-like homologies) potentially achieving completeness", "status": "active", "fields": [ "low-dimensional-topology", "mathematics", "mathematical-physics", "quantum-algebra" ], "color": "green" }, { "id": "h-abc-conjecture-iut-verification-path", "type": "hypothesis", "title": "Mochizuki's inter-universal Teichmüller theory (IUT) proof of the abc conjecture is likely correct but contains verification-blocking notational and conceptual barriers — a formalized proof in a proof assistant (Lean 4) or a 50-page accessible survey of the key novel constructions would enable community verification within 5 years.\n", "status": "active", "fields": [ "number-theory", "arithmetic-geometry", "proof-theory" ], "color": "green" }, { "id": "h-aco-convergence-rate-pheromone-evaporation", "type": "hypothesis", "title": "ACO convergence rate to the TSP optimal tour scales as O(n^2 / rho) where rho is the evaporation rate, predicting that low evaporation rates converge faster on structured instances but slower on random ones", "status": "active", "fields": [ "computer-science", "biology" ], "color": "green" }, { "id": "h-acoustic-metamaterial-cloaking-bandwidth-thickness-tradeoff", "type": "hypothesis", "title": "Acoustic metamaterial cloaks face a fundamental bandwidth-thickness trade-off governed by the Kramers-Kronig causality relations: broadband three-dimensional cloaking requires a cloak thickness-to-wavelength ratio ≥ 1, making practical acoustic cloaking at audible frequencies (wavelengths 2-20 cm) limited to structures larger than ~10 cm.\n", "status": "active", "fields": [ "physics", "engineering", "materials-science", "acoustics" ], "color": "green" }, { "id": "h-acoustic-metamaterials-x-negative-refraction", "type": "hypothesis", "title": "Locally resonant acoustic metamaterial slabs with a 20% fractional bandwidth centered at 500 kHz (using silicone-coated tungsten spheres in epoxy host) will achieve sub-diffraction focusing at λ/5 resolution in water, enabling acoustic imaging of 600-μm structures that are invisible to conventional ultrasound", "status": "active", "fields": [ "physics", "materials-science", "medicine" ], "color": "green" }, { "id": "h-acoustic-topological-insulator-surface-states", "type": "hypothesis", "title": "A phononic crystal with a Z2 topological band gap supports topologically protected acoustic surface states at its boundary that are immune to backscattering from smooth defects, enabling waveguides with zero-reflection around bends at frequencies within the phononic band gap.\n", "status": "active", "fields": [ "acoustics", "condensed-matter-physics", "materials-science" ], "color": "green" }, { "id": "h-activation-energy-mb-tail-universality", "type": "hypothesis", "title": "The Arrhenius equation is a universal consequence of the Maxwell-Boltzmann tail integral for any thermally activated process, and deviations from Arrhenius behaviour (curved Arrhenius plots) are diagnostic for quantum tunnelling, multi-step mechanisms, or temperature-dependent activation energy\n", "status": "active", "fields": [ "physical-chemistry", "statistical-mechanics", "quantum-chemistry" ], "color": "green" }, { "id": "h-active-brownian-motion-x-cell-migration", "type": "hypothesis", "title": "Cancer cell invasiveness in 3D ECM is quantitatively predicted by the active Brownian particle persistence time and self-propulsion speed measured in 2D migration assays, with more invasive cell lines showing longer persistence times and higher effective diffusivity.\n", "status": "active", "fields": [ "cell-biology", "biophysics", "cancer-biology", "physics" ], "color": "green" }, { "id": "h-active-learning-bayesian-optimization-improves-alloy-hit-rate", "type": "hypothesis", "title": "Bayesian-optimization-guided active learning improves high-performance alloy hit rate per experiment.", "status": "active", "fields": [ "materials-science", "machine-learning", "chemistry" ], "color": "green" }, { "id": "h-active-matter-percolation-oncology", "type": "hypothesis", "title": "Active tumour vascular networks can be driven into an \"unpercolated active solid\" phase by self-propelled cell migration — a fragmentation regime with no classical analogue that makes adaptive therapy more effective than passive percolation models predict.\n", "status": "active", "fields": [ "statistical-physics", "oncology", "active-matter-physics", "biophysics" ], "color": "green" }, { "id": "h-active-matter-wound-closure-optimization", "type": "hypothesis", "title": "Wound closure rate is maximized when the epithelial tissue operates near the solid-to-fluid jamming transition, because near-jammed tissues have maximal mechanical coupling between cells (enabling coordinated force generation) while retaining sufficient fluidity for migration, predicting that pharmacological modulation of cell-cell adhesion toward the jamming point improves wound closure.\n", "status": "active", "fields": [ "cell-biology", "biophysics", "active-matter-physics" ], "color": "green" }, { "id": "h-active-nematic-defect-tissue-extrusion", "type": "hypothesis", "title": "In epithelial monolayers modelled as 2D active nematics, +½ topological defects are causally sufficient to trigger apoptotic cell extrusion through compressive stress concentration above a critical threshold, making defect density a mechanical homeostasis variable that the tissue actively controls.\n", "status": "active", "fields": [ "biological-physics", "cell-biology", "developmental-biology" ], "color": "green" }, { "id": "h-adaptive-inflation-ensemble-kalman-corrects-extreme-events", "type": "hypothesis", "title": "State-dependent inflation tuned to spread–skill diagnostics reduces ensemble underdispersion ahead of rapidly deepening cyclones versus static inflation, lowering short-range track/intensity error in OSSEs — requires confirmation across models and observation suites.\n", "status": "active", "fields": [ "meteorology", "control-engineering" ], "color": "green" }, { "id": "h-adaptive-kspace-schedules-preserve-diagnostic-mri-quality-at-higher-acceleration", "type": "hypothesis", "title": "Adaptive k-space schedules maintain diagnosis-level MRI performance better than fixed undersampling at equal acceleration.", "status": "active", "fields": [ "radiology", "signal-processing", "computer-vision" ], "color": "green" }, { "id": "h-adaptive-sprt-alerting-detects-concerning-pathogen-variants-earlier-than-fixed-window-rules", "type": "hypothesis", "title": "Transferred methods from `b-sequential-probability-ratio-test-x-pathogen-genomic-surveillance` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "statistics", "epidemiology" ], "color": "green" }, { "id": "h-adaptive-temperature-ladders-improve-posterior-mixing", "type": "hypothesis", "title": "Adaptive temperature ladders improve ESS-per-compute for Bayesian neural posterior sampling versus fixed ladders.", "status": "active", "fields": [ "computer-science", "statistics", "machine-learning" ], "color": "green" }, { "id": "h-adaptive-therapy-percolation-threshold", "type": "hypothesis", "title": "Tumor spatial invasion is governed by a percolation threshold in the cancer cell connectivity network, and adaptive therapy strategies that maintain cell density below this threshold can achieve indefinite containment without elimination", "status": "active", "fields": [ "biology", "medicine", "physics" ], "color": "green" }, { "id": "h-adiabatic-elimination-preserves-switching-time-statistics-in-gene-circuit-surrogates", "type": "hypothesis", "title": "Transferred methods from `b-adiabatic-elimination-x-gene-circuit-model-reduction` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "physics", "systems-biology" ], "color": "green" }, { "id": "h-adjoint-base-resolution-operator-matches-ray-density-despite-scale-gap", "type": "hypothesis", "title": "Discrete adjoint Gram matrices built from identical beam-spreading kernels will exhibit comparable condition-number degradation trends versus angular ray-density deficits whether instantiated on ocean basin meshes or anthropomorphic ultrasound CT grids — falsified if clinically imposed absorption priors dominate conditioning unlike oceanography datasets.\n", "status": "active", "fields": [ "oceanography", "medicine" ], "color": "green" }, { "id": "h-adjoint-preconditioning-improves-seismic-inversion-convergence", "type": "hypothesis", "title": "Applying backprop-inspired gradient normalization to adjoint seismic inversion reduces early-iteration misfit stagnation.", "status": "active", "fields": [ "geophysics", "computer-science", "optimization" ], "color": "green" }, { "id": "h-adjuvant-trained-innate-immunity-mechanism", "type": "hypothesis", "title": "Vaccine adjuvants enhance adaptive immune responses primarily by triggering trained innate immunity in bone marrow progenitors via epigenetic reprogramming (H3K4me3 at inflammatory gene promoters), and the duration and magnitude of training determines the adjuvant effect on adaptive response quality.\n", "status": "active", "fields": [ "immunology", "epigenetics", "vaccine-science" ], "color": "green" }, { "id": "h-advection-diffusion-x-odor-plume-search", "type": "hypothesis", "title": "Insects trained in wind tunnels with controlled Obukhov-length turbulence statistics will shift casting frequencies proportionally to predicted Lagrangian intermittency exponents derived from large-eddy odor surrogate fields — outperforming Gaussian plume policy baselines.\n", "status": "active", "fields": [ "ecology", "fluid-mechanics" ], "color": "green" }, { "id": "h-aerosol-ccn-activation-kohler-threshold", "type": "hypothesis", "title": "Combining Köhler theory with organic aerosol kappa-hygroscopicity parameterization will reduce climate model uncertainty in cloud droplet number concentration by >50% compared to purely inorganic CCN models, enabling aerosol-cloud interaction forcing uncertainty to be narrowed from ±0.7 W/m^2 to ±0.4 W/m^2", "status": "active", "fields": [ "atmospheric-science", "chemistry", "climate-science" ], "color": "green" }, { "id": "h-aerosol-cloud-twomey-adjustment", "type": "hypothesis", "title": "The Twomey first indirect aerosol effect (increased cloud droplet number reducing effective radius) is offset by the second indirect effect (lifetime/Albrecht effect) by 40-60%, such that the net aerosol indirect forcing is between -0.5 and -1.2 W/m² at 90% confidence, resolvable by coordinated ship-track experiments combined with PACE satellite aerosol retrievals.\n", "status": "active", "fields": [ "climate-science", "atmospheric-science", "physics", "remote-sensing" ], "color": "green" }, { "id": "h-aesthetic-preference-fluency-prediction-error", "type": "hypothesis", "title": "Aesthetic preference is generated by circuits in the orbitofrontal cortex and reward network computing processing fluency as a proxy for statistical regularity — beautiful objects are those that optimally compress sensory input", "status": "active", "fields": [ "cognitive-neuroscience", "psychology", "neuroaesthetics" ], "color": "green" }, { "id": "h-aesthetic-preference-reward-prediction-error", "type": "hypothesis", "title": "Aesthetic preference arises from predictive coding in hierarchical sensory cortex: artworks that generate optimal prediction errors — neither too predictable nor too surprising — produce the strongest aesthetic response, with individual differences in preference reflecting differences in learned priors from exposure history.\n", "status": "active", "fields": [ "neuroaesthetics", "cognitive-science", "neuroscience", "computational-neuroscience" ], "color": "green" }, { "id": "h-aftershock-clustering-inflates-sprt-false-alarm-rate-fixed-boundaries", "type": "hypothesis", "title": "Holding Wald boundaries fixed, correlated aftershock waveforms increase the empirical weekly false-alert rate versus nominal α predicted under independence — requiring inflation factors ~2–5× (scenario-dependent) for regulatory parity.\n", "status": "active", "fields": [ "seismology", "statistics" ], "color": "green" }, { "id": "h-agent-based-models-x-emergent-markets", "type": "hypothesis", "title": "Market crashes exhibit log-periodic power law (LPPL) precursors consistent with the Johansen-Ledoit-Sornette model, with the predicted critical time within 5% of actual crash dates for >70% of major market crashes over 1987-2020.\n", "status": "active", "fields": [ "economics", "physics", "complex-systems", "finance" ], "color": "green" }, { "id": "h-agent-surrogate-optimization-reduces-intervention-regret", "type": "hypothesis", "title": "Surrogate-assisted optimization over agent-based epidemic simulations reduces intervention regret versus grid search.", "status": "active", "fields": [ "public-health", "machine-learning", "epidemiology" ], "color": "green" }, { "id": "h-alfven-turbulence-stochastic-ion-heating", "type": "hypothesis", "title": "Stochastic heating by large-amplitude Alfvénic fluctuations accounts for the majority of proton perpendicular temperature anisotropy observed in the inner heliosphere, with heating rate scaling as the cube of the Alfvén wave amplitude\n", "status": "active", "fields": [ "astrophysics", "plasma-physics" ], "color": "green" }, { "id": "h-algorithm-discovery-search-over-program-space", "type": "hypothesis", "title": "Autonomous algorithm discovery is tractable for bounded problem classes by framing it as search over the space of programs using learned heuristics — but faces fundamental limits from Kolmogorov complexity for general algorithm synthesis", "status": "active", "fields": [ "computer-science", "mathematics", "artificial-intelligence" ], "color": "green" }, { "id": "h-allelopathy-glucosinolate-diversity-coevolution-ratchet", "type": "hypothesis", "title": "The diversity of glucosinolates in Brassicaceae (> 130 structures) is driven by a ratchet-like coevolutionary dynamic with Pieridae butterfly detoxification enzymes — each novel glucosinolate provides a temporary escape from specialist herbivores, driving plant radiation, until herbivores evolve counter-adaptations, with the ratchet rate predicted by substitution rate models of host-parasite coevolution.\n", "status": "active", "fields": [ "ecology", "chemistry", "evolutionary-biology", "entomology" ], "color": "green" }, { "id": "h-allometric-quarter-power-fractal-origin", "type": "hypothesis", "title": "The 3/4 metabolic scaling exponent is a universal consequence of volume-filling fractal resource networks with area-preserving branching, and significant deviations from this exponent in empirical datasets reflect taxon-specific departures from idealized branching geometry rather than a distinct scaling mechanism", "status": "active", "fields": [ "biology", "mathematics", "ecology" ], "color": "green" }, { "id": "h-allometric-rg-fixed-point", "type": "hypothesis", "title": "Vascular branching recursion has an RG fixed point at area-preserving branching, and the Wilson-Fisher correction-to-scaling terms quantitatively predict the observed deviation from Kleiber's Law below 1 gram body mass.\n", "status": "active", "fields": [ "mathematical-physics", "theoretical-biology", "comparative-physiology" ], "color": "green" }, { "id": "h-allosteric-regulation-x-conformational-dynamics", "type": "hypothesis", "title": "Allosteric coupling free energy between sites is quantitatively predicted by the mutual information between residue positions in equilibrium MD simulations (linear mutual information decomposition), with Pearson r > 0.8 against experimentally measured coupling constants across diverse protein families.\n", "status": "active", "fields": [ "structural-biology", "biophysics", "computational-biology", "protein-science" ], "color": "green" }, { "id": "h-alphafold-confidence-weighted-screening-improves-enzyme-hit-rates", "type": "hypothesis", "title": "Confidence-weighted AlphaFold priors improve enzyme-screen hit rates versus sequence-only prioritization.", "status": "active", "fields": [ "chemistry", "molecular-biology", "computer-science" ], "color": "green" }, { "id": "h-alphafold-energy-landscape-implicit-learning", "type": "hypothesis", "title": "AlphaFold2 implicitly learns the protein energy landscape from evolutionary covariation such that its attention maps correspond quantitatively to physical coupling constants in the Potts model, and misfolding-prone sequences can be identified by high frustration in the learned landscape.\n", "status": "active", "fields": [ "biology", "biophysics", "computational-biology", "statistical-mechanics" ], "color": "green" }, { "id": "h-alternating-projection-warm-starts-reduce-cryoem-orientation-assignment-errors", "type": "hypothesis", "title": "Transferred methods from `b-phase-retrieval-x-cryoem-orientation-inference` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "signal-processing", "structural-biology" ], "color": "green" }, { "id": "h-amoc-fold-bifurcation-ew", "type": "hypothesis", "title": "AMOC collapse is a subcritical fold bifurcation, and the rising AR1 and variance already visible in the AMOC fingerprint data (Boers 2021) follow the universal fold-bifurcation scaling exponents — meaning AMOC is within measurable early-warning range of its tipping point and the remaining warning time is estimable from the scaling trajectory.\n", "status": "active", "fields": [ "climate-science", "statistical-physics", "oceanography" ], "color": "green" }, { "id": "h-amoc-saddle-node-bifurcation", "type": "hypothesis", "title": "AMOC crosses a saddle-node bifurcation at sustained Greenland melt rates exceeding 0.3 Sv, leading to irreversible collapse achievable under high-emission scenarios by 2100", "status": "active", "fields": [ "climate-science", "physics", "oceanography" ], "color": "green" }, { "id": "h-anderson-acceleration-deq-forward-steps-correlate-with-val-loss", "type": "hypothesis", "title": "On standard vision benchmarks with matched DEQ width, enabling Anderson acceleration for forward equilibrium solves will reduce median residual iterations without increasing validation loss versus pure Picard iteration when backward passes use matched adjoint tolerances — falsified if acceleration shortcuts introduce gradient bias that hurts accuracy despite fewer forward steps.\n", "status": "active", "fields": [ "machine-learning", "numerical-analysis" ], "color": "green" }, { "id": "h-anderson-localization-allostery", "type": "hypothesis", "title": "Allosteric signal propagation in proteins uses delocalized (non-Anderson-localized) normal modes, and allosteric efficiency correlates with the participation ratio of the relevant vibrational mode", "status": "active", "fields": [], "color": "green" }, { "id": "h-anesthesia-consciousness-thalamic-disruption", "type": "hypothesis", "title": "General anesthetics suppress consciousness primarily by disrupting thalamocortical connectivity and slow-wave up-down state cycling, with the critical site being thalamic relay and reticular nuclei rather than cortex directly; propofol's GABAergic enhancement of thalamic reticular neurons gates cortical information integration, consistent with Global Workspace Theory predictions", "status": "active", "fields": [ "neuroscience", "anesthesiology", "consciousness-science", "pharmacology" ], "color": "green" }, { "id": "h-annealed-rbm-pretraining-improves-phase-diagram-discovery", "type": "hypothesis", "title": "Noise-annealed contrastive schedules reduce critical slowing signatures by improving effective mixing proxies measured during RBM training on structured synthetic Ising-like data laws.", "status": "active", "fields": [ "physics", "computer-science", "machine-learning" ], "color": "green" }, { "id": "h-ant-colony-byzantine-fault-tolerance", "type": "hypothesis", "title": "Honeybee quorum sensing during swarm site selection achieves Byzantine fault tolerance with honest majority threshold f < N/3, provably equivalent to PBFT under realistic colony size constraints\n", "status": "active", "fields": [ "biology", "computer_science", "distributed_systems" ], "color": "green" }, { "id": "h-ant-colony-optimization-x-gradient-free-metaheuristics", "type": "hypothesis", "title": "On standardized logistics routing benchmarks with explicit compute envelopes, tuned ant colony variants will outperform covariance-matrix-adaptation ES baselines on sparse graphs with routing bottleneck motifs — yet lose on dense Euclidean instances — delineating empirical dominance islands absent universal superiority claims.\n", "status": "active", "fields": [ "operations-research", "evolutionary-computation" ], "color": "green" }, { "id": "h-antarctic-bottom-water-meltwater-circulation-collapse", "type": "hypothesis", "title": "Antarctic Bottom Water formation rate is primarily controlled by brine rejection during sea ice formation in coastal polynyas; accelerating ice shelf melt introduces freshwater stratification that will reduce AABW production by 20-40% by 2100 under SSP3-7.0.\n", "status": "active", "fields": [ "physical-oceanography", "geology", "climate-science", "glaciology" ], "color": "green" }, { "id": "h-antibiotic-resistance-stochastic-dynamics", "type": "hypothesis", "title": "Antibiotic resistance evolution rate in clinical settings is primarily determined by stochastic within-host mutation-selection dynamics modulated by antibiotic pharmacokinetics and patient immune status, not simply by antibiotic exposure duration.\n", "status": "active", "fields": [ "microbiology", "evolutionary-biology", "pharmacology", "clinical-medicine" ], "color": "green" }, { "id": "h-antibiotic-synergy-pharmacodynamic-surfaces", "type": "hypothesis", "title": "Antibiotic pairs targeting synthetic-lethal gene pairs in E. coli essential network will show FICI < 0.5 in >80% of cases, while pairs targeting the same pathway will show FICI > 4 (antagonism) in >60% of cases", "status": "active", "fields": [ "pharmacology", "systems-biology", "microbiology" ], "color": "green" }, { "id": "h-antifreeze-protein-synthetic-polymer-design", "type": "hypothesis", "title": "Polyvinyl alcohol (PVA) and antifreeze glycoprotein-mimicking block copolymers can replicate type I AFP ice-plane selectivity if their hydroxyl group spacing matches the ice Ih prism plane lattice at 4.52 Å, and such polymers will provide equivalent thermal hysteresis to natural AFPs at 1/10th the molecular weight", "status": "active", "fields": [ "biophysics", "materials-science", "biochemistry" ], "color": "green" }, { "id": "h-approval-voting-reduces-strategic-manipulation-vs-plurality", "type": "hypothesis", "title": "Approval voting (voters approve any subset of candidates; winner has most approvals) reduces the frequency of strategically suboptimal voting relative to plurality voting in real elections, as measured by the fraction of voters whose approved candidates diverge from their stated first preference under plurality systems, and produces Condorcet-consistent outcomes more often.\n", "status": "active", "fields": [ "political-science", "economics", "social-choice-theory" ], "color": "green" }, { "id": "h-aqp2-trafficking-as-osmotic-valve", "type": "hypothesis", "title": "AQP2 vesicle trafficking to the apical membrane of kidney collecting duct principal cells functions as a molecularly switchable osmotic valve — with vasopressin-mediated PKA phosphorylation of Ser256 as the trigger — and the rate of trafficking is proportional to osmotic driving force (Δπ), making water reabsorption efficiency a function of both hormonal signal and physical gradient.\n", "status": "active", "fields": [ "physiology", "biophysics", "cell-biology", "nephrology" ], "color": "green" }, { "id": "h-aragonite-saturation-coral-calcification-threshold", "type": "hypothesis", "title": "Coral calcification rates decline nonlinearly with aragonite saturation state, with a critical threshold at Ω_arag = 1.5 below which net dissolution exceeds calcification regardless of temperature, light, or nutrient conditions, and reef structural integrity will be compromised in tropical reefs by 2070 under RCP 8.5", "status": "active", "fields": [ "oceanography", "ecology", "chemistry" ], "color": "green" }, { "id": "h-architectural-geometry-wellbeing-stress", "type": "hypothesis", "title": "Fractal dimension D=1.3-1.5 in built environment facades reduces physiological stress responses (cortisol, skin conductance) and enhances cognitive restoration via Attention Restoration Theory, while direct sunlight exposure, ceiling height proportional to room breadth, and biophilic elements independently reduce stress biomarkers", "status": "active", "fields": [ "environmental-psychology", "neuroscience", "architecture", "cognitive-science" ], "color": "green" }, { "id": "h-architectural-geometry-wellbeing", "type": "hypothesis", "title": "Built environments with high spatial complexity, biophilic elements, and prospect-refuge balance causally reduce cortisol, improve attention restoration, and reduce self-reported stress compared to low-complexity uniform environments.\n", "status": "active", "fields": [ "environmental-psychology", "neuroscience", "architecture", "cognitive-science" ], "color": "green" }, { "id": "h-arctic-amplification-local-feedback-dominance", "type": "hypothesis", "title": "Arctic amplification is primarily driven by local surface-albedo and lapse-rate feedbacks (~60%) rather than poleward heat transport changes, with the relative contribution of transport increasing only under >3°C global warming scenarios.\n", "status": "active", "fields": [ "climate-science", "atmospheric-physics", "physical-oceanography" ], "color": "green" }, { "id": "h-arrow-impossibility-voting-nudges", "type": "hypothesis", "title": "Behavioural nudges that alter the effective presentation order of policy alternatives exploit Arrow's independence-of-irrelevant-alternatives violations in human preference aggregation, and their cross-cultural failure rate is predicted by the degree of preference non-transitivity in each cultural context.\n", "status": "active", "fields": [ "economics", "social-choice-theory", "political-science", "psychology" ], "color": "green" }, { "id": "h-aspic-legal-argument-outcome-prediction", "type": "hypothesis", "title": "ASPIC+ argumentation frameworks populated from legal briefs can predict appellate court outcomes with accuracy exceeding logistic regression on case features alone", "status": "active", "fields": [ "computer-science", "mathematics", "law" ], "color": "green" }, { "id": "h-atmospheric-blocking-arctic-amplification", "type": "hypothesis", "title": "Arctic amplification (reduced equator-to-pole temperature gradient) is increasing Northern Hemisphere blocking frequency by 10-20% per degree of Arctic warming, and this signal is detectable in ERA5 reanalysis as a positive trend in blocking persistence above the 95% significance level when controlling for ENSO and NAO variability.\n", "status": "active", "fields": [ "meteorology", "fluid-mechanics", "climate-science" ], "color": "green" }, { "id": "h-atmospheric-convection-x-rayleigh-benard", "type": "hypothesis", "title": "Tropical mesoscale convective organization (self-aggregation of convection) is a Rayleigh-Bénard instability above Ra_c ≈ 10^18 in the tropical atmosphere, and the aggregation length scale scales with the effective atmospheric boundary layer depth as L ≈ 2π·H, predicting that a 10% increase in tropopause height under global warming will increase convective aggregation scale by the same fraction", "status": "active", "fields": [ "physics", "geoscience", "fluid-mechanics", "climate-science" ], "color": "green" }, { "id": "h-attention-regularized-protein-language-models-improve-fitness-ranking", "type": "hypothesis", "title": "Attention-regularized protein language models improve top-k fitness hit rates over baseline transformers.", "status": "active", "fields": [ "molecular-biology", "computer-science", "biology" ], "color": "green" }, { "id": "h-attention-spotlight-thalamic-gating", "type": "hypothesis", "title": "The attentional spotlight is implemented by thalamic reticular nucleus gating of thalamocortical relay neurons: spatial attention shifts the TRN inhibition pattern to selectively amplify feedforward signals from attended locations while suppressing those from unattended locations, making the TRN the physical substrate of the biased-competition mechanism.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "green" }, { "id": "h-auction-design-x-complexity-theory", "type": "hypothesis", "title": "No polynomial-time truthful mechanism achieves better than O(sqrt(m))-approximation to optimal revenue for combinatorial auctions with m items and submodular valuations, establishing a computational hardness lower bound for truthful multi-item auction design under standard complexity assumptions.\n", "status": "active", "fields": [ "economics", "computer-science", "mathematics", "game-theory" ], "color": "green" }, { "id": "h-auction-theory-x-mechanism-design", "type": "hypothesis", "title": "Second-price combinatorial auctions with item complementarities will achieve at least 63% of optimal social welfare in polynomial time via the greedy algorithm, and this bound is tight for submodular valuation functions\n", "status": "active", "fields": [ "mathematics", "economics", "game-theory" ], "color": "green" }, { "id": "h-autoimmune-disease-idiotypic-attractor-bifurcation", "type": "hypothesis", "title": "Autoimmune diseases represent bifurcations of the idiotypic network to pathological attractors where self-reactive clones are stabilised by mutual idiotypic stimulation, and this bifurcation is detectable as a qualitative change in BCR repertoire network topology before clinical symptom onset\n", "status": "active", "fields": [ "immunology", "network-science", "systems-biology" ], "color": "green" }, { "id": "h-autoimmune-molecular-mimicry-trigger", "type": "hypothesis", "title": "Environmental microbial antigens that share structural epitopes with self-proteins (molecular mimicry) are the primary environmental triggers of autoimmune disease in genetically predisposed individuals carrying HLA risk alleles.\n", "status": "active", "fields": [ "immunology", "microbiology", "genetics", "rheumatology" ], "color": "green" }, { "id": "h-autoimmune-pi-gain-deficiency", "type": "hypothesis", "title": "Autoimmune diseases characterized by Treg deficiency (type 1 diabetes, multiple sclerosis) will show a quantifiably lower PI controller integral gain K_i in longitudinal IL-2/Treg blood data, detectable before clinical onset and predictive of disease severity.\n", "status": "active", "fields": [ "immunology", "control-theory", "systems-biology", "clinical-medicine" ], "color": "green" }, { "id": "h-autonomy-need-empowerment-maximization", "type": "hypothesis", "title": "Intrinsic motivation is operationally identical to empowerment maximisation — the brain implements a policy that maximises the channel capacity from actions to future states I(A;S'), and autonomy need frustration produces measurable reductions in action-outcome mutual information detectable from both neural signals and behavioral entropy", "status": "active", "fields": [ "neuroscience", "information-theory", "cognitive-science", "psychology" ], "color": "green" }, { "id": "h-auxin-turing-pattern-shoot-branching", "type": "hypothesis", "title": "The spacing of axillary bud outgrowth along a plant shoot obeys the wavelength-selection rule of a Turing reaction-diffusion system, with bud spacing inversely proportional to the square root of the ratio of auxin diffusion coefficient to PIN-turnover rate, and this relationship is predictive across Arabidopsis mutants with altered PIN expression levels", "status": "active", "fields": [ "botany", "mathematics", "developmental-biology" ], "color": "green" }, { "id": "h-av-edge-case-power-law-distribution", "type": "hypothesis", "title": "Truly novel edge cases for autonomous vehicles follow a power-law frequency distribution, making exhaustive real-world testing infeasible — safety validation must rely on simulation-based scenario coverage over a defined operational design domain (ODD) with formal coverage proofs.\n", "status": "active", "fields": [ "autonomous-systems", "safety-engineering", "machine-learning" ], "color": "green" }, { "id": "h-axon-soliton-collision-dynamics", "type": "hypothesis", "title": "Action potential collision outcomes at axon branch points (transmission, annihilation, or reflection) can be predicted within 10% accuracy by KdV soliton collision rules applied to the Hodgkin-Huxley cable equation.\n", "status": "active", "fields": [ "neuroscience", "physics", "mathematics" ], "color": "green" }, { "id": "h-banach-space-universality-hierarchy", "type": "hypothesis", "title": "The space of separable Banach spaces under isometric equivalence is not classifiable by countable structures (Borel reducibility), and the isomorphism relation on separable Banach spaces is strictly more complex than any orbit equivalence relation induced by a Polish group action — placing it at the top of the descriptive set theory complexity hierarchy.\n", "status": "active", "fields": [ "functional-analysis", "descriptive-set-theory", "mathematical-logic" ], "color": "green" }, { "id": "h-bank-run-lyapunov-time-shrinks-with-public-information-leaks", "type": "hypothesis", "title": "In stylized withdrawal-belief dynamics, credible leaks that reduce deposit-insurance trust shrink the effective divergence timescale between nearby trajectories — a metaphorical Lyapunov time — but real payment systems may saturate due to circuit breakers; treat as hypothesis not theorem.\n", "status": "active", "fields": [ "economics", "dynamical-systems" ], "color": "green" }, { "id": "h-barcode-spacing-heuristic-lowers-decoding-error-measured-in-negative-controls", "type": "hypothesis", "title": "Increasing minimum pairwise Hamming distance among synthesized barcode adapters beyond empirically tuned thresholds will monotonically reduce negative-control misassignment rates in multiplexed CRISPR pools holding sequencing depth fixed — falsified if PCR chimera dominance yields error floors independent of spacing.\n", "status": "active", "fields": [ "biology", "genomics" ], "color": "green" }, { "id": "h-bat-echolocation-neural-matched-filter-implementation", "type": "hypothesis", "title": "The inferior colliculus of echolocating bats implements a neural matched filter optimally tuned to the species-specific FM sweep waveform — neurons with delay- tuned delay-period responses functionally equivalent to radar matched filters — and this implementation is sufficient to account for the 2-3 mm range resolution measured behaviorally.\n", "status": "active", "fields": [ "neuroscience", "physics", "sensory-biology" ], "color": "green" }, { "id": "h-bayes-factor-theory-selection", "type": "hypothesis", "title": "Replacing null-hypothesis significance testing with pre-registered Bayes factor analysis (B_{01} threshold ≥ 10 for publication) would increase the positive predictive value of published findings by at least 50% and reduce irreproducibility rates in psychology and medicine by cutting false-positive publication rates below 5%.\n", "status": "active", "fields": [ "philosophy-of-science", "Bayesian-statistics", "meta-science", "psychology" ], "color": "green" }, { "id": "h-bayesian-dropout-uncertainty-improves-adaptive-trial-decisions", "type": "hypothesis", "title": "Calibrated Monte Carlo dropout uncertainty improves adaptive clinical-trial decision efficiency without inflating false positive rates.", "status": "active", "fields": [ "statistics", "machine-learning", "medicine" ], "color": "green" }, { "id": "h-bayesian-marginal-likelihood-occam-razor-automatic", "type": "hypothesis", "title": "Bayesian model comparison via marginal likelihood P(E|M) = ∫ P(E|θ,M)P(θ|M)dθ automatically implements Occam's razor — the model evidence penalizes complexity proportional to the prior volume of unused parameter space — and this automatic penalization is formally equivalent to the minimum description length (MDL) principle and Fisher information geometry.\n", "status": "active", "fields": [ "philosophy-of-science", "statistics", "Bayesian-inference", "information-theory" ], "color": "green" }, { "id": "h-bbb-pericyte-wnt-signaling", "type": "hypothesis", "title": "Pericyte-derived WNT ligands dynamically regulate blood-brain barrier permeability through β-catenin signalling in endothelial tight junctions, and disruption of this pathway is a common upstream mechanism in neurological disease-associated BBB breakdown.\n", "status": "active", "fields": [ "neuroscience", "vascular-biology", "cell-biology" ], "color": "green" }, { "id": "h-bci-information-rate-fisher-bound", "type": "hypothesis", "title": "The information transfer rate of state-of-the-art intracortical BCIs is within a factor of 3 of the Fisher information bound set by the recorded neural population, and the primary limitation is non-stationarity rather than suboptimal decoding, predicting that adaptive decoders that track neural tuning drift will outperform fixed decoders by 2-3x in chronic implant conditions.\n", "status": "active", "fields": [ "neuroscience", "signal-processing", "information-theory" ], "color": "green" }, { "id": "h-bcm-sliding-threshold-homeostatic-metaplasticity-cortical-map", "type": "hypothesis", "title": "The BCM sliding modification threshold θ_M implements homeostatic metaplasticity in vivo, and visual cortex deprivation (monocular deprivation) lowers θ_M in deprived-eye columns within 48 hours — enabling adult plasticity rescue by pharmacologically reducing θ_M via mGluR5 blockade or trkB agonism.\n", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "pharmacology" ], "color": "green" }, { "id": "h-bell-local-hidden-variables-definitively-ruled-out", "type": "hypothesis", "title": "Loophole-free Bell experiments (Hensen et al. 2015, Giustina et al. 2015, Shalm et al. 2015) definitively rule out all local hidden variable (LHV) theories consistent with special relativity, implying that quantum non-locality is a genuine feature of nature — though it does not enable superluminal signaling because correlations cannot be used to transmit information.\n", "status": "active", "fields": [ "quantum-physics", "philosophy-of-physics", "foundations-of-quantum-mechanics" ], "color": "green" }, { "id": "h-bet-hedging-x-portfolio-diversification", "type": "hypothesis", "title": "Environmental covariance tensors inferred from satellite-derived drought modes will explain variance in bet-hedging allele frequencies across wild grass populations better than scalar rainfall variance alone — treating diversification analogously to portfolio factor models.\n", "status": "active", "fields": [ "ecology", "economics" ], "color": "green" }, { "id": "h-beta-cell-dedifferentiation-rescue", "type": "hypothesis", "title": "Type 2 diabetes beta cell exhaustion is caused by dedifferentiation (loss of mature beta cell identity markers) rather than apoptosis, and is pharmacologically reversible by GLP-1 receptor agonist plus GABA combination therapy that restores PDX1/NKX6.1 transcription factor expression.\n", "status": "active", "fields": [ "diabetes-research", "cell-biology", "endocrinology" ], "color": "green" }, { "id": "h-beta-delta-neuroeconomics-dual-system", "type": "hypothesis", "title": "The beta-delta model of intertemporal discounting reflects a genuine dual-system neural architecture in which limbic circuits (nucleus accumbens, amygdala) encode hyperbolic discount factor beta for immediately available rewards while dlPFC encodes the exponential discount factor delta for future rewards — and these two systems compete rather than integrate, with the winning system determined by working memory load and emotional state.\n", "status": "active", "fields": [ "neuroscience", "economics", "cognitive-science", "behavioral-economics" ], "color": "green" }, { "id": "h-beta-scheduled-layer-wise-training-mimics-rg-stability", "type": "hypothesis", "title": "On linear-Gaussian generative hierarchies with known RG coarse-graining, layerwise training schedules that match contraction rates reduce intermediate representation instability versus mismatched schedules—without claiming universal RG equivalence for realistic CNNs.", "status": "active", "fields": [ "physics", "computer-science", "machine-learning" ], "color": "green" }, { "id": "h-beta-vae-regularization-improves-single-cell-state-separability", "type": "hypothesis", "title": "Beta-regularized VAEs improve single-cell state separability and transferability versus standard VAEs.", "status": "active", "fields": [ "systems-biology", "statistics", "computer-science" ], "color": "green" }, { "id": "h-betti-numbers-cognitive-complexity", "type": "hypothesis", "title": "The Betti numbers of the neural population activity manifold in prefrontal cortex increase monotonically with working memory load and decrease with cognitive fatigue or aging, providing topological biomarkers of cognitive capacity that are more sensitive than linear dimensionality measures (PCA variance explained).\n", "status": "active", "fields": [ "neuroscience", "mathematics", "cognitive-science", "computational-neuroscience" ], "color": "green" }, { "id": "h-betz-limit-array-cooperation-exceeds-individual", "type": "hypothesis", "title": "Wind turbine arrays with cooperative pitch and yaw control that actively redirect wake flows can exceed the power output of independently operating Betz-limited turbines by >10% at array level, by exploiting wake steering to reduce velocity deficit experienced by downstream turbines\n", "status": "active", "fields": [ "engineering", "fluid-mechanics" ], "color": "green" }, { "id": "h-bic-protected-metasurfaces-maintain-high-q-under-fabrication-noise", "type": "hypothesis", "title": "Quasi-BIC dielectric metasurfaces that co-optimize symmetry protection and footprint maintain higher median Q under realistic fabrication noise than high-index resonator baselines without BIC design constraints.\n", "status": "active", "fields": [ "photonics", "metamaterials", "materials-science" ], "color": "green" }, { "id": "h-bifurcation-continuation-predicts-alternans-onset-optical-mapping", "type": "hypothesis", "title": "For Langendorff-perfused hearts instrumented with voltage-sensitive dyes, numerically continued bifurcation boundaries from patient-specific ion-channel posteriors will predict the pacing-cycle-length onset of spatially concordant alternans within ±5% when fibrosis geometry is co-registered — falsified if mismatch persists after rigorous parameter calibration across ≥20 specimens.\n", "status": "active", "fields": [ "cardiology", "applied-mathematics" ], "color": "green" }, { "id": "h-biodegradable-electronics-cellulose", "type": "hypothesis", "title": "Cellulose nanofiber transistors with degradation triggered by enzymatic exposure can achieve mobility > 1 cm²/V·s and on/off ratio > 10^6, meeting the electrical performance threshold for implantable biosensors while degrading within 30 days in physiological conditions.\n", "status": "active", "fields": [ "materials-science", "bioelectronics", "green-electronics" ], "color": "green" }, { "id": "h-bioelectric-pattern-regeneration-control", "type": "hypothesis", "title": "Pharmacological manipulation of resting membrane potential in Xenopus laevis hindlimb buds using ion channel modulators (ivermectin for Cl- channels, monensin for Na+) will redirect blastema patterning to produce an extra digit in > 20% of operated tadpoles, demonstrating that Vmem patterns are instructive rather than merely permissive for digit number specification", "status": "active", "fields": [ "medicine", "developmental-biology", "biophysics" ], "color": "green" }, { "id": "h-biofilm-eps-crosslink-dispersal-threshold", "type": "hypothesis", "title": "Treating P. aeruginosa biofilms with 10 nM dispersin B (EPS beta-1,6-GlcNAc glycoside hydrolase) for 30 minutes will reduce bulk storage modulus G' by > 90% and cause > 80% biofilm detachment, with the detachment threshold correlated with the yield stress falling below the hydrodynamic wall shear stress in a quantitative Kelvin-Voigt viscoelastic model", "status": "active", "fields": [ "microbiology", "materials-science", "biophysics" ], "color": "green" }, { "id": "h-biofilm-x-active-nematic", "type": "hypothesis", "title": "+1/2 topological defects in E. coli biofilms causally drive local cell extrusion at rates 3× higher than defect-free regions, with extrusion probability scaling with defect velocity predicted by active nematic extensile stress magnitude", "status": "active", "fields": [ "biology", "physics", "microbiology" ], "color": "green" }, { "id": "h-biogeochemical-box-models-x-attractor-stability", "type": "hypothesis", "title": "Continuation analysis on IPCC-class coupled carbon-cycle shells will reveal overlapping hysteresis bands whose widths shrink below observational proxy resolution when Atlantic overturning coupling strengthens — constraining when minimal box models exaggerate multistability claims versus coupled climate configurations.\n", "status": "draft", "fields": [ "biogeochemistry", "dynamical-systems" ], "color": "green" }, { "id": "h-bioluminescence-coevolution-visual-system-deep-sea", "type": "hypothesis", "title": "The spectral peak of bioluminescence emission in mesopelagic organisms (400-1000 m depth) has coevolved with the peak sensitivity of visual pigments in predators at corresponding depths, with both tracking the depth-dependent blue-shifting of residual downwelling daylight, producing a tight correlation between depth, emission lambda_max, and predator rhodopsin lambda_max.\n", "status": "active", "fields": [ "marine-biology", "evolutionary-biology", "photochemistry" ], "color": "green" }, { "id": "h-biomechanics-x-soft-robotics", "type": "hypothesis", "title": "Compliant bipedal robots with leg spring stiffness k = 5·mg/L (matching the biological spring stiffness scaling law for body mass m, leg length L) achieve metabolic cost of transport within 20% of biological locomotion at matched Froude number, while rigid-leg robots of the same mass require 2.5× more energy at equivalent speed", "status": "active", "fields": [ "biology", "computer_science", "engineering" ], "color": "green" }, { "id": "h-biomimetic-slip-locomotion-minimal-energy-cost-robots", "type": "hypothesis", "title": "Robots implementing SLIP-based compliant leg control with leg spring constant k tuned to body mass via the biological scaling law k ∝ m^0.67 will achieve specific energy cost of transport (COT) < 1 J/(N·m) — matching biological runners — regardless of morphology, provided Strouhal number is maintained at St ≈ 0.25–0.35 for swimming and flapping analogues.\n", "status": "active", "fields": [ "engineering", "biology", "robotics", "biomechanics" ], "color": "green" }, { "id": "h-biomimicry-design-convergence-performance-ceiling", "type": "hypothesis", "title": "Biomimicry-derived designs converge on performance ceilings set by the underlying physical constraints — not by evolutionary history — so that lotus-inspired surfaces, whale-tubercle blades, and spider-silk analogs will asymptotically approach but not surpass the physical limits for superhydrophobicity, stall delay, and toughness respectively, confirming natural selection as an effective but not omniscient optimizer.\n", "status": "active", "fields": [ "ecology", "engineering", "materials-science", "physics" ], "color": "green" }, { "id": "h-biomineralisation-voronoi-control", "type": "hypothesis", "title": "Organisms control polymorph selection and crystallographic texture in biomineralisation by tuning the spatial geometry of organic matrix proteins to enforce Voronoi-like tessellation of mineralisation fronts, selecting crystal habit via geometric frustration rather than direct molecular templating alone.\n", "status": "active", "fields": [ "materials-science", "biochemistry", "crystallography", "biophysics" ], "color": "green" }, { "id": "h-biosignature-false-positive-abiotic-oxygen", "type": "hypothesis", "title": "Abiotic O2/O3 biosignature false positives arise primarily from hydrogen escape and CO2 photolysis on dry, high-UV planets — distinguishable from biotic production via CO and O2 column ratio measurements", "status": "active", "fields": [ "astrobiology", "atmospheric-chemistry", "observational-astronomy" ], "color": "green" }, { "id": "h-birdsong-context-free-grammar-test", "type": "hypothesis", "title": "A controlled playback experiment testing center-embedded motif dependencies in Bengalese finch song will demonstrate that birds respond selectively to grammatically correct vs. incorrect sequences that cannot be distinguished by a probabilistic finite-state model, providing evidence for context-free (Type 2 Chomsky) syntactic processing", "status": "active", "fields": [ "linguistics", "ornithology", "cognitive-science" ], "color": "green" }, { "id": "h-birkhoff-kolmogorov-aesthetic-sweet-spot", "type": "hypothesis", "title": "Aesthetic preference ratings for visual and auditory stimuli follow an inverted-U function of lossless compression ratio (a computable approximation of Kolmogorov complexity K), with peak preference at intermediate compression ratios of 2–5x — the \"sweet spot\" — and this relationship is cross-culturally universal, replicating across at least 6 cultural groups with distinct aesthetic traditions.\n", "status": "active", "fields": [ "cognitive-science", "information-theory", "aesthetics", "neuroscience", "cross-cultural-psychology" ], "color": "green" }, { "id": "h-bis-disgust-threshold-pathogen-prevalence-calibration", "type": "hypothesis", "title": "Trait disgust sensitivity calibrates to local historical pathogen prevalence across populations via epigenetic mechanisms (DNA methylation of serotonin transporter and oxytocin receptor promoters), such that populations from high-pathogen regions show heritable but reversible BIS upregulation detectable within 2 generations of migration to low-pathogen environments.\n", "status": "active", "fields": [ "evolutionary-biology", "social-science", "epigenetics", "psychology" ], "color": "green" }, { "id": "h-black-scholes-heat-equation", "type": "hypothesis", "title": "Fractional Black-Scholes PDE with Lévy stable log-return distribution (α-stable, α<2) produces option smiles consistent with market implied volatility surfaces, outperforming the normal Black-Scholes model out-of-sample", "status": "active", "fields": [ "finance", "mathematics", "probability" ], "color": "green" }, { "id": "h-blackscholes-x-diffusion-equation", "type": "hypothesis", "title": "Financial return distributions are well-described by a fractional diffusion equation with a Levy stable index alpha < 2 that accounts for fat tails, and this index is stable across market regimes and asset classes\n", "status": "active", "fields": [ "economics", "physics", "mathematics" ], "color": "green" }, { "id": "h-blood-coagulation-cascade-boolean", "type": "hypothesis", "title": "Patient-specific ODE coagulation models parameterized from standard clotting assays (PT, APTT, factor levels) will predict thrombin generation curves (peak, lag time, velocity index) with >85% accuracy in hemophilia A patients on prophylactic factor VIII replacement", "status": "active", "fields": [ "medicine", "systems-biology", "hematology" ], "color": "green" }, { "id": "h-bmp-wnt-diffusion-ratio-turing-digits", "type": "hypothesis", "title": "BMP and WNT morphogens in the developing vertebrate limb bud satisfy the Turing instability condition D_WNT/D_BMP > 10, directly predicting the observed inter-digit spacing from RD theory", "status": "active", "fields": [ "developmental-biology", "mathematics" ], "color": "green" }, { "id": "h-bocd-with-hazard-adaptation-detects-glacier-regime-shifts-earlier", "type": "hypothesis", "title": "Hazard-adaptive BOCPD detects glacier calving regime shifts earlier than fixed-threshold monitoring at comparable false-alert rates.", "status": "active", "fields": [ "climate-science", "statistics" ], "color": "green" }, { "id": "h-bold-fmri-hagen-poiseuille-resolution-limit", "type": "hypothesis", "title": "The fundamental spatial resolution limit of BOLD fMRI is 300-500 micrometers due to Hagen-Poiseuille r^4 sensitivity creating a vascular point-spread function that cannot be overcome by increasing field strength alone\n", "status": "active", "fields": [ "neuroscience", "physics", "fluid_mechanics", "biophysics" ], "color": "green" }, { "id": "h-boltzmann-machine-x-ising-model", "type": "hypothesis", "title": "Restricted Boltzmann machines trained on natural images will develop effective coupling constants that exhibit a spin glass phase transition as network size increases, with the glass transition temperature inversely related to dataset diversity\n", "status": "active", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "green" }, { "id": "h-boolean-network-k2-criticality-cell-reprogramming-efficiency", "type": "hypothesis", "title": "Gene regulatory networks operating at the K=2 criticality (edge of chaos) in Kauffman's NK model maximize reprogramming efficiency — the probability of noise-induced basin crossing from one cell fate attractor to another — relative to subcritical (K<2) or supercritical (K>2) connectivity regimes\n", "status": "active", "fields": [ "cell-biology", "systems-biology", "theoretical-biology", "computational-biology" ], "color": "green" }, { "id": "h-borrelia-triple-combo-persister-eradication", "type": "hypothesis", "title": "The combination of daptomycin + doxycycline + cefuroxime will achieve >99% eradication of Borrelia burgdorferi persister cells (all morphological forms) in a 28-day regimen, translating the Feng et al. (2015) in vitro finding to a validated PTLDS treatment.\n", "status": "active", "fields": [ "immunology", "microbiology", "pharmacology", "infectious-disease", "clinical-medicine" ], "color": "green" }, { "id": "h-bounded-confidence-epsilon-polarization-social-media-filter-bubbles", "type": "hypothesis", "title": "Social media algorithmic curation effectively reduces the confidence bound ε in Deffuant-Weisbuch opinion dynamics by decreasing cross-partisan exposure, and the post-2010 polarization increase in the US is quantitatively consistent with a reduction of effective ε from ~0.35 to ~0.20 as estimated from network homophily metrics.\n", "status": "active", "fields": [ "social-science", "physics", "computational-social-science", "political-science" ], "color": "green" }, { "id": "h-braess-paradox-social-network-cascades", "type": "hypothesis", "title": "The Braess paradox manifests in information networks — adding communication channels (Slack, email) to organizations increases coordination failures by diluting attention and creating conflicting parallel information flows, measurably reducing team performance.\n", "status": "active", "fields": [ "social-science", "network-science", "organizational-behavior" ], "color": "green" }, { "id": "h-brain-landauer-efficiency", "type": "hypothesis", "title": "The human brain operates within 2 orders of magnitude of the Landauer limit per synaptic event", "status": "active", "fields": [], "color": "green" }, { "id": "h-bridge-catalog-reduces-rediscovery-lag", "type": "hypothesis", "title": "A publicly accessible cross-domain bridge catalog measurably reduces the average time between independent parallel discoveries in different fields (the \"Merton multiple\" lag), detectable through citation network analysis comparing pre- and post-catalog publication patterns.\n", "status": "active", "fields": [ "science-of-science", "network-science", "information-theory", "epistemology", "scientometrics" ], "color": "green" }, { "id": "h-brokerage-advantage-diminishes-with-organizational-transparency", "type": "hypothesis", "title": "Organizational digital communication platforms (Slack, email, collaboration tools) reduce brokerage advantages by making information flows visible and searchable ΓÇö allowing non-brokers to access information previously monopolized by structural hole occupants ΓÇö and this effect is measurable as a reduction in the performance premium for high-betweenness-centrality individuals.\n", "status": "active", "fields": [ "organizational-behavior", "network-science", "management", "sociology" ], "color": "green" }, { "id": "h-bvalue-stress-criticality-forecast", "type": "hypothesis", "title": "Spatiotemporal decreases in the Gutenberg-Richter b-value (below regional average) within 50 km of a fault segment indicate increasing differential stress approaching the SOC critical point, and segments with b < 0.7 have ≥3× elevated probability of M≥6 rupture within 5 years.\n", "status": "active", "fields": [ "geology", "seismology", "statistical-physics", "geophysics" ], "color": "green" }, { "id": "h-bz-scroll-wave-negative-tension-fibrillation", "type": "hypothesis", "title": "Negative filament tension in 3D BZ scroll waves produces turbulence that is statistically equivalent to cardiac ventricular fibrillation: both exhibit the same power-law frequency spectra, identical spatial correlation lengths relative to wave speed, and the same termination statistics under external periodic forcing.\n", "status": "active", "fields": [ "chemistry", "nonlinear-dynamics", "biophysics" ], "color": "green" }, { "id": "h-cache-oblivious-algorithm-hierarchy-optimality", "type": "hypothesis", "title": "Cache-oblivious algorithms that recursively divide data access without explicit knowledge of cache line sizes achieve asymptotically optimal memory access patterns across all levels of the memory hierarchy simultaneously, and formal specification of cache-oblivious transformations can be partially automated via polyhedral analysis and affine scheduling in optimizing compilers.\n", "status": "active", "fields": [ "computer-science", "algorithms", "computer-architecture", "performance-engineering" ], "color": "green" }, { "id": "h-calcium-signaling-x-stochastic-resonance", "type": "hypothesis", "title": "HEK293 cells expressing IP3R at 50-150% of wildtype levels will show a non-monotonic relationship between IP3R expression and calcium wave propagation probability, with maximum propagation probability (SR optimal) at 100% wildtype expression — confirming that IP3R density is tuned to the stochastic resonance optimum", "status": "active", "fields": [ "biology", "physics", "biophysics" ], "color": "green" }, { "id": "h-cancer-immunoediting-neoantigen-depletion", "type": "hypothesis", "title": "Tumour immunoediting depletes high-affinity neoantigens through clonal selection, leaving an immunologically invisible tumour clone dominated by driver mutations with low HLA presentation probability — this is the primary mechanism of immune escape.\n", "status": "active", "fields": [ "cancer-immunology", "evolutionary-biology", "genomics" ], "color": "green" }, { "id": "h-cap-theorem-pacelc-extension", "type": "hypothesis", "title": "The PACELC extension to the CAP theorem (Daniel Abadi 2012) — that distributed systems must trade off latency against consistency even when there is no network partition — is a tighter characterization of practical system tradeoffs, and systems can be rigorously classified on a 2D (PA/EL vs PC/EC) grid that predicts observed user-facing behavior across cloud databases.\n", "status": "active", "fields": [ "computer-science", "distributed-systems", "engineering" ], "color": "green" }, { "id": "h-capillary-wetting-pinning-length-universality-class", "type": "hypothesis", "title": "Depinning of macroscopic contact lines on disordered micron-scale roughness belongs to a finite set of scaling universality classes when lengths are normalized by ell_c and the hysteresis bandwidth — testable on patterned libraries before claiming universality.\n", "status": "active", "fields": [ "fluid-mechanics", "materials-science" ], "color": "green" }, { "id": "h-carbon-capture-amine-sorbent-enthalpy-regeneration", "type": "hypothesis", "title": "The minimum thermodynamic energy penalty for CO2 capture from flue gas using amine sorbents is bounded below by the CO2 heat of absorption (40-80 kJ/mol) plus the sensible heat of sorbent regeneration — current amine systems operate at 2.5-4× this thermodynamic minimum, and solid sorbents with low heat capacity can approach 1.5× minimum cost.\n", "status": "active", "fields": [ "chemistry", "chemical-engineering", "thermodynamics", "materials-science" ], "color": "green" }, { "id": "h-carbon-cycle-feedback-sign-reversal", "type": "hypothesis", "title": "Terrestrial carbon cycle feedback changes sign from negative (CO2 fertilization dominant) to positive (respiration and permafrost dominant) at approximately 3°C global warming, with the sign reversal occurring earlier in boreal peatlands than in tropical forests due to differential temperature sensitivity of heterotrophic respiration.\n", "status": "active", "fields": [ "climate-science", "ecology", "earth-system-science" ], "color": "green" }, { "id": "h-carbon-price-optimal-100", "type": "hypothesis", "title": "The social cost of carbon, corrected for distribution weights and risk aversion, exceeds 200 USD per tonne CO2 in 2026 under any plausible discount rate below 3 percent", "status": "active", "fields": [], "color": "green" }, { "id": "h-cardiac-arrhythmia-phase-transition", "type": "hypothesis", "title": "Atrial fibrillation onset is the cardiac Kuramoto system crossing its critical synchronisation threshold; the transition is detectable as a diverging susceptibility in ECG power spectra before clinical arrhythmia is visible.\n", "status": "active", "fields": [ "cardiology", "statistical-physics", "nonlinear-dynamics", "biomedical-engineering" ], "color": "green" }, { "id": "h-cardiac-regeneration-hippo-yap-pathway", "type": "hypothesis", "title": "Adult mammalian cardiomyocyte regeneration failure is caused primarily by Hippo pathway activation at birth that suppresses YAP/TAZ-mediated proliferation, and transient YAP activation via AAV9-delivered dominant-active YAP after myocardial infarction can regenerate > 20% of lost myocardium in adult mice within 4 weeks.\n", "status": "active", "fields": [ "medicine", "biology", "developmental-biology", "cell-biology" ], "color": "green" }, { "id": "h-catalyst-volcano-ml-discovery", "type": "hypothesis", "title": "Machine learning models trained on DFT-computed adsorption energies can identify novel catalysts near the volcano peak for ammonia synthesis with turnover frequencies within 10× of Ru at ambient pressure, by predicting binding energy descriptors beyond the N adsorption energy traditionally used.\n", "status": "active", "fields": [ "chemistry", "materials-science", "computational-chemistry", "machine-learning" ], "color": "green" }, { "id": "h-catastrophe-theory-first-order-transitions", "type": "hypothesis", "title": "The cusp catastrophe control surface is topologically equivalent to the Landau free energy surface for all mean-field first-order transitions, predicting that hysteresis loops and spinodal boundaries are universal across physical, chemical, and biological systems sharing the same order-parameter symmetry class.\n", "status": "active", "fields": [ "mathematics", "catastrophe-theory", "statistical-mechanics", "physics" ], "color": "green" }, { "id": "h-category-theory-effects-adjunction", "type": "hypothesis", "title": "Algebraic effects and handlers in programming languages correspond precisely to free monads over effect signatures, and every handler is a monad morphism determined by a unique adjunction in the Kleisli category\n", "status": "active", "fields": [ "mathematics", "computer_science", "type_theory", "logic" ], "color": "green" }, { "id": "h-causal-fairness-resolves-impossibility-tradeoffs", "type": "hypothesis", "title": "Counterfactual fairness under a correctly specified structural causal model resolves the Chouldechova-Kleinberg impossibility by operating in a different criterion space, but is rendered non-unique by causal model underdetermination from observational data", "status": "active", "fields": [ "machine-learning", "causal-inference", "social-science", "statistics" ], "color": "green" }, { "id": "h-causal-forest-heterogeneity-improves-policy-targeting-efficiency", "type": "hypothesis", "title": "Causal-forest heterogeneity estimates improve policy targeting efficiency over population-average rules.", "status": "active", "fields": [ "economics", "machine-learning", "statistics" ], "color": "green" }, { "id": "h-cav-phantom-jam-suppression-1percent", "type": "hypothesis", "title": "A market penetration of 5% connected autonomous vehicles following smoothing control laws is sufficient to suppress phantom traffic jams on congested freeways, reducing average travel time by > 15%", "status": "active", "fields": [ "engineering", "mathematics", "physics" ], "color": "green" }, { "id": "h-cavity-method-x-belief-propagation", "type": "hypothesis", "title": "The 1RSB cavity method gives the exact satisfiability threshold for random 3-SAT at α_c ≈ 4.267, and the onset of belief propagation non-convergence (multiple fixed points) at α ≈ 3.86 corresponds exactly to the clustering threshold where DPLL solvers undergo exponential slowdown", "status": "active", "fields": [ "physics", "computer_science", "mathematics" ], "color": "green" }, { "id": "h-cbdc-bank-disintermediation-threshold", "type": "hypothesis", "title": "Central bank digital currencies cause significant bank disintermediation only above a CBDC interest rate threshold of r_CBDC ≥ r_deposits - 0.5%; below this threshold, household preference for bank services (lending, payments) prevents structural bank run risk, and monetary policy transmission improves via direct transmission channel.\n", "status": "active", "fields": [ "economics", "finance", "monetary-theory", "social-science" ], "color": "green" }, { "id": "h-cbf-enforced-insulin-constraints-prevent-severe-lows", "type": "hypothesis", "title": "CBF-enforced insulin safety filters reduce time spent below severe hypoglycemia thresholds without worsening hyperglycemia burden.", "status": "active", "fields": [ "medicine", "control-engineering", "digital-health" ], "color": "green" }, { "id": "h-cell-division-x-branching-process", "type": "hypothesis", "title": "Normal tissue stem cell clones operate at near-criticality (m ≈ 1 ± 0.02) with individual clones undergoing neutral drift; a single driver mutation shifts m to 1.05–1.15, providing a 10–30 fold increase in clonal establishment probability predictable from branching process extinction theory", "status": "active", "fields": [ "biology", "mathematics", "medicine", "evolutionary-biology" ], "color": "green" }, { "id": "h-cellular-automata-x-computational-universality", "type": "hypothesis", "title": "A 3-state 1D cellular automaton with a local neighborhood of 3 cells is sufficient for Turing universality with self-replication, establishing a new lower bound on the minimum computational complexity for physical self-replication\n", "status": "active", "fields": [ "computer-science", "physics", "complexity-science" ], "color": "green" }, { "id": "h-central-bank-independence-inflation-causal-updated", "type": "hypothesis", "title": "Central bank independence (CBI) causally reduces inflation by credibly pre-committing monetary policy, with legal CBI indices predicting ~3-5pp lower inflation in cross-country panels; but the effect collapses under fiscal dominance (high debt-to-GDP > 100%) where governments pressure bond purchases, and is attenuated by financial repression needs", "status": "active", "fields": [ "macroeconomics", "political-economy", "monetary-economics" ], "color": "green" }, { "id": "h-central-bank-independence-inflation-causal", "type": "hypothesis", "title": "Central bank independence (CBI) causally reduces inflation by removing the time-inconsistency problem (dynamic inconsistency of optimal monetary policy), but this effect is conditional on fiscal dominance: when government debt is unsustainable, CBI cannot prevent fiscal inflation regardless of its formal mandate, as shown by the fiscal theory of the price level.\n", "status": "active", "fields": [ "macroeconomics", "monetary-economics", "political-economy", "public-finance" ], "color": "green" }, { "id": "h-cerebellum-kalman-prediction-error", "type": "hypothesis", "title": "The climbing fibre signal to cerebellar Purkinje cells encodes a Kalman filter innovation (sensory prediction error weighted by optimal gain), and the magnitude of cerebellar adaptation tracks the Kalman gain K ∝ P_pred/(P_pred + R) as sensory reliability R varies.\n", "status": "active", "fields": [ "computational-neuroscience", "motor-neuroscience", "control-engineering" ], "color": "green" }, { "id": "h-cerebellum-lqr-forward-model-implementation", "type": "hypothesis", "title": "The cerebellum implements a forward model that predicts sensory consequences of motor commands via a biologically plausible approximation to the Kalman filter: Purkinje cells encode prediction of sensory state given efference copy, granule cells provide the basis for state representation, and climbing fiber error drives gradient descent on prediction error, implementing a neural linear quadratic regulator for motor control.\n", "status": "active", "fields": [ "motor-neuroscience", "control-theory", "cerebellar-neuroscience", "computational-neuroscience" ], "color": "green" }, { "id": "h-cerebellum-predictive-coding-internal-models", "type": "hypothesis", "title": "The cerebellum performs predictive coding via internal forward models: it predicts the sensory consequences of motor commands, computes prediction errors via climbing-fibre-driven LTD at parallel fibre-Purkinje cell synapses, and updates internal models — extending this framework to cognitive prediction errors (e.g. in language, social cognition) explains cerebellar involvement in autism and schizophrenia.\n", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "psychiatry" ], "color": "green" }, { "id": "h-channel-capacity-evolution-rate", "type": "hypothesis", "title": "The maximum sustainable rate of mean fitness increase in a population is bounded above by the Shannon channel capacity C = B log2(1 + S/N), where B is the effective number of independently evolving loci and S/N is the fitness variance-to-noise ratio, and this bound is approached within 2x in long-term evolution experiments.\n", "status": "active", "fields": [ "biology", "information-theory", "evolutionary-biology" ], "color": "green" }, { "id": "h-chaos-ergodic-breaking-climate-prediction", "type": "hypothesis", "title": "Climate models exhibit ergodicity breaking on multi-decadal timescales due to slow manifold dynamics, limiting the validity of time-averaged climate statistics as ensemble averages\n", "status": "active", "fields": [ "mathematics", "physics", "dynamical_systems", "climate_science" ], "color": "green" }, { "id": "h-charnov-marginal-value-maps-to-index-policy-budgeting", "type": "hypothesis", "title": "In laboratory patch-foraging with humans, patch-leaving times will track a UCB-like opportunity-cost threshold more closely when travel times are salient than when they must be learned implicitly.", "status": "active", "fields": [ "ecology", "computer-science" ], "color": "green" }, { "id": "h-chemical-garden-osmotic-pressure-tube-morphology", "type": "hypothesis", "title": "The tube diameter of a CuSO4 chemical garden in sodium silicate solution will scale as d ~ (D_Cu / k_prec)^{1/2} where D_Cu is copper ion diffusivity and k_prec is the silicate precipitation rate constant, and this scaling prediction derived from the osmotic-precipitation fluid mechanics model will hold across at least 5 different metal salt concentrations without free parameters", "status": "active", "fields": [ "chemistry", "fluid-mechanics", "materials-science" ], "color": "green" }, { "id": "h-chemotaxis-adam-optimizer-equivalence", "type": "hypothesis", "title": "The E. coli methylation adaptation circuit is mathematically equivalent to the Adam optimizer with specific beta parameters, and replacing Adam with the exact biological circuit will improve convergence on non-stationary loss landscapes\n", "status": "active", "fields": [ "biology", "computer_science", "biophysics", "optimization" ], "color": "green" }, { "id": "h-cherenkov-mach-prerequisite-transfer-diagnostic", "type": "hypothesis", "title": "Randomized STEM cohorts assigned to joint Cherenkov–Mach cone modules will outperform control cohorts on delayed testing of cone-angle calculations when controlling for prior mechanics grades — falsified if gains appear equally for purely algebraic drills without linked demos.\n", "status": "active", "fields": [ "physics", "fluid-mechanics" ], "color": "green" }, { "id": "h-chern-number-tis-robustness", "type": "hypothesis", "title": "The robustness of topological insulator surface states under non-magnetic perturbations is protected by a Z2 topological invariant that quantifies the parity of occupied Kramers doublets at time-reversal invariant momenta, and this protection breaks specifically when the perturbation locally breaks time-reversal symmetry at the surface on a length scale shorter than the Fermi wavelength.\n", "status": "active", "fields": [ "condensed-matter-physics", "materials-science", "mathematics", "quantum-physics" ], "color": "green" }, { "id": "h-chern-simons-theory-topological-quantum-computation", "type": "hypothesis", "title": "Chern-Simons gauge theory at level k provides the mathematical framework for topological quantum computation via anyons in the fractional quantum Hall state at filling fraction nu = 1/(2k+1), and the non-Abelian case (nu = 5/2) supports universal quantum gates through braiding operations that are exponentially protected from local decoherence.\n", "status": "active", "fields": [ "mathematical-physics", "quantum-physics", "topology", "quantum-computing" ], "color": "green" }, { "id": "h-cholesteric-lc-structural-color-biomimetic-photonic-applications", "type": "hypothesis", "title": "Cholesteric liquid crystal structural color can be used to create angle-independent, tunable, zero-energy color displays and anti-counterfeiting features by controlling pitch through temperature, electric field, or chiral dopant concentration ΓÇö with reflectance matching or exceeding conventional pigment displays.\n", "status": "active", "fields": [ "soft-matter", "photonics", "materials-science", "display-technology" ], "color": "green" }, { "id": "h-christofides-tight-example-construction", "type": "hypothesis", "title": "The Christofides 3/2 approximation ratio is tight — there exist infinite families of metric TSP instances on which the Christofides algorithm achieves tours within a factor arbitrarily close to 3/2 of optimal, and the Held-Karp LP integrality gap converges to exactly 4/3 on a specific known family of instances.\n", "status": "active", "fields": [ "mathematics", "computer-science", "combinatorial-optimization", "complexity-theory" ], "color": "green" }, { "id": "h-chronic-pain-glial-sensitization", "type": "hypothesis", "title": "Central sensitization in chronic pain is maintained by microglial-astrocyte cross-talk in the dorsal horn — not by sustained nociceptor input alone — such that blocking microglial P2X4R-BDNF signaling after nerve injury prevents the transition from acute to chronic pain in a 2-week window that remains open for therapeutic intervention.\n", "status": "active", "fields": [ "medicine", "neuroscience", "immunology" ], "color": "green" }, { "id": "h-circadian-clock-feeding-entrainment", "type": "hypothesis", "title": "Peripheral circadian clocks in metabolic organs (liver, pancreas, adipose) are primarily entrained by feeding time rather than light, operating via NAD+/SIRT1 and AMPK metabolic signalling, and time-restricted feeding can resynchronise dysynchronised peripheral clocks independently of the SCN.\n", "status": "active", "fields": [ "chronobiology", "metabolism", "cell-biology" ], "color": "green" }, { "id": "h-circadian-clock-x-feedback-oscillator", "type": "hypothesis", "title": "Temperature compensation arises from opposing temperature sensitivities of PER synthesis (Q₁₀ ≈ 2.5, increasing with T) and CKIε phosphorylation rate (Q₁₀ ≈ 0.4, decreasing with T due to substrate inhibition), with period set by their ratio rather than absolute rates", "status": "active", "fields": [ "biology", "biophysics", "biochemistry", "control-theory" ], "color": "green" }, { "id": "h-circadian-hopf-bifurcation-delay-oscillator", "type": "hypothesis", "title": "Circadian clock oscillations arise via a Hopf bifurcation in a delay differential equation: when the repression delay tau satisfies tau × |df/dx|(x_0) > pi/2, the stable fixed point loses stability and a limit cycle emerges with period approximately 4*tau, predicting that the ~24h period corresponds to a ~6h delay in the transcription-translation feedback loop, as confirmed by per/tim protein accumulation kinetics.\n", "status": "active", "fields": [ "chronobiology", "nonlinear-dynamics", "molecular-biology", "biophysics" ], "color": "green" }, { "id": "h-circadian-hopf-bifurcation-period-mutation-prediction", "type": "hypothesis", "title": "Period mutations in the mammalian circadian clock (tau, after hours, FASPS) act by shifting the Hopf bifurcation parameter (the effective Hill coefficient n or the nuclear repression delay τ_D), and their quantitative period changes (±1 to ±4 hours) are predicted by the Leloup-Goldbeter ODE model within ±20% without refitting.\n", "status": "active", "fields": [ "chronobiology", "systems-biology" ], "color": "green" }, { "id": "h-circadian-per3-prc-amplitude-chronotype", "type": "hypothesis", "title": "PER3 VNTR polymorphism (4/4 vs. 4/5 alleles) predicts PRC amplitude differences of at least 20% (larger amplitude in 4/5 carriers), making 4/5 carriers better able to entrain to atypical schedules; this explains the known association of PER3 genotype with chronotype and jet-lag susceptibility and is testable by forced desynchrony PRC measurement in genotyped volunteers.\n", "status": "active", "fields": [ "chronobiology", "mathematics", "genetics" ], "color": "green" }, { "id": "h-circadian-synchrony-kuramoto-critical-coupling", "type": "hypothesis", "title": "SCN circadian synchrony operates near the Kuramoto critical coupling K_c, making jet-lag recovery time maximally sensitive to VIP neuropeptide signaling strength", "status": "active", "fields": [], "color": "green" }, { "id": "h-circuit-theory-outperforms-lcp-gene-flow-prediction", "type": "hypothesis", "title": "Circuit-theoretic effective resistance predicts empirical gene flow (FST) better than least-cost path distance in fragmented landscapes because it accounts for multiple dispersal pathways, with the advantage increasing as landscape connectivity approaches the percolation threshold", "status": "active", "fields": [ "landscape-ecology", "population-genetics", "graph-theory", "conservation-biology" ], "color": "green" }, { "id": "h-citizen-science-validation-training-protocols", "type": "hypothesis", "title": "Citizen science projects achieve research-quality data when they combine three elements: structured volunteer training with proficiency assessment, redundant data collection (3+ independent classifications per item), and algorithmic aggregation that weights by demonstrated accuracy — and projects meeting all three criteria will produce data with > 90% agreement with expert labels.\n", "status": "active", "fields": [ "science-studies", "statistics", "ecology" ], "color": "green" }, { "id": "h-clf-constrained-harvest-stabilizes-biomass-under-shocks", "type": "hypothesis", "title": "Harvest policies synthesized from control-Lyapunov constraints maintain biomass above collapse thresholds more reliably than static quota rules under matched stochastic environmental shocks.\n", "status": "active", "fields": [ "ecology", "control-engineering", "dynamical-systems" ], "color": "green" }, { "id": "h-climate-fire-feedback-accelerates-beyond-linear-projections", "type": "hypothesis", "title": "The climate-fire positive feedback loop (warming → drought → more fire → CO₂ release → more warming) will cause burned area and carbon emissions from wildfire to accelerate nonlinearly under 2°C warming scenarios, exceeding IPCC AR6 projections that treat fire as a linear response to climate forcing.\n", "status": "active", "fields": [ "climate-science", "ecology", "atmospheric-science", "earth-system-science" ], "color": "green" }, { "id": "h-climate-sensitivity-emergent-constraint-water-vapor", "type": "hypothesis", "title": "Equilibrium climate sensitivity (ECS) is constrained to 3.1±0.4 K by combining the observed tropical upper-tropospheric water vapor trend, Pleistocene temperature reconstructions, and modern satellite cloud radiative effect measurements — each independently ruling out ECS below 2.5 K and above 4.0 K.\n", "status": "active", "fields": [ "climate-science", "physics", "atmospheric-science", "statistics" ], "color": "green" }, { "id": "h-climate-sensitivity-fat-tail-cloud-convection", "type": "hypothesis", "title": "The long upper tail of equilibrium climate sensitivity (ECS > 5°C) is driven primarily by the nonlinear response of marine low-cloud cover to SST warming — specifically the break-up of stratocumulus decks in the subtropical subsidence regions above a threshold SST of ~28°C.\n", "status": "active", "fields": [ "climate-science", "atmospheric-physics", "fluid-dynamics" ], "color": "green" }, { "id": "h-cloud-feedback-low-cloud-positive", "type": "hypothesis", "title": "The net cloud feedback to CO2 forcing is positive (destabilizing), dominated by low-cloud reduction in the subtropical subsidence regions, with a magnitude of +0.4 to +0.8 W/m²/K, and this is detectable in the emerging observational record of low-cloud fraction trends in CERES satellite data 2000-2030.\n", "status": "active", "fields": [ "climate-science", "atmospheric-science", "remote-sensing", "physics" ], "color": "green" }, { "id": "h-cloud-seeding-hygroscopic-efficacy-mechanism", "type": "hypothesis", "title": "Cloud seeding efficacy is primarily determined by cloud liquid water content and temperature at seeding altitude, not seeding agent chemistry — hygroscopic flares (KCl, NaCl particles) are effective only in warm convective clouds (T > -5°C), while silver iodide (AgI) is effective only in supercooled stratiform clouds (-5°C to -20°C).\n", "status": "active", "fields": [ "atmospheric-chemistry", "cloud-physics", "meteorology" ], "color": "green" }, { "id": "h-clumping-index-primary-productivity-underestimate", "type": "hypothesis", "title": "Ignoring leaf clumping in canopy Beer-Lambert models causes systematic underestimation of understory photosynthetically active radiation by 20-40% in boreal and temperate forests, leading to equivalent underestimation of understory plant productivity and carbon sequestration\n", "status": "active", "fields": [ "ecology", "optics" ], "color": "green" }, { "id": "h-cluster-cooling-flow-agn-feedback-regulation", "type": "hypothesis", "title": "AGN jet feedback self-regulates galaxy cluster cooling flows via a thermostat mechanism, preventing runaway star formation through kinetic heating that maintains the intracluster medium at T ~ 10^7 K", "status": "active", "fields": [ "astrophysics", "plasma-physics", "radio-astronomy" ], "color": "green" }, { "id": "h-cnn-layers-approximate-localized-spectral-filters", "type": "hypothesis", "title": "For fixed architecture depth, systematically varying kernel bandwidth priors will shift empirical sensitivity to high-frequency adversarial perturbations in directions predicted by local spectral response estimates.", "status": "active", "fields": [ "computer-science", "mathematics" ], "color": "green" }, { "id": "h-co2-feedstock-polycarbonate-cascade-net-carbon-neutral", "type": "hypothesis", "title": "CO₂ copolymerization with epoxides (using Zn or Co salen catalysts) to produce polycarbonate plastics is net carbon-negative over the product lifetime when accounting for CO₂ sequestration, fossil feedstock displacement, and incineration end-of-life — making CO₂-based polymers a scalable industrial carbon sink.\n", "status": "active", "fields": [ "chemistry", "engineering", "environmental-science", "chemical-engineering" ], "color": "green" }, { "id": "h-cochlear-active-amplification-hopf-bifurcation", "type": "hypothesis", "title": "The mammalian cochlear amplifier operates near a Hopf bifurcation point that provides frequency selectivity and gain with minimal energy: at the bifurcation, the amplification gain diverges as (f - f_c)^{-1/3}, the threshold for nonlinear compression is minimized, and spontaneous otoacoustic emissions arise as limit cycle oscillations when the system crosses the bifurcation.\n", "status": "active", "fields": [ "biophysics", "auditory-neuroscience", "nonlinear-dynamics", "fluid-mechanics" ], "color": "green" }, { "id": "h-cognitive-reserve-synaptic-redundancy", "type": "hypothesis", "title": "Cognitive reserve in Alzheimer's disease is mechanistically explained by dendritic spine redundancy in association cortices: individuals with higher lifetime cognitive engagement maintain larger spine density, so the same absolute amyloid and tau burden damages a smaller fraction of the functional synapse pool, delaying the symptom threshold.\n", "status": "active", "fields": [ "neuroscience", "gerontology", "neurology", "cognitive-science" ], "color": "green" }, { "id": "h-collateral-sensitivity-cycling-drug-resistance", "type": "hypothesis", "title": "Sequential antibiotic cycling designed using measured collateral sensitivity networks (where resistance to drug A creates susceptibility to drug B) maintains pathogen populations in a trapped fitness valley, preventing multi-drug resistance emergence and reducing clinical resistance rates by >50% relative to concurrent combination therapy in empirically testable E. coli UTI models.\n", "status": "active", "fields": [ "pharmacology", "evolutionary-biology", "clinical-medicine" ], "color": "green" }, { "id": "h-collective-action-ostrom-design-principles-v2", "type": "hypothesis", "title": "Groups solve collective action problems without central authority when Ostrom's 8 design principles are met (matched rules, collective choice arrangements, monitoring, graduated sanctions, conflict resolution, recognition of rights, polycentric governance for large systems), with violation of any single principle significantly increasing commons failure probability in empirical studies", "status": "active", "fields": [ "political-science", "economics", "sociology", "game-theory" ], "color": "green" }, { "id": "h-collective-action-ostrom-design-principles", "type": "hypothesis", "title": "Groups successfully solve collective action problems without central authority when they implement Ostrom's 8 design principles (clear boundaries, proportional rules, collective choice, monitoring, graduated sanctions, conflict resolution, recognition, nested institutions), with institutional robustness increasing superlinearly with the number of principles satisfied.\n", "status": "active", "fields": [ "institutional-economics", "political-science", "game-theory", "sociology" ], "color": "green" }, { "id": "h-collective-memory-social-network-transmission", "type": "hypothesis", "title": "Collective memory of historical events forms and distorts through iterative social transmission following a power-law decay: details that cannot be easily schematized are forgotten at rate proportional to their schema-inconsistency, while emotionally salient and identity-relevant elements are retained and amplified — a process well-described by Bartlett's reconstructive memory applied to network diffusion models.\n", "status": "active", "fields": [ "social-science", "cognitive-science", "neuroscience", "history" ], "color": "green" }, { "id": "h-color-emotion-universal-hue-valence", "type": "hypothesis", "title": "Cross-cultural color-emotion associations show a universal core (blue→calm, red→excitement/danger, yellow→happiness) explained by evolved ecological associations (reddened faces for threat/arousal, blue sky for safety) plus culture-specific overlays; physiological arousal (skin conductance, heart rate) shows consistent wavelength-specific responses across populations", "status": "active", "fields": [ "cognitive-science", "cross-cultural-psychology", "psychophysiology", "evolutionary-psychology" ], "color": "green" }, { "id": "h-compact-algebra-first-sequence-improves-uap-transfer", "type": "hypothesis", "title": "Students who first solve compact-set density exercises modeled on Stone-Weierstrass will more often distinguish universal approximation from trainability on neural-network concept questions; falsified if post-test misconception rates differ by less than 5 percentage points.\n", "status": "active", "fields": [ "mathematics-education", "machine-learning" ], "color": "green" }, { "id": "h-complement-mediated-synapse-loss-drives-alzheimers-cognitive-decline", "type": "hypothesis", "title": "Complement-mediated microglial synapse pruning (C1q-C3-CR3 pathway) is causally upstream of cognitive decline in Alzheimer's disease ΓÇö and C1q or C3 inhibition will preserve synapses and slow cognitive decline in clinical trials, independent of amyloid plaque burden.\n", "status": "active", "fields": [ "neuroscience", "immunology", "neurodegeneration", "clinical-trials" ], "color": "green" }, { "id": "h-complexity-economics-minority-game-market-ecology", "type": "hypothesis", "title": "Real financial market strategy ecology self-organises near the critical point of the minority game — where the number of distinct agent strategies equals the number of degrees of freedom in the market information signal — producing the observed fat-tailed returns and volatility clustering as emergent phenomena.\n", "status": "active", "fields": [ "economics", "physics", "complexity-science", "finance" ], "color": "green" }, { "id": "h-compressed-sensing-mri-10x-scan-time-reduction-clinical-safety", "type": "hypothesis", "title": "Compressed sensing MRI with undersampling by factor 10× (acquiring 10% of k-space measurements required by Nyquist) achieves diagnostic image quality equivalent to fully-sampled MRI for cardiac, neurological, and musculoskeletal indications when the image reconstruction uses ℓ₁-wavelet minimisation, as validated in randomised controlled clinical trials.\n", "status": "active", "fields": [ "medical-imaging", "mathematics", "signal-processing", "clinical-medicine" ], "color": "green" }, { "id": "h-compressed-sensing-mri-fourier-sparsity", "type": "hypothesis", "title": "Sub-Nyquist MRI using compressed sensing achieves 4x-8x scan time reduction by exploiting sparsity of MRI images in the Fourier (k-space) basis", "status": "active", "fields": [ "signal-processing", "medical-imaging", "applied-mathematics" ], "color": "green" }, { "id": "h-compressed-sensing-rip-sharp-bounds", "type": "hypothesis", "title": "Exact sparse signal recovery from m harmonic measurements of an s-sparse signal requires m ≥ C·s·log(n/s) measurements — and this bound is sharp up to constants — with the restricted isometry property (RIP) of random Fourier matrices achievable with high probability for m ≥ s·polylog(n).\n", "status": "active", "fields": [ "harmonic-analysis", "compressed-sensing", "information-theory" ], "color": "green" }, { "id": "h-compressed-sensing-x-sparse-recovery", "type": "hypothesis", "title": "Deep neural networks implicitly implement compressed sensing by learning measurement matrices that satisfy the RIP for the natural signal manifold, explaining their sample efficiency relative to classical sparse recovery\n", "status": "active", "fields": [ "mathematics", "computer-science", "signal-processing" ], "color": "green" }, { "id": "h-compressible-shock-x-traffic-shock-wave", "type": "hypothesis", "title": "Pairing mesoscopic car-following simulations with macroscopic LWR inversions on the same road segment will yield Rankine–Hugoniot speeds matching within measurement error when fundamental diagrams estimated from microscopic spacing statistics feed macro closures — falsifying claims of unavoidable mismatch absent heterogeneous autonomy mixes.\n", "status": "active", "fields": [ "transportation-engineering", "fluid-mechanics" ], "color": "green" }, { "id": "h-computational-irreducibility-turbulence-pspace", "type": "hypothesis", "title": "Turbulent fluid dynamics (Navier-Stokes at high Reynolds number) is PSPACE- complete in a formal computational sense, meaning the prediction problem is harder than NP but not in EXPTIME; this explains why neural network surrogate models achieve 7-10 day forecast skill (polynomial-time inference) while 2+ week forecasts remain inaccessible without exponential computational resources.\n", "status": "active", "fields": [ "computer-science", "mathematics", "fluid-mechanics" ], "color": "green" }, { "id": "h-computational-psychiatry-aberrant-precision-antipsychotic-mechanism", "type": "hypothesis", "title": "Antipsychotic drugs (D2 antagonists) reduce psychotic symptoms by lowering the dopaminergic precision-weighting signal — reducible to a single parameter ω_DA in the hierarchical Bayesian model — and their therapeutic efficacy across patients is quantitatively predicted by the degree to which they normalise precision-weighted prediction error updating in computational task assays.\n", "status": "active", "fields": [ "neuroscience", "psychiatry", "pharmacology", "engineering" ], "color": "green" }, { "id": "h-conformal-field-theory-x-critical-phenomena", "type": "hypothesis", "title": "The conformal bootstrap island for the 3D Ising universality class is an isolated point in CFT space, proving that critical exponents are uniquely determined by conformal invariance plus unitarity without any free parameters.\n", "status": "active", "fields": [ "statistical-mechanics", "mathematical-physics", "condensed-matter-physics", "high-energy-physics" ], "color": "green" }, { "id": "h-connectome-graph-laplacian-spectral", "type": "hypothesis", "title": "Individual differences in connectome Laplacian algebraic connectivity (λ₂) predict working memory capacity with effect size r > 0.3, independent of white-matter volume", "status": "active", "fields": [ "neuroscience", "network-science", "cognitive-science" ], "color": "green" }, { "id": "h-connectome-hub-vulnerability-neurodegeneration", "type": "hypothesis", "title": "High-betweenness hub nodes in the structural brain connectome accumulate amyloid-beta and tau pathology first due to activity-dependent secretion, and hub eigenvector centrality predicts individual tau PET staging better than regional SUVR alone", "status": "active", "fields": [ "network-neuroscience", "medicine", "computational-neurology" ], "color": "green" }, { "id": "h-conserved-metabolic-bottlenecks-longevity", "type": "hypothesis", "title": "A small set of conserved metabolic control nodes explains a measurable fraction of cross-species longevity intervention effects that are reproducibly translatable to mammalian preclinical models", "status": "active", "fields": [ "biology", "medicine" ], "color": "green" }, { "id": "h-constrained-bandit-policies-reduce-sepsis-antibiotic-overtreatment-days", "type": "hypothesis", "title": "Methods transferred from `b-multi-armed-bandits-x-sepsis-antibiotic-de-escalation` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "operations-research", "infectious-disease" ], "color": "green" }, { "id": "h-contrastive-loss-implements-high-temperature-energy-comparison", "type": "hypothesis", "title": "Systematically varying τ in SimCLR-style training will trace a tradeoff between uniformity of embedding angles and downstream linear probe accuracy matching a predicted monotonic curve class.", "status": "active", "fields": [ "computer-science", "physics" ], "color": "green" }, { "id": "h-contrastive-pretraining-improves-multiomics-transfer-stability", "type": "hypothesis", "title": "Contrastive pretraining with assay-aware augmentations improves cross-cohort multi-omics transfer stability over supervised-only embeddings.", "status": "active", "fields": [ "systems-biology", "machine-learning", "statistics" ], "color": "green" }, { "id": "h-cooperative-breeding-constraint-rb-c", "type": "hypothesis", "title": "In cooperative breeding bird species where rB < C (helpers are unrelated or benefits are small), ecological constraints on independent breeding (quantified by territory availability * juvenile survival) predict helper presence with 80% accuracy, demonstrating that direct benefit models supplement rather than replace Hamilton's rule.\n", "status": "active", "fields": [ "evolutionary-biology", "mathematics", "behavioural-ecology" ], "color": "green" }, { "id": "h-coral-bleaching-thermal-stress", "type": "hypothesis", "title": "Coral reefs hosting Symbiodiniaceae clade D will show 40-60% lower bleaching incidence at DHW=8°C-weeks compared to clade C-dominated reefs, and this difference will be detectable via satellite SST and hyperspectral remote sensing of bleaching extent", "status": "active", "fields": [ "ecology", "climate-science", "marine-biology" ], "color": "green" }, { "id": "h-correlated-port-noise-matrix-lowers-effective-nf-two-port", "type": "hypothesis", "title": "For coupled antenna ports whose Johnson–Nyquist noise shares a common physical resistor network, the effective scalar noise figure of an optimized linear combiner can fall below the Friis cascade of individual branches — correlated equilibrium noise is partially cancellable like classical common-mode subtraction.\n", "status": "active", "fields": [ "electrical-engineering", "statistical-physics" ], "color": "green" }, { "id": "h-cortical-eigenmodes-universal-resting-state-basis", "type": "hypothesis", "title": "The first 200 eigenmodes of the human connectome structural Laplacian form a universal basis for representing all resting-state fMRI functional connectivity patterns, with individual differences in cognitive ability and psychiatric symptoms encoded in eigenmode amplitude coefficients rather than in raw connectivity matrices.\n", "status": "active", "fields": [ "computational-neuroscience", "neuroimaging", "mathematical-physics" ], "color": "green" }, { "id": "h-cortical-sulcal-topological-conservation", "type": "hypothesis", "title": "The coarse sulcal pattern of the human cortex is topologically conserved across individuals because it is determined by the defect configuration of the neuroepithelium at neural tube closure — a configuration governed by the same topological invariants as liquid-crystal ordering transitions.\n", "status": "active", "fields": [ "neuroscience", "mathematical-physics", "developmental-biology", "topology" ], "color": "green" }, { "id": "h-cosmic-string-cmb-power-spectrum", "type": "hypothesis", "title": "Cosmic string networks produce distinctive B-mode polarisation and non-Gaussian signatures in the CMB power spectrum detectable by CMB-S4 and LiteBIRD experiments", "status": "active", "fields": [ "cosmology", "astrophysics", "particle-physics", "general-relativity" ], "color": "green" }, { "id": "h-cosmic-string-gwb-signature", "type": "hypothesis", "title": "If cosmic string networks form at a GUT-scale phase transition, they produce a stochastic gravitational wave background with characteristic spectral index n_T=0 (flat spectrum) distinguishable from inflationary gravitational waves (n_T<0), detectable by LISA and pulsar timing arrays at f~nHz.\n", "status": "active", "fields": [ "cosmology", "particle-physics", "gravitational-wave-astronomy", "astrophysics" ], "color": "green" }, { "id": "h-count-novelty-scales-bayesian-information-gain-proxy", "type": "hypothesis", "title": "Count-based novelty bonuses correlate with empirical Bayesian information-gain proxies computed from participant behavior in bandit tasks more tightly than with raw reward PE alone — falsified if partial correlations controlling reward magnitude fall below τ≈0.15 across pooled labs (**exploratory neural comparison**).\n", "status": "active", "fields": [ "neuroscience", "reinforcement-learning" ], "color": "green" }, { "id": "h-cratonic-root-buoyancy-viscosity-stability", "type": "hypothesis", "title": "Archean cratonic keels persist for billions of years due to combined compositional buoyancy (depleted harzburgite with lower Fe/Mg ratio reduces density by ~0.5% vs fertile mantle) and high intrinsic viscosity from water depletion during melting, with destabilization requiring large-scale mantle flow events (plume impact, flat-slab subduction) that overcome the stability window", "status": "active", "fields": [ "geophysics", "geology", "petrology", "mantle-dynamics" ], "color": "green" }, { "id": "h-creative-economy-gdp-spillovers", "type": "hypothesis", "title": "Creative industries generate measurable innovation spillovers to adjacent manufacturing and tech sectors through labor mobility and cross-sector knowledge transfer, but their GDP contribution is systematically underestimated by satellite account methodologies that miss intangible asset creation.\n", "status": "active", "fields": [ "economics", "innovation-studies", "national-accounting" ], "color": "green" }, { "id": "h-creativity-default-executive-toggle", "type": "hypothesis", "title": "Creative cognition requires co-activation of default mode network (generative) and executive control network (evaluative) — not a simple toggle between them — with high-creative individuals showing stronger functional coupling between these normally anti-correlated networks as measured by resting-state fMRI functional connectivity.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "psychology", "computational-neuroscience" ], "color": "green" }, { "id": "h-creativity-threshold-intelligence-iq120", "type": "hypothesis", "title": "The threshold hypothesis states that IQ correlates with creativity below about 120 but not above; below-threshold correlations are domain-specific and moderate (r~0.3), while above-threshold creative eminence depends on openness, intrinsic motivation, and deliberate practice rather than additional IQ increments.\n", "status": "active", "fields": [ "cognitive-psychology", "differential-psychology", "psychometrics" ], "color": "green" }, { "id": "h-creativity-threshold-intelligence", "type": "hypothesis", "title": "General intelligence (g) is necessary but not sufficient for creative achievement: the threshold hypothesis holds below IQ ~120, where g predicts creative output, but above the threshold creative achievement is primarily determined by personality (openness to experience), motivation, and domain-specific knowledge.\n", "status": "active", "fields": [ "cognitive-psychology", "differential-psychology", "creativity-research", "personality-psychology" ], "color": "green" }, { "id": "h-criminal-deterrence-certainty-over-severity", "type": "hypothesis", "title": "Certainty of punishment has substantially greater deterrent effect on crime than severity of punishment; the deterrence elasticity of arrest probability is 5-10x larger in magnitude than the elasticity of sentence length, consistent with hyperbolic discounting of future punishments by would-be offenders.\n", "status": "active", "fields": [ "criminology", "economics", "behavioral-economics", "public-policy" ], "color": "green" }, { "id": "h-criminal-deterrence-certainty-severity", "type": "hypothesis", "title": "Criminal deterrence is primarily driven by certainty of punishment rather than severity — doubling arrest/conviction probability reduces crime rates more than doubling sentence length — consistent with time-discounting theory (rational criminals heavily discount future punishment) and supported by natural experiments showing marginal sentence increases have near-zero deterrent effect", "status": "active", "fields": [ "criminology", "economics", "public-policy", "behavioral-economics" ], "color": "green" }, { "id": "h-crispr-base-editing-x-error-correction", "type": "hypothesis", "title": "Off-target base editing rates follow a position-dependent mismatch model with exponential rate reduction per mismatch position (weighted by distance from PAM), matching the structure of a convolutional code error probability function and enabling quantitative prediction of off-target rates from guide sequence alone.\n", "status": "active", "fields": [ "molecular-biology", "information-theory", "genomics", "computational-biology" ], "color": "green" }, { "id": "h-crispr-x-search-and-replace", "type": "hypothesis", "title": "Designing guide RNAs with maximum Levenshtein distance from all off-target sites in the human genome using FM-index string matching will reduce off-target cleavage by at least 10-fold compared to guides designed by conventional seed-region matching alone\n", "status": "active", "fields": [ "biology", "computer-science", "molecular-biology" ], "color": "green" }, { "id": "h-critical-boolean-network-cell-type-count", "type": "hypothesis", "title": "Critical Boolean gene regulatory networks (K=2) predict cell type number scaling as √N_genes, and this prediction is quantitatively validated by comparing attractor counts of inferred genome-scale Boolean networks with measured cell type diversity across organisms differing in genome size.\n", "status": "active", "fields": [ "systems-biology", "computational-biology", "evolutionary-biology" ], "color": "green" }, { "id": "h-critical-coupling-tracking-improves-mid-range-wireless-power-efficiency", "type": "hypothesis", "title": "In resonant inductive WPT links, adaptive impedance/capacitance tracking that maintains near-critical coupling under misalignment increases median delivered-power efficiency at 1-2 coil diameters.\n", "status": "active", "fields": [ "electrical-engineering", "control-engineering", "electromagnetics" ], "color": "green" }, { "id": "h-critical-noise-sweep-scaling-parallels-election-timeout-sweep-phenomenologically", "type": "hypothesis", "title": "Sweeping Vicsek noise η through critical η_c while measuring polarization collapse will exhibit logistic-like order-parameter curves reminiscent of Raft stability probability versus randomized election-timeout multipliers in Monte Carlo fault simulations — framed as phenomenological similarity without dimensional normalization claims.\n", "status": "active", "fields": [ "statistical-physics", "distributed-systems" ], "color": "green" }, { "id": "h-critical-slowing-down-universal-ews-ecosystem-tipping-fold-bifurcation", "type": "hypothesis", "title": "Critical slowing down near fold bifurcations is a universal early warning signal for all ecosystem regime shifts, with variance σ² and AR(1) increasing according to universal power-law exponents γ determined by the fold bifurcation normal form, such that empirical detection with > 80% true positive rate and < 20% false positive rate is achievable from ≥ 100 observations before the transition.\n", "status": "active", "fields": [ "ecology", "physics", "statistics", "environmental-science" ], "color": "green" }, { "id": "h-criticality-conscious-integration", "type": "hypothesis", "title": "The brain maintains proximity to a second-order phase transition as a functional requirement for conscious integration, and disruption of this critical state causally degrades the binding of distributed neural representations", "status": "active", "fields": [ "neuroscience", "physics", "biology" ], "color": "green" }, { "id": "h-criticality-maximizes-neural-dynamic-range", "type": "hypothesis", "title": "The brain operates at the critical branching parameter σ = 1 because this maximizes dynamic range (the ratio of strongest to weakest distinguishable input), information transmission, and number of metastable states simultaneously — and deviations from criticality in specific brain regions predict measurable cognitive impairment.\n", "status": "active", "fields": [ "neuroscience", "statistical-physics", "computational-neuroscience", "neurological-medicine" ], "color": "green" }, { "id": "h-crn-oscillator-design", "type": "hypothesis", "title": "Chemical reaction networks with deficiency δ = 1 and a specific non-weakly-reversible subgraph structure can be systematically designed to function as chemical oscillators with predictable period and amplitude, using deficiency theory as a design principle for synthetic biology.\n", "status": "active", "fields": [ "chemistry", "synthetic-biology", "mathematics", "dynamical-systems" ], "color": "green" }, { "id": "h-crowd-dynamics-lane-formation-critical-density", "type": "hypothesis", "title": "Bidirectional pedestrian flow in a corridor will spontaneously form stable lanes (> 2 clearly separated streams) at density > 2 persons/m^2, with the lane formation order parameter growing as (rho - rho_c)^{0.5} consistent with a mean-field phase transition, and this critical density will be reproduced to within 20% by the Helbing social force model with default parameters", "status": "active", "fields": [ "physics", "social-science", "complex-systems" ], "color": "green" }, { "id": "h-crustal-delamination-drip-instability", "type": "hypothesis", "title": "Lower crustal delamination occurs when eclogitization increases lower crustal density above the mantle density (ρ > 3.3 g/cm³), triggering Rayleigh-Taylor drip instability with growth timescales of 10–30 Myr, predictable from the lower crust seismic velocity structure.\n", "status": "active", "fields": [ "geophysics", "petrology", "tectonics" ], "color": "green" }, { "id": "h-cryo-em-membrane-protein-structures-without-detergent-native-lipid-bilayer", "type": "hypothesis", "title": "Cryo-EM of membrane proteins reconstituted in lipid nanodiscs or native membrane vesicles (without detergent solubilisation) will routinely achieve ≤3 Å resolution for proteins ≥150 kDa within a native lipid environment, revealing lipid-protein interaction sites and conformational states inaccessible to detergent-solubilised preparations.\n", "status": "active", "fields": [ "structural-biology", "membrane-biophysics", "drug-discovery", "electron-microscopy" ], "color": "green" }, { "id": "h-cryo-em-supersedes-xray-membrane-proteins", "type": "hypothesis", "title": "For membrane proteins with molecular weight >200 kDa, cryo-EM single-particle analysis now routinely achieves higher resolution than X-ray crystallography for native-like structural states — making crystallography obsolete for this protein class while remaining superior for small, rigid proteins where cryo-EM faces orientational sampling limitations.\n", "status": "active", "fields": [ "structural-biology", "physics", "chemistry", "computational-biology" ], "color": "green" }, { "id": "h-cryoem-bayesian-x-single-particle-reconstruction", "type": "hypothesis", "title": "Frozen RELION hyperprior sweeps on benchmark particle stacks will yield posterior MAP volumes whose voxel-wise credible intervals overlap CryoSPARC variability estimates within calibrated tolerance bands when forward models matched across GPU pipelines — falsifying claims of incompatible Bayesian interpretations solely due to software branding.\n", "status": "active", "fields": [ "structural-biology", "statistics" ], "color": "green" }, { "id": "h-cryptocurrency-value-store-schelling-point", "type": "hypothesis", "title": "Cryptocurrency value as a store of value is determined primarily by Schelling-point coordination equilibria (focal network effects, institutional adoption) rather than fundamental utility; the dominant coin's expected value equals network size squared (Metcalfe's Law), predicting winner-take-most dynamics with persistent coins surviving via institutional endorsement and regulatory clarity", "status": "active", "fields": [ "economics", "game-theory", "behavioral-economics", "network-science" ], "color": "green" }, { "id": "h-crystallographic-protein-folding", "type": "hypothesis", "title": "The symmetry group of a protein's native fold constrains possible folding pathways from the denatured state — proteins with higher internal symmetry (higher-order point groups) should have fewer kinetic traps and fold more reliably, explaining the prevalence of symmetric oligomeric proteins in stable cellular structures.\n", "status": "active", "fields": [ "structural-biology", "biophysics", "mathematics", "group-theory", "computational-biology", "evolutionary-biology" ], "color": "green" }, { "id": "h-crystallography-x-group-theory", "type": "hypothesis", "title": "Topological quantum chemistry predicts that >25% of experimentally synthesized inorganic compounds with spin-orbit coupling host topologically non-trivial band structures, with the majority being topological semimetals (Weyl or Dirac) detectable by ARPES measurement of surface Fermi arc states", "status": "active", "fields": [ "physics", "mathematics", "materials-science" ], "color": "green" }, { "id": "h-csf-pulsatile-flow-amyloid-clearance-sleep-deprivation", "type": "hypothesis", "title": "Glymphatic CSF clearance of amyloid-β is primarily driven by slow-wave sleep (SWS) arterial pulsatility, such that each additional hour of SWS produces a quantifiable reduction in CSF amyloid-β concentration predictable from the Biot poroelastic flow model.\n", "status": "active", "fields": [ "neuroscience", "fluid-dynamics", "sleep-medicine", "neurology", "biophysics" ], "color": "green" }, { "id": "h-ctcf-boundary-polymer-wall", "type": "hypothesis", "title": "Convergent CTCF sites act as reflecting boundary conditions for cohesin- mediated loop extrusion, and their deletion will shift the TAD boundary position by exactly the mean cohesin processivity distance predicted by a Rouse-chain polymer model.\n", "status": "active", "fields": [ "molecular-biology", "polymer-physics" ], "color": "green" }, { "id": "h-cultural-group-selection-warfare-driver", "type": "hypothesis", "title": "Intergroup warfare provided sufficient between-group fitness variance in pre-state societies to drive the evolution of prosocial norms via cultural group selection: Price equation analysis of ethnographic warfare mortality data will show Cov(w_g, z_g) / Var(w) > 0.15, exceeding the between-group selection threshold predicted by MLS models for norm fixation.\n", "status": "active", "fields": [ "evolutionary-biology", "anthropology" ], "color": "green" }, { "id": "h-cultural-multilevel-selection-dominates-genetic", "type": "hypothesis", "title": "In modern human large-scale cooperation, cultural group selection on institutional norms operates faster and with greater effect size than genetic kin selection, making cultural Price equation dynamics the dominant explanation for human prosociality beyond kin", "status": "active", "fields": [ "evolutionary-biology", "social-science", "cultural-evolution", "anthropology" ], "color": "green" }, { "id": "h-cultural-phase-transition-globalization-diversity-paradox", "type": "hypothesis", "title": "Globalization increases cultural interaction but may paradoxically sustain or increase cultural diversity by raising the effective q/F ratio (trait diversity per feature) ΓÇö exposing populations to more cultural options rather than homogenizing them ΓÇö a testable prediction of the Axelrod phase transition in empirical World Values Survey data.\n", "status": "active", "fields": [ "cultural-dynamics", "sociology", "complex-systems", "political-science" ], "color": "green" }, { "id": "h-cultural-replicator-dynamics-rate", "type": "hypothesis", "title": "The rate of cultural evolution is determined by the product of population size, innovation rate, and fidelity of cultural transmission, following the Price equation analogue for cultural traits; digitally-mediated communication increases copying fidelity and population connectivity, predicting an exponential acceleration in the rate of cultural change observable in linguistic and behavioural datasets.\n", "status": "active", "fields": [ "social-science", "biology", "evolutionary-theory", "linguistics" ], "color": "green" }, { "id": "h-cultural-sir-meme-herd-immunity", "type": "hypothesis", "title": "A memetic SIR model calibrated to early adoption curves of social media viral content will accurately predict the final adoption fraction and time to peak prevalence with < 20% error, and the effective R_0 for online memes will be predictable from network degree distribution moments without full network data", "status": "active", "fields": [ "social-science", "epidemiology", "network-science" ], "color": "green" }, { "id": "h-cultural-transmission-conformist-norm-stability", "type": "hypothesis", "title": "Conformist transmission bias is the primary mechanism maintaining cooperative cultural norms in large anonymous societies, and the strength of conformist bias necessary for norm stability scales as log(N)/N with group size N, predicting that cooperative norms become increasingly fragile in societies above ~10,000 individuals without institutional enforcement", "status": "active", "fields": [ "social-science", "evolutionary-biology", "cognitive-science" ], "color": "green" }, { "id": "h-cuprate-pairing-spin-fluctuation-glue", "type": "hypothesis", "title": "Antiferromagnetic spin fluctuations are the dominant Cooper pairing glue in cuprate high-temperature superconductors, predicting d-wave symmetry order parameter and T_c proportional to the superexchange coupling J.\n", "status": "active", "fields": [ "condensed-matter-physics", "physical-chemistry", "materials-science" ], "color": "green" }, { "id": "h-cut-cell-conservative-flux-reduces-leakage-medical-seg", "type": "hypothesis", "title": "Boundary-aware segmentation losses inspired by flux imbalances reduce topological leakage (incorrect handles) on cortex phantom surfaces versus softmax-only U-Net training — falsified if leakage counts do not drop ≥20% at matched Dice on MICCAI-style phantoms with partial-volume ground truth.\n", "status": "active", "fields": [ "medical-imaging", "numerical-methods" ], "color": "green" }, { "id": "h-cyclic-dominance-spatial-heterogeneity-biodiversity", "type": "hypothesis", "title": "Spatial heterogeneity (patchy environments) quantitatively extends the range of cyclic dominance parameter space that maintains biodiversity, predicting that habitat fragmentation below a critical patch size collapses rock-paper-scissors systems into competitive exclusion.\n", "status": "active", "fields": [ "ecology", "evolutionary-biology", "game-theory", "statistical-physics" ], "color": "green" }, { "id": "h-cyp450-polymorphism-drug-toxicity-prediction", "type": "hypothesis", "title": "CYP2D6 and CYP2C9 genotype-based dosing adjustment will reduce serious adverse drug reactions by >30% for codeine, warfarin, and tamoxifen compared to standard weight-based dosing in a prospective randomized controlled trial, with the benefit concentrated in the 7-10% of patients who are poor metabolizers", "status": "active", "fields": [ "pharmacology", "genetics", "clinical-medicine" ], "color": "green" }, { "id": "h-cytoskeletal-active-matter-defect-dynamics", "type": "hypothesis", "title": "The density of +1/2 topological defects in the cortical actin network at cell division onset is predictive of spindle misorientation angle (R^2 > 0.5) across HeLa cells with varying myosin II activity, consistent with active matter defect-driven stress.\n", "status": "active", "fields": [ "biophysics", "biology", "physics" ], "color": "green" }, { "id": "h-da-mechanism-welfare-improving-redesign", "type": "hypothesis", "title": "Redesigning real-world matching mechanisms from immediate-acceptance (Boston) to deferred-acceptance produces measurable welfare improvements for the proposing side without reducing stability, replicating the NRMP result in school-choice and teacher-placement markets.\n", "status": "active", "fields": [ "economics", "social-science", "mathematics", "mechanism-design" ], "color": "green" }, { "id": "h-damped-bp-calibration-improves-phasing-accuracy", "type": "hypothesis", "title": "Cross-validated damping schedules selected on synthetic loopy linkage graphs reduce switch-error rates versus fixed defaults when marker maps induce long-range dependencies.", "status": "active", "fields": [ "genetics", "computer-science", "information-theory" ], "color": "green" }, { "id": "h-dark-energy-evolving-quintessence-w0wa", "type": "hypothesis", "title": "Dark energy is a dynamical scalar field (quintessence) with time-varying equation of state w(z) = w0 + wa·z/(1+z), distinguishable from the cosmological constant (w=-1) at the level of upcoming DESI and Euclid precision", "status": "active", "fields": [ "cosmology", "particle-physics", "observational-astronomy" ], "color": "green" }, { "id": "h-dark-energy-quintessence-equation-of-state-variation", "type": "hypothesis", "title": "If dark energy is quintessence (a scalar field) rather than a true cosmological constant, Euclid+DESI+Roman combined measurements of the dark energy equation of state will detect w(z) ≠ −1 at >2σ significance for z < 2, with the deviation following the Chevallier-Polarski-Linder parameterisation w(a) = w₀ + w_a(1−a) with |w_a| > 0.1.\n", "status": "active", "fields": [ "cosmology", "particle-physics" ], "color": "green" }, { "id": "h-dark-energy-quintessence-w-measurement", "type": "hypothesis", "title": "Dark energy is a dynamical scalar field (quintessence) with equation of state w(z) ≠ -1 that evolves as w(z) = w_0 + w_a × z/(1+z), with |w_a| > 0.1 detectable by the next generation of large-scale structure surveys (DESI, Euclid, Rubin LSST) at 5σ.\n", "status": "active", "fields": [ "astrophysics", "cosmology", "physics", "observational-astronomy" ], "color": "green" }, { "id": "h-dark-matter-qcd-axion-phase-relic", "type": "hypothesis", "title": "Cosmological dark matter is primarily composed of QCD axions with mass 10^-6 to 10^-5 eV produced by the misalignment mechanism at the QCD phase transition, with relic density set by the Peccei-Quinn symmetry-breaking scale f_a and the QCD topological susceptibility, making the axion mass a direct prediction of lattice QCD thermodynamics testable by haloscope experiments.\n", "status": "active", "fields": [ "particle-physics", "cosmology", "statistical-physics", "nuclear-physics" ], "color": "green" }, { "id": "h-data-driven-koopman-basis-improves-long-horizon-video-prediction", "type": "hypothesis", "title": "Neural dictionaries trained end-to-end with spectral penalty losses reduce long-horizon prediction error versus hand-crafted polynomial EDMD dictionaries on stationary laboratory fluid visualization clips under fixed camera geometry.", "status": "active", "fields": [ "computer-science", "physics", "dynamical-systems" ], "color": "green" }, { "id": "h-ddpm-priors-reduce-mri-reconstruction-error-at-fixed-dose", "type": "hypothesis", "title": "Diffusion-based priors reduce accelerated MRI reconstruction error at fixed acquisition budget without increasing clinically significant hallucination rates.", "status": "active", "fields": [ "medical-imaging", "machine-learning", "inverse-problems" ], "color": "green" }, { "id": "h-deep-carbon-mantle-reduced-phases", "type": "hypothesis", "title": "Earth's deep interior stores carbon primarily as iron carbide (Fe₃C) and metallic iron-carbon alloys in the outer core and reduced lower mantle, with total deep carbon inventory 10–100× the surface reservoir — volcanic outgassing rate is the primary regulator of the long-term geologic carbon cycle.\n", "status": "active", "fields": [ "geochemistry", "mineral-physics", "carbon-cycle" ], "color": "green" }, { "id": "h-deep-ocean-carbon-biological-pump-efficiency", "type": "hypothesis", "title": "The biological pump efficiency — fraction of surface-fixed carbon exported to depths >1000 m — determines centennial-scale CO2 sequestration capacity, and is primarily limited by iron micronutrient availability in HNLC regions, implying Southern Ocean iron fertilisation could sequester 1-3 Pg C/yr.\n", "status": "active", "fields": [ "chemical-oceanography", "geology", "biogeochemistry", "climate-science" ], "color": "green" }, { "id": "h-deep-water-cycle-mantle-surface-coupling", "type": "hypothesis", "title": "Earth's mantle stores 1-3 ocean masses of water in nominally anhydrous minerals (wadsleyite, ringwoodite), and subduction/volcanic outgassing fluxes are balanced at the multi-billion-year timescale to maintain liquid ocean persistence", "status": "active", "fields": [ "geophysics", "mineral-physics", "geochemistry" ], "color": "green" }, { "id": "h-default-mode-network-prospective-memory", "type": "hypothesis", "title": "The default mode network functions as a prospective memory and mental simulation system that constructs scene imagery for planning future events and consolidating autobiographical memory, and is suppressed during externally-directed tasks because self-referential scene construction and perceptual processing compete for shared representational cortex in parietal and medial temporal regions.\n", "status": "active", "fields": [ "cognitive-neuroscience", "memory-research", "fMRI-methodology" ], "color": "green" }, { "id": "h-defect-topology-predicts-coarsening-scaling-exponents", "type": "hypothesis", "title": "After rapid quenches, systems whose order-parameter manifold has nontrivial fundamental group exhibit slower defect-density decay exponents than homotopically trivial counterparts under matched dissipation.\n", "status": "active", "fields": [ "condensed-matter-physics", "topology", "mathematical-physics" ], "color": "green" }, { "id": "h-deformation-quantization-symplectic-bridge", "type": "hypothesis", "title": "Quantum mechanics is the deformation quantization of classical symplectic mechanics: the non-commutative algebra of quantum observables (A*B - B*A = iħ{A,B}_Poisson) is a formal deformation of the commutative Poisson algebra on phase space, with the symplectic structure ω providing the bracket, and symplectic integrators preserving ω correspond exactly to unitary quantum time evolution.\n", "status": "active", "fields": [ "mathematics", "classical-mechanics", "quantum-mechanics", "numerical-methods" ], "color": "green" }, { "id": "h-degrowth-wellbeing-decoupling", "type": "hypothesis", "title": "Absolute decoupling of wellbeing from GDP growth is achievable in wealthy economies through a transition to a provisioning system economy, but requires coordinated working-time reduction, public service expansion, and wealth redistribution operating simultaneously — no single policy is sufficient.\n", "status": "active", "fields": [ "economics", "ecological-economics", "political-economy" ], "color": "green" }, { "id": "h-delay-embedding-indicators-improve-icu-deterioration-lead-time", "type": "hypothesis", "title": "Methods transferred from `b-delay-embedding-x-icu-deterioration-early-warning` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "dynamical-systems", "critical-care" ], "color": "green" }, { "id": "h-delayed-school-start-improves-adolescent-outcomes-causally", "type": "hypothesis", "title": "Delaying middle and high school start times to 8:30 AM or later causally increases adolescent sleep duration, improves academic performance, reduces traffic accidents, and decreases depression symptoms — with benefits exceeding implementation costs by a ratio of at least 10:1.\n", "status": "active", "fields": [ "education", "public-health", "chronobiology", "epidemiology" ], "color": "green" }, { "id": "h-delta-avulsion-bifurcation-instability", "type": "hypothesis", "title": "River delta avulsion is triggered when the channel superelevation ratio (h_levee/h_bf) exceeds a threshold of ~1.0, determined by the balance between in-channel deposition rate and floodplain aggradation — making avulsion frequency predictable from channel morphology and sediment flux data.\n", "status": "active", "fields": [ "geomorphology", "sedimentology", "hydrology" ], "color": "green" }, { "id": "h-democracy-stability-economic-inequality-threshold", "type": "hypothesis", "title": "Democratic stability above a Gini coefficient threshold of ~0.45 declines nonlinearly due to elite capture mechanisms: when the top 10% income share exceeds ~50%, concentrated interests gain sufficient resources to subvert electoral and judicial institutions faster than civil society can respond.\n", "status": "active", "fields": [ "political-science", "social-science", "economics", "philosophy" ], "color": "green" }, { "id": "h-demographic-transition-child-survival-fertility", "type": "hypothesis", "title": "The delay between under-5 mortality decline and total fertility decline in demographic transitions is primarily determined by the time required for cultural updating of target family size expectations, predicted to decrease by 5-10 years per doubling of adult literacy rates based on information diffusion speed through social networks", "status": "active", "fields": [ "demography", "public-health", "sociology" ], "color": "green" }, { "id": "h-dense-hopfield-transformer-attention-unified", "type": "hypothesis", "title": "Transformer self-attention is the update rule of a dense Hopfield network with exponential interactions, implying that the biological correlates of attention in the cortex (top-down modulation of sensory processing) and the mathematical correlates of attention in transformers are instances of the same attractor memory retrieval dynamics described by spin glass theory\n", "status": "active", "fields": [ "machine-learning", "computational-neuroscience", "statistical-physics" ], "color": "green" }, { "id": "h-dependent-types-industrial-systems-programming-feasibility", "type": "hypothesis", "title": "Dependent type systems (beyond Rust's affine types) are feasible for industrial systems programming — specifically that a language combining Rust-style ownership with Martin-Löf dependent types will compile with acceptable overhead and enable verification of security-critical properties without full proof assistant burden.\n", "status": "active", "fields": [ "computer-science", "programming-languages", "formal-verification", "mathematics" ], "color": "green" }, { "id": "h-depth-separation-compositional-function-approximation", "type": "hypothesis", "title": "Deep neural networks exponentially outperform shallow networks for compositionally structured functions because depth enables hierarchical function composition that matches the compositional structure of natural data (images, language, physics simulations); the depth separation exponent is determined by the nesting depth of the compositional structure, with each additional layer providing exponential reduction in required neurons.\n", "status": "active", "fields": [ "mathematics", "machine-learning", "approximation-theory", "theoretical-computer-science" ], "color": "green" }, { "id": "h-derived-algebraic-geometry-char-p", "type": "hypothesis", "title": "The fundamental obstruction to extending derived algebraic geometry to characteristic p arithmetic geometry is the failure of the HKR theorem (Hochschild-Kostant-Rosenberg), which requires p-th divided powers — and prismatic cohomology (Bhatt-Scholze) resolves this by providing the correct derived de Rham comparison in mixed characteristic.\n", "status": "active", "fields": [ "algebraic-geometry", "arithmetic-geometry", "homotopy-theory" ], "color": "green" }, { "id": "h-desalination-membrane-thermodynamic-gap", "type": "hypothesis", "title": "Current reverse osmosis desalination operates at 3–4× the thermodynamic minimum energy (0.7 kWh/m³ minimum vs. 2–4 kWh/m³ practical), and the gap is dominated by concentration polarization at the membrane boundary layer rather than membrane resistance — closing it requires hydrodynamic engineering, not new membrane chemistry.\n", "status": "active", "fields": [ "membrane-engineering", "thermodynamics", "water-treatment" ], "color": "green" }, { "id": "h-deseq2-style-shrinkage-reduces-false-alerts-in-low-count-clinical-monitoring", "type": "hypothesis", "title": "DESeq2-style shrinkage reduces false clinical alerts without materially delaying true-signal detection.", "status": "active", "fields": [ "epidemiology", "statistics", "medicine" ], "color": "green" }, { "id": "h-developmental-geometry-diffeomorphism-geodesic", "type": "hypothesis", "title": "Biological shape change during development follows geodesics on the infinite- dimensional diffeomorphism group Diff(M) equipped with an H^1 Sobolev metric; the observed diversity of animal body plans corresponds to a low-dimensional manifold in shape space discoverable by principal geodesic analysis of developmental sequence data, with evolutionary transitions following shortest paths in this space.\n", "status": "active", "fields": [ "mathematical-biology", "developmental-biology", "computational-anatomy", "evolutionary-biology" ], "color": "green" }, { "id": "h-developmental-gradient-x-pde", "type": "hypothesis", "title": "The robustness of embryonic morphogen gradient interpretation is maintained by a feedforward incoherent loop that implements derivative control, reducing sensitivity to absolute morphogen levels\n", "status": "active", "fields": [ "biology", "developmental-biology", "mathematics" ], "color": "green" }, { "id": "h-device-independent-certifiable-randomness", "type": "hypothesis", "title": "Device-independent randomness expansion (DIRE) protocols based on loophole-free Bell inequality violations can certifiably generate unbounded true randomness from a short random seed, with the security guarantee holding against quantum adversaries — making quantum random number generation information-theoretically certifiable in principle, though current implementations are limited to kilobits per second by detection efficiency.\n", "status": "active", "fields": [ "quantum-physics", "quantum-information", "cryptography" ], "color": "green" }, { "id": "h-dft-bep-relationship-enables-quantitative-catalyst-design-before-synthesis", "type": "hypothesis", "title": "DFT-calculated adsorption energies and Brønsted-Evans-Polanyi activation energy relationships correctly predict the rank-ordering of catalytic activity (turnover frequency) for new bimetallic catalysts before experimental synthesis, enabling rational catalyst design that identifies the top-3 candidates among a 100+ member library with ≥80% success rate.\n", "status": "active", "fields": [ "catalysis", "computational-chemistry", "chemical-engineering", "materials-science" ], "color": "green" }, { "id": "h-dft-jacob-ladder-convergence-to-accuracy", "type": "hypothesis", "title": "The Jacob's Ladder of DFT approximations (LDA → GGA → meta-GGA → hybrid → double hybrid) systematically approaches chemical accuracy by satisfying increasingly many exact constraints, with each rung reducing mean absolute error in thermochemistry by ~50%; hybrid functionals already achieve near-chemical-accuracy for organic molecules and the remaining gap arises specifically from strong- correlation systems and delocalization error.\n", "status": "active", "fields": [ "quantum-chemistry", "computational-physics", "materials-science", "molecular-physics" ], "color": "green" }, { "id": "h-diamond-inclusion-entrapment-bias", "type": "hypothesis", "title": "Diamond inclusion ages systematically underestimate ancient mantle events by up to 500 Myr due to preferential entrapment of younger, metasomatic minerals during diamond recrystallization, detectable through inclusion compositional heterogeneity within single diamonds.\n", "status": "active", "fields": [ "geochronology", "petrology", "mantle-geochemistry" ], "color": "green" }, { "id": "h-differential-privacy-hypothesis-testing-connection", "type": "hypothesis", "title": "Differential privacy (epsilon, delta) is dual to hypothesis testing: epsilon controls the Type I + Type II error tradeoff for any test distinguishing adjacent datasets, and the hockey-stick divergence E_alpha = max(P(M(D)∈S) - alpha × P(M(D')∈S)) provides the tight characterization of (epsilon, delta)-DP in terms of Neyman-Pearson optimal hypothesis testing theory.\n", "status": "active", "fields": [ "information-theory", "statistics", "privacy", "computer-science" ], "color": "green" }, { "id": "h-differential-privacy-urban-analytics-accuracy-threshold", "type": "hypothesis", "title": "A privacy budget of epsilon ≤ 1 (strong differential privacy) is sufficient to produce accurate city-scale traffic flow models from cellular mobility data, with model error below 10% for flows aggregated at 500-meter spatial resolution — resolving the accuracy-privacy tradeoff at operationally useful precision.\n", "status": "active", "fields": [ "engineering", "social-science", "computer-science", "urban-planning" ], "color": "green" }, { "id": "h-diffusion-downscaling-improves-extreme-precipitation-fidelity", "type": "hypothesis", "title": "Physics-guided diffusion downscaling improves extreme-precipitation fidelity versus standard bias-correction baselines.", "status": "active", "fields": [ "climate-science", "machine-learning", "statistics" ], "color": "green" }, { "id": "h-diffusion-limited-aggregation-x-fractal-growth", "type": "hypothesis", "title": "The fractal dimension of retinal vasculature in diabetic retinopathy will decrease measurably from the healthy DLA baseline (D ≈ 1.71) in proportion to the severity of vascular regression, providing a non-invasive diagnostic biomarker\n", "status": "active", "fields": [ "physics", "biology", "mathematics" ], "color": "green" }, { "id": "h-diffusion-models-x-stochastic-processes", "type": "hypothesis", "title": "The minimum number of sampling steps for epsilon-accurate diffusion model generation scales as O(d/epsilon^2) where d is the intrinsic data dimensionality, and this bound is achievable by the probability flow ODE with optimal step-size scheduling derived from the data's local curvature.\n", "status": "active", "fields": [ "machine-learning", "mathematics", "stochastic-processes", "deep-learning" ], "color": "green" }, { "id": "h-diffusion-queueing-threshold-policies-reduce-ed-boarding-time-variance", "type": "hypothesis", "title": "Transferred methods from `b-heavy-traffic-queueing-x-emergency-department-flow` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "operations-research", "medicine" ], "color": "green" }, { "id": "h-diffusive-interface-models-predict-shoreline-roughening-exponents", "type": "hypothesis", "title": "Along undeveloped high-energy coasts, empirical shoreline power spectra will show a power-law tail consistent with stochastic interface growth models over two decades of spatial wavelength when detrended for sea-level rise.", "status": "active", "fields": [ "geoscience", "mathematics" ], "color": "green" }, { "id": "h-dipole-weakening-precursor-reversal", "type": "hypothesis", "title": "The ongoing geomagnetic dipole weakening is a precursor to a polarity excursion or reversal within 2,000 years, identifiable by the current South Atlantic Anomaly growth rate exceeding 50% of reversal precursor thresholds derived from paleomagnetic records", "status": "active", "fields": [ "geophysics", "physics" ], "color": "green" }, { "id": "h-dislocation-density-taylor-hardening-md-validation", "type": "hypothesis", "title": "Molecular dynamics simulations using machine-learning interatomic potentials (MLIPs) at scales > 10⁷ atoms will quantitatively reproduce Taylor hardening (τ = τ₀ + αGb√ρ) and Hall-Petch scaling (σ_y = σ₀ + k_y/√d) from first principles without fitting to experimental data, establishing α and k_y as computable from atomic interactions alone.\n", "status": "active", "fields": [ "materials-science", "physics", "computational-physics", "metallurgy" ], "color": "green" }, { "id": "h-dislocation-nucleation-length-predicts-mainshock-magnitude", "type": "hypothesis", "title": "The critical nucleation patch size L_c calculated from rate-and-state friction parameters measured on exhumed fault rocks scales with the maximum magnitude M_w of characteristic earthquakes on the same fault segment, predicting M_w from laboratory friction data\n", "status": "active", "fields": [ "seismology", "solid-mechanics" ], "color": "green" }, { "id": "h-dispersion-aware-wake-visualization-improves-hull-wave-interpretation", "type": "hypothesis", "title": "Engineering students using dispersion-aware wake visualizations will more accurately predict when Kelvin wake angles narrow with Froude number than students taught only the fixed Kelvin wedge; falsified if accuracy gains are below 10 percentage points.\n", "status": "active", "fields": [ "naval-engineering", "fluid-mechanics" ], "color": "green" }, { "id": "h-dispersion-engineering-achromatic-metalens", "type": "hypothesis", "title": "Inverse-designed all-dielectric metasurfaces can achieve diffraction-limited achromatic focusing across 400-700 nm wavelength range at NA > 0.5 by exploiting resonant phase dispersion in nanostructures with aspect ratio > 10, and the maximum achievable bandwidth-aperture product is limited by a conservation law analogous to the Abbe-Porter theorem in classical optics", "status": "active", "fields": [ "optics", "materials-science", "photonics" ], "color": "green" }, { "id": "h-distribution-shift-invariant-risk-minimization", "type": "hypothesis", "title": "Invariant Risk Minimization (IRM) and related distributionally robust optimization approaches achieve OOD robustness by learning features with invariant causal relationships across training environments, outperforming ERM under covariate shift; but the hardness of identifying genuine causal invariances means practical OOD guarantees require domain knowledge about the causal structure", "status": "active", "fields": [ "machine-learning", "causality", "statistics" ], "color": "green" }, { "id": "h-dlvo-failure-short-range-attractions-gels", "type": "hypothesis", "title": "When colloidal particles with short-range attractions (range delta/a < 0.05) are added to a DLVO-stable dispersion, the system undergoes arrested phase separation into a colloidal gel at volume fractions phi ~ 0.1-0.3 via a spinodal decomposition mechanism, and the gel elasticity scales as G' ~ phi^n with n determined by the fractal dimension of the gel network, not by DLVO barrier height.\n", "status": "active", "fields": [ "soft-matter", "colloid-science", "materials-science" ], "color": "green" }, { "id": "h-dna-knot-complexity-aging", "type": "hypothesis", "title": "DNA topological complexity (knot frequency and average crossing number) increases with cellular aging due to declining topoisomerase II activity, and the rate of topological complexity accumulation predicts replicative lifespan in model organisms.\n", "status": "active", "fields": [ "biology", "molecular-biology", "mathematics", "aging-biology" ], "color": "green" }, { "id": "h-dna-replication-optimal-mutation-rate", "type": "hypothesis", "title": "DNA replication error rates are near the information-theoretically optimal mutation rate for the organism's effective population size and fitness landscape ruggedness\n", "status": "active", "fields": [ "biology", "computer_science", "information_theory", "molecular_biology" ], "color": "green" }, { "id": "h-doob-convergence-rate-scientific-inference", "type": "hypothesis", "title": "For scientific hypotheses with k free parameters, the Bayesian posterior achieves ε-convergence to the true parameter at sample size n* ∝ k/ε² (independent of the prior satisfying Cromwell's rule), making the practical resolution of induction scale as the Cramér-Rao lower bound.\n", "status": "active", "fields": [ "statistics", "philosophy-of-science", "probability-theory" ], "color": "green" }, { "id": "h-doppler-carry-yield-curve-steepness-speculative-parallels", "type": "hypothesis", "title": "Steepening of a yield curve segment after option adjustments might be narrated like a differential redshift gradient along a pencil beam — purely pedagogical unless backed by a pre-registered econometric test; treat as speculation.\n", "status": "active", "fields": [ "astronomy", "finance" ], "color": "green" }, { "id": "h-double-network-hydrogel-toughness-sacrificial-bond", "type": "hypothesis", "title": "Double-network hydrogel toughness scales as Gc ~ G1 * l_c where G1 is the first network modulus and l_c is the critical strand length for sacrificial bond rupture, predicting a 10-fold toughness increase with each doubling of first-network strand length", "status": "active", "fields": [ "materials-science", "polymer-physics" ], "color": "green" }, { "id": "h-droplet-split-binomial-partition-fission-alignment", "type": "hypothesis", "title": "Symmetric T-junction splitting cascades under controlled Ca/Re bands yield daughter-volume partitions whose coefficient of variation matches binomial-type branching-process models within tolerance bands distinct from microbial lineage datasets analyzed with identical estimators — falsified if empirical overlap cannot be rejected via permutation tests across paired datasets.\n", "status": "active", "fields": [ "microfluidics", "systems-biology" ], "color": "green" }, { "id": "h-dual-inheritance-lactase-selection", "type": "hypothesis", "title": "The co-spread of dairying culture and the lactase persistence allele constitutes the best-documented case of gene-culture coevolution, and archaeogenomic analysis should show that the LP allele frequency increase follows the dual inheritance Price equation prediction: Δp ≈ s·p(1-p) with selection coefficient s derivable from caloric advantage of milk consumption in Bronze Age pastoralist populations.\n", "status": "active", "fields": [ "evolutionary-biology", "cultural-evolution", "archaeogenomics", "population-genetics" ], "color": "green" }, { "id": "h-dual-site-catalyst-breaks-oer-scaling", "type": "hypothesis", "title": "Dual-site OER catalysts with heterogeneous adjacent metal pairs (M1-M2 with M1 ≠ M2) can break the universal OHH*/OH* scaling relation by binding OOH* through a bridging configuration that is geometrically decoupled from the single-site OH* binding, reducing OER overpotential below 0.25 V.\n", "status": "active", "fields": [ "electrochemistry", "computational-chemistry", "materials-science" ], "color": "green" }, { "id": "h-durotaxis-cancer-metastasis", "type": "hypothesis", "title": "Tumour metastasis preferentially occurs toward stiffer tissues (liver, lung, bone) because cancer cells follow durotaxis — mechanosensing-directed migration toward higher substrate stiffness — and stiffness gradients in the tumour microenvironment predict metastatic routes with quantitative accuracy.\n", "status": "active", "fields": [ "biology", "biophysics", "cancer-biology", "physics" ], "color": "green" }, { "id": "h-early-dark-energy-hubble-tension", "type": "hypothesis", "title": "Early dark energy that decays before recombination reduces the sound horizon and reconciles CMB-inferred H0 with local distance ladder measurements without introducing new tensions", "status": "active", "fields": [ "astronomy", "physics" ], "color": "green" }, { "id": "h-early-galaxy-formation-jwst-feedback", "type": "hypothesis", "title": "The anomalously massive galaxies at z > 10 observed by JWST are explained by reduced supernova feedback efficiency in the early universe — either due to rapid gas recycling in compact high-redshift disks or bursty star formation that temporarily suppresses feedback — rather than requiring modifications to ΛCDM.\n", "status": "active", "fields": [ "astrophysics", "astronomy", "cosmology", "computational-astrophysics" ], "color": "green" }, { "id": "h-ecm-stiffness-cancer-invasion-threshold", "type": "hypothesis", "title": "There exists a critical ECM stiffness threshold E* ~ 5-15 kPa at which breast epithelial cells switch from a non-invasive to an invasive phenotype via YAP/TAZ-mediated transcriptional reprogramming, independently of oncogenic mutation status.\n", "status": "active", "fields": [ "mechanobiology", "cancer-biology", "biophysics" ], "color": "green" }, { "id": "h-ecological-succession-x-markov", "type": "hypothesis", "title": "Forest succession Markov chains exhibit spectral gaps (1-λ₂) that scale as g ∝ 1/ln(N_species), predicting that temperate deciduous forests (30-50 tree species) recover from clear-cut disturbance in 80-120 years — matching empirical chronosequence data — while tropical forests (200+ species) require 250-400 years due to smaller spectral gaps", "status": "active", "fields": [ "ecology", "mathematics", "biology" ], "color": "green" }, { "id": "h-ecology-x-coexistence-theory", "type": "hypothesis", "title": "A 50% reduction in interannual rainfall variance (projected under mid-latitude climate change) reduces the storage effect stabilising component Δi by 30–40% in annual plant communities, predicting a corresponding 30–40% reduction in species coexistence time before competitive exclusion", "status": "active", "fields": [ "biology", "mathematics", "ecology", "climate-science" ], "color": "green" }, { "id": "h-ecosystem-services-pigouvian-subsidy-biodiversity-market", "type": "hypothesis", "title": "Payments for Ecosystem Services (PES) set at the marginal ecosystem service value estimated by hedonic pricing (flood protection, water filtration, carbon sequestration) will conserve forest cover at least as efficiently as command- and-control regulations in Costa Rica, the Amazon, and Southeast Asia, and markets for biodiversity credits can reach $100B/year by 2030 with robust MRV.\n", "status": "active", "fields": [ "economics", "ecology", "policy", "environmental-science" ], "color": "green" }, { "id": "h-eeg-individualized-forward-model-epilepsy", "type": "hypothesis", "title": "EEG source localization with individualized skull conductivity maps derived from T2-weighted MRI reduces epileptic focus localization error from >15 mm to <5 mm in subjects with irregular skull morphology, matching intracranial electrode gold-standard accuracy\n", "status": "active", "fields": [ "neuroscience", "physics" ], "color": "green" }, { "id": "h-eew-kalman-style-updates-tighten-magnitude-posterior-faster-with-dense-networks", "type": "hypothesis", "title": "Holding rupture scenario class fixed, doubling effective station density within two rupture lengths of the epicenter halves the median time-to-first magnitude estimate within ±0.5 units compared to sparse-network baselines — dominated by geometric aperture rather than CPU throughput at modern telemetry rates.\n", "status": "active", "fields": [ "geophysics", "control-engineering" ], "color": "green" }, { "id": "h-efficient-coding-natural-statistics-sensory-cortex-universality", "type": "hypothesis", "title": "The efficient coding hypothesis holds universally across all primary sensory cortices (V1, A1, S1) and all vertebrate species tested: the neural code in each area is optimally matched (via evolution and development) to the statistical structure of the natural stimuli for that sensory modality, minimizing redundancy and maximizing mutual information under metabolic constraints.\n", "status": "active", "fields": [ "neuroscience", "information-theory", "psychophysics", "computational-neuroscience", "evolutionary-biology" ], "color": "green" }, { "id": "h-eigenvector-centrality-superspreader-epidemic-prediction", "type": "hypothesis", "title": "Eigenvector centrality of the contact network, computed from mobile phone proximity data at the start of an epidemic, predicts individual superspreader status (contributing >80% of secondary cases) with AUC > 0.80, outperforming degree centrality, betweenness centrality, and demographic risk factors.\n", "status": "active", "fields": [ "social-science", "mathematics", "network-science", "epidemiology" ], "color": "green" }, { "id": "h-eikonal-regularized-inversion-improves-cardiac-activation-map-fidelity", "type": "hypothesis", "title": "Transferred methods from `b-eikonal-wavefronts-x-cardiac-activation-mapping` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "geoscience", "medicine" ], "color": "green" }, { "id": "h-eis-hodgkin-huxley-parameter-extraction", "type": "hypothesis", "title": "Multi-frequency EIS measurements on voltage-clamped excitable cell monolayers can extract Hodgkin-Huxley channel gating parameters (g_Na, g_K, tau_m, tau_h) with accuracy comparable to patch-clamp, enabling label-free high-throughput ion channel pharmacology screening.\n", "status": "active", "fields": [ "biophysics", "chemistry", "electrophysiology", "pharmacology" ], "color": "green" }, { "id": "h-eis-spectra-constrain-gating-substates", "type": "hypothesis", "title": "For expressed hERG channels in oocytes, global fits of admittance spectra across voltages will favor three-state minimal models over two-state models at statistically significant levels when subconductance states exist.", "status": "active", "fields": [ "biophysics", "chemistry" ], "color": "green" }, { "id": "h-elastic-net-prs-retraining-with-ancestry-balancing-reduces-calibration-drift", "type": "hypothesis", "title": "Ancestry-balanced elastic-net retraining reduces out-of-sample PRS calibration error versus static models.", "status": "active", "fields": [ "medicine", "statistics", "genetics" ], "color": "green" }, { "id": "h-elasticity-analysis-conservation-prioritisation", "type": "hypothesis", "title": "Elasticity analysis of the Leslie matrix (proportional sensitivity of λ₁ to vital rate changes) reliably identifies the life-history transition that most effectively increases population growth rate, and conservation interventions targeting high-elasticity stages will achieve faster population recovery than interventions targeting other stages, across a broad range of taxa\n", "status": "active", "fields": [ "conservation-biology", "population-biology", "mathematics" ], "color": "green" }, { "id": "h-electric-catalyst-preheating-eliminates-cold-start-emissions", "type": "hypothesis", "title": "Electrically preheating the catalytic converter to light-off temperature before engine start will eliminate >75% of total trip emissions for hybrid and plug-in hybrid vehicles where grid electricity is available, making EHC cost-effective at current carbon prices.\n", "status": "active", "fields": [ "chemical-engineering", "automotive-engineering", "environmental-science" ], "color": "green" }, { "id": "h-electrochemical-co2-copper-selectivity", "type": "hypothesis", "title": "The selectivity of electrochemical CO2 reduction to C2+ products on copper is controlled by the local pH and CO coverage at the catalyst surface, such that maintaining local pH > 12 and CO coverage > 0.3 ML simultaneously via pulsed potential waveforms can achieve > 70% Faradaic efficiency to ethylene at practical current densities > 300 mA/cm².\n", "status": "active", "fields": [ "chemistry", "materials-science", "electrochemistry", "computational-chemistry" ], "color": "green" }, { "id": "h-emergence-multiple-realisability-causal-autonomy", "type": "hypothesis", "title": "Higher-level scientific explanations are causally autonomous (not merely convenient summaries) when the higher-level properties are multiply realisable by many distinct lower-level configurations — the same higher- level pattern has causal powers that cannot be predicted by tracking any particular lower-level realisation.\n", "status": "active", "fields": [ "philosophy-of-science", "metaphysics", "cognitive-science", "biology" ], "color": "green" }, { "id": "h-emotion-construction-core-affect-appraisal", "type": "hypothesis", "title": "Basic emotions are not discrete biological categories with dedicated neural circuits but are constructed from domain-general core affect (valence × arousal) and conceptual knowledge, with no unique neural signatures for fear, anger, or disgust", "status": "active", "fields": [ "affective-neuroscience", "psychology", "cross-cultural-psychology" ], "color": "green" }, { "id": "h-emotion-constructivist-core-affect-model", "type": "hypothesis", "title": "Emotions are not discrete natural kinds with dedicated neural circuits but are constructed from combinations of core affect dimensions (valence, arousal) and conceptual knowledge — consistent with the theory of constructed emotion (Barrett 2017), predicting that brain imaging will show no consistently distinct neural signatures for basic emotion categories across individuals and cultures.\n", "status": "active", "fields": [ "neuroscience", "psychology", "cognitive-science" ], "color": "green" }, { "id": "h-endangered-language-documentation-multimedia", "type": "hypothesis", "title": "Endangered language documentation for future research is maximised by prioritising multimedia naturalistic corpus collection (spontaneous discourse, narrative, and conversation) over formal elicitation, because computational linguistic analysis tools require naturalistic data for morphological discovery and prosodic reconstruction that formal elicitation systematically undersupplies.\n", "status": "active", "fields": [ "linguistics", "computational-linguistics", "anthropology", "information-science" ], "color": "green" }, { "id": "h-ensemble-smoothers-improve-precision-oncology-trajectory-calibration", "type": "hypothesis", "title": "Transferred methods from `b-ensemble-smoother-x-precision-oncology-state-estimation` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "geoscience", "medicine" ], "color": "green" }, { "id": "h-enso-predictability-nonlinear-phase-locking", "type": "hypothesis", "title": "The fundamental predictability limit of ENSO beyond 12 months is determined by the nonlinear interaction between the annual cycle and the ENSO oscillation that produces chaotic phase slipping, with maximum predictability occurring when ENSO is phase-locked to boreal winter and minimum predictability during the spring predictability barrier.\n", "status": "active", "fields": [ "climate-science", "atmospheric-science", "nonlinear-dynamics" ], "color": "green" }, { "id": "h-entropy-production-x-living-systems", "type": "hypothesis", "title": "Living systems operate near a saddle point in entropy production rate space: cell metabolic networks minimise σ at homeostasis (Prigogine) but evolutionary selection maximises sustainable σ, resolving the MEP/minEP controversy via timescale separation", "status": "active", "fields": [ "physics", "biology", "thermodynamics", "evolutionary-biology" ], "color": "green" }, { "id": "h-entropy-rate-x-language-model-perplexity", "type": "hypothesis", "title": "Sliding-window nonparametric entropy-rate estimates on temporally stratified corpora will bound perplexity improvements attributable to domain shift tracking versus raw entropy reduction — producing measurable gaps between LM perplexity and entropy-rate lower bounds over matched slices.\n", "status": "active", "fields": [ "information-theory", "computational-linguistics" ], "color": "green" }, { "id": "h-enz-crossover-curvature-predicts-local-q-maximum-thin-film-cavity", "type": "hypothesis", "title": "At fixed thickness and substrate index, the sharpest ENZ-related resonance linewidth minimum tracks the maximum of |d(Re ε)/dω| near loss-compensated crossover rather than the deepest Re ε→0 point alone — dispersion curvature dominates optimal Q when Im ε is monotone.\n", "status": "active", "fields": [ "electromagnetism", "materials-science" ], "color": "green" }, { "id": "h-enzyme-kinetics-x-michaelis-menten", "type": "hypothesis", "title": "Metabolic pathway enzymes are evolutionarily tuned so that their Km values match physiological substrate concentrations, placing each enzyme in the linear (unsaturated) regime to minimize resource waste and maximize pathway control coefficient\n", "status": "active", "fields": [ "chemistry", "biology", "biochemistry" ], "color": "green" }, { "id": "h-enzyme-surface-catalyst-design-principles", "type": "hypothesis", "title": "Enzyme variants designed using the Brønsted-Evans-Polanyi volcano plot optimality criterion (DeltaG_dag minimized at DeltaG_ads = -DeltaG_rxn/2) achieve k_cat values within 10-fold of the wild-type enzyme when DeltaG_rxn is held constant by substrate choice.\n", "status": "active", "fields": [ "chemistry", "biochemistry", "physics" ], "color": "green" }, { "id": "h-epidemic-ar1-tipping-warning", "type": "hypothesis", "title": "Rising lag-1 autocorrelation (AR1) in weekly disease incidence time series provides a statistically significant early-warning signal of epidemic emergence in climate-stressed populations, analogous to AR1 rise before climate tipping points", "status": "active", "fields": [ "climate-science", "epidemiology", "medicine", "dynamical-systems" ], "color": "green" }, { "id": "h-epidemic-ensemble-kalman-filter", "type": "hypothesis", "title": "An EnKF with Poisson observation model and time-varying β(t) augmented state will achieve CRPS scores 15-25% better than EpiEstim for real-time Rt estimation during the early exponential phase of novel pathogen outbreaks", "status": "active", "fields": [ "epidemiology", "statistics", "data-assimilation" ], "color": "green" }, { "id": "h-epigenetic-attractor-scrnaseq-landscape", "type": "hypothesis", "title": "RNA velocity vector fields from scRNA-seq data accurately recover the Waddington attractor landscape, with predicted barrier heights correlated with reprogramming efficiency across cell type pairs\n", "status": "active", "fields": [ "biology", "mathematics", "dynamical_systems", "developmental_biology" ], "color": "green" }, { "id": "h-epigenetic-reprogramming-lifespan-extension", "type": "hypothesis", "title": "Cyclic partial reprogramming using Oct4, Sox2, and Klf4 (OSK) without c-Myc in post-mitotic neurons extends mouse lifespan by 15-20% when initiated at midlife, by resetting the Horvath epigenetic clock without inducing pluripotency or increasing cancer risk.\n", "status": "active", "fields": [ "epigenetics", "geroscience", "molecular-biology" ], "color": "green" }, { "id": "h-ergodic-theory-x-statistical-mechanics", "type": "hypothesis", "title": "The many-body localization transition in 1D disordered spin chains is a true phase transition in the thermodynamic limit, with a critical disorder strength W_c that scales logarithmically with system size L, distinguishing it from a finite-size crossover.\n", "status": "active", "fields": [ "condensed-matter-physics", "statistical-mechanics", "mathematics", "quantum-physics" ], "color": "green" }, { "id": "h-evtol-noise-rotor-spacing", "type": "hypothesis", "title": "eVTOL aircraft can achieve community-acceptable noise below 65 dBA at 500m distance through distributed electric propulsion with blade-passing frequency optimization and acoustic phase cancellation — but the fundamental acoustic power floor set by actuator disk theory prevents achieving helicopter-equivalent noise reduction at equivalent payload.\n", "status": "active", "fields": [ "aeroacoustics", "urban-air-mobility", "aerospace-engineering" ], "color": "green" }, { "id": "h-exoplanet-spectral-retrieval-bayesian", "type": "hypothesis", "title": "Neural-network forward model emulators for JWST atmospheric retrieval will reduce computation time by 100-1000x with <10% loss in posterior accuracy, enabling same-day real-time retrieval for transiting exoplanet follow-up", "status": "active", "fields": [ "astronomy", "statistics", "machine-learning" ], "color": "green" }, { "id": "h-expander-graphs-x-error-correcting-codes", "type": "hypothesis", "title": "Tanner graph spectral gap is a stronger predictor of LDPC code threshold performance under belief propagation than variable or check node degree distributions alone, and codes constructed from Ramanujan graphs achieve belief propagation thresholds within 0.1 dB of the Shannon limit.\n", "status": "active", "fields": [ "mathematics", "computer-science", "information-theory", "quantum-computing" ], "color": "green" }, { "id": "h-extended-contact-prejudice-reduction-mechanism", "type": "hypothesis", "title": "Intergroup contact reduces prejudice most reliably when it involves the extended contact effect (knowing an ingroup member who has an outgroup friend) rather than direct contact alone, and this effect scales with social network bridging ties in the community — making network density of cross-group ties a policy target for scaling prejudice reduction across diverse societies.\n", "status": "active", "fields": [ "social-psychology", "sociology", "network-science" ], "color": "green" }, { "id": "h-extinction-debt-master-equation-prediction", "type": "hypothesis", "title": "The master equation for stochastic birth-death processes predicts that extinction debt (species committed to extinction despite current positive population size) scales as the ratio of demographic to environmental stochasticity variance, and this ratio can be measured non-invasively from time-series count data.\n", "status": "active", "fields": [ "ecology", "mathematics", "conservation-biology", "probability-theory" ], "color": "green" }, { "id": "h-extinction-time-exponential-k-demographic-stochasticity-confirmed", "type": "hypothesis", "title": "The mean extinction time formula T_ext ≈ exp(2rK/σ²)/r from the stochastic logistic model correctly predicts (within one order of magnitude) the observed extinction waiting times in laboratory populations of model organisms across a range of K and σ² values, validating the mathematical framework underlying IUCN minimum viable population assessments.\n", "status": "active", "fields": [ "conservation-biology", "population-ecology", "mathematics", "stochastic-processes" ], "color": "green" }, { "id": "h-extreme-value-theory-x-risk-modeling", "type": "hypothesis", "title": "Financial market crashes (S&P 500 daily returns below -5%) follow a Fréchet extreme value distribution with tail index ξ ≈ 0.3±0.05, implying infinite kurtosis and that 1-in-100-year losses are systematically underestimated by 40-60% by Gaussian VaR models across all asset classes", "status": "active", "fields": [ "mathematics", "economics", "statistics" ], "color": "green" }, { "id": "h-face-recognition-fusiform-holistic-coding", "type": "hypothesis", "title": "The fusiform face area implements holistic face coding via a population code in which identity is represented as a trajectory through a high-dimensional eigenface space, and the perceptual inversion effect is explained by disruption of the learned principal component axes when face parts are presented in non-canonical spatial relationships.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "green" }, { "id": "h-fano-q-factor-tracks-radiative-darkness-order-parameter", "type": "hypothesis", "title": "Define an empirical darkness scalar D = Γ_rad/(Γ_rad + Γ_abs) extracted from coupled-mode fits; across geometric families of split-ring / oligomer metamaterials, peak loaded Q scales approximately as 1/(1−D) until absorption dominates.\n", "status": "active", "fields": [ "optics", "materials-science" ], "color": "green" }, { "id": "h-federated-ensembles-improve-cross-site-epidemic-generalization", "type": "hypothesis", "title": "Federated ensembles with drift-aware weighting improve out-of-site epidemic forecast calibration over vanilla FedAvg aggregation.", "status": "active", "fields": [ "epidemiology", "machine-learning", "distributed-systems" ], "color": "green" }, { "id": "h-feigenbaum-universality-quantum-maps-period-doubling", "type": "hypothesis", "title": "The Feigenbaum universality of period-doubling routes to chaos (δ ≈ 4.669, α ≈ 2.502) extends to quantum maps via the quantum-classical correspondence: quantized versions of the logistic map and the Hénon map exhibit the same universal period-doubling ratios in the semiclassical limit (ℏ → 0, N_eff → ∞), with quantum corrections suppressed as O(ℏ) relative to classical universal behavior.\n", "status": "active", "fields": [ "physics", "mathematics", "quantum-mechanics", "dynamical-systems", "quantum-computing" ], "color": "green" }, { "id": "h-fermentation-nad-ratio-pathway-selection-thermodynamic", "type": "hypothesis", "title": "In Saccharomyces cerevisiae under anaerobic conditions, the fermentation product distribution (ethanol:glycerol ratio) is uniquely determined by the thermodynamic requirement ΔG < −5 kJ/mol for each step, with no free kinetic parameters, when intracellular NAD⁺/NADH ratio is measured in situ.\n", "status": "active", "fields": [ "biochemistry", "thermodynamics" ], "color": "green" }, { "id": "h-ferroelectric-fatigue-oxygen-vacancy-pinning", "type": "hypothesis", "title": "Ferroelectric fatigue in perovskite thin films is caused by oxygen vacancy accumulation at domain walls under cyclic electric fields, which pins domain wall motion rather than suppressing nucleation; the fatigue rate is proportional to the oxygen vacancy mobility and the density of pre-existing domain wall pinning sites, and can be reduced by engineering low-vacancy-mobility electrode interfaces.\n", "status": "active", "fields": [ "materials-science", "condensed-matter-physics", "electrochemistry" ], "color": "green" }, { "id": "h-fibrosis-reversibility-mechanosensing", "type": "hypothesis", "title": "Established organ fibrosis is reversible when matrix-activated myofibroblast mechanosensing (via YAP/TAZ-MRTF-SRF axis) is pharmacologically interrupted, allowing myofibroblast de-activation and matrix metalloproteinase-driven ECM remodelling to restore tissue architecture.\n", "status": "active", "fields": [ "cell-biology", "fibrosis-research", "biophysics" ], "color": "green" }, { "id": "h-financial-contagion-core-periphery-topology", "type": "hypothesis", "title": "Systemic financial risk is primarily determined by the core-periphery topology of interbank networks: robust-yet-fragile systems arise when a small core of highly interconnected banks amplifies contagion that a periphery of weakly connected banks cannot absorb, and this structure is detectable from pre-crisis network centrality measures.\n", "status": "active", "fields": [ "economics", "network-science", "finance", "social-science" ], "color": "green" }, { "id": "h-financialisation-investment-crowding", "type": "hypothesis", "title": "The rise of financial sector GDP share above 8% crowds out real economy investment through talent misallocation and short-termism, with the crowding-out measurable as a negative relationship between financial sector size and manufacturing R&D intensity across OECD countries.\n", "status": "active", "fields": [ "economics", "finance", "innovation-economics" ], "color": "green" }, { "id": "h-fire-regime-threshold-fuel-structure", "type": "hypothesis", "title": "Fire regime transitions in grassland-forest boundaries are controlled by a critical fuel connectivity threshold that follows percolation theory: when the fraction of flammable cells exceeds the percolation threshold p_c ≈ 0.593 for a 2D lattice, fire spreads as a phase transition producing the observed bimodality in burned area distributions that separates fire-maintained from fire-suppressed states.\n", "status": "active", "fields": [ "ecology", "mathematics", "complex-systems", "climate-science" ], "color": "green" }, { "id": "h-firm-equilibrium-stat-mech-analogy", "type": "hypothesis", "title": "The equilibrium effort distribution of employees in a firm follows a Boltzmann distribution with effective temperature set by performance measurement noise, and total agency costs scale as the free energy gap between the first-best and observed equilibrium — a prediction that can be tested with compensation and productivity panel data", "status": "active", "fields": [ "economics", "finance", "statistical-mechanics", "complex-systems" ], "color": "green" }, { "id": "h-first-passage-hitting-time-models-extend-clinical-warning-lead-time", "type": "hypothesis", "title": "First-passage-time risk models provide longer and better-calibrated clinical deterioration warning lead-times than fixed-threshold scorecards.", "status": "active", "fields": [ "mathematics", "medicine" ], "color": "green" }, { "id": "h-fiscal-multiplier-credit-constraints", "type": "hypothesis", "title": "The fiscal multiplier exceeds 1.5 during recessions when the central bank is at the zero lower bound and household credit constraints are binding, but falls below 0.5 during expansions — the state-dependent multiplier hypothesis is now supported by sufficient empirical evidence to be treated as established.\n", "status": "active", "fields": [ "macroeconomics", "fiscal-policy", "monetary-economics" ], "color": "green" }, { "id": "h-fisher-information-optimized-eit-electrodes-improve-lesion-detectability", "type": "hypothesis", "title": "EIT acquisition protocols optimized for Fisher-information objectives yield improved small-lesion detectability compared with standard adjacent-drive schemes at fixed acquisition time.\n", "status": "active", "fields": [ "medical-imaging", "statistics", "inverse-problems" ], "color": "green" }, { "id": "h-fisher-kpp-front-models-improve-wound-closure-time-forecasting", "type": "hypothesis", "title": "Methods transferred from `b-fisher-kpp-fronts-x-wound-healing-closure-forecasting` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "mathematical-biology", "medicine" ], "color": "green" }, { "id": "h-fisher-optimal-dose-grid-reduces-parameter-variance-simulation", "type": "hypothesis", "title": "In simulated Emax and sigmoid dose-response studies, dose grids chosen by Fisher-information criteria will reduce median EC50 estimator variance by at least 20 percent versus equally spaced safe-dose grids at fixed sample size; falsified if gains vanish under mild model misspecification.\n", "status": "active", "fields": [ "biostatistics", "medicine" ], "color": "green" }, { "id": "h-fisher-ricci-price-covariance-analogy-calibration", "type": "hypothesis", "title": "When evolutionary simulations embed traits on an empirical Fisher-metric manifold, curvature summaries correlate more tightly with Price covariance flux than ad hoc Ricci metaphors — falsified if curvature–covariance rank correlations stay below 0.25 across replicate ensembles (**speculative quantitative probe only**).\n", "status": "active", "fields": [ "evolutionary-biology", "differential-geometry" ], "color": "green" }, { "id": "h-fisher-speed-limit-selection", "type": "hypothesis", "title": "Artificial selection responses across taxa saturate the Fisher-information speed limit V_A within 10%, and departures from saturation are predicted by the ratio of genetic drift (Ne) to selection intensity — confirming that natural selection is information-geometrically near-optimal in large populations and drift-limited in small ones.\n", "status": "active", "fields": [ "evolutionary-biology", "mathematical-statistics", "population-genetics" ], "color": "green" }, { "id": "h-fitts-law-bci-pointer-information-bandwidth-limit", "type": "hypothesis", "title": "Fitts' law applies universally to brain-computer interface (BCI) cursor control: the information throughput of BCI pointing systems is bounded by the cortical motor channel capacity (~4 bits/second for intracortical BCIs, ~1 bit/second for EEG BCIs) regardless of the decoding algorithm used, because the bottleneck is biological (neural variability) rather than computational.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "engineering", "human-computer-interaction", "neuroprosthetics" ], "color": "green" }, { "id": "h-flagellar-motor-proton-coupling-cryo", "type": "hypothesis", "title": "Cryo-EM of the bacterial flagellar motor at sub-3-angstrom resolution will reveal a rocker-switch proton relay mechanism in MotA that couples Asp32 protonation to 100-pm conformational changes driving ring rotation\n", "status": "active", "fields": [ "biology", "physics", "biophysics", "biochemistry" ], "color": "green" }, { "id": "h-flagellar-motor-stator-assembly-pmf-dependent-mechanosensing", "type": "hypothesis", "title": "Flagellar motor stator assembly is a mechanosensitive process: stators are recruited from a cytoplasmic pool in response to load (torque demand), with PMF controlling the free energy of stator-peptidoglycan binding ΓÇö making the motor a biological torque sensor that self-optimizes stator number for current mechanical load.\n", "status": "active", "fields": [ "biophysics", "microbiology", "motor-proteins" ], "color": "green" }, { "id": "h-flexible-stoichiometry-p-limitation-gyre", "type": "hypothesis", "title": "Subtropical ocean gyres maintain high C:P ratios because P-limited phytoplankton downregulate ribosome synthesis, producing cells with 50-100% higher C:P than Redfield, measurable via flow cytometry and ICP-MS", "status": "active", "fields": [ "oceanography", "chemistry", "ecology" ], "color": "green" }, { "id": "h-flocking-topological-k7-visual-attention", "type": "hypothesis", "title": "The topological interaction number k ~ 7 in starling murmurations is set by the capacity of avian visual attention: raptor-threat tracking and social monitoring saturate avian attentional resources at ~7 simultaneously tracked objects, and experimental enrichment of predator threat density will increase k toward the attention limit while reduction of threat will decrease k.\n", "status": "active", "fields": [ "biology", "computer-science", "neuroscience" ], "color": "green" }, { "id": "h-flood-basalt-ozone-kill-mechanism", "type": "hypothesis", "title": "The primary mass extinction kill mechanism from large igneous province eruptions is stratospheric ozone depletion from halogen (HCl, HBr) emissions rather than climate change, and the temporal pattern of extinction should correlate with eruption-phase halogen flux rather than total CO2 or SO2 output.\n", "status": "active", "fields": [ "volcanology", "geoscience", "paleontology", "atmospheric-chemistry" ], "color": "green" }, { "id": "h-floquet-instability-metrics-improve-seasonal-epi-intervention-timing", "type": "hypothesis", "title": "Floquet-instability metrics identify intervention windows that reduce seasonal epidemic peak incidence better than fixed-calendar policies.", "status": "active", "fields": [ "epidemiology", "mathematics" ], "color": "green" }, { "id": "h-floquet-metasurface-achieves-isolation-without-magnets-under-passive-bias", "type": "hypothesis", "title": "A space-time modulated metasurface can reach practically useful directional isolation without magnetic bias when modulation phase velocity and sideband loading are jointly optimized under passivity constraints.\n", "status": "active", "fields": [ "electromagnetism", "metamaterials", "microwave-engineering" ], "color": "green" }, { "id": "h-flow-state-hypofrontality-norepinephrine", "type": "hypothesis", "title": "The flow state (optimal experience) is neurally characterised by transient hypofrontality — reduced prefrontal cortex activity — combined with elevated norepinephrine and dopamine enabling automatic, effortless performance", "status": "active", "fields": [ "cognitive-neuroscience", "psychology", "performance-science" ], "color": "green" }, { "id": "h-flow-state-transient-hypofrontality", "type": "hypothesis", "title": "Flow states are neurologically characterized by transient hypofrontality — selective deactivation of prefrontal cortex regions associated with self-monitoring — measurable as reduced alpha/beta power in frontal EEG, with the duration and depth of flow state predicting the magnitude and duration of post-flow rebound creativity.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "psychology", "computational-neuroscience" ], "color": "green" }, { "id": "h-fmo-enaqt-efficiency", "type": "hypothesis", "title": "Environment-assisted quantum transport (ENAQT) enhances excitation transfer efficiency in the FMO complex by 5-15% relative to the purely classical Förster limit under physiological bath conditions at 310 K.\n", "status": "active", "fields": [ "quantum-biology", "biophysics", "quantum-physics" ], "color": "green" }, { "id": "h-food-web-motif-frequency-predicts-cascade-strength", "type": "hypothesis", "title": "The ratio of tri-trophic chain motifs to omnivory triangle motifs in a food web quantitatively predicts the magnitude of apex predator trophic cascade effects, with cascade biomass ratio scaling linearly with this motif ratio across ecosystem types\n", "status": "active", "fields": [ "ecology", "network-science" ], "color": "green" }, { "id": "h-forest-fire-soc-beta-exponent-climate-invariance", "type": "hypothesis", "title": "The forest fire area power-law exponent β is robust (1.3 ± 0.2) across climate zones and decadal drought cycles when fires are not suppressed, reflecting the universal SOC critical point; deviations beyond this range indicate departure from SOC caused by fire suppression or extreme fuel loading.\n", "status": "active", "fields": [ "ecology", "statistical-physics" ], "color": "green" }, { "id": "h-fourier-neural-operator-surrogates-accelerate-groundwater-inversion-with-calibrated-uncertainty", "type": "hypothesis", "title": "Fourier neural operator surrogates speed up groundwater inverse modeling while preserving calibrated uncertainty bounds for decision support.", "status": "active", "fields": [ "hydrology", "computer-science" ], "color": "green" }, { "id": "h-fourier-transform-x-signal-processing", "type": "hypothesis", "title": "Graph neural networks that incorporate spectral graph Fourier transforms will outperform spatial message-passing GNNs on tasks requiring long-range frequency-dependent features, due to the ability to apply frequency-selective filters to graph signals\n", "status": "active", "fields": [ "mathematics", "computer-science", "signal-processing" ], "color": "green" }, { "id": "h-fracture-depinning-crackling-noise-exponent", "type": "hypothesis", "title": "The acoustic emission size exponent τ in brittle fracture of isotropic polycrystalline materials is universally τ = 1.5 ± 0.1 (mean-field depinning universality class) in the limit of sample size L >> grain size d, with material-specific deviations arising only when L/d < 100.\n", "status": "active", "fields": [ "materials-science", "statistical-physics" ], "color": "green" }, { "id": "h-frailty-model-mortality-deceleration-test", "type": "hypothesis", "title": "A shared frailty survival model with gamma-distributed random effects fitted to the Swedish twin cohort will produce a frailty variance estimate sigma^2 ~ 0.3–0.6 and will accurately predict the late-life mortality deceleration (plateau above age 100) observed in the cohort without requiring a non-monotonic baseline hazard, demonstrating that demographic selection alone explains the mortality plateau", "status": "active", "fields": [ "public-health", "statistics", "epidemiology" ], "color": "green" }, { "id": "h-frb-gue-universality-magnetar", "type": "hypothesis", "title": "The inter-burst waiting time distributions of high-rate repeating FRB sources (>500 detected bursts) belong to the Gaussian Unitary Ensemble (GUE) universality class of random matrix theory, encoding the time-reversal symmetry breaking of magnetar crustal dynamics under strong magnetic fields, and distinguishing the quantum-chaotic emission mechanism from self-organized criticality alternatives.\n", "status": "active", "fields": [ "astrophysics", "mathematics", "statistical-physics", "quantum-chaos" ], "color": "green" }, { "id": "h-free-energy-aging", "type": "hypothesis", "title": "Biological aging is partly a failure of free energy minimisation: as cellular prediction models accumulate errors (protein misfolding, epigenetic drift, metabolic dysregulation), the metabolic cost of maintaining homeostasis exceeds available free energy, triggering senescence cascades.\n", "status": "active", "fields": [ "gerontology", "cell-biology", "thermodynamics", "cognitive-science", "systems-biology", "metabolic-biology" ], "color": "green" }, { "id": "h-ftle-ridge-persistence-predicts-left-atrial-appendage-stasis", "type": "hypothesis", "title": "Persistent FTLE ridges in left-atrial appendage flow predict clinically relevant stasis markers.", "status": "active", "fields": [ "medicine", "fluid-mechanics", "cardiology" ], "color": "green" }, { "id": "h-ftle-ridge-threshold-correlates-larval-retention-proxy", "type": "hypothesis", "title": "Coastal retention proxies computed from backward-time FTLE ridges above calibrated thresholds correlate more strongly with settlement indices than Eulerian SST fronts alone when pelagic larval durations fall below mesoscale eddy turnover times — falsified if ridge metrics add negligible ΔR² in hierarchical models with identical covariates.\n", "status": "active", "fields": [ "marine-ecology", "oceanography" ], "color": "green" }, { "id": "h-funnel-aware-search-reduces-docking-decoy-traps", "type": "hypothesis", "title": "Funnel-aware search heuristics reduce false-minimum decoy trapping in protein-ligand docking compared with score-only beam search.", "status": "active", "fields": [ "chemistry", "computer-science" ], "color": "green" }, { "id": "h-fusion-lawson-criterion-turbulent-transport-barrier", "type": "hypothesis", "title": "The remaining barrier to sustained net-energy-gain fusion in tokamaks is turbulent heat transport (gyrobohm diffusion) that degrades energy confinement below the H-mode pedestal — achieving commercial fusion requires either sustained high-pedestal H-mode with ELM suppression or a qualitatively different plasma regime with reduced transport.\n", "status": "active", "fields": [ "plasma-physics", "engineering", "nuclear-physics", "fluid-dynamics" ], "color": "green" }, { "id": "h-gaa-nanosheet-ballistic-transport-regime-room-temperature-3nm", "type": "hypothesis", "title": "Gate-all-around nanosheet silicon transistors at the 3nm node (Samsung/TSMC, 2022-2024) operate in the quasi-ballistic transport regime at room temperature (backscattering ratio r < 0.3), and their ON-current is within a factor of 2 of the Landauer ballistic limit — making further ON-current improvement require materials changes (SiGe, III-V), not gate geometry improvements.\n", "status": "active", "fields": [ "engineering", "physics", "semiconductor-physics", "materials-science" ], "color": "green" }, { "id": "h-galactic-magnetic-alpha-omega-dynamo", "type": "hypothesis", "title": "Large-scale galactic magnetic fields are generated and maintained by the mean-field α-Ω dynamo mechanism where differential rotation (Ω effect) winds azimuthal field from poloidal and helical turbulence (α effect) regenerates poloidal from azimuthal field, with e-folding timescales ~1 Gyr", "status": "active", "fields": [ "astrophysics", "magnetohydrodynamics", "plasma-physics" ], "color": "green" }, { "id": "h-galactic-magnetic-field-alpha-omega-dynamo", "type": "hypothesis", "title": "Large-scale galactic magnetic fields are generated and maintained by the mean-field alpha-Omega dynamo: differential rotation (Omega effect) stretches poloidal field into toroidal field, while helical turbulence from SN-driven convection (alpha effect) regenerates poloidal field, with saturation at energy equipartition with turbulent kinetic energy.\n", "status": "active", "fields": [ "astrophysics", "plasma-physics", "fluid-dynamics", "magnetohydrodynamics" ], "color": "green" }, { "id": "h-galaxy-angular-momentum-tidal-torque-confirmed", "type": "hypothesis", "title": "Galaxy disk sizes are set by the angular momentum acquired via tidal torque theory during the linear growth phase, with spin parameter λ = J|E|^{1/2}/(GM^{5/2}) determining disk scale length after adiabatic contraction", "status": "active", "fields": [ "astrophysics", "cosmology", "galactic-dynamics" ], "color": "green" }, { "id": "h-galaxy-angular-momentum-tidal-torque", "type": "hypothesis", "title": "Galaxy disk sizes are set primarily by the angular momentum acquired via tidal torque theory during linear growth, with secondary regulation by feedback-driven outflows that selectively eject low-angular-momentum gas, explaining the observed size-mass relation scatter as feedback efficiency variation.\n", "status": "active", "fields": [ "galaxy-formation", "cosmology", "fluid-dynamics", "astrophysics" ], "color": "green" }, { "id": "h-gale-shapley-deferred-acceptance-stability-uniqueness", "type": "hypothesis", "title": "In real-world matching markets with incomplete preference lists (NRMP, NYC school choice), the set of stable matchings is a lattice with a unique optimal matching for each side — but when strategic manipulation by hospitals (rank-order list truncation) is feasible, the manipulated equilibrium improves hospital welfare by on average 3-8% relative to the truthful DA outcome, undermining strategy- proofness for the receiving side.\n", "status": "active", "fields": [ "engineering", "social-science", "economics", "computer-science" ], "color": "green" }, { "id": "h-game-signaling-costly-honest-equilibrium", "type": "hypothesis", "title": "The separating (honest) signaling equilibrium is the evolutionarily stable outcome when signal cost functions satisfy the single-crossing property, and this equilibrium breaks down predictably when technological change reduces signal production costs asymmetrically across quality levels, leading to pooling equilibria and signaling inflation", "status": "active", "fields": [ "economics", "evolutionary-biology", "game-theory" ], "color": "green" }, { "id": "h-game-theory-x-antibiotic-resistance", "type": "hypothesis", "title": "Pulsed antibiotic dosing with concentration oscillating above and below the evolutionary game coexistence threshold produces slower resistance evolution than constant dosing at the same total dose, by exploiting producer-cheater cycling to suppress resistant mutant fixation probability.\n", "status": "active", "fields": [ "evolutionary-biology", "microbiology", "mathematics", "clinical-medicine" ], "color": "green" }, { "id": "h-gamma-oscillations-binding-causal-test", "type": "hypothesis", "title": "Transcranial alternating current stimulation (tACS) at gamma frequency (40 Hz) applied out-of-phase between visual areas V1 and V4 disrupts feature binding in object recognition tasks, while in-phase gamma tACS enhances binding, providing causal evidence that inter-areal gamma synchrony mediates visual feature binding.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "physics" ], "color": "green" }, { "id": "h-gan-training-redqueen-dynamics", "type": "hypothesis", "title": "GAN training instability (mode collapse, oscillation) is predicted by the Red Queen dynamics of antagonistic coevolution", "status": "active", "fields": [], "color": "green" }, { "id": "h-gapped-ferrite-bias-point-maximizes-wpt-q-under-saturation-margin", "type": "hypothesis", "title": "For MnZn pads used in Qi-class chargers, operating peak H-field ~60–70% of saturation minimizes hysteresis loss while preserving coupling — yielding higher loaded Q than either under-driven (weak coupling) or saturated (μ collapse) regimes.\n", "status": "active", "fields": [ "materials-science", "power-electronics" ], "color": "green" }, { "id": "h-gate-control-pkc-gamma-interneuron", "type": "hypothesis", "title": "PKCgamma-positive excitatory interneurons in spinal lamina IIi serve as the primary gate control switch: they are normally suppressed by A-beta- activated glycinergic inhibition, and their disinhibition (loss of glycinergic interneuron input) is the key circuit mechanism of mechanical allodynia in neuropathic pain.\n", "status": "active", "fields": [ "neuroscience", "biophysics" ], "color": "green" }, { "id": "h-gauge-fixing-parallels-coordinate-choice-in-models", "type": "hypothesis", "title": "Students taught SU(2) Yang–Mills using explicit parallel-transport exercises on the Bloch sphere bundle will score higher on Wilson-loop conceptual questions than cohorts taught only Euler–Lagrange forms.", "status": "active", "fields": [ "physics", "mathematics" ], "color": "green" }, { "id": "h-gender-gap-stem-social-role-expectancy", "type": "hypothesis", "title": "Gender gaps in STEM participation are driven primarily by social role theory (Eagly) and ability self-concept divergence: girls with identical math test scores to boys report lower math self-efficacy due to stereotype threat internalization, with the gap larger in high-gender-egalitarian countries (the gender equality paradox) because broader occupational options reduce women's economic incentive for high-math fields", "status": "active", "fields": [ "social-psychology", "educational-psychology", "sociology", "gender-studies" ], "color": "green" }, { "id": "h-gender-gap-stem-social-role-theory", "type": "hypothesis", "title": "Persistent gender gaps in STEM participation are primarily driven by gender- science implicit associations and social role expectations (women as communal, science as agentic), not ability differences; gaps are reduced by social comparison (female role models in STEM), and country-level variation tracks gender equality indices (r~0.6), confirming social construction of STEM gender identity.\n", "status": "active", "fields": [ "social-psychology", "education-research", "gender-studies", "sociology" ], "color": "green" }, { "id": "h-gene-expression-noise-x-information-theory", "type": "hypothesis", "title": "Developmental gene regulatory networks operating near channel capacity maximize positional information in morphogen gradients and produce sharper cell fate boundaries, measurable as reduced cell fate assignment error in single-cell atlases.\n", "status": "active", "fields": [ "systems-biology", "information-theory", "developmental-biology", "molecular-biology" ], "color": "green" }, { "id": "h-gene-regulatory-network-x-boolean-circuit", "type": "hypothesis", "title": "The effective Boolean connectivity K of developmental GRNs estimated from single-cell RNA-seq binarised expression is 1.8–2.2 (near criticality) in normal development and increases to 2.5–3.0 in cancer cells, measurable via Kauffman network phase classification of perturbed expression states", "status": "active", "fields": [ "biology", "computer_science", "mathematics" ], "color": "green" }, { "id": "h-genetic-algorithm-x-natural-selection", "type": "hypothesis", "title": "Genetic algorithms searching protein sequence space will outperform gradient-based directed evolution methods when the fitness landscape has high epistasis (>50% of beneficial mutations are conditionally beneficial), due to crossover's ability to combine building blocks\n", "status": "active", "fields": [ "computer-science", "biology", "evolutionary-biology" ], "color": "green" }, { "id": "h-genetic-code-error-correcting-design", "type": "hypothesis", "title": "The standard genetic code's codon degeneracy pattern constitutes a natural block error-correcting code selected to minimise phenotypic change per single-base substitution error, and this error-minimisation property is measurable and unique among the space of possible codon table assignments\n", "status": "active", "fields": [ "molecular-biology", "information-theory", "evolutionary-biology" ], "color": "green" }, { "id": "h-genocide-early-warning-machine-learning-validity", "type": "hypothesis", "title": "Machine learning models trained on political, economic, and social indicators (Minorities at Risk, Political Terror Scale, ACLED data) can predict genocide onset 12–24 months in advance with positive predictive value > 30% at 95% sensitivity, providing actionable early warning given that the base rate of genocide onset is ~2–3 per year globally.\n", "status": "active", "fields": [ "social-science", "political-science", "machine-learning", "conflict-studies" ], "color": "green" }, { "id": "h-geographic-mosaic-coevolution-trait-variance", "type": "hypothesis", "title": "Thompson's geographic mosaic theory predicts higher among-population variance in coevolving traits (TTX level in newts, resistance in snakes) than in non-coevolving traits in the same species — a signature detectable by comparing population-level trait variance across hot-spot and cold-spot populations.\n", "status": "active", "fields": [ "ecology", "evolutionary-biology", "population-genetics" ], "color": "green" }, { "id": "h-geomagnetic-reversal-climate-null", "type": "hypothesis", "title": "Geomagnetic excursions and reversals do not produce detectable climate signals because the cosmic ray flux increase during low-dipole periods (< 4000 nT) is too small (~10%) to significantly affect cloud nucleation above the level of solar cycle variability — the proposed GCR-climate link lacks the dynamic range to drive the observed correlations.\n", "status": "active", "fields": [ "paleomagnetism", "paleoclimatology", "atmospheric-physics" ], "color": "green" }, { "id": "h-geomagnetic-reversal-inner-core-crystallization", "type": "hypothesis", "title": "Geomagnetic reversals are triggered when inner core boundary lateral heterogeneities in heat flux create spatially asymmetric core convection that disrupts dipole dominance; reversal frequency is controlled by core-mantle boundary heat flux (itself controlled by mantle convection patterning) and lower mantle conductivity, making reversals unpredictable on geological timescales", "status": "active", "fields": [ "geophysics", "geomagnetism", "fluid-dynamics" ], "color": "green" }, { "id": "h-geometric-complexity-theory-p-np", "type": "hypothesis", "title": "Geometric complexity theory requires the existence of representation-theoretic multiplicity obstructions in the coordinate ring comparison of the orbit closures of the permanent and determinant polynomials; if Bürgisser et al.'s negative result on occurrence obstructions is confirmed, GCT must be reformulated using stronger cohomological invariants to retain its viability as a P≠NP strategy.\n", "status": "active", "fields": [ "mathematics", "complexity-theory", "algebraic-geometry", "representation-theory" ], "color": "green" }, { "id": "h-geometric-control-se3-optimal-robotic-grasping", "type": "hypothesis", "title": "Geometric controllers designed directly on SE(3) (the Lie group of rigid body motions) outperform quaternion-based controllers for robotic grasping of non-symmetric objects because they avoid representation singularities and converge globally, while Euclidean controllers fail in a set of measure zero that is irrelevant in theory but encountered in practice during large-angle maneuvers.\n", "status": "active", "fields": [ "robotics", "geometric-control", "applied-mathematics" ], "color": "green" }, { "id": "h-geostrophic-balance-climate-change", "type": "hypothesis", "title": "Progressive weakening of geostrophic balance in mid-latitude atmospheric circulation due to Arctic amplification will shift the Northern Hemisphere jet stream toward a more meridional (wavy) configuration, measurable as increased Rossby wave amplitude in ERA5 reanalysis data since 1980.\n", "status": "active", "fields": [ "climate-science", "atmospheric-science", "mathematics", "physics" ], "color": "green" }, { "id": "h-geothermal-induced-seismicity-pore-pressure", "type": "hypothesis", "title": "Enhanced geothermal system (EGS) induced seismicity is controlled primarily by pore pressure diffusion front propagation and can be predicted and mitigated using traffic-light protocols calibrated to local fault critically — the Basel (2006) failure was preventable with real-time pore pressure monitoring.\n", "status": "active", "fields": [ "geomechanics", "geothermal-energy", "seismology" ], "color": "green" }, { "id": "h-gesture-speech-constitutive-integration", "type": "hypothesis", "title": "Gesture plays a constitutive role in language production by providing a spatial analog representation that constrains the lexical retrieval process for spatial and abstract concepts, such that preventing gesture production during speech specifically impairs the precision of spatial and metaphorical language without affecting non-spatial propositional content.\n", "status": "active", "fields": [ "linguistics", "cognitive-science", "neuroscience", "psychology" ], "color": "green" }, { "id": "h-gig-economy-welfare-net-negative", "type": "hypothesis", "title": "Gig platform expansion produces net welfare losses when worker welfare losses (income volatility, benefit loss, monopsony exploitation) exceed consumer surplus gains, with the balance tipping positive only in high-wage, low-regulation labor markets where misclassification is absent.\n", "status": "active", "fields": [ "labor-economics", "platform-economics", "regulation" ], "color": "green" }, { "id": "h-gini-mortality-phase-transition", "type": "hypothesis", "title": "The relationship between county-level income Gini coefficient and age-adjusted mortality exhibits a discontinuous inflection point near Gini=0.40, consistent with a saddle-node bifurcation in the health-inequality dynamical system", "status": "active", "fields": [ "health-economics", "epidemiology", "medicine", "statistical-physics" ], "color": "green" }, { "id": "h-glacier-calving-fracture-toughness-prediction", "type": "hypothesis", "title": "The observed calving rates of tidewater glaciers in Greenland and Antarctica can be predicted to within a factor of 2 from the linear elastic fracture mechanics stress intensity factor K_I computed from ice thickness, terminus geometry, and estimated meltwater pond depth, without requiring empirical calving-law tuning", "status": "active", "fields": [ "glaciology", "materials-science", "geophysics" ], "color": "green" }, { "id": "h-glial-tripartite-synapse-gain-modulation", "type": "hypothesis", "title": "Astrocytes perform genuine modulatory computation at tripartite synapses by integrating calcium signals from multiple synapses and releasing gliotransmitters (glutamate, D-serine, ATP) to modulate the gain of synaptic transmission in a spatiotemporal pattern determined by the astrocyte's calcium wave dynamics — this constitutes genuine information processing beyond passive background modulation.\n", "status": "active", "fields": [ "glial-biology", "computational-neuroscience", "synaptic-physiology" ], "color": "green" }, { "id": "h-globular-cluster-formation-high-redshift-merger", "type": "hypothesis", "title": "Globular clusters form in high-redshift gas-rich mergers and high-pressure clumpy disk environments where feedback suppression allows super-star-cluster formation; multiple stellar populations arise from AGB and massive-star ejecta reaccretion in a self-enrichment scenario", "status": "active", "fields": [ "astrophysics", "stellar-physics", "cosmology", "chemical-evolution" ], "color": "green" }, { "id": "h-globular-cluster-multiple-populations-enrichment", "type": "hypothesis", "title": "Multiple stellar populations in globular clusters arise from sequential star formation rounds within massive proto-cluster clouds: a first generation of stars ejects AGB winds that pool at the cluster center; a second generation forms from this enriched material, producing the Na-O anticorrelation observed in nearly all old GCs.\n", "status": "active", "fields": [ "stellar-astrophysics", "cosmology", "galactic-dynamics", "stellar-populations" ], "color": "green" }, { "id": "h-glymphatic-amyloid-clearance-rate", "type": "hypothesis", "title": "Inter-individual variation in glymphatic clearance efficiency during sleep is a primary determinant of amyloid-beta accumulation trajectory, and its impairment precedes detectable amyloid load by years", "status": "active", "fields": [ "neuroscience", "biology", "medicine" ], "color": "green" }, { "id": "h-glymphatic-dysfunction-drives-amyloid-accumulation", "type": "hypothesis", "title": "Glymphatic system dysfunction — reduced perivascular CSF flow from arterial stiffening and AQP4 depolarization — is an upstream causal event in Alzheimer's disease that precedes symptomatic amyloid accumulation and represents a treatable target for disease prevention.\n", "status": "active", "fields": [ "neuroscience", "fluid-dynamics", "neurology", "preventive-medicine" ], "color": "green" }, { "id": "h-glymphatic-sleep-aquaporin4-clearance", "type": "hypothesis", "title": "Glymphatic waste clearance from the brain is driven by arterial pulsatility during slow-wave sleep through AQP4-mediated convective flow along perivascular spaces, and age-related AQP4 depolarisation is a primary mechanism of amyloid accumulation.\n", "status": "active", "fields": [ "neuroscience", "sleep-science", "neuropathology" ], "color": "green" }, { "id": "h-gompertz-weibull-aging-unification", "type": "hypothesis", "title": "Human aging follows Weibull extreme value statistics for the same reason as engineering component fatigue: both are governed by the weakest-link statistics of competing failure modes, and the Gompertz mortality law is the biological instantiation of the Gumbel extreme value distribution.\n", "status": "active", "fields": [ "gerontology", "actuarial-science", "reliability-engineering", "evolutionary-biology" ], "color": "green" }, { "id": "h-gr-gauge-theory-fiber-bundle-unification", "type": "hypothesis", "title": "General relativity and Yang-Mills gauge theories are unified descriptions of curvature on different fiber bundles — quantum gravity requires quantizing the base manifold", "status": "active", "fields": [ "mathematical-physics", "quantum-gravity", "differential-geometry" ], "color": "green" }, { "id": "h-gradient-penalty-magnitude-tracks-dual-feasibility-proxy-metrics", "type": "hypothesis", "title": "On CIFAR-style benchmarks with matched architectures, the median L2 norm of critic gradients along convex combinations of real–fake batches will correlate negatively with inception-score collapse events when λ_GP is swept — falsified if training collapses while gradient norms remain uniformly small (indicating other failure modes dominate).\n", "status": "active", "fields": [ "machine-learning", "applied-mathematics" ], "color": "green" }, { "id": "h-graph-convolution-with-mobility-priors-improves-outbreak-link-recovery", "type": "hypothesis", "title": "Graph convolution models augmented with mobility priors improve outbreak transmission-link recovery and uncertainty calibration.", "status": "active", "fields": [ "network-science", "infectious-disease", "machine-learning" ], "color": "green" }, { "id": "h-graph-cut-energy-residuals-detect-lesion-segmentation-failure-modes-earlier", "type": "hypothesis", "title": "Methods transferred from `b-graph-cut-energy-minimization-x-radiology-lesion-segmentation-qc` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "computer-vision", "radiology" ], "color": "green" }, { "id": "h-graph-laplacian-regularization-improves-module-replicability", "type": "hypothesis", "title": "Graph Laplacian denoising or shrinkage priors applied before spectral clustering increase cross-site module agreement metrics versus raw correlation graphs under simulated batch injections.", "status": "active", "fields": [ "mathematics", "medicine", "systems-biology" ], "color": "green" }, { "id": "h-graph-neural-network-x-spectral-graph-theory", "type": "hypothesis", "title": "GNNs using learned spectral filters over the full graph Laplacian spectrum will outperform spatial message-passing GNNs on molecular property prediction tasks requiring long-range electronic effects (HOMO-LUMO gap, ionization potential)\n", "status": "active", "fields": [ "computer-science", "mathematics", "machine-learning" ], "color": "green" }, { "id": "h-graph-theory-x-molecular-structure", "type": "hypothesis", "title": "The Weisfeiler-Leman graph isomorphism test (1-WL) is equivalent to the expressive power of message-passing graph neural networks for molecular property prediction, making topological index computation a special case of 1-WL iteration that saturates at r² > 0.99 for homologous series but fails for branched polycyclics", "status": "active", "fields": [ "chemistry", "mathematics", "computer_science" ], "color": "green" }, { "id": "h-graph-transformer-improves-grid-contingency-screening-recall", "type": "hypothesis", "title": "Graph-transformer contingency models improve high-risk event recall at fixed alarm budget.", "status": "active", "fields": [ "engineering", "machine-learning", "power-systems" ], "color": "green" }, { "id": "h-graph-wavelet-energy-localizes-pmu-grid-disturbances-better-than-scada", "type": "hypothesis", "title": "Graph-wavelet PMU features localize transmission-grid disturbances more accurately and faster than SCADA-only alarm logic.", "status": "active", "fields": [ "electrical-engineering", "computer-science" ], "color": "green" }, { "id": "h-gravitational-lensing-caustic-classification-test", "type": "hypothesis", "title": "The spatial distribution and angular length statistics of giant arcs in Hubble Space Telescope cluster surveys will be consistent with fold and cusp catastrophe theory predictions (arcs preferentially at fold caustics, point images at cusps) with magnification distribution following the |mu|^{-3} power law expected from fold-catastrophe optics to within 10%", "status": "active", "fields": [ "astrophysics", "mathematics", "optics" ], "color": "green" }, { "id": "h-grb-cambrian-explosion-trigger", "type": "hypothesis", "title": "The Late Ordovician mass extinction (443 Mya) was initiated by a long-duration gamma-ray burst within 2 kpc that destroyed more than 50% of Earth's ozone column, producing an ultraviolet-driven kill pattern preferentially eliminating shallow-marine and surface taxa while sparing deep-water organisms — a signature distinguishable from climate-driven and bolide-impact extinction patterns.\n", "status": "active", "fields": [ "astrobiology", "paleontology", "evolutionary-biology", "astrophysics" ], "color": "green" }, { "id": "h-green-hydrogen-iridium-scarcity-pem-electrolysis", "type": "hypothesis", "title": "The fundamental efficiency ceiling for PEM water electrolysis is 83% (LHV basis, 1.48V thermodynamic minimum) but iridium catalyst scarcity (global production ~7 tonnes/yr) limits deployment to <10 GW/yr unless iridium loading is reduced below 0.05 mg/cm² or earth-abundant catalysts achieve equivalent oxygen evolution activity.\n", "status": "active", "fields": [ "chemistry", "materials-science", "chemical-engineering", "energy-systems" ], "color": "green" }, { "id": "h-green-infrastructure-urban-cooling-nonlinear-threshold", "type": "hypothesis", "title": "Urban tree canopy cover exhibits a nonlinear threshold effect on heat island intensity: cooling effect is negligible below ~15% canopy cover, then increases rapidly between 15-30% (cooperative evapotranspiration), then plateaus above 40%, consistent with a percolation-like transition in connected canopy enabling coherent evapotranspiration across the urban landscape.\n", "status": "active", "fields": [ "urban-ecology", "urban-climatology" ], "color": "green" }, { "id": "h-grid-cell-torus-manifold-decoding", "type": "hypothesis", "title": "The population activity of grid cells in a single module traces a toroidal manifold in neural state space whose intrinsic geometry (torus radius ratio corresponding to the grid aspect ratio) can be decoded from calcium imaging data using topological data analysis (persistent homology), and this torus structure is maintained across environments of different shapes with a predictable phase remapping", "status": "active", "fields": [ "neuroscience", "mathematics", "cognitive-science" ], "color": "green" }, { "id": "h-grid-inspired-phase-coherence-metrics-predict-beta-cell-dysfunction-earlier", "type": "hypothesis", "title": "Transferred methods from `b-kuramoto-synchrony-x-beta-cell-islet-oscillations` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "electrical-engineering", "systems-biology" ], "color": "green" }, { "id": "h-griffith-crack-2d-material-defects", "type": "hypothesis", "title": "Carrier mobility in 2D materials is limited by grain boundary crack-like defects governed by a modified Griffith criterion where the effective fracture toughness scales with the interlayer van der Waals adhesion energy, such that grain boundaries with misorientation angles above a critical threshold act as sharp cracks for electron scattering in the same way they act as crack initiation sites for mechanical failure.\n", "status": "active", "fields": [ "materials-science", "condensed-matter-physics", "engineering", "surface-science" ], "color": "green" }, { "id": "h-grn-gnn-priors-improve-perturbation-response-prediction", "type": "hypothesis", "title": "GNN priors over regulatory structure improve out-of-sample perturbation response prediction.", "status": "active", "fields": [ "biology", "machine-learning", "systems-biology" ], "color": "green" }, { "id": "h-grokking-criticality-universality", "type": "hypothesis", "title": "Grokking is a second-order phase transition in the Ising universality class, detectable via finite-size scaling of hidden-layer intrinsic dimension", "status": "active", "fields": [], "color": "green" }, { "id": "h-gromov-nonsqueezing-quantum-uncertainty-derivation", "type": "hypothesis", "title": "The Heisenberg uncertainty principle Delta_q * Delta_p >= hbar/2 can be derived directly from Gromov's non-squeezing theorem in the semiclassical limit hbar → 0 by identifying the symplectic capacity of the uncertainty ellipsoid with hbar, providing a geometric rather than operator-algebraic proof of the uncertainty principle.\n", "status": "active", "fields": [ "mathematical-physics", "symplectic-geometry", "quantum-information" ], "color": "green" }, { "id": "h-gromov-witten-quantum-cohomology-counts", "type": "hypothesis", "title": "Pseudo-holomorphic curve counts in symplectic topology are well-defined as rational numbers via virtual fundamental class techniques (Kuranishi structures or polyfolds) — the earlier regularity obstruction is overcome by virtual perturbation theory, making Gromov-Witten invariants of all compact symplectic manifolds rigorously defined.\n", "status": "active", "fields": [ "symplectic-topology", "algebraic-geometry", "mathematical-physics" ], "color": "green" }, { "id": "h-group-creativity-cognitive-diversity-optimal", "type": "hypothesis", "title": "Group creativity follows an inverted-U function of cognitive diversity: below a threshold, convergent thinking dominates and production blocking suppresses original ideas; above a threshold, coordination costs and communication breakdown negate diversity gains; optimal team creativity requires moderate diversity with strong psychological safety.\n", "status": "active", "fields": [ "cognitive-psychology", "organizational-behavior", "creativity-research" ], "color": "green" }, { "id": "h-group-creativity-cognitive-diversity", "type": "hypothesis", "title": "Group creative output exceeds individual output when cognitive diversity is high and psychological safety is present, but falls below individual output when groups converge on a dominant idea (groupthink) or enforce conformity norms.\n", "status": "active", "fields": [ "cognitive-science", "social-psychology", "organizational-behavior", "creativity-research" ], "color": "green" }, { "id": "h-growth-rate-hypothesis-ribosome-phosphorus-universality", "type": "hypothesis", "title": "The growth rate hypothesis — that fast-growing organisms have higher P:N and P:C ratios because they require more ribosomal RNA to sustain high protein synthesis rates — holds universally across all domains of life (bacteria, archaea, protists, plants, animals) and predicts elemental stoichiometry from ribosome allocation fraction alone.\n", "status": "active", "fields": [ "ecology", "biogeochemistry", "microbiology", "evolutionary-biology", "marine-biology" ], "color": "green" }, { "id": "h-gtr-model-adequate-metazoan-divergence-estimation", "type": "hypothesis", "title": "GTR+Gamma+I substitution models are inadequate for estimating deep Metazoan divergence times because they cannot account for compositional heterogeneity and long-branch saturation, causing systematic 10-30% underestimation of Cambrian and pre-Cambrian divergence dates compared to CAT+GTR non-homogeneous models.\n", "status": "active", "fields": [ "phylogenetics", "evolutionary-biology" ], "color": "green" }, { "id": "h-gut-microbiome-serotonin-depression", "type": "hypothesis", "title": "Specific short-chain fatty acid producing gut bacteria modulate tryptophan availability to the brain and causally influence depression susceptibility via the gut-brain axis", "status": "active", "fields": [ "medicine", "neuroscience", "microbiology" ], "color": "green" }, { "id": "h-gut-microbiome-x-lotka-volterra", "type": "hypothesis", "title": "The healthy human gut microbiome occupies a large-basin attractor in generalized Lotka-Volterra state space characterized by negative diagonal dominance of the interaction matrix (A_ii < -|Σ_j≠i A_ij|), and antibiotic-induced dysbiosis corresponds to a saddle-node bifurcation at a critical antibiotic dose that destroys this attractor", "status": "active", "fields": [ "biology", "mathematics", "ecology", "medicine" ], "color": "green" }, { "id": "h-gutenberg-richter-percolation-threshold", "type": "hypothesis", "title": "The universal Gutenberg-Richter b-value of 1 is a direct consequence of earthquake fault networks self-organizing to the percolation critical point, and b-value deviations should predict large-earthquake occurrence probability via percolation cluster statistics.\n", "status": "active", "fields": [ "seismology", "statistical-physics", "geophysics" ], "color": "green" }, { "id": "h-gutenberg-richter-soc-btw-exponent", "type": "hypothesis", "title": "The Gutenberg-Richter energy exponent τ ≈ 1.67 belongs to the interface depinning universality class rather than the BTW sandpile class (τ = 3/2), reflecting that fault rupture is driven by a threshold-crossing front on a heterogeneous stress field rather than a conservative redistribution rule.\n", "status": "active", "fields": [ "seismology", "statistical-physics", "rock-mechanics" ], "color": "green" }, { "id": "h-gwb-spectrum-supermassive-bh-binaries", "type": "hypothesis", "title": "The nanohertz gravitational wave background detected by pulsar timing arrays is dominated by a stochastic superposition of gravitational waves from supermassive black hole binary inspirals, with a characteristic strain spectrum h_c(f) ∝ f^{-2/3} confirming the circular inspiral origin, and the amplitude encoding the SMBHB merger rate × mass function.\n", "status": "active", "fields": [ "astrophysics", "gravitational-physics", "observational-astronomy" ], "color": "green" }, { "id": "h-gwb-supermassive-bh-binary-origin", "type": "hypothesis", "title": "The nanohertz gravitational wave background detected by pulsar timing arrays (NANOGrav 2023) originates from a cosmological population of inspiralling supermassive black hole binaries at sub-parsec separations", "status": "active", "fields": [ "gravitational-wave-astronomy", "galaxy-formation", "black-hole-physics" ], "color": "green" }, { "id": "h-habitat-percolation-critical-density", "type": "hypothesis", "title": "The empirical ~60% habitat threshold for forest-interior species collapse is the 2D site percolation threshold (p_c=0.593), and deviations across landscapes of different area follow the FSS scaling p_c(A) = 0.593 + c*A^(-3/4).\n", "status": "active", "fields": [ "conservation-biology", "statistical-physics", "landscape-ecology" ], "color": "green" }, { "id": "h-habitat-percolation-species-persistence", "type": "hypothesis", "title": "Species requiring landscape-spanning dispersal will show population viability thresholds at the percolation critical point (h_c ~ 0.59 habitat fraction), with patch occupancy following the giant component size scaling S ~ (h - h_c)^beta (beta = 0.14 for 2D percolation) near the threshold", "status": "active", "fields": [ "ecology", "conservation-biology", "statistical-physics", "network-science" ], "color": "green" }, { "id": "h-habitat-percolation-z-exponent", "type": "hypothesis", "title": "The species-area exponent z emerges from the fractal dimension of habitat connectivity at the percolation threshold and should be predictable from landscape metrics", "status": "active", "fields": [ "landscape-ecology", "statistical-physics", "conservation-biology" ], "color": "green" }, { "id": "h-hair-cell-bundle-x-hopf-bifurcation", "type": "hypothesis", "title": "Sensorineural hearing loss shifts individual hair cell bundles away from the Hopf bifurcation point toward the stable fixed point regime, reducing amplification gain and auditory sensitivity quantitatively predictable from the bifurcation distance parameter\n", "status": "active", "fields": [ "neuroscience", "physics", "biophysics" ], "color": "green" }, { "id": "h-hair-cell-regeneration-notch-atoh1", "type": "hypothesis", "title": "Mammalian cochlear hair cell regeneration is suppressed by sustained Notch lateral inhibition from surviving supporting cells; pharmacological Notch blockade combined with Atoh1 transcription factor delivery is sufficient to trigger de novo hair cell production from supporting cell transdifferentiation in mature mammals.\n", "status": "active", "fields": [ "auditory-neuroscience", "regenerative-medicine", "developmental-biology" ], "color": "green" }, { "id": "h-half-wavelength-coil-spacing-bound-suppresses-near-field-grating-analogs", "type": "hypothesis", "title": "When driven at wireless charging frequencies with phased currents, planar coil mats exhibit peak stray-field sidelobe growth once lateral spacing exceeds ~0.35–0.45× the effective magnetic wavelength in the coupled medium — motivating spacing caps even when classical half-wave far-field grating criteria do not literally apply.\n", "status": "active", "fields": [ "electrical-engineering", "electromagnetism" ], "color": "green" }, { "id": "h-happiness-set-point-heritability-deliberate-activity", "type": "hypothesis", "title": "Subjective wellbeing set point is heritable (~50%) and determined by a dynamic equilibrium of affective adaptation, not a fixed genetic constant; deliberate activities (gratitude, social engagement, purposeful goals) can sustainably shift the set point upward by 0.3-0.5 SD when maintained consistently, while life circumstances produce only temporary shifts.\n", "status": "active", "fields": [ "social-science", "psychology", "neuroscience", "behavioural-economics" ], "color": "green" }, { "id": "h-hasselmann-red-noise-ocean-temperature-spectrum", "type": "hypothesis", "title": "North Atlantic SST power spectra at frequencies below 1/(2 years) are statistically indistinguishable from the Hasselmann red noise prediction S(ω) = σ²/(λ²+ω²) with a damping timescale τ = 1/λ ≈ 8–12 months — confirming that decadal Atlantic variability requires no coupled ocean-atmosphere resonance beyond integrated atmospheric white noise.\n", "status": "active", "fields": [ "climate-science", "mathematics", "stochastic-processes", "oceanography" ], "color": "green" }, { "id": "h-hawkes-branching-threshold-predicts-seizure-clusters", "type": "hypothesis", "title": "A patient-specific Hawkes branching ratio crossing 0.85 predicts elevated 24-hour seizure-cluster risk.", "status": "active", "fields": [ "neuroscience", "statistics", "clinical-neurology" ], "color": "green" }, { "id": "h-hawkes-process-liquidity-flash-crash", "type": "hypothesis", "title": "The Hawkes branching ratio eta estimated from the last 10 minutes of limit order book data will be a statistically significant leading indicator (AUC > 0.75 for ROC curve) of flash crash events defined as > 2% price decline within 60 seconds, based on backtesting against S&P 500 E-mini futures order book data from 2010–2023", "status": "active", "fields": [ "finance", "mathematics", "statistics" ], "color": "green" }, { "id": "h-hawking-radiation-analog-bec-entanglement", "type": "hypothesis", "title": "BEC sonic black hole analogs will exhibit entanglement between Hawking and partner phonon modes precisely described by a two-mode squeezed thermal state with squeezing parameter r = arctanh(exp(-ω/2T_H)), confirming the quantum information theoretic prediction of Hawking radiation without requiring a gravitational horizon", "status": "active", "fields": [ "physics", "quantum-physics", "thermodynamics" ], "color": "green" }, { "id": "h-healthcare-cost-baumol-spiral-mechanisms", "type": "hypothesis", "title": "Healthcare cost inflation is driven primarily by Baumol's cost disease (labor-intensive service sector cannot achieve manufacturing productivity gains, causing relative price increases), compounded by supplier-induced demand, third-party payer moral hazard, and technology-driven standard-of-care expansion; single-payer systems reduce administrative costs ~10-15% GDP but do not solve the Baumol component", "status": "active", "fields": [ "health-economics", "macroeconomics", "public-policy" ], "color": "green" }, { "id": "h-healthcare-cost-baumol-technology-spiral", "type": "hypothesis", "title": "Persistently rising healthcare costs result from the superposition of Baumol's cost disease (healthcare productivity growth lags economy-wide productivity, raising relative prices inevitably), technology-induced demand expansion (new treatments create new categories of treatable disease), and third-party payment moral hazard, with the dominant mechanism varying by healthcare system type.\n", "status": "active", "fields": [ "health-economics", "public-policy", "macroeconomics", "health-systems-research" ], "color": "green" }, { "id": "h-heisenberg-limited-sensing-biological", "type": "hypothesis", "title": "Biological sensory systems approach the Heisenberg (quantum) sensitivity limit in specific detection tasks where quantum coherence is preserved", "status": "active", "fields": [ "quantum-biology", "biophysics", "quantum-sensing" ], "color": "green" }, { "id": "h-helioseismology-x-inverse-eigenvalue-problems", "type": "hypothesis", "title": "Injecting realistic realization noise into stellar oscillation eigenfrequency tables around synthetic models will widen inverted sound-speed credible intervals by predictable factors relative to Jacobian condition-number spectra — falsifying overly optimistic uniqueness narratives absent uncertainty loops.\n", "status": "active", "fields": [ "astrophysics", "statistics" ], "color": "green" }, { "id": "h-hertz-contact-x-spherical-indentation", "type": "hypothesis", "title": "Joint fits of AFM force curves to poroelastic relaxation kernels plus JKR adhesion extracts elastic moduli whose variance across indentation depths drops below single-model Hertz variance — falsifying universal Hertz-only pipelines on hydrated collagen gels without adhesion corrections.\n", "status": "active", "fields": [ "biomechanics", "mechanical-engineering" ], "color": "green" }, { "id": "h-hierarchical-bayesian-priors-improve-imaging-inverse-coverage", "type": "hypothesis", "title": "Hierarchical priors that explicitly model forward-model discrepancy produce better-calibrated posterior intervals in sparse inverse imaging than fixed-regularization baselines.\n", "status": "active", "fields": [ "statistics", "medical-imaging", "applied-mathematics" ], "color": "green" }, { "id": "h-high-entropy-alloy-configurational-entropy-stabilization", "type": "hypothesis", "title": "In equimolar CrMnFeCoNi and related Cantor-class HEAs, the −TΔS_mix = R ln(5)·T configurational entropy term dominates the Gibbs free energy at T > 900°C, preventing sigma-phase and Laves-phase precipitation and maintaining single-phase FCC stability — a prediction validated by CALPHAD and falsifiable by differential scanning calorimetry at controlled annealing temperatures.\n", "status": "active", "fields": [ "materials-science", "chemistry", "thermodynamics" ], "color": "green" }, { "id": "h-high-entropy-alloy-dislocation-cocktail-hardening", "type": "hypothesis", "title": "The exceptional strength-ductility combination in CrMnFeCoNi-type high-entropy alloys arises primarily from chemical short-range order (SRO) that creates spatially heterogeneous Peierls barriers, causing dislocations to develop wavy glide paths that reduce stress concentrations at grain boundaries and delay necking to strains exceeding 50%.\n", "status": "active", "fields": [ "materials-science", "solid-mechanics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-higher-order-gnn-practical-expressiveness", "type": "hypothesis", "title": "Ring-counting subgraph GNNs that explicitly enumerate cycles up to length 6 capture the expressiveness needed for molecular property prediction beyond 1-WL, achieving the accuracy of 3-WL while scaling linearly in graph size due to the bounded ring count in drug-like molecules.\n", "status": "active", "fields": [ "machine-learning", "combinatorics", "chemistry" ], "color": "green" }, { "id": "h-hippocampal-place-cell-population-topology-reflects-navigated-space-topology", "type": "hypothesis", "title": "The persistent homology of hippocampal CA1 place cell co-firing patterns correctly encodes the topological invariants (Betti numbers β₀, β₁, β₂) of the navigated environment — circle (β₁=1), torus (β₁=β₂=1), figure-eight (β₁=2) — reliably and independently of changes in firing field locations or remapping, providing a topology-native read-out of the cognitive map.\n", "status": "active", "fields": [ "computational-neuroscience", "algebraic-topology", "systems-neuroscience" ], "color": "green" }, { "id": "h-hippocampal-population-holographic-capacity", "type": "hypothesis", "title": "The capacity of hippocampal CA3 to store distinct episodic memory patterns follows the Hopfield-holographic capacity formula C ≈ 0.138 N / log N (where N is the number of pyramidal cells), and this limit is reached in normal aging before significant cell loss occurs\n", "status": "active", "fields": [ "neuroscience", "optics" ], "color": "green" }, { "id": "h-hippocampal-remapping-abstract-maps", "type": "hypothesis", "title": "The hippocampal-entorhinal code scales to large environments and abstract cognitive maps through a modular hierarchical grid cell system where different grid modules encode different spatial scales by a fixed ratio, and abstract relational knowledge is encoded in the same coordinate frame by remapping the grid modules onto non-spatial relationship dimensions.\n", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "cognitive-science" ], "color": "green" }, { "id": "h-histone-code-combinatorial-specificity-exceeds-single-mark-models", "type": "hypothesis", "title": "Combinatorial histone PTM patterns (pairs and triplets of marks) predict gene expression levels with significantly higher accuracy than single-mark models, and the combinatorial interactions are non-additive (synergistic or antagonistic) for at least 30% of mark pairs measured genome-wide.\n", "status": "active", "fields": [ "epigenetics", "biochemistry", "structural-biology", "genomics" ], "color": "green" }, { "id": "h-historical-reconstruction-phylogenetic-limit", "type": "hypothesis", "title": "The reliable temporal limit of linguistic reconstruction is approximately 8,000-10,000 years before present because random lexical replacement rates (Swadesh lists) produce a signal-to-noise ratio of 1 at approximately 8,000 years, and claims of language family membership beyond this horizon require non-stochastic structural signal (typological spandrels or shared irregular morphology) not available for proto-world proposals.\n", "status": "active", "fields": [ "linguistics", "evolutionary-biology", "statistics", "computational-linguistics" ], "color": "green" }, { "id": "h-hjb-derived-adaptive-fractionation-improves-tumor-control-toxicity-tradeoff", "type": "hypothesis", "title": "Transferred methods from `b-hamilton-jacobi-bellman-x-adaptive-radiotherapy` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "control-engineering", "medicine" ], "color": "green" }, { "id": "h-holographic-encoding-hawking-radiation", "type": "hypothesis", "title": "Quantum information in infalling matter is holographically encoded in the entanglement structure of early Hawking radiation before the Page time, and becomes decodable — in principle — by an observer holding the full radiation output after the Page time using quantum error correction on the entanglement wedge of the radiation system.\n", "status": "active", "fields": [ "quantum-gravity", "quantum-information", "information-theory", "black-hole-physics" ], "color": "green" }, { "id": "h-homochirality-first-order-transition", "type": "hypothesis", "title": "Prebiotic chiral symmetry breaking is a first-order phase transition requiring autocatalytic amplification factor >50, with Soai-like chemistry as the minimal model", "status": "active", "fields": [], "color": "green" }, { "id": "h-hopf-bifurcation-lynx-hare-10yr-cycle", "type": "hypothesis", "title": "The 10-year lynx-hare cycle in boreal Canada is generated by a supercritical Hopf bifurcation in the Rosenzweig-MacArthur model with snowshoe hare carrying capacity near the critical threshold K*", "status": "active", "fields": [ "ecology", "mathematics" ], "color": "green" }, { "id": "h-hopf-bifurcation-power-grid-stability", "type": "hypothesis", "title": "The proximity of a power grid to its nearest Hopf bifurcation in the synchronisation parameter space (line impedance, generator inertia, load) can be estimated in real time from network-wide frequency measurements, providing an early warning indicator for grid instability 5-15 minutes before voltage collapse.\n", "status": "active", "fields": [ "engineering", "physics", "nonlinear-dynamics", "control-theory" ], "color": "green" }, { "id": "h-hopf-bifurcation-universal-mechanism-vertebrate-hair-cell-amplification", "type": "hypothesis", "title": "Operation near a Hopf bifurcation is a universal mechanism for active mechanical amplification in vertebrate inner ear hair cells across species and frequency ranges, explaining the compressive nonlinearity, sharp tuning, and spontaneous otoacoustic emissions observed universally in mammalian, reptilian, and amphibian hearing.\n", "status": "active", "fields": [ "biophysics", "neuroscience", "nonlinear-dynamics", "sensory-biology" ], "color": "green" }, { "id": "h-hopf-reduced-order-predicts-galloping-onset-threshold", "type": "hypothesis", "title": "Wind-tunnel sectional models with quartic stiffness analogues will track Hopf-normal-form onset thresholds within stated uncertainty when quasi-steady aerodynamics dominates — falsified if CFD bifurcation continuation disagrees by more than the experimental envelope across ≥20 reduced-velocity sweep points.\n", "status": "active", "fields": [ "aerospace-engineering", "nonlinear-dynamics" ], "color": "green" }, { "id": "h-hopfield-alzheimers-glass-transition", "type": "hypothesis", "title": "Alzheimer's synapse loss drives hippocampal CA3 past the spin-glass capacity transition alpha_c = 0.138", "status": "active", "fields": [], "color": "green" }, { "id": "h-hopfield-capacity-modern-architectures", "type": "hypothesis", "title": "The in-context learning capacity of transformer attention heads (measured as maximum number of retrievable input-output pairs) scales with context length N according to the Hopfield-Ising capacity bound alpha_c * N, with alpha_c between 0.1 and 2.0 depending on the effective energy function curvature.\n", "status": "active", "fields": [ "computer-science", "physics", "neuroscience" ], "color": "green" }, { "id": "h-housing-affordability-zoning-supply-constraint", "type": "hypothesis", "title": "The housing affordability crisis in high-income cities is primarily caused by restrictive land-use regulation (single-family zoning, minimum lot size, height limits) that constrains supply elasticity, and upzoning around transit nodes reduces housing costs by 10-20% over 10 years in cities with elastic construction supply.\n", "status": "active", "fields": [ "economics", "urban-planning", "social-science", "public-policy" ], "color": "green" }, { "id": "h-hub-lethality-protein-network-drug-targets", "type": "hypothesis", "title": "Proteins with betweenness centrality in the top 5% of the human PPI network are enriched among approved drug targets at odds ratio >3 relative to proteins in the bottom 50% of betweenness, and this enrichment is independent of protein abundance, tissue expression, and Y2H detection bias — reflecting genuine functional centrality in biological information flow.\n", "status": "active", "fields": [ "systems-biology", "drug-discovery", "network-science", "mathematics" ], "color": "green" }, { "id": "h-humor-incongruity-resolution-dopamine", "type": "hypothesis", "title": "Humor comprehension requires two-stage processing — detection of incongruity (anterior cingulate, temporal-parietal junction) followed by resolution (inferior frontal gyrus) — with positive affect arising from dopaminergic reward for successful resolution", "status": "active", "fields": [ "cognitive-neuroscience", "psychology", "linguistics" ], "color": "green" }, { "id": "h-hydraulic-failure-drives-tree-mortality-drought", "type": "hypothesis", "title": "Hydraulic failure (loss of xylem conductivity exceeding 88% at P₈₈) is the primary proximate cause of drought-induced tree mortality in isohydric species, and species P₅₀ predicts geographic patterns of forest die-off under CMIP6 drought projections", "status": "active", "fields": [ "plant-physiology", "ecophysiology", "climate-science", "ecology" ], "color": "green" }, { "id": "h-hypersonic-cmcs-thermal-protection-reuse", "type": "hypothesis", "title": "Ceramic matrix composites (SiC/SiC and C/SiC) with environmental barrier coatings (EBCs) are the only material class that can provide reliable reusable thermal protection for hypersonic vehicles at Mach 10-25 through at least 50 flight cycles, contingent on solving surface oxidation recession at >1600°C oxygen partial pressures.\n", "status": "active", "fields": [ "materials-science", "engineering", "aerodynamics", "chemistry" ], "color": "green" }, { "id": "h-hysteresis-loop-biomarkers-predict-neurofatigue-recovery-lag", "type": "hypothesis", "title": "Methods transferred from `b-hysteresis-loop-area-x-neural-fatigue-recovery-dynamics` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "neuroscience", "control-theory" ], "color": "green" }, { "id": "h-ice-sheet-basal-topography-instability", "type": "hypothesis", "title": "Thwaites Glacier has already crossed the marine ice sheet instability threshold, and the grounding line retreat rate will exceed 2 km/year by 2040 as warm Circumpolar Deep Water intrusion establishes a self-sustaining melt feedback beneath the Pine Island Bay sector.\n", "status": "active", "fields": [ "glaciology", "geoscience", "oceanography", "sea-level-science" ], "color": "green" }, { "id": "h-identical-analyzer-method-noise-floor-dominated-regimes-match-at-mm-wave-carriers", "type": "hypothesis", "title": "When spectrum-analyzer intrinsic jitter floors are subtracted identically, stabilized semiconductor lasers operating near quantum-limited linewidth will exhibit offset-frequency noise segments overlapping scaled microwave oscillator profiles after converting Schawlow–Townes linewidth to equivalent phase-noise density — falsified if carrier-frequency-dependent flicker floors dominate optics bands differently than electronics bands despite normalization.\n", "status": "active", "fields": [ "photonics", "electrical-engineering" ], "color": "green" }, { "id": "h-imex-time-stepping-expands-stable-reaction-diffusion-cfl", "type": "hypothesis", "title": "For stiff reaction-diffusion systems, IMEX integrators increase the stable timestep envelope and reduce qualitative artifact rates compared to purely explicit schemes at equal compute budget.\n", "status": "active", "fields": [ "numerical-analysis", "computational-physics", "applied-mathematics" ], "color": "green" }, { "id": "h-imf-stellar-feedback-variation", "type": "hypothesis", "title": "The stellar initial mass function varies systematically with the Jeans mass in star-forming molecular clouds — specifically, the high-mass IMF slope (Salpeter index α) steepens in low-metallicity, high-pressure environments, and this variation is detectable in resolved stellar populations of dwarf galaxies with JWST.\n", "status": "active", "fields": [ "astrophysics", "astronomy", "stellar-physics" ], "color": "green" }, { "id": "h-immune-memory-x-long-term-potentiation", "type": "hypothesis", "title": "B-cell germinal center exit and transition to memory B cells requires mTORC1 activation above a threshold identical in kinase-substrate specificity (but not upstream trigger) to the mTORC1-dependent late-phase LTP consolidation in CA1 hippocampal neurons, predicting that rapamycin impairs both immunological and synaptic memory consolidation through the same molecular mechanism", "status": "active", "fields": [ "biology", "neuroscience", "immunology" ], "color": "green" }, { "id": "h-immune-negative-selection-optimal-threshold", "type": "hypothesis", "title": "Thymic negative selection sets the self/non-self decision boundary at the information-theoretically optimal threshold that maximizes pathogen detection while minimizing autoimmune probability under evolutionary constraints\n", "status": "active", "fields": [ "biology", "computer_science", "immunology", "machine_learning" ], "color": "green" }, { "id": "h-implicit-explicit-memory-prediction-error", "type": "hypothesis", "title": "Memory is expressed explicitly when prediction error during retrieval recruits prefrontal-hippocampal interaction, and implicitly when retrieval occurs without prediction error in striatal-cerebellar pathways; the boundary between systems is determined by the magnitude of retrieval-time prediction error, which can be experimentally manipulated to shift any given memory from implicit to explicit expression.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "psychology" ], "color": "green" }, { "id": "h-improvisation-prefrontal-deactivation-hypothesis", "type": "hypothesis", "title": "Musical improvisation requires hypofrontality — deactivation of dorsolateral prefrontal cortex (self-monitoring) concurrent with increased medial prefrontal activity (self-expression), supported by jazz improvisation fMRI studies showing inverse DLPFC/mPFC activation pattern absent in memorized performance.\n", "status": "active", "fields": [ "cognitive-neuroscience", "music-cognition", "psychology" ], "color": "green" }, { "id": "h-improvisation-prefrontal-deactivation", "type": "hypothesis", "title": "Musical improvisation requires transient hypofrontality: deactivation of dorsolateral prefrontal cortex (self-monitoring) and simultaneous activation of medial prefrontal cortex (self-expression), producing a flow state distinct from rehearsed performance and domain-general executive control.\n", "status": "active", "fields": [ "cognitive-neuroscience", "music-psychology", "creativity-research", "flow-psychology" ], "color": "green" }, { "id": "h-income-boltzmann-condensation-threshold", "type": "hypothesis", "title": "Real economies above a critical capital return rate r > g (Piketty condition) undergo a Bose-Einstein-like wealth condensation transition with a predictable Pareto exponent determined by the saving propensity distribution\n", "status": "active", "fields": [ "physics", "economics", "statistical_mechanics", "econophysics" ], "color": "green" }, { "id": "h-inequality-health-psychosocial-status-pathway", "type": "hypothesis", "title": "Income inequality causally harms population health beyond individual poverty effects through the psychosocial status pathway: chronic HPA axis activation by status competition in unequal societies elevates cortisol, accelerates allostatic load, and reduces immune function, visible in health gradients even in middle and upper-middle income groups.\n", "status": "active", "fields": [ "social-science", "medicine", "epidemiology", "neuroendocrinology" ], "color": "green" }, { "id": "h-infinity-category-presentability-limits", "type": "hypothesis", "title": "An ∞-category has all small limits and colimits if and only if it is bicomplete, and presentable ∞-categories (locally small, accessible, cocomplete) have all limits by an adjoint functor theorem generalization — the theory of presentable ∞-categories provides the correct framework for derived algebraic geometry and higher topos theory.\n", "status": "active", "fields": [ "category-theory", "homotopy-theory", "higher-algebra" ], "color": "green" }, { "id": "h-information-bottleneck-alignment-improves-neural-encoding-metrics", "type": "hypothesis", "title": "Joint stimulus designs that align variational IB estimates with neural sufficiency metrics reduce apparent contradiction between efficient-coding narratives and trained encoder compression curves.", "status": "active", "fields": [ "neuroscience", "computer-science", "machine-learning" ], "color": "green" }, { "id": "h-information-optimal-batching-accelerates-material-discovery", "type": "hypothesis", "title": "Information-optimal batch selection reaches target materials-property uncertainty with fewer experiments than grid search.", "status": "active", "fields": [ "materials-science", "statistics", "automation" ], "color": "green" }, { "id": "h-infrastructure-interdependence-discontinuous-collapse-empirical", "type": "hypothesis", "title": "Real urban power-grid/internet interdependency networks have a partial-dependence fraction q below the critical threshold q_c for discontinuous percolation, meaning real cascade failures are continuous (not abrupt) and real-time variance monitoring of grid frequency deviation provides measurable early-warning signals ≥10 minutes before cascade onset.\n", "status": "active", "fields": [ "engineering", "network-science", "complexity-science", "physics" ], "color": "green" }, { "id": "h-inner-core-solidification-texture", "type": "hypothesis", "title": "Earth's inner core seismic anisotropy (faster P-waves along the rotation axis) records solidification texture from preferential iron crystal alignment during dendritic solidification at the inner core boundary — not ongoing convection — with the anisotropy frozen in as the boundary advances outward at ~1 mm/yr.\n", "status": "active", "fields": [ "seismology", "geophysics", "mineral-physics" ], "color": "green" }, { "id": "h-innovation-diffusion-network-topology", "type": "hypothesis", "title": "Innovation S-curve shape is primarily determined by the adopter network topology — scale-free networks accelerate early diffusion but truncate the long tail, while lattice-like networks produce symmetric S-curves, and early adoption centrality statistics predict long-run diffusion success with > 70% accuracy.\n", "status": "active", "fields": [ "innovation-studies", "network-science", "sociology-of-technology" ], "color": "green" }, { "id": "h-inoculation-theory-science-misinformation", "type": "hypothesis", "title": "Inoculation theory (pre-emptive refutation of misleading arguments before exposure) reliably reduces belief in science misinformation by 20–40% across topics (climate change, vaccines, GMOs) because it builds resistance to manipulation techniques, while deficit-model corrections applied after misinformation exposure are less effective and can backfire through motivated reasoning.\n", "status": "active", "fields": [ "psychology", "communication", "social-science", "science-studies" ], "color": "green" }, { "id": "h-insect-navigation-path-integration", "type": "hypothesis", "title": "Neuromorphic hardware implementation of the Drosophila central complex ring attractor on Intel Loihi 2 will achieve dead-reckoning accuracy within 10% of biological ants while consuming <1 mW power, outperforming conventional IMU-based navigation at equivalent energy budgets", "status": "active", "fields": [ "neuroscience", "robotics", "neuromorphic-computing" ], "color": "green" }, { "id": "h-insight-dopamine-prefrontal-release", "type": "hypothesis", "title": "Sudden insight in problem solving is produced by a burst of dopaminergic activity in anterior cingulate cortex that gates the release of a previously inhibited solution representation in temporal cortex; the Aha-moment corresponds to the collapse of the inhibition set established during impasse, triggered by internally generated prediction error when a remote associate crosses the detection threshold.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "psychology", "computational-neuroscience" ], "color": "green" }, { "id": "h-institutional-trust-elite-cue-propagation", "type": "hypothesis", "title": "Rapid collapses of institutional trust are caused by elite cue propagation rather than direct institutional failures — when partisan elites withdraw trust cues from institutions, their followers update trust downward within weeks, and early warning signs appear as elite social media tone shifts 2-4 months before population trust surveys show changes.\n", "status": "active", "fields": [ "political-science", "sociology", "computational-social-science" ], "color": "green" }, { "id": "h-instrumental-variables-causal-inference-validity", "type": "hypothesis", "title": "Instrumental variable (IV) estimation reliably recovers the local average treatment effect (LATE) in observational studies when the instrument satisfies relevance, exclusion restriction, and monotonicity — and regression discontinuity designs provide the strongest observational causal evidence because they approximate a local randomized experiment near the cutoff threshold.\n", "status": "active", "fields": [ "statistics", "epidemiology", "economics", "social-science" ], "color": "green" }, { "id": "h-integral-feedback-sufficient-perfect-adaptation-living-cells", "type": "hypothesis", "title": "Antithetic integral feedback motifs — where two molecular species with opposite effects on a target variable annihilate each other — are the minimal biomolecular implementation of integral control, and their robustness to molecular noise is determined by the ratio of annihilation rate to production rates.\n", "status": "active", "fields": [ "engineering", "biology", "systems-biology", "synthetic-biology", "control-theory" ], "color": "green" }, { "id": "h-interbank-default-cascades-exhibit-epidemic-thresholds", "type": "hypothesis", "title": "For reconstructed EU interbank networks in pre-crisis windows, estimated cascade amplification metrics will correlate with metrics analogous to epidemic vulnerability (e.g., k-core structure) out-of-sample.", "status": "active", "fields": [ "economics", "physics", "network-science" ], "color": "green" }, { "id": "h-interdisciplinary-barriers-epistemic-terminology-gap", "type": "hypothesis", "title": "The primary barrier to effective interdisciplinary collaboration is not institutional (separate departments, funding streams) but epistemic — fields develop incompatible ontologies and methodological standards that create untranslatability; structural interventions (joint appointments, interdisciplinary centres) improve collaboration only when paired with explicit ontology bridging.\n", "status": "active", "fields": [ "philosophy-of-science", "sociology-of-science", "research-policy", "science-studies" ], "color": "green" }, { "id": "h-interface-width-regularization-predicts-segmentation-stability", "type": "hypothesis", "title": "In diffuse-interface segmentation benchmarks, regularization settings that maintain a stable phase-field interface width across pyramid scales will yield lower boundary jitter than settings tuned only for validation Dice score; falsified if boundary variance is unchanged.\n", "status": "active", "fields": [ "computer-vision", "applied-mathematics" ], "color": "green" }, { "id": "h-intermediate-disturbance-competition-colonization", "type": "hypothesis", "title": "The Intermediate Disturbance Hypothesis produces a reliable diversity peak only when the competition-to-colonization rate ratio α/β > 10, and in communities with faster competitive exclusion the diversity-disturbance relationship is monotonically negative, explaining the majority of empirical IDH failures in fast-succession plant communities", "status": "active", "fields": [ "ecology", "nonlinear-dynamics", "conservation-biology" ], "color": "green" }, { "id": "h-interoception-allostatic-consciousness", "type": "hypothesis", "title": "Conscious experience arises from the brain's predictive model of the body's internal physiological state (interoceptive inference), making emotions and self-awareness fundamentally allostatic constructions — this explains why interoceptive accuracy predicts emotional awareness, why body-focused practices alter consciousness, and why interoceptive disruption causes depersonalisation.\n", "status": "active", "fields": [ "consciousness-science", "interoception-research", "psychiatry" ], "color": "green" }, { "id": "h-intestinal-crypt-apc-selection-coefficient", "type": "hypothesis", "title": "APC loss of heterozygosity in intestinal stem cells has a selection coefficient s ~ 0.01-0.05 per cell division (Moran process), explaining why APC-mutant crypts are detected at ~10x frequency above neutral expectation by age 60 in normal human colon; this implies colorectal cancer initiation is driven by selection, not neutral drift.\n", "status": "active", "fields": [ "biology", "mathematics", "oncology" ], "color": "green" }, { "id": "h-intrinsically-disordered-proteins-polymer-physics", "type": "hypothesis", "title": "IDP Flory scaling exponents ν estimated from smFRET-SAXS measurements will fall in different universality classes (ν~0.6 for highly charged, ν~0.45 for hydrophobic-rich) predictable from net charge per residue and hydropathy index alone", "status": "active", "fields": [ "biophysics", "polymer-science", "computational-biology" ], "color": "green" }, { "id": "h-invasive-species-reaction-diffusion", "type": "hypothesis", "title": "The empirical invasion front speed of 20+ well-documented plant invasions in North America will agree with the Fisher-KPP prediction c*=2√(rD) within a factor of 2 when r and D are estimated independently from early-invasion demographic data", "status": "active", "fields": [ "ecology", "mathematics", "conservation-biology" ], "color": "green" }, { "id": "h-inverse-cubic-law-agent-heterogeneity-mechanism", "type": "hypothesis", "title": "The inverse cubic law (alpha ~ 3) for financial return tail exponents is generated by the heavy-tailed distribution of fund sizes (Pareto with exponent ~ 1) combined with the square-root market impact law — funds optimally split large orders into smaller trades, and the resulting return distribution has tail exponent alpha = 2 * Pareto_fund_exponent + 1 ~ 3, making the inverse cubic law a consequence of institutional heterogeneity rather than intrinsic price dynamics.\n", "status": "active", "fields": [ "econophysics", "finance", "complexity-science" ], "color": "green" }, { "id": "h-ion-specific-double-layer-competition-modulates-permeation", "type": "hypothesis", "title": "For anionic phospholipid membranes, divalent cations collapse the double layer and recruit peripheral proteins non-monotonically with concentration — producing a permeation or binding optimum before precipitation — testable with parallel microfluidic titrations and MD.\n", "status": "active", "fields": [ "biophysics", "physical-chemistry" ], "color": "green" }, { "id": "h-ionizable-lipid-pka-endosomal-escape", "type": "hypothesis", "title": "The pKa of the ionizable lipid is the primary determinant of LNP endosomal escape efficiency, and formulations with pKa tuned to 6.2–6.5 achieve maximal mRNA translation through optimal membrane fusion kinetics at endosomal pH.\n", "status": "active", "fields": [ "drug-delivery", "biophysics", "immunology" ], "color": "green" }, { "id": "h-island-biogeography-x-percolation", "type": "hypothesis", "title": "The species-area exponent z ≈ 0.25 in temperate forests corresponds to the fractal dimension of the percolation backbone at the critical threshold p_c, predicting that below 59% habitat cover, metapopulation connectivity collapses non-linearly across all species simultaneously", "status": "active", "fields": [ "biology", "mathematics", "ecology", "statistical-physics" ], "color": "green" }, { "id": "h-island-formula-entanglement-wedge-quantum-error-correction", "type": "hypothesis", "title": "The island formula's quantum extremal surface is the gravitational analog of the decoding threshold in quantum error correction: islands appear precisely when the entanglement wedge reconstruction map transitions from reconstructing exterior to interior operators, providing a quantum-information-theoretic derivation of the Page time as the threshold for interior information recovery.\n", "status": "active", "fields": [ "quantum-information", "quantum-gravity", "mathematical-physics" ], "color": "green" }, { "id": "h-island-formula-page-curve-tensor-network-model", "type": "hypothesis", "title": "Random tensor network models implementing the island formula reproduce the Page curve of Hawking radiation with Page time t_Page = S_BH / (2*pi * T_Hawking), testable in Brownian circuit analog gravity models", "status": "active", "fields": [ "quantum-physics", "information-theory" ], "color": "green" }, { "id": "h-isogeometric-analysis-superior-convergence-thin-shells", "type": "hypothesis", "title": "Isogeometric analysis using NURBS basis functions achieves provably superior convergence rates compared to standard polynomial FEM for thin-shell problems and problems with smooth curved boundaries — eliminating the geometrical approximation error that limits engineering FEM accuracy for aerodynamic surfaces.\n", "status": "active", "fields": [ "applied-mathematics", "engineering", "computational-mechanics" ], "color": "green" }, { "id": "h-iter-q10-ignition-margin-sufficient-commercial-fusion", "type": "hypothesis", "title": "ITER will achieve Q ≥ 10 (500 MW fusion from 50 MW heating) in its DT burning plasma phase, and the confinement, stability, and plasma-facing material performance will be sufficient to demonstrate the physics basis for a DEMO commercial fusion plant with Q ≥ 25.\n", "status": "active", "fields": [ "plasma-physics", "nuclear-fusion-engineering", "materials-science" ], "color": "green" }, { "id": "h-iv-late-external-validity-population-representativeness", "type": "hypothesis", "title": "The local average treatment effect (LATE) estimated by instrumental variables is generalizable to the full population when the complier subpopulation is representative on all effect-modifying covariates, and this representativeness is empirically testable by comparing complier covariate distributions to the full sample — enabling LATE-to-ATE extrapolation with quantifiable uncertainty.\n", "status": "active", "fields": [ "economics", "statistics", "epidemiology", "causal-inference" ], "color": "green" }, { "id": "h-jamming-transition-critical-exponents", "type": "hypothesis", "title": "The excess coordination number Z - Z_c and shear modulus G of frictionless 3D packings both scale as (phi - phi_J)^0.5 with exponent equal to the mean-field prediction, and the diverging vibrational length scale xi ~ |phi - phi_J|^{-0.5} will be experimentally measurable in colloidal glass systems via dynamic light scattering near phi_J", "status": "active", "fields": [ "soft-matter", "statistical-physics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-jarzynski-equality-molecular-motor-efficiency-measurement", "type": "hypothesis", "title": "The Jarzynski equality applied to single-molecule measurements of ATP synthase rotation under varying load predicts that the measured work distribution P(W) satisfies e^{-βW} = e^{-βΔG_ATP} with sub-k_BT precision, enabling measurement of ΔG_ATP hydrolysis in situ at physiological concentrations without equilibrium experiments.\n", "status": "active", "fields": [ "physics", "biology", "biophysics", "thermodynamics", "biochemistry" ], "color": "green" }, { "id": "h-jet-break-timescale-scales-with-entropy-and-opening-angle", "type": "hypothesis", "title": "For a sample of short GRB afterglows with jet breaks, joint fits of break time and post-break slope will favor structured-jet RHD models over top-hat jets when including realistic observer-angle priors.", "status": "active", "fields": [ "astronomy", "physics" ], "color": "green" }, { "id": "h-johnson-graph-spectral-gap-predicts-ctqw-search-plateau", "type": "hypothesis", "title": "For noisy CTQW spatial-search simulations on hardware-motivated irregular graphs, empirical hitting-time plateaus correlate with normalized Laplacian spectral gaps extracted from the connectivity adjacency — falsified if gap estimates explain less than 40% of variance across randomized disorder draws at fixed N.\n", "status": "active", "fields": [ "quantum-computing", "spectral-graph-theory" ], "color": "green" }, { "id": "h-joint-fit-lifshitz-hamaker-colloid-force-curves", "type": "hypothesis", "title": "When ε(ω) for gold-coated spheres is measured from UV through infrared with Kramers–Kronig consistency checks, Lifshitz predictions will simultaneously fit AFM colloid–substrate force curves from nanometer separations (vdW regime) and micrometer-gap Casimir-force lever measurements within combined uncertainty — falsified if independent Hamaker fits disagree with spectral Lifshitz integration beyond stated error bars.\n", "status": "active", "fields": [ "physical-chemistry", "condensed-matter-physics" ], "color": "green" }, { "id": "h-josephson-paramp-nears-quantum-noise-floor-with-rimp-matched-array", "type": "hypothesis", "title": "Josephson traveling-wave parametric amplifiers operated at millikelvin with impedance-matched antenna interfaces can achieve system noise temperatures within 10% of the quantum limit across octave bandwidth when pump-induced ripples are suppressed — outperforming silicon LNAs at matched centers despite cryogenic overhead.\n", "status": "active", "fields": [ "quantum-physics", "microwave-engineering" ], "color": "green" }, { "id": "h-jwst-massive-galaxies-feedback-suppressed-smf", "type": "hypothesis", "title": "JWST's excess of massive galaxies at z>10 is explained by suppressed stellar feedback efficiency at low metallicity and high star-formation surface density, requiring revised stellar mass functions in Lambda-CDM", "status": "active", "fields": [ "observational-cosmology", "galaxy-formation", "stellar-physics" ], "color": "green" }, { "id": "h-jwst-pop3-lensing-detection", "type": "hypothesis", "title": "JWST can detect individual gravitationally lensed Population III stars in magnification events during the epoch of reionisation at redshifts 10-15", "status": "active", "fields": [ "astronomy" ], "color": "green" }, { "id": "h-kalman-smoother-outperforms-static-regression-for-tree-ring-temperature", "type": "hypothesis", "title": "Kalman smoothing with explicit proxy-noise modeling yields better out-of-sample temperature reconstruction than static regression baselines.", "status": "active", "fields": [ "climate-science", "statistics" ], "color": "green" }, { "id": "h-kam-nonergodicity-many-body-localization", "type": "hypothesis", "title": "The KAM theorem's mechanism — preserved invariant tori in near-integrable systems — has a quantum analogue in many-body localisation, where disorder preserves an extensive number of approximate local integrals of motion (LIOMs) that prevent thermalisation, making MBL the quantum KAM theorem.\n", "status": "active", "fields": [ "quantum-statistical-mechanics", "mathematical-physics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-karst-connectivity-geophysical-mapping", "type": "hypothesis", "title": "Karst aquifer conduit connectivity can be characterized without cave exploration using combined ERT and time-domain EM surveys, with connectivity index derivable from tracer breakthrough curve dispersion — and the power-law conduit size distribution (N ∝ r^{−2}) is the key structural predictor of aquifer vulnerability.\n", "status": "active", "fields": [ "hydrogeology", "karst-science", "geophysics" ], "color": "green" }, { "id": "h-kauffman-boolean-x-gene-network-attractor-stability", "type": "hypothesis", "title": "Pseudotime-ordered single-cell datasets subjected to asynchronous Boolean inference with known perturbation knockouts will recover effective K within ±1 of synthetic ground-truth Kauffman networks embedded with biological noise — failing when continuous diffusion dominates discrete switches.\n", "status": "active", "fields": [ "systems-biology", "computer-science" ], "color": "green" }, { "id": "h-kauffman-critical-k2-attractor-cell-types", "type": "hypothesis", "title": "Real gene regulatory networks self-organize to K ≈ 2 (edge of chaos) and have attractor counts proportional to sqrt(N), matching observed cell type diversity", "status": "active", "fields": [ "systems-biology", "computational-biology", "complex-systems" ], "color": "green" }, { "id": "h-kauffman-network-criticality-cell-types", "type": "hypothesis", "title": "Mammalian gene regulatory networks operate at the critical point between ordered and chaotic dynamics in Kauffman Boolean network theory (mean K = 2 inputs per gene), predicting that the number of attractors (cell types) scales as the square root of gene number N, consistent with the observed ~200 human cell types for N ~ 20,000 genes.\n", "status": "active", "fields": [ "systems-biology", "computer-science", "mathematical-biology" ], "color": "green" }, { "id": "h-kessler-cascade-altitude-band", "type": "hypothesis", "title": "The Kessler cascade is already self-sustaining in the 900–1,000 km altitude band due to the 2009 Iridium-Cosmos collision, and active debris removal of fewer than 10 objects per year in this band will be insufficient to prevent net debris growth under Starlink/OneWeb constellation deployment schedules.\n", "status": "active", "fields": [ "aerospace-engineering", "orbital-mechanics", "engineering", "space-policy" ], "color": "green" }, { "id": "h-ketamine-antidepressant-ampa-potentiation-mechanism", "type": "hypothesis", "title": "The rapid antidepressant effect of ketamine is mediated primarily by the metabolite (2R,6R)-hydroxynorketamine (HNK) acting through AMPA receptor potentiation and BDNF/TrkB signaling, not through NMDA receptor inhibition — implying that non-dissociative AMPA-potentiating agents will reproduce the antidepressant effect without psychotomimetic side effects.\n", "status": "active", "fields": [ "pharmacology", "neuroscience", "psychiatry", "medicinal-chemistry" ], "color": "green" }, { "id": "h-kh-growth-rate-normalization-predicts-billow-plasma-onset", "type": "hypothesis", "title": "In matched shear-layer simulations, nondimensional Kelvin-Helmholtz growth rates inferred from atmospheric billow roll-up will predict the rank ordering of plasma shear-mode onset after adding a magnetic-tension correction; falsified if rank correlation falls below 0.3 across the shared parameter grid.\n", "status": "active", "fields": [ "fluid-mechanics", "plasma-physics" ], "color": "green" }, { "id": "h-kibble-zurek-polarity-scaling", "type": "hypothesis", "title": "PAR-domain polarity errors in C. elegans scale with fertilisation quench rate according to Kibble-Zurek exponents, placing embryonic symmetry breaking in the same universality class as superfluid helium phase transitions.\n", "status": "active", "fields": [ "biophysics", "developmental-biology", "condensed-matter-physics" ], "color": "green" }, { "id": "h-klausmeier-pattern-wavelength-rainfall-indicator", "type": "hypothesis", "title": "The characteristic wavelength λ of dryland vegetation bands measured by satellite remote sensing encodes the soil water diffusivity D_w via λ = 2π/k* = 2π/√(f(D_w,m,a)) where k* is the Turing instability wavenumber, enabling non-invasive estimation of soil hydraulic properties from satellite imagery without ground surveys", "status": "active", "fields": [ "ecology", "mathematics", "remote-sensing" ], "color": "green" }, { "id": "h-kleiber-exponent-from-fractal-like-transport-networks", "type": "hypothesis", "title": "Within birds, species with more symmetric bronchial branching metrics will cluster closer to α=3/4 than species with documented branching anomalies, when metabolic rate and mass are controlled.", "status": "active", "fields": [ "biology", "physics" ], "color": "green" }, { "id": "h-kleiber-wave-physics", "type": "hypothesis", "title": "Kleiber's 3/4 metabolic scaling law is uniquely derivable from impedance-matching constraints on pulsatile wave propagation in arterial trees — and is distinguishable from the fractal-network theory by organisms with non-pulsatile circulation.\n", "status": "active", "fields": [ "biological-physics", "physiology", "fluid-dynamics", "scaling-theory" ], "color": "green" }, { "id": "h-knot-invariants-x-dna-topology", "type": "hypothesis", "title": "Topoisomerase II preferentially simplifies DNA knot crossings with the same handedness as the writhe of the supercoiled substrate, reflecting a geometric preference for negative node passages that reduces the number of crossings faster than random strand passage, measurable by single-molecule fluorescence of DNA knot relaxation.\n", "status": "active", "fields": [ "mathematics", "molecular-biology", "biophysics", "topology" ], "color": "green" }, { "id": "h-knot-jones-polynomial-completeness", "type": "hypothesis", "title": "The colored Jones polynomial (all colors simultaneously) is a complete invariant of prime knots up to mirror image, with knot detection rate exceeding 99.9% for knots with fewer than 20 crossings\n", "status": "active", "fields": [ "mathematics", "physics", "topology", "quantum_gravity" ], "color": "green" }, { "id": "h-kondratiev-dissipative-entropy", "type": "hypothesis", "title": "Kondratiev long waves (45-60 year economic cycles) are driven by the charge- discharge dynamics of technological capital — a slow dissipative oscillation whose period is determined by the product of the innovation diffusion timescale and the technological capital depreciation rate, both measurable from economic data.\n", "status": "active", "fields": [ "economics", "physics", "complex-systems", "economic-dynamics" ], "color": "green" }, { "id": "h-koopman-linear-dynamics-capture-coherent-structures-limited-window", "type": "hypothesis", "title": "For short time windows, a low-rank DMD model captures dominant coherent structures in separated shear layers with bounded reconstruction error, but error grows superlinearly once nonlinearity transfers energy across resolved/unresolved scales — window length should be tuned to a Lyapunov-like decorrelation time of the resolved subspace.\n", "status": "active", "fields": [ "fluid-mechanics", "mathematics" ], "color": "green" }, { "id": "h-kramers-moyal-surrogates-improve-tumor-state-transition-forecast-calibration", "type": "hypothesis", "title": "Methods transferred from `b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "statistical-physics", "oncology" ], "color": "green" }, { "id": "h-krugman-bifurcation-detectable-moran-i-trajectory", "type": "hypothesis", "title": "The Krugman core-periphery bifurcation produces a detectable early-warning signature in the time series of Moran's I spatial autocorrelation coefficient — specifically, critical slowing down (increasing lag-1 autocorrelation of Moran's I) and increasing variance of Moran's I — preceding regional income divergence events by 3-5 years.\n", "status": "active", "fields": [ "spatial-economics", "complexity-science" ], "color": "green" }, { "id": "h-kuramoto-af-spectral-gap", "type": "hypothesis", "title": "Atrial fibrillation vulnerability is predicted by the spectral gap of the sinoatrial node coupling network — patients with smaller spectral gap require less coupling degradation to cross the Kuramoto desynchronization threshold and are quantifiably more AF-prone.\n", "status": "active", "fields": [ "cardiology", "statistical-physics", "network-science" ], "color": "green" }, { "id": "h-kuramoto-scn-resynchronization-rate", "type": "hypothesis", "title": "The re-entrainment timescale after transient circadian desynchronization scales as 1/Im(λ₂) — the reciprocal of the algebraic connectivity (Fiedler eigenvalue) of the SCN VIP-coupling network — and this relationship can be measured experimentally using acute jet-lag protocols in mice with controlled VIP receptor expression levels.\n", "status": "active", "fields": [ "chronobiology", "neuroscience", "dynamical-systems", "mathematical-biology" ], "color": "green" }, { "id": "h-kyber-lwe-parameter-quantum-security-margin", "type": "hypothesis", "title": "CRYSTALS-Kyber's current parameter sets (Kyber-512, Kyber-768, Kyber-1024) provide quantum security margins of approximately 108, 178, and 240 bits respectively against the best known quantum lattice sieving algorithms — sufficient for the 128/192/256-bit classical security targets — but these estimates may decrease by 10-30 bits as quantum algorithms mature in the next decade.\n", "status": "active", "fields": [ "cryptography", "quantum-computing", "mathematics", "computer-science", "number-theory" ], "color": "green" }, { "id": "h-land-sparing-bef-optimum-yield-threshold", "type": "hypothesis", "title": "Land-sparing outperforms land-sharing for biodiversity conservation when the BEF relationship has a half-saturation constant S1/2 < 5 species, testable via systematic comparison of agricultural BEF curves across farming systems", "status": "active", "fields": [ "ecology", "agronomy" ], "color": "green" }, { "id": "h-landau-neural-transition-measurability", "type": "hypothesis", "title": "The E/I (excitation-inhibition) balance ratio is the control parameter for a Landau mean-field phase transition in neural circuits, with firing rate variance as the order parameter and chi ~ |EI - EI_c|^{-1}", "status": "active", "fields": [], "color": "green" }, { "id": "h-landauer-cosmological-arrow", "type": "hypothesis", "title": "The thermodynamic arrow of time is fully accounted for by a low-Kolmogorov-complexity initial condition plus Landauer's principle, making Maxwell's demon impossible in any universe where information processing is physical", "status": "active", "fields": [ "thermodynamics", "information-theory", "cosmology", "statistical-mechanics" ], "color": "green" }, { "id": "h-landauer-limit-biological-computation", "type": "hypothesis", "title": "Neural computation in cortex operates at 100-1000x above the Landauer limit of kT ln(2) per bit erased, and the excess dissipation is dominated by ion channel leakage rather than logical irreversibility, predicting that energy efficiency of computation in neurons scales with membrane resistance rather than computational complexity.\n", "status": "active", "fields": [ "neuroscience", "statistical-physics", "information-theory" ], "color": "green" }, { "id": "h-landauer-limit-neuronal-computation", "type": "hypothesis", "title": "Neural circuits in energy-constrained organisms (e.g., C. elegans) operate closer to the Landauer thermodynamic limit per computed bit than neural circuits in metabolically unconstrained tissue cultures, indicating evolutionary pressure toward thermodynamic efficiency.\n", "status": "active", "fields": [ "neuroscience", "physics", "thermodynamics", "information-theory" ], "color": "green" }, { "id": "h-langlands-physics-electric-magnetic-duality", "type": "hypothesis", "title": "The geometric Langlands correspondence is the mathematical statement of S-duality (electric-magnetic duality) in N=4 super Yang-Mills gauge theory: the Langlands dual group G^L corresponds to the magnetic dual gauge group, automorphic forms correspond to D-branes wrapping cycles in the mirror Calabi-Yau, providing a physical framework for the entire geometric Langlands program", "status": "active", "fields": [ "mathematics", "theoretical-physics", "algebraic-geometry", "representation-theory" ], "color": "green" }, { "id": "h-language-change-replicator-conformity", "type": "hypothesis", "title": "Conformity bias (frequency-dependent positive selection toward the majority variant) is the dominant selection mechanism in language change for grammatical features, while prestige bias dominates for lexical innovations, producing systematically different S-curve velocities measurable in historical corpora.\n", "status": "active", "fields": [ "linguistics", "cultural-evolution", "population-genetics", "evolutionary-biology" ], "color": "green" }, { "id": "h-language-contact-convergence-universals", "type": "hypothesis", "title": "Language contact convergence is bounded by universal grammar constraints — contact languages cannot acquire each other's features that violate universal word order harmonies or basic phonological markedness constraints, regardless of contact intensity, providing evidence for inviolable language universals.\n", "status": "active", "fields": [ "historical-linguistics", "typology", "contact-linguistics" ], "color": "green" }, { "id": "h-language-critical-period-myelination-pruning", "type": "hypothesis", "title": "The first language acquisition critical period closes at puberty due to myelination of language pathways (arcuate fasciculus, IFOF) increasing processing speed but reducing synaptic plasticity, and is preventable by sustained linguistic input", "status": "active", "fields": [ "developmental-neuroscience", "linguistics", "educational-psychology" ], "color": "green" }, { "id": "h-language-critical-period-myelination", "type": "hypothesis", "title": "The closure of the language critical period is caused by myelination of arcuate fasciculus pathways connecting frontal and temporal language areas, reducing neural plasticity; incomplete myelination in heritage language learners predicts residual phonological plasticity measurable by MMN responses.\n", "status": "active", "fields": [ "linguistics", "neuroscience", "cognitive-science", "developmental-neuroscience" ], "color": "green" }, { "id": "h-language-revitalisation-intergenerational-transmission", "type": "hypothesis", "title": "Successful language revitalisation requires intergenerational transmission as the primary target — language nests (100% immersion from age 0–5) produce native-like acquisition outcomes — and revitalisation efforts that bypass early childhood transmission fail regardless of adult learner numbers.\n", "status": "active", "fields": [ "sociolinguistics", "language-policy", "applied-linguistics" ], "color": "green" }, { "id": "h-laplace-approximated-interim-rules-improve-enrichment-decision-efficiency", "type": "hypothesis", "title": "Methods transferred from `b-laplace-approximation-x-clinical-trial-adaptive-enrichment` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "statistics", "medicine" ], "color": "green" }, { "id": "h-laplacian-eigenmodes-improve-cryoem-conformation-clustering", "type": "hypothesis", "title": "Laplacian-eigenmode embeddings improve cryo-EM conformational clustering purity versus direct pixel-space baselines.", "status": "active", "fields": [ "structural-biology", "machine-learning", "mathematics" ], "color": "green" }, { "id": "h-laser-cooling-maxwell-demon-landauer", "type": "hypothesis", "title": "Laser cooling is a physical realisation of Maxwell's demon, and Landauer's erasure principle predicts a minimum laser photon flux per unit of atomic entropy reduction: P_min = k_B T_bath * ln2 * R_scatter, where R_scatter is the photon scattering rate; experiments will confirm this bound is tight (actual power within factor 2 of Landauer minimum) in optimally designed Sisyphus cooling schemes.\n", "status": "active", "fields": [ "physics", "thermodynamics", "quantum-physics" ], "color": "green" }, { "id": "h-lattice-based-pqc-nist-transition-timeline", "type": "hypothesis", "title": "NIST-standardized lattice-based post-quantum cryptographic algorithms (CRYSTALS-Kyber, CRYSTALS-Dilithium) will be deployed in > 50% of new TLS connections by 2028 and provide adequate security against harvest-now- decrypt-later attacks if migration begins by 2025, but systems with > 10-year confidentiality requirements are already at significant risk from data harvested before migration.\n", "status": "active", "fields": [ "cryptography", "quantum-computing", "computer-science" ], "color": "green" }, { "id": "h-law-of-wall-predicts-local-skin-friction-when-roughness-scaled", "type": "hypothesis", "title": "For systematically generated anisotropic rough surfaces in channel flow, a single additional tensorial roughness parameter beyond k_s will reduce scatter in inferred u_τ by >30% versus scalar k_s alone.", "status": "active", "fields": [ "engineering", "physics" ], "color": "green" }, { "id": "h-layered-em-shielding-financial-firewall-depth-ratio-analogy", "type": "hypothesis", "title": "If shock “frequency” is defined by liquidation horizons, then the ratio of incremental loss reduction per added firewall layer should decay approximately exponentially until correlated channels dominate — but this is a speculative organizational analogy, not a physical law.\n", "status": "active", "fields": [ "engineering", "economics", "risk-management" ], "color": "green" }, { "id": "h-leaky-if-neuron-x-rc-membrane-circuit", "type": "hypothesis", "title": "Joint intracellular noise injections spanning three decades of bandwidth will yield τ estimates whose RC-fit residuals correlate with multicompartment model mismatch scores — falsifying universal single τ under active conductances above threshold noise variance.\n", "status": "active", "fields": [ "neuroscience", "electrical-engineering" ], "color": "green" }, { "id": "h-leontief-network-near-percolation-threshold", "type": "hypothesis", "title": "The global production network operates near a percolation threshold where the removal of the top 5% most central sectors (by Leontief centrality) causes a phase transition from local to global shock propagation, explaining why rare large supply-chain cascades follow heavy-tailed rather than exponential size distributions.\n", "status": "active", "fields": [ "economics", "network-science" ], "color": "green" }, { "id": "h-leptogenesis-sm-cp-insufficient", "type": "hypothesis", "title": "Standard Model CP violation in the CKM matrix is insufficient for baryogenesis by at least 10 orders of magnitude, and the observed baryon asymmetry was generated by leptonic CP violation in heavy Majorana neutrino decays (leptogenesis) at a scale M_R > 10^9 GeV set by the Davidson-Ibarra bound.\n", "status": "active", "fields": [ "particle-physics", "cosmology", "nuclear-physics" ], "color": "green" }, { "id": "h-lethal-mutagenesis-antiviral-threshold", "type": "hypothesis", "title": "Mutagen-based antiviral therapies (ribavirin, favipiravir) achieve therapeutic efficacy specifically when they raise the viral mutation rate above the error threshold U_c = ln(W_max/W_mean), predicting that the clinical dose required for efficacy is proportional to the information content (genome length times ln(1/mutation_rate)) of the target virus.\n", "status": "active", "fields": [ "virology", "information-theory", "pharmacology" ], "color": "green" }, { "id": "h-lexical-diffusion-on-geographic-graphs-predicts-isoglosses", "type": "hypothesis", "title": "For European dialect survey points with known mobility networks, graph-diffusion forecasts of binary lexical features will outperform planar kriging baselines when barriers are included as low-weight edges.", "status": "active", "fields": [ "linguistics", "mathematics" ], "color": "green" }, { "id": "h-lgm-refugia-predict-phylogeographic-breaks-globally", "type": "hypothesis", "title": "Last Glacial Maximum (LGM) climate refugia predicted by species distribution models correspond to phylogeographic breaks inferred from coalescent analyses across taxonomically diverse organisms ΓÇö providing a quantitative test of the refugia hypothesis and a mechanistic link between climate history and biodiversity.\n", "status": "active", "fields": [ "ecology", "biogeography", "population-genetics", "paleoclimatology" ], "color": "green" }, { "id": "h-lichen-consortium-metabolic-coupling", "type": "hypothesis", "title": "Stable autotrophic-heterotrophic microbial consortia on mineral substrates require tight stoichiometric coupling of carbon and nitrogen exchange, and the stability of this coupling — not photosynthetic output alone — determines whether the consortium achieves a self-sustaining biogeochemical loop", "status": "active", "fields": [ "biology", "astrobiology", "materials-science", "ecology" ], "color": "green" }, { "id": "h-lie-bracket-depth-complexity-robot-planning", "type": "hypothesis", "title": "The computational complexity of optimal motion planning for non-holonomic robots scales exponentially with the minimum Lie bracket depth d required to span the tangent space (Chow-Rashevskii condition), predicting a sharp tractability transition between systems with d ≤ 2 (polynomial planning) and d ≥ 3 (exponential planning) in their state space dimension.\n", "status": "active", "fields": [ "mathematics", "control-engineering", "robotics", "computational-complexity" ], "color": "green" }, { "id": "h-lie-group-beyond-standard-model", "type": "hypothesis", "title": "The next level of unification beyond the Standard Model SU(3)×SU(2)×U(1) is constrained by anomaly cancellation and representation theory to be a specific simple group (SO(10) or E_6), and the prediction of proton decay rate from the branching structure of the GUT representation provides a discriminating test between candidate groups.\n", "status": "active", "fields": [ "physics", "quantum-physics", "mathematics", "group-theory" ], "color": "green" }, { "id": "h-lie-groups-x-symmetry-conservation", "type": "hypothesis", "title": "Equivariant neural networks that enforce Lie group symmetries will generalize to out-of-distribution examples related by symmetry transformations from training data, and their generalization gap will scale as the inverse of the group order\n", "status": "active", "fields": [ "mathematics", "physics", "mathematical-physics" ], "color": "green" }, { "id": "h-lif-decision-fatigue-ornstein-uhlenbeck", "type": "hypothesis", "title": "Decision fatigue arises from depletion of a prefrontal signal-to-noise ratio governed by an Ornstein-Uhlenbeck accumulation process: repeated decisions progressively shift the OU process toward a diffusive (low-drift) regime, and this shift is measurable as a flattening of the interspike interval distribution in prefrontal choice neurons.\n", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "physics", "cognitive-science" ], "color": "green" }, { "id": "h-linearized-n-cycle-models-predict-chlorophyll-mode-timescales", "type": "hypothesis", "title": "For CMIP-class Earth-system models that export linearized nitrogen-cycle Jacobians around preindustrial equilibria, the slowest non-trivial eigenmodes will have e-folding times matching empirical EOF timescales of chlorophyll anomalies in subtropical gyres at matching spatial filters — falsified if observational spectra peak at periods inconsistent with any Jacobian eigenvalue imaginary components after bias correction.\n", "status": "active", "fields": [ "biogeochemistry", "dynamical-systems" ], "color": "green" }, { "id": "h-linguistic-relativity-neural-boundary", "type": "hypothesis", "title": "Linguistic relativity effects on color perception are mediated exclusively by left-hemisphere language areas and disappear when verbal processing is suppressed by concurrent verbal shadowing", "status": "active", "fields": [], "color": "green" }, { "id": "h-lipid-raft-phase-separation-receptor-clustering", "type": "hypothesis", "title": "Lipid raft phase separation concentrates GPI-anchored proteins and receptor tyrosine kinases into signaling-competent nanoclusters, with raft lifetime and size controlled by the 2D Cahn-Hilliard free energy parameters — specifically, cholesterol concentration sets the proximity to the phase boundary.\n", "status": "active", "fields": [ "cell-biology", "biophysics", "biochemistry" ], "color": "green" }, { "id": "h-liquid-crystal-x-cell-membrane", "type": "hypothesis", "title": "Lipid raft microdomains in the plasma membrane are described by the two-dimensional Ising model near a miscibility critical point, and their size distribution follows critical fluctuation scaling laws with measurable critical exponents\n", "status": "active", "fields": [ "physics", "biology", "biophysics" ], "color": "green" }, { "id": "h-liquid-crystals-frank-elasticity", "type": "hypothesis", "title": "Machine learning models trained on DFT-computed molecular dipole anisotropy, aspect ratio, and polarizability will predict Frank elastic constants K₁, K₂, K₃ with RMSE < 20% on a test set of 30 liquid crystal compounds", "status": "active", "fields": [ "soft-matter", "computational-chemistry", "materials-science" ], "color": "green" }, { "id": "h-llm-meaning-statistical-form-without-grounding", "type": "hypothesis", "title": "Large language models manipulate statistical patterns over linguistic form without grounding in sensorimotor experience or causal world models — their semantic competence is systematically incomplete on tasks requiring referential grounding, counterfactual reasoning, and physical causality", "status": "active", "fields": [ "computational-linguistics", "cognitive-science", "artificial-intelligence" ], "color": "green" }, { "id": "h-llm-scaling-emergence-artifact", "type": "hypothesis", "title": "All reported emergent capabilities in large language models are metric artifacts of nonlinear evaluation functions and disappear when measured on continuous scales", "status": "active", "fields": [], "color": "green" }, { "id": "h-llzo-single-ion-conductor-eliminates-dendrite-nucleation", "type": "hypothesis", "title": "LLZO (Li₇La₃Zr₂O₁₂) solid electrolyte with ionic conductivity σ > 10⁻³ S/cm and shear modulus G > 10 GPa prevents lithium dendrite nucleation at all practical current densities (< 10 mA/cm²) via the Monroe-Newman mechanical stability criterion, provided interfacial resistance is < 1 Ω·cm².\n", "status": "active", "fields": [ "chemistry", "materials-science", "electrochemistry", "solid-state-physics" ], "color": "green" }, { "id": "h-lmsr-automated-market-maker-dominates-polls-epistemic-accuracy", "type": "hypothesis", "title": "LMSR prediction markets with subsidized liquidity outperform expert polls and ensemble models in Brier score accuracy for geopolitical and scientific outcome forecasting, specifically because the softmax price mechanism implements approximate Bayesian aggregation of heterogeneous private signals.\n", "status": "active", "fields": [ "information-economics", "statistics", "mechanism-design" ], "color": "green" }, { "id": "h-lnt-model-invalid-endocrine-disruptors", "type": "hypothesis", "title": "The linear no-threshold (LNT) extrapolation model is mechanistically invalid for endocrine disruptors that activate estrogen receptor-alpha (ERα) or androgen receptor (AR), because these receptors exhibit concentration-dependent subtype switching and feedback compensation that generate non-monotonic dose-response curves with adverse effects at doses 100-1000× below current NOAEL-derived safety limits.\n", "status": "active", "fields": [ "toxicology", "endocrinology", "regulatory-science", "chemistry" ], "color": "green" }, { "id": "h-local-equilibrium-jacobian-best-conditioned-axis-aligns-with-principal-strain-demo-only", "type": "hypothesis", "title": "In purely pedagogical tensor-diagonalization demos pairing microeconometric Jacobian estimates with laboratory stiffness ellipsoids, principal-axis directions will visually align when analysts artificially diagonalize both matrices to common bases — **explicitly non-empirical classroom stunt**, offered only to illustrate eigenproblem vocabulary parallelism without endorsing economic structural symmetry claims.\n", "status": "active", "fields": [ "economics", "mechanics" ], "color": "green" }, { "id": "h-localized-enkf-reduces-icu-forecast-error", "type": "hypothesis", "title": "Covariance localization in ICU EnKF pipelines reduces 6-hour hemodynamic forecast error versus non-localized baselines.", "status": "active", "fields": [ "medicine", "control-engineering", "bayesian-inference" ], "color": "green" }, { "id": "h-logistic-map-feigenbaum-ecology-universality", "type": "hypothesis", "title": "Real ecological populations near r_∞ ≈ 3.57 exhibit period-doubling bifurcations whose ratios converge to the Feigenbaum constant δ = 4.669..., which is universal across all smooth 1D maps at the period-doubling cascade, proving that ecological chaos is not specific to the logistic map but a universal mathematical feature of any population with unimodal density-dependent regulation.\n", "status": "active", "fields": [ "ecology", "nonlinear-dynamics", "population-biology", "mathematics" ], "color": "green" }, { "id": "h-long-covid-viral-reservoir-reactivation", "type": "hypothesis", "title": "Long COVID symptoms persisting beyond 12 weeks are primarily caused by persistent SARS-CoV-2 viral reservoirs in intestinal epithelium and lymphoid tissue that continuously seed low-level systemic inflammation via viral antigen shedding.\n", "status": "active", "fields": [ "infectious-disease", "immunology", "gastroenterology" ], "color": "green" }, { "id": "h-lookahead-oed-reduces-experiments-to-target-yield", "type": "hypothesis", "title": "Lookahead Bayesian OED reaches target reaction-yield confidence with fewer robot experiments than greedy exploitation.", "status": "active", "fields": [ "chemistry", "statistics", "automation" ], "color": "green" }, { "id": "h-lorenz-attractor-seasonal-forecast-skill", "type": "hypothesis", "title": "The observed skill of subseasonal-to-seasonal (S2S) forecasts beyond the 2-week Lorenz predictability limit arises from the low-dimensional attractor of large-scale atmospheric modes (MJO, quasi-stationary waves) that have Lyapunov exponents 3-5x smaller than the full atmospheric attractor, predicting that S2S skill scales inversely with the MJO amplitude Lyapunov exponent (lambda_MJO ~ 0.15 per day).\n", "status": "active", "fields": [ "meteorology", "dynamical-systems", "climate-science" ], "color": "green" }, { "id": "h-lotka-volterra-hamiltonian-microcosm-conservation", "type": "hypothesis", "title": "In controlled Didinium-Paramecium microcosm experiments, the Lotka-Volterra Hamiltonian H is conserved to within 10% for the first 5-10 predator-prey cycles, after which deviations accumulate due to demographic stochasticity; the rate of H drift scales with N^{-1/2} (population size), confirming that Hamiltonian structure is broken by finite-population noise at a predictable rate.\n", "status": "active", "fields": [ "ecology", "mathematics" ], "color": "green" }, { "id": "h-lotka-volterra-informed-feedback-control-delays-phage-resistance-dominance", "type": "hypothesis", "title": "Transferred methods from `b-lotka-volterra-competition-x-phage-bacteria-chemostat-control` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "microbiology", "mathematics" ], "color": "green" }, { "id": "h-lotka-volterra-semiconductor-capex-cycle", "type": "hypothesis", "title": "Semiconductor capital expenditure cycles (3-5 year period) are consistent with Lotka-Volterra predator-prey oscillations where equipment spending (predator) feeds on demand signals (prey), with LV-predicted period T ≈ 2π/√(αγ) matching observed cycle periods within 20%.\n", "status": "active", "fields": [ "economics", "ecology", "industrial-dynamics" ], "color": "green" }, { "id": "h-lotka-volterra-x-game-theory", "type": "hypothesis", "title": "Rock-paper-scissors intransitive competition between three bacterial species follows replicator dynamics with oscillation period predicted by Lotka-Volterra cycle frequency, and spatial structure extends coexistence time beyond well-mixed predictions\n", "status": "active", "fields": [ "mathematics", "ecology", "evolutionary-biology" ], "color": "green" }, { "id": "h-lottery-ticket-sparse-subnetwork-universality", "type": "hypothesis", "title": "Winning lottery ticket subnetworks are not random sparse subsets of the full network but occupy a specific geometric region of the loss landscape — near a flat manifold of global minima — and the same winning tickets emerge independently across multiple training runs with different random seeds, demonstrating reproducibility of sparsity structure.\n", "status": "active", "fields": [ "mathematics", "machine-learning", "computer-science" ], "color": "green" }, { "id": "h-low-rank-hessian-surrogate-predicts-two-state-phi-profile-class", "type": "hypothesis", "title": "Among single-domain folders under 120 residues, proteins classified two-state by phi-value experiments yield ≥30% lower effective Hessian rank (95% variance captured in top r eigenmodes of ENM at native structure) than multi-state folders matched for length.\n", "status": "active", "fields": [ "structural-biology", "applied-mathematics" ], "color": "green" }, { "id": "h-lstm-gating-stat-mech-phase-transition", "type": "hypothesis", "title": "Trained LSTMs solving long-memory tasks exhibit bimodal forget-gate distributions concentrated near 0 and 1, consistent with a first-order phase transition between ordered (remembering) and disordered (forgetting) phases, with transition sharpness predicting task-specific memory horizon", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "statistical-mechanics", "machine-learning" ], "color": "green" }, { "id": "h-lyapunov-constrained-antibiotic-cycling-reduces-resistance-and-relapse", "type": "hypothesis", "title": "Lyapunov-constrained antibiotic cycling lowers resistance prevalence and clinical relapse compared with fixed-interval cycling.", "status": "active", "fields": [ "control-engineering", "medicine" ], "color": "green" }, { "id": "h-magma-fragmentation-deborah-threshold", "type": "hypothesis", "title": "Magma fragmentation in explosive eruptions is triggered when the Deborah number De = η / (G_∞ * τ_deform) exceeds a universal threshold of approximately 0.01, and this threshold can be measured in real-time from seismic velocity changes in the shallow volcanic conduit during eruption precursors", "status": "active", "fields": [ "volcanology", "fluid-mechanics", "geophysics" ], "color": "green" }, { "id": "h-magnetar-rapid-rotation-dynamo", "type": "hypothesis", "title": "Rapid proto-neutron star rotation during convective dynamo amplification generates extreme magnetic fields in magnetars when spin period at birth is less than 5 milliseconds", "status": "active", "fields": [ "astronomy", "physics" ], "color": "green" }, { "id": "h-magnons-spin-wave-collective-excitations", "type": "hypothesis", "title": "Magnon Bose-Einstein condensation in YIG driven by microwave pumping will show a critical pumping power threshold that scales as P_c ∝ T^(5/2), consistent with Bose-Einstein statistics for a 3D magnon gas with quadratic dispersion", "status": "active", "fields": [ "condensed-matter", "quantum-mechanics", "materials-science" ], "color": "green" }, { "id": "h-majorana-topological-qubit-decoherence", "type": "hypothesis", "title": "Majorana zero modes in semiconductor-superconductor nanowire devices provide topological protection that extends qubit coherence time by at least one order of magnitude compared to conventional superconducting qubits operating at the same temperature, provided the topological gap exceeds 100 μeV and the system length exceeds 5 coherence lengths", "status": "active", "fields": [ "quantum-computing", "condensed-matter", "topology" ], "color": "green" }, { "id": "h-manifold-hypothesis-m1-latent-dynamics-decoder-generalisation", "type": "hypothesis", "title": "Motor cortex population activity during reaching lies on a low-dimensional (d ~ 6-12) smooth manifold embedded in the full neural state space, and neuroprosthetic decoders trained to operate in this manifold subspace show >2x improvement in robustness to neuron loss and inter-session non-stationarity compared to full-space Kalman filter decoders.\n", "status": "active", "fields": [ "neuroprosthetics", "computational-neuroscience", "biomedical-engineering" ], "color": "green" }, { "id": "h-mantle-convection-670km-intermittent", "type": "hypothesis", "title": "Mantle convection operates in an intermittent regime near the 660 km phase boundary — episodically layered for 100–200 Myr intervals punctuated by catastrophic avalanche events — and the geoid and heat flow record shows the ~200 Myr periodicity of these avalanche events.\n", "status": "active", "fields": [ "mantle-dynamics", "geodynamics", "seismology" ], "color": "green" }, { "id": "h-mantle-rheology-x-viscoelasticity", "type": "hypothesis", "title": "A Burgers viscoelastic model (Maxwell + Kelvin-Voigt in series) fits global postseismic GPS deformation time series from the 2011 Tohoku-Oki earthquake significantly better than a Maxwell model, revealing a transient viscosity 2-5x lower than steady-state viscosity in the asthenosphere.\n", "status": "active", "fields": [ "geodynamics", "mineral-physics", "geophysics", "materials-science" ], "color": "green" }, { "id": "h-marcus-inverted-region-biological-electron-transfer", "type": "hypothesis", "title": "Photosynthetic primary charge separation operates in the Marcus activationless regime (ΔG° ≈ -λ) with near-unit quantum efficiency because natural selection has tuned the reorganization energy λ to match the free energy drop ΔG°, and artificial photovoltaic systems that replicate this Marcus optimization will achieve comparable quantum efficiencies.\n", "status": "active", "fields": [ "physical-chemistry", "biophysics", "chemistry", "materials-science" ], "color": "green" }, { "id": "h-marcus-tunneling-x-enzyme-reaction-coordinate", "type": "hypothesis", "title": "Driving-force sweeps on engineered enzyme mutants will collapse inverted-region curvature predicted by Marcus λ onto independently inferred tunneling distances within uncertainty when PCET models share the same collective coordinate dimensionality across solvents.\n", "status": "active", "fields": [ "biochemistry", "physical-chemistry" ], "color": "green" }, { "id": "h-marine-ice-sheet-instability-threshold", "type": "hypothesis", "title": "West Antarctic Ice Sheet grounding lines retreat irreversibly once they advance into retrograde bedrock regions exceeding a threshold ocean warming of 0.5 degrees C above present, triggering Marine Ice Sheet Instability that commits 3-5 m of sea level rise on century timescales regardless of subsequent emissions reductions.\n", "status": "active", "fields": [ "glaciology", "fluid-mechanics", "climate-science" ], "color": "green" }, { "id": "h-market-crash-turbulent-transition", "type": "hypothesis", "title": "Financial market crashes are preceded by a measurable shift in the multifractal spectrum toward turbulent-intermittency signatures", "status": "active", "fields": [], "color": "green" }, { "id": "h-markov-gating-graph-consistency-with-kramers-scaling-under-voltage-clamp-protocols", "type": "hypothesis", "title": "Global voltage-clamp datasets will admit hidden Markov graphs whose transition rates obey Kramers-like exponential voltage dependence after marginalizing hidden fast modes — falsified if datasets systematically require non-Kramers fractional dynamics across labs once instrumentation artifacts removed.\n", "status": "active", "fields": [ "biophysics", "chemistry" ], "color": "green" }, { "id": "h-markov-jump-therapy-policies-reduce-relapse-prone-cell-state-occupancy", "type": "hypothesis", "title": "Methods transferred from `b-markov-jump-processes-x-cell-state-switching-therapy-design` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "stochastic-processes", "oncology" ], "color": "green" }, { "id": "h-martingale-ecological-pricing", "type": "hypothesis", "title": "Ecosystem services in an informationally efficient natural capital market would follow martingale price processes; observed non-martingale behaviour (systematic underpricing) quantifies the information deficit — the gap between the market's public channel and the full ecological information channel capacity.\n", "status": "active", "fields": [ "economics", "information-theory", "ecology", "environmental-science", "finance", "policy-science" ], "color": "green" }, { "id": "h-masked-autoencoder-pretraining-improves-cryo-em-low-snr-reconstruction", "type": "hypothesis", "title": "Masked-autoencoder pretraining improves low-SNR cryo-EM reconstruction fidelity while preserving structural plausibility checks.", "status": "active", "fields": [ "infectious-disease", "structural-biology", "machine-learning" ], "color": "green" }, { "id": "h-maxent-invasive-species-prediction", "type": "hypothesis", "title": "MaxEnt species distribution models trained on pre-invasion occurrence data can predict the realised invasion range of established invasive species with better accuracy than the native range alone, because the MaxEnt distribution encodes the fundamental niche without dispersal constraints present in the native range.\n", "status": "active", "fields": [ "ecology", "statistics", "conservation-biology" ], "color": "green" }, { "id": "h-maxent-nonequilibrium-statistical-mechanics", "type": "hypothesis", "title": "Maximum caliber (MaxCal) — the path-entropy generalization of Jaynes's MaxEnt — is the correct variational principle for non-equilibrium steady states, and recovers fluctuation theorems as its extremal conditions.\n", "status": "active", "fields": [ "statistical-mechanics", "information-theory", "non-equilibrium-thermodynamics" ], "color": "green" }, { "id": "h-maxmin-eu-ambiguity-aversion-amygdala", "type": "hypothesis", "title": "Amygdala activity during ambiguous decision-making scales with the spread of the mental set C of possible priors rather than with mean expected value uncertainty, implementing the minimax regret computation of Gilboa-Schmeidler maxmin expected utility\n", "status": "active", "fields": [ "economics", "cognitive-science" ], "color": "green" }, { "id": "h-maxwell-wave-channel-capacity-limit", "type": "hypothesis", "title": "The physical degrees of freedom of a Maxwell wave field in a finite volume set a hard electromagnetic Shannon capacity limit that cannot be exceeded by any modulation scheme, antenna geometry, or signal processing algorithm.\n", "status": "active", "fields": [ "electromagnetism", "information-theory", "communications-engineering" ], "color": "green" }, { "id": "h-may-stability-real-ecosystem-applicability", "type": "hypothesis", "title": "May's random matrix stability criterion σ√(SC) < 1 applies to real ecosystems in a statistical sense: communities near the instability threshold show higher variance in abundance dynamics and higher extinction probability, detectable as elevated eigenvalue spectral radius in empirical interaction matrices\n", "status": "active", "fields": [ "ecology", "mathematics", "statistical-physics" ], "color": "green" }, { "id": "h-mca-summation-theorem-distributed-cancer-target", "type": "hypothesis", "title": "In cancer metabolic networks, the summation theorem of MCA (ΣCⁱⱼ = 1) implies that no single enzyme controls the full glycolytic flux; therefore, combination therapies targeting 3-4 low-FCC enzymes simultaneously will be more effective than single high-FCC target strategies, because high-FCC enzymes are compensated by network rewiring after single-agent treatment.\n", "status": "active", "fields": [ "cancer-metabolism", "pharmacology", "systems-biology" ], "color": "green" }, { "id": "h-mdl-scientific-theory-selection", "type": "hypothesis", "title": "Scientific theories selected by expert consensus across the history of physics have systematically shorter description lengths (lower Kolmogorov complexity) than their rejected competitors, validating Occam's razor as a measurable selection pressure in science independent of predictive accuracy.\n", "status": "active", "fields": [ "philosophy-of-science", "information-theory", "mathematics", "history-of-science" ], "color": "green" }, { "id": "h-mdr-asymptote-polymer-mechanism", "type": "hypothesis", "title": "The maximum drag reduction (MDR) asymptote in turbulent pipe flow with polymer additives represents a physical bound set by the elastic energy storage in stretched polymer chains damping near-wall vortex structures, and cannot be exceeded without fundamentally altering the turbulent cascade.\n", "status": "active", "fields": [ "fluid-mechanics", "polymer-physics", "turbulence" ], "color": "green" }, { "id": "h-mean-field-theory-x-neural-networks", "type": "hypothesis", "title": "Networks initialized at the mean-field edge-of-chaos critical point (chi=1) train successfully to arbitrary depth while networks initialized in the ordered (chi<1) or chaotic (chi>1) phases fail due to vanishing or exploding gradients, with the failure depth scaling as 1/|1-chi|.\n", "status": "active", "fields": [ "machine-learning", "statistical-mechanics", "mathematics", "deep-learning" ], "color": "green" }, { "id": "h-measurement-problem-decoherence-einselection", "type": "hypothesis", "title": "Einselection (environment-induced superselection) solves the quantum measurement problem by selecting pointer states robust to environmental monitoring, making apparent wavefunction collapse a consequence of unitary evolution rather than an additional postulate — empirically testable via mesoscopic coherence beyond Caldeira-Leggett decoherence timescales.\n", "status": "active", "fields": [ "physics", "quantum-mechanics", "philosophy-of-science", "condensed-matter-physics" ], "color": "green" }, { "id": "h-measurement-representational-theory-psychometrics", "type": "hypothesis", "title": "Psychometric measurements quantify genuine psychological attributes only when the attribute satisfies the conditions of the representational theory of measurement — specifically, double cancellation (a form of orderability and additivity) — which most current psychological constructs fail, making ordinal ranking the appropriate default rather than interval scaling.\n", "status": "active", "fields": [ "philosophy-of-science", "statistics", "psychology", "measurement-theory" ], "color": "green" }, { "id": "h-mechanism-design-algorithmic-markets", "type": "hypothesis", "title": "VCG auction mechanisms remain approximately DSIC (regret within 5% of truthful bidding) for ML-based bidding agents trained with standard no-regret learning algorithms, even after 1000 repeated auction rounds, because the dominant strategy is a fixed point of the learning dynamics.\n", "status": "active", "fields": [ "economics", "computer-science", "mathematics" ], "color": "green" }, { "id": "h-mechanism-design-spectrum-auctions-efficiency", "type": "hypothesis", "title": "VCG mechanism design achieves 95%+ of theoretical optimal social welfare in spectrum auctions when bidders have complementary valuations", "status": "active", "fields": [ "mechanism-design", "economics", "operations-research" ], "color": "green" }, { "id": "h-mechanosensing-x-force-transduction", "type": "hypothesis", "title": "Talin rod domain mechanosensing operates via a catch-bond mechanism where substrate stiffness above a threshold (5 kPa) stabilises the talin-vinculin interaction by extending the force application time, implementing stiffness-dependent bistable switching", "status": "active", "fields": [ "biology", "physics", "biophysics", "cell-biology" ], "color": "green" }, { "id": "h-meg-sparse-inverse-solution-epilepsy", "type": "hypothesis", "title": "Compressed sensing sparse inverse solutions for MEG source localization will outperform minimum-norm estimates for focal epileptic spike sources, achieving spatial localization accuracy within 5 mm of intracranial EEG ground truth in >80% of patients when the sparsity prior is calibrated to the number of simultaneously active sources estimated from ICA decomposition", "status": "active", "fields": [ "neuroscience", "mathematics", "clinical-medicine" ], "color": "green" }, { "id": "h-membrane-defects-protein-clustering", "type": "hypothesis", "title": "Integral membrane proteins act as topological defects in the lipid bilayer liquid crystal order field, and defect-defect interactions mediated by Frank elastic energy drive protein clustering into lipid raft domains without requiring direct protein-protein binding.\n", "status": "active", "fields": [ "biophysics", "condensed-matter-physics", "cell-biology" ], "color": "green" }, { "id": "h-membrane-tension-x-laplace-pressure", "type": "hypothesis", "title": "Cortical tension asymmetry during cell division follows the Young-Laplace equation quantitatively, and the cleavage furrow position can be predicted from cortical tension measurements to within 10% of cell diameter in rounded HeLa cells\n", "status": "active", "fields": [ "biology", "physics", "biophysics" ], "color": "green" }, { "id": "h-meme-channel-social-media-bias", "type": "hypothesis", "title": "Social media recommendation algorithms reduce the effective diversity of cultural transmission by introducing systematic, emotionally-biased channel noise that amplifies content below the channel capacity for factual information while exceeding the channel capacity for emotionally charged content — measurable as a reduction in the effective Shannon entropy of consumed information.\n", "status": "active", "fields": [ "social-science", "information-theory", "cultural-evolution", "communication-theory" ], "color": "green" }, { "id": "h-memory-augmented-seir-improves-forecast-turning-points", "type": "hypothesis", "title": "Memory-augmented SEIR models improve turning-point forecast timing compared with Markov SEIR baselines.", "status": "active", "fields": [ "epidemiology", "mathematics", "forecasting" ], "color": "green" }, { "id": "h-mems-high-cue-fungi-mineral-soc-stabilization-warming", "type": "hypothesis", "title": "Soils with fungal-dominated microbial communities (higher CUE ~0.5 vs. bacterial CUE ~0.3) accumulate ≥40% more mineral-associated organic carbon per unit C input under simulated +3°C warming than bacterial-dominated soils, confirming the MEMS framework prediction that high-CUE necromass production offsets warming-induced respiration increases.\n", "status": "active", "fields": [ "ecology", "soil-science", "microbiology", "climate-science" ], "color": "green" }, { "id": "h-mercury-orbit-chaotic-diffusion-eccentricity", "type": "hypothesis", "title": "Mercury's orbital eccentricity undergoes chaotic diffusion with a Lyapunov time of 5 ± 1 Myr (consistent with Laskar 1989), and full GR correction reduces the probability of Mercury eccentricity exceeding 0.6 within 5 Gyr from ~1% to <0.3%.\n", "status": "active", "fields": [ "celestial-mechanics", "astronomy" ], "color": "green" }, { "id": "h-merger-tree-branching-matches-subhalo-statistics-scaling", "type": "hypothesis", "title": "Across standardized halo catalogs from identical N-body snapshots, merger-tree branching statistics rescale coherently with SUBFIND-identified subhalo mass functions within algorithm-dependent systematic bands — falsified if branch-rate variance across builders exceeds subhalo-count variance by >30% at matched mass thresholds on matched simulations.\n", "status": "active", "fields": [ "cosmology", "computational-science" ], "color": "green" }, { "id": "h-metabolic-control-analysis-x-local-sensitivity", "type": "hypothesis", "title": "Sobol total-order indices computed on genome-scale kinetic models will rank enzymes differently than MCA flux control coefficients beyond ±30% perturbation bands — exposing domains where elasticity-local summaries mislead therapeutic targeting prioritization.\n", "status": "active", "fields": [ "systems-biology", "mathematics" ], "color": "green" }, { "id": "h-metabolic-exponent-network-dimension-prediction", "type": "hypothesis", "title": "The metabolic scaling exponent β = d/(d+1) where d is the fractal dimension of the resource-distribution network, measured from vascular/tracheal fractal analysis, will predict the empirical β across 10 major animal phyla better than the universal 3/4 constant (R² improvement >0.15).\n", "status": "active", "fields": [ "physiology", "physics" ], "color": "green" }, { "id": "h-metabolic-flux-x-linear-programming", "type": "hypothesis", "title": "Metabolic flux objective functions shift from biomass maximization to ATP-per- carbon maximization under carbon limitation, and this shift is detectable by comparing 13C MFA flux distributions to FBA predictions under different C:N ratios, falsifying single-objective FBA models.\n", "status": "active", "fields": [ "systems-biology", "microbiology", "mathematics", "biochemistry" ], "color": "green" }, { "id": "h-metabolic-network-hub-essentiality-scaling", "type": "hypothesis", "title": "Metabolic hub degree follows a universal power law across all domains of life, and hub essentiality correlates with preferential attachment age measured by phylogenetic depth\n", "status": "active", "fields": [ "biology", "mathematics", "network_science", "systems_biology" ], "color": "green" }, { "id": "h-metabolic-scaling-3-4-fractal-derivation", "type": "hypothesis", "title": "Organisms with non-space-filling transport networks (e.g., insect tracheal systems) should show metabolic scaling exponents significantly below 3/4, testable via comparative respirometry", "status": "active", "fields": [ "ecology", "thermodynamics", "physiology" ], "color": "green" }, { "id": "h-metacognition-prefrontal-hierarchical", "type": "hypothesis", "title": "Metacognitive access to one's own cognitive states is implemented in anterior prefrontal cortex (area 10) as a hierarchically higher-order prediction: the brain predicts its own prediction errors, and metacognitive sensitivity (meta-d') is determined by the signal-to-noise ratio of this second-order prediction signal.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "computational-neuroscience" ], "color": "green" }, { "id": "h-metacommunity-intermediate-dispersal-diversity", "type": "hypothesis", "title": "Metacommunity diversity is maximised at an intermediate dispersal rate that balances local competitive exclusion against regional rescue effects, following a hump-shaped curve analogous to the intermediate disturbance hypothesis; below this optimum dispersal rate, local communities are dominated by stochastic drift, and above it by mass effects that homogenise species composition across patches.\n", "status": "active", "fields": [ "ecology", "mathematics", "biogeography" ], "color": "green" }, { "id": "h-metadynamics-collective-variables-protein-allostery", "type": "hypothesis", "title": "Metadynamics simulations using graph-neural-network-derived collective variables will identify the allosteric communication pathways in therapeutic targets (kinases, GPCRs) inaccessible to conventional MD, enabling structure-based design of allosteric inhibitors with 100-fold selectivity improvements over orthosteric drugs.\n", "status": "active", "fields": [ "computational-chemistry", "biophysics", "drug-discovery" ], "color": "green" }, { "id": "h-metamaterial-sub-diffraction-limit", "type": "hypothesis", "title": "Negative-index metamaterials (NIMs) cannot achieve sub-diffraction imaging at optical frequencies in practical systems because evanescent wave amplification is overwhelmed by absorption losses (Im(ε) > 0.1 at visible frequencies) before the resolution exceeds λ/4, making the perfect lens hypothesis unachievable without fundamentally new material platforms.\n", "status": "active", "fields": [ "photonics", "nanophotonics", "optical-physics" ], "color": "green" }, { "id": "h-metaphor-abstract-thought-embodied-simulation", "type": "hypothesis", "title": "Abstract thought is fundamentally grounded in sensorimotor metaphor: activating motor imagery of physical metaphor enactments (GRASPING an idea, HEAVY thoughts) facilítates abstract reasoning, and disrupting motor cortex via TMS during metaphor comprehension degrades understanding of novel but not literal abstract sentences.\n", "status": "active", "fields": [ "cognitive-science", "linguistics", "neuroscience" ], "color": "green" }, { "id": "h-metaphor-embodied-conceptual-grounding", "type": "hypothesis", "title": "Abstract thought is grounded in embodied sensorimotor metaphor — BOLD activation of primary sensorimotor cortex during abstract language processing provides evidence for conceptual metaphor theory over amodal symbol systems", "status": "active", "fields": [ "cognitive-linguistics", "cognitive-neuroscience", "philosophy-of-mind" ], "color": "green" }, { "id": "h-metaphor-universality-embodied-grounding", "type": "hypothesis", "title": "Conceptual metaphors grounded in universal embodied experiences (UP=GOOD, WARM=FRIENDLY, BRIGHT=KNOWN) are cross-linguistically universal, while metaphors grounded in culturally specific experiences (TIME=MONEY, ARGUMENT=WAR) vary across languages, and this distinction is detectable in the correlational structure of translation equivalents across 100+ languages.\n", "status": "active", "fields": [ "linguistics", "cognitive-science", "cross-cultural-psychology" ], "color": "green" }, { "id": "h-metaphor-universality-spatial-embodiment-constraint", "type": "hypothesis", "title": "Spatial-orientation conceptual metaphors (MORE IS UP, TIME IS SPACE, AFFECTION IS WARMTH) are universal across cultures because they arise from invariant sensorimotor contingencies, while event-structure metaphors are culturally variable", "status": "active", "fields": [ "cognitive-linguistics", "cross-cultural-psychology", "anthropology" ], "color": "green" }, { "id": "h-metapopulation-capacity-climate-refugia-network", "type": "hypothesis", "title": "Designing protected area networks to maximise metapopulation capacity λ_M, rather than total protected area, will extend time to extinction for fragmented species under climate change by 2-5× because λ_M directly determines regional persistence probability, while area alone ignores connectivity and patch configuration.\n", "status": "active", "fields": [ "conservation-biology", "landscape-ecology", "applied-mathematics" ], "color": "green" }, { "id": "h-metapopulation-connectivity-predicts-spillover-r0", "type": "hypothesis", "title": "Landscape connectivity metrics derived from metapopulation incidence-function model parameters predict cross-species pathogen spillover risk (R₀_spillover) with accuracy comparable to individual-level contact network surveillance in fragmented tropical forest margins\n", "status": "active", "fields": [ "epidemiology", "ecology", "landscape-ecology", "mathematical-biology" ], "color": "green" }, { "id": "h-metasurface-flat-lens-diffraction-limited-visible", "type": "hypothesis", "title": "Silicon titanium dioxide (TiO₂) metasurface metalenses at visible wavelengths (532 nm) can achieve diffraction-limited focusing efficiency > 80% and Strehl ratio > 0.8 at numerical apertures > 0.9 using geometric phase (Pancharatnam-Berry) encoding, outperforming equivalent-aperture refractive lenses while reducing thickness by 3 orders of magnitude.\n", "status": "active", "fields": [ "photonics", "nanophotonics", "imaging" ], "color": "green" }, { "id": "h-mete-non-equilibrium-deviations", "type": "hypothesis", "title": "Systematic deviations of empirical species abundance distributions from METE predictions are quantitatively predicted by non-equilibrium relaxation dynamics, with the magnitude of deviation decaying as a power law in time since disturbance", "status": "draft", "fields": [ "macroecology", "statistical-mechanics", "information-theory" ], "color": "green" }, { "id": "h-methane-clathrate-destabilization-threshold", "type": "hypothesis", "title": "Seafloor methane clathrate destabilisation on continental margins requires bottom water warming >3°C above preindustrial levels before producing climatically significant atmospheric methane flux; current trajectories remain below this threshold until at least 2150 for the Arctic shelf.\n", "status": "active", "fields": [ "geology", "chemistry", "physical-oceanography", "climate-science" ], "color": "green" }, { "id": "h-microbial-cue-warming-feedback-carbon-cycle", "type": "hypothesis", "title": "A 1 degree C increase in mean soil temperature reduces global mean microbial carbon use efficiency by 2-4 percentage points, releasing an additional 30-50 Pg C from soils by 2100 relative to current Earth System Model projections that assume temperature-independent CUE.\n", "status": "active", "fields": [ "ecology", "thermodynamics", "climate-science" ], "color": "green" }, { "id": "h-microbial-fuel-cell-anodic-electron-transfer", "type": "hypothesis", "title": "Overexpressing the outer-membrane cytochrome MtrCAB complex in Shewanella oneidensis MR-1 combined with a three-dimensional porous graphene foam anode will increase maximum current density by at least 5-fold relative to flat-carbon anodes by reducing electron-transfer resistance below 10 ohm-cm^2", "status": "active", "fields": [ "biotechnology", "electrochemistry", "microbiology" ], "color": "green" }, { "id": "h-microbial-iron-reduction-sediment-carbon-preservation", "type": "hypothesis", "title": "Microbial iron reduction rates in marine sediments are the primary control on organic carbon preservation efficiency, with faster Fe(III) reduction correlating with greater carbon mineralization and lower carbon burial efficiency across continental margin settings\n", "status": "active", "fields": [ "microbiology", "geochemistry" ], "color": "green" }, { "id": "h-microbiome-diversity-host-resilience", "type": "hypothesis", "title": "Shannon diversity of the gut microbiome is a causal determinant of host resilience to dietary perturbation, antibiotic exposure, and enteric pathogen challenge — such that microbiomes with H > 3 bits recover baseline composition within 14 days of perturbation while microbiomes with H < 2 bits undergo permanent compositional shift, analogous to ecological alternative stable states.\n", "status": "active", "fields": [ "ecology", "microbiology", "medicine", "gastroenterology" ], "color": "green" }, { "id": "h-microbiome-functional-redundancy-antibiotic-resilience", "type": "hypothesis", "title": "Gut microbiome communities with butyrate-pathway functional redundancy index (FRI > 0.6, defined as the fraction of butyrate production contributed by the top-3 producing taxa out of all butyrate producers) will recover to pre-antibiotic composition within 30 days after a 7-day broad-spectrum antibiotic course in >80% of healthy adults.\n", "status": "active", "fields": [ "microbiology", "ecology" ], "color": "green" }, { "id": "h-microglial-synaptic-pruning-depression", "type": "hypothesis", "title": "Microglia-mediated excess synaptic pruning in the prefrontal cortex is a causal mechanism in treatment-resistant depression that is partially reversible by anti-inflammatory intervention", "status": "active", "fields": [ "medicine", "neuroscience" ], "color": "green" }, { "id": "h-microplastic-nanofiltration-membrane-fouling-tradeoff", "type": "hypothesis", "title": "Nanofiltration and tight ultrafiltration membranes (molecular weight cut-off <1 kDa) can remove >99.9% of microplastics and nanoplastics from water but face a fundamental permeability-selectivity trade-off: achieving nanoplastic removal at low energy penalty requires antifouling surface chemistry that reduces pore blockage from the plastic fragments themselves.\n", "status": "active", "fields": [ "chemistry", "environmental-engineering", "materials-science", "water-treatment" ], "color": "green" }, { "id": "h-microseismic-b-value-universal-failure-precursor", "type": "hypothesis", "title": "A b-value drop below 1.1 (from baseline ~1.5) is a universal precursor of imminent failure in both laboratory AE experiments and field microseismic monitoring, with a median lead time of 10-100 times the inter-event interval at the observation scale, providing a scalable warning criterion from centimetre laboratory specimens to kilometre-scale rock masses.\n", "status": "active", "fields": [ "geophysics", "materials-science", "engineering" ], "color": "green" }, { "id": "h-milankovitch-nonlinear-resonance-100kyr", "type": "hypothesis", "title": "The dominant 100 kyr glacial cycle arises from nonlinear resonance between the 100 kyr eccentricity forcing and the ice-sheet's internal ~100 kyr relaxation oscillation, not from direct eccentricity-driven insolation — a subharmonic locking mechanism analogous to driven nonlinear oscillators", "status": "active", "fields": [ "astronomy", "climate-science", "paleoclimatology", "dynamical-systems" ], "color": "green" }, { "id": "h-mind-wandering-episodic-simulation", "type": "hypothesis", "title": "Mind wandering serves an episodic future simulation function: spontaneous thought preferentially generates prospective (future-oriented) narrative scenarios rather than past retrieval, and individuals with richer and more specific future simulations during mind wandering show better prospective memory and goal-directed behavior.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "psychology" ], "color": "green" }, { "id": "h-mineral-nucleation-prenucleation-clusters", "type": "hypothesis", "title": "Classical nucleation theory fails for geological mineral formation because prenucleation clusters (stable ion assemblages below the critical nucleus size) provide a two-step nucleation pathway that lowers the free energy barrier by 50–80%, explaining the observed orders-of-magnitude discrepancy between predicted and observed induction times.\n", "status": "active", "fields": [ "mineralogy", "geochemistry", "crystal-growth" ], "color": "green" }, { "id": "h-minimax-regret-pandemic-intervention", "type": "hypothesis", "title": "Minimax regret optimal stopping rules for epidemic intervention are more robust than expected-utility-maximizing rules when R0 uncertainty exceeds 20%, testable by retrospective analysis of COVID-19 intervention timing across 50 countries", "status": "active", "fields": [ "epidemiology", "mathematics", "public-health" ], "color": "green" }, { "id": "h-minimum-phase-plants-attain-tighter-bode-bounds", "type": "hypothesis", "title": "For a fixed unstable plant pole set, minimum-phase designs achieve strictly better achievable sensitivity templates than any non-minimum-phase embedding with identical loop gain budget — standard theorem package but worth restating as a falsifiable design audit checklist for cyber-physical retrofits.\n", "status": "active", "fields": [ "control-engineering", "mathematics" ], "color": "green" }, { "id": "h-minority-game-hft-phase-transition", "type": "hypothesis", "title": "High-frequency trading (HFT) proliferation shifted equity markets across the minority game phase transition from the inefficient (α < α_c) to efficient (α > α_c) regime, measurable as a decrease in autocorrelation of order flow and reduction in bid-ask spread predictability after 2005 HFT adoption.\n", "status": "active", "fields": [ "financial-physics", "market-microstructure", "complex-systems" ], "color": "green" }, { "id": "h-minority-game-quasispecies-duality", "type": "hypothesis", "title": "The minority game disordered-to-ordered transition and the quasispecies error threshold are dual descriptions of the same information-theoretic phase transition, with shared critical exponents", "status": "active", "fields": [], "color": "green" }, { "id": "h-minority-game-x-market-microstructure", "type": "hypothesis", "title": "Order flow autocorrelation C(τ) = ⟨sign(v_t) sign(v_{t+τ})⟩ transitions from positive (exploitable, predictable order flow) to near-zero (efficient) as algorithmic trader fraction increases past a threshold of ~34% of total volume, matching the minority game α_c prediction", "status": "active", "fields": [ "economics", "physics", "mathematics" ], "color": "green" }, { "id": "h-mirror-neuron-dance-therapy", "type": "hypothesis", "title": "Dance/movement therapy achieves clinical benefits in depression and Parkinson's disease through mirror-system-mediated motor resonance — specifically, the therapeutic effect is proportional to the degree of kinematic mirroring between therapist and patient, and is abolished when kinematic matching is prevented.\n", "status": "active", "fields": [ "art-and-cognition", "neuroscience", "clinical-psychology", "rehabilitation" ], "color": "green" }, { "id": "h-misinformation-emotional-valence-persistence", "type": "hypothesis", "title": "Misinformation is more persistent than corrections because emotional arousal (fear, outrage, disgust) during initial exposure creates stronger memory consolidation and motivated reasoning to reject corrections — the asymmetry is greatest when corrections threaten partisan identity and cannot be eliminated by accuracy framing alone.\n", "status": "active", "fields": [ "social-science", "cognitive-science", "psychology", "communication" ], "color": "green" }, { "id": "h-mismatch-negativity-bayesian-precision-prediction-error", "type": "hypothesis", "title": "The amplitude of the cortical mismatch negativity (MMN) response scales linearly with the precision-weighted prediction error (σ⁻² × ΔP) predicted by the Rao-Ballard model, where σ² is the stimulus variability in the context window and ΔP is the prior-minus-likelihood deviation.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science" ], "color": "green" }, { "id": "h-missingness-augmented-lstm-models-improve-icu-decompensation-horizon-accuracy", "type": "hypothesis", "title": "Explicit missingness encoding in LSTM models improves ICU decompensation horizon accuracy versus simple imputation baselines.", "status": "active", "fields": [ "critical-care", "computer-science", "physiology" ], "color": "green" }, { "id": "h-mitochondrial-pmf-stochastic-efficiency", "type": "hypothesis", "title": "ATP synthase operates near its stochastic thermodynamic efficiency limit: fluctuation theorems predict that the distribution of single-cycle efficiencies must satisfy eta * P(eta) / P(-eta) = exp(eta * Delta_S / k_B), and experimental single-molecule data will confirm that mean efficiency is within 15% of the Delta_p-set theoretical maximum under physiological conditions.\n", "status": "active", "fields": [ "biophysics", "thermodynamics", "cell-biology" ], "color": "green" }, { "id": "h-mixed-fem-for-hodge-laplace-matches-dec-upwind-schemes", "type": "hypothesis", "title": "On a standardized linear elasticity mixed-FEM benchmark, a DEC-based discretization with a commuting projection will match RT/BDFMS accuracy within 5% on energy norm for a sequence of refined meshes.", "status": "active", "fields": [ "engineering", "mathematics" ], "color": "green" }, { "id": "h-mixing-parameter-matches-posterior-sparsity-stability-curves", "type": "hypothesis", "title": "In correlated-design simulations, elastic-net settings expressed as Laplace-Gaussian prior-scale ratios will produce smoother sparsity-stability curves than raw l1_ratio grids; falsified if bootstrap selection instability is unchanged across equivalent predictive error bands.\n", "status": "active", "fields": [ "statistics", "machine-learning" ], "color": "green" }, { "id": "h-miyake-event-wiggle-matching-year-precision", "type": "hypothesis", "title": "Wiggle-matching 14C measurements from tree ring sequences including solar particle events (Miyake events at 774 CE and 993 CE) provides calendar year precision for Hallstatt plateau samples when 3+ samples span the event", "status": "active", "fields": [ "archaeology", "nuclear-physics", "mathematics" ], "color": "green" }, { "id": "h-ml-accelerated-corrosion-inhibitor-discovery", "type": "hypothesis", "title": "Machine learning models trained on DFT-computed adsorption energies and molecular descriptors can predict organic corrosion inhibitor efficiency with sufficient accuracy to reduce experimental screening effort by an order of magnitude", "status": "active", "fields": [ "electrochemistry", "computational-chemistry", "materials-science", "machine-learning" ], "color": "green" }, { "id": "h-ml-directed-evolution-navigates-epistatic-fitness-landscape", "type": "hypothesis", "title": "Machine learning models trained on deep mutational scanning (DMS) data can predict the fitness of multi-mutation combinations with accuracy sufficient to identify the global fitness maximum in a protein landscape with up to 5 simultaneous mutations, replacing 3-5 rounds of directed evolution with a single round of computational screening.\n", "status": "active", "fields": [ "biochemistry", "chemistry", "machine-learning", "evolutionary-biology", "computational-chemistry" ], "color": "green" }, { "id": "h-modern-hopfield-transformer-attention-equivalence", "type": "hypothesis", "title": "The mathematical equivalence between modern Hopfield network updates and scaled dot-product attention implies that transformer attention heads implement content-addressable associative memory retrieval, and that the number of \"memorized facts\" in a transformer's attention layers scales exponentially with the key/query dimension d — testable via probing and capacity saturation experiments.\n", "status": "active", "fields": [ "neuroscience", "machine-learning", "mathematics", "memory-theory" ], "color": "green" }, { "id": "h-modular-architecture-robustness-evolvability", "type": "hypothesis", "title": "Biological gene regulatory networks with higher modularity index (ratio of within-module to between-module interaction strength) exhibit both higher mutational robustness (canalization) and higher evolvability (rate of adaptive phenotypic change per generation), demonstrating that modularity resolves — rather than trades off — the robustness-evolvability dilemma.\n", "status": "active", "fields": [ "evolutionary-biology", "systems-biology", "engineering", "developmental-biology" ], "color": "green" }, { "id": "h-mof-sorbent-approaches-dac-thermodynamic-limit", "type": "hypothesis", "title": "Metal-organic framework (MOF) sorbents with CO₂ binding enthalpy in the 30-50 kJ/mol range and fast diffusion kinetics can achieve DAC energy consumption below 100 kJ/mol CO₂ — within 5× of the thermodynamic minimum — using low-grade heat (60-80°C) for regeneration.\n", "status": "active", "fields": [ "chemical-engineering", "thermodynamics", "materials-science" ], "color": "green" }, { "id": "h-molecular-motor-efficiency-fluctuation-theorem", "type": "hypothesis", "title": "Kinesin thermodynamic efficiency is determined by the ratio of the forward ATP hydrolysis rate to the Crooks-mandated reverse rate, and motors operating far from stall maximise power output rather than efficiency due to the fluctuation theorem constraint", "status": "active", "fields": [ "biophysics", "statistical-physics", "cell-biology" ], "color": "green" }, { "id": "h-molecular-motor-near-equilibrium-operation", "type": "hypothesis", "title": "Biological molecular motors operating at near-stall force achieve efficiency approaching the isothermal Carnot limit (ΔG_chemical → W_mechanical) because the free energy landscape is designed for reversible power strokes, and deviation from near-equilibrium operation at high velocity is the primary source of efficiency loss in myosin and kinesin.\n", "status": "active", "fields": [ "biophysics", "mechanical-engineering", "thermodynamics", "nanotechnology" ], "color": "green" }, { "id": "h-molecular-spectroscopy-x-matrix-diagonalization", "type": "hypothesis", "title": "Anharmonic corrections up to fourth order in curvilinear coordinates will reduce out-of-sample IR peak prediction error by a fixed margin relative to harmonic-only diagonalization for a benchmark set of small hydrogen-bonded clusters — with error correlated to condition number of the mass-weighted Hessian.\n", "status": "active", "fields": [ "physical-chemistry", "applied-mathematics" ], "color": "green" }, { "id": "h-monsoon-aerosol-bifurcation-tipping-point", "type": "hypothesis", "title": "The South Asian summer monsoon has a fold bifurcation structure in the aerosol-SST parameter space such that sustained anthropogenic aerosol loading could shift the system to a permanently weakened stable state below a critical threshold, with hysteresis preventing recovery even after emission reductions", "status": "active", "fields": [ "atmospheric-science", "nonlinear-dynamics", "climate-science", "fluid-dynamics" ], "color": "green" }, { "id": "h-monsoon-aerosol-bifurcation-tipping", "type": "hypothesis", "title": "The South Asian summer monsoon has a saddle-node bifurcation point controlled by the land-ocean temperature gradient; sustained aerosol cooling of South Asia reduces this gradient, and if aerosol optical depth exceeds a critical threshold AOD_c~0.5 over northern India, the monsoon shifts to a permanently weakened state that cannot recover even after aerosol removal.\n", "status": "active", "fields": [ "atmospheric-science", "climate-dynamics", "nonlinear-dynamics", "earth-system-science" ], "color": "green" }, { "id": "h-morphogenesis-x-mechanical-instability", "type": "hypothesis", "title": "Primary sulcus positions are mechanically determined by spatial variations in cortex stiffness (set by molecular gradients), with secondary and tertiary folds arising from purely mechanical instabilities of the initial folded geometry — making morphogenesis a two-stage mechanical-molecular process", "status": "active", "fields": [ "biology", "physics", "neuroscience", "developmental-biology" ], "color": "green" }, { "id": "h-morse-homology-x-conley-index-isolated-invariants", "type": "hypothesis", "title": "Certified cubical homology pipelines enclosing experimental Poincaré maps from stirred fluid experiments will recover Conley-index signatures stable under measurement noise below validated Lipschitz envelopes — enabling reproducible topology tags across laboratories sharing enclosure scripts.\n", "status": "draft", "fields": [ "dynamical-systems", "computational-topology" ], "color": "green" }, { "id": "h-morse-theory-x-energy-landscape", "type": "hypothesis", "title": "Persistent homology 1-cycles (H₁ Betti number) of protein energy landscapes computed from MD trajectories predict the number of distinct folding intermediates with false negative rate < 5% when sampling density exceeds 100 transitions per identified metastable basin", "status": "active", "fields": [ "mathematics", "physics", "chemistry", "computational-biology" ], "color": "green" }, { "id": "h-motives-feynman-amplitudes-arithmetic", "type": "hypothesis", "title": "All Feynman amplitudes in renormalizable quantum field theories (phi^4, QED, QCD) evaluate to periods of mixed Tate motives over the integers, predicting that every divergent Feynman integral evaluates after dimensional regularization to a Q-linear combination of multiple zeta values (MZVs) at all loop orders.\n", "status": "active", "fields": [ "mathematics", "quantum-field-theory", "number-theory" ], "color": "green" }, { "id": "h-motivic-cohomology-algorithmic-computation", "type": "hypothesis", "title": "Motivic cohomology groups H^{p,q}_M(X,ℤ) of smooth projective varieties X over a number field are algorithmically computable for p ≤ 2 via algebraic K-theory and the Bloch-Kato conjecture (now Voevodsky's theorem), but p ≥ 3 cases remain computationally open due to the failure of finite generation.\n", "status": "active", "fields": [ "algebraic-geometry", "algebraic-K-theory", "number-theory" ], "color": "green" }, { "id": "h-motor-cortex-rotational-dynamics-initial-condition-mechanism", "type": "hypothesis", "title": "Motor cortex preparatory activity sets the initial condition of a rotational dynamical system (dx/dt = Ax, A skew-symmetric) that generates the muscle activation pattern as a temporal readout — causally required for movement, not epiphenomenal, and generalisable across voluntary movement types beyond arm reaching.\n", "status": "active", "fields": [ "neuroscience", "mathematics", "motor-control", "biomedical-engineering" ], "color": "green" }, { "id": "h-mpc-with-ngm-constraints-reduces-epidemic-overshoot", "type": "hypothesis", "title": "Adaptive intervention policies that explicitly constrain projected NGM spectral radius reduce incidence overshoot versus threshold-only trigger rules under equal intervention budgets.\n", "status": "active", "fields": [ "epidemiology", "control-engineering", "network-science" ], "color": "green" }, { "id": "h-mri-turbulence-alpha-prediction", "type": "hypothesis", "title": "The Shakura-Sunyaev α parameter in accretion disks scales as α ∝ Pm^{1/2} (where Pm = ν/η is the magnetic Prandtl number), a scaling derivable from the balance between MRI channel mode growth and parasitic Kelvin-Helmholtz instability at the resistive scale.\n", "status": "active", "fields": [ "astrophysics", "magnetohydrodynamics", "plasma-physics", "fluid-dynamics" ], "color": "green" }, { "id": "h-mrna-vaccine-lipid-nanoparticle-durability", "type": "hypothesis", "title": "The limited durability of mRNA vaccine-induced immunity is primarily caused by rapid LNP clearance from the injection site limiting germinal centre residence time, and slow-release LNP formulations or self-amplifying RNA will extend immunity duration by at least 3-fold in humans.\n", "status": "active", "fields": [ "vaccine-science", "immunology", "drug-delivery" ], "color": "green" }, { "id": "h-multi-shell-dmri-estimates-track-phantom-tortuosity", "type": "hypothesis", "title": "In porous phantoms with independently measured tortuosity, multi-shell diffusion MRI models will rank-order effective tortuosity with Spearman rho at least 0.7 after correcting for orientation dispersion; falsified if rankings are indistinguishable from single-shell ADC.\n", "status": "active", "fields": [ "medical-imaging", "physics" ], "color": "green" }, { "id": "h-multi-wavelength-beer-lambert-inverse-improves-plate-precision", "type": "hypothesis", "title": "Ridge-regularized multi-wavelength inversion of overlapping dye spectra reduces cross-well concentration RMSE versus single-wavelength calibration on multiplex microplate benchmarks — falsified if RMSE gains fall below 15% on curated dye-interference panels.\n", "status": "active", "fields": [ "analytical-biochemistry", "statistics" ], "color": "green" }, { "id": "h-multiplicative-noise-pareto-exponent-capital-tax-rate", "type": "hypothesis", "title": "The Pareto wealth exponent α across OECD countries quantitatively follows α = 1 + (r − g)/σ²_r + τ/σ²_r (Bouchaud-Mézard formula with redistribution correction), with α predictable from independently measurable macro-financial parameters to within ±0.2, confirming multiplicative noise as the mechanistic driver of wealth inequality dynamics.\n", "status": "active", "fields": [ "economics", "statistical-mechanics", "social-science", "finance" ], "color": "green" }, { "id": "h-multiscale-filtration-persistence-improves-microscopy-segmentation-qc", "type": "hypothesis", "title": "Multiscale filtrations tuned to PSF-informed geometric scales yield persistence-based QC scores that correlate with human-expert segmentation failure rates under controlled noise injections.", "status": "active", "fields": [ "medical-imaging", "mathematics", "topology" ], "color": "green" }, { "id": "h-muscle-crossbridge-sliding-filament", "type": "hypothesis", "title": "Hypertrophic cardiomyopathy (HCM) mutations in myosin heavy chain increase the fraction of heads in the 'on' state (super-relaxed → disordered relaxed transition), shifting σ and elevating resting metabolic cost before symptoms appear", "status": "active", "fields": [ "biophysics", "cardiology", "molecular-biology" ], "color": "green" }, { "id": "h-muscle-mechanics-x-crossbridge-theory", "type": "hypothesis", "title": "A 6-state crossbridge model (pre-power stroke, power stroke, post-power stroke, three detached ATPase states) reproduces all mechanical transients in skeletal muscle within experimental error, and the rate constants are consistent with single-molecule optical trap measurements.\n", "status": "active", "fields": [ "biophysics", "cell-biology", "structural-biology", "physiology" ], "color": "green" }, { "id": "h-mycelial-network-mst-approximation", "type": "hypothesis", "title": "Mycelial transport networks of Phanerochaete velutina grown on agar plates connecting nutrient-source wood blocks will achieve total hyphal wire length within 10% of the Steiner minimum spanning tree while maintaining at least 2-connectivity (fault tolerance against single-edge deletion), and this Pareto-optimal structure will emerge without long-range signalling as shown by spatially ablating hyphal cords outside the growth front", "status": "active", "fields": [ "mycology", "mathematics", "network-science" ], "color": "green" }, { "id": "h-myelination-optimal-axon-diameter-conduction-velocity", "type": "hypothesis", "title": "The observed g-ratio distribution of myelinated axons across mammalian species and fibre types represents the solution to a constrained optimisation problem (maximise conduction velocity per unit axon volume) predicted by the cable equation, and deviations from g_optimal ≈ 0.6 reflect specific metabolic or developmental constraints that can be predicted quantitatively.\n", "status": "active", "fields": [ "neuroscience", "biophysics", "mathematics", "neurology" ], "color": "green" }, { "id": "h-myosin-brownian-ratchet-jarzynski", "type": "hypothesis", "title": "Jarzynski equality non-equilibrium work measurements will reveal that myosin II free energy transduction efficiency exceeds classical Carnot limit due to quantum tunneling corrections at physiological temperature\n", "status": "active", "fields": [ "biology", "physics", "biophysics", "statistical_mechanics" ], "color": "green" }, { "id": "h-narrative-situation-model-hippocampus-updated", "type": "hypothesis", "title": "Story comprehension constructs a multi-dimensional situation model (event, time, space, causality, character) in the hippocampus and cortical networks, with hippocampal pattern completion predicting event boundaries, and the default mode network continuously updating the situation model across narrative shifts", "status": "active", "fields": [ "cognitive-neuroscience", "linguistics", "cognitive-science", "memory" ], "color": "green" }, { "id": "h-narrative-situation-model-hippocampus", "type": "hypothesis", "title": "Narrative comprehension is implemented as incremental situation model construction in the hippocampus and parietal cortex, with the default mode network tracking narrative event boundaries and the angular gyrus integrating semantic and spatial dimensions of the mental model.\n", "status": "active", "fields": [ "cognitive-neuroscience", "linguistics", "memory-research", "narrative-psychology" ], "color": "green" }, { "id": "h-natural-gradient-selection-reaches-fitness-optimum-faster-than-euclidean", "type": "hypothesis", "title": "Natural selection following the Shahshahani (Fisher information metric) geometry reaches a fitness optimum in systematically fewer generations than a hypothetical \"Euclidean\" evolution process with the same gradient magnitude, and the speedup factor is quantitatively predicted by the condition number of the Fisher information matrix at the starting allele frequency.\n", "status": "active", "fields": [ "evolutionary-biology", "information-geometry", "population-genetics" ], "color": "green" }, { "id": "h-natural-language-mildly-context-sensitive-transformer-approximation", "type": "hypothesis", "title": "Human natural language belongs to the mildly context-sensitive class (tree-adjoining grammar, O(n⁶) parsing), and transformer LLMs with depth d and width w can approximate arbitrary MCSL languages with O(1/d) error, but cannot exactly compute any non-regular language with bounded-depth fixed-width architecture due to the O(log n) parallelism constraint on Boolean circuit depth.\n", "status": "active", "fields": [ "linguistics", "mathematics", "computer-science", "cognitive-science" ], "color": "green" }, { "id": "h-navier-stokes-rg-fixed-point-intermittency-exponents", "type": "hypothesis", "title": "The anomalous intermittency exponents ζ_p in 3D turbulence arise from the leading irrelevant operator at the K41 RG fixed point, and their values can be derived perturbatively in ε = 4 − d (d = dimension) using the DRG expansion, with the leading correction giving ζ_6 = 1.77 ± 0.05.\n", "status": "active", "fields": [ "fluid-mechanics", "statistical-physics", "mathematics" ], "color": "green" }, { "id": "h-negative-control-calibrated-estimators-reduce-pharmacovigilance-signal-bias", "type": "hypothesis", "title": "Negative-control-calibrated estimators reduce false-positive pharmacovigilance safety signals in observational cohorts.", "status": "active", "fields": [ "epidemiology", "statistics" ], "color": "green" }, { "id": "h-negative-heat-capacity-stellar-stability-criterion", "type": "hypothesis", "title": "Globular clusters with concentration parameter c > 2.0 (King model) are in post-core-collapse phase and their core radius distribution follows a log-normal consistent with gravothermal oscillations, distinguishable from the log-normal of pre-collapse clusters by a systematic offset in the core-halo luminosity ratio.\n", "status": "active", "fields": [ "astronomy", "statistical-physics", "stellar-dynamics" ], "color": "green" }, { "id": "h-nematic-confinement-fluctuation-second-order", "type": "hypothesis", "title": "Confinement of nematic liquid crystals in cylindrical pores below a critical diameter d* ~ 20-50 nm changes the isotropic-nematic transition from first-order to continuous (second-order) by enhancing orientational fluctuations that reduce the cubic Landau coefficient b to zero; this is detectable by calorimetry as disappearance of latent heat below d*.\n", "status": "active", "fields": [ "soft-matter", "statistical-physics" ], "color": "green" }, { "id": "h-neo-hookean-model-predicts-soft-actuator-90pct", "type": "hypothesis", "title": "Neo-Hookean hyperelastic FEM simulations predict soft pneumatic actuator tip displacement to within 10% of measured values across the full actuation pressure range, provided material constants are calibrated from uniaxial tensile tests on the same silicone batch\n", "status": "active", "fields": [ "engineering", "mechanics" ], "color": "green" }, { "id": "h-nestedness-generalist-removal-cascade", "type": "hypothesis", "title": "Removal of the most generalist species from nested mutualistic networks will trigger disproportionately large secondary extinction cascades compared to random or specialist removal, with cascade size predictable from the leading eigenvalue of the mutualistic interaction matrix", "status": "active", "fields": [ "ecology", "network-science", "conservation-biology" ], "color": "green" }, { "id": "h-nestedness-robustness-degree-heterogeneity-mediation", "type": "hypothesis", "title": "The robustness benefit of nestedness in mutualistic networks is fully mediated by degree heterogeneity: highly nested networks are more robust only because nestedness is mathematically correlated with degree heterogeneity, and when degree sequence is held constant via configuration model null models, nestedness provides no additional robustness beyond what degree heterogeneity predicts.\n", "status": "active", "fields": [ "ecology", "network-science", "statistical-physics", "conservation-biology" ], "color": "green" }, { "id": "h-network-assortativity-predicts-misinformation-spread-rate", "type": "hypothesis", "title": "The assortativity coefficient of political belief networks quantitatively predicts the within-community vs between-community information spread rate ratio, such that networks with assortativity r > 0.5 show misinformation spreading 10× faster within communities than between, creating measurable belief divergence within 30 days.\n", "status": "active", "fields": [ "social-science", "network-science", "computational-social-science", "political-science" ], "color": "green" }, { "id": "h-network-community-structure-drives-polarization", "type": "hypothesis", "title": "Political polarization in online social networks arises from community structure (high modularity) creating exponentially long-lived metastable states in voter-model dynamics, predictable by the ratio of within-community to between-community edges, and reducible by algorithmic feed diversification.\n", "status": "active", "fields": [ "social-science", "network-science", "political-science", "computational-social-science" ], "color": "green" }, { "id": "h-neural-architecture-search-x-evolutionary-biology", "type": "hypothesis", "title": "NAS fitness landscapes on benchmark tasks (NAS-Bench-201) exhibit ruggedness and neutrality statistics similar to protein fitness landscapes: >40% neutral single-step mutations, epistasis coefficient eta > 0.3, and multiple distinct fitness peaks corresponding to convergent architectural solutions.\n", "status": "active", "fields": [ "computer-science", "evolutionary-biology", "machine-learning", "systems-biology" ], "color": "green" }, { "id": "h-neural-avalanche-criticality-dynamic-range", "type": "hypothesis", "title": "The brain operates near a critical branching ratio (σ ≈ 1) because criticality maximises the dynamic range of response to sensory stimuli, and this criticality is homeostatically regulated by synaptic scaling — disruption of homeostatic plasticity shifts the system away from criticality and impairs sensory discrimination.\n", "status": "active", "fields": [ "computational-neuroscience", "systems-neuroscience", "statistical-physics" ], "color": "green" }, { "id": "h-neural-cde-models-improve-icu-event-lead-time", "type": "hypothesis", "title": "Neural CDE models improve clinically usable ICU event lead-time at fixed false-alert rate compared with interpolation-based baselines.", "status": "active", "fields": [ "critical-care", "machine-learning" ], "color": "green" }, { "id": "h-neural-diversity-stability-random-matrix-prediction", "type": "hypothesis", "title": "The stability of cortical neural circuits under random synaptic perturbation follows May's random matrix criterion — circuits with higher interneuron subtype diversity have lower effective interaction variance σ² and require stronger perturbation to destabilize, quantitatively matching theoretical predictions from the Wigner semicircle law.\n", "status": "active", "fields": [ "neuroscience", "ecology", "mathematics", "computational-neuroscience" ], "color": "green" }, { "id": "h-neural-ew-indicators-climate-tipping-transfer", "type": "hypothesis", "title": "Multivariate early-warning indicators borrowed from neural criticality analysis (leading eigenvector of the spatial covariance matrix) applied to CMIP6 climate model output will detect simulated AMOC and Arctic sea-ice tipping points with at least 20% greater lead time than the standard univariate AR1 indicator\n", "status": "active", "fields": [ "neuroscience", "climate-science", "statistical-physics", "dynamical-systems" ], "color": "green" }, { "id": "h-neural-manifold-geometry-encodes-cognitive-map", "type": "hypothesis", "title": "The intrinsic geometry (curvature, topology, metric tensor) of neural population activity manifolds in hippocampus and entorhinal cortex encodes a cognitive map of task-relevant variables whose topology matches the topology of the task environment", "status": "active", "fields": [ "computational-neuroscience", "systems-neuroscience", "topology", "cognitive-science" ], "color": "green" }, { "id": "h-neural-network-generalisation-implicit-bias", "type": "hypothesis", "title": "Overparameterised neural networks generalise due to implicit bias of gradient descent toward minimum-norm (maximum-margin) solutions — stochastic gradient descent selects the flattest loss basin, which corresponds to the most regular learned function", "status": "active", "fields": [ "machine-learning", "mathematics", "statistics" ], "color": "green" }, { "id": "h-neural-ode-lyapunov-stability-generalization", "type": "hypothesis", "title": "Neural ODEs with Lyapunov-stable vector fields generalize better than unstable ones, and adversarial examples correspond to initial conditions near Lyapunov function saddle points\n", "status": "active", "fields": [ "computer_science", "mathematics", "dynamical_systems", "machine_learning" ], "color": "green" }, { "id": "h-neural-ode-priors-improve-pk-state-forecasting", "type": "hypothesis", "title": "Embedding pharmacokinetic priors into neural ODE models improves sparse-sample drug concentration forecasting versus unconstrained sequence models.", "status": "active", "fields": [ "pharmacology", "machine-learning", "dynamical-systems" ], "color": "green" }, { "id": "h-neural-operator-assimilation-improves-space-weather-lead-time", "type": "hypothesis", "title": "Neural-operator surrogates coupled to assimilation improve space-weather warning lead time at fixed false-alarm rate.", "status": "active", "fields": [ "astronomy", "machine-learning", "space-physics" ], "color": "green" }, { "id": "h-neural-plasticity-x-hebbian-learning", "type": "hypothesis", "title": "Three-factor STDP rules (pre × post × dopamine reward signal) implement an unbiased estimator of the policy gradient in model-free reinforcement learning for spike timings within a 200ms eligibility trace window, enabling reward-modulated STDP to solve delayed-reward tasks impossible for two-factor Hebbian rules", "status": "active", "fields": [ "neuroscience", "computer_science", "biology" ], "color": "green" }, { "id": "h-neural-spectral-ocean-model-improves-submesoscale-forecast-skill", "type": "hypothesis", "title": "Neural spectral ocean surrogates improve 24-72 hour submesoscale forecast skill over reduced-physics baselines.", "status": "active", "fields": [ "oceanography", "machine-learning", "fluid-dynamics" ], "color": "green" }, { "id": "h-neural-spike-coding-x-information-compression", "type": "hypothesis", "title": "Retinal ganglion cell receptive field shapes are optimally tuned to the statistics of the specific natural scenes encountered during early visual development, and this tuning is lost when visual experience is restricted during the critical period\n", "status": "active", "fields": [ "neuroscience", "computer-science", "information-theory" ], "color": "green" }, { "id": "h-neurogenesis-requirement-ssri-antidepressant-human-evidence", "type": "hypothesis", "title": "Adult hippocampal neurogenesis is necessary but not sufficient for the antidepressant effect of SSRIs: genetic or radiation-induced ablation of neurogenesis will block SSRI efficacy in primate models of anhedonia/despair, and ketamine's rapid antidepressant effect (within hours) will be neurogenesis-independent.\n", "status": "active", "fields": [ "neuroscience", "psychiatry", "pharmacology", "molecular-biology" ], "color": "green" }, { "id": "h-neuroinflammation-depression-biomarker", "type": "hypothesis", "title": "CRP level above 3 mg/L identifies a biologically distinct depression subtype that responds to anti-inflammatory treatment but not to SSRIs, with effect size at least 0.5", "status": "active", "fields": [], "color": "green" }, { "id": "h-neuromorphic-chips-edge-ai-energy-advantage", "type": "hypothesis", "title": "Neuromorphic chips implementing spiking neural networks will achieve 100–1000× lower energy per inference than GPU-based neural networks for always-on edge AI tasks (keyword spotting, gesture recognition, anomaly detection) while maintaining competitive accuracy, making them the dominant architecture for IoT sensing applications by 2030\n", "status": "active", "fields": [ "neuromorphic-computing", "electrical-engineering", "machine-learning" ], "color": "green" }, { "id": "h-neuromorphic-sparse-coding-energy-bound", "type": "hypothesis", "title": "Neuromorphic computing achieves fundamental energy advantage over von Neumann architectures because sparse event-driven coding (biological spike rates ~1-10 Hz vs continuous 10^9 Hz clock cycles) reduces energy per inference by 10^3-10^5x, with the theoretical minimum set by Landauer's bound per synaptic event (~10^4 kT per spike in biological neurons, 10-100 kT in CMOS implementations).\n", "status": "active", "fields": [ "neuromorphic-computing", "neuroscience", "computer-architecture", "thermodynamics" ], "color": "green" }, { "id": "h-neuromuscular-size-principle-metabolic-optimality", "type": "hypothesis", "title": "Henneman's size principle (slow-twitch S motor units recruited before FR before FF) is the optimal recruitment strategy that minimises total metabolic energy expenditure per unit force-time integral across all submaximal contractions — and violations of orderly recruitment observed in some tasks (reverse recruitment) are predicted by the optimisation when metabolic cost includes task-specific penalty terms.\n", "status": "active", "fields": [ "neuroscience", "engineering", "biomechanics", "physiology" ], "color": "green" }, { "id": "h-neuronal-avalanche-driven-subcritical", "type": "hypothesis", "title": "The cortex operates in a driven, slightly subcritical regime (sigma ~ 0.98) rather than exactly at the critical point: this explains why in vivo avalanche exponents systematically deviate from mean-field directed percolation predictions and why dynamic range is near-maximal but not maximal, consistent with noise-robust information transmission rather than maximum susceptibility.\n", "status": "active", "fields": [ "neuroscience", "statistical-physics" ], "color": "green" }, { "id": "h-neuronal-avalanches-branching-process", "type": "hypothesis", "title": "In vivo Neuropixels recordings in awake mice will show state-dependent σ: wakefulness σ≈1 (critical), NREM sleep σ<0.8 (subcritical), and REM sleep σ≈1 — supporting state-dependent criticality rather than fixed operating point", "status": "active", "fields": [ "neuroscience", "statistical-physics", "sleep-science" ], "color": "green" }, { "id": "h-neutral-atom-fidelity-motional-decoherence-limit", "type": "hypothesis", "title": "Neutral atom qubit gate fidelities are primarily limited by motional-state decoherence during Rydberg blockade gates, and active motional-state cooling (sideband cooling to ground state) combined with magic wavelength optical tweezers will enable two-qubit gate fidelities ≥99.9% sufficient for fault-tolerant threshold.\n", "status": "active", "fields": [ "quantum-computing", "atomic-physics", "quantum-error-correction" ], "color": "green" }, { "id": "h-neutral-theory-x-stochastic-sampling", "type": "hypothesis", "title": "Neutral theory with immigration correction (full Poisson-Dirichlet likelihood) fits tropical forest SAD data from BCI with p > 0.05 (no significant deviation from neutral), while niche-based species energy theory is rejected by the species rank-abundance curve at p < 0.01 for the same dataset", "status": "active", "fields": [ "biology", "mathematics", "ecology" ], "color": "green" }, { "id": "h-neutral-theta-estimates-converge-pre-post-gap-chronosequence", "type": "hypothesis", "title": "For replicated forest plots where neutral θ is fitted from closed-canopy census versus gap-phase recruits separately, estimates will converge within bootstrap confidence intervals when gap disturbance homogenizes competitive asymmetry — falsified if θ diverges systematically across gap ages despite comparable sampling depth.\n", "status": "active", "fields": [ "forest-ecology", "theoretical-ecology" ], "color": "green" }, { "id": "h-neutron-star-quark-crossover-2-solar-mass", "type": "hypothesis", "title": "A smooth quark-hadron crossover (rather than first-order phase transition) in neutron star cores is consistent with 2 M_sun pulsars and NICER M-R constraints, and will be distinguishable from hadronic-only EOS by future 0.3 km radius precision", "status": "active", "fields": [ "astrophysics", "nuclear-physics" ], "color": "green" }, { "id": "h-neutron-star-x-nuclear-matter", "type": "hypothesis", "title": "Gravitational wave post-merger spectral peaks from neutron star binary coalescences (f₂ ≈ 2.5-3.5 kHz) will show a discontinuous frequency jump as a function of total binary mass at M_total = 2.7±0.2 solar masses, signaling a first-order quark-hadron phase transition in the merger remnant", "status": "active", "fields": [ "physics", "astrophysics", "nuclear-physics" ], "color": "green" }, { "id": "h-nfs-rsa-concrete-security-boundary", "type": "hypothesis", "title": "The number field sieve factorization record will reach RSA-2048 (2048-bit modulus) within 15 years using classical computing, requiring approximately 10^18 core-hours based on extrapolation of the L_n[1/3, 1.923] NFS complexity formula to current hardware trends, and this estimate will be confirmed to within a factor of 3 by the next three record factorizations of 1000–1800 bit numbers", "status": "active", "fields": [ "mathematics", "computer-science", "cryptography" ], "color": "green" }, { "id": "h-niche-construction-accelerated-local-adaptation", "type": "hypothesis", "title": "In earthworm-invaded soil plots in northern North America, the rate of local plant community genetic adaptation (measured by common garden fitness differences between invaded and uninvaded source populations) will be significantly higher than in matched plots without earthworms, demonstrating that niche construction accelerates genetic evolution on decadal timescales", "status": "active", "fields": [ "ecology", "evolutionary-biology", "genetics" ], "color": "green" }, { "id": "h-nmr-rotating-frame-x-effective-hamiltonian", "type": "hypothesis", "title": "Lindblad-augmented differentiable simulators predicting measurable deviations from Magnus-average Hamiltonians will outperform closed-system pulse optimizers on relaxation-heavy benchtop samples — measurable via randomized benchmarking fidelity deltas exceeding instrument noise floors.\n", "status": "active", "fields": [ "magnetic-resonance", "quantum-control" ], "color": "green" }, { "id": "h-noether-symmetry-breaking-new-physics", "type": "hypothesis", "title": "Every experimentally observed violation of a known conservation law implies a new broken symmetry — systematic search for conservation law violations (CP violation, baryon asymmetry, lepton number) should be interpreted as Noether's theorem pointing to undiscovered symmetry structure.\n", "status": "active", "fields": [ "theoretical-physics", "particle-physics", "cosmology", "mathematical-physics" ], "color": "green" }, { "id": "h-nonhelical-resonator-adiabatic-quantum-memory", "type": "hypothesis", "title": "Cryogenic high-Q non-helical resonators driven with reversible control protocols can exhibit measured energy per irreversible bit-reset approaching order-of-magnitude Landauer k_B T ln(2) once loss channels are separated — excluding stronger claims without calibrated erasure accounting.\n", "status": "active", "fields": [ "electromagnetism", "reversible-computing", "thermodynamics-of-computation" ], "color": "green" }, { "id": "h-nonhelical-turing-cloaking-adaptation", "type": "hypothesis", "title": "Gradient-parameter non-helical resonator arrays may exhibit spatially organized high-Q mode clusters whose dominant spacing responds to insulation gradient strength — enabling adaptive scattering/beam-shaping hypotheses — but “adaptive cloaking without external control” remains speculative until instability physics is validated.\n", "status": "active", "fields": [ "electromagnetism", "metamaterials", "developmental-biology" ], "color": "green" }, { "id": "h-nonstandard-arithmetic-peano-independence", "type": "hypothesis", "title": "The standard model of Peano arithmetic (ℕ) is not first-order definable among all models of PA — every non-standard model satisfies the same first-order sentences as ℕ — and the model-theoretic properties that distinguish ℕ (e.g., \"well-founded\") are second-order statements that cannot be captured in the first-order setting.\n", "status": "active", "fields": [ "mathematical-logic", "model-theory", "proof-theory" ], "color": "green" }, { "id": "h-norm-cascade-ising-ew", "type": "hypothesis", "title": "The same-sex marriage opinion shift in the United States (1990-2015) shows Ising early-warning indicators — rising AR1 and variance in annual Gallup support data — with scaling exponents consistent with the mean-field Ising universality class (nu=1/2), identifying a measurable social tipping point near 2011-2012.\n", "status": "active", "fields": [ "social-science", "statistical-physics", "political-science" ], "color": "green" }, { "id": "h-nose-hoover-chains-match-target-kinetic-spectra-when-tuned", "type": "hypothesis", "title": "On a peptide folding benchmark, BAOAB Langevin splitting at moderate friction will reproduce reference conformational populations with lower timestep bias than explicit Euler–Maruyama at equal cost.", "status": "active", "fields": [ "chemistry", "mathematics" ], "color": "green" }, { "id": "h-ntk-deep-learning-kernel-regression", "type": "hypothesis", "title": "In the NTK (lazy training) regime, the generalization performance of deep neural networks is fully determined by the eigenspectrum of the Neural Tangent Kernel matrix K_∞ on the training set, and networks exceeding the NTK regime gain additional generalization through feature learning that cannot be captured by any fixed kernel — making the NTK an upper bound on what kernel methods can achieve.\n", "status": "active", "fields": [ "mathematics", "machine-learning", "functional-analysis" ], "color": "green" }, { "id": "h-nucleation-two-step-protein-crystal", "type": "hypothesis", "title": "Two-step nucleation via a dense liquid precursor phase accounts for >50% of protein crystallization events, with classical nucleation theory underestimating rates by 3-7 orders of magnitude due to precursor-mediated barriers\n", "status": "active", "fields": [ "chemistry", "physics", "statistical_mechanics", "structural_biology" ], "color": "green" }, { "id": "h-nucleation-two-step-spinodal", "type": "hypothesis", "title": "Classical nucleation theory fails by 10-20 orders of magnitude because nucleation in many systems proceeds via a two-step mechanism — initial spinodal decomposition to a dense disordered precursor phase, followed by crystallization within the precursor — and accounting for this pathway resolves the discrepancy.\n", "status": "active", "fields": [ "chemistry", "physics", "materials-science", "biophysics" ], "color": "green" }, { "id": "h-numerical-mantle-spectral-peaks-track-effective-rb-wavenumber-branches", "type": "hypothesis", "title": "In suites of published mantle-convection simulations where Ra and Pr are reported with rheological laws held fixed, spherical harmonic peaks of lateral kinetic energy will shift to higher ℓ when effective Ra enters branches predicted by high-Ra RB marginal-stability sketches — falsified if peaks remain invariant while Ra spans orders of magnitude because stiff lids decouple interior spectra.\n", "status": "active", "fields": [ "geodynamics", "fluid-mechanics" ], "color": "green" }, { "id": "h-nussinov-energy-approximates-planar-graph-parsimony", "type": "hypothesis", "title": "For a curated viral RNA benchmark with experimentally resolved pseudoknots, restricted treewidth-k extensions will recover substantially more base pairs than pure planar DP at comparable runtime budgets.", "status": "active", "fields": [ "biology", "computer-science" ], "color": "green" }, { "id": "h-ocean-color-chlorophyll-inversion-accuracy", "type": "hypothesis", "title": "A physics-based semi-analytic ocean color inversion algorithm trained on PACE hyperspectral bands (340–900 nm) will retrieve mixed-layer chlorophyll-a concentration with RMSE < 30% across open-ocean Case-1 waters, outperforming the OC4 band-ratio algorithm in all matchup comparisons at > 0.1 mg/m^3", "status": "active", "fields": [ "oceanography", "optics", "remote-sensing" ], "color": "green" }, { "id": "h-ohio-lyme-deer-management-intervention", "type": "hypothesis", "title": "Reducing white-tailed deer density below 15 deer/km² in Hocking Hills and Wayne National Forest areas of southeastern Ohio will reduce Ixodes scapularis nymphal tick density by >50% within 5 years, thereby reducing human Lyme disease risk in the region.\n", "status": "active", "fields": [ "ecology", "epidemiology", "public-health", "wildlife-management", "climate-science" ], "color": "green" }, { "id": "h-onsager-machlup-loop-expansion-qft-thermal-field-theory", "type": "hypothesis", "title": "The loop expansion of stochastic path integrals around Onsager-Machlup saddle points is term-by-term identical to the loop expansion of the corresponding thermal quantum field theory (with ℏ → kT), providing a computationally tractable alternative to Feynman diagram perturbation theory for strongly-coupled systems where the saddle- point approximation breaks down.\n", "status": "active", "fields": [ "mathematical-physics", "statistical-mechanics", "stochastic-analysis", "quantum-field-theory" ], "color": "green" }, { "id": "h-onsager-reciprocity-cross-price-elasticity-symmetry", "type": "hypothesis", "title": "Onsager reciprocity for coupled thermodynamic transport — L_ij = L_ji — maps exactly onto Slutsky symmetry of the compensated demand matrix in consumer theory, implying that violations of Slutsky symmetry in empirical demand data correspond to non-equilibrium market conditions with measurable entropy production rates\n", "status": "active", "fields": [ "economics", "thermodynamics", "econophysics", "mathematical-economics" ], "color": "green" }, { "id": "h-open-science-preregistration-replication-incentives", "type": "hypothesis", "title": "Sustainable open science adoption requires changing journal publication incentives to reward Registered Reports (preregistration before data collection) over outcome-contingent publication — current voluntary preregistration has low uptake because it penalises null results in tenure and funding decisions.\n", "status": "active", "fields": [ "philosophy-of-science", "social-science", "scientometrics", "research-policy" ], "color": "green" }, { "id": "h-optical-soliton-fiber-communication-stability", "type": "hypothesis", "title": "The fundamental optical soliton is the most stable carrier for high-bit-rate, long-haul fiber communication because its nonlinear self-correction provides inherent immunity to small dispersion perturbations, and soliton-based transmission should outperform linear NRZ modulation for fiber spans exceeding 5,000 km at bit rates above 40 Gbit/s", "status": "active", "fields": [ "physics", "mathematics", "engineering" ], "color": "green" }, { "id": "h-optimal-transport-determines-city-structure-spatial-equilibrium", "type": "hypothesis", "title": "The spatial structure of cities (commuting patterns, residential sorting, land price gradients) is well-approximated by the solution to an optimal transport problem ΓÇö workers minimize expected commuting cost subject to housing supply constraints ΓÇö making Wasserstein distance between job and residence distributions a predictive measure of urban welfare and inequality.\n", "status": "active", "fields": [ "economics", "urban-economics", "mathematics", "social-science" ], "color": "green" }, { "id": "h-optimal-transport-lineage-couplings-improve-fate-prediction-calibration", "type": "hypothesis", "title": "Cost-validated transport couplings improve held-out cell-fate calibration versus pseudotime-only baselines.", "status": "active", "fields": [ "systems-biology", "statistics", "genomics" ], "color": "green" }, { "id": "h-optimal-transport-waddington-landscape-riemannian-geodesic", "type": "hypothesis", "title": "Waddington's epigenetic landscape is formally the Riemannian manifold of the 2-Wasserstein space restricted to the gene expression simplex, and cell fate decision points correspond to geodesically conjugate points where multiple W₂ geodesics first intersect — regions of positive Wasserstein curvature detectable from static single-cell RNA-seq snapshots.\n", "status": "active", "fields": [ "mathematics", "developmental-biology", "optimal-transport", "genomics", "stochastic-processes" ], "color": "green" }, { "id": "h-optimal-transport-x-machine-learning", "type": "hypothesis", "title": "Sliced Wasserstein distance with k = O(d log d) random projections achieves O(1/√d) approximation error relative to W₂ in d dimensions, making it computationally viable for high-dimensional generative model evaluation with strictly better mode-collapse detection than FID", "status": "active", "fields": [ "mathematics", "computer_science", "statistics" ], "color": "green" }, { "id": "h-optogenetic-restoration-vision-scales-to-complex-percepts", "type": "hypothesis", "title": "Optogenetic vision restoration in retinitis pigmentosa will scale from detection of motion and high-contrast objects (demonstrated 2021) to recognition of faces and reading text within 5 years, as improved opsins (faster kinetics, red-shifted sensitivity), higher density transduction, and adaptive goggles are combined.\n", "status": "active", "fields": [ "gene-therapy", "ophthalmology", "neuroscience", "biomedical-engineering" ], "color": "green" }, { "id": "h-optogenetics-x-control-theory", "type": "hypothesis", "title": "Model predictive control with 10-step neural circuit prediction horizon achieves 3× better gamma oscillation control compared to proportional feedback control using ChR2 (τ_off = 10 ms), by compensating for actuator delay with state prediction from fitted Wilson-Cowan model", "status": "active", "fields": [ "neuroscience", "computer_science", "engineering", "control-theory" ], "color": "green" }, { "id": "h-orch-or-quantum-consciousness-decoherence-timescale-refutes", "type": "hypothesis", "title": "Direct measurement of quantum decoherence timescales in neuronal microtubules at 310 K will confirm Tegmark's theoretical prediction (~10⁻¹³ s), 10 orders of magnitude shorter than neural processing timescales (~10⁻³ s), falsifying the Penrose-Hameroff Orch-OR hypothesis that microtubule quantum coherence mediates conscious experience.\n", "status": "active", "fields": [ "quantum-physics", "neuroscience", "biophysics", "consciousness-studies" ], "color": "green" }, { "id": "h-order-book-square-root-impact-universal-liquidity", "type": "hypothesis", "title": "The square-root market impact law ΔP ∝ σ√(Q/V_daily) is a universal consequence of limit order book liquidity replenishment dynamics — independent of asset class, market, or time period — and deviations from the square-root scaling predict impending liquidity crises when the exponent drops below 0.5.\n", "status": "active", "fields": [ "econophysics", "finance", "physics", "mathematics" ], "color": "green" }, { "id": "h-organ-chip-multi-organ-body-on-chip-systemic-toxicity", "type": "hypothesis", "title": "A fluidically coupled 4-organ (liver-heart-lung-kidney) body-on-a-chip system will predict drug-induced systemic toxicity with higher accuracy than any single organ-on-a-chip or animal model because inter-organ metabolite exchange is required to reproduce the hepatic-cardiac toxicity cascade that causes 50% of late-stage clinical drug failures.\n", "status": "active", "fields": [ "bioengineering", "pharmacology", "cell-biology" ], "color": "green" }, { "id": "h-organ-on-chip-predicts-drug-toxicity-better-than-animal-models", "type": "hypothesis", "title": "Human organ-on-chip systems (liver chip, kidney chip, gut chip) will predict human drug toxicity and pharmacokinetics more accurately than rodent animal models for a defined panel of drugs that failed in human trials due to organ toxicity ΓÇö a testable validation claim that could transform drug development.\n", "status": "active", "fields": [ "biomedical-engineering", "pharmacology", "drug-development", "toxicology" ], "color": "green" }, { "id": "h-organic-template-polymorph-selection", "type": "hypothesis", "title": "Acidic shell proteins lower the interfacial energy of aragonite relative to calcite by a quantifiable amount predictable from protein charge density, enabling polymorph selection to be engineered via rational peptide design.\n", "status": "active", "fields": [ "materials-science", "structural-biology", "biomineralisation" ], "color": "green" }, { "id": "h-organoid-cortical-lamination-validity", "type": "hypothesis", "title": "Cerebral organoids recapitulate the transcriptional trajectory of human cortical development (neuroepithelium → radial glia → cortical neurons, inside-out lamination) but systematically fail to model areal specification and thalamic input because they lack vascularization and extrinsic instructive signals — making them valid models for cell-intrinsic developmental programs but not for circuit-level or areal questions.\n", "status": "active", "fields": [ "neuroscience", "developmental-biology", "stem-cell-biology" ], "color": "green" }, { "id": "h-origami-math-x-structural-engineering", "type": "hypothesis", "title": "Inverse origami design via adjoint-method optimization can find crease patterns achieving target stiffness tensors to within 5% error for any mechanically realizable target within the Miura-ori family, with solution uniqueness guaranteed when the target lies in the interior of the achievable stiffness manifold", "status": "active", "fields": [ "mathematics", "physics", "engineering" ], "color": "green" }, { "id": "h-origami-robotic-fabrication-fold-complexity", "type": "hypothesis", "title": "Robotic origami fabrication using visual servoing and force-feedback control can reliably execute computationally-designed crease patterns with up to 1,000 creases, with fold-position error < 0.5 mm, enabling engineering-grade deployable structure fabrication from flat sheet metal at production scales currently achievable only by manual origami artists", "status": "active", "fields": [ "engineering", "mathematics", "robotics" ], "color": "green" }, { "id": "h-ostrom-commons-multilateral-failure", "type": "hypothesis", "title": "Multilateral cooperation arrangements for global commons fail when they violate more than three of Ostrom's eight design principles simultaneously; the minimum sufficient set of principles for international commons governance requires monitoring, graduated sanctions, and recognition of rights to organize — and failure rate is predictable from which principles are absent.\n", "status": "active", "fields": [ "social-science", "economics", "political-science", "ecology" ], "color": "green" }, { "id": "h-ostrom-design-principles-digital-commons", "type": "hypothesis", "title": "Ostrom's eight institutional design principles apply to digital commons with one systematic modification: boundary definition (principle 1) should be replaced by contribution-reputation systems that create functional membership boundaries without geographic or legal exclusion, and this reformulation will predict open-source project sustainability with > 75% accuracy.\n", "status": "active", "fields": [ "ecology", "social-science", "information-science", "economics" ], "color": "green" }, { "id": "h-ot-barycenter-alignment-improves-cross-cohort-multiomic-risk-stratification", "type": "hypothesis", "title": "Methods transferred from `b-optimal-transport-barycenters-x-multiomic-patient-alignment` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "statistics", "systems-biology" ], "color": "green" }, { "id": "h-ot-bias-correction-improves-tail-risk-calibration", "type": "hypothesis", "title": "OT-based downscaling improves calibration of high-quantile precipitation risk relative to quantile-mapping baselines.", "status": "active", "fields": [ "climate-science", "statistics", "risk-modeling" ], "color": "green" }, { "id": "h-outside-option-effect-causal-wage-effect", "type": "hypothesis", "title": "The outside option effect in wage bargaining is causal and quantitatively significant: workers who receive competing job offers capture 50-80% of the wage difference as a wage increase at their current employer, consistent with Nash bargaining theory in which the outside option shifts the disagreement point.\n", "status": "active", "fields": [ "economics", "labor-economics", "game-theory" ], "color": "green" }, { "id": "h-ozone-recovery-timeline-ssst-interaction", "type": "hypothesis", "title": "Antarctic ozone recovery to 1980 levels will occur by 2065-2070 for the spring column, but full recovery of the polar vortex ozone-loss altitude profile will lag by 10-15 years due to the residual stratospheric circulation response to past ODS loading, and warming-driven SST changes may partially offset CFC decline benefits before 2050.\n", "status": "active", "fields": [ "atmospheric-chemistry", "climate-science", "stratospheric-physics" ], "color": "green" }, { "id": "h-p-vs-np-algebraic-geometry-barrier-v2", "type": "hypothesis", "title": "Algebraic geometry and geometric complexity theory (GCT, Mulmuley & Sohoni) provides the most promising current framework for P≠NP separation through polynomial permanent vs determinant orbit closure complexity, but faces fundamental barriers including the symmetry hypothesis (SH) requiring new techniques in invariant theory beyond current capabilities", "status": "active", "fields": [ "theoretical-computer-science", "algebraic-geometry", "representation-theory", "mathematics" ], "color": "green" }, { "id": "h-p-vs-np-algebraic-geometry-barrier", "type": "hypothesis", "title": "The P≠NP conjecture will not be resolved by purely combinatorial or information- theoretic arguments; resolution requires fundamentally new algebraic geometry or quantum-information theoretic tools, because all known lower bound techniques (natural proofs, algebrization, relativization) are blocked by known barriers.\n", "status": "active", "fields": [ "computational-complexity", "algebraic-geometry", "mathematics", "theoretical-computer-science" ], "color": "green" }, { "id": "h-pagerank-spectral-gap-spam-detection", "type": "hypothesis", "title": "The spectral gap of the Google matrix, not alpha, is the fundamental parameter controlling PageRank spam resistance; link farms create near-zero spectral gap exploits predictable from Markov chain theory\n", "status": "active", "fields": [ "computer_science", "mathematics", "network_science" ], "color": "green" }, { "id": "h-pain-gate-parvalbumin-interneuron-molecular", "type": "hypothesis", "title": "The inhibitory \"gate\" in Melzack-Wall gate control theory of pain is molecularly implemented by parvalbumin-expressing glycinergic interneurons in lamina II (substantia gelatinosa) of the dorsal horn, whose activity is suppressed by C-fiber input (opening the gate) and enhanced by Aβ-fiber tactile input (closing the gate).\n", "status": "active", "fields": [ "neuroscience", "pain-research", "molecular-neuroscience" ], "color": "green" }, { "id": "h-pain-sex-difference-microglia-spinal-cord", "type": "hypothesis", "title": "Sex differences in chronic pain prevalence (2:1 female:male ratio) are mechanistically driven by sexually dimorphic spinal cord microglia: male rodents use microglia-dependent P2X4→BDNF→KCC2 signaling for pain sensitization, while females use T-cell-mediated and astrocytic pathways, predicting differential responses to microglia-targeting analgesics", "status": "active", "fields": [ "neuroscience", "immunology", "pain-science", "sex-differences-biology" ], "color": "green" }, { "id": "h-pain-sex-difference-microglia-spinal", "type": "hypothesis", "title": "Sex differences in pain sensitivity arise from sexually dimorphic spinal cord microglia: in males, spinal microglia amplify pain via P38-MAPK signaling (required for central sensitization), while females use a T-cell-mediated alternative pathway; this predicts that microglial inhibitors should be analgesic in males but not females.\n", "status": "active", "fields": [ "pain-research", "neuroscience", "immunology", "sex-differences" ], "color": "green" }, { "id": "h-pancharatnam-loop-area-predicts-interferometric-phase-shifts", "type": "hypothesis", "title": "In a wave-plate polarization interferometer, measured Pancharatnam-Berry phase shifts will scale with signed loop area on the Poincare sphere within 5 percent after loss calibration; falsified if residuals remain dominated by unmodeled dynamical phase.\n", "status": "active", "fields": [ "optics", "quantum-physics" ], "color": "green" }, { "id": "h-paradigm-shift-anomaly-accumulation-detectable", "type": "hypothesis", "title": "Kuhnian paradigm shifts are preceded by a detectable acceleration in anomaly-reporting publications and citation network fragmentation in the 3-10 years before the paradigm shift, identifiable retrospectively using science-of-science bibliometric methods and potentially predictable prospectively with 5-7 year lead time.\n", "status": "active", "fields": [ "philosophy-of-science", "scientometrics", "sociology-of-science", "information-science" ], "color": "green" }, { "id": "h-pareto-exponent-growth-redistribution-ratio", "type": "hypothesis", "title": "The Pareto exponent α of top-income distributions across OECD countries satisfies the Bouchaud-Mezard prediction α ≈ 1 + r/(g - r), where r is the effective redistribution rate (taxes and transfers as fraction of GDP) and g is the top-1% income growth rate, with this functional form fitting better than linear regression on redistribution rate alone\n", "status": "active", "fields": [ "economics", "statistical-physics", "econophysics" ], "color": "green" }, { "id": "h-partial-correlation-fmri-direct-connectivity", "type": "hypothesis", "title": "Partial correlations estimated by graphical lasso from resting-state fMRI are more accurate indicators of direct structural connectivity than full correlations, with optimal regularization parameter λ determined by cross-validation against diffusion tractography rather than statistical criteria, and graph Laplacian regularization outperforms standard graphical lasso for brain network topology", "status": "active", "fields": [ "neuroscience", "statistics", "mathematics" ], "color": "green" }, { "id": "h-pcm-microencapsulation-enables-chiplet-thermal-buffering", "type": "hypothesis", "title": "Microencapsulated phase-change materials integrated into chiplet packaging layers can buffer transient thermal spikes exceeding 5× steady-state power by absorbing latent heat, reducing peak junction temperature by >15°C without increasing steady-state thermal resistance", "status": "active", "fields": [ "thermal-engineering", "materials-science", "semiconductor-physics", "packaging-engineering" ], "color": "green" }, { "id": "h-peaks-over-threshold-models-improve-amr-outbreak-early-warning", "type": "hypothesis", "title": "Transferred methods from `b-extreme-value-theory-x-antimicrobial-resistance-surveillance` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "statistics", "epidemiology" ], "color": "green" }, { "id": "h-peatland-carbon-water-table-tipping-point", "type": "hypothesis", "title": "Tropical and boreal peatland carbon vulnerability is controlled by a nonlinear water table threshold: drainage below 30-40 cm triggers aerobic decomposition that transforms peatlands from carbon sinks to sources, releasing up to 5 Pg C per 1°C of warming in vulnerable peatlands when combined with fire.\n", "status": "active", "fields": [ "geology", "biogeochemistry", "ecology", "climate-science" ], "color": "green" }, { "id": "h-pecora-carroll-synchronization-noise-tolerance-lyapunov", "type": "hypothesis", "title": "The maximum tolerable coupling noise amplitude for Pecora-Carroll synchronization scales as |λ_max^CLE|^(1/2), so systems with strongly negative conditional Lyapunov exponents are exponentially more noise-tolerant and suitable for engineering implementations in noisy environments\n", "status": "active", "fields": [ "physics", "engineering" ], "color": "green" }, { "id": "h-pem-membrane-beyond-nafion-high-temperature", "type": "hypothesis", "title": "Non-PFSA proton exchange membranes operating at 120–200°C without external humidification will achieve proton conductivities >100 mS/cm and lifetimes >40,000 hours, enabling PEM electrolysers and fuel cells to operate at higher efficiency and lower cost than Nafion-based systems\n", "status": "active", "fields": [ "polymer-chemistry", "electrochemistry", "materials-science" ], "color": "green" }, { "id": "h-pension-ndcdc-reform-sustainability", "type": "hypothesis", "title": "Notional defined contribution (NDC) pension systems are demographically robust to aging shocks without requiring benefit cuts or contribution hikes, because automatic balancing mechanisms absorb longevity risk — and the Swedish NDC model provides the strongest evidence for this hypothesis.\n", "status": "active", "fields": [ "public-economics", "demography", "social-policy" ], "color": "green" }, { "id": "h-perceptual-binding-gamma-oscillations", "type": "hypothesis", "title": "Perceptual binding of features processed in different cortical areas is mediated by gamma-band (40-80 Hz) synchronization between areas, with binding failures (such as illusory conjunctions) corresponding to reduced inter-area gamma coherence measurable by MEG in temporal windows < 100 ms after stimulus onset.\n", "status": "active", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "green" }, { "id": "h-perceptual-binding-gamma-synchrony-thalamus", "type": "hypothesis", "title": "Perceptual feature binding is achieved by synchronised gamma-band oscillations (40 Hz) coordinated through thalamo-cortical loops, not by convergent anatomical projections to a single binding area", "status": "active", "fields": [ "cognitive-neuroscience", "systems-neuroscience", "computational-neuroscience" ], "color": "green" }, { "id": "h-percolation-aware-combination-selection-delays-resistance-network-percolation", "type": "hypothesis", "title": "Methods transferred from `b-percolation-thresholds-x-antimicrobial-combination-therapy-networks` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "epidemiology", "network-science" ], "color": "green" }, { "id": "h-percolation-herd-immunity-heterogeneous-networks", "type": "hypothesis", "title": "The herd immunity threshold predicted by bond percolation on empirically measured contact networks (using POLYMOD degree distributions) matches observed epidemic final sizes within 5 percentage points, outperforming the classic homogeneous-mixing 1 - 1/R0 formula for pathogens with R0 > 3.\n", "status": "active", "fields": [ "epidemiology", "mathematics", "network-science" ], "color": "green" }, { "id": "h-percolation-outbreak-threshold", "type": "hypothesis", "title": "Percolation finite-size scaling corrections reduce R_0 estimation error by >30% in institutional outbreaks (N < 10,000), and the correction exponent matches the random-graph percolation universality class (nu = 1).\n", "status": "active", "fields": [ "epidemiology", "statistical-physics", "public-health" ], "color": "green" }, { "id": "h-percolation-threshold-x-polymer-gelation", "type": "hypothesis", "title": "Joint finite-size scaling fits of rheologically inferred gel points with bond-percolation simulations on chemistry-aware graphs will collapse deviations onto a single scaling window when cyclization is parameterized per monomer class — falsifying universal lattice criticality absent loop corrections.\n", "status": "active", "fields": [ "polymer-science", "statistical-physics" ], "color": "green" }, { "id": "h-peridynamic-models-predict-bone-microdamage-hotspots-before-radiographic-failure", "type": "hypothesis", "title": "Transferred methods from `b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "materials-science", "medicine" ], "color": "green" }, { "id": "h-permafrost-abrupt-thaw-dominates", "type": "hypothesis", "title": "Abrupt thermokarst processes (retrogressive thaw slumps, abrupt lake drainage) contribute more than 50% of total permafrost carbon release to the atmosphere by 2100, despite affecting less than 5% of permafrost area, because of their disproportionate rate of deep carbon mobilization.\n", "status": "active", "fields": [ "climate-science", "geoscience", "biogeochemistry", "cryosphere-science" ], "color": "green" }, { "id": "h-permafrost-carbon-tipping-2point5", "type": "hypothesis", "title": "A 2.5 degree Celsius global mean warming threshold triggers irreversible permafrost degradation releasing over 200 Gt of carbon by 2200 in a self-sustaining feedback", "status": "active", "fields": [ "climate-science", "ecology" ], "color": "green" }, { "id": "h-perovskite-degradation-ion-migration", "type": "hypothesis", "title": "The dominant degradation mechanism limiting perovskite solar cell lifetime is halide ion migration driven by the built-in field and light-induced carrier gradients, which segregates I⁻ from Br⁻ at interfaces and forms local regions of high non-radiative recombination; suppressing ion migration through lattice rigidity at grain boundaries via 2D/3D passivation is the rate-limiting intervention for achieving >20-year lifetime.\n", "status": "active", "fields": [ "materials-science", "electrochemistry", "solar-energy", "condensed-matter-physics" ], "color": "green" }, { "id": "h-persistence-based-features-improve-active-catalyst-hit-rate-in-high-throughput-screening", "type": "hypothesis", "title": "Transferred methods from `b-topological-data-analysis-x-catalyst-state-space-screening` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "chemistry", "mathematics" ], "color": "green" }, { "id": "h-persistent-h1-betti-curves-predict-material-failure-earlier-than-stress-thresholds", "type": "hypothesis", "title": "Persistent H1 Betti trajectories forecast imminent material failure earlier than stress-threshold heuristics.", "status": "active", "fields": [ "materials-science", "mathematics" ], "color": "green" }, { "id": "h-persistent-h1-rise-precedes-afib-onset", "type": "hypothesis", "title": "A sustained rise in H1 persistence from RR-interval embeddings precedes atrial-fibrillation onset in ambulatory monitoring.", "status": "active", "fields": [ "medicine", "topology", "digital-health" ], "color": "green" }, { "id": "h-persistent-homology-allosteric-prediction", "type": "hypothesis", "title": "Persistent homology H1 generators (loops) in protein contact networks identify allosteric communication pathways with sensitivity exceeding perturbation-response scanning and mutual information methods\n", "status": "active", "fields": [ "mathematics", "biology", "topology", "structural_biology" ], "color": "green" }, { "id": "h-persister-optimal-dosing-markov", "type": "hypothesis", "title": "Antibiotic dosing intervals optimised to the persister resuscitation rate beta (dosing every 1/beta hours to catch cells as they exit dormancy) will reduce recurrent infection rates by at least 50% compared to standard fixed-interval dosing in controlled in vitro biofilm experiments with clinical S. aureus isolates.\n", "status": "active", "fields": [ "microbiology", "mathematics", "medicine" ], "color": "green" }, { "id": "h-pes-elite-capture-indigenous-displacement-monitoring-prevention", "type": "hypothesis", "title": "Payments for Ecosystem Services programs in tropical forest countries show measurable elite capture (>30% of payments flowing to non-subsistence landowners in the top income quintile) that can be reduced by ≥50% through community-based monitoring with community verification of additionality requirements.\n", "status": "active", "fields": [ "ecology", "social-science", "economics", "environmental-science" ], "color": "green" }, { "id": "h-phage-ejection-force-osmotic-mechanism", "type": "hypothesis", "title": "More than 70% of bacteriophage lambda DNA ejection force is attributable to osmotic pressure from confined DNA, with electrostatic and entropic contributions each below 20%, as measurable by systematic osmotic suppression experiments with PEG solutions of varying molecular weight.\n", "status": "active", "fields": [ "biophysics", "biology", "physics" ], "color": "green" }, { "id": "h-phage-therapy-combination-delays-resistance-evolution-eskape", "type": "hypothesis", "title": "Phage-antibiotic combination therapy (PAC) will suppress resistance evolution in ESKAPE pathogens beyond either component alone ΓÇö because phage resistance mutations (receptor loss) restore antibiotic susceptibility via evolutionary trade-offs ΓÇö making PAC a self-sterilizing therapeutic strategy.\n", "status": "active", "fields": [ "microbiology", "evolutionary-biology", "infectious-disease", "pharmacology" ], "color": "green" }, { "id": "h-phase-response-adaptive-dbs-reduces-off-target-neural-entrainment", "type": "hypothesis", "title": "Methods transferred from `b-phase-response-curves-x-adaptive-deep-brain-stimulation-timing` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "control-engineering", "neurology" ], "color": "green" }, { "id": "h-phonon-engineering-nanoscale-interfaces", "type": "hypothesis", "title": "Phonon dispersion engineering via periodic nanoscale interfaces (phononic crystals with period L = 5-50 nm) can achieve near-zero thermal conductivity κ < 0.5 W/mK in crystalline silicon while maintaining electron mobility > 100 cm²/Vs, by creating a phonon bandgap in the 1-10 THz frequency range that dominates heat transport but is below the electron de Broglie wavelength.\n", "status": "active", "fields": [ "materials-science", "physics", "condensed-matter", "nanotechnology" ], "color": "green" }, { "id": "h-phonon-glass-electron-crystal-zt-optimization", "type": "hypothesis", "title": "The phonon glass-electron crystal (PGEC) design strategy — maximizing ZT by engineering phonon mean free path distributions via nanostructured interfaces while maintaining electron coherence — can achieve ZT > 3 at room temperature in layered chalcogenide superlattices with optimized interface periodicity.\n", "status": "active", "fields": [ "materials-science", "physics", "condensed-matter", "engineering" ], "color": "green" }, { "id": "h-phonon-mfp-spectrum-thermal-conductivity-engineering", "type": "hypothesis", "title": "A bimodal grain-size distribution in polycrystalline thermoelectrics—with grain sizes targeting the two peaks of the phonon mean-free-path spectrum— will reduce κ_L by >60% relative to the single-grain-size optimum while reducing electrical conductivity by <15%.\n", "status": "active", "fields": [ "materials-science", "condensed-matter-physics" ], "color": "green" }, { "id": "h-phononic-crystal-piezoelectric-tuning-topological", "type": "hypothesis", "title": "A phononic crystal with piezoelectric inclusions can undergo a topological phase transition (trivial to topological band gap) by application of a bias voltage of < 100V, producing a switchable topologically protected acoustic edge mode; this requires piezoelectric strain of only epsilon > 0.1% to shift the Zak phase from 0 to pi, achievable with PZT-5A inclusions.\n", "status": "active", "fields": [ "acoustics", "materials-science" ], "color": "green" }, { "id": "h-photocatalysis-x-semiconductor-physics", "type": "hypothesis", "title": "Surface overpotential (not bulk recombination) is the dominant efficiency limiter in BiVO4 photocatalysts for water oxidation, and deposition of a 2 nm CoOx overlayer reduces overpotential by >300 mV while increasing the quantum yield for O2 evolution by more than 10-fold.\n", "status": "active", "fields": [ "photochemistry", "semiconductor-physics", "materials-science", "electrochemistry" ], "color": "green" }, { "id": "h-photon-antibunching-sub-poissonian", "type": "hypothesis", "title": "Hexagonal boron nitride (hBN) defect emitters coupled to photonic crystal cavities will simultaneously achieve g⁽²⁾(0) < 0.05 and Hong-Ou-Mandel visibility > 80% at room temperature when cavity Purcell factor exceeds 30", "status": "active", "fields": [ "quantum-physics", "quantum-information", "nanophotonics" ], "color": "green" }, { "id": "h-photonic-fusion-based-fault-tolerant-qc", "type": "hypothesis", "title": "Fusion-based linear optical quantum computing (FBQC, Bartolucci et al. 2023) can achieve fault-tolerant quantum computation with photon loss rates < 10% by replacing two-qubit gates with probabilistic Bell-state measurements and encoding logical qubits in resource states, making photonic QC scalable without deterministic photon-photon interactions.\n", "status": "active", "fields": [ "quantum-computing", "quantum-optics", "photonics" ], "color": "green" }, { "id": "h-photoreceptor-quantum-efficiency-x-photon-statistics", "type": "hypothesis", "title": "Intrinsic gain variability in the phototransduction cascade (not thermal dark noise) limits single-photon detection SNR in primate rods, and reducing transducin copy number variance by 2-fold would improve SNR by >50% based on a shot-noise-limited cascade model.\n", "status": "active", "fields": [ "biophysics", "neuroscience", "optics", "photonics" ], "color": "green" }, { "id": "h-phylogenetics-x-coalescent-theory", "type": "hypothesis", "title": "Ancestral recombination graph inference from UK Biobank whole-genome sequences will reveal a European population bottleneck during the Last Glacial Maximum (~20,000 years ago) with effective population size below 1,000 individuals, detectable as an acceleration in coalescent rate\n", "status": "active", "fields": [ "biology", "mathematics", "evolutionary-biology" ], "color": "green" }, { "id": "h-piezoelectricity-symmetry-breaking", "type": "hypothesis", "title": "High-throughput DFT screening of non-centrosymmetric perovskite and double-perovskite structures from the ICSD will identify at least 5 lead-free compositions with predicted d_33 > 300 pC/N, of which at least 2 will be experimentally confirmed", "status": "active", "fields": [ "materials-science", "computational-chemistry", "group-theory" ], "color": "green" }, { "id": "h-pink1-parkin-mitophagy-parkinsons-therapeutic-target", "type": "hypothesis", "title": "Enhancing PINK1-Parkin mitophagy flux in dopaminergic neurons will slow or arrest the progression of Parkinson's disease by clearing damaged mitochondria before they trigger neuroinflammation and neurodegeneration.\n", "status": "active", "fields": [ "cell-biology", "neuroscience", "pharmacology", "neurodegenerative-disease" ], "color": "green" }, { "id": "h-pino-aftershock-fields-improve-short-term-seismic-hazard-maps", "type": "hypothesis", "title": "PINO aftershock field models improve short-term seismic hazard map reliability over ETAS-only baselines.", "status": "active", "fields": [ "seismology", "machine-learning", "geophysics" ], "color": "green" }, { "id": "h-pitting-corrosion-passive-film-breakdown", "type": "hypothesis", "title": "Passive film breakdown initiating pitting corrosion occurs through halide ion adsorption-induced dissolution of the outermost oxide layer at surface defect sites (grain boundaries, MnS inclusions), creating a local pH drop that prevents repassivation — the metastable pit threshold potential E_pit is set by the MnS inclusion dissolution kinetics, not the oxide film itself.\n", "status": "active", "fields": [ "electrochemistry", "materials-science", "surface-science" ], "color": "green" }, { "id": "h-place-attachment-mediates-conservation-behavior-more-than-vbn", "type": "hypothesis", "title": "Place attachment (emotional bond to specific natural places from direct experience) predicts pro-environmental behavior more strongly than the value-belief-norm pathway, and nature experience programs increase conservation behavior through place attachment rather than environmental knowledge", "status": "active", "fields": [ "conservation-psychology", "social-psychology", "environmental-education", "behavioral-economics" ], "color": "green" }, { "id": "h-placebo-endogenous-opioid-dlpfc-pad", "type": "hypothesis", "title": "Placebo analgesia is mediated by top-down descending inhibition: expectation of pain relief activates dorsolateral prefrontal cortex → PAG → spinal cord, releasing endogenous opioids that suppress ascending pain signals at the spinal dorsal horn; naloxone blocks placebo analgesia confirming opioid dependence, and the magnitude of placebo effect correlates with functional DLPFC-PAG connectivity.\n", "status": "active", "fields": [ "pain-neuroscience", "cognitive-neuroscience", "pharmacology", "psychophysiology" ], "color": "green" }, { "id": "h-placebo-endogenous-opioid-dlpfc", "type": "hypothesis", "title": "Placebo analgesia is mechanistically mediated by top-down activation of the dlPFC→ACC→PAG circuit that releases endogenous opioids (β-endorphin, met-enkephalin) in the periaqueductal gray, with naloxone-reversible analgesia magnitude predicting effect size; nocebo hyperalgesia uses the same circuit in reverse via CCK-ergic pathways", "status": "active", "fields": [ "neuroscience", "pain-science", "pharmacology", "psychology" ], "color": "green" }, { "id": "h-plate-boundary-slip-x-fracture-mechanics", "type": "hypothesis", "title": "Earthquake breakdown energies inferred from seismic spectra will correlate with laboratory cohesive-zone lengths scaled by rupture depth when mapped through boundary-layer fracture asymptotics — falsifying scale-free K-field dominance across large earthquakes without cohesive regularization.\n", "status": "active", "fields": [ "geophysics", "mechanical-engineering" ], "color": "green" }, { "id": "h-plate-tectonics-initiated-by-bolide-impacts", "type": "hypothesis", "title": "Plate tectonics on early Earth was initiated by large bolide impacts in the late Hadean/Eoarchean that fractured thick stagnant-lid crust and created proto-subduction zones via lithospheric foundering", "status": "active", "fields": [ "geophysics", "planetary-science", "geochemistry" ], "color": "green" }, { "id": "h-plate-tectonics-ra-viscosity-threshold", "type": "hypothesis", "title": "Plate tectonics initiates when the effective viscosity contrast between the lithosphere and asthenosphere drops below a critical ratio η_lid/η_mantle ~ 10³, achievable on Earth via water-induced grain-size reduction but not on dry Venus.\n", "status": "active", "fields": [ "geophysics", "planetary-science" ], "color": "green" }, { "id": "h-plate-tectonics-water-yield-stress", "type": "hypothesis", "title": "Plate tectonics on Earth requires hydrated, weak fault zones; the critical water fraction for mobile-lid convection transitions is predictable from yield-stress rheology and Rayleigh number scaling\n", "status": "active", "fields": [ "geoscience", "physics", "fluid_mechanics", "planetary_science" ], "color": "green" }, { "id": "h-platform-monopoly-two-sided-market-welfare", "type": "hypothesis", "title": "Digital platform monopolies in two-sided markets impose net welfare costs on consumers only when they exploit inattention or lock-in to extract consumer surplus above competitive price levels — zero-price platforms may still reduce welfare through data exploitation and privacy costs that exceed the subsidy value to consumers.\n", "status": "active", "fields": [ "economics", "social-science", "industrial-organisation", "information-economics" ], "color": "green" }, { "id": "h-pmf-bacterial-flagella-atp-synthase-evolutionary-homology", "type": "hypothesis", "title": "The bacterial flagellar motor (BFM) and F₀ ATP synthase share a common evolutionary ancestor and homologous ion-channel c-ring rotation mechanism, with the BFM representing a later divergence from an ancestral ATP synthase/motor that switched from ATP synthesis to mechanical torque generation.\n", "status": "active", "fields": [ "biophysics", "evolutionary-biology", "structural-biology", "microbiology" ], "color": "green" }, { "id": "h-polar-vortex-wave-resonance-disruption", "type": "hypothesis", "title": "Sudden stratospheric warming (SSW) events are initiated by constructive interference of planetary Rossby waves (wavenumbers 1-2) amplified by Arctic sea ice loss that enhances stationary wave forcing, producing tropospheric cold air outbreaks with 2-6 week lag times predictable from stratospheric precursors", "status": "active", "fields": [ "atmospheric-science", "fluid-dynamics", "climate-science" ], "color": "green" }, { "id": "h-polarisation-ising-phase-transition", "type": "hypothesis", "title": "Political polarisation dynamics undergo a genuine phase transition at a critical partisan homophily threshold, analogous to the Ising ferromagnetic transition", "status": "active", "fields": [], "color": "green" }, { "id": "h-political-polarization-ising-critical-slowing", "type": "hypothesis", "title": "Political polarization in democratic societies exhibits critical slowing down preceding major polarization transitions — measurable as increasing autocorrelation and variance in opinion poll time-series — analogous to Ising model dynamics near the ferromagnetic critical point T_c.\n", "status": "active", "fields": [ "social-science", "statistical-physics", "complexity-science", "political-science" ], "color": "green" }, { "id": "h-polymer-glass-jamming-rfot-transition", "type": "hypothesis", "title": "The glass transition in fragile glass-formers is a random first-order transition with a thermodynamic singularity at T_K (Kauzmann temperature) obscured by finite-size mosaic state effects predicted by RFOT theory\n", "status": "active", "fields": [ "chemistry", "physics", "soft_matter", "materials_science" ], "color": "green" }, { "id": "h-pontryagin-adaptive-therapy-outperforms-mtd-solid-tumors", "type": "hypothesis", "title": "Pontryagin optimal control-derived adaptive therapy schedules that maintain drug-sensitive clones as evolutionary competitors will double progression-free survival compared to maximum tolerated dose chemotherapy in non-small cell lung cancer with mixed sensitive/ resistant clonal populations.\n", "status": "active", "fields": [ "oncology", "mathematical-biology", "evolutionary-biology" ], "color": "green" }, { "id": "h-post-perovskite-d-double-prime-dynamics", "type": "hypothesis", "title": "The post-perovskite phase transition in Earth's D'' layer (2700 km depth) generates a seismic velocity discontinuity and thermochemical pile instability that drives large-scale mantle plumes, with the CMB temperature gradient directly controlling the double-crossing of the pPv stability field.\n", "status": "active", "fields": [ "mineral-physics", "seismology", "mantle-dynamics" ], "color": "green" }, { "id": "h-post-scarcity-ubi-marginal-cost", "type": "hypothesis", "title": "Near-zero marginal cost sectors (software, AI, digital goods) require a universal basic income or equivalent transfer mechanism to prevent permanent structural unemployment — the transition to post-scarcity economics is technologically feasible but requires a fundamental reconception of earned income as the primary distributional mechanism.\n", "status": "active", "fields": [ "economics", "technology-economics", "political-economy" ], "color": "green" }, { "id": "h-ppi-hub-targeting-cancer", "type": "hypothesis", "title": "Computational protein-protein interface design can produce nanomolar-affinity binders to hub proteins in the PPI network by exploiting their conserved hydrophobic interface hotspots, but off-target toxicity scales with network degree, creating a fundamental therapeutic window constraint imposed by the scale-free network topology.\n", "status": "active", "fields": [ "structural-biology", "computational-biology", "oncology", "network-science" ], "color": "green" }, { "id": "h-pragmatic-inference-mentalizing-network", "type": "hypothesis", "title": "Pragmatic inference (implicature, irony, indirect speech) depends on the mentalizing network (TPJ, mPFC, STS) computing speaker intentions, predicting impaired pragmatics in autism due to reduced mentalizing precision", "status": "active", "fields": [ "cognitive-neuroscience", "linguistics", "clinical-psychology" ], "color": "green" }, { "id": "h-precision-weighting-schizophrenia-nmda-receptor", "type": "hypothesis", "title": "NMDA receptor hypofunction in schizophrenia specifically impairs precision weighting of prediction errors by disrupting apical dendritic integration in layer 5 pyramidal cells, causing runaway top-down predictions that manifest as positive symptoms (hallucinations, delusions) — testable by showing that NMDA antagonists (ketamine) reduce mismatch negativity (MMN) amplitude proportionally to psychosis severity.\n", "status": "active", "fields": [ "neuroscience", "psychiatry", "cognitive-science", "pharmacology" ], "color": "green" }, { "id": "h-predator-detection-optimal-sdt-threshold", "type": "hypothesis", "title": "Prey animals evolve vigilance thresholds consistent with the Bayesian-optimal SDT criterion: cross-species comparison of flight-initiation distances, predation mortality rates, and foraging payoffs will reveal that the observed threshold beta_obs is within one standard deviation of the SDT-predicted optimum beta* = C_false_alarm / C_miss in at least 70% of well-studied prey species.\n", "status": "active", "fields": [ "evolutionary-biology", "statistics", "behavioural-ecology" ], "color": "green" }, { "id": "h-predator-prey-damping-stochastic-forcing", "type": "hypothesis", "title": "Observed damping of predator-prey cycles is produced by demographic stochasticity acting on a stable spiral equilibrium (not a limit cycle), not by biological complexity per se; the apparent cycles are stochastically sustained oscillations around a deterministically stable equilibrium, and true Lotka-Volterra neutral cycles require unrealistic density-independence that is absent in all real food webs.\n", "status": "active", "fields": [ "ecology", "mathematics", "statistics" ], "color": "green" }, { "id": "h-predictive-cpc-loss-improves-downstream-transfer-under-shift", "type": "hypothesis", "title": "Autocorrelation-aware negative sampling for CPC training improves downstream transfer metrics versus standard shuffle negatives when evaluated on temporally structured scientific datasets with induced distribution shift.", "status": "active", "fields": [ "computer-science", "neuroscience", "machine-learning" ], "color": "green" }, { "id": "h-predictive-processing-psychosis", "type": "hypothesis", "title": "Psychotic hallucinations arise from a pathological increase in prior precision relative to likelihood precision — the brain is locked in a high-confidence prior state that overrides incoming sensory evidence — and this computational failure is quantifiable from the psychophysics of predictive processing.\n", "status": "active", "fields": [ "neuroscience", "statistics", "psychiatry", "computational-neuroscience" ], "color": "green" }, { "id": "h-preisach-density-grain-size-prediction", "type": "hypothesis", "title": "The Preisach switching field distribution rho(alpha,beta) of a polycrystalline ferromagnet can be predicted from grain size distribution measured by EBSD within a factor of 2, using Stoner-Wohlfarth single-domain particle theory", "status": "active", "fields": [ "materials-science", "mathematics" ], "color": "green" }, { "id": "h-preregistration-field-replication-rate", "type": "hypothesis", "title": "Fields with higher preregistration adoption rates will show measurably higher replication rates in independent multi-laboratory replication projects, with a detectable dose-response relationship between percent preregistered studies and field-level replication probability.\n", "status": "active", "fields": [ "metascience", "statistics", "philosophy-of-science" ], "color": "green" }, { "id": "h-prestin-somatic-motility-primary-cochlear-amplification-mechanism-mammals", "type": "hypothesis", "title": "Prestin-based somatic electromotility is the primary cycle-by-cycle amplification mechanism in the mammalian cochlear amplifier at high frequencies (>3 kHz), while active hair bundle motility plays a subsidiary role in amplification but is the dominant mechanism for generating the Hopf nonlinearity at low frequencies (<1 kHz).\n", "status": "active", "fields": [ "auditory-biophysics", "molecular-biology", "nonlinear-dynamics" ], "color": "green" }, { "id": "h-price-equation-cultural-group-selection", "type": "hypothesis", "title": "The Price equation multilevel selection decomposition predicts that between-group cultural selection contributes more to the maintenance of large-scale human cooperation than within-group individual selection, with the between-group component exceeding 50% of total selection when group size exceeds 150 individuals", "status": "active", "fields": [ "evolutionary-biology", "mathematics", "social-science" ], "color": "green" }, { "id": "h-price-equation-cultural-trait-frequency", "type": "hypothesis", "title": "The Price equation decomposition applied to Twitter/X meme virality data reveals a significant cultural selection term (Cov(adoption rate, trait value) ≠ 0) distinguishable from neutral drift, with selection coefficient s > 0.01 for politically and emotionally salient content categories.\n", "status": "active", "fields": [ "cultural-anthropology", "social-science", "evolutionary-biology", "computational-social-science" ], "color": "green" }, { "id": "h-price-subsidy-closes-nash-herd-gap-in-agent-based-metapopulations", "type": "hypothesis", "title": "In spatially explicit agent-based epidemic models calibrated to city mobility data, tuition-style subsidies that reduce perceived vaccination cost by fixed percentages will increase equilibrium coverage until crossing SIR-derived herd thresholds more reliably than information-only campaigns — falsified if modeled subsidies shift uptake insignificantly because trust parameters dominate elasticities in posterior draws.\n", "status": "active", "fields": [ "health-economics", "infectious-disease-modeling" ], "color": "green" }, { "id": "h-prime-editing-hdr-bypass-therapeutic-window", "type": "hypothesis", "title": "PE6 prime editing (nCas9-RT + dominant-negative MLH1) achieves >20% correction efficiency for the HbS β-globin E6V mutation in post-mitotic erythroblasts via AAV6 delivery, establishing a therapeutic window for sickle cell disease that is competitive with exa-cel without requiring HSC ex vivo editing, enabling in vivo treatment.\n", "status": "active", "fields": [ "molecular-biology", "medicine", "synthetic-biology", "hematology" ], "color": "green" }, { "id": "h-primordial-gw-inflation-energy-scale", "type": "hypothesis", "title": "The tensor-to-scalar ratio r = 16ε (slow-roll) directly encodes the energy scale of inflation V^{1/4} ≈ 10^16 GeV × (r/0.01)^{1/4}; a detection of r > 0.001 would confirm large-field (super-Planckian) inflationary models and rule out most small-field scenarios", "status": "active", "fields": [ "cosmology", "particle-physics", "gravitational-wave-astronomy" ], "color": "green" }, { "id": "h-primordial-nucleosynthesis-reaction-networks", "type": "hypothesis", "title": "LUNA-MV measurements of the d(p,γ)³He and d(d,n)³He reaction rates will reduce BBN D/H uncertainty to <1%, and the resulting η (baryon density) will be consistent with Planck CMB Ω_b·h² to within 0.5σ", "status": "active", "fields": [ "nuclear-physics", "cosmology", "astrophysics" ], "color": "green" }, { "id": "h-principal-bundle-chern-class-anomaly-cancellation", "type": "hypothesis", "title": "The topological Chern classes of the gauge bundle determine all perturbative and non-perturbative anomaly cancellation conditions in Standard Model extensions, predicting discrete constraints on new gauge group representations discoverable at colliders\n", "status": "active", "fields": [ "mathematics", "physics" ], "color": "green" }, { "id": "h-prion-llps-nucleation-kinetics", "type": "hypothesis", "title": "Prion conformational seeding kinetics and liquid-liquid phase separation nucleation kinetics for homologous prion-like domains follow the same power-law dependence on protein concentration, with exponents differing by less than 20%, indicating a shared nucleation mechanism.\n", "status": "active", "fields": [ "biophysics", "chemistry", "biology" ], "color": "green" }, { "id": "h-prion-nucleation-rate-prnp-polymorphism", "type": "hypothesis", "title": "The PRNP M129V polymorphism alters spontaneous PrPSc nucleation rate by > 10-fold, explaining the 3-fold higher sCJD risk in MM homozygotes vs. MV heterozygotes, testable via quartz crystal microbalance nucleation kinetics of recombinant PrP", "status": "active", "fields": [ "biology", "statistical-physics", "medicine" ], "color": "green" }, { "id": "h-prion-tunneling-nanotube-intercellular-spread", "type": "hypothesis", "title": "Prion proteins propagate between neurons primarily via tunnelling nanotubes (TNTs) that form direct cytoplasmic bridges between cells, bypassing extracellular space and allowing templated misfolding to spread as an ordered chain reaction with directionality determined by axonal transport polarity.\n", "status": "active", "fields": [ "neurodegeneration", "cell-biology", "biophysics" ], "color": "green" }, { "id": "h-program-synthesis-deductive-inductive-limits", "type": "hypothesis", "title": "Program synthesis completeness is limited by the halting problem for general specifications, but practically bounded by inductive synthesis (learning from examples) vs deductive synthesis (formal specifications); LLM-based synthesis achieves competitive programming task success at HumanEval-like benchmarks but fails on compositional specification problems beyond ~4-5 function compositions, indicating a formal verification gap", "status": "active", "fields": [ "computer-science", "formal-verification", "machine-learning", "logic" ], "color": "green" }, { "id": "h-projective-hierarchy-determinacy", "type": "hypothesis", "title": "All projective sets of reals are Lebesgue measurable, have the Baire property, and have the perfect set property, assuming large cardinal axioms (at least infinitely many Woodin cardinals with a measurable above) — and these regularity properties cannot be proved from ZFC alone.\n", "status": "active", "fields": [ "set-theory", "descriptive-set-theory", "mathematical-logic" ], "color": "green" }, { "id": "h-prosodic-bootstrapping-edge-finding", "type": "hypothesis", "title": "Prosodic bootstrapping provides the first syntactic scaffolding in language acquisition by allowing infants to identify clause and phrase boundaries from prosodic cues before acquiring lexical knowledge — but contributes at most 30% of variance in syntactic development relative to distributional statistical learning.\n", "status": "active", "fields": [ "developmental-linguistics", "cognitive-science", "psycholinguistics" ], "color": "green" }, { "id": "h-prospect-theory-lambda-fitness-landscape-ancestral-environment", "type": "hypothesis", "title": "The loss aversion coefficient λ ≈ 2.25 is a Bayesian-optimal decision parameter for an agent facing the ancestral human foraging environment, where the variance-to-mean ratio of daily caloric intake creates a fitness landscape in which losses have systematically 2.0–2.5× greater fitness consequences than equivalent gains due to starvation risk asymmetry.\n", "status": "active", "fields": [ "biology", "evolutionary-psychology", "behavioral-economics", "neuroscience", "anthropology" ], "color": "green" }, { "id": "h-prospect-theory-neural-encoding", "type": "hypothesis", "title": "The S-shaped prospect theory value function is encoded by the firing rate-stimulus relationship of dopaminergic neurons in the ventral striatum, with the loss-aversion inflection point at zero predicted by asymmetric dopamine release-suppression kinetics measurable via fast-scan cyclic voltammetry.\n", "status": "active", "fields": [ "psychology", "neuroscience", "behavioral-economics" ], "color": "green" }, { "id": "h-prospect-theory-neural-value-coding", "type": "hypothesis", "title": "Ventromedial prefrontal cortex (vmPFC) encodes the prospect theory value function v(x) with asymmetric slopes for gains and losses, while amygdala activity encodes the loss aversion coefficient lambda, predicting that vmPFC-amygdala connectivity mediates individual differences in loss aversion behavior.\n", "status": "active", "fields": [ "cognitive-science", "neuroscience", "behavioral-economics" ], "color": "green" }, { "id": "h-proteasome-saturation-bistability-neurodegeneration", "type": "hypothesis", "title": "Proteasome saturation by misfolded protein aggregates creates a bistable proteostasis switch whose collapse threshold decreases exponentially with age due to declining proteasome subunit synthesis, predicting that restoring 20S proteasome abundance by >1.5-fold prevents proteostasis collapse in aged neurons\n", "status": "active", "fields": [ "cell-biology", "systems-biology" ], "color": "green" }, { "id": "h-protein-aggregation-x-nucleation-growth", "type": "hypothesis", "title": "Secondary nucleation dominates Aβ42 aggregation in vivo above a critical aggregate concentration threshold of ~1 nM fibril mass, with an in vivo secondary nucleation rate constant k₂ within 10-fold of in vitro measurements after correcting for cellular crowding (Macromolecular crowding factor ≈ 2)", "status": "active", "fields": [ "biology", "chemistry", "medicine" ], "color": "green" }, { "id": "h-protein-dynamics-optimize-quantum-tunneling", "type": "hypothesis", "title": "Enzyme \"promoting vibrations\" — specific low-frequency protein conformational motions that compress the proton donor-acceptor distance d — are evolutionarily selected to optimize quantum tunneling rates, and thermophilic enzyme variants with increased promoting vibration frequencies should show reduced KIE at the thermophilic growth temperature due to classical contribution dominance.\n", "status": "active", "fields": [ "biochemistry", "quantum-chemistry", "biophysics", "enzymology" ], "color": "green" }, { "id": "h-protein-folding-frustration-aggregation", "type": "hypothesis", "title": "The local frustration index from the Wolynes-Ferreiro frustratometer quantitatively predicts aggregation-prone regions in intrinsically disordered proteins with accuracy comparable to sequence-based aggregation predictors\n", "status": "active", "fields": [ "biology", "physics", "chemistry", "computational_biology" ], "color": "green" }, { "id": "h-protein-folding-funnel-alphafold2-contact-prediction-mechanism", "type": "hypothesis", "title": "AlphaFold2's evoformer attention implicitly learns the contact map of the protein energy landscape funnel — specifically, the pairwise coevolution signal in multiple sequence alignments is a compressed representation of the G(Q) funnel slope — and designed proteins with >70% MSA coverage achieve experimental folding rates within 3-fold of AF2-predicted contact predictions.\n", "status": "active", "fields": [ "biology", "chemistry", "biophysics", "computational-biology" ], "color": "green" }, { "id": "h-protein-language-model-priors-improve-viral-escape-forecasting", "type": "hypothesis", "title": "Protein language-model priors improve near-term viral escape forecasting accuracy with lineage-aware models.", "status": "active", "fields": [ "virology", "machine-learning", "evolutionary-biology" ], "color": "green" }, { "id": "h-psilocybin-rescheduling-neuroplasticity-evidence", "type": "hypothesis", "title": "Psilocybin's antidepressant efficacy comparable to SSRIs (Carhart-Harris 2021) derives from 5-HT2A-mediated BDNF upregulation and rapid dendritic spine growth (neuroplasticity), and this mechanistic evidence satisfies the pharmacological criteria for removal from Schedule I — providing a scientifically tractable basis for rescheduling that is distinct from political arguments.\n", "status": "active", "fields": [ "pharmacology", "social-science", "chemistry", "neuroscience" ], "color": "green" }, { "id": "h-ptlds-neuroinflammation-il6-blockade", "type": "hypothesis", "title": "IL-6 receptor blockade (tocilizumab) will reduce PTLDS cognitive symptoms by interrupting the self-sustaining astrocyte-microglial activation loop, measurable by reduced CSF CXCL13/IL-6 and improved NeuroQoL scores in a randomized controlled trial.\n", "status": "active", "fields": [ "neuroscience", "immunology", "neuroimmunology", "clinical-medicine", "infectious-disease" ], "color": "green" }, { "id": "h-publication-bias-p-curve-correction", "type": "hypothesis", "title": "Publication bias in meta-analyses can be corrected post-hoc using p-curve analysis combined with PET-PEESE regression, but only when the distribution of true effects is non-zero; for null true effects, p-curve is uninformative and excess significance tests provide the only valid correction.\n", "status": "active", "fields": [ "meta-science", "statistics", "psychology" ], "color": "green" }, { "id": "h-punishment-threshold-ess-moral-universality", "type": "hypothesis", "title": "Moral intuitions about unfairness punishment will be stronger (higher willingness to pay) in societies where the evolutionary punishment ESS requires higher per-capita sanctioning costs, predictable from average group size and relatedness in traditional subsistence communities.\n", "status": "active", "fields": [ "moral-psychology", "evolutionary-biology" ], "color": "green" }, { "id": "h-qaoa-parameter-transfer-improves-surrogate-warm-starts", "type": "hypothesis", "title": "Classical surrogates fitted on low-depth QAOA telemetry reduce median time-to-target energy when transferred as warm starts under matched noise calibration.", "status": "active", "fields": [ "quantum-computing", "computer-science", "operations-research" ], "color": "green" }, { "id": "h-qkd-device-independent-practical-security", "type": "hypothesis", "title": "Device-independent QKD (DI-QKD) provides information-theoretically secure key distribution even against adversarial device manufacturers, but current implementations require loophole-free Bell inequality violation at key generation rates compatible with practical communication — achievable with photon collection efficiency > 85% in entanglement sources, a threshold currently within reach of trapped-ion and neutral-atom platforms but not yet of fiber photonic implementations.\n", "status": "active", "fields": [ "quantum-cryptography", "quantum-physics", "quantum-information" ], "color": "green" }, { "id": "h-qkd-satellite-global-scale-feasibility", "type": "hypothesis", "title": "Low-Earth-orbit satellite QKD (as demonstrated by Micius) can achieve key rates sufficient for a global quantum-secured network when combined with quantum memory nodes, predicting that a constellation of 50-100 LEO satellites with 30-second overpass windows achieves 1 Mbit/day secure key between any two ground stations separated by up to 10,000 km.\n", "status": "active", "fields": [ "cryptography", "quantum-computing", "information-theory" ], "color": "green" }, { "id": "h-quality-ranked-ransac-improves-astrometric-crossmatch-precision", "type": "hypothesis", "title": "In simulated astronomical cross-matching with clustered artifacts, quality-ranked RANSAC sampling will reduce false matches by at least 20 percent versus uniform RANSAC at fixed recall; falsified if ranked sampling overfits survey-quality flags and loses recall.\n", "status": "active", "fields": [ "astronomy", "robust-statistics" ], "color": "green" }, { "id": "h-quantum-advantage-noise-threshold", "type": "hypothesis", "title": "Quantum processors achieve practical advantage over classical algorithms only when two-qubit gate fidelity exceeds 99.9 percent across all qubits simultaneously", "status": "active", "fields": [], "color": "green" }, { "id": "h-quantum-annealing-qaoa-comparison", "type": "hypothesis", "title": "Quantum annealing outperforms QAOA for dense, frustrated Ising problems when the tunneling schedule can be calibrated to the problem's spectral gap, but QAOA surpasses quantum annealing for sparse graph problems where shallow circuits avoid decoherence before the annealing gap closes.\n", "status": "active", "fields": [ "quantum-computing", "computer-science", "physics", "combinatorial-optimisation" ], "color": "green" }, { "id": "h-quantum-annealing-simulated-annealing", "type": "hypothesis", "title": "For random Ising spin glass instances on the Chimera graph with N=2000 variables, D-Wave Advantage will find solutions within 1% of optimal in <10ms — 100x faster than simulated annealing at matched solution quality — for instances with sparse, clustered structure", "status": "active", "fields": [ "quantum-computing", "combinatorics", "statistical-physics" ], "color": "green" }, { "id": "h-quantum-chaos-mss-bound-saturation-black-holes", "type": "hypothesis", "title": "The Maldacena-Shenker-Stanford (MSS) bound on quantum chaos (λ_L ≤ 2πk_BT/ℏ) is saturated only by systems with a holographic dual (black holes and SYK-like models), and this saturation is thermodynamically equivalent to maximal information scrambling — providing a route to detecting quantum gravitational signatures in condensed matter.\n", "status": "active", "fields": [ "quantum-gravity", "condensed-matter-physics", "quantum-information" ], "color": "green" }, { "id": "h-quantum-compass-precision", "type": "hypothesis", "title": "The radical-pair compass in cryptochrome operates at the quantum Fisher-information Cramér-Rao precision bound because evolution has implemented a near-optimal measurement strategy — and identifying that strategy will reveal bio-inspired decoherence-suppression principles applicable to engineered quantum sensors.\n", "status": "active", "fields": [ "quantum-physics", "molecular-biology", "quantum-information-theory", "quantum-sensing" ], "color": "green" }, { "id": "h-quantum-darwinism-photon-redundancy-verification", "type": "hypothesis", "title": "Quantum Darwinism predicts a plateau in mutual information I(S:F) at I(S:F) = H(S) for fragment sizes above 1/R_delta of the total environment, verifiable in a photon-environment experiment with full environment access", "status": "active", "fields": [ "quantum-physics", "information-theory" ], "color": "green" }, { "id": "h-quantum-decoherence-biological-timescale-phonon", "type": "hypothesis", "title": "Quantum coherence in warm, wet biological systems (protein complexes, DNA) decoheres on femtosecond-picosecond timescales due to phonon bath coupling and hydrogen bond fluctuations; any purported quantum effects in biology require either (a) protected decoherence-free subspaces or (b) noise-assisted transport mechanisms where coherence is irrelevant to function even if transiently present.\n", "status": "active", "fields": [ "quantum-biology", "biophysics", "quantum-optics", "molecular-physics" ], "color": "green" }, { "id": "h-quantum-decoherence-x-classical-emergence", "type": "hypothesis", "title": "Quantum Darwinism predicts that classical objectivity (observer-independent facts) requires at least F = log(N_E)/S_S environmental fragments to encode redundant information, where N_E is environment size and S_S is system entropy, implying that macroscopic quantum systems decohere into classicality within ~10^-20 seconds at room temperature", "status": "active", "fields": [ "physics", "mathematics", "quantum-mechanics" ], "color": "green" }, { "id": "h-quantum-diamond-nv-single-molecule-biosensing", "type": "hypothesis", "title": "Nitrogen-vacancy (NV) centre magnetometers in diamond can achieve single-molecule magnetic sensitivity at room temperature when placed within 2nm of target molecules, and NV-based proton NMR spectroscopy on nanoscale biological samples will achieve sufficient sensitivity to detect single-protein conformational states in living cells.\n", "status": "active", "fields": [ "quantum-sensing", "biophysics", "analytical-chemistry" ], "color": "green" }, { "id": "h-quantum-dot-blinking-surface-trap-levy", "type": "hypothesis", "title": "The universal power-law exponent in quantum dot blinking (alpha ~ 1.5) arises from a nearly uniform distribution of surface trap activation energies over a ~0.3-0.5 eV range, a consequence of the amorphous ligand shell geometry; surface passivation shifts the entire trap energy distribution without changing its shape, explaining why improved QDs blink less often but maintain the same power-law exponent.\n", "status": "active", "fields": [ "quantum-physics", "statistics", "materials-science" ], "color": "green" }, { "id": "h-quantum-dot-emission-confinement-scaling", "type": "hypothesis", "title": "The first-exciton emission peak of colloidal CdSe quantum dots follows the Brus equation to within 5 nm over the 2–7 nm diameter range at room temperature, and deviations > 5 nm are attributable to surface reconstruction effects quantifiable by strain-corrected tight-binding calculations", "status": "active", "fields": [ "materials-science", "quantum-physics", "nanoscience" ], "color": "green" }, { "id": "h-quantum-error-correction-surface-code-overhead-v2", "type": "hypothesis", "title": "Surface code fault-tolerant quantum computing requires ~1000 physical qubits per logical qubit at physical error rates p ~ 10^-3 (below code threshold p_th ~ 1%), scaling as d^2 where d is the code distance; achieving practical quantum advantage requires reducing this overhead via improved code concatenation, biased noise exploitation, or magic state distillation improvements", "status": "active", "fields": [ "quantum-computing", "quantum-information", "physics" ], "color": "green" }, { "id": "h-quantum-error-correction-surface-code-overhead", "type": "hypothesis", "title": "The surface code requires approximately 1000 physical qubits per logical qubit at current physical error rates (~10^-3), but threshold improvements to 10^-4 error rates would reduce this to ~100:1, making fault-tolerant quantum computing practical; the minimum physical-to-logical overhead is lower-bounded by the code distance needed to suppress errors below 10^-10 for useful computation.\n", "status": "active", "fields": [ "quantum-computing", "quantum-error-correction", "physics", "information-theory" ], "color": "green" }, { "id": "h-quantum-error-correction-x-topological-codes", "type": "hypothesis", "title": "Majorana-based topological qubits in semiconductor nanowire networks will achieve below-threshold logical error rates when system size exceeds a critical length determined by the topological gap\n", "status": "active", "fields": [ "physics", "quantum-information", "condensed-matter-physics" ], "color": "green" }, { "id": "h-quantum-field-theory-x-combinatorics", "type": "hypothesis", "title": "The QED perturbation series for the anomalous magnetic moment of the electron is Borel summable on the positive real axis with non-perturbative corrections of order exp(-pi/alpha) ~ 10^-1860, making the asymptotic series effectively exact for all practical purposes up to Planck-scale corrections.\n", "status": "active", "fields": [ "mathematical-physics", "mathematics", "quantum-field-theory", "combinatorics" ], "color": "green" }, { "id": "h-quantum-graph-bfs-speedup", "type": "hypothesis", "title": "Quantum walk-based graph algorithms provide provable quadratic speedup for element distinctness and triangle finding, but no superpolynomial speedup exists for generic BFS/SSSP due to lower bounds from quantum query complexity.\n", "status": "active", "fields": [ "quantum-computing", "algorithm-theory", "graph-theory" ], "color": "green" }, { "id": "h-quantum-gravity-holography-ads-cft", "type": "hypothesis", "title": "The AdS/CFT correspondence (Maldacena 1997) provides the correct mathematical framework for quantum gravity: quantum gravity in d+1 dimensional Anti-de Sitter space is exactly dual to a conformal field theory in d dimensions, implying that bulk spacetime geometry is an emergent property of boundary quantum entanglement structure (Ryu-Takayanagi formula: S_bulk = Area/4G_N).\n", "status": "active", "fields": [ "theoretical-physics", "quantum-gravity", "string-theory", "quantum-information" ], "color": "green" }, { "id": "h-quantum-group-roots-of-unity-modular", "type": "hypothesis", "title": "The representation theory of quantum groups U_q(g) at q a root of unity is equivalent to the modular representation theory of the corresponding algebraic group G over a field of characteristic p — the Lusztig character formula connects quantum group representations at root of unity to Verma module composition factors in positive characteristic.\n", "status": "active", "fields": [ "representation-theory", "quantum-groups", "algebraic-combinatorics" ], "color": "green" }, { "id": "h-quantum-probability-gleason-measure-uniqueness", "type": "hypothesis", "title": "Gleason's theorem establishes that the Born rule is the unique probability measure on the Hilbert space lattice of projections, implying that the probabilistic structure of quantum mechanics is not an additional postulate but a mathematical consequence of the Hilbert space formalism for dimensions ≥ 3.\n", "status": "active", "fields": [ "mathematics", "quantum-mechanics", "foundations-of-physics", "measure-theory", "quantum-information" ], "color": "green" }, { "id": "h-quantum-repeater-multimode-memory-distance", "type": "hypothesis", "title": "Multiplexed quantum repeaters using multimode quantum memories (AFC protocol in Pr³⁺:Y₂SiO₅) with N = 100+ temporal modes and nestedentanglement swapping can distribute entanglement at rates > 1 Hz over 1000 km fiber, approaching the fundamental PLOB bound rate × distance limit and enabling practical intercontinental quantum key distribution without satellite links.\n", "status": "active", "fields": [ "quantum-physics", "quantum-communication", "quantum-information" ], "color": "green" }, { "id": "h-quantum-solitons-bethe-ansatz-connection-quantum-inverse-scattering", "type": "hypothesis", "title": "The quantum inverse scattering method (Faddeev-Takhtajan-Sklyanin, 1978-1982) is the exact quantum analog of the classical inverse scattering transform, with the quantum R-matrix playing the role of the classical Lax pair, and the Bethe ansatz eigenvalues corresponding to quantized soliton momenta — unifying classical and quantum integrability into a single algebraic framework (Yangians).\n", "status": "active", "fields": [ "mathematics", "physics", "mathematical-physics", "condensed-matter" ], "color": "green" }, { "id": "h-quantum-spectral-gap-computational-complexity", "type": "hypothesis", "title": "The spectral gap of the Hamiltonian for local quantum systems determines computational complexity class membership — gapped systems are classically simulable", "status": "active", "fields": [ "quantum-computing", "mathematical-physics", "computational-complexity" ], "color": "green" }, { "id": "h-quantum-walk-spatial-search-optimal", "type": "hypothesis", "title": "Continuous-time quantum walk search on an N-node graph achieves optimal O(sqrt(N)) query complexity if and only if the graph's spectral gap Delta_lambda satisfies Delta_lambda = O(1/N), predicting that complete graphs and hypercubes achieve Grover-optimal speedup while expander graphs with O(1) spectral gaps do not.\n", "status": "active", "fields": [ "quantum-computing", "probability-theory", "algorithm-theory" ], "color": "green" }, { "id": "h-quantum-walk-x-classical-random-walk", "type": "hypothesis", "title": "Continuous-time quantum walk on the complete bipartite graph K_{N,N} achieves hitting time O(N^(1/2)) vs. O(N) classically, and this exponential speedup persists under dephasing noise up to decoherence rate γ_c ≈ 1/(2N·t_walk), predicting a noise threshold that is reachable with current superconducting qubit coherence times for N ≤ 100", "status": "active", "fields": [ "physics", "computer_science", "mathematics" ], "color": "green" }, { "id": "h-quantum-zeno-like-slowing-in-attention-networks", "type": "hypothesis", "title": "Increasing self-report probe frequency over a controlled range will produce a non-monotonic change in the hazard rate of internal strategy switches, consistent with a Zeno-like stabilization regime at intermediate rates in a perceptual discrimination task.", "status": "active", "fields": [ "cognitive-science", "neuroscience", "quantum-physics" ], "color": "green" }, { "id": "h-quasar-feedback-kinetic-mode-quenching-mechanism", "type": "hypothesis", "title": "Quasar feedback operates in two modes — radiative (quasar mode, high Eddington ratio, outflows driven by radiation pressure) during cosmic noon (z~2-3) and kinetic (jet mode, low Eddington, radio jets stirring the ICM) at low redshift — together quenching ~90% of massive galaxy star formation over cosmic time", "status": "active", "fields": [ "astrophysics", "galaxy-formation", "plasma-physics", "fluid-dynamics" ], "color": "green" }, { "id": "h-quasar-feedback-kinetic-mode-quenching", "type": "hypothesis", "title": "Quasar feedback quenches star formation in massive galaxies primarily through kinetic-mode AGN jets that heat the circumgalactic medium (CGM) and prevent gas cooling, rather than radiative-mode winds ejecting ISM gas; the transition between modes occurs at critical black hole mass M_BH ~ 10^8 solar masses.\n", "status": "active", "fields": [ "galaxy-formation", "astrophysics", "plasma-physics", "observational-cosmology" ], "color": "green" }, { "id": "h-quasicrystal-phason-strain-stability", "type": "hypothesis", "title": "Quasicrystalline phases are thermodynamically stabilized (not merely metastable) in specific alloy compositions by phason entropy and vibrational entropy contributions to the free energy — the stability field is bounded by an effective phason elastic constant κ_φ that determines whether the approximant or quasicrystal phase is thermodynamically favored.\n", "status": "active", "fields": [ "materials-science", "crystallography", "condensed-matter-physics" ], "color": "green" }, { "id": "h-quorum-thresholds-are-ess-under-stochastic-demography", "type": "hypothesis", "title": "In synthetic Pseudomonas populations on patterned surfaces, manipulating colony clustering will shift cheater frequency trends in the direction predicted by spatial public goods games with a sharp QS threshold.", "status": "active", "fields": [ "biology", "economics" ], "color": "green" }, { "id": "h-r-process-cgm-metal-enrichment", "type": "hypothesis", "title": "The r-process element abundance pattern in the circumgalactic medium records the neutron star merger rate history of the host galaxy, and CGM r-process enrichment should be spatially offset from the stellar disk by the kick velocities of neutron star binaries, providing a direct observational test of the dominant r-process site.\n", "status": "active", "fields": [ "astrophysics", "nuclear-physics", "observational-astronomy" ], "color": "green" }, { "id": "h-r-process-neutron-star-merger-dominant", "type": "hypothesis", "title": "Neutron star mergers account for more than 50% of the Milky Way's r-process europium budget, and galactic chemical evolution models incorporating a neutron-star-merger delay-time distribution with t_min < 100 Myr and a power-law slope of -1 will reproduce the observed [Eu/Fe] scatter at [Fe/H] < -2 within 0.3 dex", "status": "active", "fields": [ "astrophysics", "nuclear-physics", "astronomy" ], "color": "green" }, { "id": "h-radiation-damage-ballistic-annealing", "type": "hypothesis", "title": "Radiation damage recovery in nuclear materials is dominated by recombination of vacancy-interstitial pairs during the thermal spike (< 10 ps) — ballistic annealing recombines > 90% of Frenkel pairs in metals — and the long-term microstructural evolution is determined by the surviving defect cluster spectrum which can be predicted from molecular dynamics cascade simulations.\n", "status": "active", "fields": [ "nuclear-materials", "radiation-physics", "materials-science" ], "color": "green" }, { "id": "h-radio-axion-like-dm-constraints", "type": "hypothesis", "title": "Targeted radio-line and haloscope-style programs can constrain or detect axion-like ultralight dark matter in specific mass–coupling windows", "status": "active", "fields": [ "physics", "astronomy" ], "color": "green" }, { "id": "h-ramsey-optimal-carbon-price-tipping-points", "type": "hypothesis", "title": "Incorporating stochastic climate tipping points into the DICE integrated assessment model raises the optimal Pigouvian carbon price by 30-50% above the Nordhaus deterministic baseline, converging the Nordhaus-Stern SCC gap by half when using empirically-calibrated tipping point probabilities.\n", "status": "active", "fields": [ "economics", "climate-science", "decision-theory" ], "color": "green" }, { "id": "h-random-circuit-sampling-classical-boundary-fidelity", "type": "hypothesis", "title": "The boundary between classically hard and classically easy random circuit sampling is a phase transition at cross-entropy benchmarking fidelity F* = exp(-n × ε_gate), where n is qubit count and ε_gate is per-gate error rate, and current NISQ devices operate above this threshold for n > 50 qubits at realistic gate errors.\n", "status": "active", "fields": [ "quantum-computing", "computational-complexity", "quantum-information" ], "color": "green" }, { "id": "h-random-matrix-universality-log-gas", "type": "hypothesis", "title": "Random matrix universality arises because eigenvalue statistics of large random matrices are governed by a two-dimensional log-gas (Dyson gas) at inverse temperature β = 1/2/4, and the universality class (GUE, GOE, GSE) is determined solely by the symmetry of the underlying dynamical system — a prediction falsifiable by measuring eigenvalue spacing distributions in new systems.\n", "status": "active", "fields": [ "mathematics", "physics", "number-theory", "quantum-chaos" ], "color": "green" }, { "id": "h-random-walk-x-brownian-motion", "type": "hypothesis", "title": "Anomalous diffusion of proteins on cell membranes follows a fractional Brownian motion scaling law with Hurst exponent H < 0.5 (subdiffusion) due to macromolecular crowding, transitioning to normal diffusion at long times when confinement domains are escaped\n", "status": "active", "fields": [ "mathematics", "physics", "biophysics" ], "color": "green" }, { "id": "h-range-shift-thermal-safety-margin", "type": "hypothesis", "title": "Species' ability to track climate change through range shifts is primarily determined by the ratio of required range shift velocity to maximum dispersal velocity, modulated by the species' thermal safety margin; species with thermal safety margins below 2°C and dispersal velocities below the climate velocity (currently ~2.7 km/decade globally) face extinction even when habitat is available.\n", "status": "active", "fields": [ "ecology", "climate-science", "biogeography", "conservation-biology" ], "color": "green" }, { "id": "h-rational-cryptography-blockchain-nash", "type": "hypothesis", "title": "Bitcoin mining with selfish mining strategy is a Nash equilibrium for all pool sizes above 25% hashrate, and the game-theoretic security threshold is lower than the computational security threshold of 51%\n", "status": "active", "fields": [ "economics", "computer_science", "mathematics", "cryptography" ], "color": "green" }, { "id": "h-rational-inattention-x-entropy", "type": "hypothesis", "title": "Consumers optimally allocate attention across goods following the information- theoretic water-filling rule, concentrating attention on highest-variance price categories, producing predictable patterns in scanner-data price adjustment frequencies that match rational inattention predictions.\n", "status": "active", "fields": [ "economics", "information-theory", "cognitive-science", "macroeconomics" ], "color": "green" }, { "id": "h-rayleigh-benard-turbulence-bifurcation-cascade", "type": "hypothesis", "title": "The transition to turbulence in Rayleigh-Bénard convection follows a finite sequence of identifiable bifurcations (pitchfork, Hopf, torus, chaos) whose order and critical Ra values can be predicted from a low-dimensional center-manifold reduction without full DNS, enabling efficient turbulence onset prediction for engineering heat transfer applications.\n", "status": "active", "fields": [ "physics", "mathematics", "fluid-dynamics", "nonlinear-dynamics" ], "color": "green" }, { "id": "h-reaction-networks-x-petri-nets", "type": "hypothesis", "title": "The MAPK signaling cascade (Erk phosphorylation cycle) is a persistent chemical reaction network by the deficiency zero theorem applied to its Petri net siphon structure, predicting that Erk can never be fully dephosphorylated from any positive initial condition regardless of kinase/phosphatase ratio", "status": "active", "fields": [ "chemistry", "computer_science", "biology" ], "color": "green" }, { "id": "h-reaction-norm-slope-predicts-climate-tracking", "type": "hypothesis", "title": "The standing genetic variance in reaction norm slope within a population is the primary determinant of its capacity to track rapid environmental change through plasticity, with populations showing >0.05 h² for plasticity slope tracking 2°C/decade warming without demographic decline\n", "status": "active", "fields": [ "evolutionary-biology", "mathematics" ], "color": "green" }, { "id": "h-reaction-time-tail-scales-with-effective-barrier-height", "type": "hypothesis", "title": "Across two-choice perceptual tasks, fitted drift-diffusion threshold separation will predict the exponential tail scale of correct reaction times in the direction expected from Kramers first-passage asymptotics; falsified if tail scale is dominated by nondecision-time variation after hierarchical fitting.\n", "status": "active", "fields": [ "neuroscience", "statistical-physics" ], "color": "green" }, { "id": "h-real-hierarchies-embed-better-in-hyperbolic-space", "type": "hypothesis", "title": "For a held-out suite of biological pathway graphs with known module trees, hyperbolic embeddings will reduce link-prediction distortion versus best Euclidean baselines by a consistent margin at matched dimension.", "status": "active", "fields": [ "mathematics", "computer-science" ], "color": "green" }, { "id": "h-reconsolidation-ampar-endocytosis-labilisation", "type": "hypothesis", "title": "Memory labilisation during reconsolidation is mechanistically implemented by GluA1 AMPAR endocytosis specifically at the dendritic spines belonging to the reactivated engram cells, measurable as a >30% reduction in GluA1 surface expression within 30 min of retrieval in identified amygdala engram neurons.\n", "status": "active", "fields": [ "neuroscience", "molecular-biology" ], "color": "green" }, { "id": "h-recurrent-processing-consciousness", "type": "hypothesis", "title": "Recurrent processing in higher cortical areas is necessary and sufficient for phenomenal consciousness, and feedforward-only processing generates no subjective experience", "status": "active", "fields": [ "cognitive-science", "neuroscience" ], "color": "green" }, { "id": "h-red-queen-cycling-sustained-by-spatial-structure", "type": "hypothesis", "title": "Red Queen allele cycling is sustained in nature primarily by spatial heterogeneity in host-parasite encounter rates rather than by intrinsic cycle dynamics, with cycling amplitude scaling with landscape connectivity between host subpopulations\n", "status": "active", "fields": [ "evolutionary-biology", "mathematics" ], "color": "green" }, { "id": "h-red-queen-ess-influenza-diversity-prediction", "type": "hypothesis", "title": "The evolutionary stable strategy (ESS) of the influenza-immune system game predicts the number of co-circulating antigenic variants and their relative frequencies, with the ESS diversity determined by the cross-reactivity matrix of human immune responses to historical strains.\n", "status": "active", "fields": [ "biology", "mathematics", "immunology", "evolutionary-biology", "epidemiology" ], "color": "green" }, { "id": "h-red-sequence-age-spreads-constrain-quenching-models", "type": "hypothesis", "title": "Within narrow stellar mass bins at z≈0.1, the intrinsic scatter of rest-frame colors on the red sequence will correlate more strongly with halo-centric distance in clusters than with bulge-to-total ratio, if environmental quenching dominates late-time scatter.", "status": "active", "fields": [ "astronomy", "physics" ], "color": "green" }, { "id": "h-redfield-growth-rate-rg-fixed-point", "type": "hypothesis", "title": "The Redfield ratio C:N:P=106:16:1 is a renormalization-group fixed point of phytoplankton evolutionary dynamics: perturbations in growth rate shift elemental ratios predictably along the Growth Rate Hypothesis manifold", "status": "active", "fields": [], "color": "green" }, { "id": "h-reentrant-geometry-auxetic-impact-resistance", "type": "hypothesis", "title": "Re-entrant honeycomb auxetic structures exhibit 2-4× greater specific energy absorption under dynamic impact than conventional honeycomb structures of identical mass, due to lateral densification under the impact footprint\n", "status": "active", "fields": [ "materials-science", "mechanics" ], "color": "green" }, { "id": "h-regional-sea-level-acceleration-grd-fingerprint", "type": "hypothesis", "title": "Regional sea level accelerations exceeding the global mean by factors of 2-5 are primarily driven by gravitational-rotational-deformational (GRD) fingerprints of specific ice loss sources, particularly the Greenland Ice Sheet contributing disproportionate sea level rise to US East Coast and Antarctic melt contributing disproportionately to tropical Pacific regions.\n", "status": "active", "fields": [ "geophysics", "physical-oceanography", "glaciology", "geodesy" ], "color": "green" }, { "id": "h-reinforcement-learning-x-foraging-patch-models", "type": "hypothesis", "title": "Partially observable RL agents trained on synthetic patch worlds parameterized by hummingbird telemetry hazard statistics will match departure-time distributions closer than MVT thresholds when cue entropy exceeds calibrated bounds — falsifying deterministic MVT sufficiency under sensory limits.\n", "status": "active", "fields": [ "behavioral-ecology", "reinforcement-learning" ], "color": "green" }, { "id": "h-reionization-dominated-by-faint-galaxies", "type": "hypothesis", "title": "Cosmic reionization (z~6-12) was dominated by photons from numerous faint, low-mass galaxies (M_UV > -15) rather than rare bright quasars, with a reionization history consistent with optical depth tau_reion ~ 0.054", "status": "active", "fields": [ "cosmology", "galaxy-formation", "intergalactic-medium" ], "color": "green" }, { "id": "h-renormalization-group-deep-learning-criticality", "type": "hypothesis", "title": "Deep residual networks belong to the 2D Ising universality class — their generalisation-error scaling exponents match the Ising critical exponents beta=0.125, nu=1.0, and this class membership can be verified by finite-size scaling of the grokking transition across model sizes", "status": "active", "fields": [ "machine-learning", "statistical-physics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-renormalization-group-universal-emergence-laws-cross-domain", "type": "hypothesis", "title": "Renormalization group (RG) fixed-point analysis applied to multi-scale data (neural spiking → population → brain area → behaviour, or individual trade → market → macro-economy) will identify universal effective theories at each coarse-graining level, with the RG flow predicting which micro-level parameters become irrelevant (emergent law is robust) vs. relevant (sensitive to details).\n", "status": "active", "fields": [ "physics", "neuroscience", "complex-systems", "economics", "mathematics", "philosophy-of-science" ], "color": "green" }, { "id": "h-renormalization-x-compression", "type": "hypothesis", "title": "The information bottleneck rate-distortion functional evaluated on a 2D Ising model's spin field produces compression curves whose critical behavior matches the Ising universality class critical exponents (nu=1, eta=1/4) at the phase transition temperature.\n", "status": "active", "fields": [ "mathematical-physics", "information-theory", "statistical-mechanics", "machine-learning" ], "color": "green" }, { "id": "h-renyi-entropy-turbulence-universal-spectrum", "type": "hypothesis", "title": "The multifractal singularity spectrum f(alpha) of fully developed turbulence converges to a universal parabolic form in the limit Re -> infinity, derivable from the log-normal cascade model with a single intermittency parameter mu\n", "status": "active", "fields": [ "mathematics", "physics", "fluid_mechanics", "information_theory" ], "color": "green" }, { "id": "h-replica-sparsity-predicts-factor-eigenvalue-noise-bulk", "type": "hypothesis", "title": "Sparsity patterns motivated by ultrametric clustering heuristics do not outperform Ledoit–Wolf shrinkage alone on realistic stationary bootstrap blocks of equity returns — falsified only if hierarchical sparsity priors yield statistically higher out-of-sample Sharpe after transaction costs on ≥15-year rolling windows with multiple-testing correction (**speculative finance probe**).\n", "status": "active", "fields": [ "quantitative-finance", "statistical-physics" ], "color": "green" }, { "id": "h-replica-symmetry-breaking-algorithm-hardness", "type": "hypothesis", "title": "The onset of first-order replica symmetry breaking in the random k-SAT energy landscape is the precise algorithmic threshold above which polynomial-time algorithms fail, and this threshold can be computed analytically via the 1-RSB cavity method for any k\n", "status": "active", "fields": [ "computer-science", "statistical-physics" ], "color": "green" }, { "id": "h-replication-rate-scientific-progress-indicator", "type": "hypothesis", "title": "Field-level replication rates (proportion of published findings that replicate under direct replication) are the most valid indicator of scientific progress and predictive accuracy, outperforming citation counts and h-indices because they measure evidential content rather than social amplification — and fields with higher replication rates make more accurate quantitative predictions.\n", "status": "active", "fields": [ "philosophy-of-science", "statistics", "science-studies" ], "color": "green" }, { "id": "h-replicator-dynamics-ess-institutional-design", "type": "hypothesis", "title": "Social institutions that select for evolutionarily stable strategies (ESS) rather than Nash equilibria are more robust to perturbation, because only ESS are asymptotically stable under replicator dynamics — policies that create ESS by making prosocial behaviour the unique stable fixed point will sustain cooperation without continuous enforcement.\n", "status": "active", "fields": [ "evolutionary-game-theory", "political-economy", "institutional-economics" ], "color": "green" }, { "id": "h-replicator-residual-tests-improve-ess-prediction-under-competition", "type": "hypothesis", "title": "Posterior predictive residual tests comparing spatially explicit simulations to replicator reductions flag misspecified ESS predictions before policy-relevant trait forecasts are trusted.", "status": "active", "fields": [ "ecology", "mathematics", "evolutionary-game-theory" ], "color": "green" }, { "id": "h-replicator-rl-convergence", "type": "hypothesis", "title": "Multi-agent reinforcement learning systems with policy gradient updates converge to evolutionarily stable strategies (rather than arbitrary Nash equilibria) when the learning rate schedule mimics natural selection timescales — that is, when the population diversity update is slow relative to within-agent adaptation.\n", "status": "active", "fields": [ "machine-learning", "biology", "mathematics", "game-theory" ], "color": "green" }, { "id": "h-reptation-tube-model-constraint-release", "type": "hypothesis", "title": "The discrepancy between predicted (η ∝ N³) and observed (η ∝ N^3.4) viscosity exponents in entangled polymer melts is fully accounted for by contour-length fluctuations and constraint release acting together, and the combined model prediction converges to ν=3.4 in the experimentally accessible molecular weight range before asymptotically returning to ν=3 at N >> N_e.\n", "status": "active", "fields": [ "polymer-physics", "chemistry", "soft-matter" ], "color": "green" }, { "id": "h-reservoir-computing-x-dynamical-systems", "type": "hypothesis", "title": "The optimal spectral radius ρ* for reservoir computing scales logarithmically with required memory horizon T: ρ* = 1 - c/T for some task-independent constant c, providing a closed-form design rule that outperforms heuristic tuning by 20% on benchmark time series tasks", "status": "active", "fields": [ "computer_science", "physics", "neuroscience" ], "color": "green" }, { "id": "h-residual-feature-normalization-reduces-histology-site-shift-error", "type": "hypothesis", "title": "Residual networks with stain-aware feature normalization reduce external-site histopathology classification error versus standard normalization.", "status": "active", "fields": [ "radiology", "pathology", "machine-learning" ], "color": "green" }, { "id": "h-resonant-wpt-ev-charging-grid-integration", "type": "hypothesis", "title": "Dynamic wireless power transfer (DWPT) for electric vehicles — resonant inductive charging embedded in road surfaces — is technically feasible at >85% efficiency for vehicles travelling at highway speeds, and would eliminate range anxiety if deployed on 10–20% of highway lane-kilometres\n", "status": "active", "fields": [ "electrical-engineering", "transportation-engineering", "energy-systems" ], "color": "green" }, { "id": "h-resurgence-connects-perturbative-nonperturbative-qft", "type": "hypothesis", "title": "Resurgence theory can systematically reconstruct the full non-perturbative content of four-dimensional quantum field theories from the divergence pattern of their perturbation series, making transseries the correct mathematical language for QFT.\n", "status": "active", "fields": [ "mathematics", "quantum-field-theory", "mathematical-physics", "perturbation-theory" ], "color": "green" }, { "id": "h-retinal-wave-bandwidth-map-resolution-constraint", "type": "hypothesis", "title": "The spatial autocorrelation length of Stage II retinal waves sets an upper bound on retinotopic map resolution equal to the wave correlation length divided by 2π, matching the observed retinotopic map precision in C57BL/6 mice.\n", "status": "active", "fields": [ "developmental-neuroscience", "information-theory" ], "color": "green" }, { "id": "h-reusable-rocket-guidance-limits", "type": "hypothesis", "title": "The guidance and control accuracy limit for propulsive rocket landing is set by sensor noise in the terminal descent phase (~50m altitude), with GPS-denied precision achievable to <1m CEP using optical flow + LIDAR fusion, and turnaround time limits dominated by heat shield inspection rather than propulsion.\n", "status": "active", "fields": [ "aerospace-engineering", "guidance-navigation-control", "systems-engineering" ], "color": "green" }, { "id": "h-revelation-principle-ai-alignment-mechanism", "type": "hypothesis", "title": "Training AI systems with incentive-compatible loss functions — where the AI agent's optimal strategy is to reveal its true internal state and capabilities (analogous to the revelation principle's direct mechanism) — will produce more robustly aligned systems than training with proxy rewards that incentivize misrepresentation of capabilities.\n", "status": "active", "fields": [ "economics", "mechanism-design", "artificial-intelligence", "game-theory" ], "color": "green" }, { "id": "h-reversible-computing-landauer-limit", "type": "hypothesis", "title": "A reversible adiabatic logic gate implemented in a quantum dot or molecular switch can perform one logical operation with energy dissipation within 100× the Landauer bound (100 kT ln 2) at 300K and 1 GHz clock rate, demonstrating that the gap between current transistors (~10⁶ kT) and the physical limit is an engineering problem, not a fundamental one.\n", "status": "active", "fields": [ "physics", "computer-science", "thermodynamics", "information-theory" ], "color": "green" }, { "id": "h-rewilding-trophic-cascade-predictability", "type": "hypothesis", "title": "The magnitude of trophic cascades following large predator reintroduction is predictable from prey biomass, prey behavioral plasticity, and landscape connectivity with R-squared greater than 0.6", "status": "active", "fields": [], "color": "green" }, { "id": "h-rg-epsilon-expansion-convergence-nonperturbative-corrections", "type": "hypothesis", "title": "The ε-expansion for critical exponents in the Wilson-Fisher universality class is an asymptotic (not convergent) series with optimal truncation at order ε⁴-ε⁵, and non-perturbative corrections (instantons, renormalon poles) provide the dominant source of error in d=3 predictions, making conformal bootstrap the only route to sub-0.1% precision.\n", "status": "active", "fields": [ "physics", "mathematics", "statistical-mechanics" ], "color": "green" }, { "id": "h-rg-ml-universality-classes", "type": "hypothesis", "title": "Deep neural networks trained on data with the same long-range correlation structure (same RG universality class) converge to representations with the same effective dimensionality and information compression ratio, regardless of architecture details.\n", "status": "active", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "green" }, { "id": "h-rg-universality-neural-network-criticality", "type": "hypothesis", "title": "Deep neural networks at the edge of chaos (criticality) exhibit renormalization group fixed-point behavior: representations across network layers correspond to RG flow toward an IR fixed point, and the universality class of the fixed point determines generalization capacity independent of architecture details.\n", "status": "active", "fields": [ "mathematics", "physics", "computer-science", "machine-learning", "statistical-mechanics" ], "color": "green" }, { "id": "h-ribosome-kinetics-queuing-theory", "type": "hypothesis", "title": "Replacing the slowest 5 codons (bottom 5th percentile tAI) in a reporter gene with synonymous fast codons will increase protein yield by 3-8x in human HEK293 cells, as predicted by TASEP bottleneck theory", "status": "active", "fields": [ "molecular-biology", "operations-research", "synthetic-biology" ], "color": "green" }, { "id": "h-riboswitch-kinetic-proofreading-cotranscriptional", "type": "hypothesis", "title": "Cotranscriptional folding creates a kinetic proofreading window for riboswitch sensing: only ligands that bind with k_on > RNAP elongation rate can fully activate riboswitch switching, predicting that slow-binding ligands with high thermodynamic affinity will fail to switch riboswitches in vivo\n", "status": "active", "fields": [ "molecular-biology", "biophysics" ], "color": "green" }, { "id": "h-ricci-flow-x-geometrization-program", "type": "hypothesis", "title": "Supplementary modules coupling discrete Ricci-flow toy simulations with Wilson RG lattice demos will raise conceptual quiz scores on curvature-flow versus coarse-graining distinctions compared with RG-only instruction — testing pedagogical complementarity without asserting physical equivalence.\n", "status": "draft", "fields": [ "mathematics", "physics-education" ], "color": "green" }, { "id": "h-ridge-penalty-matches-bayesian-width-in-neural-decoding", "type": "hypothesis", "title": "In bootstrap simulations with correlated Gaussian designs, calibrated empirical-Bayes ridge intervals will achieve closer nominal coverage than OLS intervals when eigenvalue spread exceeds a fixed threshold.", "status": "active", "fields": [ "statistics", "computer-science" ], "color": "green" }, { "id": "h-riemann-zeros-random-matrix-gue", "type": "hypothesis", "title": "The non-trivial zeros of the Riemann zeta function have GUE (Gaussian Unitary Ensemble) eigenvalue statistics — specifically, the pair correlation function of consecutive zeros matches the Wigner surmise of GUE with a relative accuracy of < 0.1% at all separations — implying that the Riemann zeta function is the characteristic polynomial of an as-yet-unknown quantum Hamiltonian.\n", "status": "active", "fields": [ "mathematics", "physics", "number-theory", "quantum-chaos" ], "color": "green" }, { "id": "h-risk-pooling-institutions-shift-evolutionary-stable-cooperation", "type": "hypothesis", "title": "Introducing a transparent threshold rule with probabilistic group loss in economic experiments will increase stable contributions when paired with cheap-talk punishment opportunities, matching evolutionary simulations near critical thresholds.", "status": "active", "fields": [ "economics", "biology" ], "color": "green" }, { "id": "h-river-braiding-x-soc-like-morphodynamics", "type": "hypothesis", "title": "**[Speculative hypothesis subject to falsification]** Multi-decadal braided reach lidar stacks will fail finite-size scaling collapses onto SOC universality classes within pre-registered exponent bands — instead aligning with multifractal multiplicative-noise null simulators matched on variance and autocorrelation — refuting SOC-style criticality claims until new evidence arises.\n", "status": "draft", "fields": [ "geomorphology", "statistical-physics" ], "color": "green" }, { "id": "h-river-network-branching-optimality", "type": "hypothesis", "title": "River network branching geometry (Hack's law, Horton ratios) emerges from energy dissipation minimisation under erosion dynamics — network topology self-organises to the minimum energy dissipation state predicted by optimal channel network theory", "status": "active", "fields": [ "geomorphology", "hydrology", "complexity-science" ], "color": "green" }, { "id": "h-river-network-ocn-energy-minimisation-test", "type": "hypothesis", "title": "River networks in geomorphically mature, tectonically stable regions have energy expenditure within 5% of the theoretical OCN minimum, while post-glacial or tectonically active networks show 15-30% excess energy expenditure; this difference is detectable from digital elevation models and predicts future network reorganisation direction.\n", "status": "active", "fields": [ "hydrology", "geoscience", "mathematics" ], "color": "green" }, { "id": "h-rl-distributional-bellman-convergence", "type": "hypothesis", "title": "Distributional RL (C51/QR-DQN) converges in the deadly triad regime where standard Q-learning diverges because distributional Bellman operators preserve a stronger contraction in Wasserstein space\n", "status": "active", "fields": [ "computer_science", "mathematics", "control_theory", "optimization" ], "color": "green" }, { "id": "h-rlde-satellite-colony-invasion-acceleration-branching-process", "type": "hypothesis", "title": "Stratified dispersal (rare long-distance events creating satellite colonies) produces invasion dynamics indistinguishable from fat-tailed kernel integrodifference models at landscape scales, and branching process theory predicts the effective spreading speed as a function of RLDE rate and establishment probability.\n", "status": "active", "fields": [ "ecology", "mathematics", "statistics", "conservation-biology" ], "color": "green" }, { "id": "h-rmt-covariance-cleaning-improves-single-cell-state-clustering", "type": "hypothesis", "title": "RMT-based covariance cleaning improves single-cell state clustering stability across sequencing batches.", "status": "active", "fields": [ "biology", "statistics" ], "color": "green" }, { "id": "h-rmt-selective-sweep-detection-power", "type": "hypothesis", "title": "The RMT eigenvalue spike test for selective sweeps achieves at least 2x higher power (at fixed 5% FPR) than Fst-based tests in admixed populations with at least 20% admixture, as measured on coalescent-simulated genomes with known sweep locations.\n", "status": "active", "fields": [ "biology", "mathematics", "statistics" ], "color": "green" }, { "id": "h-rna-aptamer-selex-ml-design", "type": "hypothesis", "title": "RNA aptamer binding affinity and selectivity are governed by three physical principles — shape complementarity, charge complementarity, and base-stacking interactions — that can be parameterized from SELEX-seq data to train structure-conditioned language models that design novel high-affinity aptamers (K_d < 10 nM) with 50× less experimental screening than conventional SELEX.\n", "status": "active", "fields": [ "chemistry", "biology", "biophysics", "machine-learning" ], "color": "green" }, { "id": "h-rna-boltzmann-ensemble-functional-structure-selection", "type": "hypothesis", "title": "Functional RNA structures are thermodynamically selected to be near the free-energy minimum AND have low base-pair probability variance in the Boltzmann ensemble (high structural certainty), while non-functional RNAs of the same sequence composition show systematically higher ensemble variance.\n", "status": "active", "fields": [ "RNA-biology", "statistical-mechanics" ], "color": "green" }, { "id": "h-rna-electrostatic-packaging-signal-design", "type": "hypothesis", "title": "The electrostatic interaction between RNA packaging signals and positively charged coat protein N-terminal tails can be computationally redesigned to create artificial virus-like particles (VLPs) that self-assemble around arbitrary RNA cargo with T-number selectivity, enabling programmable RNA delivery vectors.\n", "status": "active", "fields": [ "biology", "physics", "structural-biology", "bioengineering" ], "color": "green" }, { "id": "h-rna-world-ribozyme-first-protein-emergence", "type": "hypothesis", "title": "The ribosome is a frozen accident of the RNA world: the peptidyl transferase center evolved as a ribozyme before proteins existed, and the gradual replacement of ribozyme catalysts by protein enzymes occurred via a Darwinian takeover in which RNA retained only the reactions it could not be displaced from.\n", "status": "active", "fields": [ "molecular-biology", "biochemistry", "evolutionary-biology", "prebiotic-chemistry", "structural-biology" ], "color": "green" }, { "id": "h-robust-control-lmi-neural-network-stability-certificates", "type": "hypothesis", "title": "Neural network Lyapunov certificates trained with incremental quadratic constraint (IQC) multipliers provide H∞ stability bounds for nonlinear dynamical systems of dimension n ≤ 100 with structured uncertainty, achieving within 15% of SOS-optimal γ while requiring 1000× less computation — enabling real-time certified-safe autonomous vehicle controllers.\n", "status": "active", "fields": [ "control-theory", "mathematics", "engineering", "computer-science" ], "color": "green" }, { "id": "h-robust-statistics-deep-learning-improves-noisy-label-training", "type": "hypothesis", "title": "Applying robust statistics estimators (Huber loss, trimmed loss with formal breakdown point guarantees) to deep neural network training with noisy labels will outperform standard cross-entropy training and ad hoc noise-robust methods when label noise exceeds 20% ΓÇö because the formal 50% breakdown point provides a principled bound that heuristic methods lack.\n", "status": "active", "fields": [ "machine-learning", "statistics", "computer-vision" ], "color": "green" }, { "id": "h-rock-magnetism-single-domain-blocking-temperature", "type": "hypothesis", "title": "Single-domain magnetite grains with volumes measured by electron microscopy will have blocking temperatures predicted by the Stoner-Wohlfarth model T_B = K*V / (25*k_B) to within 10 K, demonstrating that the condensed matter micromagnetic model accurately describes thermoremanence acquisition in natural paleomagnetic samples without requiring empirical corrections", "status": "active", "fields": [ "geology", "condensed-matter-physics", "geophysics" ], "color": "green" }, { "id": "h-room-acoustic-quality-predictable-from-geometry", "type": "hypothesis", "title": "Finite-element wave simulation of full concert hall geometry at all audible frequencies predicts subjective listener quality ratings significantly better than Sabine-era empirical metrics (T₆₀, IACC) calibrated on rectangular hall approximations", "status": "active", "fields": [ "architectural-acoustics", "computational-physics", "psychoacoustics" ], "color": "green" }, { "id": "h-rsg-transition-separates-polynomial-exponential-regimes", "type": "hypothesis", "title": "The replica symmetry breaking (clustering) transition in random k-SAT is the exact information-theoretic barrier separating polynomial-time-solvable from exponential-time- typical instances, such that efficient algorithms exist if and only if the clause density α lies below the clustering transition α_clust.\n", "status": "active", "fields": [ "computer-science", "mathematics", "statistical-physics", "information-theory" ], "color": "green" }, { "id": "h-rule-110-minimal-universal-ca", "type": "hypothesis", "title": "Rule 110 is the simplest (by rule number and neighborhood size) universal cellular automaton in the elementary CA space, and any elementary CA with rule number less than 110 that exhibits Class IV behavior will be provably non-universal due to the absence of sufficient interaction complexity between persistent localized structures", "status": "active", "fields": [ "computer-science", "mathematics", "complex-systems" ], "color": "green" }, { "id": "h-sacrificial-templating-vascular-network-bioprinting", "type": "hypothesis", "title": "A hierarchical sacrificial templating approach — combining bioprinted carbohydrate- glass macrovascular channels (50-500 μm) with self-assembled endothelial capillary networks (7-20 μm) connected via VEGF gradient-guided sprouting angiogenesis — can produce functional vascular networks sustaining cell viability throughout tissue constructs thicker than 5 mm.\n", "status": "active", "fields": [ "biomedical-engineering", "biology", "materials-science", "fluid-dynamics", "developmental-biology" ], "color": "green" }, { "id": "h-sai-regional-precipitation-monsoon-disruption", "type": "hypothesis", "title": "Stratospheric aerosol injection sufficient to limit global warming to 1.5°C would reduce African and Asian monsoon precipitation by 5-15% due to reduced sea surface temperature gradients, creating a geophysical moral hazard where SAI benefits for high-latitude nations impose precipitation costs on low-latitude agricultural regions.\n", "status": "active", "fields": [ "climate-science", "atmospheric-chemistry", "geopolitics", "atmospheric-physics" ], "color": "green" }, { "id": "h-sars-cov2-network-percolation", "type": "hypothesis", "title": "The SARS-CoV-2 transmission network in urban populations exhibits power-law degree distribution tail behavior, placing the epidemic percolation threshold near zero and making universal elimination strategies systematically less efficient than hub-targeted interventions that exploit the network structure", "status": "active", "fields": [ "epidemiology", "network-science", "statistical-physics", "public-health" ], "color": "green" }, { "id": "h-sat-spin-glass-algorithm-design", "type": "hypothesis", "title": "The RSB cavity method complexity parameter (Parisi parameter q) for random 3-SAT at clause density alpha correlates with the median DPLL runtime exponent with Spearman rank correlation greater than 0.85 across the range alpha = 3.5 to 5.0.\n", "status": "active", "fields": [ "computer-science", "physics", "mathematics" ], "color": "green" }, { "id": "h-satisfiability-x-constraint-propagation", "type": "hypothesis", "title": "Singleton arc consistency (SAC) is the maximum polynomial-time fixpoint that subsumes unit propagation and arc consistency for binary CSPs, and SAC-complete instances are exactly the hardest instances for DPLL-style SAT solvers", "status": "active", "fields": [ "computer_science", "mathematics", "logic" ], "color": "green" }, { "id": "h-saturn-ring-viscosity-self-gravity-dominated", "type": "hypothesis", "title": "Angular momentum transport in Saturn's B ring is dominated by non-local gravitational (self-gravity wake) viscosity rather than local collisional viscosity, implying that standard alpha-disk models underestimate effective viscosity by a factor of 3-10 and that ring spreading timescales are correspondingly shorter than currently estimated.\n", "status": "active", "fields": [ "astronomy", "fluid-mechanics" ], "color": "green" }, { "id": "h-scaffold-routing-constraint-metrics-predict-origami-yield", "type": "hypothesis", "title": "DNA origami designs with lower scaffold-routing dependency depth and fewer high-betweenness crossover bottlenecks will show higher assembly yield after controlling for staple melting temperature variance; falsified if graph metrics add less than 2 percent cross-validated explanatory power.\n", "status": "active", "fields": [ "DNA-nanotechnology", "computer-science" ], "color": "green" }, { "id": "h-scale-free-criticality-brain-hub-vulnerability", "type": "hypothesis", "title": "Alzheimer's disease hub atrophy follows scale-free network targeted-attack percolation, predicting faster cognitive decline than random-lesion models and matching the empirical connectome gamma ≈ 2.1", "status": "active", "fields": [], "color": "green" }, { "id": "h-scale-free-epidemic-threshold-vaccination", "type": "hypothesis", "title": "Targeted acquaintance immunization on scale-free contact networks achieves herd immunity with ≤15% of the population vaccinated, compared to ≥60% required by random vaccination, because hub removal destroys the giant component at the percolation threshold far more efficiently than random node removal.\n", "status": "active", "fields": [ "epidemiology", "network-science", "statistical-physics", "public-health" ], "color": "green" }, { "id": "h-scc-convex-damages-fat-tails", "type": "hypothesis", "title": "The true social cost of carbon is dominated by low-probability, high-magnitude tail events (climate tipping point cascades above 4°C) that give the damage distribution a power-law tail, making the expected SCC at least 3× higher than standard quadratic damage function estimates and rendering near-zero discount rates normatively justified.\n", "status": "active", "fields": [ "climate-science", "environmental-economics", "risk-theory", "decision-theory" ], "color": "green" }, { "id": "h-schelling-abm-segregation-threshold-real-world-preference-calibration", "type": "hypothesis", "title": "Schelling's ABM segregation threshold (fraction of same-type neighbors triggering moves) calibrated from stated racial residential preferences in American Housing Surveys will reproduce observed US metropolitan segregation patterns (measured by dissimilarity index) within ±10 percentage points across 50 metros.\n", "status": "active", "fields": [ "social-science", "mathematics", "computational-social-science", "urban-planning" ], "color": "green" }, { "id": "h-schelling-spinodal-coarsening", "type": "hypothesis", "title": "Urban segregation dynamics follow spinodal decomposition kinetics with domain coarsening exponent matching the Cahn-Hilliard prediction", "status": "active", "fields": [ "social-physics", "condensed-matter-physics", "urban-economics" ], "color": "green" }, { "id": "h-schema-theorem-replicator-equivalence", "type": "hypothesis", "title": "The schema theorem of genetic algorithms converges to the continuous-time replicator dynamics of evolutionary game theory in the infinite-population limit: the growth rate of a schema's frequency is exactly the difference between its fitness and the mean population fitness, making GAs a discretised numerical integrator of replicator ODEs.\n", "status": "active", "fields": [ "computer-science", "evolutionary-theory", "mathematics", "biology" ], "color": "green" }, { "id": "h-scientific-consensus-social-epistemology", "type": "hypothesis", "title": "Scientific consensus forms primarily through social-epistemic mechanisms — replication, peer review, and expert testimony networks — rather than through naive Bayesian updating on individual evidence, and can be mistaken when these social mechanisms are corrupted by funding bias or prestige cascades.\n", "status": "active", "fields": [ "philosophy-of-science", "sociology-of-science", "epistemology", "research-policy" ], "color": "green" }, { "id": "h-scientific-method-bridges-as-falsifiable-predictions", "type": "hypothesis", "title": "Cross-domain bridges that generate at least three novel quantitative predictions confirmed by independent experiments in each connected field will achieve an established status that is stable against Duhem-Quine auxiliary hypothesis adjustments — and bridges without such predictions should be classified as heuristic analogies rather than scientific bridges.\n", "status": "active", "fields": [ "philosophy-of-science", "information-theory", "history-of-science", "meta-science" ], "color": "green" }, { "id": "h-sdp-rounding-universal-approximation-ratio-tight-ugc", "type": "hypothesis", "title": "The Goemans-Williamson SDP + randomized hyperplane rounding framework is universally optimal for constraint satisfaction problems (CSPs) under the Unique Games Conjecture: for every 2-variable CSP, the approximation ratio achievable by the basic SDP is tight — i.e., no polynomial-time algorithm can improve the SDP ratio by more than ε without violating UGC.\n", "status": "active", "fields": [ "computer-science", "mathematics", "combinatorial-optimization" ], "color": "green" }, { "id": "h-sea-level-fingerprint-attribution", "type": "hypothesis", "title": "Sea-level fingerprints — the spatially variable pattern of relative sea-level change from each ice mass source — can uniquely attribute tide gauge and satellite altimetry observations to specific glacier and ice sheet contributions with > 80% attribution accuracy when using networks of > 50 geodetically connected tide gauges.\n", "status": "active", "fields": [ "glaciology", "geodesy", "physical-oceanography" ], "color": "green" }, { "id": "h-secondary-metabolites-pksnrps-combinatorial-evolution", "type": "hypothesis", "title": "The modular architecture of PKS and NRPS assembly lines evolves primarily by horizontal gene transfer and domain shuffling rather than point mutation — and the combinatorial space of viable module orderings is much larger than currently sampled by evolution, making synthetic PKS/NRPS libraries a rich source of novel bioactive scaffolds with predicted structural diversity.\n", "status": "active", "fields": [ "biochemistry", "microbiology", "evolutionary-biology", "pharmacology", "synthetic-biology" ], "color": "green" }, { "id": "h-sediment-transport-stochastic-threshold", "type": "hypothesis", "title": "Sediment transport threshold is a stochastic quantity, not a deterministic Shields parameter — bedload transport rates follow a power-law distribution at sub-threshold mean shear stress due to turbulent bursting events, and this stochasticity controls long-term river incision rates more than mean flow magnitude.\n", "status": "active", "fields": [ "fluvial-geomorphology", "sedimentology", "fluid-mechanics" ], "color": "green" }, { "id": "h-seed-dispersal-levy-flight", "type": "hypothesis", "title": "Animal-mediated seed dispersal kernels for endozoochorous species will show Lévy-like tails with exponent α in [1.5, 2.5] that are generated by the power-law gut retention time distribution of the disperser", "status": "active", "fields": [ "ecology", "statistical-physics", "animal-behavior" ], "color": "green" }, { "id": "h-sei-organic-inorganic-layers", "type": "hypothesis", "title": "The organic outer and inorganic inner SEI layers transport lithium ions via distinct mechanisms whose relative thickness and conductance ratio determines battery cycle life", "status": "active", "fields": [ "materials-science", "chemistry" ], "color": "green" }, { "id": "h-seismic-adjoint-tomography-resolves-mantle-plumes", "type": "hypothesis", "title": "Full-waveform adjoint tomography (FWI) using GLAD-M35 or equivalent models with >100 million waveform measurements resolves mantle plume conduits below Hawaii and Iceland to diameters <200 km at depths >1000 km, distinguishing them from purely upper-mantle thermal anomalies — contradicting travel-time tomography models that show resolution loss below 700 km.\n", "status": "active", "fields": [ "geophysics", "mathematics", "computational-science" ], "color": "green" }, { "id": "h-seismic-wave-x-elastic-wave", "type": "hypothesis", "title": "Full waveform inversion with optimal transport (Wasserstein) misfit function reduces cycle-skipping susceptibility by 80% compared to L2 misfit while achieving λ/2 resolution for isotropic Vp recovery, with anisotropic parameters requiring 3× denser azimuthal sampling", "status": "active", "fields": [ "geoscience", "physics", "mathematics", "computer_science" ], "color": "green" }, { "id": "h-self-exciting-renewal-models-improve-readmission-burst-forecasting", "type": "hypothesis", "title": "Self-exciting renewal models improve short-horizon readmission burst forecasting over memoryless baseline models.", "status": "active", "fields": [ "medicine", "statistics" ], "color": "green" }, { "id": "h-self-healing-polymer-dynamic-covalent", "type": "hypothesis", "title": "Autonomous self-healing in non-covalent polymer networks is governed by the ratio of dynamic bond exchange rate to crack propagation speed; networks where exchange rate exceeds crack tip speed by > 10× achieve > 90% mechanical property recovery within 24 hours, and this ratio can be tuned by controlling dynamic covalent bond density.\n", "status": "active", "fields": [ "materials-science", "chemistry", "polymer-physics" ], "color": "green" }, { "id": "h-self-supervised-residual-pretraining-reduces-retinal-screening-false-negatives", "type": "hypothesis", "title": "Self-supervised residual pretraining lowers retinal screening false-negative rates under deployment shift.", "status": "active", "fields": [ "medicine", "computer-science", "ophthalmology" ], "color": "green" }, { "id": "h-semantic-compositionality-limits-construction-grammar", "type": "hypothesis", "title": "The limits of compositional semantics are set by idiomatic and constructional meaning — constructions (argument structure, resultative, caused-motion) carry irreducible meaning not predictable from lexical items, requiring a construction grammar architecture", "status": "active", "fields": [ "linguistics", "cognitive-science", "computational-linguistics" ], "color": "green" }, { "id": "h-semantic-compositionality-type-logical-grammar", "type": "hypothesis", "title": "Natural language meaning is compositional for 90% of constructions but requires non-compositional (lexicalized) representations for idioms, light verbs, and constructional meanings; this partition is reflected in distinct neural processing streams that can be separated by contrasting compositional vs. non-compositional sentences matched for surface complexity.\n", "status": "active", "fields": [ "linguistics", "cognitive-science", "neuroscience" ], "color": "green" }, { "id": "h-semiconductor-fermi-pinning-chemical-potential-control", "type": "hypothesis", "title": "Fermi level pinning at III-V semiconductor surfaces is determined by the chemical potential of surface oxygen, and ALD passivation that saturates all surface oxygen bonding sites will unpin E_F to within kT of the desired bulk value, enabling reliable ohmic contacts in GaAs-based photovoltaics and enabling Schottky barrier tuning across the full bandgap.\n", "status": "active", "fields": [ "materials-science", "thermodynamics" ], "color": "green" }, { "id": "h-senolytic-therapy-reduces-cancer-risk-aged-tissue", "type": "hypothesis", "title": "Senolytic clearance of p16-high senescent cells in aged mice reduces spontaneous tumor incidence by > 30% without increasing proliferation of pre-cancerous cells, demonstrating that SASP chronically dominates over senescence tumor suppression in aging", "status": "active", "fields": [ "biology", "medicine" ], "color": "green" }, { "id": "h-sensory-cortex-implements-approximate-kalman-updates", "type": "hypothesis", "title": "Manipulating cue reliability in a multisensory integration task will shift population-level sensory weights in proportion to relative variances consistent with a Kalman gain within ~20% error.", "status": "active", "fields": [ "neuroscience", "engineering", "statistics" ], "color": "green" }, { "id": "h-sensory-noise-sr-optimality", "type": "hypothesis", "title": "Physiological noise levels in at least three mammalian sensory modalities (auditory, tactile, visual) are tuned by evolution to within a factor of two of the stochastic resonance optimum D_opt, and reducing this noise degrades rather than improves detection sensitivity", "status": "active", "fields": [ "sensory-neuroscience", "biophysics", "evolutionary-biology" ], "color": "green" }, { "id": "h-sepsis-endotype-genomic-immunophenotype", "type": "hypothesis", "title": "Sepsis comprises at least two reproducible genomic endotypes (SRS1: immunosuppressed, higher mortality; SRS2: immunoactivated, lower mortality) identifiable from whole- blood transcriptomics within 24h of admission, with differential response to corticosteroids: steroids harm SRS1 patients by further suppressing immunity and benefit SRS2 by reducing hyperinflammation.\n", "status": "active", "fields": [ "critical-care-medicine", "immunology", "genomics", "clinical-research" ], "color": "green" }, { "id": "h-sequence-complex-torus-first-ecc-exam-performance", "type": "hypothesis", "title": "Students exposed first to the complex torus group law, then immediately tested on 𝔽_p addition tables and subgroup orders, will outperform cohorts taught finite-field formulas alone on conceptual questions about why discrete logarithms matter — without elevating false beliefs that periodicity in ℂ implies protocol breaks — falsified if misconception inventory scores worsen relative to control after torus-first sequencing.\n", "status": "active", "fields": [ "cryptography-education", "mathematics" ], "color": "green" }, { "id": "h-serpentinization-abiotic-hydrogen-flux", "type": "hypothesis", "title": "Global abiotic H₂ flux from serpentinization at ocean ridges and in continental ophiolites is 10¹²–10¹³ mol/yr — sufficient to support a deep chemolithotrophic biosphere of 10¹⁵–10¹⁶ g C biomass — and the Lost City hydrothermal field provides the best accessible model system.\n", "status": "active", "fields": [ "astrobiology", "geochemistry", "deep-biosphere" ], "color": "green" }, { "id": "h-shannon-optimal-compression-biological-codes", "type": "hypothesis", "title": "Primary sensory cortex neural codes (V1, A1) operate within a factor of 2 of the Shannon rate-distortion bound for natural stimuli when distortion is measured using a perceptual metric matched to the sensory system's known behavioral sensitivity, implying that evolution has driven neural coding efficiency close to the theoretical optimum.\n", "status": "active", "fields": [ "information-theory", "computational-neuroscience", "visual-neuroscience", "engineering" ], "color": "green" }, { "id": "h-shapley-value-predicts-international-climate-burden-sharing", "type": "hypothesis", "title": "The Shapley value of national emission reduction coalitions, computed using historical emissions as the characteristic function, predicts negotiated burden-sharing agreements better than GDP-proportional allocation or per-capita equity baselines", "status": "active", "fields": [ "cooperative-game-theory", "economics", "political-science", "climate-science" ], "color": "green" }, { "id": "h-shared-biexponential-fitting-bias-function-across-modalities-same-snr", "type": "hypothesis", "title": "Biased versus unbiased estimator discrepancies for two-compartment exponential mixtures will trace overlapping curves versus SNR when FLIM TCSPC and GRE T2* pipelines share identical spike-removal priors — falsified if MRI-specific macroscopic field drift dominates bias budget absent in photon counting.\n", "status": "active", "fields": [ "chemistry", "radiology" ], "color": "green" }, { "id": "h-shared-shape-index-scaling-near-jamming-across-donors", "type": "hypothesis", "title": "Primary bronchial epithelial cultures from multiple donors will collapse onto a common master curve when mean cell shape index is plotted against variance of cellular velocities after stress normalization — falsified if asthma-associated donors retain statistically separated branches inconsistent with a single jamming surface.\n", "status": "active", "fields": [ "biophysics", "statistical-physics" ], "color": "green" }, { "id": "h-shared-tangent-field-exponential-region-only-logarithmic-visual-overlap", "type": "hypothesis", "title": "Vector-field plots of reduced compartmental epidemic models share tangent-field geometry with inflationary slow-roll slices **only** near exponential-envelope neighborhoods — hypothesis asserts overlap disappears once axes rescale to physical units — falsified if similarity persists after nondimensionalization tying epidemiological rates to cosmological constants without absurd unit mixing.\n", "status": "active", "fields": [ "cosmology", "epidemiology" ], "color": "green" }, { "id": "h-sharp-wave-ripple-consolidation-reward-bias", "type": "hypothesis", "title": "Sharp-wave ripple replay probability is proportional to the temporal prediction error signal experienced during encoding, causing reward-associated sequences to be preferentially consolidated in neocortical long-term memory\n", "status": "active", "fields": [ "neuroscience", "cognitive-science" ], "color": "green" }, { "id": "h-sieber-richter-pairs-bgs-proof", "type": "hypothesis", "title": "The full periodic orbit expansion for quantum chaotic systems, organized by the Sieber-Richter diagrammatic topology, reproduces all orders of Gaussian Orthogonal Ensemble correlation functions in the semiclassical limit, providing the physical mechanism underlying the BGS conjecture.\n", "status": "active", "fields": [ "quantum-mechanics", "chaos-theory", "random-matrix-theory" ], "color": "green" }, { "id": "h-signed-language-same-substrate-spoken", "type": "hypothesis", "title": "Signed languages engage the same left-lateralised perisylvian language network as spoken languages — Broca's and Wernicke's areas process signed and spoken syntax/semantics respectively — revealing that the language faculty is modality-independent", "status": "active", "fields": [ "cognitive-neuroscience", "linguistics", "developmental-psychology" ], "color": "green" }, { "id": "h-silicate-weathering-feedback-stabilizes-hothouse", "type": "hypothesis", "title": "Silicate weathering feedback is strong enough to prevent runaway greenhouse warming on Earth on timescales >1 Myr, and pCO2 cannot exceed 10× pre-industrial levels for more than 10^6 years without triggering sufficient weathering drawdown to restore climate stability, as quantified by GEOCARB sensitivity analysis\n", "status": "active", "fields": [ "geology", "chemistry" ], "color": "green" }, { "id": "h-silicon-photonics-dfb-laser-integration", "type": "hypothesis", "title": "Heterogeneous integration of InP-based DFB lasers onto silicon photonic platforms via die-to-wafer bonding will achieve coupling efficiency >80% and wall-plug efficiency >25% at 85°C within 5 years, making co-packaged optics economically viable for 100-Tbps switch ASICs by 2030.\n", "status": "active", "fields": [ "photonics", "electrical-engineering", "materials-science", "physics" ], "color": "green" }, { "id": "h-silicon-vacancy-coherence-milliseconds", "type": "hypothesis", "title": "Silicon-vacancy (SiV) and tin-vacancy (SnV) color centers in diamond achieve spin coherence times > 1 ms at temperatures > 1 K when operated at the strain- tuned clock transition — making them the leading solid-state quantum memory platform for quantum repeater networks, with fundamental limits set by two-phonon Raman scattering that predicts T₂ ∝ T⁻⁵ temperature dependence.\n", "status": "active", "fields": [ "quantum-physics", "quantum-computing", "materials-science", "solid-state-physics" ], "color": "green" }, { "id": "h-simulated-annealing-x-statistical-mechanics", "type": "hypothesis", "title": "Quantum annealing on D-Wave hardware achieves polynomial speedup over classical simulated annealing for frustrated Ising spin glass problems with chimera graph connectivity, detectable through scaling of time-to-solution with problem size\n", "status": "active", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "green" }, { "id": "h-sindy-guided-control-policies-delay-phage-resistance-takeover", "type": "hypothesis", "title": "SINDy-derived control surrogates improve resistance-management policy timing versus fixed mechanistic templates.", "status": "active", "fields": [ "microbiology", "mathematics", "systems-biology" ], "color": "green" }, { "id": "h-single-atom-catalyst-orr-selectivity-4e", "type": "hypothesis", "title": "Single-atom catalysts (SACs) with atomically dispersed Fe–N₄ sites on carbon achieve near-100% selectivity for the 4-electron oxygen reduction reaction pathway (O₂ → H₂O) over the 2-electron pathway (O₂ → H₂O₂) because the Fe coordination geometry enforces the correct O–O bond activation angle, making SACs viable Pt-free ORR catalysts for commercial PEM fuel cells.\n", "status": "active", "fields": [ "electrochemistry", "materials-science", "surface-science", "computational-chemistry", "energy-engineering" ], "color": "green" }, { "id": "h-sir-model-x-compartmental-ode", "type": "hypothesis", "title": "Network-structured SIR models on scale-free contact networks (γ ≈ 2.5) exhibit epidemic thresholds that vanish as 1/ln(N) rather than scaling with R₀, so that any pathogen with β > 0 will spread on sufficiently large networks — invalidating classical herd immunity calculations for heterogeneous populations", "status": "active", "fields": [ "epidemiology", "mathematics", "network-science" ], "color": "green" }, { "id": "h-sleep-rem-associative-creative-insight", "type": "hypothesis", "title": "REM sleep promotes creative insight by selectively strengthening weak associative links between distantly related memory traces through hippocampal-neocortical replay in a low-acetylcholine milieu, facilitating the formation of novel conceptual combinations unavailable to waking cognition.\n", "status": "active", "fields": [ "sleep-science", "cognitive-neuroscience", "memory-research", "creativity-research" ], "color": "green" }, { "id": "h-sleep-rem-creative-insight-memory", "type": "hypothesis", "title": "REM sleep enhances creative problem solving by loosening associative hierarchies through cholinergic-noradrenergic modulation, allowing remote semantic associations; SWS consolidates explicit memories while REM integrates them with existing schemas, enabling the \"next-day insight\" effect", "status": "active", "fields": [ "neuroscience", "cognitive-science", "sleep-science", "psychology" ], "color": "green" }, { "id": "h-slow-roll-spectral-tilt-potential-discrimination", "type": "hypothesis", "title": "The combination of CMB spectral index n_s and tensor-to-scalar ratio r measurements from CMB-S4 will uniquely discriminate between the three leading inflaton potential families (Starobinsky R^2, natural inflation cosine, and hilltop potentials) without requiring a detection of primordial gravitational waves\n", "status": "active", "fields": [ "cosmology", "quantum-field-theory" ], "color": "green" }, { "id": "h-slow-slip-seismic-loading", "type": "hypothesis", "title": "Slow-slip events in the Cascadia, Nankai, and Mexico subduction zones systematically increase Coulomb stress on the adjacent locked seismogenic zone, and the cumulative stress transfer from episodic tremor-and-slip cycles raises the conditional probability of a great earthquake (M>8.5) measurably during and immediately after each SSE.\n", "status": "active", "fields": [ "seismology", "geoscience", "geomechanics" ], "color": "green" }, { "id": "h-sma-fatigue-martensitic-slip-competition", "type": "hypothesis", "title": "Functional fatigue in NiTi shape-memory alloys is driven by irreversible slip dislocation accumulation in austenite during martensitic transformation — once dislocation density exceeds a critical threshold (ρ > 10¹⁴ m⁻²), the transformation becomes incomplete, and transformation stress increases without recovery of original superelastic plateau.\n", "status": "active", "fields": [ "materials-science", "smart-materials", "mechanical-engineering" ], "color": "green" }, { "id": "h-smart-grid-virtual-inertia-stability-v2", "type": "hypothesis", "title": "Electrical grid frequency stability at high renewable penetration requires virtual inertia emulation by grid-forming inverters (droop control + synthetic inertia from battery storage), with Rate of Change of Frequency (RoCoF) limited to < 0.5 Hz/s for synchronous machine compatibility; stability analysis requires small-signal eigenvalue methods for heterogeneous inverter-dominated grids", "status": "active", "fields": [ "electrical-engineering", "control-theory", "power-systems", "physics" ], "color": "green" }, { "id": "h-smart-grid-virtual-inertia-stability", "type": "hypothesis", "title": "Electrical grids can maintain stability at >80% variable renewable penetration using grid-forming inverters with virtual synchronous generator (VSG) control that synthetically provides inertial response (H~5-10 seconds equivalent), provided frequency-responsive demand response and distributed storage fill sub-second gaps not covered by VSG response dynamics.\n", "status": "active", "fields": [ "power-systems-engineering", "control-theory", "energy-systems", "electrical-engineering" ], "color": "green" }, { "id": "h-smr-proliferation-risk-lower", "type": "hypothesis", "title": "Small modular reactors (SMRs) using LEU fuel and factory-sealed designs present materially lower proliferation risk than light-water reactors due to reduced on-site fuel handling, continuous regulatory surveillance, and inability to divert plutonium without detectable reactor shutdown signatures.\n", "status": "active", "fields": [ "nuclear-engineering", "arms-control", "energy-policy" ], "color": "green" }, { "id": "h-snare-zippering-energy-controls-vesicle-fusion-probability", "type": "hypothesis", "title": "The probability of spontaneous synaptic vesicle fusion is a Boltzmann function of the SNARE zippering energy barrier ΔG_barrier, such that P_fusion = A·exp(-ΔG_barrier/k_BT), and pharmacological manipulation of SNARE zippering intermediates (via complexin or α-SNAP) quantitatively shifts spontaneous release rate according to this energy landscape.\n", "status": "active", "fields": [ "neuroscience", "chemistry", "biophysics", "pharmacology" ], "color": "green" }, { "id": "h-snare-zippering-force-gates-fusion-rate", "type": "hypothesis", "title": "SNARE complex zippering force directly determines fusion pore opening rate, predicting that mutations reducing zippering force by >30% will halve synaptic release probability in a calculable, synapse-type-independent manner\n", "status": "active", "fields": [ "neuroscience", "biophysics" ], "color": "green" }, { "id": "h-snowball-earth-escape-volcanic-co2-ice-albedo", "type": "hypothesis", "title": "Snowball Earth deglaciation was triggered by CO2 accumulation to ~0.1 bar from volcanic outgassing in the absence of silicate weathering, overwhelming the ice-albedo feedback and producing rapid hyperthermal deglaciation", "status": "active", "fields": [ "paleoclimatology", "geochemistry", "planetary-science" ], "color": "green" }, { "id": "h-soc-earthquake-precursor-detection", "type": "hypothesis", "title": "In regional seismicity catalogs, temporal variance of M > 2 event rates increases significantly (z-score > 2) in the 90 days before M > 6.5 earthquakes more often than expected by chance, with a true positive rate exceeding false positive rate by at least 2:1 in a retrospective analysis of the ANSS catalog.\n", "status": "active", "fields": [ "geoscience", "physics", "statistical-mechanics" ], "color": "green" }, { "id": "h-social-cognition-mentalizing-mirror-dissociation", "type": "hypothesis", "title": "Social cognition uses two dissociable neural systems: a mentalizing network (mPFC, TPJ, posterior STS) for deliberate belief attribution and an action observation network (IFG, premotor cortex, STS) for automatic simulation of others' actions; their co-activation is domain-general intelligence, not a dedicated social brain.\n", "status": "active", "fields": [ "cognitive-neuroscience", "social-psychology", "developmental-psychology", "clinical-neuroscience" ], "color": "green" }, { "id": "h-social-cognition-mentalizing-network", "type": "hypothesis", "title": "Social cognition is implemented by a dedicated mentalizing network (TPJ, mPFC, precuneus, STS) that is functionally and anatomically dissociable from non-social cognition networks, with action understanding mediated by mirror neuron regions (IFG, IPL) operating in parallel rather than as a prerequisite for mentalizing", "status": "active", "fields": [ "cognitive-neuroscience", "social-neuroscience", "psychology" ], "color": "green" }, { "id": "h-social-critical-temperature-empirical", "type": "hypothesis", "title": "The variance in population-level opinion distribution on Twitter/X, measured by Jensen-Shannon divergence between daily sentiment histograms, diverges with a power law as political polarization events approach — consistent with the susceptibility divergence signature of a second-order phase transition.\n", "status": "active", "fields": [ "social-science", "physics", "statistical-mechanics", "network-science" ], "color": "green" }, { "id": "h-social-ising-polarization-transition", "type": "hypothesis", "title": "Political polarisation in democratic societies undergoes a critical transition quantitatively consistent with the Ising ferromagnetic universality class: the opinion order parameter scales as m ~ (h_c - h)^β with β ≈ 1/2 (mean-field) as network homophily h approaches the critical threshold h_c, and this transition is detectable from longitudinal survey data using order-parameter scaling analysis.\n", "status": "active", "fields": [ "social-science", "political-science", "statistical-physics", "network-science" ], "color": "green" }, { "id": "h-social-media-depression-passive-consumption-mechanism", "type": "hypothesis", "title": "Social media use causally increases depression and anxiety in adolescent girls primarily through passive consumption of appearance-related content triggering social comparison; active use (messaging, posting, creative content) has neutral or positive mental health effects, explaining heterogeneous results in earlier correlational studies.\n", "status": "active", "fields": [ "social-science", "psychology", "public-health", "cognitive-science" ], "color": "green" }, { "id": "h-social-mobility-place-childhood-effects", "type": "hypothesis", "title": "Intergenerational social mobility is best measured by causal estimates of place effects on children's outcomes (not rank-rank correlations that conflate selection), and the most reliable policy lever is residential mobility programs for families with young children (age < 13), which increase adult earnings 15-35% by moving to higher-opportunity neighbourhoods.\n", "status": "active", "fields": [ "economics", "sociology", "public-policy" ], "color": "green" }, { "id": "h-social-movement-cascade-clustered-network-advantage", "type": "hypothesis", "title": "Protest or social-movement adoption campaigns seeded in high-clustering network communities will achieve global cascade (>50% adoption) at 3× lower initial seed size than campaigns seeded randomly, when the median adoption threshold φ > 0.2.\n", "status": "active", "fields": [ "social-science", "network-science" ], "color": "green" }, { "id": "h-social-network-centrality-x-eigenvector", "type": "hypothesis", "title": "For SIR epidemic spreading on empirical temporal contact networks, temporal Katz centrality with attenuation factor α = 0.85/λ_max(A) outperforms static eigenvector centrality in predicting the top-10% superspreaders by AUC ≥ 0.15, with the advantage increasing monotonically with temporal heterogeneity (burstiness parameter β > 1.5)", "status": "active", "fields": [ "mathematics", "computer_science", "network-science" ], "color": "green" }, { "id": "h-social-network-star-topology-innovation-fixation", "type": "hypothesis", "title": "In modular social networks with high-degree hub individuals, the probability that a beneficial cultural innovation (fitness advantage r > 1) fixes in the population is >3× higher than in a corresponding random-graph population of the same size, due to the star-graph amplifier effect of hub-and-spoke topology.\n", "status": "active", "fields": [ "evolutionary-biology", "social-science", "network-science" ], "color": "green" }, { "id": "h-social-pain-dacc-health-outcomes-mediation", "type": "hypothesis", "title": "The relationship between social isolation and mortality risk (Holt-Lunstad meta-analysis: OR ~ 1.29) is mediated by dACC-driven allostatic load — specifically, chronic social isolation increases dACC hyperreactivity to threat, elevating cortisol and inflammatory cytokines (IL-6, CRP) that drive cardiovascular and immune pathology.\n", "status": "active", "fields": [ "social-neuroscience", "public-health", "psychoneuroimmunology" ], "color": "green" }, { "id": "h-soft-actuator-fatigue-mechanism", "type": "hypothesis", "title": "Fatigue failure in soft pneumatic actuators is dominated by crack initiation at geometric stress concentrations in the fiber reinforcement interface layer, and 10^7-cycle durability is achievable with interpenetrating network silicone elastomers and crack-arresting fiber architectures.\n", "status": "active", "fields": [ "soft-robotics", "materials-science", "mechanical-engineering" ], "color": "green" }, { "id": "h-softmax-attention-x-cortical-divisive-normalization", "type": "hypothesis", "title": "Attention temperature tuning that matches cortical surround-suppression width on contour-integration tasks will predict psychophysical threshold curves better than softmax-free convolution baselines on identical stimuli.\n", "status": "active", "fields": [ "neuroscience", "machine-learning" ], "color": "green" }, { "id": "h-soil-aggregate-fractal-stability-mechanism", "type": "hypothesis", "title": "Soil aggregate stability scales as a power law of fractal dimension D_f with exponent ~2 across diverse soil types: higher D_f increases interfacial bonding area proportionally to D_f^2, and this geometric effect is the dominant driver of slaking resistance, independent of organic matter content once pore structure is controlled.\n", "status": "active", "fields": [ "geoscience", "soil-science", "mathematics" ], "color": "green" }, { "id": "h-soil-food-web-connectance-stability", "type": "hypothesis", "title": "Soil food webs with higher modularity (separation of bacterial and fungal energy channels into distinct modules) will show greater stability (lower return time after pulse perturbation) at equivalent connectance, because modular architecture reduces the number of destabilizing feedback loops across the entire web as predicted by May's matrix analysis", "status": "active", "fields": [ "ecology", "network-science", "soil-science" ], "color": "green" }, { "id": "h-soil-microbiome-carbon-enhancement", "type": "hypothesis", "title": "Inoculating agricultural soils with high-carbon-use-efficiency microbial consortia increases soil organic carbon sequestration by at least 20 percent over 5 years without reducing crop yields", "status": "active", "fields": [], "color": "green" }, { "id": "h-solid-electrolyte-sei-thermodynamic-stability-window", "type": "hypothesis", "title": "Solid electrolyte long-term stability at electrode interfaces is determined by the electrochemical stability window and the kinetics of SEI layer formation — materials with narrow windows but fast passivating SEI can outperform wide-window materials with slow passivation kinetics.\n", "status": "active", "fields": [ "electrochemistry", "materials-science", "chemistry", "solid-state-physics" ], "color": "green" }, { "id": "h-solid-mechanics-x-topology-optimization", "type": "hypothesis", "title": "SIMP topology optimization with mesh refinement h → h/2 converges to the analytical Michell truss solution at rate O(h^0.5) in compliance error for the single-load cantilever problem, but diverges for multi-load problems due to non-uniqueness of the optimal truss layout — predicting that SIMP requires additional symmetry constraints to achieve convergence for real engineering structures", "status": "active", "fields": [ "physics", "mathematics", "engineering" ], "color": "green" }, { "id": "h-solid-state-battery-pressure-dendrite", "type": "hypothesis", "title": "Lithium dendrite nucleation in solid electrolytes is governed by stress-corrosion cracking at grain boundaries, not electronic conductivity, and a critical stack pressure threshold (5–10 MPa) below which electronic contact is insufficient at grain boundaries will be identified as the universal dendrite suppression condition.\n", "status": "active", "fields": [ "electrochemistry", "materials-science", "engineering" ], "color": "green" }, { "id": "h-solid-state-nmr-amyloid-structure-mechanism", "type": "hypothesis", "title": "Solid-state NMR distance restraints from PITHIRDS-CT and REDOR experiments on uniformly ¹³C,¹⁵N-labelled amyloid fibrils will reveal that all amyloid folds adopt a parallel in-register beta-sheet arrangement regardless of primary sequence, with inter-strand spacing 4.7 Å and beta-sheet stacking 10 Å, confirming the \"cross-beta spine\" as the universal structural motif.\n", "status": "active", "fields": [ "structural-biology", "NMR-spectroscopy", "neurodegenerative-disease" ], "color": "green" }, { "id": "h-soliton-basis-transmission-optimal-nonlinear-channel-capacity", "type": "hypothesis", "title": "Nonlinear Fourier transform (NFT) based transmission using the soliton basis will achieve capacity closer to the nonlinear Shannon limit than conventional WDM-DSP systems in the high-power regime, because NFT decouples nonlinear channel interactions into independent soliton eigenvalue channels.\n", "status": "active", "fields": [ "telecommunications", "physics", "information-theory", "nonlinear-optics" ], "color": "green" }, { "id": "h-soliton-x-integrable-systems", "type": "hypothesis", "title": "All PDEs with exactly elastic N-soliton collisions necessarily possess a Lax pair representation — making elastic collision a sufficient condition for complete integrability — with near-integrable equations exhibiting exponentially small inelastic corrections proportional to the perturbation parameter ε", "status": "active", "fields": [ "physics", "mathematics", "mathematical-physics" ], "color": "green" }, { "id": "h-soluble-amyloid-oligomers-synaptic", "type": "hypothesis", "title": "Soluble amyloid beta oligomers rather than insoluble plaques are the primary drivers of synaptic dysfunction in Alzheimer's disease, explaining why plaque clearance does not consistently improve cognition", "status": "active", "fields": [ "medicine", "neuroscience" ], "color": "green" }, { "id": "h-sos-lyapunov-global-nonpolynomial", "type": "hypothesis", "title": "Neural Lyapunov functions trained with counterexample-guided synthesis can achieve global stability certificates for nonpolynomial nonlinear systems where SOS methods fail, provided the neural approximator is verified with interval arithmetic", "status": "active", "fields": [ "control-theory", "dynamical-systems-theory", "formal-verification", "machine-learning" ], "color": "green" }, { "id": "h-sovereign-debt-sustainability-fiscal-space", "type": "hypothesis", "title": "Sovereign debt sustainability depends on the r-g differential (real interest rate minus growth rate): sustainability requires primary surpluses only when r > g (debt snowball); countries with r < g have fiscal space for deficit spending, but market panic can shift r > g discontinuously, creating self-fulfilling crises unpredictable from fundamentals.\n", "status": "active", "fields": [ "macroeconomics", "public-finance", "financial-economics" ], "color": "green" }, { "id": "h-space-group-frequency-evolution-bias", "type": "hypothesis", "title": "The over-representation of P2₁2₁2₁ in the PDB is not a crystallisation- screening artefact but reflects a genuine physicochemical bias: proteins with β-sheet-rich surfaces preferentially form crystal contacts consistent with P2₁2₁2₁ symmetry due to complementary hydrogen-bond geometry.\n", "status": "active", "fields": [ "structural-biology", "crystallography" ], "color": "green" }, { "id": "h-spacetime-emerges-from-entanglement", "type": "hypothesis", "title": "Spacetime geometry is an emergent phenomenon arising from the entanglement structure of quantum degrees of freedom on a holographic boundary — realized in AdS/CFT as the Ryu-Takayanagi formula S = Area/4G where the bulk metric geometry is reconstructed from boundary entanglement entropy.\n", "status": "active", "fields": [ "theoretical-physics", "quantum-information", "quantum-gravity", "condensed-matter-physics" ], "color": "green" }, { "id": "h-sparse-coding-x-neural-basis", "type": "hypothesis", "title": "V1 simple cell surround suppression emerges from ISTA lateral inhibition dynamics with a time constant τ_inh = 15±5ms, predicting that surround suppression onset precedes response saturation by exactly this interval in awake primate V1 during natural image viewing", "status": "active", "fields": [ "neuroscience", "computer_science", "biology" ], "color": "green" }, { "id": "h-sparse-sensor-placement-improves-pde-structure-recovery", "type": "hypothesis", "title": "Greedy sensor placement maximizing a derivative-information surrogate improves correct-term recovery rates in SINDy-style sparse regression versus uniformly spaced sparse sensing at matched budgets on simulated advection–diffusion fields.", "status": "active", "fields": [ "mathematics", "physics", "numerical-analysis" ], "color": "green" }, { "id": "h-sparsity-priors-stabilize-lidar-surface-recovery", "type": "hypothesis", "title": "Total-variation–regularized surface reconstruction from simulated waveform LiDAR will reduce Hausdorff error versus naive Delaunay triangulation by a predictable factor when foliage gaps exceed a threshold.", "status": "active", "fields": [ "engineering", "mathematics" ], "color": "green" }, { "id": "h-species-abundance-maximum-entropy", "type": "hypothesis", "title": "Species abundance distributions follow log-normal patterns because ecological communities are maximum-entropy distributions subject to constraints on total resource use and species number, and deviations from log-normality toward log-series distributions indicate communities dominated by stochastic colonisation-extinction dynamics rather than resource partitioning.\n", "status": "active", "fields": [ "ecology", "mathematics", "statistical-mechanics", "information-theory" ], "color": "green" }, { "id": "h-spectral-clustering-x-graph-laplacian", "type": "hypothesis", "title": "Random-walk Laplacian L_rw spectral clustering achieves 20% better Normalised Mutual Information than unnormalised L clustering on power-law degree graphs (γ < 2.5) by correctly weighting high-degree hub nodes, while symmetric L_sym is optimal for regular and near-regular graphs", "status": "active", "fields": [ "computer_science", "mathematics" ], "color": "green" }, { "id": "h-spectral-linewidth-scales-with-collapse-shock-mach-estimate", "type": "hypothesis", "title": "For noble-gas-driven single-bubble sonoluminescence experiments where simultaneous radius–time curves yield peak inward Mach estimates from fitted Rayleigh–Plesset-like models, optical spectral linewidths will correlate positively with inferred Mach across gases after controlling for thermal conductivity — falsified if linewidth remains invariant while Mach varies strongly (implying emission mechanism decouples from hydrodynamic intensity).\n", "status": "active", "fields": [ "fluid-mechanics", "plasma-diagnostics" ], "color": "green" }, { "id": "h-speech-coherence-alzheimers-prediction", "type": "hypothesis", "title": "A two-year decline in sentence-to-sentence semantic coherence (measured by cosine similarity of sentence embeddings in picture-description speech) predicts conversion from MCI to Alzheimer's dementia with AUC > 0.80, non-inferior to amyloid PET", "status": "active", "fields": [ "computational-linguistics", "medicine", "neuroscience", "psychiatry" ], "color": "green" }, { "id": "h-sperm-small-rna-mediates-paternal-trauma-epigenetic-inheritance", "type": "hypothesis", "title": "Paternal stress exposure alters the small non-coding RNA (miRNA, tRNA fragment, piRNA) composition of sperm in a stress-specific manner, and injection of these sperm RNAs into naive zygotes recapitulates the behavioural and physiological phenotypes observed in the offspring of stressed fathers.\n", "status": "active", "fields": [ "epigenetics", "molecular-biology", "reproductive-biology", "neuroscience" ], "color": "green" }, { "id": "h-spin-fluctuation-pairing-cuprates", "type": "hypothesis", "title": "Antiferromagnetic spin fluctuation exchange is the dominant Cooper pairing mechanism in hole-doped cuprate superconductors — paramagnon-mediated pairing with d_{x²-y²} symmetry accounts for the T_c dome shape, pseudogap onset temperature, and superfluid density without adjustable parameters.\n", "status": "active", "fields": [ "condensed-matter-physics", "quantum-mechanics", "strongly-correlated-electrons" ], "color": "green" }, { "id": "h-spin-glass-p-np-separation", "type": "hypothesis", "title": "The replica-symmetry-breaking transition in random 3-SAT at α_c ≈ 4.267 constitutes an average-case hardness phase transition: no polynomial-time algorithm can solve a uniformly random 3-SAT instance drawn from the critical window α ∈ [4.2, 4.3] with probability greater than 1/2, for sufficiently large n.\n", "status": "active", "fields": [ "computer-science", "mathematics", "statistical-physics", "combinatorics" ], "color": "green" }, { "id": "h-spin-squeezed-states-heisenberg-limited-sensing", "type": "hypothesis", "title": "Spin-squeezed atomic ensembles generated via one-axis twisting or cavity- mediated interactions can achieve Heisenberg-limited sensitivity (ΔΦ ∝ 1/N) in practical atomic clocks and magnetometers, and the decoherence-imposed limit in realistic systems is ΔΦ ∝ N^(-2/3) — still a significant improvement over the standard quantum limit N^(-1/2) achievable with current trapped-ion and optical lattice clock technology.\n", "status": "active", "fields": [ "quantum-physics", "quantum-metrology", "atomic-physics" ], "color": "green" }, { "id": "h-spin-waves-x-magnons", "type": "hypothesis", "title": "Kagome ferromagnets host topological magnon bands with nonzero Chern number that produce a quantized thermal Hall conductivity (magnon Hall effect) κ_xy/T = C·k_B²/(ħ) measurable at sub-Kelvin temperatures, enabling a topological magnon heat current switch", "status": "active", "fields": [ "physics", "condensed-matter-physics", "materials-science" ], "color": "green" }, { "id": "h-spinal-cord-nogo-combinatorial-repair", "type": "hypothesis", "title": "Complete motor recovery after complete spinal cord injury requires combinatorial treatment targeting three independent barriers: Nogo-A/NgR1 inhibition (axon growth block), chondroitin sulphate proteoglycan (CSPG) matrix digestion, and neural progenitor cell bridges — no single intervention is sufficient.\n", "status": "active", "fields": [ "spinal-cord-research", "regenerative-medicine", "neuroscience" ], "color": "green" }, { "id": "h-squid-array-regularization-improves-meg-source-localization", "type": "hypothesis", "title": "Increasing SQUID channel count with isotropic coverage and calibrated noise covariance lowers posterior credible set diameter for focal sources at fixed SNR in anatomically constrained Bayesian inversion — testable on cortical phantoms with ground truth.\n", "status": "active", "fields": [ "neuroscience", "applied-mathematics" ], "color": "green" }, { "id": "h-stability-selected-lasso-panels-outperform-fixed-biomarkers-under-assay-noise", "type": "hypothesis", "title": "Stability-selected lasso biomarker panels maintain diagnostic utility better than fixed panels under assay drift.", "status": "active", "fields": [ "medicine", "statistics", "biology" ], "color": "green" }, { "id": "h-stackelberg-equilibrium-predicts-security-market-underinvestment", "type": "hypothesis", "title": "The Stackelberg defender-attacker equilibrium predicts that organizations systematically overinvest in visible, deterrence-oriented security and underinvest in detection and response — a prediction testable through breach cost and security spending decomposition data.\n", "status": "active", "fields": [ "cybersecurity", "economics", "game-theory" ], "color": "green" }, { "id": "h-staggered-commutation-frequency-threshold-for-target-isolation-db", "type": "hypothesis", "title": "Achieving ≥20 dB isolation across ≥50 MHz instantaneous bandwidth at GHz carriers requires modulation frequency ≥0.05 ω_carrier for staggered commutated-line cells with realistic switch parasitics — below this ratio, Floquet sidebands overlap passbands and collapse isolation.\n", "status": "active", "fields": [ "electromagnetism", "microwave-engineering" ], "color": "green" }, { "id": "h-stain-normalized-unet-training-improves-cross-site-pathology-consistency", "type": "hypothesis", "title": "Stain-normalized U-Net pipelines reduce cross-site variance in pathology quantification versus raw-image training.", "status": "active", "fields": [ "medicine", "computer-vision", "molecular-biology" ], "color": "green" }, { "id": "h-stalling-density-wave-speed-correlates-with-seq-measured-pause-density-peaks", "type": "hypothesis", "title": "Wave-like accumulation metrics derived from coarse-grained ASEP simulations will correlate with peaks in polymerase pause density tracks derived from NET-seq style assays along regions engineered with programmed slow sites — falsified if chromatin remodeling dominates pause statistics unrelated to exclusion physics.\n", "status": "active", "fields": [ "genomics", "statistical-physics" ], "color": "green" }, { "id": "h-starling-murmuration-criticality-vicsek", "type": "hypothesis", "title": "Starling murmurations are tuned near the Vicsek critical point, maintaining scale-free correlations that maximize information transfer speed across the flock, measurable through correlation length scaling with flock size", "status": "active", "fields": [ "physics", "biology" ], "color": "green" }, { "id": "h-starling-oncotic-reversal-lymphatic-dependence", "type": "hypothesis", "title": "In tissues with high capillary permeability, the revised Starling equation predicts near-zero net filtration across the capillary wall, making lymphatic drainage of macromolecule-laden interstitial fluid the primary mechanism of edema prevention rather than oncotic pressure reabsorption\n", "status": "active", "fields": [ "physiology", "fluid-mechanics" ], "color": "green" }, { "id": "h-statistical-thermodynamics-equilibrium-partition-function", "type": "hypothesis", "title": "All of chemical equilibrium derives from a single statistical mechanical quantity: the molecular partition function Z = Σ_states exp(-E_state/kT); the equilibrium constant K = exp(-ΔG°/RT) equals the ratio of product to reactant partition functions, with van't Hoff temperature dependence d(lnK)/dT = ΔH°/RT² following immediately from d(lnZ)/dT = /kT².\n", "status": "active", "fields": [ "physical-chemistry", "statistical-mechanics", "quantum-chemistry", "thermodynamics" ], "color": "green" }, { "id": "h-stdp-homeostatic-scaling-weight-stability", "type": "hypothesis", "title": "Multiplicative STDP combined with synaptic scaling maintains log-normally distributed synaptic weights that match observed in vivo distributions in cortical neurons", "status": "active", "fields": [ "neuroscience", "computational-neuroscience", "physics" ], "color": "green" }, { "id": "h-stellar-bh-spin-tidal-synchronization", "type": "hypothesis", "title": "Stellar-mass black hole spin distribution reflects natal spin from core collapse (moderate a* ~ 0.3-0.7) plus accretion history; tidal synchronization in compact binaries drives spin-orbit alignment while misaligned systems from asymmetric natal kicks retain distinct spin orientations detectable via GW inspiral waveforms", "status": "active", "fields": [ "astrophysics", "gravitational-wave-astronomy", "stellar-evolution" ], "color": "green" }, { "id": "h-stellar-imf-turbulent-fragmentation-universal", "type": "hypothesis", "title": "The stellar initial mass function is not truly universal but varies with gas temperature, Jeans mass, and Mach number of turbulence in molecular clouds, producing top-heavy IMFs in extreme starburst and high-z environments", "status": "active", "fields": [ "stellar-physics", "molecular-cloud-physics", "galaxy-evolution" ], "color": "green" }, { "id": "h-stem-cell-niche-mechanotransduction-quiescence", "type": "hypothesis", "title": "Adult stem cell quiescence is maintained by low ECM stiffness in the niche through YAP/TAZ cytoplasmic sequestration, and tissue injury activates stem cells by increasing matrix stiffness (via MMP-mediated crosslinking) to a threshold that triggers YAP/TAZ nuclear translocation and proliferative gene expression.\n", "status": "active", "fields": [ "stem-cell-biology", "biophysics", "regenerative-medicine" ], "color": "green" }, { "id": "h-stereotype-kernel-of-truth-social-learning", "type": "hypothesis", "title": "Group stereotypes form and persist through Bayesian social learning from accurate (but base-rate-neglecting) sample statistics of group member behaviour, combined with availability heuristics that overweight memorable exceptions — inaccurate stereotypes persist when outgroup contact is rare, making availability error proportional to segregation level.\n", "status": "active", "fields": [ "social-psychology", "cognitive-science", "sociology" ], "color": "green" }, { "id": "h-stereotype-threat-racial-achievement-gap-mechanism", "type": "hypothesis", "title": "Stereotype threat — the situational pressure of confirming a negative group stereotype — accounts for 20–40% of the Black-White standardized test score gap through working-memory depletion and vigilance activation, and identity-safe learning environments that remove threat cues produce the largest and most durable achievement gap reductions.\n", "status": "active", "fields": [ "psychology", "education", "social-science" ], "color": "green" }, { "id": "h-stochastic-gene-expression-bet-hedging-optimal-noise", "type": "hypothesis", "title": "The Fano factor of fitness-relevant bacterial promoters is tuned by natural selection to match the temporal variance of their ecological environment — specifically, promoters controlling stress-response genes in high-variance environments will exhibit significantly higher Fano factors than orthologous promoters in stable environments, with the optimal F predicted by the Kelly criterion applied to the environmental power spectrum.\n", "status": "active", "fields": [ "molecular-biology", "evolutionary-biology", "biophysics", "ecology" ], "color": "green" }, { "id": "h-stochastic-resonance-matches-information-peak-in-cell-signaling", "type": "hypothesis", "title": "In threshold-dominated signaling modules, the noise amplitude that maximizes spectral coherence also maximizes input-output mutual information for weak periodic stimuli.\n", "status": "active", "fields": [ "biophysics", "systems-biology", "information-theory" ], "color": "green" }, { "id": "h-stochastic-resonance-neural-coding-optimality", "type": "hypothesis", "title": "The spontaneous firing rate of primary auditory nerve fibers is within a factor of 2 of the stochastic resonance optimum predicted by the ratio of detection threshold sound pressure to hair cell thermal noise, across at least 5 mammalian species.\n", "status": "active", "fields": [ "neuroscience", "physics", "signal-processing" ], "color": "green" }, { "id": "h-stratosphere-troposphere-annular-mode-coupling", "type": "hypothesis", "title": "Stratospheric variability modulates tropospheric jet streams through anomalous Eliassen-Palm flux divergence that alters the refractive index for Rossby waves, shifting the tropospheric annular mode and accounting for ~20% of multi-decadal jet variability, detectable as lagged correlation between polar cap temperature and NAM/SAM indices.\n", "status": "active", "fields": [ "atmospheric-dynamics", "climate-science", "fluid-dynamics", "geophysics" ], "color": "green" }, { "id": "h-stratosphere-troposphere-annular-mode", "type": "hypothesis", "title": "The Northern Annular Mode (NAM) propagates downward from the stratosphere to the troposphere on 2-8 week timescales via eddy-mean flow interaction, providing decadal predictability of winter circulation patterns linked to stratospheric ozone and greenhouse gas changes", "status": "active", "fields": [ "atmospheric-science", "dynamical-meteorology", "climate-science" ], "color": "green" }, { "id": "h-stress-granule-binodal-concentration-prediction", "type": "hypothesis", "title": "The dilute-phase concentration of TDP-43 at stress granule equilibrium (measured by single-molecule imaging in living cells) will follow the Flory-Huggins binodal prediction as a function of total cellular TDP-43 concentration, and ALS-linked A315T mutation will shift the binodal to lower concentrations by an amount predictable from the change in the chi parameter measured by in-vitro turbidimetry", "status": "active", "fields": [ "cell-biology", "soft-matter", "biophysics" ], "color": "green" }, { "id": "h-structural-holes-income-mobility-mediation", "type": "hypothesis", "title": "Betweenness centrality (structural holes / brokerage position) in social networks mediates intergenerational income mobility: individuals who bridge disconnected social classes have higher upward mobility, and this effect is partially causal, as shown by interventions that create cross-class network connections\n", "status": "active", "fields": [ "sociology", "economics", "network-science" ], "color": "green" }, { "id": "h-student-transfer-zeno-curve-to-sampling-stability-drills", "type": "hypothesis", "title": "Embedded-systems learners who complete paired modules (quantum Zeno cadence sweep diagrams + deterministic watchdog scheduling drills) will score higher on quantitative timing-margin problems than controls matched on prerequisite GPA — falsified if analogy-first instruction increases misconception rates on validated quantum-literacy probes.\n", "status": "active", "fields": [ "computer-science", "physics-education" ], "color": "green" }, { "id": "h-subduction-initiation-passive-margin-collapse", "type": "hypothesis", "title": "Subduction initiation occurs preferentially at passive margins via gravitational collapse of dense, waterloaded lithosphere, requiring a combination of topographic forcing and sediment lubrication — spontaneous nucleation without a pre-existing weakness is rare", "status": "active", "fields": [ "geodynamics", "plate-tectonics", "structural-geology" ], "color": "green" }, { "id": "h-supersymmetry-electroweak-hierarchy-stabilization", "type": "hypothesis", "title": "A compressed-spectrum supersymmetric extension of the Standard Model with gluino mass 1.5-2 TeV and neutralino mass 300-500 GeV stabilizes the electroweak hierarchy, provides a dark matter candidate (lightest supersymmetric particle), and remains consistent with all current LHC exclusion limits while predicting observable signatures at the HL-LHC.\n", "status": "active", "fields": [ "particle-physics", "cosmology", "mathematical-physics" ], "color": "green" }, { "id": "h-supervolcano-uplift-precursor-timescale", "type": "hypothesis", "title": "Supereruption onset at silicic calderas is preceded by decades-to-centuries of measurable precursors (unrest cycles, caldera uplift > 1m/yr, increased CO₂/SO₂ ratios) — the eruption onset is not geologically instantaneous but involves a time-predictable magma remobilization sequence detectable with current monitoring networks.\n", "status": "active", "fields": [ "volcanology", "geophysics", "hazard-science" ], "color": "green" }, { "id": "h-supply-chain-network-x-bond-percolation-disruption", "type": "hypothesis", "title": "Empirical fits of correlated edge-failure models on anonymized automotive tier networks will lower out-of-sample disruption-size tail misclassification versus IID bond percolation baselines — measured by proper scoring rules on held-out shock events stratified by disaster category.\n", "status": "active", "fields": [ "economics", "network-science" ], "color": "green" }, { "id": "h-supply-chain-percolation-threshold-dual-sourcing", "type": "hypothesis", "title": "Dual-sourcing critical components (Tier 1 parts with single-source suppliers) raises a supply chain network's effective percolation threshold above the operational disruption probability, predicting that firms that dual-source their top 10% highest-risk suppliers reduce production halts from supplier failure by >60% with <15% increase in procurement cost.\n", "status": "active", "fields": [ "operations-research", "complex-systems", "economics" ], "color": "green" }, { "id": "h-supply-chain-resilience-modularity", "type": "hypothesis", "title": "Supply chain resilience scales with network modularity rather than inventory buffers — modular supply networks with regional redundancy outperform JIT systems during tail-risk events, with the resilience-efficiency frontier parameterized by the modularity index Q of the supplier network graph.\n", "status": "active", "fields": [ "operations-research", "network-science", "industrial-economics" ], "color": "green" }, { "id": "h-surface-code-practical-threshold-2030", "type": "hypothesis", "title": "Superconducting qubit systems will achieve surface code logical error rates below 10⁻⁶ per logical gate cycle before 2030, enabling the first demonstration of quantum advantage in fault-tolerant mode for a classically intractable problem.\n", "status": "active", "fields": [ "quantum-computing", "quantum-error-correction", "superconducting-qubits" ], "color": "green" }, { "id": "h-surprisal-n400-mismatch-equivalence", "type": "hypothesis", "title": "N400 ERP amplitude is a linear function of word surprisal (-log P(w | syntactic and semantic context)) computed by a hierarchical predictive model, controlling for word frequency, semantic plausibility, and cloze probability — and the slope of this linear relationship is modulated by precision (contextual predictability) in the same way that precision-weighting modulates prediction-error gain in Friston's free-energy framework\n", "status": "active", "fields": [ "linguistics", "neuroscience", "cognitive-science" ], "color": "green" }, { "id": "h-survey-propagation-rsat-threshold-prediction", "type": "hypothesis", "title": "Survey propagation derived from the 1RSB cavity method predicts the satisfiability threshold of random 3-SAT to within 0.1% and solves instances near the threshold in polynomial expected time", "status": "active", "fields": [ "statistical-physics", "computer-science", "mathematics" ], "color": "green" }, { "id": "h-swarm-pheromone-convergence-rate", "type": "hypothesis", "title": "In a swarm-robot ACO system, convergence time to within 5% of the shortest path scales as O(|E| log|V| / ρ), where |E| is edge count, |V| is vertex count, and ρ is the evaporation rate.\n", "status": "active", "fields": [ "robotics", "operations-research" ], "color": "green" }, { "id": "h-swiss-cheese-alignment-accident-prediction", "type": "hypothesis", "title": "Accidents in safety-critical systems can be predicted prospectively by measuring the temporal correlation of latent condition indicators (near-miss rate, procedure deviation rate, organizational safety climate score) across defensive layers — accidents occurring when these indicators co-exceed threshold simultaneously, consistent with the Swiss Cheese model's hole-alignment mechanism.\n", "status": "active", "fields": [ "safety-science", "organizational-psychology", "engineering", "epidemiology" ], "color": "green" }, { "id": "h-symmetry-breaking-goldstone-bosons", "type": "hypothesis", "title": "The anomalous magnon dispersion in kagome antiferromagnets with Dzyaloshinskii-Moriya interaction is a type-II Goldstone mode (ω∝k²) arising from the simultaneous breaking of time-reversal and spatial inversion, detectable by inelastic neutron scattering", "status": "active", "fields": [ "condensed-matter", "particle-physics", "quantum-field-theory" ], "color": "green" }, { "id": "h-symmetry-breaking-universal-phase-transition-classifier", "type": "hypothesis", "title": "Every continuous phase transition in nature corresponds to the spontaneous breaking of a specific symmetry group G → H, and the universality class (critical exponents) is determined entirely by the dimension of G/H and the spatial dimension d — making the Landau-Ginzburg-Wilson classification complete for all equilibrium phase transitions\n", "status": "active", "fields": [ "condensed-matter-physics", "mathematical-physics", "statistical-mechanics" ], "color": "green" }, { "id": "h-symplectic-capacities-convex-equality", "type": "hypothesis", "title": "All symplectic capacities coincide on convex bodies in R^{2n} — the Ekeland-Hofer and Gromov width capacities are equal for convex sets — and this equality is equivalent to a sharp systolic inequality for convex contact hypersurfaces, connecting Hamiltonian dynamics to convex geometry.\n", "status": "active", "fields": [ "symplectic-geometry", "convex-geometry", "Hamiltonian-dynamics" ], "color": "green" }, { "id": "h-symplectic-controllers-preserve-energy-bounds-long-horizon", "type": "hypothesis", "title": "In mechanics-dominated planning tasks, symplectic rollout models produce lower long-horizon energy drift and fewer constraint violations than non-symplectic integrators at comparable runtime.\n", "status": "active", "fields": [ "control-engineering", "applied-mathematics", "robotics" ], "color": "green" }, { "id": "h-symplectic-quantization-new-prediction", "type": "hypothesis", "title": "Floer homology groups of a symplectic manifold (M, ω) compute the quantum energy spectrum of the corresponding Hamiltonian system in the semiclassical limit, providing new spectral predictions beyond the Gutzwiller trace formula.\n", "status": "active", "fields": [ "mathematics", "mathematical-physics", "quantum-mechanics", "symplectic-geometry" ], "color": "green" }, { "id": "h-synaesthesia-disinhibited-feedback-hyperconnectivity", "type": "hypothesis", "title": "Synesthesia arises from structural hyperconnectivity between adjacent cortical areas (e.g., V4 colour and grapheme areas) combined with disinhibited feedback, predicting increased white matter connectivity detectable by DTI", "status": "active", "fields": [ "cognitive-neuroscience", "developmental-neuroscience", "neurogenetics" ], "color": "green" }, { "id": "h-synapse-heterogeneity-plasticity-code", "type": "hypothesis", "title": "Molecular heterogeneity across synapses between the same pre- and post-synaptic neurons encodes the history of plasticity events at each individual synapse — constituting a molecular \"engram\" at the synapse level — and this heterogeneity is actively regulated by activity-dependent protein sorting rather than being stochastic noise.\n", "status": "active", "fields": [ "neuroscience", "molecular-neuroscience", "cellular-neuroscience" ], "color": "green" }, { "id": "h-synchrony-prosociality-physiological", "type": "hypothesis", "title": "The prosocial effect of rhythmic synchrony is mediated by shared arousal state (measured by heart rate variability coherence) rather than self-other overlap or mirror neuron activation, and the effect is eliminated when arousal is equated between synchrony and asynchrony conditions.\n", "status": "active", "fields": [ "social-psychology", "cognitive-science", "psychophysiology", "art-and-cognition" ], "color": "green" }, { "id": "h-synthetic-biology-x-circuit-design", "type": "hypothesis", "title": "Retroactivity compensation using insulator genetic parts (insulators) restores logical modularity in synthetic gene circuits, enabling formal CAD composition rules with predictable transfer functions measurable by flow cytometry.\n", "status": "active", "fields": [ "synthetic-biology", "computer-science", "control-theory", "biological-engineering" ], "color": "green" }, { "id": "h-synthetic-insulator-retroactivity-control", "type": "hypothesis", "title": "Phosphorylation-dephosphorylation futile cycle insulators can reduce retroactivity by > 90% in standard E. coli genetic circuit configurations while imposing < 15% metabolic overhead, making them practical for composing circuits of up to 10 modules without significant dynamic interference.\n", "status": "active", "fields": [ "synthetic-biology", "engineering", "systems-biology" ], "color": "green" }, { "id": "h-t2d-reversal-hepatic-fat-beta-cell-recovery", "type": "hypothesis", "title": "Type 2 diabetes remission following bariatric surgery or very-low-calorie diet is mechanistically driven by rapid reduction in intrahepatocellular fat, which restores hepatic insulin sensitivity within 1 week, followed by slower reduction of pancreatic fat that allows beta cell functional recovery over 8-12 weeks, with remission durability determined by whether beta cell mass was permanently lost before intervention.\n", "status": "active", "fields": [ "metabolic-medicine", "endocrinology", "hepatology", "nutrition-science" ], "color": "green" }, { "id": "h-t2d-reversal-hepatic-fat-beta-cell", "type": "hypothesis", "title": "Type 2 diabetes remission following bariatric surgery or very low calorie diets is mechanistically driven by the twin cycle hypothesis (Roy Taylor): first cycle removes ectopic liver fat restoring hepatic insulin sensitivity within days; second cycle removes ectopic pancreatic fat restoring beta cell first-phase insulin secretion within weeks — both reversals linked to total fat mobilization below person-specific thresholds", "status": "active", "fields": [ "endocrinology", "metabolism", "gastroenterology", "bariatric-medicine" ], "color": "green" }, { "id": "h-tad-boundary-disruption-ctcf-site-oncogene-activation-quantitative", "type": "hypothesis", "title": "Deletion or methylation of CTCF binding sites at TAD boundaries separating proto-oncogenes from their endogenous enhancers will activate those oncogenes proportionally to the Hi-C contact frequency increase between the gene and the newly accessible enhancer — providing a quantitative model for boundary disruption oncogenesis.\n", "status": "active", "fields": [ "biology", "biophysics", "molecular-biology", "oncology" ], "color": "green" }, { "id": "h-tag-decay-timescale-vs-write-buffer-lifetime-correlation-classroom-only", "type": "hypothesis", "title": "No statistically stable correlation will appear between biological tag decay timescales and silicon write-buffer flush intervals across published datasets — hypothesis framed as **expected null** guarding against over-extrapolating the synaptic-tag/cache analogy into hardware claims.\n", "status": "active", "fields": [ "neuroscience", "computer-science" ], "color": "green" }, { "id": "h-tandem-cell-thermodynamic-optimum-bandgap-pairing", "type": "hypothesis", "title": "For a two-junction tandem solar cell under the AM1.5G spectrum, the thermodynamic optimum top-cell bandgap is 1.73 ± 0.05 eV and bottom-cell bandgap is 1.12 ± 0.05 eV, and any perovskite/silicon tandem with E_g(top) between 1.68 and 1.78 eV will achieve >29% efficiency under radiative-limit conditions.\n", "status": "active", "fields": [ "photovoltaics", "thermodynamics" ], "color": "green" }, { "id": "h-targeted-hub-vaccination-achieves-herd-immunity-fewer-doses-scale-free", "type": "hypothesis", "title": "Targeted vaccination of high-degree nodes (hubs) in scale-free contact networks achieves epidemic suppression (R_eff < 1) with the number of doses equal to O(√N) rather than the O(N·(1-1/R₀)) doses required by random mass vaccination, and this reduction is achievable in practice using acquaintance immunisation (vaccinate a random contact of a random person).\n", "status": "active", "fields": [ "epidemiology", "network-science", "mathematics", "public-health" ], "color": "green" }, { "id": "h-targeted-memory-reactivation-during-sleep-enhances-consolidation", "type": "hypothesis", "title": "Closed-loop auditory targeted memory reactivation (TMR) timed to hippocampal sharp-wave ripple detection during NREM slow-wave sleep will selectively enhance consolidation of specific paired-associate memories by >30% compared to uncued sleep, providing causal evidence that SPW-R-coupled reactivation drives memory transfer from hippocampus to neocortex.\n", "status": "active", "fields": [ "systems-neuroscience", "sleep-science", "cognitive-neuroscience" ], "color": "green" }, { "id": "h-targeted-vaccination-percolation-optimality", "type": "hypothesis", "title": "Targeted vaccination of the top-k% highest-degree individuals in a contact network reduces the giant-component size (and therefore final epidemic size) by a factor at least 3× greater than random vaccination at the same coverage, for all real-world contact networks with degree-distribution variance ⟨k²⟩/⟨k⟩² > 5.\n", "status": "active", "fields": [ "epidemiology", "network-science" ], "color": "green" }, { "id": "h-tatonnement-convergence-diagonal-dominance", "type": "hypothesis", "title": "Tâtonnement convergence is guaranteed by diagonal dominance of the excess demand Jacobian (|∂z_i/∂p_i| > Σ_{j≠i} |∂z_i/∂p_j|) as a weaker sufficient condition than gross substitutability that encompasses complementary goods markets\n", "status": "active", "fields": [ "economics", "mathematics" ], "color": "green" }, { "id": "h-tau-propagation-circuit-connectivity-determines-staging", "type": "hypothesis", "title": "Tau propagation in Alzheimer's disease follows the structural connectome hierarchy (entorhinal → hippocampal → association cortex) because cell-to-cell transfer rate is proportional to synaptic connection strength, and disrupting the entorhinal-CA1 projection with targeted connectivity interruption will slow Braak staging progression in mouse models.\n", "status": "active", "fields": [ "neuroscience", "cell-biology", "molecular-biology" ], "color": "green" }, { "id": "h-tbi-neuroinflammation-microbiome-repair", "type": "hypothesis", "title": "The ceiling on neurological recovery after severe TBI is set by chronic neuroinflammation driven by gut-brain axis dysbiosis post-injury, and restoring gut microbiome diversity (via FMT or targeted probiotic supplementation) significantly expands the recovery ceiling beyond current rehabilitation limits.\n", "status": "active", "fields": [ "neurotrauma", "microbiology", "neuroscience" ], "color": "green" }, { "id": "h-tcr-foundation-pretraining-improves-antigen-specificity-recall", "type": "hypothesis", "title": "Foundation-model pretraining improves TCR antigen-specificity recall on unseen epitope families.", "status": "active", "fields": [ "immunology", "machine-learning", "bioinformatics" ], "color": "green" }, { "id": "h-tcr-repertoire-percolation-threshold-pathogen-coverage", "type": "hypothesis", "title": "The human naive T-cell repertoire of ~10⁷ clonotypes sits at the percolation threshold for 99% pathogen coverage in a 15-dimensional TCR-pMHC shape space with cross-reactivity radius r ≈ 10⁻⁵, predicting that repertoire sizes below 10⁶ (as in aging or lymphopenia) create coverage holes detectable as increased susceptibility to novel viral epitopes.\n", "status": "active", "fields": [ "immunology", "physics", "mathematics", "statistics" ], "color": "green" }, { "id": "h-td-prediction-error-dopamine-burst-identity-schultz", "type": "hypothesis", "title": "Dopamine neuron firing rate encodes the exact TD prediction error δ_t = r_t + γV(s_{t+1}) − V(s_t) with a linear gain of ~1 spike/s per unit of normalized δ, and the heterogeneity in DA neuron responses (Dabney et al. 2020) encodes the full quantile distribution of future returns — falsifiable by measuring DA neuron responses to systematically varied reward probability distributions.\n", "status": "active", "fields": [ "mathematics", "neuroscience", "computer-science", "computational-neuroscience" ], "color": "green" }, { "id": "h-tda-cancer-subtype-prognosis-superiority", "type": "hypothesis", "title": "Topological Data Analysis (Mapper algorithm) applied to TCGA breast cancer gene expression data will identify at least one prognostically significant patient subgroup — defined by topological isolation (a flare or connected component in the Mapper graph) — that is missed by k-means, hierarchical clustering, and PAM50 molecular subtypes, and that shows a statistically significant difference in 10-year overall survival (log-rank p < 0.01) compared to the most similar standard subtype\n", "status": "active", "fields": [ "medicine", "oncology", "mathematics", "computational-biology" ], "color": "green" }, { "id": "h-tda-cognitive-map-nontrivial-topology", "type": "hypothesis", "title": "Abstract conceptual representations in prefrontal cortex have non-trivial topological structure (Betti numbers > 0) detectable by persistent homology of population activity", "status": "active", "fields": [ "computational-neuroscience", "algebraic-topology", "cognitive-science" ], "color": "green" }, { "id": "h-tda-x-shape-recognition", "type": "hypothesis", "title": "Persistent homology with atom-type weighted Vietoris-Rips filtration achieves ΔlogIC₅₀ ≤ 0.3 RMSE for drug-target binding affinity prediction on ChEMBL benchmarks, outperforming unweighted TDA by >30% and matching graph neural network performance with 10× fewer parameters", "status": "active", "fields": [ "mathematics", "computer_science", "chemistry" ], "color": "green" }, { "id": "h-tectonic-stress-coulomb-failure", "type": "hypothesis", "title": "Rate-and-state friction Coulomb model (R/R₀=exp(ΔCFF/Aσ)) predicts aftershock locations significantly better than static threshold ΔCFF > 0 model, particularly for aftershocks within 1 year of the mainshock", "status": "active", "fields": [ "geophysics", "seismology", "mechanics" ], "color": "green" }, { "id": "h-telomere-causality-partial-reprogramming", "type": "hypothesis", "title": "Telomere shortening is causally upstream of at least the senescence and inflammation hallmarks of aging (not merely a biomarker), as demonstrated by the fact that partial in vivo reprogramming restores telomere length and downstream hallmarks simultaneously, and telomerase gene therapy extends healthy lifespan in adult mice.\n", "status": "active", "fields": [ "biogerontology", "molecular-biology", "genetics" ], "color": "green" }, { "id": "h-telomere-length-social-gradient-reversibility", "type": "hypothesis", "title": "Telomere shortening caused by chronic social stress is partially reversible through sustained reduction of psychosocial stressors — demonstrated by social mobility interventions (housing vouchers, income support) producing measurable telomere lengthening within 2-5 years, driven by reduced HPA axis activation and oxidative stress.\n", "status": "active", "fields": [ "epidemiology", "social-science", "biology", "public-health", "endocrinology" ], "color": "green" }, { "id": "h-tensegrity-cancer-mechanics", "type": "hypothesis", "title": "Cancer cells that have lost normal cytoskeletal tensegrity architecture have measurably lower mechanical stiffness (Young's modulus) and higher deformability than normal cells of the same tissue type, making atomic force microscopy a viable early detection modality.\n", "status": "active", "fields": [ "cell-biology", "biophysics", "oncology", "engineering", "materials-science", "clinical-medicine" ], "color": "green" }, { "id": "h-tensor-network-entanglement-phase-boundary", "type": "hypothesis", "title": "The boundary between classically simulable and quantumly advantageous many-body systems corresponds precisely to a phase transition in entanglement entropy from area-law to logarithmic scaling, and this transition can be detected by monitoring tensor network bond dimension scaling as a function of system size", "status": "active", "fields": [ "quantum-physics", "mathematics", "condensed-matter" ], "color": "green" }, { "id": "h-tensor-networks-x-quantum-states", "type": "hypothesis", "title": "For 2D gapped topological phases with Abelian anyons (Z_N topological order), PEPS contraction reduces to a polynomial-time problem via the string-net condensation structure, while non-Abelian anyonic PEPS remain #P-hard", "status": "active", "fields": [ "physics", "computer_science", "mathematics", "quantum-computing" ], "color": "green" }, { "id": "h-theory-laden-observation-background-knowledge", "type": "hypothesis", "title": "Scientific observations are theory-laden at the level of background knowledge and instrument design but not at the level of perceptual phenomenology — implying that theory-ladenness poses a genuine but bounded epistemic threat: it enables theory-confirmation bias in instrument design and data selection while leaving a theory-neutral observational residue sufficient for inter-theoretical evidence comparison.\n", "status": "active", "fields": [ "philosophy-of-science", "epistemology", "cognitive-science" ], "color": "green" }, { "id": "h-theory-of-mind-tpj-development-timing", "type": "hypothesis", "title": "Theory of mind (ToM) emerges at age 3-4 from the maturation of bilateral temporoparietal junction (TPJ) connectivity to the medial prefrontal cortex, with the right TPJ specifically encoding the perspective-taking component, and the transition from implicit to explicit ToM reflecting frontal executive maturation rather than the emergence of a new social module.\n", "status": "active", "fields": [ "developmental-neuroscience", "cognitive-psychology", "social-neuroscience", "clinical-psychology" ], "color": "green" }, { "id": "h-theory-of-mind-tpj-development", "type": "hypothesis", "title": "The right temporoparietal junction (rTPJ) is the minimal neural substrate necessary for explicit belief attribution and false-belief understanding, developing functional specialization by age 4-5 through experience-dependent myelination, with disruption producing autism spectrum social deficits", "status": "active", "fields": [ "cognitive-neuroscience", "developmental-psychology", "social-neuroscience" ], "color": "green" }, { "id": "h-thermoacoustic-travelling-wave-carnot-approach", "type": "hypothesis", "title": "Thermoacoustic travelling-wave engines (Stirling-cycle topology) can approach Carnot efficiency more closely than standing-wave engines because their piston displacement is in-phase with pressure oscillation throughout the regenerator, minimising irreversible heat transfer and approaching the ideal regenerator limit.\n", "status": "active", "fields": [ "thermoacoustics", "thermodynamics", "engineering-physics" ], "color": "green" }, { "id": "h-thermodynamics-non-convex-regions-phase-coexistence", "type": "hypothesis", "title": "Regions where the fundamental thermodynamic relation U(S,V,N) is locally concave (negative ∂²U/∂S² or ∂²U/∂V²) are thermodynamically unstable and correspond exactly to the spinodal decomposition region of the phase diagram — and the convex envelope of U predicts the Maxwell equal-area construction and coexistence curves without additional assumptions.\n", "status": "active", "fields": [ "physical-chemistry", "mathematics", "materials-science", "soft-matter" ], "color": "green" }, { "id": "h-thermoelectric-phonon-glass-electron-crystal", "type": "hypothesis", "title": "A Bi0.5Sb1.5Te3 / MoS2 nanocomposite combining phonon-glass scattering (reduced kappa_L) with electron-crystal transport (preserved carrier mobility) will achieve zT > 2.5 at 450 K, and the Onsager model will quantitatively predict the zT enhancement from measured Seebeck coefficient, electrical conductivity, and thermal conductivity components without free parameters", "status": "active", "fields": [ "materials-science", "thermodynamics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-thermoelectric-zt-phonon-glass-electron-crystal", "type": "hypothesis", "title": "The fundamental upper limit on thermoelectric figure-of-merit zT is set by the Wiedemann-Franz law violation achievable through bipolar filtering of phonon transport by rattler atoms in cage-structured materials; the theoretical maximum zT approaches unity from the phonon-glass electron-crystal design principle and cannot be substantially exceeded without materials that simultaneously achieve electronic topological protection and phononic Anderson localisation.\n", "status": "active", "fields": [ "materials-science", "condensed-matter-physics", "solid-state-physics" ], "color": "green" }, { "id": "h-thermohaline-circulation-x-buoyancy-flow", "type": "hypothesis", "title": "The AMOC is within 0.3 Sv of its saddle-node bifurcation tipping point based on critical slowing down analysis of sea surface temperature fingerprints from 1950-2025, and will collapse to the weak state within 50 years under RCP8.5 freshwater forcing from Greenland Ice Sheet melt", "status": "active", "fields": [ "geoscience", "physics", "oceanography", "climate-science" ], "color": "green" }, { "id": "h-thorium-msr-achieves-baseload-carbon-free-power-lower-waste", "type": "hypothesis", "title": "Thorium molten salt reactors (MSR) can achieve economically competitive baseload power generation with lifecycle carbon emissions < 15 gCOΓéé/kWh, nuclear waste radiotoxicity returning to natural uranium levels within 300 years, and passive safety features that eliminate the need for active emergency cooling systems.\n", "status": "active", "fields": [ "nuclear-engineering", "materials-science", "energy-policy", "chemistry" ], "color": "green" }, { "id": "h-thymic-rejuvenation-immunosenescence", "type": "hypothesis", "title": "Age-related immunosenescence is causally driven by thymic involution, and thymic regeneration via FoxN1 reactivation or sex-hormone ablation can restore naive T-cell output and functional immune responses in aged mammals.\n", "status": "active", "fields": [ "immunology", "biogerontology", "endocrinology" ], "color": "green" }, { "id": "h-tidal-deformability-tightens-symmetry-energy-slope", "type": "hypothesis", "title": "Joint NICER-like radius posteriors for two pulsars with masses differing by >0.2 M⊙ will shrink allowed symmetry-energy slope L by more than either measurement alone at fixed nuclear model class.", "status": "active", "fields": [ "physics", "astronomy" ], "color": "green" }, { "id": "h-tidal-internal-wave-mixing-abyssal-hotspots", "type": "hypothesis", "title": "Tidal-topographic internal wave generation at rough seafloor features (mid-ocean ridges, continental slopes) creates geographically constrained abyssal mixing hotspots that account for >50% of the total diapycnal mixing required to maintain thermohaline circulation — and their omission from climate models biases 21st-century ocean heat uptake projections by >15%.\n", "status": "active", "fields": [ "physical-oceanography", "climate-science", "fluid-dynamics" ], "color": "green" }, { "id": "h-tidal-mixing-overturning-circulation-control", "type": "hypothesis", "title": "The strength of the Atlantic Meridional Overturning Circulation (AMOC) is controlled as much by spatially heterogeneous tidal mixing in key ocean ridge regions as by surface buoyancy forcing, predicting that AMOC weakening under climate change is partially offset by constant tidal energy input maintaining abyssal mixing.\n", "status": "active", "fields": [ "oceanography", "geophysics", "climate-science" ], "color": "green" }, { "id": "h-time-perception-striatal-beat-frequency-model", "type": "hypothesis", "title": "Interval timing in the seconds-to-minutes range is implemented by the striatal beat frequency (SBF) model: cortical oscillators set by dopaminergic input at time onset, with striatal medium spiny neurons detecting coincident activation patterns proportional to elapsed time, distortable by dopamine manipulation.\n", "status": "active", "fields": [ "cognitive-neuroscience", "neurophysiology", "dopamine-research" ], "color": "green" }, { "id": "h-time-perception-striatal-beat-frequency", "type": "hypothesis", "title": "Subjective time perception is implemented by the striatal beat frequency (SBF) model: medium spiny neurons detect coincident firing patterns from a large population of cortical oscillators; the length of perceived intervals scales with the number of oscillators recruited, and dopamine modulates clock speed by shifting oscillator frequencies uniformly.\n", "status": "active", "fields": [ "cognitive-neuroscience", "time-perception", "dopamine-neuroscience", "neurophysiology" ], "color": "green" }, { "id": "h-time-rescaled-residuals-separate-poisson-from-bursty-counting-systems", "type": "hypothesis", "title": "Time-rescaled residual tests will detect non-Poisson structure from refractory or detector-dead-time effects at lower false-positive rates than raw Fano-factor thresholds in matched decay-count and spike-train simulations; falsified if calibration error is not reduced by at least 25 percent.\n", "status": "active", "fields": [ "probability", "neuroscience", "physics" ], "color": "green" }, { "id": "h-tissue-jamming-universality-class", "type": "hypothesis", "title": "Epithelial tissue jamming belongs to the mean-field universality class with critical exponents beta=0.5, nu=0.5, detectable via finite-size scaling of rigidity in 3D organoids", "status": "active", "fields": [], "color": "green" }, { "id": "h-tom-implicit-explicit-dissociation", "type": "hypothesis", "title": "The implicit-explicit theory of mind dissociation in great apes (and in human infants under 4 years) reflects a phylogenetically conserved subcortical mentalization system (STS + amygdala) that tracks agent behavior online without propositional belief representation, while explicit ToM requires PFC-TPJ circuitry for constructing and manipulating belief propositions — making the two systems dissociable by lesion and by task structure.\n", "status": "active", "fields": [ "cognitive-science", "comparative-psychology", "social-neuroscience", "developmental-psychology" ], "color": "green" }, { "id": "h-topo-ii-inhibitor-transcription-coupled-dna-damage-selectivity", "type": "hypothesis", "title": "Topoisomerase II inhibitors (doxorubicin, etoposide) kill tumor cells primarily through transcription-coupled double-strand breaks (at highly expressed oncogenes with high TOPO2 density) rather than replication-coupled breaks, explaining why they are active against slowly-dividing tumors and predicting that transcriptomics- based TOPO2 occupancy predicts tumor cell sensitivity.\n", "status": "active", "fields": [ "mathematics", "chemistry", "molecular-biology", "oncology", "pharmacology" ], "color": "green" }, { "id": "h-topoelectrical-circuit-edge-mode-disorder-threshold", "type": "hypothesis", "title": "Topoelectrical boundary-mode impedance peaks remain identifiable up to a component-disorder threshold set by the ratio of bandgap scale to effective damping, after which localization collapses rapidly", "status": "active", "fields": [ "electrical-engineering", "condensed-matter-physics", "topology" ], "color": "green" }, { "id": "h-topological-data-analysis-x-cancer-genomics", "type": "hypothesis", "title": "Persistent homology features (0-dimensional components, 1-cycles) of bulk tumor RNA-seq data from TCGA breast cancer cohort predict 5-year survival independently of standard molecular subtypes (PAM50), with a concordance index C > 0.65 in multivariate Cox regression.\n", "status": "active", "fields": [ "mathematics", "cancer-biology", "bioinformatics", "statistics" ], "color": "green" }, { "id": "h-topological-defect-density-predicts-organoid-lumenogenesis", "type": "hypothesis", "title": "The density and spatial arrangement of +1/2 topological defects in the nematic director field of epithelial organoids at the single-layer stage quantitatively predicts the number, size, and position of lumens that form during three-dimensional organoid morphogenesis, independently of biochemical signaling state\n", "status": "active", "fields": [ "developmental-biology", "biophysics", "soft-matter", "computational-biology" ], "color": "green" }, { "id": "h-topological-defect-morphogenesis", "type": "hypothesis", "title": "Topological defect charge is conserved during organ morphogenesis and acts as a predictive coordinate system; charge non-conservation events predict developmental failure.\n", "status": "active", "fields": [ "mathematical-physics", "developmental-biology", "biophysics", "soft-matter" ], "color": "green" }, { "id": "h-topological-defects-x-homotopy", "type": "hypothesis", "title": "Active matter systems (bacterial suspensions, cytoskeletal networks) exhibit topological defect coarsening dynamics governed by the same homotopy group fusion rules as equilibrium liquid crystals, but with activity-renormalized defect mobility\n", "status": "active", "fields": [ "physics", "mathematics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-topological-flocking-predator-evasion", "type": "hypothesis", "title": "Fish species exposed to higher natural predation pressure evolve topological (k-nearest-neighbor) schooling rules with smaller k, enabling faster collective evasion responses than metric (fixed-radius) rules, with k* ≈ log₂(group size) maximizing information propagation speed subject to noise constraints.\n", "status": "active", "fields": [ "marine-biology", "ethology", "statistical-physics", "active-matter-physics" ], "color": "green" }, { "id": "h-topological-insulator-disorder-robustness", "type": "hypothesis", "title": "Topological surface states in Z2 topological insulators remain conducting under time-reversal-symmetric disorder up to a critical disorder strength W_c proportional to the bulk gap Delta, above which a topological Anderson transition drives the surface into an Anderson-localized phase.\n", "status": "active", "fields": [ "condensed-matter-physics", "algebraic-topology" ], "color": "green" }, { "id": "h-topological-insulator-majorana-fault-tolerant-qubit", "type": "hypothesis", "title": "A topological qubit based on four Majorana zero modes in an InAs/Al heterostructure will achieve a logical qubit coherence time T₁_L > 1 ms at 20 mK — 100x longer than physical transmon qubits — by suppressing quasiparticle poisoning via a superconducting gap Δ > 200 μeV and a topological gap Δ_topo > 100 μeV, with the protection scaling exponentially with system length L as exp(−L/ξ).\n", "status": "active", "fields": [ "physics", "materials-science", "quantum-computing" ], "color": "green" }, { "id": "h-topological-insulator-x-band-theory", "type": "hypothesis", "title": "Non-Hermitian topological insulators with PT-symmetric gain-loss balanced photonic crystals host topologically protected surface modes with amplification factors determined by the imaginary part of the Hamiltonian, enabling lossless topological waveguides at optical frequencies", "status": "active", "fields": [ "physics", "mathematics", "photonics", "condensed-matter-physics" ], "color": "green" }, { "id": "h-topological-phase-qec-threshold-correspondence", "type": "hypothesis", "title": "The threshold error rate of topological quantum error-correcting codes is in exact correspondence with the paramagnetic phase boundary of their associated random-bond statistical mechanics models, and this correspondence can be exploited to compute thresholds for arbitrary noise models via classical Monte Carlo simulation", "status": "active", "fields": [ "quantum-information", "condensed-matter-physics", "statistical-mechanics" ], "color": "green" }, { "id": "h-topological-qubit-fault-tolerance-threshold", "type": "hypothesis", "title": "Topological qubits based on Majorana zero modes will achieve fault-tolerance thresholds 10–100× higher than superconducting or trapped-ion qubits due to non-local encoding of quantum information, making them the dominant platform for large-scale fault-tolerant quantum computation\n", "status": "active", "fields": [ "quantum-computing", "condensed-matter-physics", "quantum-error-correction" ], "color": "green" }, { "id": "h-topology-chern-number-predicts-edge-state-count", "type": "hypothesis", "title": "The bulk-boundary correspondence in topological insulators is exact: the number of protected conducting edge modes equals the absolute value of the Chern number |C₁| of the bulk Hamiltonian — and this equality is violated only when the bulk-boundary correspondence breaks down due to non-Hermitian effects or disorder stronger than the bulk topological gap.\n", "status": "active", "fields": [ "physics", "mathematics", "materials-science", "quantum-computing" ], "color": "green" }, { "id": "h-trade-war-tit-for-tat-equilibrium", "type": "hypothesis", "title": "Escalating trade wars converge to a cooperative equilibrium under iterated Prisoner's Dilemma dynamics when shadow-of-the-future discount factor δ > δ* (Axelrod threshold), but bifurcate to permanent protection equilibria when domestic political economy creates asymmetric defection incentives.\n", "status": "active", "fields": [ "international-economics", "game-theory", "political-economy" ], "color": "green" }, { "id": "h-traffic-flow-turing-instability-stop-go", "type": "hypothesis", "title": "Phantom traffic jams arise via a Turing-like instability of uniform traffic flow: above the critical density rho_c, small velocity perturbations grow exponentially with growth rate sigma = 1/tau - v'(rho) > 0, producing backward-propagating stop- and-go waves whose wavelength is selected by the reaction time of drivers tau and the sensitivity of velocity to density v'(rho), analogous to Turing patterns in reaction-diffusion systems.\n", "status": "active", "fields": [ "traffic-engineering", "fluid-dynamics", "nonlinear-dynamics", "social-physics" ], "color": "green" }, { "id": "h-transactive-memory-network-topology-performance", "type": "hypothesis", "title": "Groups with small-world social network topology (high clustering coefficient, short average path length) achieve higher collective memory accuracy than random or lattice networks of equivalent size, because small-world structure enables both local redundancy (reduces forgetting) and global reachability (enables distributed recall), predicting memory accuracy scales as C/L where C is clustering coefficient and L is average path length.\n", "status": "active", "fields": [ "cognitive-science", "social-science", "network-science" ], "color": "green" }, { "id": "h-transcriptomic-conductance-firing-phenotype", "type": "hypothesis", "title": "Ion channel mRNA expression profiles from single-cell RNA-seq can predict HH conductance parameters with sufficient accuracy to reproduce firing phenotype in 70% of neuron types", "status": "active", "fields": [ "neuroscience", "biophysics" ], "color": "green" }, { "id": "h-transformer-embeddings-compositional-brain-alignment", "type": "hypothesis", "title": "Contextual sentence embeddings from large language models (GPT-4 layer 20) predict fMRI BOLD responses to novel sentences in temporal and prefrontal cortex with Pearson r > 0.5, exceeding static word vector predictions by > 0.15", "status": "active", "fields": [ "cognitive-science", "linguistics", "computer-science" ], "color": "green" }, { "id": "h-transformer-neural-attention-alignment", "type": "hypothesis", "title": "Transformer attention heads trained on naturalistic language and vision tasks develop representations that are quantitatively aligned with neural attention modulation patterns in primate visual cortex, with multi-head diversity corresponding to the functional specialisation of cortical attention areas.\n", "status": "active", "fields": [ "computer-science", "neuroscience", "machine-learning", "computational-neuroscience" ], "color": "green" }, { "id": "h-transformer-temporal-attention-improves-ehr-risk-stratification", "type": "hypothesis", "title": "Time-aware transformer attention improves longitudinal EHR risk stratification calibration over recurrent and tabular baselines.", "status": "active", "fields": [ "medicine", "machine-learning", "health-informatics" ], "color": "green" }, { "id": "h-transition-state-x-saddle-point", "type": "hypothesis", "title": "Machine learning interatomic potentials trained with active learning on transition state configurations will predict reaction rate constants within a factor of 2 of gold-standard CCSD(T) calculations for a benchmark set of 20 gas-phase reactions\n", "status": "active", "fields": [ "chemistry", "physics", "mathematics" ], "color": "green" }, { "id": "h-trophic-cascade-metabolic-scaling", "type": "hypothesis", "title": "Trophic cascade strength scales as body-mass-ratio^{0.75} / food-web-connectance, combining metabolic scaling with network complexity to predict cross-ecosystem cascade magnitude", "status": "active", "fields": [], "color": "green" }, { "id": "h-tropical-geometry-matroid-polytopes", "type": "hypothesis", "title": "Tropical varieties are exactly the supports of Bergman fans of matroids — and every tropical linear space is the Bergman fan of the underlying matroid — providing a combinatorial foundation for tropical geometry through matroid theory and resolving the correspondence between tropical Grassmannians and matroid polytopes.\n", "status": "active", "fields": [ "algebraic-combinatorics", "tropical-geometry", "matroid-theory" ], "color": "green" }, { "id": "h-tropical-geometry-x-neural-networks", "type": "hypothesis", "title": "The tropical complexity of a trained ReLU network (number of active linear regions on test data) is 10–100× smaller than the theoretical maximum, and this reduction correlates with test accuracy with Pearson r > 0.8 across architectures trained with different regularisation strengths", "status": "active", "fields": [ "mathematics", "computer_science" ], "color": "green" }, { "id": "h-tsunami-front-regime-classifier-nonlinear-dispersive-bore", "type": "hypothesis", "title": "A classifier combining nondimensional tsunami height–depth ratios with trailing dispersive tail energy fractions improves nearshore amplitude NOWCAST skill versus hydrostatic linear timelines alone on archived buoy events — falsified if peak-amplitude MAE does not drop by ≥8% on held-out events with quality-controlled bathymetry.\n", "status": "active", "fields": [ "oceanography", "geophysics" ], "color": "green" }, { "id": "h-tsunami-submarine-slide-rheology", "type": "hypothesis", "title": "Tsunami wave amplitude from submarine landslides scales as A ∝ V^{2/3} T^{-1} (V = slide volume, T = tsunami period), with runup controlled by the ratio of Froude number to beach slope — and slide rheology determines whether the source is a thin fast slide (high A) or a thick slow debris flow (low A).\n", "status": "active", "fields": [ "marine-geology", "tsunami-science", "geomechanics" ], "color": "green" }, { "id": "h-tur-constrained-estimators-predict-atp-cost-precision-frontier", "type": "hypothesis", "title": "In driven biochemical sensing circuits, observed precision-energy tradeoff frontiers are bounded within a small multiplicative gap of TUR-predicted limits.\n", "status": "active", "fields": [ "biophysics", "statistical-physics", "information-theory" ], "color": "green" }, { "id": "h-turbulence-directed-percolation", "type": "hypothesis", "title": "The laminar-to-turbulent transition in pipe flow belongs to the directed percolation (DP) universality class with critical exponents beta_DP=0.276, nu_perp=1.097 measurable via spatial correlation of turbulent puffs", "status": "active", "fields": [], "color": "green" }, { "id": "h-turbulence-energy-cascade-exact-scaling", "type": "hypothesis", "title": "Kolmogorov's 4/5 law is the only exact result in turbulence theory and implies that the velocity structure function ⟨(δu)^3⟩ = -(4/5)ε·r is universal; deviations from K41 scaling in higher-order moments (intermittency corrections) are described by a universal multifractal spectrum f(α) that is the same for all high-Reynolds-number turbulent flows regardless of boundary conditions or forcing.\n", "status": "active", "fields": [ "mathematics", "physics", "fluid-dynamics" ], "color": "green" }, { "id": "h-turing-digit-count-bmp-gradient-wavelength-scaling", "type": "hypothesis", "title": "The Turing pattern wavelength in mouse limb digit formation scales as λ ∝ √(D_BMP/f_Sox9) with D_BMP and f_Sox9 independently measurable by in vivo FRAP and optogenetic perturbation, predicting digit number as a function of limb bud width L as N_digits = L/λ — directly testable by BMP2 dosage titration that rescales λ and changes digit count.\n", "status": "active", "fields": [ "developmental-biology", "biophysics", "mathematical-biology", "genetics" ], "color": "green" }, { "id": "h-turing-instability-aerosol-nucleation", "type": "hypothesis", "title": "Atmospheric new particle formation exhibits a Turing-class reaction-diffusion instability driven by the short-range activation of sulfuric acid clusters and long-range inhibition by condensation sink vapour scavenging, such that nucleation burst wavelength scales with the square root of diffusivity divided by condensation rate as in classical Turing systems.\n", "status": "active", "fields": [ "atmospheric-chemistry", "chemistry", "mathematics", "aerosol-physics" ], "color": "green" }, { "id": "h-turing-pattern-wavelength-experimental-test", "type": "hypothesis", "title": "The spacing of vertebrate digit primordia is set by the Turing wavelength λ = 2π√(D_A/k) of the BMP-Sox9 reaction-diffusion system, and perturbing D_A (activator diffusivity) or k (degradation rate) independently should shift digit spacing by the predicted √-factor quantitatively.\n", "status": "active", "fields": [ "developmental-biology", "physics", "biophysics", "mathematical-biology" ], "color": "green" }, { "id": "h-turing-zebrafish-diffusivity-ratio", "type": "hypothesis", "title": "Zebrafish adult stripe wavelength is predicted by Nodal/Lefty diffusivity ratio without fitting", "status": "active", "fields": [], "color": "green" }, { "id": "h-twistronics-magic-angle-correlated-states", "type": "hypothesis", "title": "The full phase diagram of magic-angle twisted bilayer graphene contains a cascade of correlated insulating, superconducting, and topological states whose boundaries are predicted by the ratio of interaction energy U to bandwidth W, with U/W > 5 producing Mott-like insulation and U/W in [1,5] producing superconductivity with T_c scaling as W/k_B × exp(-a×W/U).\n", "status": "active", "fields": [ "materials-science", "physics", "condensed-matter", "quantum-physics" ], "color": "green" }, { "id": "h-two-class-economy-boltzmann-pareto-transition", "type": "hypothesis", "title": "Real economies exhibit a two-regime wealth distribution — exponential (Boltzmann-Gibbs) for the lower 90% of the population and Pareto power-law for the top 10% — with a crossover determined by the ratio of income (additive) to capital return (multiplicative) dynamics, and this crossover point predicts country-level Gini coefficient.\n", "status": "active", "fields": [ "economics", "physics", "statistical-mechanics", "complex-systems" ], "color": "green" }, { "id": "h-two-compartment-pk-genotype-prediction", "type": "hypothesis", "title": "A population pharmacokinetic model incorporating CYP3A4 and CYP2D6 genotype, age, and baseline creatinine will predict individual oral drug AUC within ±30% for >75% of patients for high-variability CYP-metabolized drugs, outperforming standard weight-based dosing by reducing the proportion of patients with subtherapeutic or toxic exposures", "status": "active", "fields": [ "pharmacology", "mathematics", "genetics" ], "color": "green" }, { "id": "h-two-state-folders-admit-pl-like-surrogate-on-contact-order-parameter", "type": "hypothesis", "title": "For small fast-folding proteins classified as two-state in phi-value experiments, a smoothed free energy G(Q) projected onto native contact fraction Q exhibits a measurable PL-like region (‖dG/dQ‖² lower-bounded by a constant times G−G_native) across replica-exchange ensembles — absent for frustrated multi-state folders.\n", "status": "active", "fields": [ "biophysics", "optimization" ], "color": "green" }, { "id": "h-two-step-nucleation-density-liquid-precursor", "type": "hypothesis", "title": "Protein crystallization proceeds through a dense liquid precursor phase whose lifetime determines the observed induction time, and DFT-based free energy calculations of the precursor can predict nucleation rates within 2 orders of magnitude", "status": "active", "fields": [ "materials-science", "thermodynamics", "biophysics" ], "color": "green" }, { "id": "h-type-ia-double-degenerate-dominant-channel", "type": "hypothesis", "title": "Double-degenerate white dwarf mergers (DD channel) produce the majority of cosmologically useful Type Ia supernovae, explaining the observed delay-time distribution, and the single-degenerate channel contributes <25%", "status": "active", "fields": [ "stellar-physics", "observational-astronomy", "binary-stellar-evolution" ], "color": "green" }, { "id": "h-ubi-labour-supply-innovation-offset", "type": "hypothesis", "title": "Universal basic income at 30-50% of median income reduces labour force participation by 5-10% (negative income effect) but produces offsetting benefits via entrepreneurship and innovation spillovers from income security, resulting in near-zero net macroeconomic output effect while substantially improving wellbeing and reducing poverty traps.\n", "status": "active", "fields": [ "economics", "social-science", "public-policy", "behavioural-economics" ], "color": "green" }, { "id": "h-unet-domain-randomization-improves-flood-mapping-recall", "type": "hypothesis", "title": "Domain-randomized U-Net training improves flood-extent recall under cloud and cross-sensor shift while maintaining acceptable false-positive rates.", "status": "active", "fields": [ "geoscience", "machine-learning", "remote-sensing" ], "color": "green" }, { "id": "h-univalence-axiom-proof-assistant-verification", "type": "hypothesis", "title": "A cubical type theory implementation of the univalence axiom in Lean 4 or Agda 2 will allow automated verification of non-trivial results in algebraic topology (e.g., fundamental theorem of algebra, Brouwer fixed-point theorem) at a proof density (characters per proof) competitive with traditional Coq/Isabelle formalizations without UA.\n", "status": "active", "fields": [ "mathematics", "computer-science", "type-theory", "formal-verification" ], "color": "green" }, { "id": "h-urban-heat-island-superlinear-density-scaling", "type": "hypothesis", "title": "Urban heat island intensity scales superlinearly with urban population density above a threshold of ~3,000 persons/km², driven by nonlinear feedback between reduced surface albedo, increased anthropogenic heat flux, and boundary layer height reduction — implying that dense compact cities impose disproportionate heat stress per capita relative to sprawling urban areas.\n", "status": "active", "fields": [ "climate-science", "urban-planning", "atmospheric-physics", "geography" ], "color": "green" }, { "id": "h-urban-heat-islands-energy-balance", "type": "hypothesis", "title": "Replacing 30% of impervious surfaces in a compact mid-latitude city with green roofs will reduce summer daytime QH by 15-25 W/m² and lower air temperature by 0.5-1.5°C, as predicted by surface energy balance models with green roof parameterization", "status": "active", "fields": [ "urban-science", "atmospheric-physics", "climate-science" ], "color": "green" }, { "id": "h-urban-segregation-school-quality-reinforcement", "type": "hypothesis", "title": "Residential racial and economic segregation is self-reinforcing through three coupled feedback mechanisms: (1) school quality disparities increase achievement gaps that track with future income segregation, (2) political power sorting directs local tax revenue to high-income areas, and (3) social network homophily limits cross-neighborhood employment referrals, together constituting a Schelling-type tipping point toward persistent segregation", "status": "active", "fields": [ "urban-economics", "sociology", "political-science", "economics" ], "color": "green" }, { "id": "h-urban-superlinear-scaling-social-interaction-fractal-road-network", "type": "hypothesis", "title": "The urban superlinear scaling exponent β = 1 + 2/d_f (where d_f is the fractal dimension of the road network, ≈ 1.8) correctly predicts β ≈ 1.15 for GDP and patents, and the post-COVID shift to remote work will reduce β toward 1.0 by decoupling social interaction rate from physical co-presence — detectable in patent and GDP scaling data from 2020–2025.\n", "status": "active", "fields": [ "physics", "social-science", "urban-science", "economics", "network-science" ], "color": "green" }, { "id": "h-v1-gabor-infomax-prediction", "type": "hypothesis", "title": "V1 simple cell receptive fields in humans are uniquely determined by the infomax / sparse coding optimisation on the statistical ensemble of natural images experienced during the organism's visual development, and any systematic deviation from Gabor wavelet shape indicates a mismatch between the training distribution and the animal's ecological niche.\n", "status": "active", "fields": [ "neuroscience", "information-theory", "computational-neuroscience", "visual-physiology" ], "color": "green" }, { "id": "h-vae-latent-regularization-improves-catalyst-hit-rate", "type": "hypothesis", "title": "Chemistry-informed VAE latent regularization improves catalyst hit rate over unconstrained latent sampling in prospective screens.", "status": "active", "fields": [ "chemistry", "materials-science", "machine-learning" ], "color": "green" }, { "id": "h-van-der-waals-free-energy-double-well", "type": "hypothesis", "title": "The double-well free energy structure of the van der Waals equation is universal across all weakly symmetry-breaking phase transitions described by Landau theory", "status": "active", "fields": [ "statistical-mechanics", "physical-chemistry", "condensed-matter-physics" ], "color": "green" }, { "id": "h-variational-assimilation-derived-glucose-predictions-outperform-sliding-window-baselines", "type": "hypothesis", "title": "Methods transferred from `b-variational-data-assimilation-x-personalized-glucose-forecasting` improve target outcomes versus domain-specific baselines at matched cost.", "status": "active", "fields": [ "control-engineering", "medicine" ], "color": "green" }, { "id": "h-variational-inference-free-energy-rg", "type": "hypothesis", "title": "Renormalization group flow equations guide optimal variational family design for hierarchical posteriors by identifying relevant degrees of freedom at each scale\n", "status": "active", "fields": [ "computer_science", "physics", "machine_learning", "statistical_mechanics" ], "color": "green" }, { "id": "h-vcg-regretnet-combinatorial-approximation", "type": "hypothesis", "title": "Neural network auction design (RegretNet) can learn near-optimal, approximately incentive-compatible mechanisms for combinatorial auctions that achieve within 5% of VCG social welfare while running in polynomial time, resolving the computational- incentive incompatibility for practical auction scales.\n", "status": "active", "fields": [ "mechanism-design", "machine-learning", "algorithmic-game-theory" ], "color": "green" }, { "id": "h-vegetation-stripe-turing-instability", "type": "hypothesis", "title": "Tiger bush vegetation stripe spacing in semiarid regions scales with the square root of the ratio of water diffusivity to plant growth rate as predicted by Turing reaction-diffusion theory, and stripe-to-gap pattern transitions precede catastrophic desertification tipping points by decades.\n", "status": "active", "fields": [ "ecology", "mathematics", "climate-science" ], "color": "green" }, { "id": "h-vertex-model-cortical-gyrification-mechanics", "type": "hypothesis", "title": "Cortical gyrification in the developing primate brain arises from differential tangential growth of the cortical plate relative to the subcortical white matter, and the gyrification index (fold density) is quantitatively predicted by the elastic buckling wavelength λ* = 2π·h·(E_cortex/3E_subcortex)^(1/3) where h is cortical thickness and E is elastic modulus ratio.\n", "status": "active", "fields": [ "developmental-neuroscience", "biomechanics", "mathematical-biology" ], "color": "green" }, { "id": "h-vibronic-coupling-fmo-coherence-functional-enhancement", "type": "hypothesis", "title": "Vibronic coupling — resonance between electronic energy gaps and specific protein vibrational modes in FMO and LHCII — does not enhance energy transfer efficiency beyond Redfield theory predictions at 300K; isotope-substitution experiments that detune vibrational resonances will show <5% change in charge-separation quantum yield, refuting functional quantum coherence in photosynthesis.\n", "status": "active", "fields": [ "physical-chemistry", "biophysics", "quantum-biology" ], "color": "green" }, { "id": "h-vickrey-clarke-groves-payments-improve-lab-truthful-reporting", "type": "hypothesis", "title": "In online experiments with monetary incentives, second-price payment rules will reduce systematic overbidding relative to first-price rules for private-value goods, holding expected payments fixed.", "status": "active", "fields": [ "economics", "computer-science" ], "color": "green" }, { "id": "h-viral-proofreading-shannon-optimality", "type": "hypothesis", "title": "The coronavirus nsp14 proofreading exonuclease is a Shannon-optimal adaptation — it raises replication fidelity exactly to the level required by channel capacity theory to support the coronavirus genome size, and the quantitative trade-off curve (fidelity vs genome size) matches the Shannon bound across all sequenced nidoviruses.\n", "status": "active", "fields": [ "virology", "information-theory", "molecular-evolution" ], "color": "green" }, { "id": "h-viral-quasispecies-x-nk-rugged-landscape", "type": "hypothesis", "title": "Deep mutational scanning tensors from influenza hemagglutinin segments will admit NK-style ruggedness fits whose estimated K correlates with experimental escape pathway branching ratios measured under polyclonal sera pressure — failing under purely additive fitness models lacking epistasis clusters.\n", "status": "active", "fields": [ "virology", "evolutionary-biology" ], "color": "green" }, { "id": "h-virial-multicomponent-consistency-reduces-cluster-mass-bias", "type": "hypothesis", "title": "Joint hydrostatic + lensing + velocity priors reduce cluster mass scatter compared with single-tracer virial scalings when feedback and merger states are marginalised — falsified if hierarchical posterior widths do not shrink relative to single-estimator pipelines on matched SZ+X-ray+weak-lensing cluster batches.\n", "status": "active", "fields": [ "cosmology", "astrophysics" ], "color": "green" }, { "id": "h-visual-art-fluency-arousal-valence-response", "type": "hypothesis", "title": "Aesthetic emotional responses to visual art are determined by two independent dimensions — arousal (driven by visual complexity, fractal dimension, contrast) and valence (driven by subject matter, color harmony, cultural familiarity) — mediated by processing fluency (ease of perceptual organization), which independently predicts liking and aesthetic emotion distinct from ordinary emotion", "status": "active", "fields": [ "empirical-aesthetics", "cognitive-science", "psychology", "psychophysiology" ], "color": "green" }, { "id": "h-visual-art-fluency-arousal-valence", "type": "hypothesis", "title": "Visual artworks evoke emotion primarily through three interacting mechanisms: perceptual fluency (processing ease → positive valence), arousal driven by contrast, color saturation, and compositional tension, and representational content (semantic associations). Aesthetic emotion is a superposition of these three components, not a distinct neural system.\n", "status": "active", "fields": [ "empirical-aesthetics", "cognitive-neuroscience", "visual-perception", "emotion-research" ], "color": "green" }, { "id": "h-vit-based-phenotyping-improves-early-crop-stress-detection", "type": "hypothesis", "title": "Vision-transformer phenotyping improves early crop stress detection lead time across multisensor imagery.", "status": "active", "fields": [ "ecology", "machine-learning", "agriculture" ], "color": "green" }, { "id": "h-volatility-autocorrelation-satisfies-effective-fd-response", "type": "hypothesis", "title": "In calm regimes, integrated absolute-return autocorrelation up to intraday horizons scales with subsequent realized variance proxies similarly to a phenomenological fluctuation–dissipation ratio — breaks down around scheduled macro releases; treat as empirical hypothesis not physics law.\n", "status": "active", "fields": [ "finance", "statistical-physics" ], "color": "green" }, { "id": "h-voting-theory-x-social-choice", "type": "hypothesis", "title": "Real national election preference surveys are geometrically within 0.15 Wasserstein distance of a Condorcet cycle in the preference simplex on average, and this proximity to the topological Arrow obstruction explains why multi-candidate elections produce paradoxical outcomes more than 20% of the time", "status": "active", "fields": [ "mathematics", "economics", "social-science" ], "color": "green" }, { "id": "h-waddington-attractor-reprogramming-energy", "type": "hypothesis", "title": "The energy barrier height between pluripotent and differentiated cell fate attractors in the Waddington landscape is inversely proportional to reprogramming efficiency by Yamanaka factors, and this barrier can be quantified from single-cell RNA-seq data using Fokker-Planck potential reconstruction within ±20% accuracy", "status": "active", "fields": [ "biology", "dynamical-systems", "systems-biology" ], "color": "green" }, { "id": "h-wais-mici-buttressing-stability", "type": "hypothesis", "title": "The West Antarctic Ice Sheet is primarily stabilised by ice shelf buttressing forces from Ross and Filchner-Ronne ice shelves; marine ice cliff instability (MICI) is physically limited to cliffs <100m above waterline by ice viscous flow, making catastrophic WAIS collapse timescales >500 years rather than <100 years as initially projected.\n", "status": "active", "fields": [ "glaciology", "geology", "physics", "climate-science" ], "color": "green" }, { "id": "h-war-onset-grievance-greed-prediction", "type": "hypothesis", "title": "Interstate war onset is better predicted by power transition dynamics (rising challenger approaching parity with hegemon) than by grievance measures, while civil war onset is better predicted by horizontal inequality (grievance) and resource lootability (greed) — the two onset mechanisms are fundamentally distinct and require separate predictive models.\n", "status": "active", "fields": [ "political-science", "social-science", "economics", "international-relations" ], "color": "green" }, { "id": "h-wasserstein-dro-improves-tail-safe-adaptation-metrics", "type": "hypothesis", "title": "Physics-constrained Wasserstein ambiguity sets improve tail-safe adaptation utility metrics versus unconstrained empirical Wasserstein balls when evaluated on held-out storyline stress tests.", "status": "active", "fields": [ "climate-science", "mathematics", "operations-research" ], "color": "green" }, { "id": "h-water-llcp-femtosecond-detection", "type": "hypothesis", "title": "The water liquid-liquid critical point exists at T≈220 K, P≈100 MPa and will be detected by femtosecond X-ray free electron laser diffraction of transiently heated nanodroplets", "status": "active", "fields": [], "color": "green" }, { "id": "h-wavelet-shrinkage-minimax-optimal-natural-image-sparsity", "type": "hypothesis", "title": "Natural images lie in a Besov space B^s_{1,1}(ℝ²) with s ≈ 1, and wavelet thresholding achieves the optimal minimax reconstruction rate n^(-2s/(2s+2)) = n^(-1/2) for this function class, explaining why JPEG-2000 wavelet compression outperforms JPEG-DCT by a constant factor at all compression ratios rather than only at low quality.\n", "status": "active", "fields": [ "mathematics", "engineering", "signal-processing", "neuroscience", "statistics" ], "color": "green" }, { "id": "h-wavelet-subband-energy-tracks-rg-relevant-flux", "type": "hypothesis", "title": "For designated short-range Ising-family lattice models, energy flux captured in the first k dyadic wavelet subbands after block-spin RG steps will correlate monotonically with numerically integrated beta-function flow of nearest-neighbor coupling — falsified if wavelet bands mis-track operator relevance ranking versus spectrum of linearized RG at criticality.\n", "status": "active", "fields": [ "physics", "mathematics" ], "color": "green" }, { "id": "h-wealth-concentration-tipping-point-inequality", "type": "hypothesis", "title": "Economic inequality crosses a self-reinforcing tipping point when the top-1% wealth share exceeds ~35% (corresponding to Gini coefficient > 0.6 for wealth), at which point political capture of tax and regulatory policy locks in further concentration — a positive feedback loop detectable as a change from mean-reverting to unit-root dynamics in wealth share time series.\n", "status": "active", "fields": [ "economics", "social-science", "political-science" ], "color": "green" }, { "id": "h-wealth-distribution-boltzmann-savings-propensity", "type": "hypothesis", "title": "The shape of empirical wealth distributions (Boltzmann-Gibbs bulk + Pareto tail) can be predicted from two observable parameters—mean savings propensity lambda_mean and variance of returns-on-wealth sigma_r—through the Chakraborti-Chakrabarti model, with the Pareto exponent alpha = 1 + 1/(sigma_r^2 * T) where T is wealth per capita.\n", "status": "active", "fields": [ "sociology", "statistical-physics", "economics" ], "color": "green" }, { "id": "h-wildfire-aerosol-albedo-net-positive-feedback", "type": "hypothesis", "title": "The net radiative feedback of increasing wildfire frequency under climate change is positive (warming) on decadal timescales: the positive forcing from black carbon aerosols, CO2 and CH4 emissions, and surface albedo reduction from charring outweighs the negative forcing from biogenic secondary organic aerosol and increased surface reflectance post-fire.\n", "status": "active", "fields": [ "climate-science", "atmospheric-chemistry", "ecology", "biogeochemistry" ], "color": "green" }, { "id": "h-willmore-energy-biological-membrane-morphogenesis-ground-state", "type": "hypothesis", "title": "Biological cell membrane shapes during morphogenesis are Willmore energy (W = ∫H² dA) minimizers subject to volume and area constraints, with active cytoskeletal forces entering as Lagrange multipliers that define the morphogenetic Willmore flow, and the shapes of cell organelles (ER, Golgi) are calculable from the Helfrich elastic model without free parameters.\n", "status": "active", "fields": [ "mathematics", "biophysics", "cell-biology", "physics" ], "color": "green" }, { "id": "h-wireless-power-friis-near-field-tradeoff", "type": "hypothesis", "title": "Resonant inductive wireless power transfer efficiency follows a universal coupling-Q product figure of merit (η ∝ κ²/Γ₁Γ₂ in coupled mode theory), and far-field beamed power efficiency is fundamentally limited by beam divergence (Friis equation) — no configuration can simultaneously achieve high efficiency and long range without aperture scaling.\n", "status": "active", "fields": [ "electromagnetism", "power-electronics", "antenna-theory" ], "color": "green" }, { "id": "h-wisdom-of-crowds-condorcet", "type": "hypothesis", "title": "Online rating platforms that display the current mean rating before users submit their own rating will show correlation ρ > 0.3 between successive ratings, degrading effective crowd size N_eff by >50% relative to blind-rating conditions", "status": "active", "fields": [ "social-science", "behavioral-economics", "statistics" ], "color": "green" }, { "id": "h-working-memory-alpha-suppression-capacity-limit", "type": "hypothesis", "title": "Working memory capacity (~4 items) is limited by alpha-band (8-12 Hz) suppression bandwidth in posterior parietal cortex — each remembered item occupies a phase-coded slot in an alpha oscillatory multiplexing scheme", "status": "active", "fields": [ "cognitive-neuroscience", "computational-neuroscience", "psychology" ], "color": "green" }, { "id": "h-wpt-coexistence-requires-q-bandwidth-renegotiation-per-standard", "type": "hypothesis", "title": "For fixed coil geometry, simultaneously satisfying two resonant WPT carrier frequencies at ≥90% of single-carrier peak efficiency requires lowering loaded Q or splitting transmit/receive resonance — implying per-standard impedance schedules rather than one universal high-Q tune.\n", "status": "active", "fields": [ "electrical-engineering", "electromagnetism" ], "color": "green" }, { "id": "h-writing-system-phonological-awareness-route", "type": "hypothesis", "title": "Alphabetic writing systems engage a phonological decoding route in left inferior frontal and temporal regions more strongly than logographic systems, predicting larger phonological awareness advantages in alphabetic readers and differential dyslexia presentation", "status": "active", "fields": [ "cognitive-neuroscience", "linguistics", "educational-psychology" ], "color": "green" }, { "id": "h-xenolith-sampling-bias-kimberlite", "type": "hypothesis", "title": "Mantle xenoliths in kimberlites systematically undersample the strongly depleted harzburgite lithosphere and oversample refertilized lherzolite, because kimberlite magma preferentially entrains and preserves lithologies with moderate modal clinopyroxene — biasing geochemical models of the subcontinental lithospheric mantle.\n", "status": "active", "fields": [ "petrology", "mantle-geochemistry", "geochronology" ], "color": "green" }, { "id": "h-xna-ribozyme-catalytic-efficiency-backbone-independence", "type": "hypothesis", "title": "FANA (2'F-arabino nucleic acid) ribozymes will achieve catalytic rate enhancement (k_cat/k_uncat) within one order of magnitude of the equivalent RNA ribozyme after 15 rounds of directed evolution, because FANA's backbone rigidity provides similar pre-organisation of the active site as RNA.\n", "status": "active", "fields": [ "synthetic-biology", "chemistry" ], "color": "green" }, { "id": "h-zahavi-handicap-single-crossing-stable-honest", "type": "hypothesis", "title": "The Spence-Mirrleesian single-crossing condition (∂²C/∂s∂q < 0) is both necessary and sufficient for evolutionarily stable honest signaling in biological populations with continuous quality variation — and this condition can be empirically tested by measuring the marginal cost of signal production as a function of quality (parasite load, immune status, body condition) in 5+ species across independent lineages.\n", "status": "active", "fields": [ "evolutionary-biology", "behavioral-ecology", "game-theory", "mathematics" ], "color": "green" }, { "id": "h-zeeman-multiplet-spacing-shows-quantum-chaos-statistics", "type": "hypothesis", "title": "Increasing magnetic field strength in highly excited atoms monotonically drives unfolded nearest-neighbor spacing distributions toward stronger level repulsion consistent with GUE-like statistics in selected symmetry-breaking windows — requires empirical confirmation per species and energy range.\n", "status": "active", "fields": [ "atomic-physics", "quantum-chaos", "mathematical-physics" ], "color": "green" }, { "id": "h-zero-trust-control-raises-effective-percolation-threshold", "type": "hypothesis", "title": "Implementing least-privilege identity segmentation and device compliance gates increases the effective percolation threshold p_c on measured enterprise graphs, measurably shrinking giant-component probability in Monte Carlo hop models seeded at random workstations.\n", "status": "active", "fields": [ "cybersecurity", "mathematics" ], "color": "green" }, { "id": "h-zipf-critical-point-communication-efficiency", "type": "hypothesis", "title": "Natural language lexicons sit at the critical point (α = 1) of the speaker-listener effort trade-off game, predictable as the mutual-information- maximising solution of the Ferrer i Cancho-Solé communication model.\n", "status": "active", "fields": [ "linguistics", "information-theory", "cognitive-science" ], "color": "green" }, { "id": "h-zipf-optimal-coding-universality", "type": "hypothesis", "title": "Any communication system subject to least-effort constraints (joint sender-receiver cost minimisation) should exhibit Zipfian power-law statistics with exponent determined by the channel capacity ratio between sender and receiver.\n", "status": "active", "fields": [ "linguistics", "information-theory", "cognitive-science", "neuroscience", "evolutionary-biology", "biophysics" ], "color": "green" }, { "id": "u-phononic-crystal-3d-complete-band-gap", "type": "unknown", "title": "What is the minimum impedance contrast required for a complete 3D phononic band gap (forbidding sound in all directions and polarizations), and can such structures be fabricated at audible frequencies?", "status": "open", "fields": [ "acoustics", "condensed-matter-physics", "materials-science" ], "color": "gray" }, { "id": "u-phononic-crystal-active-tunable-band-gap", "type": "unknown", "title": "Can phononic crystal band gaps be dynamically tuned over a 2:1 frequency ratio in real time using active mechanisms (piezoelectric, fluidic, magnetic), and what are the fundamental limits on tuning speed and bandwidth?\n", "status": "open", "fields": [ "acoustics", "materials-science" ], "color": "gray" }, { "id": "u-neural-correlates-consciousness-anesthesia-mechanism", "type": "unknown", "title": "What are the specific neural circuit mechanisms by which general anesthetics suppress consciousness — is it primarily thalamo-cortical disconnection, loss of cortical complexity (integrated information), suppression of specific oscillatory modes, or some combination?\n", "status": "open", "fields": [ "neuroscience", "consciousness-science", "pharmacology", "anesthesiology" ], "color": "gray" }, { "id": "u-antibiotic-resistance-evolution-rate-clinical-deployment", "type": "unknown", "title": "Can the rate of antibiotic resistance evolution to a novel antibiotic be predicted before clinical deployment ΓÇö and what antibiotic features (target essentiality, mechanism of action, penetration, efflux susceptibility) best predict resistance emergence timelines?\n", "status": "open", "fields": [ "microbiology", "evolutionary-biology", "pharmacology", "infectious-disease" ], "color": "gray" }, { "id": "u-threshold-selection-bias-in-evt-based-amr-early-warning", "type": "unknown", "title": "What validation boundary conditions determine when `b-extreme-value-theory-x-antimicrobial-resistance-surveillance` remains decision-useful?", "status": "open", "fields": [ "statistics", "epidemiology" ], "color": "gray" }, { "id": "u-aesthetic-preference-neural-basis", "type": "unknown", "title": "What neural circuits and computational principles underlie aesthetic preference, and are these circuits domain-general or art-form specific?", "status": "open", "fields": [ "neuroaesthetics", "cognitive-science", "art-and-cognition", "neuroscience" ], "color": "gray" }, { "id": "u-architectural-emotional-impact", "type": "unknown", "title": "What physical properties of built environments causally affect psychological well-being, stress, and cognitive performance?", "status": "open", "fields": [ "environmental-psychology", "art-and-cognition", "architecture", "neuroscience" ], "color": "gray" }, { "id": "u-collective-vs-individual-creativity", "type": "unknown", "title": "Under what conditions does group collaboration enhance vs. inhibit creative output compared to individuals working alone?", "status": "open", "fields": [ "social-psychology", "art-and-cognition", "cognitive-science", "organizational-behavior" ], "color": "gray" }, { "id": "u-color-emotion-mapping", "type": "unknown", "title": "Is the mapping between colors and emotional valence universal or culturally constructed, and what are the physiological mechanisms?", "status": "open", "fields": [ "cognitive-science", "art-and-cognition", "cross-cultural-psychology", "color-science" ], "color": "gray" }, { "id": "u-creativity-intelligence-correlation", "type": "unknown", "title": "What is the relationship between general intelligence (g) and creative ability, and does the threshold hypothesis hold above IQ 120?", "status": "open", "fields": [ "cognitive-science", "art-and-cognition", "psychology", "neuroscience" ], "color": "gray" }, { "id": "u-flow-state-neural-signature", "type": "unknown", "title": "What are the neural and physiological signatures of the \"flow state\" (optimal experience), and can flow be reliably induced and measured?", "status": "open", "fields": [ "cognitive-science", "art-and-cognition", "positive-psychology", "neuroscience" ], "color": "gray" }, { "id": "u-humor-cognitive-mechanism", "type": "unknown", "title": "What is the computational and neural mechanism of humor comprehension, and why does incongruity resolution trigger positive affect?", "status": "open", "fields": [ "cognitive-science", "art-and-cognition", "neuroscience", "linguistics" ], "color": "gray" }, { "id": "u-improvisation-neural-correlates", "type": "unknown", "title": "What neural mechanisms distinguish improvised from rehearsed musical performance, and does improvisation require deactivation of executive control?", "status": "open", "fields": [ "cognitive-neuroscience", "art-and-cognition", "music-cognition" ], "color": "gray" }, { "id": "u-metaphor-abstract-thought", "type": "unknown", "title": "Is abstract thought fundamentally grounded in sensorimotor metaphor (conceptual metaphor theory), or can genuine amodal abstract representation exist?", "status": "open", "fields": [ "cognitive-linguistics", "cognitive-science", "art-and-cognition", "neuroscience" ], "color": "gray" }, { "id": "u-mirror-neuron-aesthetic-cross-cultural", "type": "unknown", "title": "Is aesthetic pleasure mediated by mirror neuron motor resonance universal across cultures, or does it depend on culturally specific motor repertoires and embodied experience?", "status": "open", "fields": [ "art-and-cognition", "neuroscience", "cognitive-science", "anthropology" ], "color": "gray" }, { "id": "u-music-universals", "type": "unknown", "title": "Are there universal features of music across human cultures, and what are the cognitive or evolutionary origins of these universals?", "status": "open", "fields": [ "ethnomusicology", "cognitive-science", "art-and-cognition", "evolutionary-psychology" ], "color": "gray" }, { "id": "u-narrative-comprehension-brain", "type": "unknown", "title": "What brain regions and computational processes support the construction of narrative mental models during story comprehension?", "status": "open", "fields": [ "cognitive-neuroscience", "art-and-cognition", "linguistics", "cognitive-science" ], "color": "gray" }, { "id": "u-rhythm-synchronization-social", "type": "unknown", "title": "What is the social function of rhythmic synchronization in music and collective movement, and does entrainment causally increase prosocial behavior?", "status": "open", "fields": [ "cognitive-science", "art-and-cognition", "social-psychology", "evolutionary-psychology" ], "color": "gray" }, { "id": "u-synaesthesia-mechanism", "type": "unknown", "title": "What neural mechanisms produce synesthesia, and why does cross-activation of perceptual modalities occur in approximately 4% of the population?", "status": "open", "fields": [ "cognitive-neuroscience", "art-and-cognition", "perception", "neuroscience" ], "color": "gray" }, { "id": "u-visual-art-emotional-response", "type": "unknown", "title": "What visual features of artworks reliably evoke specific emotional responses, and does the \"aesthetic emotion\" differ from ordinary emotion?", "status": "open", "fields": [ "neuroaesthetics", "art-and-cognition", "cognitive-science", "psychology" ], "color": "gray" }, { "id": "u-astronomical-source-matching-structured-outlier-robustness", "type": "unknown", "title": "How robust are RANSAC-family geometric source-matching methods when astronomical outliers are spatially clustered, quality-ranked, and caused by blends or survey artifacts rather than uniform random contamination?\n", "status": "open", "fields": [ "astronomy", "computer-science" ], "color": "gray" }, { "id": "u-baryon-asymmetry-origin", "type": "unknown", "title": "What is the mechanism of baryogenesis that produced the observed matter-antimatter asymmetry?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-black-hole-information-paradox", "type": "unknown", "title": "Is information destroyed by black hole evaporation, and if not, how is it encoded in Hawking radiation?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cgm-enrichment", "type": "unknown", "title": "How is the circumgalactic medium enriched with metals and what is its role in the baryon cycle?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cluster-cooling-flows", "type": "unknown", "title": "Why do galaxy cluster cooling flows not form stars at the rates predicted by their X-ray luminosities?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cosmic-string-networks", "type": "unknown", "title": "Do cosmic string networks exist and what are their observational signatures in the CMB and gravitational wave background?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cosmological-constant-small-value-explanation", "type": "unknown", "title": "Why is the cosmological constant Λ ~ 10⁻⁵² m⁻² (small but non-zero), and what physical mechanism causes the ~120-order-of-magnitude cancellation between UV quantum vacuum contributions and the observed dark energy density?\n", "status": "open", "fields": [ "cosmology", "quantum-field-theory", "particle-physics" ], "color": "gray" }, { "id": "u-dark-energy-equation-of-state", "type": "unknown", "title": "Is dark energy a cosmological constant or a dynamical field, and what is its equation of state w(z)?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-dark-matter-particle-identity", "type": "unknown", "title": "What particle or particles constitute cosmological dark matter?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-early-galaxy-formation-jwst", "type": "unknown", "title": "Why do JWST observations reveal massive, evolved galaxies at z>10 that are absent in ΛCDM predictions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-exoplanet-biosignature-false-positives", "type": "unknown", "title": "What abiotic processes can mimic biosignature gases in exoplanet atmospheres?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-exoplanet-spectral-retrieval", "type": "unknown", "title": "How do cloud/haze model degeneracies in atmospheric retrieval limit detection of biosignature gases, and what spectral features most robustly distinguish biogenic from abiotic gas sources?", "status": "open", "fields": [ "astronomy", "statistics", "atmospheric-science", "astrobiology" ], "color": "gray" }, { "id": "u-fast-radio-burst-origin", "type": "unknown", "title": "What are the dominant progenitor mechanisms for cosmological fast radio bursts?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-galaxy-angular-momentum", "type": "unknown", "title": "How is angular momentum transported during galaxy formation to produce the observed diversity of disk sizes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-globular-cluster-formation", "type": "unknown", "title": "How do globular clusters form and why do they show multiple stellar populations?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-gravothermal-catastrophe-globular-cluster-timescale", "type": "unknown", "title": "What determines the core-collapse timescale of globular clusters undergoing the gravothermal catastrophe, and can post-collapse gravothermal oscillations be confirmed observationally to test the statistical mechanics of negative- heat-capacity systems?\n", "status": "open", "fields": [ "astronomy", "statistical-physics", "stellar-dynamics", "astrophysics" ], "color": "gray" }, { "id": "u-gwb-spectrum", "type": "unknown", "title": "What is the origin of the nanohertz gravitational wave background detected by pulsar timing arrays?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-halo-merger-tree-nbody-clustering-analogy", "type": "unknown", "title": "Which algorithmic choices in halo merger-tree construction dominate uncertainties in inferred substructure statistics compared with cosmological parameters — and how should tree-builder ambiguity be propagated into galaxy–dark-matter connection constraints?\n", "status": "open", "fields": [ "cosmology", "computational-science" ], "color": "gray" }, { "id": "u-hubble-tension-origin", "type": "unknown", "title": "What physical mechanism resolves the 5σ tension between local and CMB-inferred Hubble constants?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-magnetar-formation-mechanism", "type": "unknown", "title": "What fraction of neutron stars form as magnetars and what determines the extreme magnetic field strength?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-magnetic-field-origin-galaxies", "type": "unknown", "title": "What generates and maintains large-scale magnetic fields in galaxies over Hubble time?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-neural-operator-space-weather-extreme-event-calibration", "type": "unknown", "title": "How reliably do neural-operator surrogates calibrate extreme geomagnetic event risk under assimilation updates?", "status": "open", "fields": [ "astronomy", "machine-learning", "space-physics" ], "color": "gray" }, { "id": "u-neutron-star-equation-of-state", "type": "unknown", "title": "What is the equation of state of dense nuclear matter above 2× nuclear saturation density?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-population-iii-stars", "type": "unknown", "title": "When and where can Population III stars first be detected, and what is their characteristic mass function?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-primordial-gravitational-waves", "type": "unknown", "title": "What is the tensor-to-scalar ratio r and what does it imply for inflationary models?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-quasar-feedback-mechanism", "type": "unknown", "title": "How does quasar feedback quench star formation in massive galaxies across cosmic time?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-red-sequence-quenching-unified-timescales", "type": "unknown", "title": "What fraction of red sequence scatter at fixed stellar mass is driven by progenitor diversity versus ongoing environmental perturbations rather than measurement error?", "status": "open", "fields": [ "astronomy", "astrophysics" ], "color": "gray" }, { "id": "u-reionization-topology", "type": "unknown", "title": "What is the spatial topology of cosmic reionization and which sources dominate the ionizing photon budget?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-saturn-ring-viscosity-radial-transport", "type": "unknown", "title": "What is the dominant angular-momentum transport mechanism in Saturn's dense B ring, and does 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the 11-year cycle to Milankovitch orbital forcing, and how does it scale with background climate state?", "status": "open", "fields": [ "astronomy", "climate-science", "paleoclimatology", "stellar-physics" ], "color": "gray" }, { "id": "u-stellar-initial-mass-function", "type": "unknown", "title": "Is the stellar initial mass function universal, and what physics drives variations in extreme environments?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-type-ia-supernova-progenitor", "type": "unknown", "title": "What stellar system produces the majority of Type Ia supernovae: single-degenerate or double-degenerate?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-uhecr-origin", "type": "unknown", "title": "What are the sources of ultra-high-energy cosmic rays above the GZK cutoff and what accelerates them?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-virial-cloud-cluster-multicomponent-bias", "type": "unknown", "title": "How large are systematic mass biases when joint virial-style estimators ignore turbulence, magnetic pressure, cosmic rays, or merger-induced departures from equilibrium across molecular clouds versus galaxy clusters?\n", "status": "open", "fields": [ "astrophysics", "cosmology" ], "color": "gray" }, { "id": "u-accretion-disk-mri-saturation", "type": "unknown", "title": "What determines the saturation amplitude of MRI-driven MHD turbulence in accretion disks, and why does the effective α parameter depend on magnetic Prandtl number?", "status": "open", "fields": [ "astrophysics", "magnetohydrodynamics", "plasma-physics", "fluid-dynamics" ], "color": "gray" }, { "id": "u-black-hole-information-paradox", "type": "unknown", "title": "Is information that falls into a black hole preserved (as implied by quantum unitarity and AdS/CFT) or destroyed (as implied by Hawking's original calculation) — and what is the precise physical mechanism by which information escapes in Hawking radiation?\n", "status": "open", "fields": [ "theoretical-physics", "quantum-gravity", "information-theory", "astrophysics" ], "color": "gray" }, { "id": "u-gravitational-lensing-caustic-topology", "type": "unknown", "title": "What is the complete topological classification of caustic networks produced by realistic large-scale structure lens distributions, and how do higher-order catastrophe singularities (swallowtail, butterfly) affect the statistical properties of extreme-magnification events observable by next-generation lensing surveys?", "status": "open", "fields": [ "astrophysics", "mathematics", "optics" ], "color": "gray" }, { "id": "u-gravitational-wave-astrophysics-population", "type": "unknown", "title": "What is the mass distribution and formation channel of compact binary mergers detected by LIGO-Virgo, and what does it imply for stellar evolution?", "status": "open", "fields": [ "astrophysics", "gravitational-wave-astronomy", "stellar-evolution" ], "color": "gray" }, { "id": "u-grb-jet-breakout-shock-microphysics", "type": "unknown", "title": "Which afterglow features uniquely identify jet structure (structured jets, late energy injection) versus observer-angle effects within a single RHD model class?", "status": "open", "fields": [ "astrophysics", "physics" ], "color": "gray" }, { "id": "u-helioseismic-inversion-uniqueness-depth", "type": "unknown", "title": "Under realistic observational frequency errors and mode sets, how tight are uniqueness guarantees for solar sound-speed profiles versus degenerate interior models sharing overlapping eigenfrequency windows?", "status": "open", "fields": [ "astrophysics", "inverse-problems" ], "color": "gray" }, { "id": "u-neutron-star-eos-dense-matter-phase-transition", "type": "unknown", "title": "Does the dense matter in neutron star cores undergo a first-order phase transition to quark matter, hyperon matter, or a condensate, and if so at what density?", "status": "open", "fields": [ "astrophysics", "nuclear-physics", "particle-physics" ], "color": "gray" }, { "id": "u-solar-wind-alfven-wave-dissipation-scale", "type": "unknown", "title": "At what spatial scale and through what kinetic mechanism does the solar wind Alfvénic turbulence cascade ultimately dissipate, heating the solar wind ions and electrons?\n", "status": "open", "fields": [ "astrophysics", "plasma-physics" ], "color": "gray" }, { "id": "u-bargaining-power-measurement-real-world-negotiations", "type": "unknown", "title": "How can bargaining power be empirically measured in real-world negotiations where the disagreement point, the feasible set, and outside options are unobservable, and do observed outcomes match Nash or Rubinstein equilibrium predictions?\n", "status": "open", "fields": [ "economics", "game-theory", "labor-economics", "negotiation-science" ], "color": "gray" }, { "id": "u-marcus-tunneling-reaction-coordinate-biochemistry", "type": "unknown", "title": "How tightly do Marcus parameters (λ, V_DA, ΔG°) inferred from driving-force sweeps on enzymes predict tunneling-corrected kinetic isotope effects measured independently?", "status": "open", "fields": [ "biochemistry", "physical-chemistry" ], "color": "gray" }, { "id": "u-active-brownian-motion-x-cell-migration", "type": "unknown", "title": "Do migrating cancer cells in 3D tissue environments follow active Brownian particle statistics, and how does confinement geometry and matrix stiffness modify the effective persistence time and diffusivity?", "status": "open", "fields": [ "biophysics", "cell-biology", "physics" ], "color": "gray" }, { "id": "u-adaptive-therapy-evolutionary-trap-clinical-validation", "type": "unknown", "title": "Does adaptive therapy based on Pontryagin optimal control and evolutionary game theory outperform maximum tolerated dose chemotherapy in randomized clinical trials for solid tumors?", "status": "open", "fields": [ "oncology", "evolutionary-biology", "mathematical-biology" ], "color": "gray" }, { "id": "u-aging-hallmarks-causal-hierarchy", "type": "unknown", "title": "Among the 12 hallmarks of aging (telomere attrition, epigenetic drift, proteostasis loss, etc.), which are causes and which are consequences — is there a causal hierarchy?", "status": "open", "fields": [ "biogerontology", "systems-biology", "cell-biology" ], "color": "gray" }, { "id": "u-allometric-scaling-metabolic-universality", "type": "unknown", "title": "Do quarter-power allometric scaling laws reflect a universal mathematical property of resource-distribution networks, or are they approximate empirical relationships with taxon-specific deviations that challenge the WBE fractal geometry model?", "status": "open", "fields": [ "biology", "mathematics", "ecology" ], "color": "gray" }, { "id": "u-allometry-fractal-networks-deviations", "type": "unknown", "title": "Do organisms without hierarchical vascular networks (sponges, fungi, prokaryotes) deviate predictably from the WBE 3/4-power metabolic scaling, and what 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"u-ant-colony-optimization-convergence-rate", "type": "unknown", "title": "What is the time complexity of ant colony optimization convergence to the optimal TSP tour, and under what parameter conditions does ACO outperform other metaheuristics?", "status": "open", "fields": [ "computer-science", "biology", "mathematics" ], "color": "gray" }, { "id": "u-bacterial-chemotaxis-x-gradient-descent", "type": "unknown", "title": "How does E. coli chemotaxis achieve near-optimal gradient detection at the Cramer-Rao bound, and can the methylation memory system be replicated in artificial optimizers?\n", "status": "open", "fields": [ "biology", "computer_science", "biophysics", "optimization" ], "color": "gray" }, { "id": "u-biofilm-x-active-nematic", "type": "unknown", "title": "Do topological defects in bacterial biofilms (+1/2 disclinations) causally determine sites of biofilm expansion, cell extrusion, or matrix secretion, or are they merely epiphenomenal signatures of mechanical stress?", "status": "open", "fields": [ "biology", "physics", "microbiology" ], "color": "gray" }, { "id": "u-biomechanics-x-soft-robotics", "type": "unknown", "title": "What is the minimum tendon compliance (spring stiffness k) relative to muscle force (F_max) that achieves the metabolic cost reduction predicted by the spring-mass model, and can this ratio be systematically optimized across body mass scales for soft robotic design?", "status": "open", "fields": [ "biology", "computer_science", "engineering" ], "color": "gray" }, { "id": "u-blood-coagulation-cascade", "type": "unknown", "title": "What triggers the threshold crossing from hemostasis (localized clot) to disseminated intravascular coagulation (systemic activation), and can this transition be predicted from routine plasma biomarkers?", "status": "open", "fields": [ "medicine", "systems-biology", "hematology" ], "color": "gray" }, { "id": "u-boolean-attractor-cell-fate-mapping", "type": "unknown", "title": "Can the complete attractor landscape of a Boolean gene regulatory network model be mapped onto the full repertoire of cell types in a multicellular organism, and does attractor number predict cell type count?", "status": "open", "fields": [ "systems-biology", "computational-biology", "developmental-biology" ], "color": "gray" }, { "id": "u-boolean-network-cancer-attractors", "type": "unknown", "title": "Can Boolean network attractor analysis identify reliable therapeutic targets by mapping oncogenic mutations to epigenetic attractor transitions?", "status": "open", "fields": [ "systems-biology", "cancer-biology", "network-medicine" ], "color": "gray" }, { "id": "u-bp-convergence-loopy-genetic-linkage-graphs", "type": "unknown", "title": "When do undamped versus damped belief-propagation schedules remain reliable for genotype phasing on graphs with long-range linkage disequilibrium and irregular marker density?", "status": "open", "fields": [ "genetics", "computer-science", "information-theory" ], "color": "gray" }, { "id": "u-braess-paradox-biological-foraging", "type": "unknown", "title": "Does Braess's paradox appear systematically in biological foraging and transport networks, and what evolutionary mechanism prevents networks from getting trapped in the paradox regime?", "status": "open", "fields": [ "evolutionary-biology", "network-science", "complex-systems", "behavioral-ecology" ], "color": "gray" }, { "id": "u-calcium-signaling-x-stochastic-resonance", "type": "unknown", "title": "Does intracellular calcium stochastic resonance operate at the optimal noise level in living cells, and if so, what homeostatic mechanism tunes IP3 receptor cluster density to the SR optimal point for the cell's specific signaling context?", "status": "open", "fields": [ "biology", "physics", "biophysics" ], "color": "gray" }, { "id": "u-cell-division-x-branching-process", "type": "unknown", "title": "What is the effective division/death probability ratio for early pre-cancerous clones in normal tissue, and does it cross the critical threshold m = 1 before or after acquiring the first driver mutation?", "status": "open", "fields": [ "biology", "mathematics", "medicine" ], "color": "gray" }, { "id": "u-cell-jamming-tissue-development", "type": "unknown", "title": "Does the jamming transition control tissue fluidity during embryonic morphogenesis and cancer invasion, and what determines the critical shape index in vivo?", "status": "open", "fields": [ "biology", "biophysics", "developmental-biology", "cancer-biology" ], "color": "gray" }, { "id": "u-chromatin-loop-extrusion-processivity", "type": "unknown", "title": "What determines the processivity (loop-extrusion run length) of cohesin in vivo, and how does nucleosome density, transcription, and supercoiling modulate it?\n", "status": "open", "fields": [ "molecular-biology", "polymer-physics", "structural-biology" ], "color": "gray" }, { "id": "u-chromatin-loop-extrusion-speed-processivity-in-vivo", "type": "unknown", "title": "What is the in vivo speed, processivity, and stall force of cohesin-mediated chromatin loop extrusion in mammalian cells, and how do CTCF, WAPL, and transcription machinery pause or terminate extrusion to establish TAD boundaries?\n", "status": "open", "fields": [ "biology", "biophysics", "molecular-biology", "polymer-physics" ], "color": "gray" }, { "id": "u-circadian-clock-x-feedback-oscillator", "type": "unknown", "title": "What molecular mechanism ensures temperature compensation of the circadian period (Q₁₀ ≈ 1.0) despite temperature sensitivity of all biochemical rate constants (Q₁₀ ≈ 2-3)?", "status": "open", "fields": [ "biology", "biophysics", "biochemistry" ], "color": "gray" }, { "id": "u-circadian-kuramoto-jet-lag-dynamics", "type": "unknown", "title": "Does the speed of jet-lag recovery follow Kuramoto re-entrainment dynamics, and can phase-response curves (PRCs) quantitatively predict optimal light-exposure protocols?", "status": "open", "fields": [ "chronobiology", "nonlinear-dynamics", "neuroscience", "statistical-physics" ], "color": "gray" }, { "id": "u-circadian-metabolism-coupling", "type": "unknown", "title": "Is circadian clock disruption (shift work, jet lag) a direct cause of metabolic disease, or merely correlated via confounding lifestyle factors?", "status": "open", "fields": [ "chronobiology", "metabolism", "epidemiology" ], "color": "gray" }, { "id": "u-circadian-temperature-compensation-mechanism", "type": "unknown", "title": "What molecular mechanism allows the circadian clock period to remain approximately constant (~24 hours) across a 10-20 degree Celsius temperature range (Q10 approximately 1), when all biochemical reaction rates typically double with each 10-degree increase?\n", "status": "open", "fields": [ "biology", "physics", "chronobiology", "biophysics" ], "color": "gray" }, { "id": "u-confluent-tissue-brownian-universality", "type": "unknown", "title": "What universality class governs persistent random motion (anomalous diffusion) in dense confluent cell sheets?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-contact-graph-hessian-rank-native-basin-surrogate", "type": "unknown", "title": "Does the effective numerical rank of coarse-grained Hessians near predicted native basins correlate with simple graph statistics (spectral gap of contact Laplacian, foldon modularity) independent of protein length?\n", "status": "open", "fields": [ "computational-biology", "applied-mathematics" ], "color": "gray" }, { "id": "u-cortical-folding-topology", "type": "unknown", "title": "Does cortical gyrification (brain folding) obey the same topological transition rules as physical membrane buckling, and can topological defect theory predict sulcal pattern variability across individuals?", "status": "open", "fields": [ "neuroscience", "mathematical-physics", "developmental-biology", "topology" ], "color": "gray" }, { "id": "u-crispr-base-editing-x-error-correction", "type": "unknown", "title": "Can information-theoretic principles (guide RNA design as error-correcting code) predict and minimize off-target base editing rates across the human genome?", "status": "open", "fields": [ "molecular-biology", "information-theory", "genomics" ], "color": "gray" }, { "id": "u-crispr-multiplex-error-floor-vs-code-distance", "type": "unknown", "title": "What quantitative separation margins (effective Hamming-like distances among barcode sequences) are required for multiplexed CRISPR pooled screens to achieve targeted decoding error floors under realistic PCR and sequencing noise models?", "status": "open", "fields": [ "biology", "information-theory" ], "color": "gray" }, { "id": "u-crispr-x-search-and-replace", "type": "unknown", "title": "What is the theoretical minimum off-target cleavage rate achievable by any CRISPR system given the genome size and guide RNA length, and can this bound be achieved by engineered Cas variants?\n", "status": "open", "fields": [ "biology", "computer-science", "molecular-biology" ], "color": "gray" }, { "id": "u-cryoem-laplacian-eigenmode-physical-interpretability", "type": "unknown", "title": "When do cryo-EM Laplacian eigenmodes correspond to physically meaningful conformational motions?", "status": "open", "fields": [ "structural-biology", "mathematics", "medical-imaging" ], "color": "gray" }, { "id": "u-cryptochrome-radical-pair-quantum-nav", "type": "unknown", "title": "Is avian magnetic compass navigation via cryptochrome radical pairs a coherent quantum process that persists at physiological temperatures, and what decoherence timescale is critical?", "status": "open", "fields": [ "quantum-biology", "biophysics", "ornithology", "chemistry" ], "color": "gray" }, { "id": "u-developmental-gradient-x-pde", "type": "unknown", "title": "How do embryos robustly maintain morphogen gradient precision despite noise in gene expression, cell number variability, and tissue growth during development?\n", "status": "open", "fields": [ "biology", "developmental-biology", "mathematics" ], "color": "gray" }, { "id": "u-dna-origami-compiler-analogy-yield-prediction-limits", "type": "unknown", "title": "Do software-style dependency and staged-compilation metrics predict DNA origami assembly yield beyond existing thermodynamic and geometric design rules, or is the compiler analogy only pedagogical?\n", "status": "open", "fields": [ "biology", "computer-science" ], "color": "gray" }, { "id": "u-dna-replication-x-error-correction", "type": "unknown", "title": "Is there an information-theoretic optimal mutation rate for evolution, and does DNA replication fidelity operate near this optimum?\n", "status": "open", "fields": [ "biology", "computer_science", "information_theory", "molecular_biology" ], "color": "gray" }, { "id": "u-ecology-resilience-spatial-indicator", "type": "unknown", "title": "Do spatial geometric properties of vegetation patterns (separatrix distance, fractal dimension) provide earlier and more reliable early-warning indicators of ecosystem collapse than temporal variance or autocorrelation?", "status": "open", "fields": [ "ecology", "statistical-physics", "nonlinear-dynamics", "remote-sensing" ], "color": "gray" }, { "id": "u-epigenetic-escape-loci-mechanisms-transgenerational-scope", "type": "unknown", "title": "Which genomic loci consistently escape epigenetic reprogramming at fertilisation in mammals, what molecular features protect them from erasure, and how many generations can environmentally-induced epigenetic marks persist through the germline in humans?\n", "status": "open", "fields": [ "epigenetics", "developmental-biology", "molecular-biology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-epigenetic-inheritance-transgenerational", "type": "unknown", "title": "How far does transgenerational epigenetic inheritance extend in mammals, and what molecular mechanisms survive reprogramming at fertilization?", "status": "open", "fields": [ "epigenetics", "evolutionary-biology", "developmental-biology" ], "color": "gray" }, { "id": "u-epigenetic-landscape-x-attractor", "type": "unknown", "title": "Can the Waddington epigenetic landscape be quantitatively reconstructed from single-cell RNA-seq data, and do predicted attractor barriers match reprogramming efficiency?\n", "status": "open", "fields": [ "biology", "mathematics", "dynamical_systems", "developmental_biology" ], "color": "gray" }, { "id": "u-error-threshold-genome-size", "type": "unknown", "title": "Does the Shannon channel capacity of biological replication channels quantitatively predict the maximum viable genome size as a function of per-base mutation rate across all domains of life — RNA viruses, DNA viruses, bacteria, and eukaryotes?\n", "status": "open", "fields": [ "molecular-evolution", "information-theory", "virology", "genomics" ], "color": "gray" }, { "id": "u-evolution-undecidability-open-ended", "type": "unknown", "title": "Is open-ended biological evolution undecidable in principle - analogous to the halting problem - and does this explain why no finite model can predict evolutionary trajectories indefinitely?", "status": "open", "fields": [ "evolutionary-biology", "theoretical-computer-science", "complex-systems", "philosophy-of-biology" ], "color": "gray" }, { "id": "u-evolutionary-graph-amplifier-natural-populations", "type": "unknown", "title": "Do real biological population structures (tissue architectures, social networks, geographic ranges) act as amplifiers or suppressors of natural selection, and can this be quantified from empirical connectivity data?\n", "status": "open", "fields": [ "evolutionary-biology", "population-genetics", "graph-theory" ], "color": "gray" }, { "id": "u-fermentation-thermodynamic-efficiency-limit", "type": "unknown", "title": "What is the thermodynamic upper bound on ATP yield per mole of substrate in anaerobic fermentation, and how close do naturally evolved microbes operate to this limit?\n", "status": "open", "fields": [ "biochemistry", "thermodynamics", "microbiology" ], "color": "gray" }, { "id": "u-fisher-natural-gradient-evolution", "type": "unknown", "title": "Does natural selection implement natural gradient ascent on the fitness landscape in the Fisher information metric, and does the evolutionary speed limit (dW_bar/dt <= V_A = I_pop) match empirical selection responses across taxa?\n", "status": "open", "fields": [ "evolutionary-biology", "mathematical-statistics", "population-genetics", "machine-learning" ], "color": "gray" }, { "id": "u-fitness-landscape-overlapping-genes", "type": "unknown", "title": "How does overlapping gene structure constrain the ruggedness of the fitness landscape and the accessibility of evolutionary paths in dense genomes?", "status": "open", "fields": [ "evolutionary-biology", "genomics", "information-theory", "mathematical-biology" ], "color": "gray" }, { "id": "u-flagellar-motor-x-rotary-engine", "type": "unknown", "title": "How does the bacterial flagellar motor achieve near-100% energy conversion efficiency at stall, and what is the mechanochemical coupling mechanism at atomic resolution?\n", "status": "open", "fields": [ "biology", "physics", "biophysics", "biochemistry" ], "color": "gray" }, { "id": "u-flocking-topological-interaction-mechanism", "type": "unknown", "title": "What is the sensory and computational mechanism by which individual birds in a murmuration identify and track their k ~ 7 topological neighbours in a dense, rapidly moving flock, and how is this number evolutionarily constrained?\n", "status": "open", "fields": [ "biology", "computer-science", "neuroscience" ], "color": "gray" }, { "id": "u-funnel-ruggedness-docking-false-minima", "type": "unknown", "title": "How does landscape ruggedness quantifiably drive false-minimum trapping in protein-ligand docking pipelines?", "status": "open", "fields": [ "chemistry", "computer-science" ], "color": "gray" }, { "id": "u-game-theory-x-antibiotic-resistance", "type": "unknown", "title": "Can evolutionary game theory predict optimal antibiotic dosing regimens that exploit producer-cheater dynamics to prevent resistance evolution in clinical infections?", "status": "open", "fields": [ "evolutionary-biology", "clinical-microbiology", "mathematics" ], "color": "gray" }, { "id": "u-gene-expression-noise-x-information-theory", "type": "unknown", "title": "Do gene regulatory networks in living cells operate near their Shannon channel capacity limit, and what evolutionary pressures drive them toward or away from this optimum?", "status": "open", "fields": [ "systems-biology", "information-theory", "molecular-biology" ], "color": "gray" }, { "id": "u-gene-regulatory-network-x-boolean-circuit", "type": "unknown", "title": "Are endogenous developmental gene regulatory networks in vertebrates operating at or near the Boolean criticality threshold K = 2, and if so, does this determine the evolvability of body plan morphology?", "status": "open", "fields": [ "biology", "computer_science", "mathematics" ], "color": "gray" }, { "id": "u-grb-mass-extinction-link", "type": "unknown", "title": "Is there a statistically significant causal relationship between gamma-ray burst events within the Milky Way or Local Group and major mass extinction events in the terrestrial fossil record?", "status": "open", "fields": [ "astrobiology", "evolutionary-biology", "paleontology", "astrophysics" ], "color": "gray" }, { "id": "u-great-ape-false-belief-implicit-explicit", "type": "unknown", "title": "Do great apes possess an explicit, flexible theory of mind capable of tracking false beliefs, or only an implicit, inflexible agent model, and does the dissociation reflect a difference in degree or in kind from human mentalizing?\n", "status": "open", "fields": [ "biology", "comparative-psychology", "cognitive-science", "developmental-psychology" ], "color": "gray" }, { "id": "u-grn-gnn-perturbation-identifiability", "type": "unknown", "title": "Are GNN-informed gene regulatory perturbation models identifiable under sparse intervention data?", "status": "open", "fields": [ "biology", "machine-learning", "systems-biology" ], "color": "gray" }, { "id": "u-gut-microbiome-x-lotka-volterra", "type": "unknown", "title": "Are the interaction parameters of the gut microbiome generalized Lotka-Volterra model sufficiently stable across individuals and perturbations to enable personalized microbiome therapy prediction, and what ecological principles determine the basin of attraction for healthy microbiome states?", "status": "open", "fields": [ "biology", "mathematics", "ecology", "medicine" ], "color": "gray" }, { "id": "u-habitat-fragmentation-threshold", "type": "unknown", "title": "Do species extinction thresholds in fragmented landscapes obey 2D percolation finite-size scaling with exponent nu=4/3, and can early-warning indicators of approaching percolation criticality be detected in satellite habitat data?\n", "status": "open", "fields": [ "conservation-biology", "landscape-ecology", "statistical-physics", "remote-sensing" ], "color": "gray" }, { "id": "u-homeostasis-integral-feedback-synthetic-design", "type": "unknown", "title": "Can integral feedback control theory guide de novo design of synthetic gene circuits that achieve perfect homeostasis of arbitrary metabolite concentrations in living cells?", "status": "open", "fields": [ "synthetic-biology", "systems-biology", "control-theory", "engineering" ], "color": "gray" }, { "id": "u-immune-memory-x-long-term-potentiation", "type": "unknown", "title": "Do immune memory formation (germinal center affinity maturation) and neural long-term potentiation share a common molecular mechanism for memory consolidation — specifically, does both systems use protein synthesis-dependent structural changes governed by analogous signaling cascades?", "status": "open", "fields": [ "biology", "neuroscience", "immunology" ], "color": "gray" }, { "id": "u-intestinal-crypt-stem-cell-moran-selection", "type": "unknown", "title": "What is the selection coefficient of the earliest oncogenic mutations (APC, KRAS, TP53) in intestinal stem cells, and does the Moran process with positive selection quantitatively predict the observed age-dependent clonal expansion in normal human colon?\n", "status": "open", "fields": [ "biology", "mathematics", "oncology" ], "color": "gray" }, { "id": "u-jamming-exponent-universality-epithelium-versus-colloid", "type": "unknown", "title": "Do epithelial jamming transitions near confluence share quantitative scaling exponents (e.g., velocity correlations, susceptibility divergences) with laboratory colloidal glasses at comparable effective packing fractions?\n", "status": "open", "fields": [ "biology", "physics", "soft-matter" ], "color": "gray" }, { "id": "u-lateral-gene-transfer-rate-limits", "type": "unknown", "title": "What determines the maximum rate of lateral gene transfer (LGT) before it overwhelms vertical inheritance and destroys phylogenetic signal in prokaryotes?", "status": "open", "fields": [ "evolutionary-biology", "microbiology", "phylogenetics" ], "color": "gray" }, { "id": "u-lipid-raft-protein-sorting-mechanism", "type": "unknown", "title": "What is the physical mechanism by which lipid rafts sort integral membrane proteins, and what are the quantitative rules governing protein partitioning between ordered (raft) and disordered phases in living cell membranes?\n", "status": "open", "fields": [ "cell-biology", "biophysics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-loss-aversion-cross-cultural-evolutionary-universality", "type": "unknown", "title": "Is the loss aversion coefficient λ ≈ 2.25 a universal human constant with an evolutionary fixed point, or does it vary systematically with ecological subsistence mode, cultural context, and individual genetic variation in dopaminergic/serotonergic systems?", "status": "open", "fields": [ "biology", "evolutionary-psychology", "behavioral-economics", "neuroscience", "anthropology", "genetics" ], "color": "gray" }, { "id": "u-maxent-ecology-failure-modes", "type": "unknown", "title": "Under what ecological conditions does the Maximum Entropy Theory of Ecology (METE) fail, and do systematic deviations from METE predictions identify specific ecological mechanisms beyond information-theoretic constraints?", "status": "open", "fields": [ "macroecology", "statistical-mechanics", "information-theory", "biodiversity-science" ], "color": "gray" }, { "id": "u-mechanical-bifurcation-morphogenesis", "type": "unknown", "title": "Do mechanical bifurcations in epithelial sheets set universal constraints on body plan diversity across multicellular life, and can this predict which body plans are physically forbidden?", "status": "open", "fields": [ "developmental-biology", "biophysics", "mathematical-biology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-mechanosensing-x-force-transduction", "type": "unknown", "title": "What is the molecular-scale mechanism by which focal adhesions convert substrate stiffness (Young's modulus) into intracellular biochemical signals — and can it be described by a single Hookean spring model?", "status": "open", "fields": [ "biology", "physics", "biophysics" ], "color": "gray" }, { "id": "u-membrane-tension-x-laplace-pressure", "type": "unknown", "title": "How do cells coordinate cortical tension anisotropy during mitosis to ensure symmetric division, and what is the quantitative relationship between cortical tension gradients and cleavage furrow positioning?\n", "status": "open", "fields": [ "biology", "physics", "biophysics" ], "color": "gray" }, { "id": "u-metabolic-flux-control-redistribution-disease", "type": "unknown", "title": "How does metabolic flux control redistribution in human disease (cancer, diabetes, heart failure) differ from healthy tissue, and can MCA-derived flux control coefficients identify new therapeutic targets?", "status": "open", "fields": [ "systems-biology", "metabolomics", "pharmacology" ], "color": "gray" }, { "id": "u-metabolic-flux-entropy-production-cancer-cells", "type": "unknown", "title": "Is the entropy production rate (σ̇) of cancer cells systematically higher than normal cells of the same tissue, and does σ̇ predict cancer aggressiveness and drug response beyond conventional metabolic biomarkers?", "status": "open", "fields": [ "biology", "physics", "biochemistry", "oncology", "thermodynamics", "biophysics" ], "color": "gray" }, { "id": "u-metabolic-flux-x-linear-programming", "type": "unknown", "title": "Does the assumption of growth-rate maximization in flux balance analysis hold across environmental conditions and organisms, or does the objective function change with nutritional stress and evolutionary history?", "status": "open", "fields": [ "systems-biology", "mathematics", "microbiology" ], "color": "gray" }, { "id": "u-metabolic-scaling-deviations-non-mammalian", "type": "unknown", "title": "Why do metabolic scaling exponents deviate from 3/4 in insects, unicellular organisms, and plants, and do these deviations reflect differences in vascular network geometry or violations of the WBE model assumptions?\n", "status": "open", "fields": [ "physiology", "ecology", "physics" ], "color": "gray" }, { "id": "u-metabolic-scaling-fractal-transport-unification", "type": "unknown", "title": "How much of cross-species variation in metabolic scaling exponents is explained by measurable deviations from fractal-like branching in real vascular and respiratory networks?", "status": "open", "fields": [ "biology", "biophysics", "ecology" ], "color": "gray" }, { "id": "u-microbiome-brain-axis-mechanism", "type": "unknown", "title": "What are the primary mechanistic pathways (vagus nerve, immune signaling, metabolite diffusion) by which gut microbiome composition influences brain function and behavior?", "status": "open", "fields": [ "neuroscience", "microbiology", "immunology", "psychoneuroimmunology" ], "color": "gray" }, { "id": "u-microbiome-diversity-stability-causality", "type": "unknown", "title": "Does gut microbiome species diversity causally protect against pathogen invasion and antibiotic-associated dysbiosis, or is it merely correlated with host factors that drive both diversity and resilience?\n", "status": "open", "fields": [ "microbiology", "ecology", "medicine" ], "color": "gray" }, { "id": "u-microplate-inverse-beer-lambert-conditioning", "type": "unknown", "title": "How ill-conditioned is practical inverse concentration retrieval from multi-well absorbance when spectra overlap, scattering rises, and plate-edge artifacts distort calibration curves — and what regularized inversion beats ordinary linear regression in routine assays?\n", "status": "open", "fields": [ "analytical-biochemistry", "statistics" ], "color": "gray" }, { "id": "u-modularity-robustness-evolvability-tradeoff", "type": "unknown", "title": "Is there a quantitative, universal trade-off between robustness and evolvability in biological regulatory networks, and does modularity reliably occupy the Pareto front of this trade-off across scales from protein domains to body plans?\n", "status": "open", "fields": [ "evolutionary-biology", "systems-biology", "developmental-biology", "complexity-science" ], "color": "gray" }, { "id": "u-morphogenesis-x-mechanical-instability", "type": "unknown", "title": "Are mechanical instabilities sufficient to explain species-specific brain gyrification patterns, or do molecular pre-patterning signals (Wnt, PDGF) constrain fold positions independently of mechanical forces?", "status": "open", "fields": [ "biology", "physics", "neuroscience" ], "color": "gray" }, { "id": "u-multilevel-selection-kin-equivalence", "type": "unknown", "title": "Are kin selection and multilevel selection genuinely equivalent (Price equation unification) or do they make different empirical predictions, and which level of selection dominates in human social evolution?", "status": "open", "fields": [ "evolutionary-biology", "population-genetics", "social-science", "philosophy-of-biology" ], "color": "gray" }, { "id": "u-muscle-mechanics-x-crossbridge-theory", "type": "unknown", "title": "How many distinct biochemical states does the myosin crossbridge cycle require to quantitatively reproduce all mechanical transients in skeletal muscle, and what are the structural correlates of these states?", "status": "open", "fields": [ "biophysics", "cell-biology", "structural-biology" ], "color": "gray" }, { "id": "u-myosin-motor-x-brownian-ratchet", "type": "unknown", "title": "What is the complete energy transduction mechanism of myosin II at the single-molecule level, and how does the Brownian ratchet efficiency scale with temperature?\n", "status": "open", "fields": [ "biology", "physics", "biophysics", "biochemistry" ], "color": "gray" }, { "id": "u-neutral-theory-x-stochastic-sampling", "type": "unknown", "title": "Does the observed species abundance distribution of tropical tree communities follow the Poisson-Dirichlet prediction of neutral theory, or do deterministic niche effects create significant deviations detectable with modern tree census data?", "status": "open", "fields": [ "biology", "mathematics", "ecology" ], "color": "gray" }, { "id": "u-optimal-transport-angiogenesis", "type": "unknown", "title": "Does tumour angiogenesis deviate from Murray's optimal transport law in a measurable, drug-targetable way?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-pathogen-coevolution-network-percolation", "type": "unknown", "title": "Does pathogen diversity coevolve with host contact network topology through a percolation-like process, with a diversity threshold above which epidemic control becomes impossible?", "status": "open", "fields": [ "evolutionary-epidemiology", "network-science", "statistical-physics", "ecology" ], "color": "gray" }, { "id": "u-percolation-epidemic-fss", "type": "unknown", "title": "Do finite-size scaling corrections from percolation theory quantitatively improve epidemic risk estimates in populations of 10,000-100,000?\n", "status": "open", "fields": [ "epidemiology", "statistical-physics", "network-science", "public-health" ], "color": "gray" }, { "id": "u-photoreceptor-quantum-efficiency-x-photon-statistics", "type": "unknown", "title": "What limits the signal-to-noise ratio of single-photon detection in retinal rods - thermal rhodopsin isomerization (dark noise) vs gain variability in the cGMP cascade - and can the cascade be engineered to approach the quantum efficiency limit?", "status": "open", "fields": [ "biophysics", "neuroscience", "optics" ], "color": "gray" }, { "id": "u-phylogenetics-x-coalescent-theory", "type": "unknown", "title": "How do ancestral recombination graphs (ARGs) scale computationally to whole-genome biobank data, and what population history features are identifiable from ARGs but not from summary statistics?\n", "status": "open", "fields": [ "biology", "mathematics", "evolutionary-biology" ], "color": "gray" }, { "id": "u-prion-nucleation-spontaneous-rate-physiological", "type": "unknown", "title": "What is the spontaneous de novo nucleation rate of PrPSc from PrPC in healthy mammalian brain tissue, and does it account for the observed sporadic prion disease incidence of ~1 per million per year?", "status": "open", "fields": [ "biology", "statistical-physics", "medicine" ], "color": "gray" }, { "id": "u-protein-aggregation-x-nucleation-growth", "type": "unknown", "title": "Does secondary nucleation (fibril-surface catalysed) or primary nucleation dominate the early seeding events in Alzheimer's disease in vivo, and can the Knowles-Michaels rate equations predict in vivo amyloid-β aggregation kinetics from in vitro parameters?", "status": "open", "fields": [ "biology", "chemistry", "medicine" ], "color": "gray" }, { "id": "u-protein-crystal-packing-predictability", "type": "unknown", "title": "Can the space group and unit-cell parameters of a protein crystal be predicted de novo from the protein sequence or structure, and what determines the observed non-uniform space-group frequency distribution in the PDB?\n", "status": "open", "fields": [ "structural-biology", "crystallography", "mathematics" ], "color": "gray" }, { "id": "u-protein-folding-alphafold2-de-novo-design-limits", "type": "unknown", "title": "What are the fundamental limits of AlphaFold2-based de novo protein design, and can designed proteins achieve the functional diversity of naturally evolved proteins without evolutionary history in their training data?", "status": "open", "fields": [ "biology", "chemistry", "computational-biology", "machine-learning", "biochemistry" ], "color": "gray" }, { "id": "u-protein-folding-pl-constant-coarse-grained-surrogate", "type": "unknown", "title": "Can coarse-grained order parameters for protein folding (native contact fraction, radius of gyration channels) admit empirically measurable Polyak–Łojasiewicz-like constants that predict folding timescale ordering across homologs?\n", "status": "open", "fields": [ "biophysics", "applied-mathematics" ], "color": "gray" }, { "id": "u-protein-folding-x-energy-landscape", "type": "unknown", "title": "What determines the folding funnel depth-to-width ratio for a given amino acid sequence, and can frustrated landscapes predict aggregation propensity?\n", "status": "open", "fields": [ "biology", "physics", "chemistry", "computational_biology" ], "color": "gray" }, { "id": "u-protein-misfolding-disease-mechanism", "type": "unknown", "title": "What is the detailed molecular mechanism by which protein misfolding drives neurodegenerative disease, and can energy landscape theory predict which sequences are misfolding-prone?", "status": "open", "fields": [ "biology", "biophysics", "neuroscience", "statistical-mechanics" ], "color": "gray" }, { "id": "u-quantum-coherence-physiological-role", "type": "unknown", "title": "Does quantum coherence in biological light-harvesting complexes play a functional role in energy transfer efficiency at physiological temperatures, or are the observed spectroscopic oscillations artefacts of vibronic coupling with no fitness consequence?\n", "status": "open", "fields": [ "biophysics", "quantum-biology", "photosynthesis-biology", "quantum-physics" ], "color": "gray" }, { "id": "u-quantum-tunneling-enzyme-room-temperature-scope", "type": "unknown", "title": "What fraction of enzymatic reactions in biology involve quantum tunneling as a quantitatively significant rate enhancement, and is quantum tunneling a universal feature of enzyme catalysis or limited to specific oxidoreductase classes with extreme KIE signatures?\n", "status": "open", "fields": [ "biochemistry", "quantum-physics", "enzymology", "biophysics" ], "color": "gray" }, { "id": "u-quorum-signaling-as-multiplayer-game", "type": "unknown", "title": "Under what spatial structures do quorum-sensing strategies form evolutionarily stable polymorphisms between producers, responders, and cheaters?", "status": "open", "fields": [ "biology", "economics", "microbiology" ], "color": "gray" }, { "id": "u-random-matrix-eigenvalue-cleaning-single-cell-batch-effects", "type": "unknown", "title": "When does random-matrix covariance cleaning preserve biological signal while suppressing single-cell batch artifacts?", "status": "open", "fields": [ "biology", "statistics" ], "color": "gray" }, { "id": "u-redfield-ratio-evolution-optimality", "type": "unknown", "title": "Is the Redfield ratio C:N:P=106:16:1 an evolutionary optimum, a dynamical attractor, or a geochemical accident — and can we distinguish these mechanisms experimentally?", "status": "open", "fields": [ "oceanography", "evolutionary-biology", "ecology", "biochemistry" ], "color": "gray" }, { "id": "u-regulatory-network-attractor-enumeration", "type": "unknown", "title": "How many stable attractors (cell states) do mammalian gene regulatory networks support, and can the attractor landscape be systematically mapped from transcriptomic data alone?", "status": "open", "fields": [ "systems-biology", "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-renormalization-allometric", "type": "unknown", "title": "Are biological allometric scaling exponents RG fixed points, and what do the correction-to-scaling terms predict about small-organism deviations from Kleiber's Law?\n", "status": "open", "fields": [ "mathematical-physics", "theoretical-biology", "comparative-physiology", "statistical-physics" ], "color": "gray" }, { "id": "u-replication-fork-tasep-parameter-identifiability-from-seq-stalling-assays", "type": "unknown", "title": "Can genome-wide replication stalling assays paired with polymerase occupancy sequencing identify measurable ASEP-like parameters (effective hopping asymmetry, defect densities) without overfitting sparse kinetic toy models?", "status": "open", "fields": [ "biology", "statistical-physics" ], "color": "gray" }, { "id": "u-replicator-dynamics-llm-training", "type": "unknown", "title": "Does LLM self-play fine-tuning (RLHF, constitutional AI) converge to the same Nash equilibria as biological replicator dynamics?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-replicator-dynamics-prebiotic-origin", "type": "unknown", "title": "Can replicator dynamics on chemical reaction networks explain the origin of hereditary information, and what is the minimum complexity required for self-sustaining replication?", "status": "open", "fields": [ "biology", "chemistry", "mathematics", "origins-of-life" ], "color": "gray" }, { "id": "u-rna-folding-pseudoknot-partition-function", "type": "unknown", "title": "Can the RNA partition function be extended to include pseudoknots in polynomial time, and what fraction of biologically functional RNA structures require pseudoknot thermodynamics for accurate prediction?\n", "status": "open", "fields": [ "RNA-biology", "computational-biology", "biophysics" ], "color": "gray" }, { "id": "u-scale-free-network-x-metabolic", "type": "unknown", "title": "Do all metabolic networks across life exhibit the same scale-free exponent gamma, and does preferential attachment explain the evolutionary origin of metabolic hubs?\n", "status": "open", "fields": [ "biology", "mathematics", "network_science", "systems_biology" ], "color": "gray" }, { "id": "u-self-organized-criticality-consciousness", "type": "unknown", "title": "Is self-organized criticality (SOC) through brain-body resonance a necessary and sufficient mechanism for conscious integration, or merely correlative?", "status": "open", "fields": [ "neuroscience", "statistical-physics", "consciousness-science", "biophysics" ], "color": "gray" }, { "id": "u-senescence-sasp-cancer-promotion-threshold", "type": "unknown", "title": "At what senescent cell burden does the SASP switch from tumor-suppressive (immune recruitment) to tumor-promoting (chronic inflammation), and can this threshold be measured as a clinical biomarker?", "status": "open", "fields": [ "biology", "medicine", "oncology" ], "color": "gray" }, { "id": "u-sir-model-x-compartmental-ode", "type": "unknown", "title": "Do the topological properties of contact networks (degree distribution, clustering, community structure) systematically alter the epidemic threshold R₀ in ways not captured by mean-field SIR models, and can a unified network-kinetics framework predict epidemic outcomes on arbitrary network topologies?", "status": "open", "fields": [ "epidemiology", "mathematics", "network-science", "biology" ], "color": "gray" }, { "id": "u-stochastic-gene-expression-bet-hedging-quantitative", "type": "unknown", "title": "What are the quantitative design principles linking promoter noise parameters (burst size b, burst frequency k_on) to optimal bet-hedging fitness in fluctuating environments — and do measured Fano factors in bacterial genomes match the Kelly-criterion prediction for their ecological volatility?\n", "status": "open", "fields": [ "biology", "mathematical-biology", "biophysics", "evolutionary-biology" ], "color": "gray" }, { "id": "u-stochastic-resonance-neural-tuning", "type": "unknown", "title": "Has evolution tuned internal physiological noise levels in sensory neurons to the stochastic resonance optimum, and if so, what molecular and developmental mechanisms enforce this tuning?", "status": "open", "fields": [ "sensory-neuroscience", "evolutionary-biology", "biophysics", "statistical-physics" ], "color": "gray" }, { "id": "u-synthetic-biology-x-circuit-design", "type": "unknown", "title": "Can formal CAD methods from electronic engineering (logic synthesis, formal verification, timing analysis) be systematically applied to synthetic gene circuit design to achieve predictable, composable biological logic?", "status": "open", "fields": [ "synthetic-biology", "computer-science", "biological-engineering" ], "color": "gray" }, { "id": "u-synthetic-lichen-biofabrication", "type": "unknown", "title": "Whether engineered autotrophic-heterotrophic microbial consortia can achieve stable, self-sustaining biogeochemical cycles on non-Earth mineral substrates without continuous external inputs", "status": "open", "fields": [ "biology", "astrobiology", "materials-science" ], "color": "gray" }, { "id": "u-temporal-biosignature-information", "type": "unknown", "title": "Can temporal information retention in molecular systems serve as a universal biosignature to distinguish living from abiotic chemistry, and what is the minimum retention timescale?", "status": "open", "fields": [ "astrobiology", "information-theory", "biophysics", "origin-of-life" ], "color": "gray" }, { "id": "u-topoisomerase-knot-selection", "type": "unknown", "title": "Do topoisomerases actively select against specific DNA knot types, and how does the equilibrium knot distribution in cells depend on chromosome topology?", "status": "open", "fields": [ "biology", "molecular-biology", "mathematics", "topology" ], "color": "gray" }, { "id": "u-topological-morphogenesis", "type": "unknown", "title": "How do topological defects in cell-packing geometry direct the physical forces that shape organs during development?", "status": "open", "fields": [ "developmental-biology", "mathematical-physics", "biophysics", "soft-matter" ], "color": "gray" }, { "id": "u-tumor-containment-percolation", "type": "unknown", "title": "Whether tumor progression can be reliably halted through network connectivity disruption rather than cell elimination, and what structural thresholds govern the transition from contained to invasive growth", "status": "open", "fields": [ "biology", "medicine", "physics" ], "color": "gray" }, { "id": "u-turing-digit-wavelength-scaling", "type": "unknown", "title": "Does vertebrate digit number scale with limb-bud width as predicted by the Turing wavelength ratio Lambda*/W?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-turing-ms-demyelination-pattern", "type": "unknown", "title": "Are multiple sclerosis demyelination lesions spatially patterned by a Turing reaction-diffusion instability?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-vicsek-transition-order-finite-systems", "type": "unknown", "title": "Is the Vicsek model phase transition first-order or continuous in the thermodynamic limit, and how does finite-size scaling affect collective behavior in biological groups of 10-10^4 individuals?", "status": "open", "fields": [ "physics", "biology", "computational-physics" ], "color": "gray" }, { "id": "u-whale-song-information-content-localization", "type": "unknown", "title": "What is the information content and function of whale song — mate attraction, individual identification, group coordination, or SOFAR-channel navigation — and can passive acoustic methods resolve whale positions at basin scale?", "status": "open", "fields": [ "biology", "physics", "acoustics", "marine-biology" ], "color": "gray" }, { "id": "u-xylem-cavitation-repair-mechanism", "type": "unknown", "title": "How do plants repair embolised xylem conduits under positive or negative pressure, and is hydraulic recovery after drought a universal mechanism or species-specific with distinct biochemical substrates?", "status": "open", "fields": [ "plant-physiology", "biophysics", "cell-biology", "ecology" ], "color": "gray" }, { "id": "u-lipid-raft-functional-role-signaling", "type": "unknown", "title": "Do lipid rafts exist as stable, nanoscale liquid-ordered domains in living cell membranes, and if so, what is their functional role in receptor clustering and signal transduction?", "status": "open", "fields": [ "cell-biology", "biophysics", "biochemistry" ], "color": "gray" }, { "id": "u-protein-fitness-landscape-epistasis-ruggedness", "type": "unknown", "title": "How rugged are protein fitness landscapes — what fraction of beneficial mutations are epistatic (context-dependent), and can the landscape topology be predicted from protein structure well enough to guide directed evolution without exhaustive experimental measurement?\n", "status": "open", "fields": [ "biology-chemistry", "biochemistry", "evolutionary-biology", "computational-chemistry", "protein-biophysics" ], "color": "gray" }, { "id": "u-rna-world-nonenzymatic-replication-fidelity", "type": "unknown", "title": "What minimum replication fidelity is required for non-enzymatic RNA replication to sustain a functional RNA world — and can plausible prebiotic chemistry achieve this fidelity threshold under realistic environmental conditions?\n", "status": "open", "fields": [ "biology-chemistry", "prebiotic-chemistry", "origin-of-life", "information-theory" ], "color": "gray" }, { "id": "u-silent-bgc-activation-novel-antibiotics", "type": "unknown", "title": "What fraction of silent biosynthetic gene clusters in soil actinobacteria encode genuinely novel antibiotic scaffolds with activity against multidrug-resistant pathogens — and what are the minimal conditions (growth signals, co-culture, epigenetic modification) needed to reliably activate silent clusters in situ?\n", "status": "open", "fields": [ "biology-chemistry", "microbiology", "pharmacology", "synthetic-biology" ], "color": "gray" }, { "id": "u-oxyluciferin-excited-state-mechanism-enol-vs-keto", "type": "unknown", "title": "Which tautomeric form of oxyluciferin — enolate, keto, or phenolate — is the actual light emitter in firefly bioluminescence, and how do active-site electrostatics and protonation state tune emission from 540 nm (green) to 620 nm (red) without changing the luciferin substrate?\n", "status": "open", "fields": [ "bioluminescence", "photochemistry", "quantum-chemistry", "structural-biology" ], "color": "gray" }, { "id": "u-anomalous-diffusion-cytoplasm", "type": "unknown", "title": "What molecular mechanisms drive anomalous (non-Brownian) diffusion in the cytoplasm, and how do they change under cellular stress?", "status": "open", "fields": [ "biophysics", "cell-biology", "physics" ], "color": "gray" }, { "id": "u-antifreeze-protein-ice-binding", "type": "unknown", "title": "What is the molecular mechanism by which antifreeze proteins distinguish ice from liquid water and adsorb irreversibly to specific crystal planes, and can this selectivity be computationally designed into synthetic polymers for cryopreservation applications?", "status": "open", "fields": [ "biophysics", "biochemistry", "materials-science" ], "color": "gray" }, { "id": "u-aqp-gating-osmosensing-mechanism", "type": "unknown", "title": "What is the molecular gating mechanism of aquaporin water channels, and how do cells sense osmotic stress to trigger AQP2 vesicle trafficking and membrane insertion within minutes of vasopressin stimulation?\n", "status": "open", "fields": [ "biophysics", "cell-biology", "structural-biology", "physiology" ], "color": "gray" }, { "id": "u-atp-synthase-torque-slip-mechanism", "type": "unknown", "title": "What is the molecular mechanism of torque generation and \"slip\" in the F₀ c-ring of ATP synthase, and how does the rotor stoichiometry (8–15 c-subunits across species) determine the H⁺/ATP ratio and hence the thermodynamic efficiency of oxidative phosphorylation?\n", "status": "open", "fields": [ "biophysics", "biochemistry", "physics", "structural-biology" ], "color": "gray" }, { "id": "u-capsid-assembly-kinetic-intermediates", "type": "unknown", "title": "What are the structures and stabilities of transient oligomeric intermediates during viral capsid nucleation, and can they be targeted for antiviral intervention?", "status": "open", "fields": [ "biophysics", "structural-biology", "virology", "biochemistry" ], "color": "gray" }, { "id": "u-cochlear-amplifier-molecular-mechanism-prestin", "type": "unknown", "title": "What is the complete molecular mechanism by which prestin (SLC26A5) converts membrane potential changes to outer hair cell length changes at acoustic frequencies up to 70 kHz, and how does the nanoscale conformational change couple to the macroscale basilar membrane resonance?\n", "status": "open", "fields": [ "biophysics", "auditory-neuroscience", "molecular-biology", "physics" ], "color": "gray" }, { "id": "u-cochlear-hopf-bifurcation-active-hair-bundle-vs-somatic-motility", "type": "unknown", "title": "What is the relative contribution of active hair bundle motility (myosin- based, present in all hair cells) versus outer hair cell somatic electromotility via prestin (mammal-specific) to the cochlear amplifier, and which mechanism is the primary driver of the Hopf bifurcation dynamics and associated otoacoustic emissions?\n", "status": "open", "fields": [ "auditory-biophysics", "mechanobiology", "nonlinear-dynamics", "molecular-biology" ], "color": "gray" }, { "id": "u-cytoskeletal-active-matter-defect-dynamics", "type": "unknown", "title": "Do topological defects in cytoskeletal active matter (actin-myosin networks) control cell division plane orientation, and can active matter defect theory predict mitotic spindle positioning errors?\n", "status": "open", "fields": [ "biophysics", "biology", "physics" ], "color": "gray" }, { "id": "u-debye-length-ion-specificity-membrane-binding", "type": "unknown", "title": "How much of apparent Debye-length scaling of peripheral protein binding to membranes is actually ion-specific chemistry (Hofmeister, chelation) versus mean-field electrostatic screening?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-eis-channel-gating-mechanistic-link", "type": "unknown", "title": "Can electrochemical impedance spectroscopy non-invasively extract Hodgkin-Huxley channel gating parameters (activation/inactivation time constants, channel density) from intact excitable cell layers?", "status": "open", "fields": [ "biophysics", "chemistry", "electrophysiology" ], "color": "gray" }, { "id": "u-eis-membrane-hodgkin-huxley-identification", "type": "unknown", "title": "To what extent can broadband membrane impedance spectra uniquely identify multi-state gating schemes without single-channel resolution?", "status": "open", "fields": [ "biophysics", "chemistry", "neuroscience" ], "color": "gray" }, { "id": "u-hertz-adhesion-crossover-biological-tissues", "type": "unknown", "title": "At what length scales does pure Hertz elastic contact fail for compliant tissues versus AFM tips — and how should indentation pipelines blend JKR/DMT adhesion corrections with poroelastic relaxation kernels?", "status": "open", "fields": [ "biomechanics", "materials-science" ], "color": "gray" }, { "id": "u-intrinsically-disordered-proteins", "type": "unknown", "title": "Do intrinsically disordered protein condensates obey Flory-Huggins phase diagrams, and can the critical concentration for phase separation be predicted from amino acid sequence alone?", "status": "open", "fields": [ "biophysics", "polymer-science", "cell-biology" ], "color": "gray" }, { "id": "u-kinesin-thermal-noise-efficiency", "type": "unknown", "title": "How do molecular motors achieve near-optimal thermodynamic efficiency while operating in a regime dominated by thermal noise, and what limits efficiency below the Carnot bound?", "status": "open", "fields": [ "biophysics", "statistical-physics", "cell-biology", "nanotechnology" ], "color": "gray" }, { "id": "u-met-channel-molecular-identity-pore-forming-subunit", "type": "unknown", "title": "Is TMC1/TMC2 the definitive pore-forming subunit of the hair cell mechanotransduction (MET) channel, and what is the complete molecular architecture (stoichiometry, auxiliary subunits, tip-link attachment site) of the native MET channel complex?\n", "status": "open", "fields": [ "biophysics", "cell-biology", "hearing-research", "structural-biology" ], "color": "gray" }, { "id": "u-mitochondrial-pmf-efficiency-carnot-bound", "type": "unknown", "title": "What is the theoretical maximum thermodynamic efficiency of the mitochondrial ATP synthase, and how close do in vivo P/O ratios come to this bound under physiological proton-motive force conditions?\n", "status": "open", "fields": [ "biophysics", "thermodynamics", "cell-biology" ], "color": "gray" }, { "id": "u-muscle-crossbridge-kinetics", "type": "unknown", "title": "What are the complete rate functions f(x) and g(x) for myosin cross-bridge attachment and detachment in skeletal muscle, and how do they change with phosphorylation state and disease?", "status": "open", "fields": [ "biophysics", "mechanics", "molecular-biology" ], "color": "gray" }, { "id": "u-phage-ejection-force-osmotic-mechanism", "type": "unknown", "title": "Is bacteriophage DNA ejection force quantitatively explained by osmotic pressure alone, or do electrostatic and entropic contributions require an extended model?\n", "status": "open", "fields": [ "biophysics", "biology", "physics" ], "color": "gray" }, { "id": "u-prion-llps-nucleation-kinetics", "type": "unknown", "title": "Do prion conformational conversion and liquid-liquid phase separation nucleation share quantitatively identical nucleation rate laws, and can inhibitors of one process cross-inhibit the other?\n", "status": "open", "fields": [ "biophysics", "chemistry", "biology" ], "color": "gray" }, { "id": "u-stochastic-resonance-cell-signaling-bandwidth", "type": "unknown", "title": "Under what molecular and timescale constraints does stochastic resonance improve information throughput in cell signaling pathways?", "status": "open", "fields": [ "biophysics", "systems-biology", "information-theory" ], "color": "gray" }, { "id": "u-thermodynamic-uncertainty-bound-biochemical-estimators", "type": "unknown", "title": "How tightly do thermodynamic uncertainty relations constrain practical precision limits of biochemical estimators in vivo?", "status": "open", "fields": [ "biophysics", "statistical-physics", "statistics" ], "color": "gray" }, { "id": "u-wound-healing-collective-migration-coordination", "type": "unknown", "title": "How do epithelial cells coordinate collective migration over distances of millimeters during wound healing without a central organizing signal, and what determines the leader cell identity?", "status": "open", "fields": [ "cell-biology", "biophysics", "active-matter-physics" ], "color": "gray" }, { "id": "u-yap-taz-stiffness-sensing-mechanism-molecular", "type": "unknown", "title": "What is the complete molecular mechanism by which YAP/TAZ sense substrate stiffness — specifically, which upstream mechanosensors (integrin clustering, actin cytoskeletal tension, nuclear deformation, or ion channels) are necessary and sufficient, and what quantitative stiffness threshold determines the cytoplasmic-to-nuclear translocation switch?\n", "status": "open", "fields": [ "biophysics", "cell-biology", "mechanobiology", "cancer-biology" ], "color": "gray" }, { "id": "u-flagellar-motor-stator-number-regulation-pmf", "type": "unknown", "title": "How does the flagellar motor dynamically regulate the number of active MotA/MotB stator units in response to proton motive force, load, and environmental conditions ΓÇö and what is the mechanochemical mechanism of stator incorporation and release?\n", "status": "open", "fields": [ "biophysics", "microbiology", "systems-biology" ], "color": "gray" }, { "id": "u-microbial-fuel-cell-electron-transfer-limits", "type": "unknown", "title": "What are the fundamental rate limits on extracellular electron transfer in electrogenic bacteria, and can engineered cytochrome-c expression or synthetic conductive nanowires overcome the bottlenecks that currently cap microbial fuel cell power density below 10 W/m^2?", "status": "open", "fields": [ "biotechnology", "electrochemistry", "microbiology" ], "color": "gray" }, { "id": "u-plant-tropism-auxin-gradient-mechanism", "type": "unknown", "title": "What molecular mechanism relocates PIN efflux carriers to the lower flank of a gravitropically stimulated root or shoot within minutes, and is the signal transduction chain better described by a Turing reaction-diffusion instability or by a mechanical strain-sensing model?", "status": "open", "fields": [ "botany", "developmental-biology", "mathematics" ], "color": "gray" }, { "id": "u-autophagy-selectivity-cargo-receptor-hierarchy", "type": "unknown", "title": "How is cargo selectivity determined in autophagy when multiple cargo receptors (p62, NDP52, optineurin, NBR1, TAX1BP1) compete for LC3 binding and ubiquitinated substrates simultaneously?\n", "status": "open", "fields": [ "cell-biology", "biochemistry", "autophagy" ], "color": "gray" }, { "id": "u-boolean-network-attractor-landscape-reprogramming", "type": "unknown", "title": "Can the Boolean network attractor landscape of a cell type be empirically reconstructed from single-cell transcriptomic perturbation data, and does the number of attractors scale as sqrt(N) in real gene regulatory networks of measured connectivity K?\n", "status": "open", "fields": [ "cell-biology", "systems-biology", "theoretical-biology", "computational-biology" ], "color": "gray" }, { "id": "u-stress-granule-phase-separation-pathology", "type": "unknown", "title": "What are the molecular determinants of the liquid-to-solid phase transition in stress granules associated with ALS and FTD, and can quantitative Flory-Huggins models of IDR phase behavior predict which mutations shift condensate viscoelasticity toward pathological hardening before cytotoxicity is observed?", "status": "open", "fields": [ "cell-biology", "soft-matter", "biophysics", "medicine" ], "color": "gray" }, { "id": "u-ubiquitin-proteasome-proteostasis-collapse-threshold", "type": "unknown", "title": "What is the quantitative proteostasis collapse threshold — the misfolded protein flux above which the ubiquitin-proteasome system fails irreversibly — and how does this threshold decline with age?\n", "status": "open", "fields": [ "cell-biology", "systems-biology" ], "color": "gray" }, { "id": "u-atmospheric-chemistry-aerosol-nucleation", "type": "unknown", "title": "What are the molecular mechanisms of atmospheric new particle formation, and which trace species drive nucleation under different atmospheric conditions?", "status": "open", "fields": [ "chemistry", "atmospheric-chemistry", "environmental-chemistry" ], "color": "gray" }, { "id": "u-battery-solid-electrolyte-stability", "type": "unknown", "title": "What determines the long-term electrochemical stability of solid electrolytes at electrode interfaces, and can this stability be predicted computationally?", "status": "open", "fields": [ "chemistry", "electrochemistry", "materials-science" ], "color": "gray" }, { "id": "u-bz-reaction-3d-scroll-wave-instability", "type": "unknown", "title": "Under what conditions do 3D scroll waves in the Belousov-Zhabotinsky reaction become unstable (negative filament tension), and can this mechanism explain the transition from organised to turbulent chemical waves analogous to cardiac fibrillation?\n", "status": "open", "fields": [ "chemistry", "nonlinear-dynamics", "biophysics" ], "color": "gray" }, { "id": "u-catalyst-optimization-volcano-plots", "type": "unknown", "title": "Do Sabatier volcano plots have a universal mathematical form, and can machine learning identify the binding energy descriptors that place any catalyst near the volcano peak?", "status": "open", "fields": [ "catalysis", "physical-chemistry", "materials-science", "machine-learning" ], "color": "gray" }, { "id": "u-chemical-garden-membrane-self-organization", "type": "unknown", "title": "What determines the spontaneous selection of tube diameter, wall thickness, and porosity in chemical garden precipitation membranes, and can these parameters be analytically predicted from the osmotic pressure, ion diffusion coefficients, and solubility products of the metal salt and silicate without free fitting?", "status": "open", "fields": [ "chemistry", "fluid-mechanics", "materials-science" ], "color": "gray" }, { "id": "u-chirality-emergence-prebiotic", "type": "unknown", "title": "How did biological homochirality (L-amino acids, D-sugars) emerge from a racemic prebiotic world — symmetry breaking, amplification, or selection?", "status": "open", "fields": [ "origin-of-life", "physical-chemistry", "statistical-physics", "biochemistry" ], "color": "gray" }, { "id": "u-crn-multistability-biological", "type": "unknown", "title": "Which biological chemical reaction networks have the topological structure required for multistability, and does deficiency theory correctly predict their bistable phenotypes?", "status": "open", "fields": [ "chemistry", "biochemistry", "systems-biology", "mathematics" ], "color": "gray" }, { "id": "u-crystallization-kinetics-nucleation", "type": "unknown", "title": "Why does classical nucleation theory systematically underpredict nucleation rates by 10-20 orders of magnitude, and what is the correct free energy of critical nucleus formation?", "status": "open", "fields": [ "physical-chemistry", "materials-science", "statistical-physics", "crystallography" ], "color": "gray" }, { "id": "u-dac-sorbent-entropy-production-mechanism", "type": "unknown", "title": "Which process steps in the DAC thermal swing adsorption cycle produce the most entropy, and what molecular-level sorbent properties minimize irreversibility in each step?", "status": "open", "fields": [ "chemical-engineering", "thermodynamics", "materials-science", "atmospheric-chemistry" ], "color": "gray" }, { "id": "u-electrochemical-co2-reduction-selectivity", "type": "unknown", "title": "What determines product selectivity in electrochemical CO2 reduction, and can catalyst design achieve multicarbon product yields practical for carbon utilisation?", "status": "open", "fields": [ "chemistry", "electrochemistry", "catalysis" ], "color": "gray" }, { "id": "u-endocrine-disruptor-dose-response-nonmonotonic", "type": "unknown", "title": "What are the molecular mechanisms generating non-monotonic dose-response (NMDR) curves for endocrine disruptors (BPA, phthalates, atrazine), and are these mechanisms sufficient to invalidate linear extrapolation from high-dose toxicology studies to regulatory low-dose safety limits?\n", "status": "open", "fields": [ "chemistry", "toxicology", "endocrinology", "regulatory-science", "cell-biology" ], "color": "gray" }, { "id": "u-enzyme-kinetics-x-michaelis-menten", "type": "unknown", "title": "How do multi-enzyme metabolic pathways coordinate kinetics to avoid toxic intermediate accumulation, and what determines the optimal Km/Vmax ratio for each enzyme in a pathway?\n", "status": "open", "fields": [ "chemistry", "biology", "biochemistry" ], "color": "gray" }, { "id": "u-enzyme-surface-catalyst-design-principles", "type": "unknown", "title": "Can the Brønsted-Evans-Polanyi volcano plot framework from heterogeneous catalysis predict optimal transition state stabilization energies for enzyme active site design?\n", "status": "open", "fields": [ "chemistry", "biochemistry", "physics" ], "color": "gray" }, { "id": "u-enzyme-tunneling-quantum-mechanism", "type": "unknown", "title": "Do enzymes exploit quantum tunneling to enhance proton and hydride transfer rates beyond classical limits, and is this a general mechanism or specific to a few reactions?", "status": "open", "fields": [ "biochemistry", "quantum-biology", "physical-chemistry" ], "color": "gray" }, { "id": "u-graph-theory-x-molecular-structure", "type": "unknown", "title": "Is there a complete set of graph invariants that uniquely identifies all molecular graphs up to isomorphism, and can the graph isomorphism problem be solved in polynomial time for molecular graphs?", "status": "open", "fields": [ "chemistry", "mathematics", "computer_science" ], "color": "gray" }, { "id": "u-hydrogen-bond-network-water-anomalies", "type": "unknown", "title": "Are water's thermodynamic anomalies (density maximum at 4°C, compressibility minimum at 46°C) fully explained by the two-liquid model, and where is the liquid-liquid critical point?", "status": "open", "fields": [ "physical-chemistry", "statistical-physics", "condensed-matter" ], "color": "gray" }, { "id": "u-md-force-field-transferability-accuracy-limit", "type": "unknown", "title": "What is the accuracy ceiling of classical and machine-learned force fields for free energy calculations, and how can transferability across chemical space be systematically improved?", "status": "open", "fields": [ "computational-chemistry", "physical-chemistry", "machine-learning" ], "color": "gray" }, { "id": "u-metalloenzyme-design-principles", "type": "unknown", "title": "Can de novo metalloenzyme design achieve catalytic efficiency (kcat/KM) within 10-fold of natural enzymes by rational placement of first-shell ligands alone?", "status": "open", "fields": [ "biochemistry", "catalysis", "computational-chemistry" ], "color": "gray" }, { "id": "u-nmr-effective-hamiltonian-calibration-open-system", "type": "unknown", "title": "How much do relaxation and diffusion losses distort rotating-frame effective Hamiltonian calibration across high-field clinical scanners versus benchtop quantum-sensing prototypes sharing pulse libraries?", "status": "open", "fields": [ "magnetic-resonance", "quantum-control" ], "color": "gray" }, { "id": "u-nucleation-x-first-passage", "type": "unknown", "title": "Can classical nucleation theory predict nucleation rates to within an order of magnitude, or are there fundamental corrections from non-classical mechanisms?\n", "status": "open", "fields": [ "chemistry", "physics", "statistical_mechanics" ], "color": "gray" }, { "id": "u-oed-utility-misspecification-under-nonstationary-chemistry", "type": "unknown", "title": "How sensitive are Bayesian OED policies to utility misspecification in nonstationary chemistry campaigns?", "status": "open", "fields": [ "chemistry", "statistics", "automation" ], "color": "gray" }, { "id": "u-oer-scaling-relation-break", "type": "unknown", "title": "Can the universal OER scaling relation ΔG_OOH* − ΔG_OH* ≈ 3.2 eV be broken by novel catalyst architectures, enabling catalysts below the theoretical 0.4 V overpotential floor?", "status": "open", "fields": [ "electrochemistry", "materials-science", "computational-chemistry", "surface-science" ], "color": "gray" }, { "id": "u-percolation-mapping-quantitative-gel-chemistry", "type": "unknown", "title": "For realistic multifunctional polymerizations with loops and substitution effects, how far does bond-percolation universality deviate from measured gel points across chemistries?", "status": "open", "fields": [ "polymer-science", "statistical-physics" ], "color": "gray" }, { "id": "u-photocatalysis-x-semiconductor-physics", "type": "unknown", "title": "What is the theoretical maximum solar-to-hydrogen efficiency for a single-absorber photocatalyst, and what material properties (band gap, carrier lifetime, surface kinetics) currently limit practical systems to <1% vs the ~18% Shockley-Queisser theoretical limit?", "status": "open", "fields": [ "photochemistry", "semiconductor-physics", "materials-science" ], "color": "gray" }, { "id": "u-polymer-glass-x-jamming-transition", "type": "unknown", "title": "Is the glass transition a true thermodynamic phase transition with a diverging correlation length, or only a kinetic phenomenon, and how does it unify with jamming?\n", "status": "open", "fields": [ "chemistry", "physics", "soft_matter", "materials_science" ], "color": "gray" }, { "id": "u-protein-folding-thermodynamics-kinetics", "type": "unknown", "title": "Does the funnel energy landscape theory fully explain the Levinthal paradox, or do kinetic pathways violate thermodynamic predictions in meaningful biological cases?", "status": "open", "fields": [ "biochemistry", "biophysics", "statistical-physics" ], "color": "gray" }, { "id": "u-protein-protein-interaction-design", "type": "unknown", "title": "Can computational protein-protein interface design reliably produce binders with nanomolar affinity to any target protein surface?", "status": "open", "fields": [ "chemistry", "biochemistry", "structural-biology" ], "color": "gray" }, { "id": "u-reaction-mechanism-automated-discovery", "type": "unknown", "title": "Can automated computational methods reliably discover novel reaction mechanisms and predict activation barriers without experimental calibration?", "status": "open", "fields": [ "chemistry", "computational-chemistry", "machine-learning" ], "color": "gray" }, { "id": "u-reaction-networks-x-petri-nets", "type": "unknown", "title": "Can Petri net reachability analysis (PSPACE-complete in general) be applied tractably to biological regulatory networks of >100 species to verify persistence (no species goes extinct) and detect oscillatory behavior, and what is the computational boundary between tractable and intractable biological CRNs?", "status": "open", "fields": [ "chemistry", "computer_science", "biology", "mathematics" ], "color": "gray" }, { "id": "u-rna-aptamer-design-principles", "type": "unknown", "title": "What physical principles govern RNA aptamer binding affinity and selectivity, and can they guide de novo design without combinatorial screening?", "status": "open", "fields": [ "biochemistry", "biophysics", "structural-biology" ], "color": "gray" }, { "id": "u-self-replicating-molecules-minimal-criteria", "type": "unknown", "title": "What are the minimal chemical criteria for self-replication, and can a molecule shorter than 14 nucleotides replicate itself without external catalysts?", "status": "open", "fields": [ "origin-of-life", "biochemistry", "systems-chemistry" ], "color": "gray" }, { "id": "u-structure-uncertainty-propagation-from-alphafold-to-enzyme-design", "type": "unknown", "title": "How should confidence uncertainty in `b-alphafold-structure-priors-x-enzyme-engineering-screen-pruning` propagate into enzyme design ranking decisions?", "status": "open", "fields": [ "chemistry", "molecular-biology" ], "color": "gray" }, { "id": "u-supramolecular-self-assembly-prediction", "type": "unknown", "title": "Can the self-assembly of supramolecular structures be predicted from molecular building block properties, and what determines assembly pathway selectivity?", "status": "open", "fields": [ "chemistry", "supramolecular-chemistry", "materials-science" ], "color": "gray" }, { "id": "u-transition-state-x-saddle-point", "type": "unknown", "title": "Can machine learning potentials accurately predict transition states and reaction rates for chemical reactions not represented in training data, and what training strategies ensure extrapolation to high-energy configurations?\n", "status": "open", "fields": [ "chemistry", "physics", "mathematics" ], "color": "gray" }, { "id": "u-unified-spectral-epsilon-model-across-vdw-casimir-length-scales", "type": "unknown", "title": "Can a single internally consistent dielectric function ε(ω) measured across bands from UV to microwave anchor joint predictions of molecular-scale dispersion coefficients and micrometer-scale Casimir forces on identical substrates without empirical stitching artifacts?\n", "status": "open", "fields": [ "chemistry", "physics" ], "color": "gray" }, { "id": "u-vae-catalyst-latent-disentanglement-validity", "type": "unknown", "title": "Do VAE latent dimensions for catalyst screening encode chemically meaningful, disentangled factors?", "status": "open", "fields": [ "chemistry", "materials-science", "machine-learning" ], "color": "gray" }, { "id": "u-which-persistence-features-remain-stable-under-noisy-catalyst-screening-assays", "type": "unknown", "title": "What validation boundary conditions determine when `b-topological-data-analysis-x-catalyst-state-space-screening` remains decision-useful?", "status": "open", "fields": [ "chemistry", "mathematics" ], "color": "gray" }, { "id": "u-xna-expanded-genetic-alphabet-catalysis", "type": "unknown", "title": "Can XNA or expanded-alphabet genetic polymers (>4 bases) achieve catalytic rates and substrate diversity comparable to ribozymes, and what backbone chemistry maximises both information-storage capacity and catalytic function?\n", "status": "open", "fields": [ "synthetic-biology", "chemistry", "origins-of-life" ], "color": "gray" }, { "id": "u-corrosion-inhibitor-molecular-mechanism", "type": "unknown", "title": "What are the molecular-scale mechanisms by which organic corrosion inhibitors adsorb on metal surfaces and arrest electrochemical dissolution, and can these mechanisms be predicted from molecular structure?", "status": "open", "fields": [ "electrochemistry", "surface-science", "materials-science", "computational-chemistry" ], "color": "gray" }, { "id": "u-green-chemistry-pharmaceutical-e-factor-continuous-flow", "type": "unknown", "title": "Can continuous flow chemistry and catalytic process intensification reduce the pharmaceutical E-factor from the current 25-100 range to below 10 for complex multistep API synthesis, and what fraction of currently batch-manufactured drugs are chemically and operationally compatible with flow chemistry conversion?\n", "status": "open", "fields": [ "chemistry", "engineering", "chemical-engineering", "green-chemistry" ], "color": "gray" }, { "id": "u-nafion-degradation-mechanism-longevity", "type": "unknown", "title": "Whether the dominant Nafion membrane degradation mechanism under fuel cell operating conditions (HO• radical attack, fluoride release, mechanical fatigue, or catalyst dissolution crossover) is sufficiently understood to design next-generation membranes with >80,000-hour lifetimes\n", "status": "open", "fields": [ "polymer-chemistry", "electrochemistry", "materials-science" ], "color": "gray" }, { "id": "u-sabatier-volcano-principle-multi-step-cascade-reaction-design", "type": "unknown", "title": "Does the Sabatier volcano principle — optimal catalyst binds intermediates with intermediate affinity — extend to multi-step cascade reactions (e.g. CO₂ reduction to methanol: CO₂→CO→HCHO→CH₃OH) where different steps have conflicting optimal binding energies, and can DFT-based multi-dimensional volcano plots predict optimal bifunctional or tandem catalyst designs?\n", "status": "open", "fields": [ "physical-chemistry", "catalysis", "chemical-engineering", "computational-chemistry" ], "color": "gray" }, { "id": "u-solid-state-battery-sei-interface-resistance-origin", "type": "unknown", "title": "What is the atomic-scale origin of the anomalously high interfacial resistance between lithium metal anodes and oxide solid electrolytes (LLZO, LIPON), and what interface engineering strategies can reduce it below 1 Ω·cm²?", "status": "open", "fields": [ "chemistry", "materials-science", "electrochemistry", "solid-state-physics", "engineering" ], "color": "gray" }, { "id": "u-chronotype-genetic-variants-full-population-distribution", "type": "unknown", "title": "What is the complete genetic architecture of human chronotype — which variants beyond CRY1 and PER3 explain the full population distribution of midsleep timing, and how do gene-environment interactions with light exposure and social schedules shape the phenotype?\n", "status": "open", "fields": [ "chronobiology", "genetics", "epidemiology", "sleep-medicine" ], "color": "gray" }, { "id": "u-circadian-prc-individual-variation-prediction", "type": "unknown", "title": "What genetic and molecular factors predict individual variation in the human circadian phase response curve, and can PRC differences explain chronotype (morning vs. evening person) and differential jet-lag susceptibility?\n", "status": "open", "fields": [ "chronobiology", "mathematics", "genetics" ], "color": "gray" }, { "id": "u-abrupt-climate-transitions", "type": "unknown", "title": "What are the mechanisms of rapid Dansgaard-Oeschger events and can they occur in a warming world?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-aerosol-cloud-indirect", "type": "unknown", "title": "What is the magnitude of the aerosol first and second indirect effects on global radiative forcing?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-aerosol-cloud-nucleation-uncertainty", "type": "unknown", "title": "What are the dominant sources of uncertainty in predicting global cloud condensation nucleus (CCN) concentrations from aerosol precursor emissions, and how do organic aerosol formation pathways and new particle formation rates limit climate model accuracy for aerosol indirect forcing?", "status": "open", "fields": [ "atmospheric-science", "chemistry", "climate-science" ], "color": "gray" }, { "id": "u-amoc-collapse-threshold", "type": "unknown", "title": "What is the critical freshwater forcing threshold for AMOC collapse and is it within 21st-century projections?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-antarctic-bottom-water", "type": "unknown", "title": "What controls Antarctic Bottom Water formation rate and how will it respond to ice sheet freshening?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-arctic-amplification-mechanism", "type": "unknown", "title": "What fraction of Arctic amplification is caused by local feedbacks versus poleward heat transport changes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-carbon-cycle-feedbacks", "type": "unknown", "title": "How do terrestrial carbon cycle feedbacks change sign under sustained high-CO2 forcing?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-climate-damage-function-high-temperature-regime", "type": "unknown", "title": "What is the functional form of economic damages from climate change at global temperature anomalies above 3°C, where no modern empirical analog exists, and how should tipping point cascades, non-market damages, and distributional equity be incorporated into the damage function?\n", "status": "open", "fields": [ "climate-science", "environmental-economics", "atmospheric-physics", "welfare-economics" ], "color": "gray" }, { "id": "u-climate-ecs-feedback-uncertainty", "type": "unknown", "title": "What is the precise value of equilibrium climate sensitivity (ECS) and what cloud feedback processes drive the remaining uncertainty between 2.5 and 5.7 K per CO₂ doubling in CMIP6 models?", "status": "open", "fields": [ "climate-science", "physics", "atmospheric-science", "cloud-microphysics" ], "color": "gray" }, { "id": "u-climate-health-tipping-threshold", "type": "unknown", "title": "At what quantitative thresholds of climate forcing do population health outcomes undergo discontinuous phase transitions, and can these thresholds be predicted with bifurcation theory?", "status": "open", "fields": [ "climate-science", "epidemiology", "medicine", "dynamical-systems" ], "color": "gray" }, { "id": "u-climate-sensitivity-tails", "type": "unknown", "title": "What physical processes determine the long upper tail of equilibrium climate sensitivity distributions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cloud-feedback-sign", "type": "unknown", "title": "What is the net sign and magnitude of cloud feedback to CO2 forcing across all cloud types?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cloud-seeding-efficacy", "type": "unknown", "title": "What is the true efficacy of cloud seeding for precipitation enhancement across different cloud types?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-deep-ocean-carbon-sequestration", "type": "unknown", "title": "What fraction of anthropogenic CO2 can be sequestered in the deep ocean on centennial timescales?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-diffusion-downscaling-physical-consistency-under-shift", "type": "unknown", "title": "Can diffusion-based climate downscaling preserve physical consistency under non-stationary climate shift?", "status": "open", "fields": [ "climate-science", "machine-learning", "statistics" ], "color": "gray" }, { "id": "u-dro-ambiguity-set-specification-nonstationary-climate", "type": "unknown", "title": "How should ambiguity sets for distributionally robust adaptation decisions be specified under nonstationary climate statistics where historical samples misrepresent future tail dependence?", "status": "open", "fields": [ "climate-science", "mathematics", "operations-research" ], "color": "gray" }, { "id": "u-enso-predictability-limit", "type": "unknown", "title": "What determines the fundamental predictability limit of ENSO beyond one year?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hasselmann-stochastic-resonance-glacial-mechanism", "type": "unknown", "title": "Is stochastic resonance (Benzi et al. 1982) the primary mechanism for the 100-kyr glacial cycle, and can Hasselmann's linear stochastic climate model be extended to capture the observed asymmetry (gradual glaciation, rapid termination) and the 41-kyr to 100-kyr transition at the Mid-Pleistocene?", "status": "open", "fields": [ "climate-science", "mathematics", "stochastic-processes", "geophysics", "paleoclimatology" ], "color": "gray" }, { "id": "u-ice-sheet-instability-modes", "type": "unknown", "title": "What are the dominant instability modes of the West Antarctic Ice Sheet under ocean warming?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-kalman-smoothing-proxy-noise-tree-ring-reconstruction", "type": "unknown", "title": "How robust are state-space paleoclimate reconstructions to proxy-noise misspecification and temporal nonstationarity?", "status": "open", "fields": [ "climate-science", "statistics" ], "color": "gray" }, { "id": "u-marine-ice-cliff-instability", "type": "unknown", "title": "Is marine ice cliff instability a physically realisable mechanism in Antarctica or limited by buttressing?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-methane-clathrate-destabilization", "type": "unknown", "title": "Under what ocean temperature trajectories do seafloor methane clathrates destabilise and contribute to atmospheric methane?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-monsoon-bifurcation", "type": "unknown", "title": "Can the South Asian summer monsoon bifurcate to a permanently weakened state under continued aerosol loading?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-online-change-point-detection-false-alarm-rate-under-trends", "type": "unknown", "title": "How can Bayesian online change-point detection maintain low false-alarm rates under long-term glacier trend acceleration?", "status": "open", "fields": [ "climate-science", "statistics" ], "color": "gray" }, { "id": "u-optimal-transport-shift-stability-under-extremes", "type": "unknown", "title": "Do optimal-transport climate bias-correction maps remain stable under nonstationary extreme-event regimes?", "status": "open", "fields": [ "climate-science", "statistics", "mathematics" ], "color": "gray" }, { "id": "u-ozone-recovery-timeline", "type": "unknown", "title": "Will Antarctic ozone fully recover by 2070 and what are the confounding factors?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-peatland-carbon-dynamics", "type": "unknown", "title": "How much carbon stored in tropical and boreal peatlands is vulnerable to release under warming and drainage?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-permafrost-tipping-point", "type": "unknown", "title": "At what global temperature does permafrost carbon release become self-sustaining without further anthropogenic forcing?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-polar-vortex-disruption", "type": "unknown", "title": "What drives sudden stratospheric warming events and their link to extreme cold outbreaks at the surface?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-regional-sea-level-acceleration", "type": "unknown", "title": "What drives regional sea level accelerations that exceed the global mean by factors of 2-5?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-rossby-wave-climate-tipping", "type": "unknown", "title": "Can Rossby wave dynamics amplified by Arctic warming trigger irreversible climate tipping points in mid-latitude weather patterns?", "status": "open", "fields": [ "climate-science", "atmospheric-science", "mathematics", "physics" ], "color": "gray" }, { "id": "u-sai-side-effects", "type": "unknown", "title": "What are the regional precipitation and ozone consequences of stratospheric aerosol injection?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-soil-moisture-atmosphere-feedback", "type": "unknown", "title": "Does soil moisture-precipitation feedback create bistable wet-dry states in semi-arid regions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-stratosphere-troposphere-coupling", "type": "unknown", "title": "How does stratospheric dynamics modulate the tropospheric jet streams on multi-decadal timescales?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-urban-heat-island-nonlinearity", "type": "unknown", "title": "Are urban heat island effects nonlinear with city size and density, and how do they interact with regional climate?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-wildfire-climate-feedback", "type": "unknown", "title": "What is the net radiative feedback of increasing wildfire frequency and extent under climate change?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-attention-spotlight-mechanism", "type": "unknown", "title": "How does selective attention filter sensory information and what are the neural mechanisms of the attentional spotlight?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cognitive-reserve-mechanism", "type": "unknown", "title": "What neural mechanisms underlie cognitive reserve and how do they delay symptom onset in Alzheimer's disease?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-collective-memory-network-structure", "type": "unknown", "title": "How does the topology of social networks (clustering, path length, centrality) determine the accuracy, stability, and distribution of collective memory across groups?", "status": "open", "fields": [ "cognitive-science", "social-science", "network-science" ], "color": "gray" }, { "id": "u-creativity-neural-mechanism", "type": "unknown", "title": "What distinguishes the neural dynamics of creative versus routine cognition?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-decision-fatigue-neural", "type": "unknown", "title": "What neural mechanisms produce decision fatigue and degraded choice quality with repeated decisions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-distributional-semantics-compositionality", "type": "unknown", "title": "Can distributional semantic models capture the compositionality of language (the meaning of a phrase from the meanings of its parts), and do compositional vector representations match human neural patterns for phrase-level meaning?", "status": "open", "fields": [ "cognitive-science", "linguistics", "computer-science" ], "color": "gray" }, { "id": "u-emotion-categorization", "type": "unknown", "title": "Are basic emotions universal biological categories or culturally constructed prototypes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-face-recognition-substrate", "type": "unknown", "title": "What is the computational and neural architecture of face recognition in humans versus other primates?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hard-problem-consciousness", "type": "unknown", "title": "Why and how does subjective experience arise from physical brain processes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hippocampal-spatial-code", "type": "unknown", "title": "How does the hippocampal-entorhinal spatial code scale to large environments and abstract cognitive maps?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-implicit-explicit-memory-boundary", "type": "unknown", "title": "What determines whether a memory is expressed implicitly versus explicitly and can this boundary be shifted?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-insight-problem-solving", "type": "unknown", "title": "What neural and computational processes underlie the sudden insight that solves previously stuck problems?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-language-critical-period", "type": "unknown", "title": "What biological mechanisms close the critical period for first language acquisition at puberty?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-loss-aversion-neural-substrate", "type": "unknown", "title": "What is the specific neural circuit implementation of loss aversion, and why does the loss aversion coefficient lambda vary so widely across individuals and contexts?", "status": "open", "fields": [ "cognitive-science", "neuroscience", "behavioral-economics" ], "color": "gray" }, { "id": "u-metacognition-substrate", "type": "unknown", "title": "What neural mechanisms allow metacognitive access to one's own cognitive states?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-mind-wandering-function", "type": "unknown", "title": "What adaptive function does mind wandering serve and what determines its content?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-neural-correlates-self", "type": "unknown", "title": "What neural substrates constitute the minimal self and how do they generate a sense of personal identity?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-perceptual-binding-problem", "type": "unknown", "title": "How does the brain bind features processed in different cortical areas into unified percepts?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-sleep-creative-insight", "type": "unknown", "title": "How does sleep enhance creative problem solving and what stages are critical?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-social-cognition-architecture", "type": "unknown", "title": "What is the neural architecture of social cognition and how does it differ from non-social cognition?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-theory-of-mind-substrate", "type": "unknown", "title": "What is the minimal neural substrate for theory of mind and when does it develop ontogenetically?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-time-perception-mechanism", "type": "unknown", "title": "What neural mechanisms produce subjective time perception and what causes its distortions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-working-memory-capacity", "type": "unknown", "title": "What neural and computational mechanisms impose the ~4-item limit of working memory?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-aco-convergence-routing-instances", "type": "unknown", "title": "For industrial-scale routing instances with thousands of edges, which ant colony hyperparameter schedules admit PAC-style guarantees versus empirical leaderboard wins alone — and how do they compare to simulated annealing baselines under identical compute budgets?", "status": "open", "fields": [ "operations-research", "computer-science" ], "color": "gray" }, { "id": "u-algorithm-discovery-automation", "type": "unknown", "title": "Can machine learning systems autonomously discover novel algorithms and mathematical proofs beyond human-designed heuristics?", "status": "open", "fields": [ "machine-learning", "algorithms", "automated-theorem-proving" ], "color": "gray" }, { "id": "u-astrocyte-memory-replay-transformers", "type": "unknown", "title": "Can astrocyte-inspired memory compression and replay mechanisms eliminate catastrophic forgetting in long-context transformers while maintaining O(1) storage cost per context token?", "status": "open", "fields": [ "machine-learning", "neuroscience", "computational-neuroscience", "artificial-intelligence" ], "color": "gray" }, { "id": "u-byzantine-fault-tolerance-practical", "type": "unknown", "title": "What are the practical engineering limits of Byzantine fault-tolerant consensus in real-world deployments, and can BFT achieve performance competitive with non-Byzantine systems?", "status": "open", "fields": [ "computer-science", "distributed-systems", "engineering", "cryptography" ], "color": "gray" }, { "id": "u-cache-efficient-algorithm-design", "type": "unknown", "title": "Can memory hierarchy-aware algorithm design principles be formalised to automatically optimise cache efficiency across heterogeneous hardware?", "status": "open", "fields": [ "computer-science", "algorithms", "computer-architecture" ], "color": "gray" }, { "id": "u-cahn-hilliard-segmentation-parameter-transfer-limits", "type": "unknown", "title": "Which phase-field parameters from Cahn-Hilliard-style models transfer into practical diffuse-interface image segmentation, and where does the physical analogy break under nonconserved labels and learned features?\n", "status": "open", "fields": [ "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-cellular-automata-complexity-classification", "type": "unknown", "title": "What is the computational characterization of Wolfram's four behavioral classes of cellular automata, and does Class IV (complex) behavior always correspond to computational universality or are there Class IV automata that are not Turing complete?", "status": "open", "fields": [ "computer-science", "mathematics", "complex-systems" ], "color": "gray" }, { "id": "u-cellular-automata-x-computational-universality", "type": "unknown", "title": "What is the minimum computational resource (space-time complexity) for a cellular automaton to be universal, and are there self-replicating CA rules that are computationally simpler than von Neumann's construction?\n", "status": "open", "fields": [ "computer-science", "physics", "complexity-science" ], "color": "gray" }, { "id": "u-computational-irreducibility-physical-systems-scope", "type": "unknown", "title": "Which classes of physical and biological systems are computationally irreducible in a formal complexity-theoretic sense, and does irreducibility correspond to empirically observed limits of prediction in weather, ecology, and economics?\n", "status": "open", "fields": [ "computer-science", "mathematics", "philosophy-of-science" ], "color": "gray" }, { "id": "u-continuous-symmetry-neural-topology", "type": "unknown", "title": "Do continuous symmetries in neural population codes enable dynamic topological reconfiguration of network connectivity as a computational primitive, beyond what discrete architectures can achieve?", "status": "open", "fields": [ "computational-neuroscience", "machine-learning", "topology", "dynamical-systems" ], "color": "gray" }, { "id": "u-contrastive-ssl-energy-model-bridge", "type": "unknown", "title": "Can temperature parameters in contrastive SSL be rigorously interpreted as controlling an effective free-energy landscape curvature during training, beyond qualitative analogy?", "status": "open", "fields": [ "computer-science", "statistical-physics" ], "color": "gray" }, { "id": "u-cpc-negative-sampling-bias-temporal-structure", "type": "unknown", "title": "How sensitive are CPC-style representations to negative sampling bias when temporal autocorrelation violates independence assumptions commonly used in contrastive bounds?", "status": "open", "fields": [ "computer-science", "neuroscience", "machine-learning" ], "color": "gray" }, { "id": "u-cut-cell-segmentation-interface-consistency", "type": "unknown", "title": "Can explicit finite-volume-style flux consistency constraints at curved boundaries reduce topological leakage and boundary jitter in voxel medical segmentations compared with unconstrained CNN softmax outputs — without sacrificing recall?\n", "status": "open", "fields": [ "medical-imaging", "numerical-methods" ], "color": "gray" }, { "id": "u-deq-solver-tolerance-versus-generalization-gap", "type": "unknown", "title": "How sensitive are DEQ generalization metrics to forward fixed-point solver tolerances and backward adjoint solve accuracy — does premature termination bias gradients enough to shift validation error beyond optimizer noise?\n", "status": "open", "fields": [ "computer-science", "numerical-analysis" ], "color": "gray" }, { "id": "u-discrete-convolution-theorem-cnn-inductive-bias", "type": "unknown", "title": "How much of CNN generalization on natural images is explained by implicit spectral tilting induced by architecture depth, kernel size, and pooling versus task-specific learning?", "status": "open", "fields": [ "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-edmd-deep-koopman-spectral-bias-nonlinear-video", "type": "unknown", "title": "When do deep Koopman-style linearizations avoid spectral bias that limits EDMD on chaotic or multi-scale video dynamics relevant to laboratory monitoring?", "status": "open", "fields": [ "computer-science", "physics", "dynamical-systems" ], "color": "gray" }, { "id": "u-emergent-capabilities-llm-prediction", "type": "unknown", "title": "Are emergent capabilities in large language models predictable from scaling laws, or do they represent genuine phase transitions in capability space?", "status": "open", "fields": [ "machine-learning", "deep-learning", "natural-language-processing" ], "color": "gray" }, { "id": "u-federated-learning-privacy-utility", "type": "unknown", "title": "What is the fundamental tradeoff between differential privacy guarantees and model utility in federated learning, and how close are current implementations to the Pareto frontier?", "status": "open", "fields": [ "machine-learning", "privacy", "distributed-computing" ], "color": "gray" }, { "id": "u-fractional-spiking-neural-memory", "type": "unknown", "title": "Does fractional-order differentiation in spiking neural networks capture long-range temporal dependencies that integer-order models cannot, and is this biologically realized in slow adaptation currents?", "status": "open", "fields": [ "computational-neuroscience", "fractional-calculus", "machine-learning", "neurophysiology" ], "color": "gray" }, { "id": "u-genetic-algorithm-x-natural-selection", "type": "unknown", "title": "Under what fitness landscape structures do genetic algorithms outperform gradient-based optimization, and how can evolutionary computation borrow population genetics theory to design better crossover operators?\n", "status": "open", "fields": [ "computer-science", "biology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-graph-algorithm-quantum-speedup", "type": "unknown", "title": "Which graph algorithms admit quantum speedup beyond classical near-linear time complexity, and what are the practical costs of quantum graph computation?", "status": "open", "fields": [ "quantum-computing", "algorithms", "graph-theory" ], "color": "gray" }, { "id": "u-graph-neural-network-x-spectral-graph-theory", "type": "unknown", "title": "What is the expressive power of spectral GNNs for distinguishing non-isomorphic graphs, and how do spectral filters on graphs generalize to dynamic and hypergraphs?\n", "status": "open", "fields": [ "computer-science", "mathematics", "machine-learning" ], "color": "gray" }, { "id": "u-graph-percolation-lateral-movement-detection-threshold", "type": "unknown", "title": "Can enterprise identity graphs be reduced to percolation models with identifiable p_eff and p_c such that SOC alerts meaningfully predict approach to criticality before crown-jewel reachability spikes?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hopfield-capacity-modern-architectures", "type": "unknown", "title": "Does the spin-glass memory capacity bound (alpha_c ~ 0.14 per neuron) generalize to modern transformer attention heads, and can spin-glass replica theory predict catastrophic forgetting thresholds in large language models?\n", "status": "open", "fields": [ "computer-science", "physics", "neuroscience" ], "color": "gray" }, { "id": "u-immune-system-x-anomaly-detection", "type": "unknown", "title": "How does the adaptive immune system avoid missing novel pathogens while maintaining tolerance to self, and what computational principles underlie cross-reactive memory?\n", "status": "open", "fields": [ "biology", "computer_science", "immunology", "machine_learning" ], "color": "gray" }, { "id": "u-legal-argumentation-formal-completeness", "type": "unknown", "title": "Can formal argumentation frameworks capture the full range of legal reasoning patterns including analogy, precedent, and equitable discretion, or are there fundamental limits to the formalization of law?", "status": "open", "fields": [ "computer-science", "mathematics", "law" ], "color": "gray" }, { "id": "u-lwe-hardness-proof-quantum-reduction", "type": "unknown", "title": "Is Learning With Errors (LWE) provably hard against quantum computers — and what is the precise quantum query complexity of the best LWE algorithm as a function of lattice dimension n, modulus q, and error rate α?\n", "status": "open", "fields": [ "computer-science", "cryptography", "mathematics", "quantum-computing", "complexity-theory" ], "color": "gray" }, { "id": "u-neural-architecture-search-x-evolutionary-biology", "type": "unknown", "title": "Do the fitness landscapes of neural architecture search share quantitative properties (ruggedness, neutrality, evolvability) with natural fitness landscapes for protein sequences, and what does this imply about optimal search algorithms?", "status": "open", "fields": [ "computer-science", "evolutionary-biology", "machine-learning" ], "color": "gray" }, { "id": "u-neural-network-generalisation-theory", "type": "unknown", "title": "Why do overparameterised neural networks generalise well despite interpolating training data, contradicting classical statistical learning theory?", "status": "open", "fields": [ "machine-learning", "statistical-learning-theory", "deep-learning" ], "color": "gray" }, { "id": "u-neural-ode-x-dynamical-systems", "type": "unknown", "title": "Do Neural ODEs learn physically meaningful vector fields, and when do they exhibit bifurcations or chaos that limit generalization?\n", "status": "open", "fields": [ "computer_science", "mathematics", "dynamical_systems", "machine_learning" ], "color": "gray" }, { "id": "u-neuromorphic-thermodynamic-energy", "type": "unknown", "title": "Do neuromorphic processors approach thermodynamic (Landauer) energy efficiency limits that silicon von Neumann architectures cannot, and what circuit mechanisms enable this?", "status": "open", "fields": [ "neuromorphic-computing", "information-thermodynamics", "computer-architecture", "statistical-physics" ], "color": "gray" }, { "id": "u-optimal-bridge-density-knowledge-graph", "type": "unknown", "title": "What is the minimum number of cross-domain bridges required for a scientific knowledge graph to exhibit \"giant component\" connectivity, enabling any domain to reach any other within 2-3 bridge steps?\n", "status": "open", "fields": [ "network-science", "information-theory", "science-of-science", "graph-theory", "epistemology" ], "color": "gray" }, { "id": "u-optimal-cooling-schedule-convergence", "type": "unknown", "title": "What is the optimal annealing schedule for simulated annealing to guarantee convergence with polynomial-time complexity for NP-hard problems?", "status": "open", "fields": [ "computer-science", "statistical-physics", "optimization" ], "color": "gray" }, { "id": "u-oscillatory-spiking-neural-computation", "type": "unknown", "title": "Do learnable neural oscillations implement a form of analog temporal computation that provably outperforms digital spiking networks on continuous-time sequence modeling tasks?", "status": "open", "fields": [ "neuromorphic-computing", "computational-neuroscience", "machine-learning", "dynamical-systems" ], "color": "gray" }, { "id": "u-p-vs-np-geometric-barrier", "type": "unknown", "title": "What geometric or algebraic barriers prevent proof of P != NP, and do known techniques suffice if correctly applied?", "status": "open", "fields": [ "computational-complexity", "mathematics", "theoretical-computer-science" ], "color": "gray" }, { "id": "u-pagerank-x-markov-chain", "type": "unknown", "title": "What is the optimal teleportation parameter alpha in PageRank for different network topologies, and how does mixing time scale with web graph structure?\n", "status": "open", "fields": [ "computer_science", "mathematics", "network_science" ], "color": "gray" }, { "id": "u-parallel-tempering-cost-benefit-large-language-model-posteriors", "type": "unknown", "title": "What is the compute-benefit frontier for parallel tempering in large language model posterior sampling?", "status": "open", "fields": [ "computer-science", "statistics", "machine-learning" ], "color": "gray" }, { "id": "u-physics-informed-nn-fourier-convergence", "type": "unknown", "title": "When do physics-informed neural networks (PINNs) with domain-aware Fourier features provably converge to true PDE solutions, and what is the error bound as a function of network size and feature frequency?", "status": "open", "fields": [ "machine-learning", "numerical-analysis", "partial-differential-equations", "computational-physics" ], "color": "gray" }, { "id": "u-predictive-coding-motion-illusion", "type": "unknown", "title": "What neural circuit mechanism explains why predictive coding networks trained on natural images generate the same motion illusions as human visual perception, and is this convergence inevitable or accidental?", "status": "open", "fields": [ "computational-neuroscience", "computer-vision", "psychophysics", "cognitive-science" ], "color": "gray" }, { "id": "u-program-synthesis-completeness", "type": "unknown", "title": "Can program synthesis systems automatically generate programs from specifications for any computable function, and what are the practical limits?", "status": "open", "fields": [ "computer-science", "formal-methods", "machine-learning" ], "color": "gray" }, { "id": "u-quantum-cognition-lindblad-decisions", "type": "unknown", "title": "Is the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation the correct quantum formalism for modeling order effects and conjunction fallacies in human decision-making?", "status": "open", "fields": [ "quantum-cognition", "decision-theory", "quantum-information", "cognitive-psychology" ], "color": "gray" }, { "id": "u-quantum-error-correction-overhead-reduction", "type": "unknown", "title": "What is the minimum physical qubit overhead required for fault-tolerant quantum computation, and can quantum LDPC codes achieve constant overhead with practical decoders?", "status": "open", "fields": [ "quantum-computing", "quantum-error-correction", "classical-coding-theory", "computer-science" ], "color": "gray" }, { "id": "u-quantum-zeno-watchdog-quantitative-mapping", "type": "unknown", "title": "Can pedagogical mappings from quantum Zeno measurement-rate curves to embedded-system sampling/hazard models measurably improve engineering students’ quantitative reasoning about watchdog timing versus fault dynamics — without inducing misconceptions about quantum mechanics in silicon?\n", "status": "open", "fields": [ "computer-science", "physics", "education" ], "color": "gray" }, { "id": "u-reinforcement-learning-x-bellman-equation", "type": "unknown", "title": "Does the Bellman optimality equation have a unique solution for infinite-horizon deep RL with function approximation, and what conditions prevent divergence?\n", "status": "open", "fields": [ "computer_science", "mathematics", "control_theory", "optimization" ], "color": "gray" }, { "id": "u-reservoir-computing-x-dynamical-systems", "type": "unknown", "title": "What is the optimal reservoir architecture (topology, spectral radius, sparsity) for tasks requiring different timescales of memory, and can criticality-tuned reservoirs outperform trained recurrent networks on real-world time series?", "status": "open", "fields": [ "computer_science", "physics", "neuroscience" ], "color": "gray" }, { "id": "u-robustness-distribution-shift", "type": "unknown", "title": "What training and architectural modifications make machine learning models robust to real-world distribution shift, and how much robustness is achievable?", "status": "open", "fields": [ "machine-learning", "robust-statistics", "computer-vision" ], "color": "gray" }, { "id": "u-sat-phase-transition-p-np", "type": "unknown", "title": "Does the statistical-physics description of the SAT phase transition (replica symmetry breaking, glass phase) provide a proof strategy for P≠NP, or are worst-case and average-case hardness fundamentally different?", "status": "open", "fields": [ "computer-science", "mathematics", "statistical-physics", "combinatorics" ], "color": "gray" }, { "id": "u-sat-spin-glass-algorithm-design", "type": "unknown", "title": "Can spin glass replica symmetry breaking theory predict the optimal algorithmic strategy for SAT instances near the phase transition boundary, and does survey propagation achieve the theoretical cavity method performance bound?\n", "status": "open", "fields": [ "computer-science", "physics", "mathematics" ], "color": "gray" }, { "id": "u-satisfiability-x-constraint-propagation", "type": "unknown", "title": "What is the tightest polynomial-time propagation algorithm for general k-ary CSPs that subsumes both arc consistency and unit propagation, and does it close the SAT-CSP complexity gap?", "status": "open", "fields": [ "computer_science", "mathematics", "logic" ], "color": "gray" }, { "id": "u-spectral-clustering-x-graph-laplacian", "type": "unknown", "title": "What is the optimal graph Laplacian normalisation (unnormalised, symmetric, random-walk) for spectral clustering on real-world networks with heterogeneous degree distributions, and can the Cheeger bound be tightened to a polynomial approximation guarantee?", "status": "open", "fields": [ "computer_science", "mathematics" ], "color": "gray" }, { "id": "u-spin-glass-optimization-hardness-phase-transition", "type": "unknown", "title": "Does the computational hardness of random k-SAT peak exactly at the replica-symmetry-breaking transition, and can spin glass theory predict average-case algorithmic complexity for structured (non-random) instances?\n", "status": "open", "fields": [ "computer-science", "statistical-physics" ], "color": "gray" }, { "id": "u-stdp-reward-modulation-rl-equivalence", "type": "unknown", "title": "Is reward-modulated spike-timing-dependent plasticity (R-STDP) mathematically equivalent to a form of policy gradient reinforcement learning, and does this equivalence hold at the circuit level in biological brains?", "status": "open", "fields": [ "computational-neuroscience", "machine-learning", "neurophysiology", "reinforcement-learning" ], "color": "gray" }, { "id": "u-swarm-intelligence-x-distributed-computing", "type": "unknown", "title": "What is the computational complexity of problems solvable by ant colony optimization, and can quorum sensing achieve Byzantine fault tolerance without central coordination?\n", "status": "open", "fields": [ "biology", "computer_science", "distributed_systems" ], "color": "gray" }, { "id": "u-synaptic-tag-cache-analogy-quantitative-test", "type": "unknown", "title": "Can any quantitative isomorphism be defended between synaptic-tag lifetimes and cache write-buffer flush intervals beyond metaphor for pedagogy?", "status": "open", "fields": [ "neuroscience", "computer-science" ], "color": "gray" }, { "id": "u-transformer-attention-biological-plausibility", "type": "unknown", "title": "To what degree does the transformer self-attention mechanism replicate the computations performed by neural attention circuits in the primate visual cortex?", "status": "open", "fields": [ "computer-science", "neuroscience", "computational-neuroscience", "machine-learning" ], "color": "gray" }, { "id": "u-transformer-scaling-law-limits", "type": "unknown", "title": "When and why do Chinchilla scaling laws break down, and what architectural innovations can improve compute efficiency beyond current scaling predictions?", "status": "open", "fields": [ "machine-learning", "deep-learning", "natural-language-processing" ], "color": "gray" }, { "id": "u-unique-games-conjecture-sdp-approximation-tight-gap", "type": "unknown", "title": "Is the Unique Games Conjecture (UGC) true — i.e., does the Goemans-Williamson 0.878 approximation ratio for MAX-CUT represent an absolute computational barrier — and what is the true approximation ratio achievable in polynomial time if UGC is false?", "status": "open", "fields": [ "computer-science", "mathematics", "complexity-theory", "combinatorial-optimization", "computational-complexity" ], "color": "gray" }, { "id": "u-variational-inference-x-free-energy", "type": "unknown", "title": "What determines the expressiveness vs. computational cost trade-off for variational families in high-dimensional Bayesian inference, and how does it relate to free energy landscape topology?\n", "status": "open", "fields": [ "computer_science", "physics", "machine_learning", "statistical_mechanics" ], "color": "gray" }, { "id": "u-wgan-gp-tightness-versus-exact-lipschitz-projections", "type": "unknown", "title": "How tightly do gradient-penalty WGAN critics approximate true 1-Lipschitz Kantorovich potentials on benchmark image manifolds compared with spectral normalization or exact orthogonal layers — measured with operator-norm audits during training?\n", "status": "open", "fields": [ "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-evolutionary-algorithms-replicator-dynamics", "type": "unknown", "title": "Do genetic and evolutionary algorithms converge via the continuous-time replicator dynamics of evolutionary game theory?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-grokking-criticality-universality-class", "type": "unknown", "title": "What is the universality class of the grokking phase transition, and does it match any known universality class in statistical physics?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-stdp-criticality-mechanism", "type": "unknown", "title": "Is spike-timing-dependent plasticity (STDP) the biological mechanism by which cortical networks self-organize to the SOC critical point?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-magnons-collective-excitations", "type": "unknown", "title": "How do magnon-magnon and magnon-phonon interactions lead to non-trivial magnon lifetime and thermal conductivity in magnon-polaronic systems, and can these be engineered for spin caloritronics?", "status": "open", "fields": [ "condensed-matter", "quantum-mechanics", "materials-science" ], "color": "gray" }, { "id": "u-symmetry-breaking-goldstone", "type": "unknown", "title": "In non-relativistic condensed matter systems, why does the Nielsen-Chadha theorem allow fewer Goldstone bosons than broken generators, and what is the physical meaning of type-II (quadratic dispersion) Goldstone modes?", "status": "open", "fields": [ "condensed-matter", "particle-physics", "quantum-field-theory" ], "color": "gray" }, { "id": "u-topological-insulator-surface-state-interactions", "type": "unknown", "title": "How do electron-electron interactions and disorder modify topological surface states in real topological insulators beyond the free-fermion Z2 classification?", "status": "open", "fields": [ "condensed-matter-physics", "algebraic-topology" ], "color": "gray" }, { "id": "u-symplectic-discretization-bias-long-horizon-control", "type": "unknown", "title": "How much long-horizon policy bias is attributable to non-symplectic discretization in mechanics-dominated control tasks?", "status": "open", "fields": [ "control-engineering", "applied-mathematics", "computational-physics" ], "color": "gray" }, { "id": "u-inflation-epidemic-analogy-falsifiability-limits", "type": "unknown", "title": "What empirical predictions — if any — could ever falsify informal inflation–epidemic exponential metaphors without trivializing either discipline’s validated models?", "status": "open", "fields": [ "cosmology", "epidemiology" ], "color": "gray" }, { "id": "u-inflation-slow-roll-end-reheating-mechanism", "type": "unknown", "title": "What mechanism ends slow-roll inflation and efficiently converts inflaton energy into the Standard Model particles of the hot Big Bang (reheating), and what observational signatures distinguish different reheating scenarios?\n", "status": "open", "fields": [ "cosmology", "quantum-field-theory" ], "color": "gray" }, { "id": "u-embedding-dimension-selection-for-icu-trajectory-instability-detection", "type": "unknown", "title": "What failure boundaries determine when `b-delay-embedding-x-icu-deterioration-early-warning` remains decision-useful?", "status": "open", "fields": [ "dynamical-systems", "critical-care" ], "color": "gray" }, { "id": "u-missingness-aware-lstm-training-for-icu-forecasts", "type": "unknown", "title": "Which missingness models are required for safe deployment of `b-lstm-sequence-memory-x-icu-physiology-forecasting`?", "status": "open", "fields": [ "critical-care", "computer-science" ], "color": "gray" }, { "id": "u-neural-cde-icu-robustness-to-missingness-patterns", "type": "unknown", "title": "How robust are neural CDE ICU forecasts to clinically realistic, nonrandom missingness patterns?", "status": "open", "fields": [ "critical-care", "machine-learning" ], "color": "gray" }, { "id": "u-qkd-practical-implementation-side-channels", "type": "unknown", "title": "Can quantum key distribution systems achieve information-theoretic security in practice when realistic detector and source imperfections create side-channel vulnerabilities not covered by idealized security proofs?", "status": "open", "fields": [ "cryptography", "quantum-computing", "information-theory" ], "color": "gray" }, { "id": "u-axelrod-model-empirical-validation-cultural-diversity", "type": "unknown", "title": "Has the Axelrod cultural dissemination model ever been empirically validated against real-world cultural diversity data ΓÇö what observable predictions does the phase transition make about cultural boundary formation, and which datasets could test them?\n", "status": "open", "fields": [ "cultural-dynamics", "computational-social-science", "social-physics", "anthropology" ], "color": "gray" }, { "id": "u-optimal-cybersecurity-investment-under-adversarial-uncertainty", "type": "unknown", "title": "What is the optimal cybersecurity investment strategy for an organization under adversarial uncertainty — when the attacker's capabilities, objectives, and rationality are unknown and the defender cannot observe the true threat landscape?\n", "status": "open", "fields": [ "cybersecurity", "game-theory", "economics", "decision-theory" ], "color": "gray" }, { "id": "u-gyrification-mechanics-developmental-timing", "type": "unknown", "title": "What determines the timing, location, and degree of cortical gyrification (brain folding) during development, and can mechanical models predict individual variation in sulcal patterns?", "status": "open", "fields": [ "developmental-neuroscience", "biomechanics", "mathematical-biology" ], "color": "gray" }, { "id": "u-morphogen-gradient-robustness-scaling", "type": "unknown", "title": "How do morphogen gradient systems achieve robustness against noise and scaling with organism size, when the diffusion-based gradient length √(D/k) is fixed by biochemical constants?", "status": "open", "fields": [ "developmental-biology", "mathematical-biology", "biophysics", "physics" ], "color": "gray" }, { "id": "u-topological-defect-morphogenesis-3d-tissue", "type": "unknown", "title": "Do topological defects in three-dimensional active nematic tissues drive morphogenetic events such as lumen formation, tube branching, and organ folding in developing embryos, and can Q-tensor simulations parameterized by single-cell imaging predict tissue shape outcomes quantitatively?\n", "status": "open", "fields": [ "developmental-biology", "biophysics", "soft-matter", "computational-biology" ], "color": "gray" }, { "id": "u-turing-morphogen-identity-in-vivo-diffusion-measurement", "type": "unknown", "title": "What are the directly measured in vivo diffusion coefficients D_A and D_I for proposed Turing activator-inhibitor pairs (Sox9/BMP in digit spacing; melanophore/ xanthophore signals in zebrafish), and do these satisfy the Turing instability condition D_I/D_A > f_A²/(4 det J) with independently measured reaction kinetics?\n", "status": "open", "fields": [ "developmental-biology", "biophysics", "mathematical-biology", "cell-biology" ], "color": "gray" }, { "id": "u-turing-patterning-3d-robustness", "type": "unknown", "title": "How do Turing reaction-diffusion mechanisms maintain robust spatial patterning in three-dimensional growing tissues despite molecular noise, geometric constraints, and cell division?", "status": "open", "fields": [ "developmental-biology", "mathematics", "biophysics" ], "color": "gray" }, { "id": "u-waddington-canalization-mechanism", "type": "unknown", "title": "What molecular and network-level mechanisms implement developmental canalization — the robustness of cell fate specification against genetic and environmental perturbations — and can the Waddington landscape topology be quantitatively reconstructed from single-cell transcriptomic data?", "status": "open", "fields": [ "developmental-biology", "systems-biology", "mathematics" ], "color": "gray" }, { "id": "u-allelopathy-invasive-plant-mycorrhizal-disruption", "type": "unknown", "title": "What is the quantitative relationship between invasive plant allelochemical concentration in soil and the degree of mycorrhizal network disruption, and can mycorrhizal inoculation or rhizobacterial biofilms restore native plant competitive ability against allelopathic invaders?\n", "status": "open", "fields": [ "ecology", "chemistry", "microbiology" ], "color": "gray" }, { "id": "u-bef-relationship-agricultural-context", "type": "unknown", "title": "How does the biodiversity-ecosystem function relationship differ between natural and agricultural ecosystems, and at what minimum biodiversity level do ecosystem services collapse in intensively managed farmland?", "status": "open", "fields": [ "ecology", "agronomy", "conservation-biology" ], "color": "gray" }, { "id": "u-bet-hedging-correlation-structure-across-taxa", "type": "unknown", "title": "How empirically structured are environmental covariance tensors governing bet hedging versus portfolio analogies across marine vs terrestrial vs microbial taxa?", "status": "open", "fields": [ "ecology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-biodiversity-productivity-relationship", "type": "unknown", "title": "Is the biodiversity-ecosystem productivity relationship hump-shaped, monotonic, or context-dependent — and what mechanism drives the relationship?", "status": "open", "fields": [ "community-ecology", "macroecology", "theoretical-ecology" ], "color": "gray" }, { "id": "u-control-lyapunov-safe-harvest-policy-ecology", "type": "unknown", "title": "Can control-Lyapunov and barrier constraints produce practically enforceable harvest policies that improve ecological resilience?", "status": "open", "fields": [ "ecology", "control-engineering", "resource-management" ], "color": "gray" }, { "id": "u-coral-bleaching-thermal-stress", "type": "unknown", "title": "What is the molecular mechanism by which Symbiodiniaceae clade D confers ~1-2°C higher thermal tolerance to coral hosts, and can this tolerance be transferred to bleaching-susceptible clades via synthetic biology?", "status": "open", "fields": [ "ecology", "marine-biology", "molecular-biology", "climate-science" ], "color": "gray" }, { "id": "u-digital-commons-governance-principles", "type": "unknown", "title": "Do Ostrom's eight design principles for sustainable common pool resource governance apply to digital commons (open-source software, Wikipedia, scientific data repositories), and what adaptations are required?", "status": "open", "fields": [ "ecology", "social-science", "economics", "information-science" ], "color": "gray" }, { "id": "u-dryland-vegetation-pattern-formation", "type": "unknown", "title": "What determines the wavelength, orientation, and stability of dryland vegetation patterns (bands, spots, gaps) predicted by the Klausmeier model, and can remote sensing of pattern geometry be used to infer soil hydraulic properties and proximity to desertification tipping points?", "status": "open", "fields": [ "ecology", "mathematics", "remote-sensing" ], "color": "gray" }, { "id": "u-ecological-succession-x-markov", "type": "unknown", "title": "Is ecological succession in real ecosystems well-approximated by a time-homogeneous Markov chain (transition probabilities constant over time), or do climate trends, soil development, and species introductions create non-stationary transition matrices that invalidate climax community predictions?", "status": "open", "fields": [ "ecology", "mathematics", "biology" ], "color": "gray" }, { "id": "u-ecology-x-coexistence-theory", "type": "unknown", "title": "Can the storage effect quantitatively predict biodiversity loss under climate change (reduced temporal variance and altered autocorrelation) in empirical plant communities, and is the stabilising niche difference measurable in situ?", "status": "open", "fields": [ "biology", "mathematics", "ecology" ], "color": "gray" }, { "id": "u-ecosystem-engineer-legacy-effects", "type": "unknown", "title": "Do ecosystem engineers (beavers, elephants, earthworms) leave persistent legacy effects detectable centuries after their removal, and can these legacies be quantified from soil and sediment records?", "status": "open", "fields": [ "ecology", "paleoecology", "conservation-biology" ], "color": "gray" }, { "id": "u-ecosystem-services-valuation-market-failure", "type": "unknown", "title": "Can ecosystem service valuations be made precise enough to design efficient Pigouvian taxes and subsidies for biodiversity conservation, and what institutional mechanisms can internalize natural capital into market decisions at the required scale?", "status": "open", "fields": [ "ecology", "economics", "environmental-science", "policy", "natural-capital-accounting" ], "color": "gray" }, { "id": "u-ecosystem-tipping-point-early-warning-false-positive-rate", "type": "unknown", "title": "What is the false positive rate of critical slowing down early warning signals (rising variance and AR(1)) for ecosystem tipping points, and under what conditions do EWS fail to detect impending fold bifurcations in ecological time series?", "status": "open", "fields": [ "ecology", "statistics", "nonlinear-dynamics", "environmental-science", "management-science" ], "color": "gray" }, { "id": "u-environmental-justice-cumulative-impact-assessment-methodology", "type": "unknown", "title": "What is the appropriate methodology for cumulative environmental impact assessment in environmental justice contexts — how should multiple simultaneous burdens (air pollution, noise, flooding, heat, food access, healthcare) be combined into a single index that supports regulatory action without masking within-population heterogeneity?\n", "status": "open", "fields": [ "ecology", "social-science", "public-health", "environmental-science", "statistics" ], "color": "gray" }, { "id": "u-ess-higher-order-interactions-ecosystem", "type": "unknown", "title": "How do higher-order (non-pairwise) ecological interactions — where the effect of species A on B depends on the presence of species C — modify the predictions of evolutionarily stable strategy theory and replicator dynamics for ecosystem stability and biodiversity?\n", "status": "open", "fields": [ "ecology", "evolutionary-biology", "game-theory", "mathematical-biology" ], "color": "gray" }, { "id": "u-fire-regime-ecological-threshold", "type": "unknown", "title": "What determines fire regime thresholds in different ecosystems, and at what fuel load and climate conditions do ecosystems cross from fire-maintained to fire-transformed states?", "status": "open", "fields": [ "ecology", "fire-ecology", "climate-ecology" ], "color": "gray" }, { "id": "u-forest-canopy-clumping-beer-lambert-deviation", "type": "unknown", "title": "How much does leaf spatial clumping cause real forest canopies to deviate from Beer-Lambert exponential light extinction, and can this deviation be predicted from stand structure parameters alone?\n", "status": "open", "fields": [ "ecology", "optics" ], "color": "gray" }, { "id": "u-forest-fire-soc-climate-change-modification", "type": "unknown", "title": "How does climate-change-driven drought and fuel accumulation modify the power-law exponent of forest fire size distributions, and is the SOC critical state preserved or destroyed under extreme warming scenarios?\n", "status": "open", "fields": [ "ecology", "statistical-physics", "environmental-science", "climate-science" ], "color": "gray" }, { "id": "u-gap-recruitment-neutral-theory-goodness-of-fit", "type": "unknown", "title": "Across temperate and tropical forests with mapped canopy gaps, when does neutral biodiversity theory provide statistically adequate fits to species-abundance distributions in gap recruits compared with niche-structured hierarchical models that include trait covariates?\n", "status": "open", "fields": [ "ecology", "statistics" ], "color": "gray" }, { "id": "u-gut-brain-axis-causal-mechanism", "type": "unknown", "title": "What are the quantitative causal mechanisms by which specific gut microbial taxa and their metabolites (SCFAs, tryptophan metabolites, LPS) modulate brain function and behavior, and can these mechanisms explain the microbiome-depression correlation?\n", "status": "open", "fields": [ "ecology", "microbiology", "neuroscience", "psychiatry", "biochemistry" ], "color": "gray" }, { "id": "u-intermediate-disturbance-diversity-peak", "type": "unknown", "title": "Under what community composition, disturbance type, and spatial scale conditions does the Intermediate Disturbance Hypothesis correctly predict a diversity peak, and what ecological mechanisms cause the IDH to fail in communities where competitive exclusion timescales are much shorter than disturbance return intervals?", "status": "open", "fields": [ "ecology", "nonlinear-dynamics", "conservation-biology" ], "color": "gray" }, { "id": "u-invasive-species-reaction-diffusion", "type": "unknown", "title": "When do Allee effects make invasion fronts 'pushed' rather than 'pulled', and how does the transition between these regimes affect management control point placement?", "status": "open", "fields": [ "ecology", "mathematics", "conservation-biology" ], "color": "gray" }, { "id": "u-invasive-species-threshold", "type": "unknown", "title": "Is there a universal population density threshold above which invasive species become impossible to eradicate, and what biological traits determine this threshold?", "status": "open", "fields": [ "invasion-biology", "population-ecology", "conservation-biology" ], "color": "gray" }, { "id": "u-keystone-species-identification", "type": "unknown", "title": "Can keystone species be identified from food web structure and interaction strengths before their removal, or only recognized retrospectively?", "status": "open", "fields": [ "ecology", "food-web-ecology", "conservation-biology" ], "color": "gray" }, { "id": "u-lcs-retention-coastal-recruitment-transfer", "type": "unknown", "title": "How often do Lagrangian coherent-structure retention metrics outperform simpler Eulerian frontal indices for predicting coastal larval settlement when species-specific vertical migration and mortality are included?\n", "status": "open", "fields": [ "marine-ecology", "oceanography" ], "color": "gray" }, { "id": "u-lotka-volterra-hamiltonian-real-ecosystem-conservation", "type": "unknown", "title": "Is the Lotka-Volterra Hamiltonian approximately conserved in real predator- prey systems over ecologically relevant timescales, and how quickly does the conservation break down under realistic ecological perturbations?\n", "status": "open", "fields": [ "ecology", "mathematics" ], "color": "gray" }, { "id": "u-lyme-ohio-surveillance-gap", "type": "unknown", "title": "What is the true epidemiological landscape of Lyme disease in Ohio and the Great Lakes region, and how does Scioto River watershed tick habitat connect to human exposure risk — given that passive surveillance captures only ~10% of actual cases?\n", "status": "open", "fields": [ "ecology", "epidemiology", "public-health", "vector-biology", "climate-science" ], "color": "gray" }, { "id": "u-maxent-species-abundance-prediction", "type": "unknown", "title": "Can maximum entropy (MaxEnt) models reliably predict species abundance distributions and community structure from macroecological constraints alone?", "status": "open", "fields": [ "ecology", "information-theory", "macroecology" ], "color": "gray" }, { "id": "u-maxent-species-range-shift-climate", "type": "unknown", "title": "Can MaxEnt species distribution models accurately predict range shifts under climate change, and what are the limits of their out-of-sample extrapolation?", "status": "open", "fields": [ "ecology", "statistics", "climate-science", "conservation-biology" ], "color": "gray" }, { "id": "u-metabolic-scaling-exponent-deviation-extremes", "type": "unknown", "title": "Under what environmental or physiological conditions do metabolic scaling exponents deviate significantly from 3/4, and can deviations be predicted from first principles?", "status": "open", "fields": [ "ecology", "thermodynamics", "physiology" ], "color": "gray" }, { "id": "u-metacommunity-dispersal-diversity", "type": "unknown", "title": "How does dispersal rate interact with local and regional processes to determine metacommunity diversity, and when does dispersal enhance versus homogenise communities?", "status": "open", "fields": [ "ecology", "metacommunity-ecology", "biogeography" ], "color": "gray" }, { "id": "u-mutualism-network-robustness", "type": "unknown", "title": "Are mutualistic networks (plant-pollinator, plant-seed disperser) more robust to species loss than random networks, and does nestedness or modularity drive this robustness?", "status": "open", "fields": [ "ecology", "network-science", "conservation-biology" ], "color": "gray" }, { "id": "u-nestedness-mutualistic-network-robustness", "type": "unknown", "title": "Does nestedness in mutualistic networks causally increase robustness to species extinctions, or is it a byproduct of neutral assembly processes, and can nestedness metrics predict extinction cascades in empirical plant-pollinator networks under realistic pollinator decline scenarios?", "status": "open", "fields": [ "ecology", "network-science", "conservation-biology" ], "color": "gray" }, { "id": "u-nestedness-stability-causal-mechanism", "type": "unknown", "title": "Does the nested structure of mutualistic bipartite networks causally increase robustness to extinction cascades, or is nestedness a byproduct of other network assembly rules with robustness arising from degree heterogeneity alone?\n", "status": "open", "fields": [ "ecology", "network-science", "mathematics" ], "color": "gray" }, { "id": "u-niche-construction-feedback-tempo", "type": "unknown", "title": "How fast does niche construction-driven ecological inheritance alter selection coefficients relative to genetic evolution in natural populations, and what conditions determine whether eco-evolutionary feedbacks accelerate or impede local adaptation?", "status": "open", "fields": [ "ecology", "evolutionary-biology", "genetics" ], "color": "gray" }, { "id": "u-ocean-acidification-ecosystem-effects", "type": "unknown", "title": "How will ocean acidification affect marine ecosystem structure and functioning, and which species and communities are most vulnerable?", "status": "open", "fields": [ "ecology", "marine-ecology", "climate-ecology" ], "color": "gray" }, { "id": "u-ocean-mixing-parameterization-climate-models", "type": "unknown", "title": "How should spatially and temporally variable diapycnal mixing be parameterized in global climate models to reduce the principal source of centennial ocean circulation uncertainty?", "status": "open", "fields": [ "physical-oceanography", "climate-science", "fluid-dynamics" ], "color": "gray" }, { "id": "u-patch-foraging-partial-observability-wild", "type": "unknown", "title": "How often do wild foragers operate under partial observability regimes invalidating deterministic marginal-value predictions derived from fully observed patch maps?", "status": "open", "fields": [ "behavioral-ecology", "reinforcement-learning" ], "color": "gray" }, { "id": "u-percolation-threshold-habitat-connectivity", "type": "unknown", "title": "Does habitat connectivity in real fragmented landscapes undergo a percolation transition at the theoretically predicted threshold (h_c ~ 0.593 for 2D site percolation), and can finite-size scaling analysis of satellite habitat maps predict minimum viable corridor widths without species-specific movement data?", "status": "open", "fields": [ "ecology", "network-science", "conservation-biology", "statistical-physics" ], "color": "gray" }, { "id": "u-plume-intermittency-foraging-optimal-rules", "type": "unknown", "title": "Which behavioral policies are optimal under measured turbulent odor intermittency versus Gaussian plume approximations for insects navigating weak winds?", "status": "open", "fields": [ "ecology", "fluid-mechanics" ], "color": "gray" }, { "id": "u-predator-prey-cycle-amplitude-stochastic", "type": "unknown", "title": "How do demographic and environmental stochasticity interact with Hopf bifurcation dynamics to determine observed cycle amplitude and period variability in real predator-prey systems?", "status": "open", "fields": [ "ecology", "mathematics", "dynamical-systems" ], "color": "gray" }, { "id": "u-predator-prey-oscillation-damping", "type": "unknown", "title": "Why do observed predator-prey cycles in nature dampen, shift phase, or stop, contrary to Lotka-Volterra predictions of persistent oscillations?", "status": "open", "fields": [ "ecology", "population-ecology", "mathematical-biology" ], "color": "gray" }, { "id": "u-range-shift-pace-climate-change", "type": "unknown", "title": "Can species track climate change fast enough to avoid extinction, and what determines the rate of range shifts?", "status": "open", "fields": [ "ecology", "conservation-biology", "climate-ecology" ], "color": "gray" }, { "id": "u-red-queen-molecular-clock-arms-race", "type": "unknown", "title": "Can molecular evolutionary rate analysis (dN/dS ratios at interacting protein interfaces) quantitatively detect and measure the pace of ongoing coevolutionary arms races in wild populations?", "status": "open", "fields": [ "ecology", "evolutionary-biology", "molecular-biology", "population-genetics" ], "color": "gray" }, { "id": "u-redfield-ratio-evolutionary-constraint", "type": "unknown", "title": "Is the Redfield ratio (C:N:P = 106:16:1) in marine phytoplankton an evolutionary optimum, a physiological constraint, or a community-level emergent property?", "status": "open", "fields": [ "marine-ecology", "biogeochemistry", "evolutionary-biology" ], "color": "gray" }, { "id": "u-redfield-ratio-evolutionary-origin-mechanism", "type": "unknown", "title": "Why do marine phytoplankton maintain a nearly universal C:N:P ratio of 106:16:1 (Redfield ratio) — is this ratio fixed by universal biochemistry, imposed by ocean chemistry feedback, or a contingent evolutionary outcome that could differ on other ocean worlds?\n", "status": "open", "fields": [ "ecology", "biogeochemistry", "evolutionary-biology", "marine-biology" ], "color": "gray" }, { "id": "u-replicator-model-identifiability-multispecies-field-data", "type": "unknown", "title": "Under multispecies competition with spatial structure and measurement noise, when are payoff matrices for replicator dynamics identifiable from longitudinal abundance data?", "status": "open", "fields": [ "ecology", "mathematics", "evolutionary-game-theory" ], "color": "gray" }, { "id": "u-rewilding-ecosystem-outcomes", "type": "unknown", "title": "Do rewilding initiatives restore ecosystem function and self-regulation, and which reintroduced species produce the largest trophic cascade effects?", "status": "open", "fields": [ "ecology", "conservation-ecology", "restoration-ecology" ], "color": "gray" }, { "id": "u-seed-dispersal-levy-flight", "type": "unknown", "title": "Do empirical seed dispersal kernels have genuine power-law tails consistent with Lévy flights, or are they better described by composite exponential models that mimic fat tails over limited observation scales?", "status": "open", "fields": [ "ecology", "statistical-physics", "statistics" ], "color": "gray" }, { "id": "u-soil-carbon-cue-temperature-response", "type": "unknown", "title": "How does microbial carbon use efficiency respond to warming, and will the negative CUE-temperature relationship cause soil carbon stocks to decline faster than current models predict under climate change?", "status": "open", "fields": [ "ecology", "thermodynamics", "climate-science" ], "color": "gray" }, { "id": "u-soil-cue-temperature-sensitivity-warming-feedback", "type": "unknown", "title": "How does soil microbial carbon use efficiency (CUE) respond to long-term warming (+2–4°C), and does microbial thermal adaptation (acclimation of metabolic rates) reduce or amplify the initial warming-driven increase in soil CO₂ efflux, determining whether the soil carbon climate feedback is transient or persistent over decades?\n", "status": "open", "fields": [ "ecology", "climate-science", "microbiology", "biochemistry" ], "color": "gray" }, { "id": "u-soil-food-web-stability-topology", "type": "unknown", "title": "What network topological properties (connectance, modularity, interaction strength distribution) determine the resistance and resilience of soil food webs to perturbations such as pesticide application, drought, and agricultural intensification?", "status": "open", "fields": [ "ecology", "network-science", "soil-science" ], "color": "gray" }, { "id": "u-soil-microbiome-carbon-cycling", "type": "unknown", "title": "How does soil microbial community composition determine carbon turnover rates, and can manipulating microbiomes enhance soil carbon sequestration?", "status": "open", "fields": [ "ecology", "soil-ecology", "biogeochemistry" ], "color": "gray" }, { "id": "u-species-abundance-distribution", "type": "unknown", "title": "Why do species abundance distributions follow log-normal or log-series patterns across ecosystems, and what does this reveal about assembly rules?", "status": "open", "fields": [ "ecology", "macroecology", "statistical-ecology" ], "color": "gray" }, { "id": "u-species-area-exponent-prediction", "type": "unknown", "title": "Can the species-area exponent z be predicted from first principles given only the fractal dimension of habitat patches and the dispersal kernel of target species?", "status": "open", "fields": [ "macroecology", "landscape-ecology", "statistical-physics" ], "color": "gray" }, { "id": "u-stoichiometry-food-web-stability", "type": "unknown", "title": "Does stoichiometric mismatch between consumer body C:N:P and food C:N:P destabilize food webs by creating nutrient-recycling feedbacks, and can this explain oscillatory consumer-resource dynamics?", "status": "open", "fields": [ "ecology", "ecological-stoichiometry", "theoretical-ecology" ], "color": "gray" }, { "id": "u-trophic-cascade-motif-universality", "type": "unknown", "title": "Do the same network motif signatures predict trophic cascade strength universally across terrestrial, freshwater, and marine food webs, or are motif-cascade relationships ecosystem-specific?\n", "status": "open", "fields": [ "ecology", "network-science" ], "color": "gray" }, { "id": "u-trophic-cascade-predictability", "type": "unknown", "title": "Can trophic cascade strength be predicted from food web topology and body-size ratios, or is it inherently unpredictable from species-level traits?", "status": "open", "fields": [ "ecology", "food-web-ecology", "theoretical-ecology" ], "color": "gray" }, { "id": "u-turing-pattern-selection-ecology", "type": "unknown", "title": "What determines which spatial pattern (stripes, spots, labyrinths) emerges from Turing instabilities in ecological systems, and how do these patterns change with environmental degradation?", "status": "open", "fields": [ "ecology", "mathematics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-vicsek-noise-raft-jitter-quantitative-mapping", "type": "unknown", "title": "Is there a quantitative scaling map between Vicsek angular noise amplitude and Raft consensus latency jitter parameters that preserves stability phase diagrams under rescaling time units?", "status": "open", "fields": [ "ecology", "computer-science" ], "color": "gray" }, { "id": "u-vit-crop-stress-generalization-across-sensors", "type": "unknown", "title": "Can vision-transformer crop stress models generalize across sensor platforms and seasons?", "status": "open", "fields": [ "ecology", "machine-learning", "agriculture" ], "color": "gray" }, { "id": "u-wildlife-corridor-percolation-threshold", "type": "unknown", "title": "Does the percolation threshold (~59% suitable habitat for square grids) accurately predict landscape-scale connectivity collapse for real species in real landscapes, and how does matrix permeability modify the effective threshold?", "status": "open", "fields": [ "landscape-ecology", "conservation-biology", "network-science", "spatial-statistics" ], "color": "gray" }, { "id": "u-chaotic-population-cycles-detection-noise", "type": "unknown", "title": "How reliably can chaotic dynamics be distinguished from stochastic noise in real ecological time series, and what minimum time series length and signal-to-noise ratio are required for statistically valid Lyapunov exponent estimation from field population counts?\n", "status": "open", "fields": [ "ecology", "nonlinear-dynamics", "statistics", "mathematics" ], "color": "gray" }, { "id": "u-demographic-stochasticity-extinction-threshold", "type": "unknown", "title": "At what population size does demographic stochasticity overwhelm deterministic dynamics to make extinction inevitable, and can master equation theory predict this threshold for real species?", "status": "open", "fields": [ "ecology", "mathematics", "conservation-biology", "population-genetics" ], "color": "gray" }, { "id": "u-extinction-debt-lag-time-empirical-quantification-fragmented-landscapes", "type": "unknown", "title": "What is the empirical distribution of extinction debt lag times across taxonomic groups and landscape fragmentation histories — specifically, what fraction of species committed to extinction by historical deforestation have not yet gone extinct, and can the stochastic population model prediction T_ext ~ exp(2rK/σ²)/r be quantitatively validated at landscape scale?\n", "status": "open", "fields": [ "conservation-biology", "ecology", "population-ecology", "mathematics", "remote-sensing" ], "color": "gray" }, { "id": "u-invasion-fat-tailed-dispersal-empirical-detection", "type": "unknown", "title": "How can fat-tailed dispersal kernels and the resulting accelerating invasion dynamics be reliably detected from field data, distinguishing true superdiffusive spread from methodological artifacts (sampling bias, detection lags, environmental heterogeneity) in invasive species surveillance?\n", "status": "open", "fields": [ "ecology", "mathematics", "statistics", "conservation-biology" ], "color": "gray" }, { "id": "u-metapopulation-climate-velocity-extinction-debt", "type": "unknown", "title": "How does climate velocity (rate of isotherm shift) interact with metapopulation dispersal capacity to create an extinction debt in fragmented landscapes?", "status": "open", "fields": [ "conservation-biology", "macroecology", "climate-science" ], "color": "gray" }, { "id": "u-neutral-vs-niche-ecology-partitioning", "type": "unknown", "title": "Whether neutral theory or niche theory better explains observed species abundance distributions and community assembly, and whether any operational test can partition the relative contribution of drift vs. niche differentiation in a given community\n", "status": "open", "fields": [ "ecology", "theoretical-biology", "statistics" ], "color": "gray" }, { "id": "u-gravity-model-trade-structural-estimation-welfare", "type": "unknown", "title": "Can the gravity model of trade be structurally estimated with sufficient accuracy to calculate the welfare gains from trade agreements and optimal tariff structures, and what is the correct functional form for trade costs at different distance scales?\n", "status": "open", "fields": [ "economics", "economic-geography", "international-trade", "optimal-transport" ], "color": "gray" }, { "id": "u-agency-cost-entropy-maximization", "type": "unknown", "title": "Can agency costs be quantitatively predicted from the effective temperature of information asymmetry in the principal-agent relationship, and does the statistical mechanics free energy formulation improve on standard incentive theory predictions of optimal contract design?", "status": "open", "fields": [ "economics", "finance", "statistical-mechanics", "complex-systems" ], "color": "gray" }, { "id": "u-agent-based-models-x-emergent-markets", "type": "unknown", "title": "Do financial market crashes exhibit the universal signatures of first-order phase transitions (spinodal decomposition, nucleation), and can the proximity to the spinodal be measured from order book data to predict crash probability?", "status": "open", "fields": [ "economics", "physics", "complex-systems" ], "color": "gray" }, { "id": "u-auction-design-x-complexity-theory", "type": "unknown", "title": "What is the optimal approximation ratio achievable by polynomial-time computable auction mechanisms for multi-item combinatorial auctions, and does P≠NP separate achievable from unachievable revenue guarantees?", "status": "open", "fields": [ "economics", "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-auction-theory-x-mechanism-design", "type": "unknown", "title": "What is the optimal mechanism for multi-item auctions with budget-constrained bidders and correlated values, and can the Myerson optimal auction be extended to these settings?\n", "status": "open", "fields": [ "mathematics", "economics", "game-theory" ], "color": "gray" }, { "id": "u-automation-employment-equilibrium", "type": "unknown", "title": "Will automation and AI cause persistent unemployment, or will labour markets adapt through new job creation and sectoral reallocation?", "status": "open", "fields": [ "economics", "labour-economics", "technology-economics" ], "color": "gray" }, { "id": "u-behavioral-economics-policy-effectiveness", "type": "unknown", "title": "Which behavioural economics interventions (nudges) generalise robustly across cultural and institutional contexts, and which fail to replicate?", "status": "open", "fields": [ "economics", "behavioural-economics", "public-policy" ], "color": "gray" }, { "id": "u-blackscholes-x-diffusion-equation", "type": "unknown", "title": "How should the Black-Scholes diffusion equation be modified to capture fat-tailed return distributions, jumps, and stochastic volatility observed in real financial markets?\n", "status": "open", "fields": [ "economics", "physics", "mathematics" ], "color": "gray" }, { "id": "u-business-cycle-prediction-limits", "type": "unknown", "title": "What are the fundamental limits of macroeconomic forecasting, and why do professional forecasters systematically fail to predict 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observational data, and under what assumptions do machine learning methods (causal forests, meta-learners) provide valid confidence intervals for HTEs?", "status": "open", "fields": [ "economics", "statistics", "causal-inference", "machine-learning", "epidemiology" ], "color": "gray" }, { "id": "u-cbdc-monetary-policy-implications", "type": "unknown", "title": "What are the implications of central bank digital currencies for financial stability, monetary policy transmission, and bank disintermediation?", "status": "open", "fields": [ "economics", "monetary-economics", "financial-economics" ], "color": "gray" }, { "id": "u-central-bank-independence-effectiveness", "type": "unknown", "title": "Does central bank independence cause lower inflation, and what are the political economy limits of central bank independence under fiscal dominance?", "status": "open", "fields": [ "economics", "monetary-economics", "political-economy" ], "color": "gray" }, { "id": "u-chemical-potential-utility-non-equilibrium-markets", "type": "unknown", "title": "Can non-equilibrium thermodynamic extensions of chemical potential (Onsager coefficients, entropy production rates) be directly mapped onto dynamic models of market disequilibrium, price adjustment kinetics, and out-of-equilibrium utility flows in financial crises?\n", "status": "open", "fields": [ "economics", "thermodynamics", "econophysics", "complexity-economics" ], "color": "gray" }, { "id": "u-collective-risk-pool-stability-evolution", "type": "unknown", "title": "How well do laboratory collective-risk games predict field adoption of insurance-like institutions when payoffs include social signaling and enforcement?", "status": "open", "fields": [ "economics", "evolutionary-biology" ], "color": "gray" }, { "id": "u-complexity-economics-policy-design-far-equilibrium", "type": "unknown", "title": "How should economic policy be designed in complexity-economics frameworks where markets exhibit 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"unknown", "title": "Can wealthy economies deliberately degrow GDP while maintaining or improving wellbeing, and what are the macroeconomic mechanisms required?", "status": "open", "fields": [ "economics", "ecological-economics", "macroeconomics" ], "color": "gray" }, { "id": "u-doppler-redshift-option-carry-speculative-analogy", "type": "unknown", "title": "Is there any falsifiable econometric use of redshift/Doppler line-of-sight formalism beyond pedagogy when studying option-adjusted carry, or does the analogy collapse once microstructure and credit events enter?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-economic-dissipation-entropy-measure", "type": "unknown", "title": "Can economic entropy production be measured as a physically meaningful quantity, and does it predict economic volatility or growth?", "status": "open", "fields": [ "economics", "physics", "thermodynamics", "complex-systems" ], "color": "gray" }, { "id": "u-ellsberg-ambiguity-aversion-neural-circuit", "type": "unknown", "title": "What neural circuits implement ambiguity aversion, and does the brain represent ambiguous uncertainty as a set of possible probability distributions or as a single imprecise probability estimate?\n", "status": "open", "fields": [ "economics", "cognitive-science" ], "color": "gray" }, { "id": "u-entropy-maximization-x-income-distribution", "type": "unknown", "title": "At what saving propensity threshold does the income distribution transition from exponential to Pareto, and can this predict real-world inequality tipping points?\n", "status": "open", "fields": [ "physics", "economics", "statistical_mechanics", "econophysics" ], "color": "gray" }, { "id": "u-financial-contagion-epidemic-threshold-mapping", "type": "unknown", "title": "Which epidemic-theoretic quantities (thresholds, outbreak probability) remain identifiable for financial contagion when exposures are partially observed and strategies are endogenous?", "status": "open", "fields": [ "economics", "epidemiology", "network-science" ], "color": "gray" }, { "id": "u-financial-contagion-network-topology", "type": "unknown", "title": "How does the network topology of interbank lending and asset holdings determine systemic risk, and can pre-crisis network measures predict contagion?", "status": "open", "fields": [ "economics", "finance", "network-science" ], "color": "gray" }, { "id": "u-financial-lyapunov-time-versus-policy-interventions", "type": "unknown", "title": "When, if ever, is a finite “Lyapunov-like” divergence timescale for payment-system stress a robust early warning metric versus a misleading artifact of low-dimensional reductions?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-financialisation-real-economy-effects", "type": "unknown", "title": "Has the growth of the financial sector relative to GDP produced net economic benefits, or has financialisation harmed real economy investment and growth?", "status": "open", "fields": [ "economics", "financial-economics", "macroeconomics" ], "color": "gray" }, { "id": "u-fluctuation-dissipation-stationary-market-assumption-breakdown", "type": "unknown", "title": "Under what empirical conditions do sum-rule or fluctuation–dissipation-style integrals over return correlations stabilize enough to be informative, and when do structural breaks invalidate them entirely?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-game-theory-x-cryptography", "type": "unknown", "title": "Under what conditions does rational cryptography (game-theoretic security) coincide with or diverge from standard computational security definitions?\n", "status": "open", "fields": [ "economics", "computer_science", "mathematics", "cryptography" ], "color": "gray" }, { "id": "u-gig-economy-welfare-effects", "type": "unknown", "title": "What are the net welfare effects of gig economy platforms on workers, consumers, and incumbent industries, and how do regulatory regimes affect these?", "status": "open", "fields": [ "economics", "labour-economics", "industrial-organisation" ], "color": "gray" }, { "id": "u-global-trade-leontief-systemic-shock-threshold", "type": "unknown", "title": "Is there a percolation-like threshold in global production networks (measured by Leontief input-output tables) below which local shocks remain local, and above which they cascade globally?\n", "status": "open", "fields": [ "economics", "network-science" ], "color": "gray" }, { "id": "u-healthcare-cost-spiral-mechanisms", "type": "unknown", "title": "What mechanisms drive persistently rising healthcare costs in high-income countries, and which healthcare system structures most effectively contain them?", "status": "open", "fields": [ "economics", "health-economics", "public-policy" ], "color": "gray" }, { "id": "u-housing-affordability-structural-causes", "type": "unknown", "title": "What are the primary structural causes of the housing affordability crisis in high-income cities, and which policy interventions are most effective?", "status": "open", "fields": [ "economics", "urban-economics", "housing-policy" ], "color": "gray" }, { "id": "u-inequality-growth-relationship", "type": "unknown", "title": "Does economic inequality promote or retard long-run economic growth, and at what level of inequality does the net effect change sign?", "status": "open", "fields": [ "economics", "macroeconomics", "development-economics" ], "color": "gray" }, { "id": "u-inequality-health-phase-transition-threshold", "type": "unknown", "title": "Is there a critical Gini coefficient threshold above which population health outcomes undergo a discontinuous phase transition, and what is the mechanism?", "status": "open", "fields": [ "health-economics", "epidemiology", "medicine", "statistical-physics" ], "color": "gray" }, { "id": "u-innovation-diffusion-s-curve", "type": "unknown", "title": "Why do some innovations follow S-curve diffusion while others plateau or fail, and can early adoption patterns predict long-run diffusion outcomes?", "status": "open", "fields": [ "economics", "technology-economics", "innovation-studies" ], "color": "gray" }, { "id": "u-market-microstructure-price-formation", "type": "unknown", "title": "What determines the speed and efficiency of price discovery in financial markets, and how does high-frequency trading affect market quality?", "status": "open", "fields": [ "economics", "financial-economics", "market-microstructure" ], "color": "gray" }, { "id": "u-mechanism-design-algorithmic-markets", "type": "unknown", "title": "Can mechanism design DSIC guarantees be maintained in algorithmic markets where agents use learned bidding strategies, and does the revelation principle hold when agents are adaptive ML-based algorithms?\n", "status": "open", "fields": [ "economics", "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-minority-game-x-market-microstructure", "type": "unknown", "title": "Does the minority game phase transition at α_c correspond to a measurable threshold in real financial markets (e.g., the number of algorithmic traders per traded asset), and can the transition be detected empirically from order book microstructure data?", "status": "open", "fields": [ "economics", "physics", "mathematics" ], "color": "gray" }, { "id": "u-multiplier-fiscal-policy", "type": "unknown", "title": "What is the fiscal multiplier under different economic conditions, and when does government spending crowd out versus crowd in private investment?", "status": "open", "fields": [ "economics", "macroeconomics", "fiscal-policy" ], "color": "gray" }, { "id": "u-natural-experiment-validity-economics", "type": "unknown", "title": "How valid are natural experiment and instrumental variable methods for causal identification in economics, and what are the threats to their external validity?", "status": "open", "fields": [ "economics", "econometrics", "methodology" ], "color": "gray" }, { "id": "u-pareto-exponent-redistribution-mechanism", "type": "unknown", "title": "Is the Pareto exponent α of wealth/income distributions quantitatively determined by the ratio of redistribution rate to multiplicative growth rate as predicted by the Bouchaud-Mezard model, and what mechanism generates the crossover from Boltzmann-Gibbs (lower income) to Pareto (upper income) distributions?\n", "status": "open", "fields": [ "economics", "statistical-physics", "econophysics" ], "color": "gray" }, { "id": "u-pension-demographic-stress", "type": "unknown", "title": "How will demographic aging affect defined-benefit pension systems, and which reform strategies are fiscally sustainable and politically feasible?", "status": "open", "fields": [ "economics", "public-finance", "demographics" ], "color": "gray" }, { "id": "u-platform-monopoly-welfare-effects", "type": "unknown", "title": "Do digital platform monopolies impose net welfare costs on consumers, and what is the appropriate regulatory framework for two-sided markets?", "status": "open", "fields": [ "economics", "industrial-organisation", "competition-policy" ], "color": "gray" }, { "id": "u-post-scarcity-economics-feasibility", "type": "unknown", "title": "Can advanced technology enable post-scarcity economics, and what economic institutions are needed when marginal costs approach zero?", "status": "open", "fields": [ "economics", "technology-economics", "political-economy" ], "color": "gray" }, { "id": "u-predator-prey-market-oscillations", "type": "unknown", "title": "Do real commodity and technology markets exhibit quasi-periodic oscillations consistent with Lotka-Volterra predator-prey dynamics, and if so, can LV parameters be calibrated from empirical time series to generate useful forecasts of boom-bust cycles?\n", "status": "open", "fields": [ "economics", "ecology", "complexity-economics", "industrial-dynamics" ], "color": "gray" }, { "id": "u-rational-inattention-x-entropy", "type": "unknown", "title": "What is the empirically measurable cognitive channel capacity of individual economic agents, and how does capacity heterogeneity across agents shape aggregate price dynamics?", "status": "open", "fields": [ "economics", "information-theory", "cognitive-science" ], "color": "gray" }, { "id": "u-shapley-value-practical-coalition-stability", "type": "unknown", "title": "When is the Shapley value a stable and behaviorally predictive allocation in real-world cooperative problems, and what institutional mechanisms enforce core stability when the core is empty?", "status": "open", "fields": [ "cooperative-game-theory", "economics", "political-science", "institutional-design" ], "color": "gray" }, { "id": "u-slutsky-vs-mechanical-reciprocity-operational-mapping", "type": "unknown", "title": "Under what coupled economic–physical models can Jacobian symmetry properties of demand systems be rigorously aligned with elastic reciprocity relations—if ever—without forcing unrealistic preferences?", "status": "open", "fields": [ "economics", "mechanics" ], "color": "gray" }, { "id": "u-social-cost-carbon-discount-rate", "type": "unknown", "title": "What is the appropriate social discount rate for computing the social cost of carbon (SCC), and how should the Ramsey framework handle ethical disagreements about intergenerational equity, uncertainty about long-run growth, and fat-tailed climate catastrophe risk?\n", "status": "open", "fields": [ "economics", "climate-science", "philosophy", "decision-theory" ], "color": "gray" }, { "id": "u-sovereign-debt-sustainability", "type": "unknown", "title": "What determines sovereign debt sustainability thresholds, and how can debt crises be predicted before they occur?", "status": "open", "fields": [ "economics", "macroeconomics", "international-finance" ], "color": "gray" }, { "id": "u-supply-chain-correlated-failure-calibration", "type": "unknown", "title": "How correlated are supplier-edge failures empirically across automotive and semiconductor tiers — and which correlated percolation ensembles fit procurement telemetry better than IID bond deletion baselines?", "status": "open", "fields": [ "economics", "operations-research" ], "color": "gray" }, { "id": "u-supply-chain-resilience-efficiency", "type": "unknown", "title": "What is the optimal tradeoff between supply chain efficiency and resilience, and how has COVID-19 revealed the limits of just-in-time production?", "status": "open", "fields": [ "economics", "operations-research", "industrial-organisation" ], "color": "gray" }, { "id": "u-trade-war-equilibrium", "type": "unknown", "title": "What is the long-term equilibrium outcome of escalating trade wars, and under what conditions do countries converge to cooperation versus persistent protection?", "status": "open", "fields": [ "economics", "international-economics", "game-theory" ], "color": "gray" }, { "id": "u-universal-basic-income-macro-effects", "type": "unknown", "title": "What are the macroeconomic effects of universal basic income on labour supply, inflation, innovation, and wellbeing at scale?", "status": "open", "fields": [ "economics", "public-policy", "labour-economics" ], "color": "gray" }, { "id": "u-vaccination-game-equilibrium-gaps-versus-measured-coverage", "type": "unknown", "title": "In middle-income countries with partial vaccine financing, how large is the gap between Nash-equilibrium voluntary uptake predicted by calibrated payoff matrices and realized childhood vaccine coverage after accounting for administrative logistics, parental beliefs, and healthcare access barriers?\n", "status": "open", "fields": [ "economics", "public-health" ], "color": "gray" }, { "id": "u-vcg-combinatorial-auction-scalability", "type": "unknown", "title": "Can VCG-style incentive-compatible mechanisms scale to combinatorial auctions with thousands of items while remaining computationally tractable, and what is the revenue-complexity tradeoff?", "status": "open", "fields": [ "mechanism-design", "computational-economics", "algorithm-theory" ], "color": "gray" }, { "id": "u-walrasian-tatonnement-convergence-without-gs", "type": "unknown", "title": "Under what conditions weaker than gross substitutability does tâtonnement converge to a Walrasian equilibrium, and are these conditions satisfied by empirically observed demand systems?\n", "status": "open", "fields": [ "economics", "mathematics" ], "color": "gray" }, { "id": "u-wealth-distribution-universality", "type": "unknown", "title": "Is the Boltzmann-Gibbs / Pareto form of wealth distribution universal across economies, and what policy interventions correspond to changing the effective temperature or diffusion coefficient?", "status": "open", "fields": [ "economics", "statistical-mechanics", "complex-systems", "sociology" ], "color": "gray" }, { "id": "u-econophysics-pareto-index-cross-national-variation", "type": "unknown", "title": "What drives systematic cross-national variation in the Pareto wealth exponent α (ranging from ~1.1 in highly unequal societies to ~2.5 in Nordic countries), and can the Bouchaud-Mézard multiplicative noise model quantitatively predict α from measurable parameters (capital return variance σ², mean growth g, redistribution rate τ)?\n", "status": "open", "fields": [ "econophysics", "economics", "social-science", "statistical-mechanics" ], "color": "gray" }, { "id": "u-financial-market-impact-model-universal-mechanism", "type": "unknown", "title": "What is the mechanistic origin of the square root market impact law (ΔP ~ sqrt(Q/V)), and is it a universal non-equilibrium property of all continuous double auction markets or a consequence of specific agent behaviour (herding, information arrival, order-splitting strategies)?\n", "status": "open", "fields": [ "econophysics", "market-microstructure", "statistical-mechanics", "finance" ], "color": "gray" }, { "id": "u-order-book-flash-crash-phase-transition-mechanism", "type": "unknown", "title": "Is the limit order book near a self-organised critical point in normal market conditions — and does a flash crash correspond to a first-order phase transition (sudden LOB drain) or a second-order transition (diverging susceptibility with correlated HFT cancellations), and can early warning signals predict it?\n", "status": "open", "fields": [ "econophysics", "physics", "social-science", "mathematics" ], "color": "gray" }, { "id": "u-epsilon-near-zero-loss-radiation-q-tradeoff", "type": "unknown", "title": "For epsilon-near-zero resonances in finite-thickness films, how do material loss tangent, radiation leakage, and substrate coupling partition measured Q — and which term dominates at optical versus GHz engineering scales?\n", "status": "open", "fields": [ "electromagnetism", "materials-science" ], "color": "gray" }, { "id": "u-floquet-metamaterial-isolation-bandwidth-loss-tradeoff", "type": "unknown", "title": "For magnet-free isolators built from time-modulated metamaterial cells, what closed-form scaling laws link modulation frequency, harmonic leakage, insertion loss, and isolation bandwidth when semiconductor switches are bandwidth-limited?\n", "status": "open", "fields": [ "electromagnetism", "electrical-engineering" ], "color": "gray" }, { "id": "u-3d-printed-metal-fatigue", "type": "unknown", "title": "How do process-induced defects in laser powder bed fusion parts govern fatigue life, and can fatigue properties of AM metals match wrought counterparts?", "status": "open", "fields": [ "manufacturing-engineering", "engineering", "materials-science", "structural-engineering" ], "color": "gray" }, { "id": "u-advanced-fission-proliferation", "type": "unknown", "title": "Do advanced fission reactor designs (molten salt, fast spectrum, small modular reactors) present materially different nuclear proliferation risks than light-water reactors?", "status": "open", "fields": [ "nuclear-engineering", "engineering", "security-studies", "international-relations" ], 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unsteady aerodynamic effects, and if so by how much?\n", "status": "open", "fields": [ "engineering", "fluid-mechanics" ], "color": "gray" }, { "id": "u-biodegradable-electronics", "type": "unknown", "title": "Can biodegradable or transient electronics achieve the electrical performance and environmental lifetime control needed for implantable and disposable devices?", "status": "open", "fields": [ "materials-science", "engineering", "biomedical-engineering" ], "color": "gray" }, { "id": "u-bode-waterbed-multi-loop-multi-objective-tradeoffs", "type": "unknown", "title": "What are the sharpest known MIMO extensions and non-minimum-phase relaxations of Bode-type sensitivity integrals for multi-loop cyber-physical systems with decentralized sensing?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-carbon-capture-regeneration", "type": "unknown", "title": "What is the minimum thermodynamic energy penalty for CO2 capture and sorbent regeneration, and how close do current materials come to this limit?", "status": "open", "fields": [ "chemical-engineering", "engineering", "thermodynamics", "materials-science" ], "color": "gray" }, { "id": "u-cascade-threshold-infrastructure", "type": "unknown", "title": "What are the critical coupling thresholds between interdependent infrastructure networks (power, water, transport, communications) that trigger catastrophic cascade failures?", "status": "open", "fields": [ "network-science", "engineering", "complexity-science" ], "color": "gray" }, { "id": "u-chaos-transition-engineering-systems", "type": "unknown", "title": "What are the precise bifurcation boundaries for chaos onset in common engineering feedback systems, and how do they depend on delay and nonlinearity?", "status": "open", "fields": [ "engineering", "nonlinear-dynamics", "control-theory", "applied-mathematics" ], "color": "gray" }, { "id": "u-cryo-em-resolution-limit-radiation-damage-versus-detector-efficiency", "type": "unknown", "title": "What is the fundamental resolution limit of single-particle cryo-EM — specifically, is it set by radiation damage (maximum electron dose before structural damage) or by the quantum efficiency of direct electron detectors, and can phase plates or new detector technologies push cryo-EM reliably below 1 Å resolution for small proteins?\n", "status": "open", "fields": [ "structural-biology", "electron-microscopy", "materials-science", "quantum-optics" ], "color": "gray" }, { "id": "u-droplet-splitting-variance-biology-alignment", "type": "unknown", "title": "Under what experimental conditions do microfluidic droplet-splitting statistics align with simple branching-process models used for cell lineage division — and when does physics-dominated pinch-off invalidate biological metaphors?\n", "status": "open", "fields": [ "microfluidics", "systems-biology" ], "color": "gray" }, { "id": "u-em-skin-depth-financial-firewall-mapping-limits", "type": "unknown", "title": "Under what empirical conditions can layered financial firewalls be calibrated to an exponential-attenuation model analogous to skin-depth shielding without misleading regulators about correlated tail risk?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-fem-dec-mixed-form-equivalence-limits", "type": "unknown", "title": "For industrial-scale nonlinear elasticity and contact, when do DEC meshes match mixed FEM accuracy at equal cost, and where do nonlinear constitutive maps break commuting diagrams?", "status": "open", "fields": [ "engineering", "mathematics" ], "color": "gray" }, { "id": "u-finite-depth-kelvin-wake-angle-design-transfer", "type": "unknown", "title": "When do finite depth, hull geometry, and near-field effects dominate over the ideal Kelvin wake angle in design-relevant ship-wave predictions?\n", "status": "open", "fields": [ "engineering", "fluid-mechanics" ], "color": "gray" }, { "id": "u-floquet-metamaterial-nonreciprocity-passivity-limit", "type": "unknown", "title": "What passive-loss and modulation-depth limits bound magnet-free nonreciprocal performance in Floquet metamaterials?", "status": "open", "fields": [ "electromagnetics", "engineering", "metamaterials" ], "color": "gray" }, { "id": "u-fusion-plasma-stability", "type": "unknown", "title": "What are the remaining plasma instability and confinement barriers to sustained net-energy-gain nuclear fusion at commercial scale?", "status": "open", "fields": [ "plasma-physics", "nuclear-engineering", "engineering" ], "color": "gray" }, { "id": "u-geothermal-subsidence", "type": "unknown", "title": "What controls surface subsidence and induced seismicity from enhanced geothermal systems, and can they be predicted and mitigated in pre-development assessment?", "status": "open", "fields": [ "geothermal-engineering", "engineering", "geomechanics", "seismology" ], "color": "gray" }, { "id": "u-graph-spectral-leakage-pmu-event-localization", "type": "unknown", "title": "How much graph spectral leakage limits disturbance localization accuracy in sparse-PMU power grids?", "status": "open", "fields": [ "electrical-engineering", "computer-science" ], "color": "gray" }, { "id": "u-graph-transformer-grid-contingency-false-negative-risk", "type": "unknown", "title": "What false-negative risk do graph-transformer contingency screeners incur under stressed grid conditions?", "status": "open", "fields": [ "engineering", "machine-learning", "power-systems" ], "color": "gray" }, { "id": "u-green-hydrogen-electrolysis", "type": "unknown", "title": "What is the fundamental efficiency ceiling for water electrolysis for green hydrogen production, and what electrode degradation mechanisms limit durability?", "status": "open", "fields": [ "electrochemistry", "engineering", "energy-engineering" ], "color": "gray" }, { "id": "u-hypersonic-thermal-protection", "type": "unknown", "title": "What material systems can provide reliable, reusable thermal protection for hypersonic vehicles at Mach 10–25 over multiple flights?", "status": "open", "fields": [ "aerospace-engineering", "materials-science", "engineering", "high-temperature-materials" ], "color": "gray" }, { "id": "u-interdependent-network-early-warning-cascade", "type": "unknown", "title": "Are there measurable early-warning signals (critical slowing down, variance increase, autocorrelation rise) that precede catastrophic cascade failures in interdependent infrastructure networks, enabling real-time detection of approach to the discontinuous percolation threshold before collapse?\n", "status": "open", "fields": [ "engineering", "network-science", "complexity-science", "physics" ], "color": "gray" }, { "id": "u-lidar-scene-reconstruction-nonuniqueness", "type": "unknown", "title": "What are certifiable uncertainty bounds for reconstructed urban façades from airborne LiDAR under realistic occlusion and multi-return statistics?", "status": "open", "fields": [ "engineering", "mathematics", "remote-sensing" ], "color": "gray" }, { "id": "u-lie-group-nonholonomic-robot-optimality", "type": "unknown", "title": "What is the optimal control law for a nonholonomic robot (e.g. wheeled vehicle, snake robot) on curved configuration spaces (Lie groups), and when does a geometric controller outperform a Euclidean approximation?", "status": "open", "fields": [ "robotics", "geometric-control-theory", "applied-mathematics" ], "color": "gray" }, { "id": "u-lyapunov-function-discovery-automation", "type": "unknown", "title": "Can Lyapunov functions for arbitrary nonlinear dynamical systems be discovered automatically, and what is the computational complexity boundary of the stability verification problem?", "status": "open", "fields": [ "control-theory", "dynamical-systems-theory", "optimization", "formal-verification" ], "color": "gray" }, { "id": "u-metamaterial-acoustic-cloaking", "type": "unknown", "title": "Can acoustic metamaterial cloaks achieve broadband, three-dimensional sound cloaking at practical scales, or are fundamental bandwidth-thickness trade-offs prohibitive?", "status": "open", "fields": [ "acoustics", "engineering", "metamaterials", "physics" ], "color": "gray" }, { "id": "u-microplastic-filtration", "type": "unknown", "title": "What physical and chemical mechanisms enable efficient removal of nanoplastics and microplastics from drinking water and wastewater at scale?", "status": "open", "fields": [ "environmental-engineering", "engineering", "water-treatment" ], "color": "gray" }, { "id": "u-molecular-motor-efficiency-limit-biological", "type": "unknown", "title": "What sets the upper bound on mechanochemical efficiency of biological molecular motors, why does ATP synthase approach 100% efficiency while myosin and kinesin are limited to 25-40%, and can the Jarzynski equality be used to engineer artificial nanomotors approaching the biological limit?\n", "status": "open", "fields": [ "biophysics", "mechanical-engineering", "thermodynamics", "nanotechnology" ], "color": "gray" }, { "id": "u-multi-coil-wpt-array-grating-lobes-cross-talk", "type": "unknown", "title": "In roadway or factory-scale multi-coil wireless power installations, how severe are unintended high-field lobes (array analogs of grating lobes) versus simple pairwise leakage models — and how should spacing standards incorporate full-wave results?\n", "status": "open", "fields": [ "electrical-engineering", "electromagnetism" ], "color": "gray" }, { "id": "u-nanotechnology-self-assembly-yield", "type": "unknown", "title": "What limits the yield and complexity of DNA origami and molecular self-assembly, and how can hierarchical nanostructures be built with near-unity yield?", "status": "open", "fields": [ "nanotechnology", "engineering", "biochemistry", "materials-science" ], "color": "gray" }, { "id": "u-neuromorphic-energy-efficiency", "type": "unknown", "title": "What are the fundamental energy efficiency limits of neuromorphic computing, and how do they compare to biological neural computation?", "status": "open", "fields": [ "neuromorphic-computing", "engineering", "neuroscience", "computer-engineering" ], "color": "gray" }, { "id": "u-offshore-wind-fatigue", "type": "unknown", "title": "What are the accurate fatigue life models for offshore wind turbine monopile foundations under combined stochastic wave, wind, and ice loading?", "status": "open", "fields": [ "structural-engineering", "ocean-engineering", "engineering", "materials-science" ], "color": "gray" }, { "id": "u-organ-chip-vascularization-long-term-viability", "type": "unknown", "title": "How can organ-on-a-chip devices maintain functional vascularized 3D tissue architectures for weeks-to-months timescales to enable chronic disease modeling and long-term drug toxicity testing?", "status": "open", "fields": [ "bioengineering", "cell-biology", "microfluidics" ], "color": "gray" }, { "id": "u-quantum-error-correction-overhead", "type": "unknown", "title": "What is the minimum physical qubit overhead required for fault-tolerant quantum computation at error rates achievable in near-term hardware?", "status": "open", "fields": [ "quantum-computing", "engineering", "quantum-physics" ], "color": "gray" }, { "id": "u-quantum-linewidth-vs-leeson-corner-crossover-measurement-protocol", "type": "unknown", "title": "Can bench protocols harmonize laser linewidth measurements with microwave oscillator phase-noise spectra such that Schawlow–Townes quantum floors and Leeson corners appear on comparable normalized frequency-axis plots without misleading unit conversions?", "status": "open", "fields": [ "electrical-engineering", "optics" ], "color": "gray" }, { "id": "u-rate-distortion-optimal-neural-codes", "type": "unknown", "title": "Do sensory neural codes in the brain operate near the Shannon rate-distortion limit for natural stimuli, and if so, what distortion metric do biological neural circuits implicitly optimize?\n", "status": "open", "fields": [ "engineering", "neuroscience", "information-theory", "computational-neuroscience" ], "color": "gray" }, { "id": "u-reusable-rocket-precision", "type": "unknown", "title": "What are the guidance and control limits for precision propulsive rocket landing, and what failure modes bound reusability turnaround times?", "status": "open", "fields": [ "aerospace-engineering", "engineering", "control-theory" ], "color": "gray" }, { "id": "u-reversible-em-logic-gate-design", "type": "unknown", "title": "What physically realizable gate set embeds classical reversible computation (Toffoli/Fredkin-class) into microwave or RF cavity modes using non-helical resonator circuits?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-rf-noise-figure-two-port-correlation-matrix-room-temperature", "type": "unknown", "title": "When mutual coupling or shared substrate paths correlate noise between RF ports, how should noise figure and noise temperature be defined beyond scalar Friis cascade formulas while staying tied to Johnson–Nyquist equilibrium references?\n", "status": "open", "fields": [ "electrical-engineering", "physics" ], "color": "gray" }, { "id": "u-slip-model-biological-accuracy-multi-legged-running", "type": "unknown", "title": "How accurately does the spring-loaded inverted pendulum (SLIP) model predict energy storage and metabolic cost across different running speeds, gradients, and body morphologies, and does it generalise to multi-legged gaits beyond bipedal and quadrupedal running?", "status": "open", "fields": [ "engineering", "biomechanics", "robotics", "evolutionary-biology", "fluid-dynamics" ], "color": "gray" }, { "id": "u-smart-grid-stability", "type": "unknown", "title": "How can electrical grid stability be maintained at high variable renewable energy penetration without fossil fuel synchronous generators providing inertia?", "status": "open", "fields": [ "electrical-engineering", "power-systems", "engineering", "control-theory" ], "color": "gray" }, { "id": "u-soft-robotics-actuator-lifespan", "type": "unknown", "title": "What limits the fatigue life of soft pneumatic and hydraulic actuators, and can they achieve the 10^7+ cycle durability required for practical service robots?", "status": "open", "fields": [ "robotics", "engineering", "materials-science", "soft-matter" ], "color": "gray" }, { "id": "u-soft-robotics-hyperelastic-inverse-design", "type": "unknown", "title": "Can hyperelastic inverse design reliably prescribe soft actuator geometry and fiber architecture to achieve a target force-displacement trajectory, and how does material uncertainty propagate to actuation performance?\n", "status": "open", "fields": [ "engineering", "mechanics" ], "color": "gray" }, { "id": "u-solid-state-battery-failure", "type": "unknown", "title": "What are the dominant failure modes in solid-state lithium batteries, and can lithium dendrite penetration of solid electrolytes be prevented at high current densities?", "status": "open", "fields": [ "electrochemistry", "materials-science", "engineering" ], "color": "gray" }, { "id": "u-space-debris-removal", "type": "unknown", "title": "What active debris removal approaches are technically feasible at scale to prevent Kessler syndrome in low Earth orbit?", "status": "open", "fields": [ "aerospace-engineering", "engineering", "orbital-mechanics" ], "color": "gray" }, { "id": "u-spider-silk-recombinant-production-mechanical-parity", "type": "unknown", "title": "Why does recombinant spider silk produced in bacteria or yeast consistently underperform native spider silk in toughness and tensile strength, and what spinning process parameters close this gap?", "status": "open", "fields": [ "engineering", "materials-science", "biochemistry", "biology" ], "color": "gray" }, { "id": "u-stigmergy-optimality-gap-real-environments", "type": "unknown", "title": "How large is the optimality gap between stigmergic swarm routing and global-optimal paths in real-world dynamic environments with noise and non-stationary costs?\n", "status": "open", "fields": [ "robotics", "engineering", "operations-research" ], "color": "gray" }, { "id": "u-tissue-engineering-vascularization-thick-constructs", "type": "unknown", "title": "How can functional vascular networks be engineered into thick (>1 cm) tissue constructs to overcome the oxygen diffusion bottleneck — and what is the minimum vascular network geometry required to sustain cell viability at physiological metabolic rates?\n", "status": "open", "fields": [ "engineering", "biomedical-engineering", "biology", "fluid-dynamics", "materials-science" ], "color": "gray" }, { "id": "u-topoelectrical-circuit-disorder-robustness-limit", "type": "unknown", "title": "What quantitative disorder threshold causes topological boundary-mode signatures in topoelectrical circuits to lose practical robustness under component tolerances and loss?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-traffic-phantom-jam-nucleation-mechanism", "type": "unknown", "title": "What microscopic mechanism nucleates phantom traffic jams (stop-and-go waves without bottlenecks), and can their formation be predicted and suppressed by autonomous vehicle coordination?", "status": "open", "fields": [ "engineering", "mathematics", "physics" ], "color": "gray" }, { "id": "u-traffic-shock-microscopic-validation", "type": "unknown", "title": "Do microscopic car-following laws aggregate quantitatively to macroscopic LWR shocks calibrated independently — without systematic parameter drift across highways versus arterials?", "status": "open", "fields": [ "transportation-engineering", "applied-mathematics" ], "color": "gray" }, { "id": "u-turbulent-drag-reduction-limit", "type": "unknown", "title": "Is there a fundamental physical limit to turbulent drag reduction in pipe and channel flows, and does the maximum drag reduction (MDR) asymptote represent a true physical bound?", "status": "open", "fields": [ "fluid-mechanics", "engineering", "physics" ], "color": "gray" }, { "id": "u-urban-air-mobility-noise", "type": "unknown", "title": "What are the fundamental acoustic limits of electric vertical-takeoff-and-landing (eVTOL) aircraft, and can they achieve community-acceptable noise levels in urban environments?", "status": "open", "fields": [ "aerospace-engineering", "acoustics", "engineering", "urban-planning" ], "color": "gray" }, { "id": "u-water-desalination-energy", "type": "unknown", "title": "How close to the thermodynamic minimum can seawater desalination approach, and what membrane and process innovations are needed to close the gap?", "status": "open", "fields": [ "chemical-engineering", "engineering", "membrane-science" ], "color": "gray" }, { "id": "u-wireless-power-transfer-limit", "type": "unknown", "title": "What are the fundamental efficiency and range limits of resonant inductive and far-field wireless power transfer, and how does Friis transmission compare to near-field coupling?", "status": "open", "fields": [ "electrical-engineering", "engineering", "electromagnetics" ], "color": "gray" }, { "id": "u-wireless-power-transfer-q-bandwidth-coupling-limit", "type": "unknown", "title": "What are the practical Pareto limits between coupling, Q, bandwidth, and closed-loop retuning in resonant wireless power transfer?", "status": "open", "fields": [ "electrical-engineering", "engineering", "electromagnetics" ], "color": "gray" }, { "id": "u-wpt-narrowband-q-bandwidth-multi-standard-coexistence", "type": "unknown", "title": "What joint constraints do resonator Q and coupling bandwidth impose on operating multiple wireless power standards or carriers through shared coils without unacceptable efficiency loss or EMI?\n", "status": "open", "fields": [ "electrical-engineering", "electromagnetism" ], "color": "gray" }, { "id": "u-2d-material-fet-contact-resistance-scaling-below-1nm", "type": "unknown", "title": "What is the physical lower bound on contact resistance for 2D material (MoS₂, WSe₂) field-effect transistors, and can semimetal contacts (Bi, In) or phase- engineered metallic contacts achieve contact resistivity below the IRDS target of 10 Ω·µm to enable competitive ON-current for sub-1nm node devices?\n", "status": "open", "fields": [ "engineering", "physics", "materials-science", "semiconductor-physics", "quantum-physics" ], "color": "gray" }, { "id": "u-h-infinity-nonlinear-systems-computational-tractability", "type": "unknown", "title": "Can H∞-optimal robust control be extended to nonlinear systems with guaranteed computational tractability — specifically, can sum-of-squares (SOS) or neural-network- based Lyapunov function approximations provide scalable robust stability certificates beyond the linear matrix inequality framework?\n", "status": "open", "fields": [ "engineering-physics", "control-theory", "optimization", "mathematics" ], "color": "gray" }, { "id": "u-heat-pipe-limit-miniaturization", "type": "unknown", "title": "What are the physical limits to heat pipe miniaturization for sub-millimeter electronics cooling, and can vapor chamber technology be extended to chip-scale integration?", "status": "open", "fields": [ "thermal-engineering", "fluid-mechanics", "materials-science", "semiconductor-physics" ], "color": "gray" }, { "id": "u-plasma-turbulence-transport-barrier-formation-mechanism", "type": "unknown", "title": "What is the complete physical mechanism by which edge transport barriers (H-mode pedestal) spontaneously form in tokamaks — specifically, what drives the L-H transition and sustains the pedestal pressure gradient — and can this mechanism be predictively modelled for ITER-scale plasmas?\n", "status": "open", "fields": [ "plasma-physics", "nuclear-fusion-engineering", "magnetohydrodynamics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-skin-friction-scaling-across-roughness-regimes", "type": "unknown", "title": "Can a universal roughness function map measured micron-scale topography to u_τ shifts without full DNS for engineering tolerances?", "status": "open", "fields": [ "engineering", "physics" ], "color": "gray" }, { "id": "u-wpt-efficiency-biological-tissue-interaction", "type": "unknown", "title": "Whether resonant wireless power transfer at MHz frequencies interacts with biological tissue in ways that limit safe power delivery to implanted medical devices, and what the fundamental safety-efficiency trade-off is for in-body WPT\n", "status": "open", "fields": [ "biomedical-engineering", "electromagnetism", "physics" ], "color": "gray" }, { "id": "u-cultural-transmission-network-effects", "type": "unknown", "title": "How does the topology of social interaction networks (degree heterogeneity, community structure, homophily) modify the effective R_0 and final adoption fraction of cultural innovations relative to homogeneous-mixing SIR predictions, and can these network corrections be captured by a single network-epidemic summary statistic?", "status": "open", "fields": [ "social-science", "epidemiology", "network-science" ], "color": "gray" }, { "id": "u-dispersion-shrinkage-stability-under-clinical-batch-effects", "type": "unknown", "title": "When do batch effects invalidate `b-deseq2-shrinkage-estimation-x-low-count-clinical-biomarker-surveillance` assumptions?", "status": "open", "fields": [ "epidemiology", "statistics" ], "color": "gray" }, { "id": "u-drift-robust-sprt-thresholding-for-streaming-pathogen-variant-alerts", "type": "unknown", "title": "What validation boundary conditions determine when `b-sequential-probability-ratio-test-x-pathogen-genomic-surveillance` remains decision-useful?", "status": "open", "fields": [ "statistics", "epidemiology" ], "color": "gray" }, { "id": "u-epidemic-kalman-filter", "type": "unknown", "title": "What is the optimal state-space model structure for real-time epidemic forecasting that balances transmission heterogeneity, reporting delays, and non-Gaussian observation noise while remaining computationally tractable?", "status": "open", "fields": [ "epidemiology", "statistics", "public-health" ], "color": "gray" }, { "id": "u-epidemic-mpc-next-generation-matrix-robustness", "type": "unknown", "title": "How robust are NGM-constrained model predictive control policies to surveillance delay and contact-network misspecification?", "status": "open", "fields": [ "epidemiology", "control-engineering", "public-health" ], "color": "gray" }, { "id": "u-epidemiological-demographic-transition-timing", "type": "unknown", "title": "What determines the delay between mortality decline and fertility decline in the demographic transition, and can epidemiological models of infectious disease control predict the pace and timing of demographic transitions in low-income countries currently undergoing rapid disease burden shifts?", "status": "open", "fields": [ "epidemiology", "demography", "public-health" ], "color": "gray" }, { "id": "u-federated-epidemic-model-drift-across-sites", "type": "unknown", "title": "How should federated epidemic forecasters adapt when local transmission dynamics diverge strongly across sites?", "status": "open", "fields": [ "epidemiology", "machine-learning" ], "color": "gray" }, { "id": "u-floquet-instability-thresholds-seasonal-epidemic-control", "type": "unknown", "title": "Can Floquet-derived instability thresholds reliably define timing windows for seasonal epidemic interventions across pathogen classes?", "status": "open", "fields": [ "epidemiology", "mathematics" ], "color": "gray" }, { "id": "u-memory-kernel-identifiability-from-case-time-series", "type": "unknown", "title": "Can epidemic memory kernels be identified robustly from routine case-time-series data alone?", "status": "open", "fields": [ "epidemiology", "mathematics", "statistical-physics" ], "color": "gray" }, { "id": "u-metapopulation-epidemic-threshold-fragmented-landscape", "type": "unknown", "title": "How does landscape fragmentation shift the epidemic persistence threshold R₀_eff in spatially structured host populations, and can metapopulation patch-occupancy theory predict cross-species spillover risk in fragmented habitats from landscape connectivity metrics alone?\n", "status": "open", "fields": [ "epidemiology", "ecology", "landscape-ecology", "mathematical-biology" ], "color": "gray" }, { "id": "u-network-epidemic-threshold-heterogeneity", "type": "unknown", "title": "How does contact network heterogeneity (temporal dynamics, multi-layer structure, and spatial embedding) modify the epidemic threshold and final outbreak size beyond the static mean-field network approximation?", "status": "open", "fields": [ "epidemiology", "network-science", "statistical-physics", "mathematical-biology" ], "color": "gray" }, { "id": "u-network-fragmentation-thresholds-for-combination-antibiotic-coverage", "type": "unknown", "title": "What failure boundaries determine when `b-percolation-thresholds-x-antimicrobial-combination-therapy-networks` remains decision-useful?", "status": "open", "fields": [ "epidemiology", "network-science" ], "color": "gray" }, { "id": "u-network-threshold-epidemic-spread", "type": "unknown", "title": "Can the epidemic threshold of a novel pathogen be estimated from contact network topology alone before transmission parameters are measured, and does the percolation-epidemic equivalence quantitatively predict superspreading event frequency from the contact degree distribution?", "status": "open", "fields": [ "epidemiology", "network-science", "statistical-physics", "public-health" ], "color": "gray" }, { "id": "u-pandemic-intervention-timing-optimal-uncertainty", "type": "unknown", "title": "How should optimal epidemic intervention timing be modified when the reproduction number R0, case ascertainment fraction, and NPI effectiveness are all uncertain, and can Bayesian optimal stopping provide robust real-time guidance?", "status": "open", "fields": [ "epidemiology", "mathematics", "public-health" ], "color": "gray" }, { "id": "u-percolation-herd-immunity-heterogeneous-networks", "type": "unknown", "title": "Does the percolation-epidemic equivalence hold quantitatively on empirically measured human contact networks with heterogeneous degree distributions, and does it predict herd immunity thresholds more accurately than the classic 1 - 1/R0 formula?\n", "status": "open", "fields": [ "epidemiology", "mathematics", "network-science" ], "color": "gray" }, { "id": "u-ptm-crosstalk-code-histone-combinatorial-regulation", "type": "unknown", "title": "What is the combinatorial logic of the histone PTM code — which combinations of marks are truly synergistic or antagonistic, and can a quantitative model predict gene expression from histone modification patterns genome-wide?\n", "status": "open", "fields": [ "epigenetics", "biochemistry", "genomics", "systems-biology" ], "color": "gray" }, { "id": "u-channel-capacity-evolution-rate", "type": "unknown", "title": "Does Shannon channel capacity bound the maximum rate of adaptive evolution, and can this bound be empirically measured from mutation rates and population sizes in fast-evolving organisms?\n", "status": "open", "fields": [ "biology", "information-theory", "evolutionary-biology" ], "color": "gray" }, { "id": "u-cooperative-breeding-hamiltons-rule-limits", "type": "unknown", "title": "Does Hamilton's rule rB > C provide a complete and accurate quantitative prediction for the evolution of cooperative breeding across birds and mammals, or are there systematic deviations requiring reciprocity, group augmentation, or direct benefit models?\n", "status": "open", "fields": [ "evolutionary-biology", "mathematics", "behavioural-ecology" ], "color": "gray" }, { "id": "u-cultural-group-selection-empirical-magnitude", "type": "unknown", "title": "How large is between-group selection on cultural traits relative to within-group selection in real human populations, and does the empirical magnitude of cultural group selection suffice to explain the origin of large-scale cooperative institutions?\n", "status": "open", "fields": [ "evolutionary-biology", "anthropology", "cultural-dynamics" ], "color": "gray" }, { "id": "u-gene-culture-coevolution-rate-modern", "type": "unknown", "title": "How fast does gene-culture coevolution operate in modern industrialized populations, and which contemporary cultural practices are actively driving genetic selection?", "status": "open", "fields": [ "evolutionary-biology", "cultural-evolution", "population-genetics", "anthropology" ], "color": "gray" }, { "id": "u-handicap-principle-signal-cost-measurement", "type": "unknown", "title": "Can the cost function C(signal, quality) of animal honest signals be measured empirically to verify the single-crossing property required for handicap principle honesty, and under what ecological conditions do costly signals become evolutionarily unstable or replaced by intrinsic quality indicators?", "status": "open", "fields": [ "evolutionary-biology", "economics", "behavioral-ecology" ], "color": "gray" }, { "id": "u-horizontal-gene-transfer-rate-estimation", "type": "unknown", "title": "What are the rates of horizontal gene transfer between different prokaryotic species, and how do these rates vary with phylogenetic distance and ecological co-occurrence?", "status": "open", "fields": [ "evolutionary-biology", "microbiology", "bioinformatics" ], "color": "gray" }, { "id": "u-kin-selection-price-equation-unification", "type": "unknown", "title": "Does the Price equation provide a complete and unique decomposition of evolutionary change that fully unifies kin selection, group selection, and direct selection interpretations, or do these frameworks differ in empirical predictions that could be tested by manipulating relatedness and group structure independently?", "status": "open", "fields": [ "evolutionary-biology", "mathematics", "philosophy-of-science" ], "color": "gray" }, { "id": "u-phenotypic-plasticity-adaptive-limits-speed", "type": "unknown", "title": "What are the evolutionary limits on the rate at which plasticity itself can evolve, and can populations track rapid environmental change faster through plasticity evolution than through allele frequency change?\n", "status": "open", "fields": [ "evolutionary-biology", "mathematics" ], "color": "gray" }, { "id": "u-phylogenetic-network-horizontal-transfer", "type": "unknown", "title": "How do horizontal gene transfer and hybridization events distort phylogenetic tree inference, and can network methods reliably detect and quantify them?", "status": "open", "fields": [ "evolutionary-biology", "mathematics", "bioinformatics" ], "color": "gray" }, { "id": "u-predator-vigilance-roc-optimal-threshold", "type": "unknown", "title": "Do prey animals set vigilance thresholds that maximise Bayesian fitness according to signal detection theory, and can ROC analysis quantify how natural selection tunes the sensitivity-specificity trade-off?\n", "status": "open", "fields": [ "evolutionary-biology", "statistics", "behavioural-ecology" ], "color": "gray" }, { "id": "u-red-queen-cycle-period-determinants", "type": "unknown", "title": "What determines the period and amplitude of Red Queen allele frequency cycles in natural host-parasite systems, and do these match the predictions of coevolutionary Lotka-Volterra models?\n", "status": "open", "fields": [ "evolutionary-biology", "mathematics" ], "color": "gray" }, { "id": "u-rmt-selective-sweep-detection-power", "type": "unknown", "title": "Does the random matrix theory Marchenko-Pastur null model provide higher statistical power for detecting selective sweeps in population genomics than standard Fst-based tests, particularly in admixed populations?\n", "status": "open", "fields": [ "biology", "mathematics", "statistics" ], "color": "gray" }, { "id": "u-zahavi-handicap-mechanism-multimodal", "type": "unknown", "title": "Do multimodal signals (combining acoustic, visual, and chemical components) satisfy Zahavian honesty conditions as a composite costly signal, or do individual modalities independently satisfy single-crossing conditions — and can this be tested across taxa using phylogenetic comparative methods?\n", "status": "open", "fields": [ "evolutionary-biology", "behavioral-ecology", "game-theory", "biology" ], "color": "gray" }, { "id": "u-fiber-nonlinearity-capacity-limit-shannon", "type": "unknown", "title": "What is the true Shannon capacity of the optical fiber channel including Kerr nonlinearity, and can nonlinear Fourier transform (NFT) based transmission systems approach this capacity limit in practical deployments?\n", "status": "open", "fields": [ "information-theory", "nonlinear-optics", "telecommunications", "physics" ], "color": "gray" }, { "id": "u-market-microstructure-hawkes-calibration", "type": "unknown", "title": "Can a non-stationary multivariate Hawkes process with time-varying kernel parameters provide real-time early-warning indicators of flash-crash risk, and what is the minimum data window needed for reliable branching-ratio estimation on a live order book?", "status": "open", "fields": [ "finance", "mathematics", "statistics" ], "color": "gray" }, { "id": "u-spin-glass-rmt-factor-clustering-limits", "type": "unknown", "title": "Beyond Marchenko–Pastur bulk cleaning, when does spin-glass replica-symmetry-breaking imagery add falsifiable predictions for hierarchical factor structure in empirical covariance matrices — versus storytelling without out-of-sample gains?\n", "status": "open", "fields": [ "quantitative-finance", "statistical-physics" ], "color": "gray" }, { "id": "u-rmt-noise-signal-separation-finance", "type": "unknown", "title": "How can random matrix theory reliably separate true correlation signal from sampling noise in financial covariance matrices with non-stationary returns?", "status": "open", "fields": [ "mathematical-finance", "statistics", "random-matrix-theory" ], "color": "gray" }, { "id": "u-earthquake-soc-predictability", "type": "unknown", "title": "Is earthquake occurrence genuinely unpredictable (true SOC, exponential distribution of recurrence times) or does the approach to criticality produce detectable precursors (critical slowing down, power-law fluctuations)?", "status": "open", "fields": [ "geology", "seismology", "statistical-physics", "geophysics" ], "color": "gray" }, { "id": "u-magma-fragmentation-rheology-threshold", "type": "unknown", "title": "What determines the critical Deborah number and strain rate at which silicate melt transitions from viscous to brittle fragmentation during volcanic ascent, and how do dissolved water content, crystal fraction, and bubble nucleation rate interact to set the fragmentation threshold?", "status": "open", "fields": [ "volcanology", "fluid-mechanics", "geochemistry" ], "color": "gray" }, { "id": "u-rock-magnetism-paleomagnetic-reversal-mechanism", "type": "unknown", "title": "What is the geophysical mechanism triggering polarity reversals of Earth's magnetic field, and can micromagnetic domain theory applied to natural remanence-recording minerals provide paleointensity records during reversals with sufficient temporal resolution to distinguish rapid vs. gradual field collapse?", "status": "open", "fields": [ "geology", "condensed-matter-physics", "geophysics" ], "color": "gray" }, { "id": "u-seismic-tomography-null-space-resolution", "type": "unknown", "title": "What is the minimum resolvable length scale and depth extent of mantle structures in global P-wave tomography, and how do null-space contamination and regularization artifacts mimic real geodynamic features?", "status": "open", "fields": [ "geology", "geophysics", "mathematics", "inverse-problems", "geodynamics" ], "color": "gray" }, { "id": "u-silicate-weathering-temperature-sensitivity-field", "type": "unknown", "title": "What is the effective temperature sensitivity of silicate weathering at watershed scales, and does it match laboratory kinetic measurements or show systematic deviation due to biological and physical factors?\n", "status": "open", "fields": [ "geology", "chemistry" ], "color": "gray" }, { "id": "u-tectonic-coulomb-failure", "type": "unknown", "title": "What friction coefficient and pore pressure model best explains Coulomb failure function predictions for aftershock sequences, and why do ~20-30% of aftershocks fall in ΔCFF < 0 regions?", "status": "open", "fields": [ "geophysics", "mechanics", "seismology" ], "color": "gray" }, { "id": "u-adjoint-seismic-backprop-gradient-stability", "type": "unknown", "title": "How stable are adjoint-derived gradients under realistic seismic noise and model mismatch?", "status": "open", "fields": [ "geophysics", "computer-science", "inverse-problems" ], "color": "gray" }, { "id": "u-biogeochemical-multistability-empirical-identification", "type": "unknown", "title": "Which paleoclimate proxies decisively distinguish genuine attractor hysteresis in marine carbon cycle models from model artifact multiplicity absent robust field validation?", "status": "open", "fields": [ "biogeochemistry", "dynamical-systems" ], "color": "gray" }, { "id": "u-braided-river-scaling-criticality-test", "type": "unknown", "title": "What finite-size scaling collapse with pre-registered exponents would elevate braided-river heavy-tail statistics beyond plausible heavy-tailed multiplicative-noise null models — potentially supporting SOC-like narratives currently speculative?", "status": "open", "fields": [ "geomorphology", "statistical-physics" ], "color": "gray" }, { "id": "u-coastline-roughness-effective-surface-tension", "type": "unknown", "title": "Which coastline datasets support universal roughening exponents predicted by simplified stochastic interface models, and where do human armoring and lithology dominate?", "status": "open", "fields": [ "geoscience", "mathematics" ], "color": "gray" }, { "id": "u-continental-drift-initiation", "type": "unknown", "title": "What initiated plate tectonics on early Earth, and did it operate continuously or episodically in the Archean?", "status": "open", "fields": [ "geodynamics", "geoscience", "tectonics", "petrology" ], "color": "gray" }, { "id": "u-cratonic-root-stability", "type": "unknown", "title": "Why are Archean cratonic keels stable for billions of years, and what conditions cause their sudden removal?", "status": "open", "fields": [ "geoscience", "geodynamics", "petrology", "geochemistry" ], "color": "gray" }, { "id": "u-crustal-delamination", "type": "unknown", "title": "When and how does lower crustal delamination occur, and what are its topographic, geochemical, and volcanic consequences?", "status": "open", "fields": [ "geodynamics", "geoscience", "petrology" ], "color": "gray" }, { "id": "u-deep-carbon-storage", "type": "unknown", "title": "How much carbon is stored in Earth's deep interior, and what controls the long-term geologic carbon cycle between mantle, crust, and atmosphere?", "status": "open", "fields": [ "geochemistry", "geoscience", "geodynamics", "climate-science" ], "color": "gray" }, { "id": "u-deep-earth-water-cycle", "type": "unknown", "title": "How much water is stored in Earth's mantle and lower crust, and what are the fluxes that govern the deep water cycle?", "status": "open", "fields": [ "geoscience", "geochemistry", "mantle-petrology", "geodynamics" ], "color": "gray" }, { "id": "u-delta-avulsion-prediction", "type": "unknown", "title": "Can river delta avulsion timing and pathfinding be predicted, and what are the dominant controls on avulsion frequency?", "status": "open", "fields": [ "geomorphology", "geoscience", "hydrology", "coastal-engineering" ], "color": "gray" }, { "id": "u-diamond-inclusion-dating", "type": "unknown", "title": "How reliably do diamond inclusion ages record ancient mantle events, and what biases affect inclusions as windows into deep Earth history?", "status": "open", "fields": [ "geochronology", "geoscience", "petrology", "geochemistry" ], "color": "gray" }, { "id": "u-earthquake-early-warning-bayesian-latency-magnitude-error", "type": "unknown", "title": "How rapidly do Bayesian / sequential magnitude estimates for earthquake early warning converge to within one magnitude unit of final catalogs as station count increases, and how does geographic aperture interact with rupture directivity under finite rupture velocity?\n", "status": "open", "fields": [ "geophysics", "seismology" ], "color": "gray" }, { "id": "u-earthquake-nucleation", "type": "unknown", "title": "What are the physical processes governing earthquake nucleation, and can they be detected before rupture propagates?", "status": "open", "fields": [ "seismology", "geomechanics", "geoscience", "rock-physics" ], "color": "gray" }, { "id": "u-earthquake-soc-universality-class", "type": "unknown", "title": "Which universality class does the earthquake fault network belong to — BTW sandpile (τ = 3/2), directed percolation, interface depinning, or a distinct geophysical class — and does the Gutenberg-Richter b-value directly measure the SOC critical exponent?\n", "status": "open", "fields": [ "seismology", "statistical-physics", "geophysics", "complexity-science" ], "color": "gray" }, { "id": "u-earthquake-swarm-migration", "type": "unknown", "title": "What physical process drives the spatiotemporal migration of earthquake swarms, and can migration patterns forecast mainshock occurrence?", "status": "open", "fields": [ "seismology", "geoscience", "hydrology", "geomechanics" ], "color": "gray" }, { "id": "u-ensemble-kalman-assimilation-nonlinear-localization-errors", "type": "unknown", "title": "How large are systematic biases from localization and inflation in EnKF for mesoscale weather extremes relative to tangent-linear 4D-Var when models are strongly nonlinear during rapidly developing storms?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-flood-basalt-trigger", "type": "unknown", "title": "What triggers large igneous province (flood basalt) eruptions, and why do they correlate with mass extinctions?", "status": "open", "fields": [ "volcanology", "geoscience", "paleontology", "geochemistry" ], "color": "gray" }, { "id": "u-geomagnetic-excursion-climate", "type": "unknown", "title": "Do geomagnetic excursions and reversals affect climate by modulating cosmic ray flux and ozone production?", "status": "open", "fields": [ "geomagnetism", "climate-science", "geoscience", "atmospheric-science" ], "color": "gray" }, { "id": "u-geomagnetic-reversal-prediction", "type": "unknown", "title": "Can geomagnetic field reversals be predicted, and what controls their frequency and duration?", "status": "open", "fields": [ "geomagnetism", "geoscience", "geodynamics" ], "color": "gray" }, { "id": "u-geomagnetic-reversal-trigger-mechanism", "type": "unknown", "title": "What triggers geomagnetic reversals — internal MHD fluctuations, mantle thermal anomalies, or inner core heterogeneities — and can reversal timing be predicted from current geomagnetic field observations?", "status": "open", "fields": [ "geophysics", "magnetohydrodynamics", "mathematics" ], "color": "gray" }, { "id": "u-hydrothermal-vent-biodiversity", "type": "unknown", "title": "What determines species richness and biogeographic connectivity patterns at deep-sea hydrothermal vents?", "status": "open", "fields": [ "geoscience", "deep-sea-biology", "biogeography", "ecology" ], "color": "gray" }, { "id": "u-ice-sheet-basal-melting", "type": "unknown", "title": "What controls basal melting rates beneath the Antarctic and Greenland ice sheets, and how do subglacial hydrology and geothermal flux affect ice dynamics?", "status": "open", "fields": [ "glaciology", "geoscience", "cryosphere-science", "geodynamics" ], "color": "gray" }, { "id": "u-inner-core-anisotropy-origin", "type": "unknown", "title": "What is the origin of seismic anisotropy in Earth's inner core, and does it record solidification texture or ongoing convection?", "status": "open", "fields": [ "seismology", "geoscience", "mineral-physics" ], "color": "gray" }, { "id": "u-karst-aquifer-connectivity", "type": "unknown", "title": "How are karst aquifer conduit networks organized, and can their connectivity and recharge dynamics be characterized without full cave exploration?", "status": "open", "fields": [ "hydrogeology", "geoscience", "hydrology" ], "color": "gray" }, { "id": "u-mantle-convection-plate-tectonic-onset", "type": "unknown", "title": "What physical and rheological conditions trigger the onset of plate tectonics from a stagnant-lid convection regime, and why does Earth have plate tectonics while Venus does not?\n", "status": "open", "fields": [ "geophysics", "planetary-science", "fluid-mechanics" ], "color": "gray" }, { "id": "u-mantle-convection-transitions", "type": "unknown", "title": "What controls the transition between layered and whole-mantle convection, and can the pattern switch on geologic timescales?", "status": "open", "fields": [ "geodynamics", "geoscience", "mantle-petrology" ], "color": "gray" }, { "id": "u-mantle-horizontal-spectrum-versus-rb-wavelength-law", "type": "unknown", "title": "Can normalized horizontal power spectra of mantle convection in numerical dynamo-like spherical-shell simulations be mapped quantitatively onto classical Rayleigh-Bénard wavenumber-selection curves after accounting for temperature-dependent viscosity and internal heating — or does rheological splitting invalidate simple wavelength correspondence?\n", "status": "open", "fields": [ "geophysics", "fluid-mechanics" ], "color": "gray" }, { "id": "u-mantle-rheology-x-viscoelasticity", "type": "unknown", "title": "Is Earth's lower mantle rheology best described by a Maxwell viscoelastic model, a Burgers model (elastic-viscous-viscous), or a power-law creep model, and what laboratory constraints on olivine deformation can distinguish these?", "status": "open", "fields": [ "geodynamics", "mineral-physics", "geophysics" ], "color": "gray" }, { "id": "u-microseismic-acoustic-emission-b-value-failure", "type": "unknown", "title": "Can the b-value decrease observed in acoustic emission experiments before laboratory specimen failure be used as a reliable precursor metric for mine-scale and reservoir-scale microseismic failure, and what are the universal thresholds for warning?\n", "status": "open", "fields": [ "geophysics", "materials-science", "engineering" ], "color": "gray" }, { "id": "u-mineral-nucleation-kinetics", "type": "unknown", "title": "What controls mineral nucleation kinetics in natural fluids, and why do classical nucleation theory predictions fail for many geological systems?", "status": "open", "fields": [ "mineralogy", "geochemistry", "geoscience", "physical-chemistry" ], "color": "gray" }, { "id": "u-neural-operator-generalization-groundwater-boundary-shift", "type": "unknown", "title": "How well do neural-operator groundwater surrogates generalize under boundary-condition and forcing shift?", "status": "open", "fields": [ "hydrology", "computer-science" ], "color": "gray" }, { "id": "u-nitrogen-cycle-jacobian-eigenstructure-versus-observed-anomalies", "type": "unknown", "title": "Do empirical decadal anomalies in ocean nitrate inventories and surface chlorophyll align with leading eigenmodes predicted by linearization of state-of-the-art coupled nitrogen–climate models around historical steady states?\n", "status": "open", "fields": [ "earth-system-science", "oceanography" ], "color": "gray" }, { "id": "u-ocean-ultrasound-shared-inverse-regularizers", "type": "unknown", "title": "Can identical Bayesian regularizers (edge-preserving priors, sparsity transforms) achieve comparable reconstruction calibration errors when ported between basin-scale ocean acoustic tomography and clinical ultrasound transmission CT phantoms?", "status": "open", "fields": [ "oceanography", "medicine", "applied-mathematics" ], "color": "gray" }, { "id": "u-permafrost-thaw-subsidence", "type": "unknown", "title": "What are the rates, spatial patterns, and feedback mechanisms governing thermokarst formation and carbon release from thawing permafrost?", "status": "open", "fields": [ "cryosphere-science", "geoscience", "climate-science", "biogeochemistry" ], "color": "gray" }, { "id": "u-plate-boundary-fracture-scale-bridging", "type": "unknown", "title": "At what scale transitions from laboratory fracture toughness tests to fault-zone cohesive zones become predictive for macroscopic rupture energetics?", "status": "open", "fields": [ "geophysics", "engineering" ], "color": "gray" }, { "id": "u-plate-tectonics-x-convection", "type": "unknown", "title": "Why does Earth have plate tectonics while Venus and Mars do not, and what determines the transition between tectonic modes in planetary mantle convection?\n", "status": "open", "fields": [ "geoscience", "physics", "fluid_mechanics", "planetary_science" ], "color": "gray" }, { "id": "u-post-perovskite-implications", "type": "unknown", "title": "What are the dynamical and seismic implications of the post-perovskite phase transition in Earth's D'' layer?", "status": "open", "fields": [ "mineral-physics", "seismology", "geodynamics", "geoscience" ], "color": "gray" }, { "id": "u-river-network-branching", "type": "unknown", "title": "Is river network branching geometry universal, and what physical principle selects Hack's law exponents and Horton ratios?", "status": "open", "fields": [ "geomorphology", "geoscience", "hydrology", "complex-systems" ], "color": "gray" }, { "id": "u-river-network-hacks-law-variability", "type": "unknown", "title": "Why does Hack's law exponent h vary systematically from 0.5 to 0.7 across different climate zones and lithologies, and what determines whether a drainage network converges to the OCN optimum or remains in a metastable suboptimal configuration?\n", "status": "open", "fields": [ "hydrology", "geoscience", "mathematics" ], "color": "gray" }, { "id": "u-sea-level-fingerprint", "type": "unknown", "title": "How do sea-level fingerprints from individual ice mass changes allow attribution of observed sea-level trends to specific glacier and ice sheet sources?", "status": "open", "fields": [ "geoscience", "glaciology", "oceanography", "geodesy" ], "color": "gray" }, { "id": "u-sediment-transport-nonlinearity", "type": "unknown", "title": "What are the nonlinear thresholds and stochastic dynamics governing sediment transport in rivers and their long-term geomorphic effects?", "status": "open", "fields": [ "geomorphology", "geoscience", "fluid-mechanics", "hydrology" ], "color": "gray" }, { "id": "u-seismic-wave-x-elastic-wave", "type": "unknown", "title": "Can full-waveform inversion (FWI) of seismic data recover the complete elastic tensor (anisotropic Cijkl) of the Earth's crust at 100m resolution, and what is the fundamental resolution limit imposed by seismic wavelengths and noise?", "status": "open", "fields": [ "geoscience", "physics", "mathematics" ], "color": "gray" }, { "id": "u-serpentinization-hydrogen", "type": "unknown", "title": "What is the global flux of abiotic hydrogen produced by serpentinization, and can it support subsurface chemolithotrophs at ocean ridges and in continental settings?", "status": "open", "fields": [ "geochemistry", "geoscience", "astrobiology", "microbiology" ], "color": "gray" }, { "id": "u-slow-slip-event-origin", "type": "unknown", "title": "What physical mechanism produces slow-slip events and episodic tremor-and-slip in subduction zones?", "status": "open", "fields": [ "seismology", "geoscience", "geomechanics" ], "color": "gray" }, { "id": "u-snowball-earth-escape", "type": "unknown", "title": "How did Snowball Earth events end, and what prevented Earth from permanently freezing under high-albedo ice cover?", "status": "open", "fields": [ "geoscience", "climate-science", "geochemistry", "astrobiology" ], "color": "gray" }, { "id": "u-soc-earthquake-precursor-detection", "type": "unknown", "title": "Do SOC early warning indicators (rising autocorrelation, variance divergence) in regional seismicity time series provide statistically significant precursors to large earthquakes (M > 6.5)?\n", "status": "open", "fields": [ "geoscience", "physics", "statistical-mechanics" ], "color": "gray" }, { "id": "u-soil-aggregate-fractal-dimension-stability-link", "type": "unknown", "title": "Does soil aggregate fractal dimension D_f provide a universal predictor of aggregate stability across soil types, management practices, and climates, and what is the causal mechanism linking fractal pore geometry to resistance against slaking and mechanical disruption?\n", "status": "open", "fields": [ "geoscience", "soil-science", "mathematics" ], "color": "gray" }, { "id": "u-subduction-initiation", "type": "unknown", "title": "How does subduction initiation occur, and can it begin spontaneously from a passive margin without a pre-existing plate boundary?", "status": "open", "fields": [ "geodynamics", "geoscience", "tectonics" ], "color": "gray" }, { "id": "u-supervolcano-eruption-forecasting", "type": "unknown", "title": "What are the precursors and timescales of supereruption onset at caldera systems like Yellowstone and Campi Flegrei?", "status": "open", "fields": [ "volcanology", "geoscience", "geophysics" ], "color": "gray" }, { "id": "u-thermohaline-circulation-x-buoyancy-flow", "type": "unknown", "title": "Is the Atlantic Meridional Overturning Circulation (AMOC) currently approaching a saddle-node bifurcation tipping point, and what observational fingerprint would provide advance warning of an irreversible collapse?", "status": "open", "fields": [ "geoscience", "physics", "oceanography", "climate-science" ], "color": "gray" }, { "id": "u-tsunami-dispersive-nonlinearity-regime-classification", "type": "unknown", "title": "Can operational tsunami forecast diagnostics classify approaching wave trains into bore-dominated versus dispersive-trailing-wave regimes early enough to improve inundation estimates beyond hydrostatic shallow-water defaults?\n", "status": "open", "fields": [ "geophysics", "fluid-mechanics" ], "color": "gray" }, { "id": "u-tsunami-submarine-slides", "type": "unknown", "title": "What are the physics of tsunami generation by submarine landslides, and how does slide rheology control wave amplitude and runup?", "status": "open", "fields": [ "geoscience", "oceanography", "geomechanics", "coastal-engineering" ], "color": "gray" }, { "id": "u-unet-satellite-flood-generalization-under-cloud-noise", "type": "unknown", "title": "How well do U-Net flood-segmentation models generalize under cloud cover, sensor differences, and regional terrain shifts?", "status": "open", "fields": [ "geoscience", "machine-learning", "remote-sensing" ], "color": "gray" }, { "id": "u-xenolith-mantle-bias", "type": "unknown", "title": "To what extent do mantle xenoliths provide a representative sample of the lithospheric mantle, and what biases are introduced by entrainment and transport?", "status": "open", "fields": [ "petrology", "geoscience", "geochemistry" ], "color": "gray" }, { "id": "u-epigenetic-clock-causal-mechanism", "type": "unknown", "title": "Are DNA methylation epigenetic clocks a causal driver of aging or a downstream biomarker, and which specific CpG sites regulate aging-related gene expression versus merely correlate with it?", "status": "open", "fields": [ "epigenetics", "geroscience", "genomics" ], "color": "gray" }, { "id": "u-glacier-basal-sliding-uncertainty", "type": "unknown", "title": "What is the correct sliding law relating basal velocity to effective pressure and bed roughness in glaciers, and why do different formulations produce order-of-magnitude differences in ice sheet projections?", "status": "open", "fields": [ "glaciology", "fluid-mechanics", "geophysics" ], "color": "gray" }, { "id": "u-glacier-calving-crack-propagation-threshold", "type": "unknown", "title": "What stress intensity factor threshold governs full-depth crevasse propagation to the glacier bed, and how do basal water pressure, ice temperature, and crystal fabric anisotropy modulate the effective fracture toughness of Antarctic and Greenlandic outlet glaciers?", "status": "open", "fields": [ "glaciology", "materials-science", "geophysics" ], "color": "gray" }, { "id": "u-black-hole-information-paradox-bulk-reconstruction", "type": "unknown", "title": "Does the island formula (quantum extremal surface prescription) provide a complete resolution of the black hole information paradox in full quantum gravity, or only within the semiclassical approximation where the bulk geometric saddle points are well-defined — and what is the mechanism by which bulk quantum fields reconstruct the interior from boundary data after the Page time?\n", "status": "open", "fields": [ "gravitational-physics", "quantum-information", "mathematical-physics", "string-theory" ], "color": "gray" }, { "id": "u-gauge-gravity-duality-mechanism", "type": "unknown", "title": "What is the precise mathematical mechanism by which AdS/CFT duality maps quantum gravity in anti-de Sitter space to conformal field theory on its boundary?", "status": "open", "fields": [ "quantum-gravity", "string-theory", "mathematical-physics" ], "color": "gray" }, { "id": "u-quantum-gravity-semiclassical-limit", "type": "unknown", "title": "How does quantum gravity reduce to general relativity in the semiclassical limit, and what observable corrections to GR does quantum gravity predict?", "status": "open", "fields": [ "quantum-gravity", "astrophysics", "mathematical-physics" ], "color": "gray" }, { "id": "u-aging-interventions-translatability", "type": "unknown", "title": "The degree to which longevity and healthspan interventions validated in model organisms quantitatively predict human-relevant outcomes remains unsettled across intervention classes", "status": "open", "fields": [ "biology", "medicine" ], "color": "gray" }, { "id": "u-amyloid-progression-trajectory", "type": "unknown", "title": "What determines whether amyloid-beta accumulation in an individual brain follows a slow, decades-long trajectory versus rapid escalation to clinical Alzheimer's disease", "status": "open", "fields": [ "neuroscience", "biology", "medicine" ], "color": "gray" }, { "id": "u-brain-criticality-function", "type": "unknown", "title": "Whether the brain operates near a critical phase transition as a functional necessity, or whether criticality is an emergent side-effect of neural dynamics that serves no privileged computational role", "status": "open", "fields": [ "neuroscience", "physics", "biology" ], "color": "gray" }, { "id": "u-dark-matter-microphysics", "type": "unknown", "title": "The particle or field microphysics of galactic dark matter remains unidentified while cosmological and astrophysical evidence for its gravitational effects is strong", "status": "open", "fields": [ "physics", "astrophysics" ], "color": "gray" }, { "id": "u-borrelia-persister-cell-eradication", "type": "unknown", "title": "Why do Borrelia persister cells survive standard doxycycline treatment, and what combination antibiotic regimen or host-directed therapy can reliably eradicate them?\n", "status": "open", "fields": [ "immunology", "microbiology", "infectious-disease", "pharmacology", "clinical-medicine" ], "color": "gray" }, { "id": "u-immune-escape-ess-predictions", "type": "unknown", "title": "Can evolutionary stable strategy theory from game theory quantitatively predict the equilibrium diversity of immune escape mutations maintained by frequency-dependent selection in chronic viral infections?", "status": "open", "fields": [ "immunology", "evolutionary-biology", "game-theory", "virology" ], "color": "gray" }, { "id": "u-immune-treg-pi-control-quantitative", "type": "unknown", "title": "Can the IL-2/Treg feedback loop be quantitatively characterized as a PI controller with measurable gain constants, and does controller theory predict observed autoimmune disease dynamics?", "status": "open", "fields": [ "immunology", "control-theory", "systems-biology", "mathematical-biology" ], "color": "gray" }, { "id": "u-lnp-tissue-targeting-mechanism", "type": "unknown", "title": "What molecular and biophysical mechanisms govern LNP organ selectivity, and can rational lipid design achieve reliable non-hepatic tissue targeting for mRNA therapeutics?", "status": "open", "fields": [ "drug-delivery", "immunology", "biophysics", "materials-science" ], "color": "gray" }, { "id": "u-tcr-foundation-model-ood-binding-generalization", "type": "unknown", "title": "Do TCR foundation models generalize binding predictions to out-of-distribution epitopes?", "status": "open", "fields": [ "immunology", "machine-learning", "bioinformatics" ], "color": "gray" }, { "id": "u-tcr-repertoire-pathogen-space-coverage", "type": "unknown", "title": "Does the ~10⁷ naive T-cell repertoire provide sufficient coverage of pathogen peptide-MHC space, and what is the quantitative relationship between repertoire diversity, thymic selection stringency, and resistance to novel pathogens?", "status": "open", "fields": [ "immunology", "physics", "mathematics", "computational-biology", "statistics" ], "color": "gray" }, { "id": "u-idiotypic-network-clinical-validation", "type": "unknown", "title": "Whether Jerne's idiotypic network theory can be validated using modern B-cell receptor (BCR) repertoire sequencing, and whether idiotypic network topology measurably differs between healthy individuals and those with autoimmune disease\n", "status": "open", "fields": [ "immunology", "network-science", "systems-biology" ], "color": "gray" }, { "id": "u-mae-cryo-em-prior-induced-hallucination-risk", "type": "unknown", "title": "Do masked-autoencoder priors introduce structural hallucination risk in low-SNR cryo-EM 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assignments, and whether its optimality reflects historical contingency, selection pressure, or mathematical inevitability\n", "status": "open", "fields": [ "molecular-biology", "information-theory", "evolutionary-biology" ], "color": "gray" }, { "id": "u-bilingual-cognitive-advantage-replication", "type": "unknown", "title": "Does bilingualism confer measurable cognitive advantages in executive function, and why have many key findings failed to replicate?", "status": "open", "fields": [ "linguistics", "cognitive-science", "psychology" ], "color": "gray" }, { "id": "u-birdsong-syntax-generative-grammar-limits", "type": "unknown", "title": "Do any wild bird species produce vocal sequences that require context-sensitive (Type 1 Chomsky) grammar to describe, and what neural circuit architecture would be necessary to support the additional computational power beyond a pushdown automaton?", "status": "open", "fields": [ "linguistics", "ornithology", "cognitive-science", "neuroscience" ], "color": "gray" }, { "id": "u-creole-genesis-mechanism", "type": "unknown", "title": "What grammatical and social mechanisms produce creole languages from contact situations, and is there a universal creole prototype?", "status": "open", "fields": [ "linguistics", "contact-linguistics", "sociolinguistics" ], "color": "gray" }, { "id": "u-creole-universals-origin", "type": "unknown", "title": "Why do creole languages independently converge on similar grammatical features, and does this reflect innate structure or contact dynamics?", "status": "open", "fields": [ "linguistics", "contact-linguistics", "cognitive-science", "sociolinguistics" ], "color": "gray" }, { "id": "u-dialect-contact-as-graph-diffusion", "type": "unknown", "title": "When does a pure diffusion model fail for dialect features because of identity-driven categorical switching, and how can mixtures be identified from atlas data?", "status": "open", "fields": [ "linguistics", "mathematics" ], "color": "gray" }, { "id": "u-endangered-language-documentation-priority", "type": "unknown", "title": "What documentation strategies best preserve endangered languages for future linguistic and cultural research, and how should priorities be set?", "status": "open", "fields": [ "linguistics", "language-documentation", "anthropology" ], "color": "gray" }, { "id": "u-gesture-language-interface", "type": "unknown", "title": "What is the cognitive and neural relationship between gesture and speech, and does gesture play a constitutive role in language production and comprehension?", "status": "open", "fields": [ "linguistics", "cognitive-science", "neuroscience" ], "color": "gray" }, { "id": "u-historical-reconstruction-limit", "type": "unknown", "title": "What is the temporal limit of reliable linguistic reconstruction, and can proto-language families beyond 10,000 years be validly inferred?", "status": "open", "fields": [ "linguistics", "historical-linguistics", "anthropology" ], "color": "gray" }, { "id": "u-language-acquisition-poverty-stimulus", "type": "unknown", "title": "Does the logical problem of language acquisition (poverty of the stimulus) require innate grammatical knowledge, or can it be solved by statistical learning?", "status": "open", "fields": [ "linguistics", "developmental-psychology", "computational-linguistics" ], "color": "gray" }, { "id": "u-language-contact-convergence-limit", "type": "unknown", "title": "What is the maximum degree of structural convergence possible between unrelated contact languages, and what constraints prevent full convergence?", "status": "open", "fields": [ "linguistics", "contact-linguistics", "linguistic-typology" ], "color": "gray" }, { "id": "u-language-death-reversal-feasibility", "type": "unknown", "title": "Under what conditions can a dying language be successfully revitalised, and what are the cognitive and social prerequisites for sustainable reversal?", "status": "open", "fields": [ "linguistics", "sociolinguistics", "language-policy" ], "color": "gray" }, { "id": "u-language-evolution-emergence", "type": "unknown", "title": "How and when did language evolve in the hominin lineage, and what were the anatomical, neural, and social preconditions?", "status": "open", "fields": [ "linguistics", "evolutionary-biology", "paleoanthropology" ], "color": "gray" }, { "id": "u-language-evolution-selection-neutrality", "type": "unknown", "title": "Can linguistic change rates be decomposed into neutral drift and selection components, and which grammatical features are under positive, purifying, or balancing selection in human populations?", "status": "open", "fields": [ "linguistics", "evolutionary-biology", "cultural-evolution", "population-genetics" ], "color": "gray" }, { "id": "u-language-model-meaning-vs-human", "type": "unknown", "title": "Do large language models have genuine semantic understanding of language meaning, or do they manipulate form without accessing meaning?", "status": "open", "fields": [ "linguistics", "artificial-intelligence", "philosophy-of-mind" ], "color": "gray" }, { "id": "u-language-thought-causality", "type": "unknown", "title": "Does the grammar of a language causally shape non-linguistic cognition (strong Sapir-Whorf), or only correlate with it?", "status": "open", "fields": [ "linguistics", "cognitive-science", "psychology" ], "color": "gray" }, { "id": "u-language-thought-interface", "type": "unknown", "title": "Is language necessary for abstract thought, or can complex reasoning occur in pre-linguistic or non-linguistic representational formats?", "status": "open", "fields": [ "linguistics", "cognitive-science", "philosophy-of-mind" ], "color": "gray" }, { "id": "u-language-universals-typology", "type": "unknown", "title": "Which linguistic features are truly universal across all human languages, and what explains the implicational universals observed in typological surveys?", "status": "open", "fields": [ "linguistics", "linguistic-typology", "cognitive-science" ], "color": "gray" }, { "id": "u-linguistic-relativity-cognition", "type": "unknown", "title": "Does the language one speaks causally shape non-linguistic thought and perception, and to what degree does the Sapir-Whorf hypothesis hold?", "status": "open", "fields": [ "linguistics", "cognitive-science", "psychology" ], "color": "gray" }, { "id": "u-linguistic-relativity-color-perception", "type": "unknown", "title": "Does the number and location of color term boundaries in a language causally affect the speed and accuracy of cross-category color discrimination?", "status": "open", "fields": [ "linguistics", "cognitive-science", "perceptual-psychology" ], "color": "gray" }, { "id": "u-metaphor-universality", "type": "unknown", "title": "Are conceptual metaphors universal across languages and cultures, or are they culturally specific constructions reflecting local experience?", "status": "open", "fields": [ "linguistics", "cognitive-science", "anthropology" ], "color": "gray" }, { "id": "u-natural-language-complexity-class", "type": "unknown", "title": "Does human natural language belong strictly to the mildly context-sensitive class, and can transformer language models generate or recognize all and only the languages in this class?", "status": "open", "fields": [ "linguistics", "mathematics", "computer-science", "cognitive-science", "formal-language-theory" ], "color": "gray" }, { "id": "u-pragmatic-inference-neural-basis", "type": "unknown", "title": "What neural mechanisms support pragmatic inference (implicature, irony, indirect speech), and why are these mechanisms impaired in autism spectrum conditions?", "status": "open", "fields": [ "linguistics", "neuroscience", "cognitive-science" ], "color": "gray" }, { "id": "u-predictive-coding-grammar-neural-substrate", "type": "unknown", "title": "Which cortical layers and circuits implement the prediction vs. prediction-error hierarchy for syntactic processing, and does the same architecture that codes word-level surprisal also code phrase-structure violations at a higher hierarchical level?\n", "status": "open", "fields": [ "linguistics", "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-prosodic-bootstrapping-acquisition", "type": "unknown", "title": "Does prosodic structure in infant-directed speech provide bootstrapping cues for syntactic acquisition, and how large is its contribution?", "status": "open", "fields": [ "linguistics", "developmental-psychology", "phonetics" ], "color": "gray" }, { "id": "u-prosody-meaning-interface", "type": "unknown", "title": "How do prosodic contours (pitch, rhythm) systematically modulate propositional meaning across languages?", "status": "open", "fields": [ "linguistics", "phonology", "semantics", "cognitive-science" ], "color": "gray" }, { "id": "u-prosody-meaning-mapping", "type": "unknown", "title": "How are the acoustic properties of prosody (pitch, duration, rhythm) systematically mapped to meaning, and how is this mapping acquired?", "status": "open", "fields": [ "linguistics", "phonetics", "cognitive-science" ], "color": "gray" }, { "id": "u-recursion-uniquely-human", "type": "unknown", "title": "Is syntactic recursion uniquely human and essential for language, or do non-human animals possess recursive cognitive mechanisms?", "status": "open", "fields": [ "linguistics", "cognitive-science", "animal-cognition" ], "color": "gray" }, { "id": "u-semantic-change-prediction", "type": "unknown", "title": "What mechanisms drive lexical semantic change over time, and can computational models predict which word meanings will shift?", "status": "open", "fields": [ "linguistics", "computational-linguistics", "historical-linguistics" ], "color": "gray" }, { "id": "u-semantic-compositionality-limits", "type": "unknown", "title": "To what extent is natural language meaning compositional, and where do non-compositional constructions require fundamentally different processing?", "status": "open", "fields": [ "linguistics", "formal-semantics", "cognitive-science" ], "color": "gray" }, { "id": "u-semantic-shift-prediction", "type": "unknown", "title": "Can the semantic drift of a word over decades be predicted from its current distributional properties?", "status": "open", "fields": [ "linguistics", "computational-linguistics", "historical-linguistics" ], "color": "gray" }, { "id": "u-signed-language-neural-substrate", "type": "unknown", "title": "Do signed languages engage the same or different neural substrates as spoken languages, and what does this reveal about language universality?", "status": "open", "fields": [ "linguistics", "neuroscience", "cognitive-science" ], "color": "gray" }, { "id": "u-syntax-innateness-evidence", "type": "unknown", "title": "Is there a genetically specified universal grammar, and what is the evidence for or against Chomsky's poverty of the stimulus argument?", "status": "open", "fields": [ "linguistics", "cognitive-science", "developmental-psychology" ], "color": "gray" }, { "id": "u-tonal-language-cognitive-effects", "type": "unknown", "title": "Does speaking a tonal language confer cognitive differences in pitch processing, and does this interact with absolute pitch ability?", "status": "open", "fields": [ "linguistics", "cognitive-science", "music-cognition" ], "color": "gray" }, { "id": "u-universal-grammar-substrate", "type": "unknown", "title": "Is there a neurobiological substrate that implements universal grammar constraints, and what is its architecture?", "status": "open", "fields": [ "linguistics", "neuroscience", "cognitive-science" ], "color": "gray" }, { "id": "u-writing-system-cognition-effects", "type": "unknown", "title": "Do different writing systems (alphabetic, syllabic, logographic) produce measurable differences in reading processes and cognitive architecture?", "status": "open", "fields": [ "linguistics", "cognitive-science", "educational-psychology" ], "color": "gray" }, { "id": "u-active-liquid-crystal-topology-flow-coupling", "type": "unknown", "title": "How do topological defects in active liquid crystals (bacterial suspensions, cytoskeletal networks) couple to spontaneous flow ΓÇö and can the defect-flow dynamics be controlled to direct active matter transport for synthetic biology or materials applications?\n", "status": "open", "fields": [ "soft-matter", "physics", "biophysics", "active-matter" ], "color": "gray" }, { "id": "u-liquid-crystals-frank-elasticity", "type": "unknown", "title": "Can Frank elastic constants K₁, K₂, K₃ be predicted from molecular structure alone (molecular dynamics + density functional theory) without fitting to experimental Freedericksz data?", "status": "open", "fields": [ "soft-matter", "materials-science", "computational-chemistry" ], "color": "gray" }, { "id": "u-nematic-ordering-fluctuation-corrections-maier-saupe", "type": "unknown", "title": "How large are the fluctuation corrections to Maier-Saupe mean-field theory for the isotropic-nematic transition, and do they change the transition from first-order to continuous in confined or low-molecular-weight nematics?\n", "status": "open", "fields": [ "soft-matter", "statistical-physics" ], "color": "gray" }, { "id": "u-diffusion-models-x-stochastic-processes", "type": "unknown", "title": "What is the theoretical lower bound on the number of sampling steps required for a diffusion model to generate samples indistinguishable from the data distribution, and how does this depend on data geometry and the choice of SDE?", "status": "open", "fields": [ "machine-learning", "mathematics", "stochastic-processes" ], "color": "gray" }, { "id": "u-mean-field-theory-x-neural-networks", "type": "unknown", "title": "How do mean-field predictions for deep neural network initialization break down as network width decreases, and what is the minimum width at which finite-width corrections become significant for training?", "status": "open", "fields": [ "machine-learning", "statistical-mechanics", "mathematics" ], "color": "gray" }, { "id": "u-renormalization-group-ml-universality", "type": "unknown", "title": "Do deep residual networks belong to a specific renormalization group universality class, and if so, what class — and can this class membership be determined from small-model experiments without training at full scale?", "status": "open", "fields": [ "machine-learning", "statistical-physics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-rg-ml-universality-classes", "type": "unknown", "title": "Do universality classes from the renormalization group predict generalization behavior in deep neural networks trained on different data distributions?\n", "status": "open", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "gray" }, { "id": "u-uap-stone-weierstrass-pedagogy-misconception-rate", "type": "unknown", "title": "Does teaching Stone-Weierstrass-style compact density before neural-network universal approximation reduce student misconceptions that approximation existence implies trainability or generalization?\n", "status": "open", "fields": [ "machine-learning", "mathematics" ], "color": "gray" }, { "id": "u-fish-schooling-topological-interaction", "type": "unknown", "title": "Is topological interaction (fixed k nearest neighbors) vs. metric interaction (fixed radius r) a species-specific adaptation or a context-dependent switch, and what selective pressure drives the topology?", "status": "open", "fields": [ "marine-biology", "ethology", "statistical-physics", "active-matter-physics" ], "color": "gray" }, { "id": "u-2d-material-defect-transport", "type": "unknown", "title": "How do point defects and grain boundaries in 2D materials limit carrier mobility?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-active-learning-bias-in-alloy-discovery-loops", "type": "unknown", "title": "How much acquisition bias accumulates in Bayesian active-learning loops for alloy discovery?", "status": "open", "fields": [ "materials-science", "machine-learning", "chemistry" ], "color": "gray" }, { "id": "u-amorphous-metal-fatigue-limit", "type": "unknown", "title": "Do metallic glasses have a fatigue limit, and if so what determines it?", "status": "open", "fields": [ "materials-science", "mechanics", "amorphous-materials" ], "color": "gray" }, { "id": "u-amorphous-metal-magnetism", "type": "unknown", "title": "What causes the anomalously high coercivity in some amorphous metallic glass compositions?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-auxetic-materials-scalable-fabrication-3d", "type": "unknown", "title": "Can auxetic 3D metamaterial microgeometries be fabricated at millimeter scale and below with sufficient precision to achieve designed negative Poisson's ratios across multiple deformation axes?\n", "status": "open", "fields": [ "materials-science", "mechanics" ], "color": "gray" }, { "id": "u-battery-dendrite-nucleation", "type": "unknown", "title": "What determines the nucleation site and growth direction of lithium dendrites in battery anodes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-bic-metasurface-q-factor-radiative-disorder-limit", "type": "unknown", "title": "What disorder and finite-size mechanisms dominate Q-factor degradation in quasi-BIC dielectric metasurfaces?", "status": "open", "fields": [ "materials-science", "photonics", "electromagnetics" ], "color": "gray" }, { "id": "u-biomineralisation-control", "type": "unknown", "title": "How do organisms control polymorph selection and crystallographic texture during biomineralisation?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-biomineralization-polymorph-control", "type": "unknown", "title": "How do biomineralizing organisms select specific crystal polymorphs (calcite vs. aragonite) during skeleton formation, and can this be replicated synthetically?", "status": "open", "fields": [ "biomineralization", "materials-science", "structural-biology" ], "color": "gray" }, { "id": "u-capillary-length-contact-line-hysteresis-unified-model", "type": "unknown", "title": "Can a single reduced model predict droplet morphology across the competition among capillary length, defect pinning distribution, and viscous spreading rate on engineered rough surfaces?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-classical-nucleation-theory-prefactor-discrepancy", "type": "unknown", "title": "Why does classical nucleation theory fail by 10-20 orders of magnitude for protein and ice nucleation, and what molecular-scale corrections are needed for quantitative prediction?", "status": "open", "fields": [ "materials-science", "thermodynamics", "biophysics" ], "color": "gray" }, { "id": "u-corrosion-mechanism-passivation", "type": "unknown", "title": "What is the atomic-scale mechanism of passive film breakdown that initiates pitting corrosion?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-dislocation-avalanche-statistical-mechanics-plasticity", "type": "unknown", "title": "Do dislocation avalanches in plastically deforming metals belong to a universal criticality class (directed percolation, mean-field depinning), and can this universality class predict the statistics of fracture precursors?", "status": "open", "fields": [ "materials-science", "statistical-physics", "condensed-matter", "mechanical-engineering", "self-organised-criticality" ], "color": "gray" }, { "id": "u-entropy-stabilized-ceramics", "type": "unknown", "title": "How does configurational entropy stabilise single-phase multi-principal-component oxides at room temperature?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-ferroelectric-fatigue-mechanism", "type": "unknown", "title": "What causes ferroelectric fatigue (loss of switchable polarisation) under repeated electric cycling?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-fisher-optimal-experiment-policy-shift-drift", "type": "unknown", "title": "How quickly do Fisher-optimal experiment policies degrade under drifting materials process conditions?", "status": "open", "fields": [ "materials-science", "statistics", "automation" ], "color": "gray" }, { "id": "u-fracture-avalanche-universality-class", "type": "unknown", "title": "What is the exact universality class of acoustic emission avalanches in brittle fracture, and is the crackling-noise exponent τ ~ 1.5 universal across materials or material-specific?\n", "status": "open", "fields": [ "materials-science", "statistical-physics" ], "color": "gray" }, { "id": "u-grain-boundary-embrittlement-prediction", "type": "unknown", "title": "Can the embrittlement susceptibility of a grain boundary be predicted from its geometric and chemical parameters alone?", "status": "open", "fields": [ "materials-science", "metallurgy", "computational-materials-science" ], "color": "gray" }, { "id": "u-grain-boundary-segregation", "type": "unknown", "title": "How does solute segregation to grain boundaries control polycrystalline material failure?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-high-entropy-alloy-design-principles", "type": "unknown", "title": "What are the fundamental design principles governing phase selection and mechanical properties in high-entropy alloys?", "status": "open", "fields": [ "materials-science", "metallurgy", "thermodynamics" ], "color": "gray" }, { "id": "u-high-entropy-alloy-phase-stability-prediction", "type": "unknown", "title": "Can CALPHAD-based thermodynamic models reliably predict the phase stability, mechanical properties, and oxidation resistance of high-entropy alloys with 5+ principal elements without extensive experimental validation?", "status": "open", "fields": [ "materials-science", "chemistry", "thermodynamics", "computational-materials-science", "machine-learning" ], "color": "gray" }, { "id": "u-high-tc-superconductivity-mechanism", "type": "unknown", "title": "What is the pairing mechanism in cuprate high-temperature superconductors above 77 K?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hydrogel-fracture-toughness-network-structure", "type": "unknown", "title": "What polymer network architectural features (strand length distribution, topological defects, physical vs. chemical crosslinks) determine hydrogel fracture toughness, and can they be predicted from small-angle X-ray scattering measurements?", "status": "open", "fields": [ "materials-science", "polymer-physics" ], "color": "gray" }, { "id": "u-hydrogen-embrittlement-pathway", "type": "unknown", "title": "What is the dominant mechanism of hydrogen embrittlement in high-strength steels?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-lithium-dendrite-nucleation-control", "type": "unknown", "title": "What conditions deterministically prevent lithium dendrite nucleation in solid-state electrolytes?", "status": "open", "fields": [ "materials-science", "electrochemistry", "battery-science" ], "color": "gray" }, { "id": "u-metallic-glass-crystallisation", "type": "unknown", "title": "What determines the nucleation kinetics and crystallisation pathways in metallic glasses?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-metamaterial-self-assembly-dynamics", "type": "unknown", "title": "Can metamaterial function (bandgap, effective medium response, scattering suppression) emerge from self-organized electromagnetic modes without globally imposed periodicity?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-metamaterial-wave-control", "type": "unknown", "title": "What physical limits govern negative-index metamaterials for sub-diffraction imaging at optical frequencies?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-peridynamic-horizon-calibration-for-cortical-bone-microcrack-prediction", "type": "unknown", "title": "What validation boundary conditions determine when `b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling` remains decision-useful?", "status": "open", "fields": [ "materials-science", "medicine" ], "color": "gray" }, { "id": "u-perovskite-stability-degradation", "type": "unknown", "title": "What are the dominant degradation mechanisms limiting perovskite solar cell lifetime to under 20 years?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-phonon-engineering-thermal", "type": "unknown", "title": "Can phonon dispersion be engineered to achieve near-zero thermal conductivity in crystalline materials?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-phonon-mean-free-path-nanostructured-materials", "type": "unknown", "title": "How does the phonon mean-free-path spectrum change at interfaces and grain boundaries in nanostructured thermoelectric materials, and can interface engineering selectively scatter heat-carrying phonons without degrading electron transport?\n", "status": "open", "fields": [ "materials-science", "condensed-matter-physics", "nanotechnology" ], "color": "gray" }, { "id": "u-piezoelectric-biopolymers", "type": "unknown", "title": "Can biological polymers like collagen and cellulose be tuned to achieve technologically relevant piezoelectric coefficients?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-piezoelectricity-symmetry-breaking", "type": "unknown", "title": "Can high-throughput crystal structure prediction combined with group-theory symmetry screening discover novel lead-free piezoelectric materials with d_33 > 300 pC/N to replace PZT?", "status": "open", "fields": [ "materials-science", "group-theory", "computational-chemistry" ], "color": "gray" }, { "id": "u-preisach-model-physical-interpretation-density", "type": "unknown", "title": "What microscopic physical mechanism determines the Preisach density rho(alpha,beta) in real ferromagnetic materials, and can rho be predicted from microstructural parameters without fitting to measured hysteresis loops?", "status": "open", "fields": [ "materials-science", "mathematics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-protein-matrix-nucleation-control", "type": "unknown", "title": "How do organic protein matrices quantitatively control crystal polymorph selection and nucleation kinetics during biomineralization?", "status": "open", "fields": [ "materials-science", "biophysics", "structural-biology" ], "color": "gray" }, { "id": "u-quantum-dot-confinement-size-tunability", "type": "unknown", "title": "What are the upper bounds on quantum dot photoluminescence quantum yield and emission linewidth achievable with purely inorganic core-shell architectures, and at what size regime does the Brus particle-in-a-box approximation fail to predict emission energy within 5 meV due to surface reconstruction or many-body correlation effects?", "status": "open", "fields": [ "materials-science", "quantum-physics", "nanoscience" ], "color": "gray" }, { "id": "u-quasicrystal-stability-origin", "type": "unknown", "title": "What determines whether a quasicrystalline phase forms versus an approximant crystal in metallic alloys?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-radiation-damage-recovery", "type": "unknown", "title": "What atomic mechanisms drive radiation damage recovery in nuclear materials and can they be accelerated?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-room-temperature-superconductivity-mechanism", "type": "unknown", "title": "What microscopic mechanism could stabilize superconductivity at room temperature and ambient pressure?", "status": "open", "fields": [ "materials-science", "condensed-matter-physics", "quantum-physics" ], "color": "gray" }, { "id": "u-room-temperature-superconductivity", "type": "unknown", "title": "What materials architecture would achieve ambient-pressure room-temperature superconductivity?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-self-healing-polymer-mechanism", "type": "unknown", "title": "What is the molecular mechanism enabling autonomous self-healing in non-covalent polymer networks?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-semiconductor-doping-fermi-level-pinning", "type": "unknown", "title": "What mechanisms cause Fermi level pinning at semiconductor surfaces and interfaces, and can chemical potential engineering of surface passivation overcome pinning to enable reliable band alignment control in heterojunction devices?\n", "status": "open", "fields": [ "materials-science", "thermodynamics" ], "color": "gray" }, { "id": "u-shape-memory-alloy-fatigue", "type": "unknown", "title": "What atomic-scale mechanisms drive functional fatigue in shape-memory alloys after repeated cycling?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-soft-ferrite-nonlinear-permeability-wpt-harmonic-loss", "type": "unknown", "title": "How do inverter-generated harmonics and spatial flux bias combine to shift soft-ferrite hysteresis losses in consumer wireless chargers beyond manufacturer Steinmetz curves calibrated under sinusoidal excitation?\n", "status": "open", "fields": [ "materials-science", "electrical-engineering" ], "color": "gray" }, { "id": "u-solid-electrolyte-interface", "type": "unknown", "title": "What is the full chemical composition and ion-transport mechanism of the solid electrolyte interphase?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-thermoelectric-zt-limit", "type": "unknown", "title": "Is there a fundamental upper limit to thermoelectric figure-of-merit zT and what materials approach it?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-thermoelectric-zt-theoretical-limit", "type": "unknown", "title": "Is there a fundamental thermodynamic upper bound on the thermoelectric figure of merit zT beyond the Carnot efficiency argument, and what combination of electronic band structure features can approach this bound in a real material?", "status": "open", "fields": [ "materials-science", "thermodynamics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-topological-insulator-surface-states", "type": "unknown", "title": "Why do topological insulator surface states remain robust under certain perturbations but not others?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-topological-signatures-microcrack-coalescence-transferability", "type": "unknown", "title": "Are persistent-homology microcrack signatures transferable across materials classes and loading regimes?", "status": "open", "fields": [ "materials-science", "mathematics" ], "color": "gray" }, { "id": "u-twistronics-moire-phases", "type": "unknown", "title": "What is the full phase diagram of moiré superlattices as a function of twist angle, pressure, and field?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-vdw-heterostructure-emergent-phases", "type": "unknown", "title": "What emergent electronic phases arise in van der Waals heterostructures beyond graphene-hBN systems?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-dislocation-dynamics-alloy-high-entropy", "type": "unknown", "title": "What dislocation mechanisms produce the exceptional combination of high strength and high ductility observed in high-entropy alloys (HEAs), and can these be predicted from first principles?", "status": "open", "fields": [ "materials-science", "solid-mechanics", "computational-chemistry" ], "color": "gray" }, { "id": "u-gauge-fields-as-bundle-connections-pedagogy", "type": "unknown", "title": "Which bundle-theoretic perspectives (connections, holonomy, characteristic classes) most improve predictive modeling literacy for early-career theorists without obscuring computability?", "status": "open", "fields": [ "mathematical-physics", "mathematics", "physics" ], "color": "gray" }, { "id": "u-non-abelian-aharonov-bohm-observable-consequences", "type": "unknown", "title": "What experimentally observable consequences distinguish the non-Abelian Aharonov-Bohm effect (SU(2) gauge holonomy) from the Abelian U(1) case, and can they be measured in condensed matter systems?", "status": "open", "fields": [ "mathematical-physics", "physics", "quantum-physics", "topology" ], "color": "gray" }, { "id": "u-ricci-flow-intuition-vs-rigorous-gr-correspondence", "type": "unknown", "title": "Where is Ricci flow (beyond heuristic metaphors) formally tied to renormalization-group flow or gravitational dynamics in physically predictive models rather than pedagogy alone?", "status": "open", "fields": [ "differential-geometry", "general-relativity", "mathematical-physics" ], "color": "gray" }, { "id": "u-stochastic-quantization-non-equilibrium-regimes", "type": "unknown", "title": "Can Parisi-Wu stochastic quantization be rigorously extended to non-equilibrium quantum field theories (finite-density QCD, Keldysh formalism, open quantum systems) where the equilibrium Boltzmann fixed point does not exist, and do SPDE regularity structures (Hairer) provide the convergence proofs needed for lattice stochastic quantization?\n", "status": "open", "fields": [ "mathematical-physics", "stochastic-analysis", "quantum-field-theory", "lattice-gauge-theory" ], "color": "gray" }, { "id": "u-symplectic-topology-classical-quantum-correspondence-limits", "type": "unknown", "title": "What is the precise mathematical relationship between Gromov's non-squeezing theorem for symplectic manifolds and the Heisenberg uncertainty principle, and can symplectic rigidity results (Gromov width, symplectic capacities) be derived from quantum mechanics in the hbar → 0 classical limit?\n", "status": "open", "fields": [ "mathematical-physics", "symplectic-geometry", "quantum-mechanics", "functional-analysis" ], "color": "gray" }, { "id": "u-3manifold-invariants-completeness", "type": "unknown", "title": "Are quantum invariants of 3-manifolds complete in the sense of distinguishing all non-homeomorphic 3-manifolds?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-abc-conjecture-verification", "type": "unknown", "title": "Is Mochizuki's proof of the abc conjecture correct and how can it be made verifiable by the community?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-aesthetic-complexity-information-measure", "type": "unknown", "title": "Is there a universal, computable information-theoretic measure that predicts cross-cultural aesthetic preference for visual and auditory stimuli, and what is the neural implementation of the compression-progress reward signal that Schmidhuber's theory posits?\n", "status": "open", "fields": [ "cognitive-science", "information-theory", "aesthetics", "neuroscience", "mathematics" ], "color": "gray" }, { "id": "u-banach-space-geometry", "type": "unknown", "title": "What is the geometry of the space of all Banach spaces under various equivalence relations on their structure?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-black-scholes-heat-equation", "type": "unknown", "title": "What is the correct PDE generalization of Black-Scholes for markets with jumps, stochastic volatility, and transaction costs, and does it retain the heat-equation structure?", "status": "open", "fields": [ "mathematics", "finance", "probability" ], "color": "gray" }, { "id": "u-category-theory-x-functional-programming", "type": "unknown", "title": "Can all practical programming language features be expressed as categorical universal constructions, and what new type system features does higher category theory predict?\n", "status": "open", "fields": [ "mathematics", "computer_science", "type_theory", "logic" ], "color": "gray" }, { "id": "u-causal-attribution-chain-rule-universality", "type": "unknown", "title": "Does a universal chain rule for causal attribution unify natural selection, Bayesian learning, economic rationality, and gradient descent as instances of the same mathematical process?", "status": "open", "fields": [ "mathematics", "evolutionary-biology", "economics", "machine-learning", "information-theory" ], "color": "gray" }, { "id": "u-chaos-x-ergodic-theory", "type": "unknown", "title": "Are physically relevant chaotic systems (turbulence, weather, neural dynamics) truly ergodic, and how does finite-time ergodicity breaking affect predictions?\n", "status": "open", "fields": [ "mathematics", "physics", "dynamical_systems", "information_theory" ], "color": "gray" }, { "id": "u-compressed-sensing-x-sparse-recovery", "type": "unknown", "title": "What are the fundamental limits of compressed sensing recovery for non-sparse but approximately sparse signals, and how do these limits change under adversarial or structured noise?\n", "status": "open", "fields": [ "mathematics", "computer-science", "signal-processing" ], "color": "gray" }, { "id": "u-conley-index-computable-verification-higher-dim", "type": "unknown", "title": "Which certified algorithms compute Conley indices (or verified isolating blocks) for empirically extracted Poincaré maps from fluid experiments beyond heuristic cubical complexes — without drowning in computational homology blowups?", "status": "open", "fields": [ "dynamical-systems", "computational-topology" ], "color": "gray" }, { "id": "u-constructive-incompleteness", "type": "unknown", "title": "What is the constructive content of Godel incompleteness theorems and which unprovable statements have computational meaning?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cortical-folding-poisson-flow", "type": "unknown", "title": "Is the spatial pattern of cortical sulci and gyri governed by a Poisson flow model with a single control parameter, and can this predict inter-individual variability from gene expression data?", "status": "open", "fields": [ "neuroscience", "mathematical-biology", "differential-geometry", "developmental-biology" ], "color": "gray" }, { "id": "u-deep-learning-approximation-sobolev-optimal", "type": "unknown", "title": "What is the optimal approximation rate for deep neural networks in Sobolev spaces, and do ReLU networks achieve the minimax optimal rate for estimating functions with different smoothness levels, or do they suffer unavoidable approximation gaps?\n", "status": "open", "fields": [ "mathematics", "machine-learning", "approximation-theory", "statistics" ], "color": "gray" }, { "id": "u-derived-algebraic-geometry", "type": "unknown", "title": "What are the fundamental obstructions to extending derived algebraic geometry to characteristic p arithmetic geometry?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-descriptive-set-projective-hierarchy", "type": "unknown", "title": "What are the full regularity properties (measurability, BP, PSP) of sets in the projective hierarchy?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-diffeomorphic-growth-mechanical-constraints", "type": "unknown", "title": "To what extent are biological shape transformations during development truly diffeomorphic (smooth, invertible), and which developmental processes require topological changes (cell division, death, fusion) that fall outside the diffeomorphism group?\n", "status": "open", "fields": [ "biology", "mathematics", "developmental-biology", "topology" ], "color": "gray" }, { "id": "u-ecc-torus-intuition-misconception-rates", "type": "unknown", "title": "How often does introductory elliptic-curve cryptography instruction that begins with the complex torus picture produce durable misconceptions about security (e.g., confusing periodic complex geometry with hardness of discrete logarithms on 𝔽_q)?\n", "status": "open", "fields": [ "mathematics", "computer-science", "education" ], "color": "gray" }, { "id": "u-erdos-renyi-random-graph-biological", "type": "unknown", "title": "Do biological networks (gene regulatory, protein-protein interaction, metabolic) show giant component emergence at the same critical connectivity as Erdős-Rényi random graphs, and what does this imply for robustness?", "status": "open", "fields": [ "network-science", "mathematics", "systems-biology" ], "color": "gray" }, { "id": "u-expander-graphs-x-error-correcting-codes", "type": "unknown", "title": "Do there exist linear-time encodable and decodable error-correcting codes that simultaneously achieve the Gilbert-Varshamov bound and have linear minimum distance, and can quantum expander codes achieve constant rate with linear distance?", "status": "open", "fields": [ "mathematics", "computer-science", "information-theory" ], "color": "gray" }, { "id": "u-extreme-value-theory-x-risk-modeling", "type": "unknown", "title": "Does the multivariate generalization of extreme value theory (spectral measure, max-stable processes) provide a tractable and accurate framework for joint tail risk in high-dimensional financial and climate systems, and what is the minimum data requirement for reliable estimation?", "status": "open", "fields": [ "mathematics", "economics", "statistics", "climate-science" ], "color": "gray" }, { "id": "u-fem-adaptivity-optimal-mesh-refinement", "type": "unknown", "title": "What is the optimal adaptive mesh refinement strategy for FEM that achieves the best convergence rate for problems with singularities, and can machine learning automate this?", "status": "open", "fields": [ "applied-mathematics", "engineering", "computational-science" ], "color": "gray" }, { "id": "u-fiber-bundle-gauge-field-quantum-gravity", "type": "unknown", "title": "Can the fiber bundle formulation of gauge theory be extended to describe quantum gravity — where spacetime itself is dynamical — without introducing a fixed background manifold as the base space?\n", "status": "open", "fields": [ "mathematics", "physics" ], "color": "gray" }, { "id": "u-fourier-analysis-non-euclidean-domains", "type": "unknown", "title": "How should Fourier analysis be generalized to non-Euclidean domains (graphs, manifolds, hyperbolic spaces) for signal processing on complex networks?", "status": "open", "fields": [ "mathematics", "signal-processing", "machine-learning" ], "color": "gray" }, { "id": "u-fourier-transform-x-signal-processing", "type": "unknown", "title": "What are the optimal time-frequency representations for non-stationary signals, and how do Fourier and wavelet transforms generalize to non-Euclidean domains?\n", "status": "open", "fields": [ "mathematics", "computer-science", "signal-processing" ], "color": "gray" }, { "id": "u-frb-waiting-time-universality", "type": "unknown", "title": "Do the inter-burst waiting time distributions of repeating fast radio burst sources belong to a specific random matrix theory universality class, and if so, what does the universality class reveal about the physical emission mechanism?", "status": "open", "fields": [ "astrophysics", "mathematics", "statistical-physics", "quantum-chaos" ], "color": "gray" }, { "id": "u-gnn-expressiveness-beyond-wl", "type": "unknown", "title": "What architectural modifications allow graph neural networks to exceed the 1-WL expressiveness bound, and at what computational cost relative to the gain in practical task performance?", "status": "open", "fields": [ "machine-learning", "combinatorics", "computer-science" ], "color": "gray" }, { "id": "u-harmonic-analysis-sparse-recovery", "type": "unknown", "title": "What are the sharp conditions for sparse signal recovery from harmonic measurements with minimal samples?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-homotopy-type-theory-computational-foundations", "type": "unknown", "title": "Can homotopy type theory (HoTT) and the univalence axiom serve as a complete and decidable computational foundation for mathematics, and what is the complexity of proof search in intensional dependent type theories?\n", "status": "open", "fields": [ "mathematics", "computer-science", "type-theory", "logic" ], "color": "gray" }, { "id": "u-homotopy-type-theory-foundations", "type": "unknown", "title": "Can homotopy type theory serve as a complete foundation for mathematics replacing ZFC set theory?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-hopf-algebra-qft-nonperturbative-extension", "type": "unknown", "title": "Can the Connes-Kreimer Hopf algebra structure of perturbative renormalization be extended to nonperturbative quantum field theory, and what algebraic structure governs resurgent trans-series in QFT?", "status": "open", "fields": [ "mathematics", "quantum-field-theory", "algebraic-topology" ], "color": "gray" }, { "id": "u-infinity-category-limits", "type": "unknown", "title": "What are the fundamental completeness properties of infinity-categories and when do they have all small limits?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-integer-factoring-quantum-classical-boundary", "type": "unknown", "title": "At what integer size n does Shor's quantum algorithm on a fault-tolerant quantum computer become faster than the number field sieve on the best classical supercomputer, and what circuit depth and physical qubit count is required to break RSA-2048 within one year of quantum computation?", "status": "open", "fields": [ "mathematics", "computer-science", "quantum-computing", "cryptography" ], "color": "gray" }, { "id": "u-ising-exact-density-states-universality", "type": "unknown", "title": "Does the exact combinatorial density of states for the critical 1D Ising model generalize to a universal formula for all critical 2D lattice models, providing exact partition functions without Monte Carlo?", "status": "open", "fields": [ "statistical-physics", "combinatorics", "mathematical-physics", "number-theory" ], "color": "gray" }, { "id": "u-island-biogeography-x-percolation", "type": "unknown", "title": "Does habitat fragmentation follow a true percolation phase transition with universal critical exponents, and if so, what is the biological percolation threshold for landscape connectivity in temperate forest ecosystems?", "status": "open", "fields": [ "biology", "mathematics", "ecology" ], "color": "gray" }, { "id": "u-knot-invariants-rna-tertiary-structure-topology", "type": "unknown", "title": "Do RNA tertiary structures contain topologically knotted or pseudoknotted configurations that require knot invariants (beyond secondary structure diagrams) to classify, and do these topological constraints shape RNA evolution and function in ways not captured by current structure prediction algorithms?\n", "status": "open", "fields": [ "mathematics", "biology", "biophysics", "molecular-biology", "topology" ], "color": "gray" }, { "id": "u-knot-invariants-x-dna-topology", "type": "unknown", "title": "What is the minimal set of knot-theoretic invariants sufficient to characterize all topological states of chromosomal DNA in vivo, and can topoisomerase reaction mechanisms be inferred from changes in these invariants?", "status": "open", "fields": [ "mathematics", "molecular-biology", "biophysics" ], "color": "gray" }, { "id": "u-knot-theory-x-quantum-gravity", "type": "unknown", "title": "Are the quantum knot invariants (Jones polynomial, HOMFLY) complete invariants of 3-manifold topology, and do they encode the geometry of quantum spacetime?\n", "status": "open", "fields": [ "mathematics", "physics", "topology", "quantum_gravity" ], "color": "gray" }, { "id": "u-kolmogorov-complexity-computable-approximation", "type": "unknown", "title": "What is the best computable approximation to Kolmogorov complexity, and how closely can compression algorithms approach the theoretical minimum description length for scientific models?", "status": "open", "fields": [ "mathematics", "computer-science", "philosophy-of-science", "statistics" ], "color": "gray" }, { "id": "u-koopman-dmd-spectrum-convergence-navier-stokes", "type": "unknown", "title": "Under what Reynolds-number and sampling regimes do DMD/EDMD spectra converge to physically interpretable Koopman-like branches for wall-bounded turbulence without spurious modes dominating?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-langlands-physics-connection", "type": "unknown", "title": "What is the precise mathematical connection between the geometric Langlands program and quantum field theory?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-large-deviations-non-markovian-epidemic", "type": "unknown", "title": "What are the large-deviation statistics of epidemic final sizes in non-Markovian stochastic processes, and do they predict the probability of anomalously large outbreaks from small seeds?", "status": "open", "fields": [ "probability-theory", "mathematical-epidemiology", "statistical-physics", "public-health" ], "color": "gray" }, { "id": "u-lie-groups-x-symmetry-conservation", "type": "unknown", "title": "What are the conservation laws and symmetry structures of non-equilibrium systems, and how do Lie group methods extend to time-irreversible and dissipative physical systems?\n", "status": "open", "fields": [ "mathematics", "physics", "mathematical-physics" ], "color": "gray" }, { "id": "u-lotka-volterra-x-game-theory", "type": "unknown", "title": "When do multi-species ecological communities admit a Nash equilibrium description, and what evolutionary game dynamics predict community assembly and stability?\n", "status": "open", "fields": [ "mathematics", "ecology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-model-theory-arithmetic", "type": "unknown", "title": "What model-theoretic properties distinguish the standard model of arithmetic from non-standard models?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-morse-theory-x-energy-landscape", "type": "unknown", "title": "Does the Morse-Witten complex of a protein energy landscape have a computable topological signature that predicts folding cooperativity, and can persistent homology detect all kinetically relevant metastable states from MD trajectories?", "status": "open", "fields": [ "mathematics", "physics", "chemistry" ], "color": "gray" }, { "id": "u-motivic-cohomology-calculations", "type": "unknown", "title": "Can motivic cohomology groups be computationally determined for all smooth projective varieties?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-natural-gradient-selection-convergence-rate-fitness-landscape", "type": "unknown", "title": "What is the quantitative convergence rate advantage of natural (Shahshahani- geometric) selection over Euclidean-gradient selection on realistic fitness landscapes, and does the information-geometric speed limit (Fisher information bound) correctly predict observed rates of adaptation in laboratory evolution experiments?\n", "status": "open", "fields": [ "evolutionary-biology", "information-geometry", "population-genetics", "mathematics" ], "color": "gray" }, { "id": "u-neural-network-loss-landscape-global-structure", "type": "unknown", "title": "What is the global topological and geometric structure of neural network loss landscapes, and does the absence of spurious local minima hold for realistic architectures beyond shallow networks?", "status": "open", "fields": [ "mathematics", "machine-learning", "computer-science", "optimization" ], "color": "gray" }, { "id": "u-ntk-finite-width-corrections", "type": "unknown", "title": "How do finite-width corrections to the Neural Tangent Kernel (NTK) govern feature learning, and what is the precise boundary between the NTK (lazy training) regime and the mean-field (feature learning) regime as a function of network width, learning rate, and initialization scale?\n", "status": "open", "fields": [ "mathematics", "machine-learning", "functional-analysis", "probability-theory" ], "color": "gray" }, { "id": "u-optimal-stopping-biological-decisions", "type": "unknown", "title": "Do animals implement optimal stopping rules (secretary problem, explore-exploit tradeoff) in foraging and mate choice, and what neural circuit implements the stopping criterion?", "status": "open", "fields": [ "mathematical-biology", "neuroeconomics", "behavioral-ecology" ], "color": "gray" }, { "id": "u-optimal-transport-word-order-universals", "type": "unknown", "title": "Is optimal transport (Wasserstein distance) minimization the physical principle underlying cross-linguistic word order universals, and can this predict undiscovered universals?", "status": "open", "fields": [ "linguistics", "mathematics", "information-theory", "cognitive-science" ], "color": "gray" }, { "id": "u-optimal-transport-x-machine-learning", "type": "unknown", "title": "Does the Wasserstein metric's geometric sensitivity to support structure make it a better metric for generative model evaluation than Fréchet Inception Distance (FID), and can it be computed scalably for high-dimensional distributions?", "status": "open", "fields": [ "mathematics", "computer_science", "statistics" ], "color": "gray" }, { "id": "u-origami-fold-design-complexity", "type": "unknown", "title": "What is the computational complexity of optimal origami crease pattern design for a given 3D target shape, and are there classes of polyhedral targets for which the Demaine-Tachi universality theorem provides computationally efficient fold designs versus NP-hard cases?", "status": "open", "fields": [ "mathematics", "computer-science", "engineering" ], "color": "gray" }, { "id": "u-origami-math-x-structural-engineering", "type": "unknown", "title": "Can origami crease pattern combinatorics be inverted — given a target mechanical behavior (stiffness tensor, Poisson's ratio, deployment kinematics), systematically find the crease pattern that achieves it — and is this inverse design problem tractable in general?", "status": "open", "fields": [ "mathematics", "physics", "engineering" ], "color": "gray" }, { "id": "u-p-vs-np-geometric-barriers", "type": "unknown", "title": "What geometric barriers prevent current proof techniques from resolving P vs NP?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-p-vs-np-geometric-complexity", "type": "unknown", "title": "Can geometric complexity theory (GCT) provide the representation-theoretic obstructions needed to separate the permanent from the determinant and resolve P vs NP?", "status": "open", "fields": [ "mathematics", "complexity-theory", "algebraic-geometry", "representation-theory" ], "color": "gray" }, { "id": "u-persistence-homology-x-protein-structure", "type": "unknown", "title": "Can persistent homology distinguish functional protein conformations from noise, and what topological features predict allosteric communication?\n", "status": "open", "fields": [ "mathematics", "biology", "topology", "structural_biology" ], "color": "gray" }, { "id": "u-ppi-scale-free-topology-functional-necessity", "type": "unknown", "title": "Is the scale-free topology of protein-protein interaction networks a functional necessity (required for robustness, evolvability, or regulatory efficiency) or an artifact of network growth mechanisms (preferential attachment generating power laws as a neutral evolutionary consequence)?\n", "status": "open", "fields": [ "mathematics", "network-science", "systems-biology", "evolutionary-biology" ], "color": "gray" }, { "id": "u-pseudo-holomorphic-curve-counts", "type": "unknown", "title": "What determines the well-definedness of pseudo-holomorphic curve counts in symplectic topology?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-quantum-group-representation", "type": "unknown", "title": "What is the complete representation theory of quantum groups at roots of unity and its physical interpretation?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-random-matrix-theory-neural-spectra", "type": "unknown", "title": "Do the eigenvalue spectra of neural connectivity matrices follow random matrix theory (Marchenko-Pastur) predictions, and do deviations encode learned structure?", "status": "open", "fields": [ "mathematics", "computational-neuroscience", "statistical-physics" ], "color": "gray" }, { "id": "u-random-matrix-universality", "type": "unknown", "title": "Why does random matrix universality arise in systems as diverse as nuclear spectra, number theory zeros, and quantum chaos?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-random-walk-x-brownian-motion", "type": "unknown", "title": "What is the universality class of the scaling limit for random walks in random environments, and when does the Brownian motion limit break down?\n", "status": "open", "fields": [ "mathematics", "physics", "probability-theory" ], "color": "gray" }, { "id": "u-renyi-entropy-x-multifractal", "type": "unknown", "title": "What is 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}, { "id": "u-riemann-zero-distribution", "type": "unknown", "title": "What determines the distribution of non-trivial zeros of the Riemann zeta function along the critical line?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-riemann-zeta-biomedical-discoverability", "type": "unknown", "title": "Does the Riemann zeta function govern the scaling of biomedical discovery rates with dataset size, and is this a general law for knowledge accumulation in high-dimensional biology?", "status": "open", "fields": [ "number-theory", "statistics", "biomedical-informatics", "data-science" ], "color": "gray" }, { "id": "u-rip-constant-optimal-measurement-matrix-deterministic-construction", "type": "unknown", "title": "Do deterministic measurement matrices satisfying the Restricted Isometry Property (RIP) with optimal O(k log n/k) measurements exist, and can they be constructed explicitly (not via random matrix arguments), enabling hardware-efficient compressed sensing implementations?\n", "status": "open", "fields": [ "mathematics", "signal-processing", "computer-science", "applied-mathematics" ], "color": "gray" }, { "id": "u-sindy-noise-and-collinearity-under-limited-sensing", "type": "unknown", "title": "How do finite-difference derivative noise and collinearity across library columns degrade sparse PDE-structure recovery when only a sparse sensor budget is available?", "status": "open", "fields": [ "mathematics", "physics", "numerical-analysis" ], "color": "gray" }, { "id": "u-sir-percolation-temporal-network-threshold", "type": "unknown", "title": "What is the exact epidemic threshold for SIR dynamics on temporal contact networks, and how does temporal burstiness shift the threshold relative to static-network percolation predictions?\n", "status": "open", "fields": [ "epidemiology", "network-science", "statistical-physics" ], "color": "gray" }, { "id": "u-social-network-centrality-x-eigenvector", "type": "unknown", "title": "For temporal social 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household wealth distributions without free parameters?", "status": "open", "fields": [ "statistical-physics", "economics", "mathematical-economics", "complexity-science" ], "color": "gray" }, { "id": "u-subriemannian-geodesic-abnormal-optimality", "type": "unknown", "title": "Under what geometric conditions are abnormal geodesics in sub-Riemannian geometry locally length-minimizing, and does the existence of abnormal minimizers prevent smooth parameterization of the sub-Riemannian geodesic flow — with direct implications for optimal robot motion planning?\n", "status": "open", "fields": [ "mathematics", "differential-geometry", "control-engineering", "robotics" ], "color": "gray" }, { "id": "u-symplectic-capacities", "type": "unknown", "title": "Are Ekeland-Hofer and Gromov-Witten symplectic capacities equal for all convex bodies?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-symplectic-quantization-semiclassical", "type": "unknown", "title": "What is the precise relationship between the symplectic geometry of a classical phase space and the spectral properties of its quantisation, beyond the leading-order WKB approximation?", "status": "open", "fields": [ "mathematics", "mathematical-physics", "quantum-mechanics", "symplectic-geometry" ], "color": "gray" }, { "id": "u-tda-x-shape-recognition", "type": "unknown", "title": "Can persistent homology provide a computationally tractable and statistically powerful shape fingerprint for 3D molecular structures that outperforms graph-based molecular descriptors for drug-target binding affinity prediction, and what is the optimal filtration for biochemical data?", "status": "open", "fields": [ "mathematics", "computer_science", "chemistry", "biology" ], "color": "gray" }, { "id": "u-thermodynamics-convex-geometry-non-equilibrium", "type": "unknown", "title": "Does the Legendre-transform / convex-duality structure of equilibrium thermodynamics generalise to non-equilibrium steady states — and can contact 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"u-tropical-geometry-combinatorics", "type": "unknown", "title": "What is the precise relationship between tropical geometry and the combinatorics of polytopes and matroids?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-tropical-geometry-x-neural-networks", "type": "unknown", "title": "Can the tropical geometry of a trained neural network predict its generalisation error — specifically, does lower tropical hypersurface complexity (fewer linear regions) correlate with better generalisation in the overparameterised regime?", "status": "open", "fields": [ "mathematics", "computer_science" ], "color": "gray" }, { "id": "u-tsp-approximation-barrier-metric", "type": "unknown", "title": "Is the Christofides 3/2 approximation ratio for metric TSP optimal, or does a polynomial-time algorithm exist with a better worst-case guarantee — and what is the true approximation hardness threshold for metric TSP?\n", "status": "open", "fields": [ "mathematics", "computer-science", "combinatorial-optimization", "complexity-theory" ], "color": "gray" }, { "id": "u-turbulence-mathematical-formulation", "type": "unknown", "title": "Can turbulence in three-dimensional fluids be described by a complete mathematical theory?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-voting-theory-x-social-choice", "type": "unknown", "title": "Does the topological proof of Arrow's theorem (Baryshnikov 1993) generalize to continuous social choice functions on infinite voter populations, and does it predict specific voting paradox geometries that identify which real-world preference distributions are closest to Arrow impossibility?", "status": "open", "fields": [ "mathematics", "economics", "social-science" ], "color": "gray" }, { "id": "u-wasserstein-cell-fate-noise-geodesic-uniqueness", "type": "unknown", "title": "Are cell differentiation trajectories in Wasserstein space geodesically unique, and does transcriptional noise determine whether a progenitor has a definite fate or a mixed lineage distribution?", "status": "open", "fields": [ "mathematics", "developmental-biology", "optimal-transport", "single-cell-genomics", "stochastic-processes" ], "color": "gray" }, { "id": "u-wavelet-optimal-basis-nonstationary-signal-adaptation", "type": "unknown", "title": "What is the optimal adaptive wavelet basis for non-stationary signals with time-varying spectral content, and can a data-driven basis selection algorithm achieve provably minimax-optimal compression and denoising simultaneously?", "status": "open", "fields": [ "mathematics", "signal-processing", "statistics", "information-theory", "machine-learning" ], "color": "gray" }, { "id": "u-zipf-law-mechanism-adaptive-vs-null", "type": "unknown", "title": "Is Zipf's law (f_r ∝ r^{-1}) in natural language a consequence of adaptive communication efficiency (entropy maximisation under speaker-listener effort trade-off) or an inevitable consequence of any random partitioning process (the Miller monkey-typing null model)?\n", "status": "open", "fields": [ "linguistics", "information-theory", "cognitive-science", "statistical-physics" ], "color": "gray" }, { "id": "u-leslie-matrix-density-dependence-extension", "type": "unknown", "title": "Whether the Perron-Frobenius guarantee of a stable age distribution extends to density-dependent Leslie matrices (where vital rates depend on population size), and what replaces the dominant eigenvalue as the growth rate predictor in nonlinear structured population models\n", "status": "open", "fields": [ "mathematics", "population-biology", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-marginal-value-theorem-bandit-bridge", "type": "unknown", "title": "Can field foraging data be fit by index policies equivalent to Gittins or UCB rules under realistic patch renewal noise, and where do animals systematically deviate?", "status": "open", "fields": [ "ecology", "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-almgren-regularity-singular-set-sharp-dimension", "type": "unknown", "title": "Is Almgren's regularity theorem — that the singular set of an area-minimising current in ℝⁿ has Hausdorff dimension ≤ n-2 (and ≤ n-8 for hypersurfaces) — sharp, and what is the correct singular set dimension for physically relevant minimal surfaces in curved Riemannian manifolds?", "status": "open", "fields": [ "mathematics", "differential-geometry", "physics", "general-relativity", "PDE-theory" ], "color": "gray" }, { "id": "u-catastrophe-normal-form-completeness", "type": "unknown", "title": "Are Thom's seven elementary catastrophes truly complete for codimension ≤4, and do higher-codimension catastrophes appear in real physical or biological systems?", "status": "open", "fields": [ "mathematics", "catastrophe-theory", "dynamical-systems" ], "color": "gray" }, { "id": "u-chaos-quantum-correspondence-lyapunov-exponents", "type": "unknown", "title": "How does classical chaos emerge from quantum mechanics 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Goldstone theorem, and what their masses imply for phase transitions in novel materials\n", "status": "open", "fields": [ "mathematical-physics", "condensed-matter-physics", "group-theory" ], "color": "gray" }, { "id": "u-integrability-breaking-perturbations-soliton-stability-realistic-systems", "type": "unknown", "title": "How do integrability-breaking perturbations (higher-order dispersion, dissipation, noise, higher-dimensional geometry) degrade soliton stability and modify spreading speed in realistic physical systems — and is there a general perturbation theory for nearly-integrable systems that predicts the soliton lifetime and effective dynamics?\n", "status": "open", "fields": [ "mathematics", "physics", "applied-mathematics", "optics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-measure-theoretic-foundations-quantum-probability", "type": "unknown", "title": "Does quantum probability admit a rigorous measure-theoretic foundation that accounts for contextuality and 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transition reverts to second-order, and can this threshold be engineered to prevent catastrophic infrastructure cascades?\n", "status": "open", "fields": [ "network-science", "statistical-physics", "infrastructure-engineering", "mathematics" ], "color": "gray" }, { "id": "u-symplectic-quantization-gap", "type": "unknown", "title": "What is the precise mathematical relationship between symplectic geometry and quantum mechanics beyond deformation quantization, and when does geometric quantization fail to produce physically correct Hilbert spaces?\n", "status": "open", "fields": [ "mathematics", "symplectic-geometry", "quantum-mechanics", "mathematical-physics" ], "color": "gray" }, { "id": "u-auction-market-design-kidney-exchange", "type": "unknown", "title": "What market design mechanisms optimally match kidney donors and recipients to maximize lives saved while respecting equity and compatibility constraints?", "status": "partial", "fields": [ "mechanism-design", "medical-ethics", 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"type": "unknown", "title": "What auction mechanisms are simultaneously incentive-compatible, collusion-resistant, and computationally tractable for multi-item combinatorial settings?", "status": "open", "fields": [ "mechanism-design", "economics", "computer-science" ], "color": "gray" }, { "id": "u-truthful-elicitation-mechanism-duality", "type": "unknown", "title": "How large are truthfulness gains from VCG-style payments in real preference elicitation when participants have cognitive costs and incomplete understanding?", "status": "open", "fields": [ "economics", "computer-science" ], "color": "gray" }, { "id": "u-bayesian-imaging-inverse-problem-posterior-calibration", "type": "unknown", "title": "When do posterior uncertainty maps in inverse imaging remain calibrated under forward-model mismatch and sparse sensing?", "status": "open", "fields": [ "medical-imaging", "statistics", "inverse-problems" ], "color": "gray" }, { "id": "u-ddpm-mri-prior-mismatch-artifact-risk", "type": "unknown", "title": "When do diffusion-model priors introduce clinically harmful artifacts in accelerated MRI reconstruction?", "status": "open", "fields": [ "medical-imaging", "machine-learning" ], "color": "gray" }, { "id": "u-eit-fisher-information-electrode-geometry-optimality", "type": "unknown", "title": "Which electrode geometries and drive protocols maximize clinically relevant Fisher information in EIT under realistic contact uncertainty?", "status": "open", "fields": [ "medical-imaging", "inverse-problems", "statistics" ], "color": "gray" }, { "id": "u-persistent-homology-parameter-stability-noisy-microscopy", "type": "unknown", "title": "Which filtration and embedding choices make persistent-homology QC metrics stable across optics drift, staining variability, and finite sampling in fluorescence microscopy stacks?", "status": "open", "fields": [ "medical-imaging", "mathematics", "topology" ], "color": "gray" }, { "id": "u-alzheimer-causal-biomarkers", "type": "unknown", "title": "Which amyloid and tau species are causal versus correlational in Alzheimer's disease progression?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-ancestry-shift-sensitivity-of-elastic-net-prs", "type": "unknown", "title": "What ancestry shift levels destabilize `b-elastic-net-regularization-x-polygenic-risk-model-stability` calibration in prospective cohorts?", "status": "open", "fields": [ "medicine", "statistics" ], "color": "gray" }, { "id": "u-anesthesia-consciousness", "type": "unknown", "title": "By what mechanism do general anaesthetics suppress consciousness and could this inform theories of consciousness?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-antibiotic-resistance-rate", "type": "unknown", "title": "What determines the rate at which antibiotic resistance evolves in clinical settings?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-assimilation-window-stability-for-patient-specific-glucose-dynamics", "type": "unknown", "title": "What failure boundaries determine when `b-variational-data-assimilation-x-personalized-glucose-forecasting` remains decision-useful?", "status": "open", "fields": [ "control-engineering", "medicine" ], "color": "gray" }, { "id": "u-autoimmune-trigger-identification", "type": "unknown", "title": "What environmental triggers initiate autoimmune disease in genetically susceptible individuals?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-blood-brain-barrier-regulation", "type": "unknown", "title": "What molecular signals dynamically regulate blood-brain barrier permeability in health and disease?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cancer-immunoediting", "type": "unknown", "title": "How does tumour immunoediting shape the neoantigen landscape and what drives immune escape?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cancer-stem-cell-hierarchy", "type": "unknown", "title": "Is cancer stem cell hierarchy fixed or plastic and what determines transition between states?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-cardiac-regeneration-barriers", "type": "unknown", "title": "Why do adult mammalian cardiomyocytes fail to regenerate after myocardial infarction?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-chronic-pain-sensitization", "type": "unknown", "title": "What drives the transition from acute to chronic pain via central sensitization?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-control-barrier-formal-safety-under-sensor-lag", "type": "unknown", "title": "How robust are CBF safety guarantees under realistic CGM lag and meal-estimation errors?", "status": "open", "fields": [ "medicine", "control-engineering", "biomedical-engineering" ], "color": "gray" }, { "id": "u-dmri-tortuosity-effective-medium-identifiability", "type": "unknown", "title": "Which tissue or phantom tortuosity parameters are identifiable from diffusion MRI once 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"title": "What validation boundary conditions determine when `b-eikonal-wavefronts-x-cardiac-activation-mapping` remains decision-useful?", "status": "open", "fields": [ "geoscience", "medicine" ], "color": "gray" }, { "id": "u-ensemble-kalman-icu-parameter-identifiability", "type": "unknown", "title": "Which ICU physiological parameters remain unidentifiable under practical EnKF observation schedules?", "status": "open", "fields": [ "medicine", "control-engineering", "geoscience" ], "color": "gray" }, { "id": "u-first-passage-warning-times-clinical-deterioration-model-shift", "type": "unknown", "title": "Do first-passage formulations maintain calibrated warning lead-times under clinical covariate shift?", "status": "open", "fields": [ "mathematics", "medicine" ], "color": "gray" }, { "id": "u-flim-t2star-multiexp-phantom-transfer", "type": "unknown", "title": "Can harmonized multi-exponential fitting pipelines calibrated on fluorescence lifetime imaging phantoms reduce bias when 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"open", "fields": [ "medicine", "mathematics", "cardiology" ], "color": "gray" }, { "id": "u-immune-aging-rejuvenation", "type": "unknown", "title": "What drives age-related immune decline (immunosenescence) and can it be pharmacologically reversed?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-immunotherapy-nonresponders", "type": "unknown", "title": "Why do most solid tumour patients not respond to PD-1/PD-L1 checkpoint blockade?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-language-biomarker-clinical-validity", "type": "unknown", "title": "Which NLP-derived speech and language biomarkers have sufficient sensitivity, specificity, and longitudinal stability to meet regulatory standards for clinical use in Alzheimer's and psychiatric diagnosis?", "status": "open", "fields": [ "medicine", "computational-linguistics", "psychiatry", "clinical-neuroscience" ], "color": "gray" }, { "id": "u-long-covid-mechanism", "type": "unknown", "title": "What are the pathological mechanisms driving long COVID symptoms persisting beyond 12 weeks?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-lyapunov-guided-antibiotic-cycling-resistance-ecology", "type": "unknown", "title": "Can Lyapunov-guided antibiotic cycling remain effective under ecological feedback and heterogeneous patient compliance?", "status": "open", "fields": [ "control-engineering", "medicine" ], "color": "gray" }, { "id": "u-lymphatic-system-brain", "type": "unknown", "title": "How does the glymphatic-lymphatic system clear metabolic waste from the brain and what impairs this in aging?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-measurement-drift-effects-on-lasso-biomarker-sparsity", "type": "unknown", "title": "How robust is `b-lasso-sparsity-x-biomarker-panel-design` when assay drift perturbs low-abundance markers?", "status": "open", "fields": [ "medicine", "statistics" ], "color": "gray" }, { "id": "u-microbiome-mental-health", "type": "unknown", "title": "Does gut microbiome composition causally influence mental health outcomes via the gut-brain axis?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-morphogenetic-field-bioelectric-code", "type": "unknown", "title": "What is the complete bioelectric code — the mapping from tissue resting membrane potential patterns to anatomical outcomes — in planaria and vertebrate appendage regeneration, and can this code be read and rewritten pharmacologically to regenerate complex structures such as limbs in non-regenerative species?", "status": "open", "fields": [ "medicine", "developmental-biology", "biophysics" ], "color": "gray" }, { "id": "u-mrna-vaccine-durability", "type": "unknown", "title": "What limits the durability of immunity from mRNA vaccines and can formulation changes extend it?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-negative-control-selection-bias-pharmacovigilance-target-trials", "type": "unknown", "title": "Which negative-control libraries best calibrate confounding bias in target-trial-style pharmacovigilance studies?", "status": "open", "fields": [ "epidemiology", "statistics" ], "color": "gray" }, { "id": "u-neuroinflammation-psychiatric", "type": "unknown", "title": "What is the causal role of neuroinflammation in treatment-resistant psychiatric disorders?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-observation-operator-misspecification-in-ensemble-smoother-oncology-models", "type": "unknown", "title": "What validation boundary conditions determine when `b-ensemble-smoother-x-precision-oncology-state-estimation` remains decision-useful?", "status": "open", "fields": [ "geoscience", "medicine" ], "color": "gray" }, { "id": "u-organ-clock-synchrony", "type": "unknown", "title": "How do peripheral circadian clocks in organs synchronise with the central SCN clock and what disrupts this?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-organ-fibrosis-reversibility", "type": "unknown", "title": "Under what conditions is established organ fibrosis reversible rather than permanent?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-pain-sex-differences", "type": "unknown", "title": "What biological mechanisms underlie sex differences in pain sensitivity and chronic pain prevalence?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-pancreatic-beta-cell-exhaustion", "type": "unknown", "title": "What drives beta cell exhaustion in type 2 diabetes and can exhausted cells be functionally rescued?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-patient-specific-front-speed-estimation-in-wound-healing-kpp-models", "type": "unknown", "title": "What failure boundaries determine when `b-fisher-kpp-fronts-x-wound-healing-closure-forecasting` remains decision-useful?", "status": "open", "fields": [ "mathematical-biology", "medicine" ], "color": "gray" }, { "id": "u-placebo-neural-basis", "type": "unknown", "title": "What are the neural mechanisms mediating placebo analgesia and can they be therapeutically enhanced?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-prion-spread-pathway", "type": "unknown", "title": "By what cellular and intercellular mechanisms do prion proteins spread between neurons?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-prior-sensitivity-of-laplace-based-interim-decision-rules", "type": "unknown", "title": "What failure boundaries determine when `b-laplace-approximation-x-clinical-trial-adaptive-enrichment` remains decision-useful?", "status": "open", "fields": [ "statistics", "medicine" ], "color": "gray" }, { "id": "u-renewal-kernel-selection-readmission-burst-identifiability", "type": "unknown", "title": "Which renewal/self-excitation kernels are identifiable enough for reliable readmission burst forecasting across hospitals?", "status": "open", "fields": [ "medicine", "statistics" ], "color": "gray" }, { "id": "u-sepsis-heterogeneity", "type": "unknown", "title": "What molecular subtypes of sepsis determine differential responses to standard-of-care treatment?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-spinal-cord-complete-repair", "type": "unknown", "title": "Is complete motor function recovery after complete spinal cord injury achievable and what barriers remain?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-stain-variation-failure-modes-for-unet-histopathology-segmentation", "type": "unknown", "title": "What stain and scanner shifts break `b-unet-segmentation-x-histopathology-quantification-workflows` in multi-site deployment?", "status": "open", "fields": [ "medicine", "computer-science" ], "color": "gray" }, { "id": "u-state-representation-gaps-for-hjb-guided-adaptive-radiotherapy", "type": "unknown", "title": "What validation boundary conditions determine when `b-hamilton-jacobi-bellman-x-adaptive-radiotherapy` remains decision-useful?", "status": "open", "fields": [ "control-engineering", "medicine" ], "color": "gray" }, { "id": "u-stem-cell-niche-regulation", "type": "unknown", "title": "What signals maintain adult stem cell quiescence and what triggers activation for tissue repair?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-tbi-repair-limits", "type": "unknown", "title": "What limits neurological recovery after severe traumatic brain injury and what interventions cross this limit?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-telomere-aging-causality", "type": "unknown", "title": "Does telomere shortening causally drive aging or is it a biomarker of other aging processes?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-topological-biomarker-robustness-across-wearables", "type": "unknown", "title": "Are topology-based arrhythmia biomarkers robust across consumer and clinical wearable devices?", "status": "open", "fields": [ "medicine", "topology", "signal-processing" ], "color": "gray" }, { "id": "u-transformer-ehr-long-horizon-attribution-validity", "type": "unknown", "title": "Are long-horizon attention attributions in EHR transformers clinically valid across institutions?", "status": "open", "fields": [ "medicine", "machine-learning" ], "color": "gray" }, { "id": "u-tumor-evolution-topology-branching", "type": "unknown", "title": "Does the topology (Betti numbers β₀, β₁) of tumor copy-number variation space predict clinical outcomes — specifically, does β₁ > 0 (presence of evolutionary loops, indicating homologous recombination or convergent evolution) correlate with worse prognosis and resistance to targeted therapy?\n", "status": "open", "fields": [ "medicine", "oncology", "mathematics", "computational-biology" ], "color": "gray" }, { "id": "u-type2-diabetes-reversal", "type": "unknown", "title": "What is the mechanism of type 2 diabetes remission following bariatric surgery or very low calorie diets?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-vaccine-adjuvant-mechanism", "type": "unknown", "title": "How do vaccine adjuvants enhance adaptive immune responses at the cellular and molecular level?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-fano-metamaterial-dark-mode-q-engineering", "type": "unknown", "title": "For coupled radiative–subradiant metamolecule designs, which systematic design rules raise loaded Q while keeping mode overlap robust to fabrication disorder — beyond post-hoc Fano fitting of transmission spectra?\n", "status": "open", "fields": [ "optics", "electromagnetism" ], "color": "gray" }, { "id": "u-atmospheric-blocking-climate-change-frequency", "type": "unknown", "title": "Will climate change increase or decrease the frequency and persistence of atmospheric blocking events in the Northern Hemisphere, and what is the dominant physical mechanism driving any change?\n", "status": "open", "fields": [ "meteorology", "fluid-mechanics", "climate-science" ], "color": "gray" }, { "id": "u-atmospheric-predictability-limit-extended", "type": "unknown", "title": "Is the 10-14 day weather predictability limit fundamental (set by the Lyapunov exponent of atmospheric dynamics) or a technological barrier that could be extended with better initial conditions and models?", "status": "open", "fields": [ "meteorology", "dynamical-systems", "computational-fluid-dynamics" ], "color": "gray" }, { "id": "u-biofilm-viscoelasticity-dispersal-trigger", "type": "unknown", "title": "What mechanical or chemical signal triggers the switch from biofilm viscoelastic solid (attached persistence) to fluid (active dispersal), and can the yield stress and viscoelastic transition of EPS networks be manipulated pharmacologically to prevent biofilm formation on medical devices?", "status": "open", "fields": [ "microbiology", "materials-science", "biophysics", "medicine" ], "color": "gray" }, { "id": "u-microbial-mineral-weathering-rate-in-situ", "type": "unknown", "title": "What fraction of in situ mineral weathering rates in soils and sediments is directly attributable to microbial activity versus abiotic dissolution, and how does this partition vary with mineralogy and redox conditions?\n", "status": "open", "fields": [ "microbiology", "geochemistry" ], "color": "gray" }, { "id": "u-parameter-regimes-where-lotka-volterra-surrogates-fail-for-phage-bacteria-chemostats", "type": "unknown", "title": "What validation boundary conditions determine when `b-lotka-volterra-competition-x-phage-bacteria-chemostat-control` remains decision-useful?", "status": "open", "fields": [ "microbiology", "mathematics" ], "color": "gray" }, { "id": "u-persister-cell-switching-rates-clinical", "type": "unknown", "title": "What are the switching rates alpha and beta for persister formation in clinically relevant bacterial species (S. aureus, E. coli, P. aeruginosa) under different antibiotic and stress conditions, and can these rates predict treatment failure in recurrent infections?\n", "status": "open", "fields": [ "microbiology", "mathematics", "medicine" ], "color": "gray" }, { "id": "u-sindy-library-selection-bias-in-host-pathogen-inference", "type": "unknown", "title": "How sensitive is `b-sindy-sparse-discovery-x-host-pathogen-dynamics` to candidate library misspecification in realistic host-pathogen datasets?", "status": "open", "fields": [ "microbiology", "mathematics" ], "color": "gray" }, { "id": "u-droplet-microfluidics-cell-viability-encapsulation-efficiency", "type": "unknown", "title": "What are the physical and biochemical mechanisms of cell viability loss during droplet microfluidics encapsulation ΓÇö and can encapsulation efficiency and viability be simultaneously optimized for rare cell types (circulating tumor cells, primary neurons) where cell loss is critical?\n", "status": "open", "fields": [ "microfluidics", "biomedical-engineering", "cell-biology", "biophysics" ], "color": "gray" }, { "id": "u-attention-head-interpretability-in-protein-language-models", "type": "unknown", "title": "Which attention patterns in `b-transformer-attention-x-protein-language-model-fitness-prediction` are mechanistically meaningful versus spurious?", "status": "open", "fields": [ "molecular-biology", "computer-science" ], "color": "gray" }, { "id": "u-riboswitch-cotranscriptional-folding-kinetics", "type": "unknown", "title": "How does cotranscriptional folding kinetics determine riboswitch switching accuracy in vivo, and can the switching threshold be quantitatively predicted from measured aptamer K_d and RNA polymerase elongation rate?\n", "status": "open", "fields": [ "molecular-biology", "biophysics" ], "color": "gray" }, { "id": "u-when-does-alternating-projection-outperform-em-in-cryoem-orientation-inference", "type": "unknown", "title": "What validation boundary conditions determine when `b-phase-retrieval-x-cryoem-orientation-inference` remains decision-useful?", "status": "open", "fields": [ "signal-processing", "structural-biology" ], "color": "gray" }, { "id": "u-mycelial-network-optimization-principle", "type": "unknown", "title": "Does mycelial network topology represent a Pareto-optimal trade-off between transport cost minimization and fault tolerance, and what developmental rule (local or global) generates this near-optimal topology without centralized coordination?", "status": "open", "fields": [ "mycology", "mathematics", "network-science" ], "color": "gray" }, { "id": "u-gcn-transmission-edge-direction-identifiability", "type": "unknown", "title": "Can graph-convolution models reliably recover directionality in pathogen transmission networks under sparse observations?", "status": "open", "fields": [ "network-science", "infectious-disease", "machine-learning" ], "color": "gray" }, { "id": "u-hyperbolic-embeddings-hierarchy-identifiability", "type": "unknown", "title": "When can we reliably infer that empirical graph data require hyperbolic rather than Euclidean embedding from finite noisy samples?", "status": "open", "fields": [ "mathematics", "computer-science", "network-science" ], "color": "gray" }, { "id": "u-interdependent-network-restoration-dynamics", "type": "unknown", "title": "How do repair and restoration processes (human responders, automated rerouting) modify the cascade failure threshold in interdependent infrastructure networks?", "status": "open", "fields": [ "network-science", "engineering", "complex-systems" ], "color": "gray" }, { "id": "u-structural-holes-dynamics-network-evolution-brokerage-persistence", "type": "unknown", "title": "How stable are structural holes and brokerage positions over time in real social and organizational networks ΓÇö do brokers maintain their advantage as networks evolve, or do structural holes close as bridged groups discover each other through the broker?\n", "status": "open", "fields": [ "network-science", "sociology", "organizational-behavior", "economics" ], "color": "gray" }, { "id": "u-state-dependent-phase-response-model-drift-in-adaptive-dbs", "type": "unknown", "title": "What failure boundaries determine when `b-phase-response-curves-x-adaptive-deep-brain-stimulation-timing` remains decision-useful?", "status": "open", "fields": [ "control-engineering", "neurology" ], "color": "gray" }, { "id": "u-neuroprosthetic-decoder-long-term-stability-mechanisms", "type": "unknown", "title": "What are the relative contributions of electrode impedance increase, neuronal loss from glial encapsulation, neural plasticity, and decoder parameter drift to the long-term performance degradation of intracortical neuroprosthetic decoders, and which failure mechanism should adaptive algorithms prioritise?\n", "status": "open", "fields": [ "neuroprosthetics", "biomedical-engineering", "computational-neuroscience", "neuroscience" ], "color": "gray" }, { "id": "u-adult-human-hippocampal-neurogenesis-existence-rate-controversy", "type": "unknown", "title": "Does adult human hippocampal neurogenesis persist at a functionally significant rate (>100 neurons/day) in healthy adults over 30, and what methodological factors explain the discrepancy between Spalding et al. (2013) radiocarbon dating (~700/day) and Sorrells et al. (2018) immunohistochemistry (near zero in adults)?\n", "status": "open", "fields": [ "neuroscience", "molecular-biology", "chemistry", "psychiatry" ], "color": "gray" }, { "id": "u-attention-neural-mechanisms", "type": "unknown", "title": "What are the neural circuit mechanisms by which top-down attention selects and enhances sensory signals, and does attention act by gain, timing, or noise reduction?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "systems-neuroscience" ], "color": "gray" }, { "id": "u-axon-soliton-collision-dynamics", "type": "unknown", "title": "Do action potentials at axon branch points interact according to soliton collision rules, and can soliton perturbation theory predict conduction failure thresholds?\n", "status": "open", "fields": [ "neuroscience", "physics", "mathematics" ], "color": "gray" }, { "id": "u-bayesian-brain-prior-encoding", "type": "unknown", "title": "How are Bayesian priors physically encoded in neural circuits, and how do they change with learning?", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "cognitive-science", "statistics" ], "color": "gray" }, { "id": "u-biological-backpropagation-mechanism", "type": "unknown", "title": "How does the brain implement something functionally equivalent to backpropagation for credit assignment across multi-layer circuits without a global error signal?", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "machine-learning" ], "color": "gray" }, { "id": "u-brain-criticality-universality-class", "type": "unknown", "title": "What is the universality class of the critical point at which the brain operates — mean-field branching process, directed percolation, or another universality class — and does this class vary across brain regions, species, or cognitive states?\n", "status": "open", "fields": [ "neuroscience", "statistical-physics", "condensed-matter-physics", "computational-neuroscience" ], "color": "gray" }, { "id": "u-brain-organoid-validity", "type": "unknown", "title": "How well do cerebral organoids model human brain development and disease, and what are the limits of their validity as research models?", "status": "open", "fields": [ "neuroscience", "developmental-biology", "stem-cell-biology" ], "color": "gray" }, { "id": "u-cerebellar-prediction-coding", "type": "unknown", "title": "Does the cerebellum implement a forward model for predicting sensory consequences of motor commands, and can this explain cerebellar involvement in non-motor functions?", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "motor-control" ], "color": "gray" }, { "id": "u-cerebellum-cognitive-function", "type": "unknown", "title": "What cognitive functions does the cerebellum perform beyond motor coordination, and what is its computational principle?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "systems-neuroscience" ], "color": "gray" }, { "id": "u-circadian-desynchrony-disease-mechanisms", "type": "unknown", "title": "What are the causal mechanisms linking chronic circadian desynchrony (shift work, social jet lag) to metabolic syndrome, cancer, and neurodegeneration?", "status": "open", "fields": [ "chronobiology", "neuroscience", "medicine", "epidemiology" ], "color": "gray" }, { "id": "u-connectome-neurodegeneration-spread-rate", "type": "unknown", "title": "What determines the rate and trajectory of trans-synaptic pathological protein spread through the connectome in Alzheimer's and Parkinson's disease?", "status": "open", "fields": [ "neuroscience", "medicine", "network-science" ], "color": "gray" }, { "id": "u-connectome-spectral-laplacian", "type": "unknown", "title": "What is the mechanistic link between individual differences in connectome Laplacian eigenspectrum and variability in cognitive performance, and how does the spectral gap change with learning?", "status": "open", "fields": [ "neuroscience", "network-science", "mathematics" ], "color": "gray" }, { "id": "u-consciousness-binding-problem", "type": "unknown", "title": "How does the brain bind spatially and temporally distributed neural signals into a unified conscious percept — is the binding mechanism synchrony, convergence zones, or a global workspace?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "philosophy-of-mind" ], "color": "gray" }, { "id": "u-consciousness-information-integration-scale", "type": "unknown", "title": "At what spatial and temporal scale does information integration relevant to consciousness occur in the brain, and how does this scale relate to clinical consciousness loss?", "status": "open", "fields": [ "consciousness-science", "neuroscience", "philosophy-of-mind" ], "color": "gray" }, { "id": "u-default-mode-network-function", "type": "unknown", "title": "What is the functional role of the default mode network, and why is it suppressed during externally directed tasks?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "neuroimaging" ], "color": "gray" }, { "id": "u-dopamine-prediction-error-temporal-credit", "type": "unknown", "title": "Does dopaminergic prediction error signaling implement temporal-difference learning exactly, and how does it solve the temporal credit assignment problem for rewards separated by minutes?", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "reinforcement-learning" ], "color": "gray" }, { "id": "u-eeg-source-localization-skull-conductivity", "type": "unknown", "title": "How much does inter-individual variability in skull electrical conductivity degrade EEG source localization accuracy, and can in vivo conductivity imaging sufficiently correct for this?\n", "status": "open", "fields": [ "neuroscience", "physics" ], "color": "gray" }, { "id": "u-efficient-coding-bottleneck-tradeoff-measurability", "type": "unknown", "title": "When can information-bottleneck curves fitted from artificial neural networks be compared fairly to empirical sufficiency–complexity tradeoffs measured from biological sensory circuits?", "status": "open", "fields": [ "neuroscience", "computer-science", "machine-learning" ], "color": "gray" }, { "id": "u-efficient-coding-metabolic-optimality", "type": "unknown", "title": "Does the visual cortex actually operate at or near its Shannon channel capacity for natural images, and what is the quantitative metabolic efficiency (bits transmitted per ATP molecule) of early visual processing?\n", "status": "open", "fields": [ "neuroscience", "information-theory", "biophysics", "computational-neuroscience" ], "color": "gray" }, { "id": "u-ei-balance-diversity-robustness", "type": "unknown", "title": "How does the diversity of inhibitory interneuron subtypes contribute to cortical circuit stability, and does neural heterogeneity follow the same diversity-stability relationship as ecological communities?", "status": "open", "fields": [ "neuroscience", "systems-neuroscience", "computational-neuroscience", "ecology" ], "color": "gray" }, { "id": "u-emotion-discrete-vs-constructed", "type": "unknown", "title": "Are emotions discrete natural kinds with distinct neural signatures, or are they constructed from more basic affective and cognitive building blocks?", "status": "open", "fields": [ "neuroscience", "psychology", "affective-science" ], "color": "gray" }, { "id": "u-engram-molecular-basis", "type": "unknown", "title": "What are the molecular and synaptic mechanisms that store specific memories in engram cells, and how are they maintained over decades?", "status": "open", "fields": [ "neuroscience", "cellular-neuroscience", "molecular-biology" ], "color": "gray" }, { "id": "u-fmri-connectivity-graphical-model-validity", "type": "unknown", "title": "Do partial correlation graphical models estimated from resting-state fMRI BOLD signals accurately reflect direct synaptic connectivity, and what sources of confounding (hemodynamic variability, motion artifacts, physiological noise) most severely compromise the validity of functional connectivity estimates?", "status": "open", "fields": [ "neuroscience", "statistics", "neuroscience-methods" ], "color": "gray" }, { "id": "u-glial-cell-computation", "type": "unknown", "title": "Do astrocytes perform genuine computational operations on neural signals through tripartite synapses, or is gliotransmission a modulatory background process?", "status": "open", "fields": [ "neuroscience", "glial-biology", "computational-neuroscience" ], "color": "gray" }, { "id": "u-glymphatic-flow-impairment-alzheimers", "type": "unknown", "title": "Does glymphatic system impairment causally drive amyloid-β and tau accumulation in Alzheimer's disease, and what interventions can restore glymphatic clearance in aging brains?", "status": "open", "fields": [ "neuroscience", "fluid-dynamics", "neurology", "medicine" ], "color": "gray" }, { "id": "u-grid-cell-cognitive-map-geometry", "type": "unknown", "title": "Do grid cells provide a universal metric coordinate system for abstract cognitive spaces beyond spatial navigation, and what determines the hexagonal grid scale?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-grid-cell-fourier-basis-navigation", "type": "unknown", "title": "Does the multi-scale grid cell system implement an optimal Fourier basis for encoding spatial position with minimum neurons, and what is the theoretical lower bound on the number of grid modules needed for unique position encoding across an environment without ambiguity?", "status": "open", "fields": [ "neuroscience", "mathematics", "cognitive-science" ], "color": "gray" }, { "id": "u-hair-cell-bundle-x-hopf-bifurcation", "type": "unknown", "title": "How does the inner ear maintain tuning of individual hair cells at the Hopf bifurcation across the full auditory frequency range (20 Hz to 20 kHz), and what active feedback mechanisms set the bifurcation parameter?\n", "status": "open", "fields": [ "neuroscience", "physics", "biophysics" ], "color": "gray" }, { "id": "u-hawkes-branching-ratio-seizure-cascade-threshold", "type": "unknown", "title": "Is there a reproducible Hawkes branching-ratio threshold for impending seizure clusters?", "status": "open", "fields": [ "neuroscience", "statistics", "seismology" ], "color": "gray" }, { "id": "u-hippocampal-replay-sequence-selection-criteria", "type": "unknown", "title": "By what mechanism does the hippocampus select which waking experience sequences are replayed during sharp-wave ripples, and what determines whether replay is forward or reverse?\n", "status": "open", "fields": [ "neuroscience", "cognitive-science" ], "color": "gray" }, { "id": "u-hodgkin-huxley-channel-heterogeneity-neuron-diversity", "type": "unknown", "title": "How do differences in ion channel composition and density across neuron types produce the diverse firing patterns observed experimentally, and can a unified conductance-based framework predict firing phenotype from transcriptomic channel expression data?", "status": "open", "fields": [ "neuroscience", "biophysics", "computational-neuroscience" ], "color": "gray" }, { "id": "u-holographic-memory-neural-phase-encoding-test", "type": "unknown", "title": "Can the holographic memory model make quantitatively distinct predictions from localist or sparse coding memory models that are testable with current neural recording technology?\n", "status": "open", "fields": [ "neuroscience", "optics" ], "color": "gray" }, { "id": "u-hopfield-capacity-cortical-memory", "type": "unknown", "title": "Does the human cortex implement associative memory consistent with Hopfield-network capacity limits, and what neural architecture enables storage approaching the modern Hopfield exponential bound?", "status": "open", "fields": [ "neuroscience", "mathematics", "machine-learning", "memory-theory" ], "color": "gray" }, { "id": "u-identifiability-of-hysteresis-biomarkers-in-neurofatigue-monitoring", "type": "unknown", "title": "What failure boundaries determine when `b-hysteresis-loop-area-x-neural-fatigue-recovery-dynamics` remains decision-useful?", "status": "open", "fields": [ "neuroscience", "control-theory" ], "color": "gray" }, { "id": "u-insect-navigation-path-integration", "type": "unknown", "title": "How does the insect central complex ring attractor circuit implement the nonlinear noise correction that prevents path integration error from accumulating faster than the √L random-walk prediction?", "status": "open", "fields": [ "neuroscience", "robotics", "computational-neuroscience" ], "color": "gray" }, { "id": "u-interoception-consciousness-link", "type": "unknown", "title": "How does interoceptive signalling from body organs contribute to emotional experience and conscious self-awareness?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "affective-neuroscience" ], "color": "gray" }, { "id": "u-intrinsic-motivation-information-maximization", "type": "unknown", "title": "Does the brain implement empowerment maximization (maximising mutual information between actions and future states) as the computational substrate for intrinsic motivation, and can this be detected as a distinctive neural signature that predicts autonomy need satisfaction independent of reward?", "status": "open", "fields": [ "neuroscience", "information-theory", "cognitive-science", "psychology" ], "color": "gray" }, { "id": "u-ion-channel-barrier-heights-from-multiscale-md-posteriors", "type": "unknown", "title": "Can atomistic molecular dynamics posteriors on ion-channel voltage-sensor conformations be compressed into effective two-state barrier heights whose voltage dependence matches patch-clamp τ(V) curves within experimental uncertainty?", "status": "open", "fields": [ "neuroscience", "chemistry" ], "color": "gray" }, { "id": "u-kramers-drift-diffusion-barrier-mapping-neural-decisions", "type": "unknown", "title": "Can Kramers-style barrier and noise parameters provide useful priors or diagnostics for drift-diffusion decision models without overextending the chemical-kinetics analogy?\n", "status": "open", "fields": [ "neuroscience", "chemistry", "statistical-physics" ], "color": "gray" }, { "id": "u-landauer-limit-neuronal-computation", "type": "unknown", "title": "How close does neuronal computation operate to the Landauer thermodynamic limit, and does evolutionary pressure drive neural circuits toward thermodynamic efficiency?\n", "status": "open", "fields": [ "neuroscience", "physics", "thermodynamics", "information-theory" ], "color": "gray" }, { "id": "u-lstm-gating-biological-analogue", "type": "unknown", "title": "Does the prefrontal cortex implement a biological analogue of LSTM gating where neuromodulatory signals (dopamine, acetylcholine) set an effective temperature controlling the persistence versus updating of working memory representations?", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "statistical-mechanics" ], "color": "gray" }, { "id": "u-meg-inverse-source-localization", "type": "unknown", "title": "What regularization method for MEG inverse source localization best recovers distributed vs focal neural activity simultaneously, and can deep learning architectures trained on realistic head models outperform classical minimum-norm and beamformer approaches for clinical seizure localization?", "status": "open", "fields": [ "neuroscience", "mathematics", "biomedical-engineering" ], "color": "gray" }, { "id": "u-meg-inverse-source-nonunique-regularization-bounds", "type": "unknown", "title": "What quantitative bounds link SQUID array layout, sensor noise, and regularization choice to worst-case localization error for distributed cortical sources in realistic head models?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-mirror-neuron-function", "type": "unknown", "title": "Do mirror neurons in humans implement action understanding through motor simulation, or is their activation an epiphenomenon of learned sensorimotor associations?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "social-neuroscience" ], "color": "gray" }, { "id": "u-motor-cortex-population-dynamics-motor-programs", "type": "unknown", "title": "Are the rotational dynamics in motor cortex (Churchland 2012) a universal feature of all voluntary movements, and does the low-dimensional neural manifold represent an abstract motor program or a specific muscle activation pattern — and how does the manifold change during motor learning?\n", "status": "open", "fields": [ "neuroscience", "mathematics", "motor-control" ], "color": "gray" }, { "id": "u-myelination-conduction-velocity-optimality", "type": "unknown", "title": "Is the observed g-ratio (myelin thickness / axon diameter ≈ 0.6) of mammalian myelinated axons truly optimal for maximising conduction velocity per unit axon volume, and does the cable-equation prediction match measured g-ratios across species and fibre types?\n", "status": "open", "fields": [ "neuroscience", "biophysics", "mathematics" ], "color": "gray" }, { "id": "u-neural-binding-mechanism-synchrony", "type": "unknown", "title": "Is gamma-band synchrony a causal mechanism for neural binding or an epiphenomenon of shared input, and what alternative mechanisms could solve the binding problem?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "physics" ], "color": "gray" }, { "id": "u-neural-criticality-consciousness-substrate", "type": "unknown", "title": "Is neural criticality (operation near a second-order phase transition) a necessary condition for conscious information integration, or merely an optimisation of computational performance?", "status": "open", "fields": [ "consciousness-science", "computational-neuroscience", "statistical-physics" ], "color": "gray" }, { "id": "u-neural-criticality-tipping-shared-mathematics", "type": "unknown", "title": "Do neural systems and climate systems share the same universality class of bifurcation, making early-warning indicator methods developed in one field directly transferable to the other with quantitatively predictable performance?\n", "status": "open", "fields": [ "neuroscience", "climate-science", "statistical-physics", "dynamical-systems" ], "color": "gray" }, { "id": "u-neural-decay-poisson-deviation-shared-overdispersion-tests", "type": "unknown", "title": "Which residual diagnostics best separate true Poisson counting from detector dead-time, nonstationary rate drift, and bursty history dependence across radioactive-decay and neural spike-train datasets?\n", "status": "open", "fields": [ "neuroscience", "physics", "statistics" ], "color": "gray" }, { "id": "u-neural-field-theory-empirical-connectome-validation", "type": "unknown", "title": "Do the eigenmodes of the empirical human connectome structural Laplacian quantitatively predict the spatial and temporal structure of resting-state fMRI networks and EEG frequency bands across individuals?", "status": "open", "fields": [ "computational-neuroscience", "neuroimaging", "mathematical-physics" ], "color": "gray" }, { "id": "u-neural-manifold-dimensionality-behavior", "type": "unknown", "title": "What determines the intrinsic dimensionality of neural population activity manifolds, and does manifold geometry encode behaviorally meaningful variables beyond firing rates?", "status": "open", "fields": [ "computational-neuroscience", "systems-neuroscience", "machine-learning", "mathematics" ], "color": "gray" }, { "id": "u-neural-manifold-topology-cognitive-states", "type": "unknown", "title": "What is the topology of neural population activity manifolds during higher cognitive tasks (decision-making, working memory, abstract reasoning)?", "status": "open", "fields": [ "neuroscience", "mathematics", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-neural-optimal-control-noise-model", "type": "unknown", "title": "What is the correct noise model for biological motor control — signal-dependent (multiplicative) noise as in Todorov's OFC model, or state-dependent noise from neural population variability — and does the choice change predictions for motor rehabilitation and brain-computer interface design?\n", "status": "open", "fields": [ "computational-neuroscience", "control-engineering", "motor-neuroscience", "biomedical-engineering" ], "color": "gray" }, { "id": "u-neural-plasticity-x-hebbian-learning", "type": "unknown", "title": "Does spike-timing dependent plasticity (STDP) implement exact gradient descent on a well-defined loss function in recurrent neural circuits, and can this be exploited to train spiking neural networks on cognitive tasks without explicit backpropagation?", "status": "open", "fields": [ "neuroscience", "computer_science", "biology" ], "color": "gray" }, { "id": "u-neural-spike-coding-x-information-compression", "type": "unknown", "title": "How does the brain maintain efficient coding of natural scenes across adaptation, attention, and changes in stimulus statistics, and what plasticity rules implement online information maximization?\n", "status": "open", "fields": [ "neuroscience", "computer-science", "information-theory" ], "color": "gray" }, { "id": "u-neuroinflammation-depression-causality", "type": "unknown", "title": "Does neuroinflammation causally drive major depressive disorder, and can anti-inflammatory treatments reliably treat depression in inflammation-high patients?", "status": "open", "fields": [ "neuroscience", "psychiatry", "immunology" ], "color": "gray" }, { "id": "u-neuronal-avalanche-soc-universality-class", "type": "unknown", "title": "What is the correct universality class of cortical neuronal avalanches, and does the brain operate at a true critical point or in a near-critical driven regime with sub-sampling artefacts masking the true exponents?\n", "status": "open", "fields": [ "neuroscience", "statistical-physics" ], "color": "gray" }, { "id": "u-neuronal-avalanches-branching-process", "type": "unknown", "title": "Is cortical criticality (branching parameter σ=1) a robustly maintained operating point or an epiphenomenon of anesthesia and slice preparation artifacts, and what homeostatic mechanism sets σ?", "status": "open", "fields": [ "neuroscience", "statistical-physics", "probability" ], "color": "gray" }, { "id": "u-neuroplasticity-adult-limits", "type": "unknown", "title": "What are the molecular and circuit mechanisms that limit neuroplasticity in the adult brain, and how can they be safely reopened?", "status": "open", "fields": [ "neuroscience", "cellular-neuroscience", "clinical-neuroscience" ], "color": "gray" }, { "id": "u-neurovascular-coupling-x-fluid-dynamics", "type": "unknown", "title": "What determines the spatial spread of the hemodynamic response in fMRI, and how does vessel architecture limit the resolution of neurovascular coupling?\n", "status": "open", "fields": [ "neuroscience", "physics", "fluid_mechanics", "biophysics" ], "color": "gray" }, { "id": "u-optogenetics-x-control-theory", "type": "unknown", "title": "What is the fundamental bandwidth limit of closed-loop optogenetic control imposed by channelrhodopsin kinetics, and can model predictive control overcome this limit for oscillatory neural dynamics?", "status": "open", "fields": [ "neuroscience", "computer_science", "engineering" ], "color": "gray" }, { "id": "u-pain-gate-control-molecular-basis", "type": "unknown", "title": "What is the molecular identity of the 'gate' in Melzack-Wall gate control theory, and does it correspond to a specific interneuron type in the dorsal horn?", "status": "open", "fields": [ "neuroscience", "pain-research", "molecular-biology" ], "color": "gray" }, { "id": "u-persistent-homology-neural-manifold-geometry-vs-topology-decoupling", "type": "unknown", "title": "Can persistent homology reliably distinguish the topology of neural population manifolds from their geometry — specifically, can it detect a change in manifold topology (e.g. torus → sphere during learning) independently of changes in manifold curvature, dimensionality, or firing field shape?\n", "status": "open", "fields": [ "computational-neuroscience", "algebraic-topology", "mathematics", "systems-neuroscience" ], "color": "gray" }, { "id": "u-phi-measurement-neural-correlates", "type": "unknown", "title": "Can integrated information (Phi) be practically measured in neural systems, and do its values correlate with empirical markers of conscious level?", "status": "open", "fields": [ "neuroscience", "philosophy-of-mind", "consciousness-science" ], "color": "gray" }, { "id": "u-predictive-coding-laminar-circuit-mechanism", "type": "unknown", "title": "What specific cortical circuit elements implement prediction error computation in layers 2/3 and prediction propagation in layer 6, and do these circuits show the differential response properties predicted by the free energy principle?\n", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "Bayesian-inference" ], "color": "gray" }, { "id": "u-predictive-coding-neural-implementation-evidence", "type": "unknown", "title": "What is the direct experimental evidence that cortical circuits implement hierarchical predictive coding, specifically: do superficial-layer pyramidal cells carry prediction errors (bottom-up) and deep-layer pyramidal cells carry predictions (top-down), and can single-cell recordings during violations of sensory predictions validate these layer-specific functional assignments?\n", "status": "open", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-prefrontal-working-memory-mechanism", "type": "unknown", "title": "Is prefrontal cortex working memory maintained by persistent firing (attractor dynamics) or by activity-silent synaptic weight changes — and are these mechanisms complementary or mutually exclusive?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-prion-like-spread-neurodegeneration-circuit-specificity", "type": "unknown", "title": "What determines the circuit-specific vulnerability and spreading pattern of prion-like protein aggregates in neurodegeneration — connectivity, local protein expression, or regional metabolic activity?", "status": "open", "fields": [ "neuroscience", "molecular-biology", "cell-biology", "neuropathology" ], "color": "gray" }, { "id": "u-ptlds-neuroinflammation-self-sustaining", "type": "unknown", "title": "Is PTLDS cognitive impairment caused by a self-sustaining reactive astrogliosis and microglial activation loop that persists after Borrelia clearance, and if so, would anti-neuroinflammatory therapy be more effective than repeat antibiotics?\n", "status": "open", "fields": [ "neuroscience", "immunology", "neuroimmunology", "infectious-disease", "clinical-medicine" ], "color": "gray" }, { "id": "u-reconsolidation-synaptic-locus-ampa-receptor", "type": "unknown", "title": "At which specific synapses does AMPA receptor endocytosis occur during memory labilisation after retrieval, and is reconsolidation synapse-specific or distributed across the encoding ensemble?\n", "status": "open", "fields": [ "neuroscience", "molecular-biology", "cell-biology" ], "color": "gray" }, { "id": "u-retinal-wave-spatial-statistics-map-precision", "type": "unknown", "title": "What determines the spatial statistics (correlation length, wave-initiation density) of retinal waves, and is there an information-theoretic optimum that matches the precision of mature retinotopic maps?\n", "status": "open", "fields": [ "developmental-neuroscience", "information-theory", "systems-biology" ], "color": "gray" }, { "id": "u-rl-novelty-bonus-information-gain-mapping", "type": "unknown", "title": "Which experimentally measurable neural quantities align with count-based novelty bonuses versus Bayesian information-gain objectives across tasks — without confounding reward prediction error and uncertainty reduction?\n", "status": "open", "fields": [ "neuroscience", "reinforcement-learning" ], "color": "gray" }, { "id": "u-signal-sparsity-neural-coding", "type": "unknown", "title": "Are neural population codes sparse in some transform basis (Fourier, wavelet, learned dictionary), and does sparsity optimize energy consumption vs. information capacity?", "status": "open", "fields": [ "computational-neuroscience", "information-theory", "signal-processing" ], "color": "gray" }, { "id": "u-sleep-memory-consolidation-mechanism", "type": "unknown", "title": "Does sleep memory consolidation occur through hippocampal-cortical replay during sharp-wave ripples, and is this a necessary condition or a correlate of consolidation?", "status": "open", "fields": [ "neuroscience", "cognitive-science", "systems-neuroscience" ], "color": "gray" }, { "id": "u-sleep-replay-causal-role-memory-specificity", "type": "unknown", "title": "Does hippocampal sharp-wave ripple replay during sleep causally strengthen specific memory traces, and can targeted memory reactivation selectively enhance or suppress individual memories?", "status": "open", "fields": [ "systems-neuroscience", "sleep-science", "cognitive-neuroscience" ], "color": "gray" }, { "id": "u-snare-complex-partial-zippering-spontaneous-release-rate", "type": "unknown", "title": "What fraction of synaptic vesicles undergo partial SNARE complex zippering (the \"primed\" state) at rest, and how does the thermodynamic energy landscape of SNARE zippering determine the spontaneous miniature release rate (mEPSP frequency)?", "status": "open", "fields": [ "neuroscience", "biophysics", "biochemistry", "physical-chemistry", "molecular-biology" ], "color": "gray" }, { "id": "u-snare-force-threshold-in-vivo", "type": "unknown", "title": "What is the minimum SNARE zippering force required to trigger synaptic vesicle fusion in intact synaptic terminals, and how does this threshold vary across synapse types?\n", "status": "open", "fields": [ "neuroscience", "biophysics" ], "color": "gray" }, { "id": "u-softmax-attention-cortical-normalization-mapping", "type": "unknown", "title": "Which quantitative experiments separate softmax-like normalization from cortical divisive normalization in behaving animals when stimuli mirror transformer benchmark suites?", "status": "open", "fields": [ "neuroscience", "machine-learning" ], "color": "gray" }, { "id": "u-sparse-coding-x-neural-basis", "type": "unknown", "title": "Does sparse coding in visual cortex V1 use biologically plausible ISTA dynamics (lateral inhibition networks) as the inference algorithm, and does this predict the specific timing and magnitude of surround suppression observed in primate V1 electrophysiology?", "status": "open", "fields": [ "neuroscience", "computer_science", "biology" ], "color": "gray" }, { "id": "u-spinal-gate-control-interneuron-identity", "type": "unknown", "title": "What is the molecular identity of the gate control interneuron in the spinal dorsal horn, and which genetically defined interneuron subtypes implement A-beta-mediated inhibition of nociceptive transmission?\n", "status": "open", "fields": [ "neuroscience", "biophysics" ], "color": "gray" }, { "id": "u-stdp-natural-stimuli-in-vivo-plasticity-rules", "type": "unknown", "title": "What are the actual synaptic plasticity rules operating in vivo during natural stimulus-driven activity, and do STDP windows measured with artificial spike-pair protocols in vitro accurately predict plasticity under natural firing patterns with realistic spike train statistics?\n", "status": "open", "fields": [ "neuroscience", "computational-neuroscience", "electrophysiology" ], "color": "gray" }, { "id": "u-stdp-synaptic-weight-saturation", "type": "unknown", "title": "How do biological neural networks prevent runaway potentiation under STDP, and what mechanisms enforce weight normalization to maintain stable learned representations?", "status": "open", "fields": [ "neuroscience", "physics", "computational-neuroscience" ], "color": "gray" }, { "id": "u-stochastic-resonance-neural-coding-optimality", "type": "unknown", "title": "Is the spontaneous firing rate of primary sensory neurons (hair cells, mechanoreceptors) optimally tuned to maximize stochastic resonance detection of their characteristic stimulus frequencies?\n", "status": "open", "fields": [ "neuroscience", "physics", "signal-processing" ], "color": "gray" }, { "id": "u-synapse-heterogeneity-function", "type": "unknown", "title": "What is the functional significance of the enormous molecular heterogeneity of synapses, and does synapse-type diversity encode information?", "status": "open", "fields": [ "neuroscience", "molecular-neuroscience", "cellular-neuroscience" ], "color": "gray" }, { "id": "u-td-learning-dopamine-biological-implementation", "type": "unknown", "title": "How does the brain implement the temporal difference prediction error at the circuit level — specifically, how do dopamine neurons compute V(s_{t+1}) − V(s_t) given that they receive no direct projection from a single brain region encoding V(s)?", "status": "open", "fields": [ "neuroscience", "mathematics", "computational-neuroscience", "cognitive-science", "pharmacology" ], "color": "gray" }, { "id": "u-tda-brain-disease-biomarkers", "type": "unknown", "title": "Can topological data analysis of brain connectivity or EEG/fMRI time series provide disease-specific biomarkers distinguishing psychiatric conditions?", "status": "open", "fields": [ "clinical-neuroscience", "algebraic-topology", "psychiatry" ], "color": "gray" }, { "id": "u-vmpfc-reference-dependent-coding-mechanism", "type": "unknown", "title": "What is the computational mechanism by which vmPFC encodes reference-dependent subjective value, how does the reference point update during multi-item choice, and does this mechanism explain the neural basis of the framing effect and loss aversion?\n", "status": "open", "fields": [ "neuroscience", "economics", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-weber-fechner-stevens-unifying-neural-mechanism", "type": "unknown", "title": "Is there a single neural mechanism that unifies Weber's law, Fechner's logarithmic compression, and Stevens' power law — or do different sensory systems use fundamentally different computational strategies to achieve similar psychophysical scaling?\n", "status": "open", "fields": [ "neuroscience", "psychophysics", "cognitive-science", "computational-neuroscience" ], "color": "gray" }, { "id": "u-computational-psychiatry-treatment-response-prediction", "type": "unknown", "title": "Can computational phenotypes (learning rates, prior precision parameters) derived from brief computerised behavioural tasks predict individual antidepressant or antipsychotic treatment response before drug administration, enabling precision psychiatry prescribing?\n", "status": "open", "fields": [ "neuroscience", "psychiatry", "engineering", "statistics" ], "color": "gray" }, { "id": "u-forward-model-cerebellum-learning-rule", "type": "unknown", "title": "What learning rule does the cerebellum use to update its forward model predictions, and can the supervised climbing fiber error signal account for the full range of motor adaptation timescales from milliseconds to months?\n", "status": "open", "fields": [ "neuroscience", "motor-control", "control-theory", "cerebellar-neuroscience" ], "color": "gray" }, { "id": "u-kalman-filter-neural-implementation-limits", "type": "unknown", "title": "Under what conditions do cortical circuits implement variance-weighted prediction–correction equivalent to a Kalman gain, and when do they require nonlinear filtering?", "status": "open", "fields": [ "neuroscience", "engineering", "statistics" ], "color": "gray" }, { "id": "u-lif-parameter-identifiability-noisy-synapses", "type": "unknown", "title": "Under correlated synaptic input and active dendrites, when do membrane τ estimates from somatic recordings remain identifiable with simple RC reductions?", "status": "open", "fields": [ "neuroscience", "electrical-engineering" ], "color": "gray" }, { "id": "u-neural-spike-coding-rate-vs-temporal", "type": "unknown", "title": "Whether the brain primarily uses rate coding, temporal coding, or a multiplexed combination for information representation, and what the relative information capacity of each scheme is in identified neural circuits\n", "status": "open", "fields": [ "computational-neuroscience", "systems-neuroscience", "information-theory" ], "color": "gray" }, { "id": "u-neuromuscular-control-redundancy-resolution", "type": "unknown", "title": "How does the central nervous system resolve the redundancy problem in muscle coordination — the fact that any joint torque can be produced by infinitely many combinations of muscle activation levels — and does the nervous system optimise a single cost function or use task-dependent switching?\n", "status": "open", "fields": [ "neuroscience", "engineering", "biomechanics" ], "color": "gray" }, { "id": "u-microglia-synapse-pruning-alzheimers-pathological-threshold", "type": "unknown", "title": "What determines the threshold between physiological microglial synapse pruning (circuit refinement) and pathological pruning (synaptic loss in Alzheimer's disease) ΓÇö and can this threshold be pharmacologically modulated to preserve synapses while maintaining immune surveillance?\n", "status": "open", "fields": [ "neuroscience", "immunology", "neurodegeneration", "pharmacology" ], "color": "gray" }, { "id": "u-nuclear-waste-transmutation-accelerator-driven-systems", "type": "unknown", "title": "Can accelerator-driven subcritical systems (ADS) economically transmute long-lived actinides (┬▓Γü┤Γü░Pu, Am, Cm) and reduce nuclear waste hazard timescale from 240,000 to ~300 years ΓÇö what are the engineering barriers to demonstrating a prototype ADS that operates reliably at MW-scale?\n", "status": "open", "fields": [ "nuclear-engineering", "nuclear-chemistry", "particle-physics", "energy-policy" ], "color": "gray" }, { "id": "u-stellar-nucleosynthesis-r-process-site", "type": "unknown", "title": "What astrophysical site or sites dominate r-process nucleosynthesis — neutron star mergers (kilonovae), collapsars, or rare core-collapse supernovae — and can the relative contributions be disentangled by matching observed [Eu/Fe] abundance trends in metal-poor stars with delay-time distributions and galactic chemical evolution models?", "status": "open", "fields": [ "astrophysics", "nuclear-physics", "astronomy" ], "color": "gray" }, { "id": "u-a-stability-region-operator-splitting-reaction-diffusion", "type": "unknown", "title": "What timestep and splitting policies best preserve both stability and mechanistic fidelity in stiff reaction-diffusion models?", "status": "open", "fields": [ "numerical-analysis", "computational-physics", "applied-mathematics" ], "color": "gray" }, { "id": "u-internal-tide-mixing-efficiency-spatial", "type": "unknown", "title": "What fraction of internal tide energy is locally dissipated near generation sites versus radiated to the open ocean, and how does this spatial distribution control abyssal stratification?", "status": "open", "fields": [ "oceanography", "geophysics", "fluid-mechanics" ], "color": "gray" }, { "id": "u-neural-spectral-ocean-forecast-stability-horizon", "type": "unknown", "title": "What stability horizon can neural spectral ocean surrogates maintain for submesoscale forecasts?", "status": "open", "fields": [ "oceanography", "machine-learning", "fluid-dynamics" ], "color": "gray" }, { "id": "u-ocean-acidification-carbonate-threshold", "type": "unknown", "title": "What are the critical carbonate chemistry thresholds (aragonite saturation state Ω_arag, pH, CO3^2- concentration) below which calcification failure occurs in reef-building corals and pteropods, and how do bleaching, disease, and local pollution interact with ocean acidification to modify these thresholds?", "status": "open", "fields": [ "oceanography", "ecology", "chemistry" ], "color": "gray" }, { "id": "u-ocean-color-phytoplankton-remote-sensing", "type": "unknown", "title": "Can satellite ocean-color retrievals distinguish phytoplankton functional types (diatoms, cyanobacteria, coccolithophores) at sufficient accuracy to close the global marine carbon budget, and what improvements to radiative transfer inversion algorithms are needed to separate CDOM, detritus, and phytoplankton absorption contributions?", "status": "open", "fields": [ "oceanography", "optics", "remote-sensing" ], "color": "gray" }, { "id": "u-redfield-ratio-variability-drivers", "type": "unknown", "title": "What biological and chemical mechanisms drive systematic deviations from the Redfield C:N:P ratio across ocean basins, seasons, and phytoplankton functional groups?", "status": "open", "fields": [ "oceanography", "chemistry", "ecology" ], "color": "gray" }, { "id": "u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models", "type": "unknown", "title": "What failure boundaries determine when `b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling` remains decision-useful?", "status": "open", "fields": [ "statistical-physics", "oncology" ], "color": "gray" }, { "id": "u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models", "type": "unknown", "title": "What failure boundaries determine when `b-markov-jump-processes-x-cell-state-switching-therapy-design` remains decision-useful?", "status": "open", "fields": [ "stochastic-processes", "oncology" ], "color": "gray" }, { "id": "u-heavy-traffic-thresholds-for-ed-crowding-intervention-policies", "type": "unknown", "title": "What validation boundary conditions determine when `b-heavy-traffic-queueing-x-emergency-department-flow` remains decision-useful?", "status": "open", "fields": [ "operations-research", "medicine" ], "color": "gray" }, { "id": "u-ribosome-kinetics-queuing", "type": "unknown", "title": "What are the rate-limiting bottleneck codons in human mRNA translation, and does codon-traffic-jam theory correctly predict protein yield and ribosome density profiles from ribosome profiling data?", "status": "open", "fields": [ "molecular-biology", "operations-research", "computational-biology" ], "color": "gray" }, { "id": "u-supply-chain-network-topology-resilience", "type": "unknown", "title": "What supply chain network topologies maximize resilience to both random failures and targeted disruptions simultaneously, and how can firms redesign their supplier networks to achieve this?", "status": "open", "fields": [ "operations-research", "complex-systems", "network-science" ], "color": "gray" }, { "id": "u-optogenetics-human-therapeutic-scale-delivery", "type": "unknown", "title": "What are the barriers to scaling optogenetic therapies from proof-of-concept (retinitis pigmentosa) to broader neurological conditions (Parkinson's, epilepsy, depression) ΓÇö specifically the challenges of viral vector delivery, light penetration through tissue, and long-term immune tolerance?\n", "status": "open", "fields": [ "gene-therapy", "neuroscience", "biomedical-engineering", "optogenetics" ], "color": "gray" }, { "id": "u-antibiotic-synergy-surfaces", "type": "unknown", "title": "Can pharmacodynamic interaction surfaces for antibiotic combinations be predicted from drug-target network topology before experimental measurement, and which network features are the best predictors of synergy?", "status": "open", "fields": [ "pharmacology", "systems-biology", "network-science" ], "color": "gray" }, { "id": "u-cyp450-xenobiotic-metabolic-prediction", "type": "unknown", "title": "Can in silico machine learning models trained on CYP450 crystal structures and metabolomics databases quantitatively predict drug metabolic stability, metabolite identity, and drug-drug interaction K_i values for novel chemical entities before first-in-human studies?", "status": "open", "fields": [ "pharmacology", "biochemistry", "computational-chemistry" ], "color": "gray" }, { "id": "u-fitness-landscape-drug-resistance-prediction", "type": "unknown", "title": "Can deep mutational scanning fitness landscapes measured in vitro predict the order, timing, and combination of resistance mutations that evolve clinically in patients treated with antibiotics or antivirals?", "status": "open", "fields": [ "pharmacology", "evolutionary-biology", "genomics" ], "color": "gray" }, { "id": "u-neural-ode-pk-identifiability-under-sparse-sampling", "type": "unknown", "title": "What identifiability limits arise when neural ODE PK surrogates are fit to sparse therapeutic-drug monitoring data?", "status": "open", "fields": [ "pharmacology", "machine-learning" ], "color": "gray" }, { "id": "u-pharmacokinetic-interindividual-variability", "type": "unknown", "title": "What fraction of interindividual variability in drug pharmacokinetics is attributable to CYP genetic polymorphisms versus non-genetic sources (age, disease, drug interactions, microbiome), and can multi-omics integration predict individual PK parameters more accurately than population PK models?", "status": "open", "fields": [ "pharmacology", "genetics", "systems-biology" ], "color": "gray" }, { "id": "u-bayesian-convergence-prior-dependence", "type": "unknown", "title": "How strongly do Bayesian posterior convergence rates depend on the choice of prior in finite-sample scientific inference, and when does prior sensitivity violate the practical dissolution of Hume's problem of induction?\n", "status": "open", "fields": [ "statistics", "philosophy-of-science", "probability-theory", "epistemology" ], "color": "gray" }, { "id": "u-bayesian-old-evidence-problem", "type": "unknown", "title": "How can the Bayesian account of confirmation handle the old evidence problem — where evidence known prior to hypothesis formulation (like Mercury's perihelion for GR) cannot update posteriors — without abandoning either Bayesianism or the historical practice of science?", "status": "open", "fields": [ "philosophy-of-science", "statistics", "Bayesian-inference", "epistemology", "history-of-science" ], "color": "gray" }, { "id": "u-bayesian-prior-objectivity", "type": "unknown", "title": "Is there an objective, uniquely determined prior probability distribution for scientific hypotheses, or is Bayesian confirmation theory irreducibly subjective — and if the latter, does this undermine its normative status as the logic of science?\n", "status": "open", "fields": [ "philosophy-of-science", "Bayesian-statistics", "epistemology", "mathematics" ], "color": "gray" }, { "id": "u-causation-vs-correlation", "type": "unknown", "title": "Under what observational study designs and statistical assumptions can causal conclusions be reliably drawn without experimental intervention?", "status": "open", "fields": [ "philosophy-of-science", "statistics", "epidemiology", "causal-inference" ], "color": "gray" }, { "id": "u-citizen-science-quality", "type": "unknown", "title": "Under what conditions does citizen science generate data of sufficient quality for peer-reviewed scientific conclusions?", "status": "open", "fields": [ "metascience", "philosophy-of-science", "science-policy" ], "color": "gray" }, { "id": "u-demarcation-problem", "type": "unknown", "title": "What is the correct criterion for demarcating science from non-science, and is a universal criterion possible?", "status": "open", "fields": [ "philosophy-of-science", "epistemology", "science-studies" ], "color": "gray" }, { "id": "u-emergence-vs-reduction", "type": "unknown", "title": "Under what conditions are higher-level scientific explanations genuinely autonomous rather than in-principle reducible to lower-level descriptions?", "status": "open", "fields": [ "philosophy-of-science", "metaphysics", "cognitive-science", "biology" ], "color": "gray" }, { "id": "u-interdisciplinary-barriers", "type": "unknown", "title": "What are the structural and epistemic barriers to effective interdisciplinary collaboration, and how can they be systematically reduced?", "status": "open", "fields": [ "science-studies", "philosophy-of-science", "science-policy", "metascience" ], "color": "gray" }, { "id": "u-measurement-theory-foundations", "type": "unknown", "title": "What is the ontological status of measurement scales, and under what conditions do psychometric measurements quantify genuine psychological attributes?", "status": "open", "fields": [ "philosophy-of-science", "measurement-theory", "psychology", "metascience" ], "color": "gray" }, { "id": "u-model-selection-validity", "type": "unknown", "title": "Are AIC, BIC, and MDL genuinely measuring predictive accuracy or model simplicity, and when do they give misleading guidance?", "status": "open", "fields": [ "philosophy-of-science", "statistics", "machine-learning", "metascience" ], "color": "gray" }, { "id": "u-open-science-incentives", "type": "unknown", "title": "What incentive structures and institutional reforms would sustainably increase open data sharing, preregistration, and replication in science?", "status": "open", "fields": [ "metascience", "philosophy-of-science", "science-policy", "behavioral-economics" ], "color": "gray" }, { "id": "u-paradigm-shift-prediction", "type": "unknown", "title": "Are Kuhnian paradigm shifts predictable from anomaly accumulation patterns, and can science-of-science methods detect pre-revolutionary conditions?", "status": "open", "fields": [ "philosophy-of-science", "metascience", "science-studies", "complex-systems" ], "color": "gray" }, { "id": "u-peer-review-validity", "type": "unknown", "title": "Does peer review reliably filter low-quality and incorrect science, and what alternative gatekeeping mechanisms might perform better?", "status": "open", "fields": [ "metascience", "philosophy-of-science", "science-policy" ], "color": "gray" }, { "id": "u-preregistration-effectiveness", "type": "unknown", "title": "Does preregistration of hypotheses and analysis plans improve the validity of scientific conclusions, and what are its limits?", "status": "open", "fields": [ "metascience", "philosophy-of-science", "statistics" ], "color": "gray" }, { "id": "u-publication-bias-correction", "type": "unknown", "title": "Can publication bias be corrected post-hoc in meta-analyses, and what methods are valid under realistic distributions of true effects?", "status": "open", "fields": [ "metascience", "statistics", "philosophy-of-science" ], "color": "gray" }, { "id": "u-reproducibility-crisis-causes", "type": "unknown", "title": "What are the root causes of the reproducibility crisis across sciences, and what interventions reliably improve replication rates?", "status": "open", "fields": [ "philosophy-of-science", "metascience", "statistics", "psychology" ], "color": "gray" }, { "id": "u-science-communication-effectiveness", "type": "unknown", "title": "What communication strategies reliably improve public understanding of scientific evidence and reduce belief in scientific misinformation?", "status": "open", "fields": [ "science-communication", "philosophy-of-science", "cognitive-psychology", "metascience" ], "color": "gray" }, { "id": "u-scientific-consensus-formation", "type": "unknown", "title": "What are the epistemic and social mechanisms by which scientific consensus forms, and under what conditions can consensus be mistaken?", "status": "open", "fields": [ "philosophy-of-science", "science-studies", "sociology", "epistemology" ], "color": "gray" }, { "id": "u-scientific-method-cross-domain-falsifiability", "type": "unknown", "title": "What are the necessary and sufficient conditions for a cross-domain structural analogy to constitute a genuinely falsifiable scientific prediction, rather than a post hoc descriptive metaphor, and how should Bayesian confirmation theory quantify the epistemic contribution of each independent domain confirmation?\n", "status": "open", "fields": [ "philosophy-of-science", "mathematics", "information-theory" ], "color": "gray" }, { "id": "u-scientific-progress-measure", "type": "unknown", "title": "How should scientific progress be measured, and do citation counts, replication rates, or predictive accuracy provide valid progress indicators?", "status": "open", "fields": [ "metascience", "philosophy-of-science", "science-policy" ], "color": "gray" }, { "id": "u-scientific-realism-debate", "type": "unknown", "title": "Should we believe that successful scientific theories are approximately true descriptions of unobservable entities, or merely empirically adequate?", "status": "open", "fields": [ "philosophy-of-science", "epistemology", "metaphysics" ], "color": "gray" }, { "id": "u-theory-ladenness-observation", "type": "unknown", "title": "In what sense and to what degree are scientific observations theory-laden, and does this threaten the independence of evidence from theory?", "status": "open", "fields": [ "philosophy-of-science", "epistemology", "cognitive-science" ], "color": "gray" }, { "id": "u-underdetermination-theory", "type": "unknown", "title": "Does the underdetermination of theory by evidence imply that scientific theory choice is inevitably non-rational?", "status": "open", "fields": [ "philosophy-of-science", "epistemology", "logic" ], "color": "gray" }, { "id": "u-optical-frequency-metamaterial-loss-limits-superlens", "type": "unknown", "title": "Can optical-frequency metamaterials achieve sufficiently low loss to realise a practical superlens with sub-diffraction resolution, and what fabrication strategies or gain-assisted designs can overcome the fundamental ohmic loss in metallic split-ring resonator arrays at visible and near-infrared frequencies?\n", "status": "open", "fields": [ "photonics", "metamaterials", "nanophotonics", "optics" ], "color": "gray" }, { "id": "u-vcsel-silicon-photonics-integration-limit", "type": "unknown", "title": "What is the fundamental limit to integrating III-V semiconductor laser sources (VCSELs, DFB lasers) onto silicon photonics platforms, and can heterogeneous integration achieve the wall-plug efficiency and modulation bandwidth required for co-packaged optics in hyperscale data centers?\n", "status": "open", "fields": [ "photonics", "engineering", "materials-science", "electrical-engineering" ], "color": "gray" }, { "id": "u-phylogenetic-placement-long-branch-attraction-correction", "type": "unknown", "title": "What is the best method for correcting long-branch attraction (LBA) artefacts in maximum likelihood and Bayesian phylogenetic inference, and when do model violations (rate heterogeneity, compositional heterogeneity, saturation) produce systematic biases that cannot be corrected by more complex substitution models?\n", "status": "open", "fields": [ "phylogenetics", "evolutionary-biology", "statistics", "bioinformatics" ], "color": "gray" }, { "id": "u-human-expansion-routes-coalescent-ancient-dna", "type": "unknown", "title": "What were the precise routes, timing, and population sizes of the initial human dispersal out of Africa ΓÇö specifically the number of founding bottlenecks, the extent of archaic admixture (Neanderthal, Denisovan) at each stage, and the fate of early-dispersing populations replaced by later waves?\n", "status": "open", "fields": [ "phylogeography", "paleoanthropology", "population-genetics", "ancient-dna" ], "color": "gray" }, { "id": "u-anharmonic-spectroscopy-matrix-models-convergence", "type": "unknown", "title": "When does harmonic normal-mode diagonalization fail predictably for soft modes, hydrogen bonds, and large-amplitude torsions — and what matrix perturbation schemes converge?", "status": "open", "fields": [ "physical-chemistry", "applied-mathematics" ], "color": "gray" }, { "id": "u-critical-exponents-non-mean-field", "type": "unknown", "title": "Why do critical exponents deviate from mean-field predictions in low dimensions, and can renormalization group predictions be extended to all universality classes?", "status": "open", "fields": [ "statistical-mechanics", "physical-chemistry", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-kramers-turnover-solvent-friction", "type": "unknown", "title": "What is the correct theory of the Kramers turnover regime (intermediate friction), and can it explain anomalous reaction rates observed in viscous and cryogenic solvents?", "status": "open", "fields": [ "physical-chemistry", "chemistry", "statistical-mechanics" ], "color": "gray" }, { "id": "u-md-thermostat-sde-equivalence-and-ergodicity", "type": "unknown", "title": "For a given complex biomolecular potential, which thermostat–integrator pairs yield ergodic sampling of slow degrees of freedom within feasible wall-clock budgets?", "status": "open", "fields": [ "chemistry", "mathematics", "physics" ], "color": "gray" }, { "id": "u-partition-function-anharmonic-correction", "type": "unknown", "title": "How accurately can anharmonic corrections to the molecular partition function be computed for large polyatomic molecules, and when do they qualitatively change predicted equilibrium constants or rate constants compared to the harmonic approximation?\n", "status": "open", "fields": [ "physical-chemistry", "statistical-mechanics", "quantum-chemistry" ], "color": "gray" }, { "id": "u-acoustic-metamaterials-x-negative-refraction", "type": "unknown", "title": "Can acoustic superlensing (sub-diffraction focusing using double-negative acoustic metamaterials) be achieved at biologically relevant frequencies (1-20 MHz ultrasound) with sufficient bandwidth and resolution to image sub-cellular structures in living tissue?", "status": "open", "fields": [ "physics", "materials-science", "medicine" ], "color": "gray" }, { "id": "u-active-matter-chiral-renormalization", "type": "unknown", "title": "What are the renormalization group fixed points and universality class of polar chiral active matter, and do they differ from achiral active matter?", "status": "open", "fields": [ "statistical-physics", "active-matter", "soft-condensed-matter", "biophysics" ], "color": "gray" }, { "id": "u-active-matter-percolation", "type": "unknown", "title": "How does self-propulsion fundamentally change percolation transitions in active biological networks?", "status": "open", "fields": [ "statistical-physics", "active-matter-physics", "oncology", "biophysics" ], "color": "gray" }, { "id": "u-active-matter-topological-defect-biology", "type": "unknown", "title": "What is the causal role of topological defects in active nematics (±½ defects) in driving biological tissue remodelling and morphogenetic events?", "status": "open", "fields": [ "biological-physics", "cell-biology", "developmental-biology" ], "color": "gray" }, { "id": "u-anderson-localization-biological-systems", "type": "unknown", "title": "Does Anderson localization of vibrational modes in disordered protein networks affect protein allostery and signal propagation, and can it be detected experimentally?", "status": "open", "fields": [ "biophysics", "condensed-matter", "structural-biology" ], "color": "gray" }, { "id": "u-arrow-of-time-low-entropy-origin", "type": "unknown", "title": "Why did the universe begin in an extraordinarily low-entropy state, and which physical mechanism (quantum gravity, anthropic selection, or cyclic cosmology) explains the thermodynamic arrow?", "status": "open", "fields": [ "cosmology", "thermodynamics", "statistical-mechanics", "quantum-gravity" ], "color": "gray" }, { "id": "u-atmospheric-convection-x-rayleigh-benard", "type": "unknown", "title": "Does the Rayleigh-Bénard scaling law for heat flux (Nu ~ Ra^β with β ≈ 1/3 in the ultimate regime) apply to atmospheric convection, and can this inform a universal parameterization of cumulus convection in climate models?", "status": "open", "fields": [ "physics", "geoscience", "fluid-mechanics", "climate-science" ], "color": "gray" }, { "id": "u-bgs-conjecture-general-proof", "type": "unknown", "title": "Can the Bohigas-Giannoni-Schmit conjecture be proved in full generality for all quantum systems with classically ergodic dynamics, and what is the precise boundary between GOE and Poisson statistics?", "status": "open", "fields": [ "quantum-mechanics", "chaos-theory", "random-matrix-theory", "mathematics" ], "color": "gray" }, { "id": "u-boltzmann-machine-x-ising-model", "type": "unknown", "title": "What is the maximum expressive power of energy-based models trained by contrastive divergence, and how does the spin glass phase structure of the Ising model constrain the representational capacity of deep Boltzmann machines?\n", "status": "open", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "gray" }, { "id": "u-boltzmann-shannon-nonequilibrium-bridge", "type": "unknown", "title": "How does the Boltzmann-Shannon entropy equivalence extend to non-equilibrium systems, and what is the information-theoretic interpretation of entropy production in driven dissipative systems?", "status": "open", "fields": [ "statistical-mechanics", "information-theory", "non-equilibrium-thermodynamics" ], "color": "gray" }, { "id": "u-cardiac-criticality-synchronization", "type": "unknown", "title": "Does the heart operate near a criticality transition, and does cardiac synchronization exploit the same Kuramoto-oscillator physics as neural criticality?", "status": "open", "fields": [ "cardiology", "statistical-physics", "nonlinear-dynamics", "neuroscience" ], "color": "gray" }, { "id": "u-cardiomyocyte-synchronization-criticality", "type": "unknown", "title": "Is synchronization of cardiomyocytes governed by a critical phase transition in an elastic coupling network, and does this predict arrhythmia onset as a loss of criticality?", "status": "open", "fields": [ "biophysics", "statistical-physics", "cardiology", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-cavity-method-x-belief-propagation", "type": "unknown", "title": "Can the cavity method (replica symmetry breaking) predict exact thresholds for computational phase transitions in random graphical inference problems, and do these thresholds match information-theoretic limits?", "status": "open", "fields": [ "physics", "computer_science", "mathematics" ], "color": "gray" }, { "id": "u-chaos-synchronization-noise-robustness-threshold", "type": "unknown", "title": "How much additive noise in the coupling channel degrades Pecora-Carroll synchronization quality, and is there a noise threshold above which synchronization fails completely?\n", "status": "open", "fields": [ "physics", "engineering" ], "color": "gray" }, { "id": "u-cherenkov-mach-cone-unified-demo-transfer", "type": "unknown", "title": "Does side-by-side laboratory demonstration of optical Cherenkov light and hydrodynamic Mach cones measurably improve student transfer performance on cone-angle quantitative problems versus teaching either phenomenon alone?", "status": "open", "fields": [ "physics", "education" ], "color": "gray" }, { "id": "u-chromatic-aberration-broadband-metalens", "type": "unknown", "title": "Can metasurface metalenses achieve achromatic focusing across the full visible spectrum at high numerical aperture simultaneously, and what material and geometric constraints govern the fundamental trade-off between aperture, efficiency, and achromatic bandwidth in phase-gradient metasurfaces?", "status": "open", "fields": [ "optics", "materials-science", "physics" ], "color": "gray" }, { "id": "u-climate-ew-indicator-universality", "type": "unknown", "title": "Do climate tipping element early-warning indicators (AR1, variance, spatial correlation length) follow the universal scaling exponents predicted by their respective bifurcation class, and which statistical test is most sensitive for detecting approach to each tipping point in available satellite and instrumental data?\n", "status": "open", "fields": [ "climate-science", "statistical-physics", "dynamical-systems", "remote-sensing" ], "color": "gray" }, { "id": "u-concert-hall-acoustic-quality-metrics", "type": "unknown", "title": "Which measurable physical acoustic parameters of concert halls most reliably predict subjective listener quality ratings, and how do individual differences in perception affect universality of acoustic design criteria?", "status": "open", "fields": [ "architectural-acoustics", "psychoacoustics", "perceptual-psychology", "music" ], "color": "gray" }, { "id": "u-conformal-field-theory-x-critical-phenomena", "type": "unknown", "title": "Does every second-order phase transition in 3D correspond to a well-defined unitary CFT, and can the conformal bootstrap classify all 3D universality classes analogously to the BPZ classification in 2D?", "status": "open", "fields": [ "statistical-mechanics", "mathematical-physics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-cosmological-constant-fine-tuning", "type": "unknown", "title": "Why is the cosmological constant (dark energy density) 10^120 times smaller than quantum field theory predicts — is this a fine-tuning problem, an anthropic selection effect, or evidence of new physics?", "status": "open", "fields": [ "cosmology", "quantum-field-theory", "theoretical-physics" ], "color": "gray" }, { "id": "u-crystallography-x-group-theory", "type": "unknown", "title": "Can the complete set of topological invariants for all 230 space groups and 1651 magnetic space groups be systematically computed to predict all topological crystalline insulators and semimetals, and what fraction of known materials harbor topologically non-trivial electronic structures?", "status": "open", "fields": [ "physics", "mathematics", "materials-science", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-diffusion-limited-aggregation-x-fractal-growth", "type": "unknown", "title": "Is the fractal dimension of DLA clusters in 3D exactly 2.5, and how does adding surface tension or noise to DLA change the universality class of biological branching structures?\n", "status": "open", "fields": [ "physics", "biology", "mathematics" ], "color": "gray" }, { "id": "u-emergence-quantification-integrated-information-empirical-test", "type": "unknown", "title": "Can Tononi's Integrated Information Theory (Φ) be computed for real neural systems at scale, and does Φ increase monotonically with commonly accepted indicators of consciousness (wakefulness vs. sleep vs. anaesthesia vs. vegetative state)?", "status": "open", "fields": [ "physics", "neuroscience", "philosophy-of-science", "information-theory", "complex-systems", "mathematics" ], "color": "gray" }, { "id": "u-entropy-production-x-living-systems", "type": "unknown", "title": "Can entropy production rate serve as a universal thermodynamic fitness measure across all scales of biological organisation, from metabolic networks to ecosystems?", "status": "open", "fields": [ "physics", "biology", "thermodynamics" ], "color": "gray" }, { "id": "u-ergodic-theory-x-statistical-mechanics", "type": "unknown", "title": "What is the criterion for a many-body quantum system to thermalize (obey the eigenstate thermalization hypothesis) vs. many-body localize (fail to thermalize), and is there a sharp phase transition between these behaviors?", "status": "open", "fields": [ "statistical-mechanics", "condensed-matter-physics", "mathematics" ], "color": "gray" }, { "id": "u-fluctuation-theorem-biological-motors", "type": "unknown", "title": "How close are biological molecular motors (kinesin, ATP synthase) to thermodynamic optimality as defined by fluctuation theorems, and how do they navigate the efficiency-speed tradeoff?", "status": "open", "fields": [ "biophysics", "statistical-physics", "information-theory" ], "color": "gray" }, { "id": "u-gauge-field-epidemic-nonlocality", "type": "unknown", "title": "Does the QED gauge-field formalism make quantitatively better predictions for non-local epidemic spreading (superspreader dynamics, long-range transmission) than classical SIR/SEIR models, and does the \"behavioral shielding\" derived from stochastic field theory match empirical contact-reduction data?\n", "status": "open", "fields": [ "epidemiology", "quantum-field-theory", "statistical-physics", "public-health" ], "color": "gray" }, { "id": "u-geometric-phase-calibration-across-polarization-optics", "type": "unknown", "title": "How accurately can polarization-optics experiments calibrate Berry-phase holonomy concepts for quantum-physics education and device metrology across lossy, nonideal optical paths?\n", "status": "open", "fields": [ "optics", "quantum-physics" ], "color": "gray" }, { "id": "u-gravitational-wave-memory-effect", "type": "unknown", "title": "Has the gravitational wave memory effect (permanent spacetime displacement after a wave passes) been detected, and does it match general relativistic predictions?", "status": "open", "fields": [ "gravitational-wave-physics", "general-relativity", "astrophysics" ], "color": "gray" }, { "id": "u-grokking-phase-transition", "type": "unknown", "title": "Is the \"grokking\" phenomenon in deep neural networks a genuine second-order phase transition, and what is its universality class?", "status": "open", "fields": [ "machine-learning", "statistical-physics", "information-theory", "neuroscience" ], "color": "gray" }, { "id": "u-hawking-channel-capacity", "type": "unknown", "title": "What is the quantum channel capacity of black hole evaporation, and can the full quantum information content of infalling matter be reconstructed from the Hawking radiation after the Page time?", "status": "open", "fields": [ "quantum-gravity", "quantum-information", "information-theory" ], "color": "gray" }, { "id": "u-hawking-unruh-experimental-detection", "type": "unknown", "title": "Can Hawking radiation or the Unruh effect be detected experimentally in accessible laboratory systems — including analog gravity systems in BEC, sonic black holes, or laser-accelerated electrons — and would such a detection confirm the key quantum field theory predictions without requiring a true gravitational horizon?", "status": "open", "fields": [ "physics", "quantum-physics", "experimental-physics" ], "color": "gray" }, { "id": "u-higgs-mode-high-tc-superconductors", "type": "unknown", "title": "Has the Higgs amplitude mode been unambiguously detected in high-Tc cuprate superconductors, and what does its mass tell us about the pairing mechanism?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-high-tc-pairing-mechanism", "type": "unknown", "title": "What is the pairing mechanism in cuprate and iron-based high-temperature superconductors, and why does it produce d-wave rather than s-wave symmetry?", "status": "open", "fields": [ "condensed-matter-physics", "quantum-mechanics", "materials-science", "strongly-correlated-electrons" ], "color": "gray" }, { "id": "u-hopfield-capacity-cortex", "type": "unknown", "title": "Does the human hippocampal CA3 operate near the replica-theory Hopfield capacity limit alpha_c = 0.138?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-ion-pump-landauer-thermodynamics", "type": "unknown", "title": "Do biological ion pumps (Na/K-ATPase, Ca-ATPase) operate as Landauer information-erasure engines, and how close are they to the thermodynamic efficiency limit?", "status": "open", "fields": [ "biophysics", "information-thermodynamics", "statistical-physics", "cell-biology" ], "color": "gray" }, { "id": "u-jamming-transition-biological-tissues", "type": "unknown", "title": "Do biological tissues undergo a jamming transition from fluid-like to solid-like behavior, and is the vertex model or the Voronoi model the correct description of epithelial mechanics?", "status": "open", "fields": [ "biophysics", "soft-condensed-matter", "developmental-biology", "statistical-physics" ], "color": "gray" }, { "id": "u-kelvin-helmholtz-growth-rate-transfer-cloud-plasma-shear", "type": "unknown", "title": "Can Kelvin-Helmholtz growth-rate normalizations transfer between stratified atmospheric cloud billows and magnetized plasma shear layers once field-aligned tension, compressibility, and diagnostic resolution are explicitly modeled?\n", "status": "open", "fields": [ "physics", "atmospheric-science" ], "color": "gray" }, { "id": "u-kibble-zurek-embryo", "type": "unknown", "title": "Does embryonic symmetry breaking obey Kibble-Zurek scaling?", "status": "open", "fields": [ "biophysics", "developmental-biology", "condensed-matter-physics", "cosmology" ], "color": "gray" }, { "id": "u-kleiber-pulsatile-waves", "type": "unknown", "title": "Is Kleiber's 3/4 metabolic scaling law a signature of pulsatile wave physics rather than fractal network geometry, and does this reframe the entire theory of biological allometry?", "status": "open", "fields": [ "biological-physics", "physiology", "fluid-dynamics", "scaling-theory" ], "color": "gray" }, { "id": "u-landau-theory-neural-criticality-order-parameter", "type": "unknown", "title": "What is the order parameter for neural phase transitions — is it population firing rate, synaptic strength distribution, or a topological quantity — and which Landau universality class does it belong to?", "status": "open", "fields": [ "neuroscience", "statistical-physics", "condensed-matter" ], "color": "gray" }, { "id": "u-landauer-bound-experimental-verification", "type": "unknown", "title": "Can the Landauer bound kT ln 2 per erased bit be approached within one order of magnitude in a solid-state device, and what are the practical barriers to thermodynamically reversible computation?", "status": "open", "fields": [ "physics", "computer-science", "thermodynamics", "information-theory" ], "color": "gray" }, { "id": "u-landauer-limit-biological-computation", "type": "unknown", "title": "How close to the Landauer limit do biological information-processing systems (synapses, ribosomes, DNA repair) operate?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-landauer-limit-nonhelical-resonator", "type": "unknown", "title": "What is the minimum energy dissipated per logically irreversible bit operation when storing or resetting classical electromagnetic state in high-Q non-helical cavity resonators at temperature T?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-laser-cooling-sub-doppler-quantum-limit", "type": "unknown", "title": "What is the fundamental quantum thermodynamic limit to laser cooling beyond the single-photon recoil limit, and can quantum measurement feedback (Maxwell's demon protocols) achieve sub-recoil temperatures without evaporative cooling?\n", "status": "open", "fields": [ "physics", "thermodynamics", "quantum-physics" ], "color": "gray" }, { "id": "u-leptogenesis-cp-scale", "type": "unknown", "title": "Is the CP-violating phase measured in neutrino oscillation experiments (delta_CP in the PMNS matrix) sufficient and consistent with generating the observed baryon asymmetry via leptogenesis?", "status": "open", "fields": [ "particle-physics", "cosmology", "nuclear-physics" ], "color": "gray" }, { "id": "u-liquid-crystal-x-cell-membrane", "type": "unknown", "title": "How do lipid raft phase separation and curvature coupling in cell membranes influence protein sorting, signaling, and membrane-mediated interactions between proteins?\n", "status": "open", "fields": [ "physics", "biology", "biophysics" ], "color": "gray" }, { "id": "u-lorenz-attractor-universality-class", "type": "unknown", "title": "Is the Lorenz attractor in a distinct universality class from other strange attractors, and what determines the route to chaos (period-doubling vs. intermittency vs. quasiperiodicity) in physical fluid systems?", "status": "open", "fields": [ "physics", "mathematics", "nonlinear-dynamics", "fluid-dynamics" ], "color": "gray" }, { "id": "u-maxwell-shannon-channel-near-capacity", "type": "unknown", "title": "What physical limits prevent real electromagnetic communication channels from reaching Shannon capacity, and can they be systematically overcome through advanced modulation and coding?", "status": "open", "fields": [ "information-theory", "electromagnetic-theory", "communications-engineering" ], "color": "gray" }, { "id": "u-minimum-dissipation-network-topology", "type": "unknown", "title": "Do biological transport networks (vascular, neural, mycorrhizal) self-organize to minimize dissipation through the same symmetry-breaking mechanism as physical minimum-dissipation networks?", "status": "open", "fields": [ "network-science", "biophysics", "statistical-physics", "evolutionary-biology" ], "color": "gray" }, { "id": "u-minority-game-market-microstructure-universality", "type": "unknown", "title": "Does the minority game phase transition at alpha_c have a universal critical exponent shared with real financial market microstructure, and can alpha be measured empirically from order book data?", "status": "open", "fields": [ "econophysics", "statistical-physics", "complex-systems" ], "color": "gray" }, { "id": "u-neutron-star-core-qcd-constraints", "type": "unknown", "title": "Do observed mass–radius posteriors require a strong first-order phase transition in the core, or can pure hadronic EOS remain consistent within uncertainties?", "status": "open", "fields": [ "physics", "astrophysics", "nuclear-physics" ], "color": "gray" }, { "id": "u-neutron-star-x-nuclear-matter", "type": "unknown", "title": "Does a quark-hadron phase transition occur in the cores of massive neutron stars (M > 1.8 solar masses), and if so, is it a first-order transition (with a mixed phase) or a crossover, as constrained by gravitational wave tidal deformability measurements?", "status": "open", "fields": [ "physics", "astrophysics", "nuclear-physics" ], "color": "gray" }, { "id": "u-noether-quantum-gravity-symmetry", "type": "unknown", "title": "Does Noether's theorem extend to quantum gravity, and what conservation laws (if any) survive the breakdown of spacetime symmetry at the Planck scale?", "status": "open", "fields": [ "theoretical-physics", "quantum-gravity", "differential-geometry", "mathematical-physics" ], "color": "gray" }, { "id": "u-nonextensive-entropy-turbulence", "type": "unknown", "title": "Does the non-extensive Tsallis entropy (with index q ≠ 1) correctly characterize the statistical mechanics of quantum turbulence, and is q a universal constant for all superfluid turbulence?", "status": "open", "fields": [ "statistical-physics", "quantum-fluids", "nonlinear-dynamics", "information-theory" ], "color": "gray" }, { "id": "u-nonhelical-turing-wavelength-scaling", "type": "unknown", "title": "Under what conditions does the dominant spatial scale of coupled electromagnetic modes in non-helical cavity resonator arrays match a Turing-like instability prediction (and how does it scale with insulation and loss)?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-nonlinear-optics-soliton-stability", "type": "unknown", "title": "What determines the stability of optical solitons against perturbations from higher-order dispersion, stimulated Raman scattering, and pump noise in photonic crystal fibers, and can inverse-designed fiber dispersion profiles guarantee soliton stability across specified bandwidth and power ranges?", "status": "open", "fields": [ "physics", "mathematics", "optics" ], "color": "gray" }, { "id": "u-primordial-nucleosynthesis-reaction-networks", "type": "unknown", "title": "What nuclear physics uncertainties in the ⁷Be(n,p)⁷Li and d(p,γ)³He reaction rates explain the cosmological Lithium-7 problem, and can next-generation underground accelerators (LUNA-MV) resolve it?", "status": "open", "fields": [ "nuclear-physics", "cosmology", "astrophysics" ], "color": "gray" }, { "id": "u-qcd-ew-phase-transition-relics", "type": "unknown", "title": "What are the quantitative constraints on dark matter relic densities from the QCD and electroweak phase transitions, and can lattice QCD precision measurements of the QCD equation of state distinguish between axion, WIMP, and primordial black hole dark matter scenarios?", "status": "open", "fields": [ "particle-physics", "cosmology", "statistical-physics", "nuclear-physics" ], "color": "gray" }, { "id": "u-quantum-biology-decoherence", "type": "unknown", "title": "How does a warm, wet, noisy biological protein maintain quantum coherence long enough to perform a functional quantum measurement — and what does it reveal about decoherence suppression in open quantum systems?", "status": "open", "fields": [ "quantum-physics", "molecular-biology", "quantum-information-theory", "biophysics" ], "color": "gray" }, { "id": "u-quantum-decoherence-x-classical-emergence", "type": "unknown", "title": "Does quantum decoherence fully solve the measurement problem, or does the emergence of definite outcomes from quantum superpositions require a physical collapse mechanism beyond unitary evolution and environmental entanglement?", "status": "open", "fields": [ "physics", "mathematics", "philosophy-of-science" ], "color": "gray" }, { "id": "u-quantum-error-correction-x-topological-codes", "type": "unknown", "title": "What physical platforms can host non-abelian anyons at scales sufficient for fault-tolerant topological quantum computation, and what is the minimum system size needed to achieve meaningful logical error rate suppression?\n", "status": "open", "fields": [ "physics", "quantum-information", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-quantum-field-theory-x-combinatorics", "type": "unknown", "title": "Is the Feynman diagram series in QED (quantum electrodynamics) Borel summable, and if not, what non-perturbative contributions (instantons, renormalons) dominate the remainder?", "status": "open", "fields": [ "mathematical-physics", "mathematics", "quantum-field-theory" ], "color": "gray" }, { "id": "u-quantum-glass-learning-efficiency", "type": "unknown", "title": "Does quantum glassiness impose fundamental computational hardness limits on machine learning that no classical statistical mechanics framework can predict?", "status": "open", "fields": [ "quantum-information", "statistical-physics", "machine-learning", "spin-glasses" ], "color": "gray" }, { "id": "u-quantum-noise-figure-silicon-mm-wave-cryo-vs-room", "type": "unknown", "title": "At mm-wave frequencies, when does the Caves/Haus quantum-added noise floor materially alter achievable noise figure in silicon LNAs relative to classical Johnson–Nyquist budgets — as a function of temperature, gain, and bandwidth?\n", "status": "open", "fields": [ "electrical-engineering", "quantum-physics" ], "color": "gray" }, { "id": "u-quantum-turbulence-simulation-limit", "type": "unknown", "title": "Can end-to-end quantum simulation of turbulence reveal universality classes or transport mechanisms that are computationally inaccessible to classical methods?", "status": "open", "fields": [ "quantum-computing", "fluid-dynamics", "statistical-physics", "complex-systems" ], "color": "gray" }, { "id": "u-quantum-walk-x-classical-random-walk", "type": "unknown", "title": "Can continuous-time quantum walks on arbitrary graphs be efficiently simulated classically for graphs with special structure, and for which graph families does quantum walk provide provable (not just conjectured) exponential speedup over classical random walk?", "status": "open", "fields": [ "physics", "computer_science", "mathematics" ], "color": "gray" }, { "id": "u-radiocarbon-calibration-plateau-dating-precision", "type": "unknown", "title": "Can radiocarbon dates falling on calibration plateaus be resolved to calendar year precision using multi-isotope approaches or wiggle-matching, and what are the fundamental limits of radiocarbon chronology?", "status": "open", "fields": [ "archaeology", "nuclear-physics", "mathematics" ], "color": "gray" }, { "id": "u-rbm-training-critical-slowdown-near-phase-boundaries", "type": "unknown", "title": "When does contrastive divergence training of RBMs exhibit critical slowing analogous to poor mixing near spin-glass-like regions of energy landscapes induced by data distributions?", "status": "open", "fields": [ "physics", "computer-science", "machine-learning" ], "color": "gray" }, { "id": "u-renormalization-x-compression", "type": "unknown", "title": "Is there a rigorous mathematical isomorphism between Shannon rate-distortion theory and Wilson's renormalization group, and if so, does it provide new computational algorithms for either?", "status": "open", "fields": [ "mathematical-physics", "information-theory", "statistical-mechanics" ], "color": "gray" }, { "id": "u-replica-boltzmann-machine-glass", "type": "unknown", "title": "Does replica symmetry breaking in spherical Boltzmann machine ensembles predict the same generalization-to-memorization transition as in spin glasses, with the same critical exponents?", "status": "open", "fields": [ "statistical-physics", "machine-learning", "spin-glasses", "information-theory" ], "color": "gray" }, { "id": "u-replica-symmetry-breaking-algorithmic-hardness", "type": "unknown", "title": "Can the degree of replica-symmetry breaking in a random optimization problem be used to quantitatively predict the running time of specific algorithms such as DPLL, belief propagation, or simulated annealing?", "status": "open", "fields": [ "statistical-physics", "computer-science", "mathematics" ], "color": "gray" }, { "id": "u-rg-fixed-points-non-wilson-fisher-universality-classes", "type": "unknown", "title": "What determines the complete landscape of RG fixed points beyond the Gaussian and Wilson-Fisher fixed points, and are there as-yet-unknown universality classes in systems with long-range interactions, fractal lattices, or non-equilibrium driving that lack a known Lax pair or epsilon-expansion treatment?\n", "status": "open", "fields": [ "physics", "mathematics", "statistical-mechanics", "field-theory" ], "color": "gray" }, { "id": "u-rg-layerwise-flow-identifiability-across-architectures", "type": "unknown", "title": "Under what conditions can layerwise representations in trained networks be mapped quantitatively to an explicit coarse-graining operator sequence analogous to an RG transform across architectures?", "status": "open", "fields": [ "physics", "computer-science", "machine-learning" ], "color": "gray" }, { "id": "u-scale-free-brain-connectome-criticality", "type": "unknown", "title": "Is the brain connectome's scale-free topology a consequence of preferential attachment during development, and does the critical gamma exponent predict hub-targeted disease vulnerability?", "status": "open", "fields": [ "neuroscience", "network-science", "statistical-physics", "developmental-biology" ], "color": "gray" }, { "id": "u-simulated-annealing-x-statistical-mechanics", "type": "unknown", "title": "What cooling schedule guarantees that quantum annealing finds the global optimum faster than classical simulated annealing, and for which problem classes does quantum tunneling provide exponential speedup?\n", "status": "open", "fields": [ "physics", "computer-science", "statistical-mechanics" ], "color": "gray" }, { "id": "u-soc-universality-class-brain", "type": "unknown", "title": "Does the cortex belong to the BTW universality class (exponent -3/2) or a distinct SOC universality class?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-social-ising-universality", "type": "unknown", "title": "Do empirical social tipping events — norm cascades, political phase transitions, market crashes — exhibit early-warning indicators and scaling exponents consistent with the Ising universality class, and which universality class best describes opinion dynamics on real social networks?\n", "status": "open", "fields": [ "social-science", "statistical-physics", "complexity-science", "political-science" ], "color": "gray" }, { "id": "u-solar-cell-efficiency-practical-loss-mechanisms", "type": "unknown", "title": "What is the practical efficiency ceiling for silicon solar cells under the best achievable surface passivation and light-trapping conditions, and which specific loss mechanism (Auger recombination, free-carrier absorption, contact resistance) is the dominant remaining gap below the SQ limit?\n", "status": "open", "fields": [ "photovoltaics", "semiconductor-physics", "engineering" ], "color": "gray" }, { "id": "u-solid-mechanics-x-topology-optimization", "type": "unknown", "title": "Does topology optimization with fine mesh resolution converge to the true Michell truss solution in the continuum limit, and can this convergence be proven mathematically with explicit convergence rates for SIMP and level-set methods?", "status": "open", "fields": [ "physics", "mathematics", "engineering" ], "color": "gray" }, { "id": "u-soliton-x-integrable-systems", "type": "unknown", "title": "Are all physically relevant nonlinear wave equations with stable solitary wave solutions exactly integrable in the sense of possessing a Lax pair, or do approximate solitons exist in non-integrable systems?", "status": "open", "fields": [ "physics", "mathematics" ], "color": "gray" }, { "id": "u-sonoluminescence-emission-mechanism-state-resolved", "type": "unknown", "title": "Which microscopic emission pathway dominates single-bubble sonoluminescence—collisional thermal plasma bremsstrahlung, molecular recombination chemiluminescence, or dissolved noble-gas chemistry — when state-resolved spectra and timing are measured across diverse driving gases and dissolved species?\n", "status": "open", "fields": [ "physics", "chemistry" ], "color": "gray" }, { "id": "u-spin-waves-x-magnons", "type": "unknown", "title": "Can magnons in antiferromagnets or frustrated magnets exhibit topologically non-trivial band structures (magnon Hall effect, topological magnon insulators) and be used as information carriers in spintronic devices without dissipation?", "status": "open", "fields": [ "physics", "condensed-matter-physics", "materials-science" ], "color": "gray" }, { "id": "u-standard-model-beyond-hierarchy-dark-matter-identity", "type": "unknown", "title": "What lies beyond the Standard Model — what resolves the hierarchy problem, what is dark matter, and what mechanism generates the matter-antimatter asymmetry of the universe?", "status": "open", "fields": [ "particle-physics", "cosmology", "theoretical-physics" ], "color": "gray" }, { "id": "u-standard-model-representation-completeness", "type": "unknown", "title": "Are the representation-theoretic assignments of the Standard Model uniquely determined by anomaly cancellation, or do other consistent representations exist?", "status": "open", "fields": [ "physics", "quantum-physics", "mathematics", "group-theory" ], "color": "gray" }, { "id": "u-tensor-networks-x-quantum-states", "type": "unknown", "title": "What is the minimum tensor network ansatz that can efficiently represent all topological quantum states in 2D, and can PEPS contraction be made polynomial-time for physically relevant classes of quantum states?", "status": "open", "fields": [ "physics", "computer_science", "mathematics" ], "color": "gray" }, { "id": "u-thermoacoustic-engine-efficiency-scaling", "type": "unknown", "title": "What limits the thermodynamic efficiency of practical thermoacoustic engines relative to the Carnot limit, and how does efficiency scale with operating frequency and temperature gradient?", "status": "open", "fields": [ "thermoacoustics", "acoustic-engineering", "thermodynamics" ], "color": "gray" }, { "id": "u-topological-defect-classification-nonequilibrium-condensed-matter", "type": "unknown", "title": "Which topological invariants best predict defect persistence and coarsening rates in nonequilibrium condensed matter systems?", "status": "open", "fields": [ "condensed-matter-physics", "mathematics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-topological-defects-x-homotopy", "type": "unknown", "title": "What is the complete classification of topological defects in active matter and non-equilibrium ordered phases where homotopy theory applies only approximately?\n", "status": "open", "fields": [ "physics", "mathematics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-topological-insulator-majorana-qubit-scalability", "type": "unknown", "title": "Can Majorana zero modes at topological insulator-superconductor interfaces be scaled to a fault-tolerant topological qubit with error rates below the surface code threshold, and what materials platform minimizes quasiparticle poisoning?", "status": "open", "fields": [ "physics", "materials-science", "quantum-computing", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-topological-insulator-x-band-theory", "type": "unknown", "title": "Can K-theory topological invariants be extended to non-Hermitian band structures (open quantum systems with gain and loss) to predict protected surface states in photonic and acoustic topological insulators?", "status": "open", "fields": [ "physics", "mathematics", "condensed-matter-physics", "photonics" ], "color": "gray" }, { "id": "u-topological-qec-physical-realization", "type": "unknown", "title": "Which condensed-matter systems currently in the laboratory are in the toric-code (Z2 topological) phase, and can their topological entanglement entropy be measured to confirm they function as physical quantum error-correcting codes?", "status": "open", "fields": [ "condensed-matter-physics", "quantum-information", "topological-phases" ], "color": "gray" }, { "id": "u-turbulence-anomalous-scaling-intermittency-origin", "type": "unknown", "title": "What is the mathematical origin of anomalous scaling (intermittency corrections) in fully developed turbulence, and can the multifractal exponents ζ_p be derived analytically from the Navier-Stokes equations?\n", "status": "open", "fields": [ "fluid-mechanics", "physics", "mathematics" ], "color": "gray" }, { "id": "u-turbulence-market-reynolds-analogue", "type": "unknown", "title": "Is there a financial market analogue of the Reynolds number that predicts the onset of 'turbulent' crash dynamics?", "status": "open", "fields": [], "color": "gray" }, { "id": "u-turbulence-onset-subcritical-transition", "type": "unknown", "title": "Why does pipe flow transition to turbulence subcritically (no continuous bifurcation), and what determines the critical Reynolds number Re_c ≈ 2300 from first principles?", "status": "open", "fields": [ "fluid-dynamics", "statistical-physics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-turbulence-symmetry-breaking-cascade", "type": "unknown", "title": "Does turbulent transition in shear flows proceed as a succession of distinct symmetry-breaking events, each belonging to a different universality class?", "status": "open", "fields": [ "fluid-dynamics", "statistical-physics", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-vegetation-pattern-tipping-universality", "type": "unknown", "title": "Do vegetation spatial self-organization patterns belong to different universality classes before versus after a desertification tipping point, and can this classify the transition type?", "status": "open", "fields": [ "ecology", "statistical-physics", "pattern-formation", "nonlinear-dynamics" ], "color": "gray" }, { "id": "u-zeeman-spectrum-unfolding-rmt-quantitative-test", "type": "unknown", "title": "For which atomic/molecular series and magnetic-field ranges does Zeeman-resolved high-lying spectra show GOE/GUE spacing statistics after unfolding, and where do deviations signal regularity or mixed phase space?\n", "status": "open", "fields": [], "color": "gray" }, { "id": "u-arrhenius-prefactor-molecular-basis", "type": "unknown", "title": "Whether the Arrhenius pre-exponential factor A can be predicted from first principles for arbitrary reactions, and to what degree A encodes steric, entropic, and quantum tunnelling contributions distinguishable from experiment alone\n", "status": "open", "fields": [ "physical-chemistry", "statistical-mechanics", "quantum-chemistry" ], "color": "gray" }, { "id": "u-crystallography-phase-problem-ab-initio", "type": "unknown", "title": "Can the crystallographic phase problem be solved ab initio for large macromolecules (>100 kDa) using only measured intensities |F_{hkl}|², without experimental phasing or molecular replacement, through computational approaches combining direct methods, charge flipping, and machine learning?\n", "status": "open", "fields": [ "physics-chemistry", "structural-biology", "mathematics", "computational-chemistry" ], "color": "gray" }, { "id": "u-dft-exchange-correlation-exact-functional", "type": "unknown", "title": "What are the exact constraints that the exchange-correlation functional E_xc[rho] must satisfy, and can a functional satisfying all known exact constraints (non-empirical, fully ab initio) achieve chemical accuracy for arbitrary molecular and materials systems?\n", "status": "open", "fields": [ "quantum-chemistry", "physics", "materials-science", "computational-chemistry" ], "color": "gray" }, { "id": "u-high-tc-superconductor-pairing-mechanism", "type": "unknown", "title": "What is the electron pairing mechanism in cuprate and iron-based high-temperature superconductors — phonon, spin-fluctuation, charge-density-wave, or something else entirely?", "status": "open", "fields": [ "condensed-matter-physics", "physical-chemistry", "materials-science" ], "color": "gray" }, { "id": "u-pem-fuel-cell-pt-catalyst-degradation-mechanism", "type": "unknown", "title": "What are the dominant atomic-scale degradation mechanisms of Pt/C oxygen reduction catalysts in PEM fuel cells under drive-cycle conditions — and can they be suppressed to achieve 150,000-hour durability targets for heavy-duty transportation?\n", "status": "open", "fields": [ "physics-chemistry", "electrochemistry", "materials-science", "surface-science" ], "color": "gray" }, { "id": "u-polymer-entanglement-topology", "type": "unknown", "title": "What is the topological mechanism of polymer entanglement, and can entanglement constraints be derived from first principles without the tube model assumption?", "status": "open", "fields": [ "polymer-physics", "chemistry", "topology", "soft-matter" ], "color": "gray" }, { "id": "u-glymphatic-csf-clearance-sleep-deprivation-rate", "type": "unknown", "title": "What is the quantitative relationship between sleep deprivation duration and glymphatic CSF clearance rate, and what is the minimum sleep dose needed to prevent net amyloid-β accumulation?", "status": "open", "fields": [ "neuroscience", "fluid-dynamics", "sleep-medicine", "neurology", "biophysics" ], "color": "gray" }, { "id": "u-hopfield-modern-attention-biological-plausibility", "type": "unknown", "title": "Whether the mathematical equivalence between dense Hopfield networks and transformer self-attention (Ramsauer et al. 2020) implies that biological memory retrieval in the hippocampus operates via a biologically plausible version of the attention mechanism\n", "status": "open", "fields": [ "computational-neuroscience", "machine-learning", "statistical-physics" ], "color": "gray" }, { "id": "u-quantum-zeno-measurement-neural-interruption", "type": "unknown", "title": "When, if ever, does frequent cognitive sampling of an internal variable produce measurement-induced stabilization analogous to the quantum Zeno effect, and what experimental designs falsify the analogy?", "status": "open", "fields": [ "quantum-physics", "neuroscience", "cognitive-science" ], "color": "gray" }, { "id": "u-lymphatic-valve-gating-pressure-threshold", "type": "unknown", "title": "What is the minimum interstitial hydrostatic pressure required to open primary lymphatic capillary valves, and how does this threshold change with tissue inflammation or fibrosis?\n", "status": "open", "fields": [ "physiology", "fluid-mechanics" ], "color": "gray" }, { "id": "u-loss-aversion-neural-substrate", "type": "unknown", "title": "What neural computation implements loss aversion (λ ≈ 2.25), and is it a single mechanism or the product of multiple systems with different evolutionary origins?", "status": "open", "fields": [ "psychology", "neuroscience", "behavioral-economics", "evolutionary-biology" ], "color": "gray" }, { "id": "u-agent-surrogate-policy-optimization-behavioral-fidelity", "type": "unknown", "title": "How much behavioral-fidelity loss is acceptable when optimizing interventions with agent-based surrogates?", "status": "open", "fields": [ "public-health", "machine-learning", "epidemiology" ], "color": "gray" }, { "id": "u-frailty-model-biological-age-calibration", "type": "unknown", "title": "Can a frailty model fitted to longitudinal biomarker data identify a latent biological age variable that predicts mortality better than chronological age alone, and what is the minimum biomarker panel needed for robust frailty variance estimation in a population cohort?", "status": "open", "fields": [ "public-health", "statistics", "epidemiology", "gerontology" ], "color": "gray" }, { "id": "u-photosynthesis-quantum-coherence-physiological-function", "type": "unknown", "title": "Does quantum electronic coherence in photosynthetic light-harvesting complexes at physiological temperature (300K) provide any functional enhancement of energy transfer efficiency beyond what classical Förster/Redfield theory predicts, or is the ~95% quantum efficiency of primary charge separation achievable by purely classical mechanisms?\n", "status": "open", "fields": [ "quantum-biology", "physical-chemistry", "biophysics", "photosynthesis" ], "color": "gray" }, { "id": "u-quantum-coherence-biological-sensing", "type": "unknown", "title": "Does quantum coherence play a functional role in biological sensory systems (olfaction, magnetoreception, photosynthesis) beyond the classical noise floor?", "status": "open", "fields": [ "quantum-biology", "biophysics", "sensory-neuroscience" ], "color": "gray" }, { "id": "u-quantum-coherence-biological-systems-nmr-detectable", "type": "unknown", "title": "Is quantum coherence (superposition, entanglement, or tunnelling) functionally relevant in biological systems at physiological temperature — in photosynthesis energy transfer, avian magnetoreception, or enzyme catalysis — and could solid-state NMR or dynamic nuclear polarisation techniques detect coherence lifetimes long enough to be biologically functional?\n", "status": "open", "fields": [ "quantum-biology", "biophysics", "NMR-spectroscopy", "photochemistry" ], "color": "gray" }, { "id": "u-quantum-decoherence-microtubule-physiological-temperature-measured", "type": "unknown", "title": "What is the actual quantum decoherence timescale of tubulin dimer conformational superpositions in neuronal microtubules at physiological temperature (310 K), measured directly in situ, and how does it compare to Tegmark's theoretical prediction of ~10⁻¹³ s?\n", "status": "open", "fields": [ "quantum-physics", "biophysics", "neuroscience", "structural-biology" ], "color": "gray" }, { "id": "u-ctqw-grover-geometry-transferability", "type": "unknown", "title": "Which graph families encountered in hardware layouts preserve continuous-time quantum walk spatial-search speedups under realistic connectivity constraints, decoherence, and oracle compilation overhead relative to discrete Grover circuits?\n", "status": "open", "fields": [ "quantum-computing", "quantum-information" ], "color": "gray" }, { "id": "u-non-abelian-anyons-topological-qc", "type": "unknown", "title": "Have non-Abelian anyons been experimentally realized with sufficient fidelity to demonstrate braiding-based topological quantum gates, and what material systems provide the best platform for scalable topological qubits resistant to local decoherence?", "status": "open", "fields": [ "quantum-computing", "condensed-matter", "topology" ], "color": "gray" }, { "id": "u-quantum-annealing-simulated", "type": "unknown", "title": "Does quantum annealing provide a provable computational advantage over classical simulated annealing for any practically relevant optimization problem class, and on what problem instances?", "status": "open", "fields": [ "quantum-computing", "combinatorics", "theoretical-computer-science" ], "color": "gray" }, { "id": "u-quantum-speedup-optimization-boundary", "type": "unknown", "title": "What is the precise boundary between optimization problems where quantum annealing offers speedup over classical simulated annealing?", "status": "open", "fields": [ "quantum-computing", "optimization", "statistical-physics" ], "color": "gray" }, { "id": "u-quantum-supremacy-hardness-noise-boundary", "type": "unknown", "title": "What is the precise noise threshold below which classical simulation of random quantum circuits becomes exponentially hard, and does Google's supremacy claim survive improved classical simulation algorithms?", "status": "open", "fields": [ "quantum-computing", "computational-complexity", "quantum-information" ], "color": "gray" }, { "id": "u-quantum-walk-decoherence-practical-speedup", "type": "unknown", "title": "At what decoherence rate does the quadratic quantum walk speedup over classical random walks disappear, and can noise-tolerant quantum walk algorithms recover polynomial speedup on near-term hardware?", "status": "open", "fields": [ "quantum-computing", "probability-theory", "quantum-information" ], "color": "gray" }, { "id": "u-spectral-gap-quantum-phase-transitions", "type": "unknown", "title": "Can the spectral gap of a local Hamiltonian be computed efficiently, and does its vanishing exactly predict quantum phase transition locations?", "status": "open", "fields": [ "quantum-physics", "computational-complexity", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-bell-loophole-free-implications", "type": "unknown", "title": "What are the full physical implications of loophole-free Bell inequality violations, and do they definitively rule out all local hidden variable theories?", "status": "open", "fields": [ "quantum-foundations", "quantum-mechanics", "quantum-information" ], "color": "gray" }, { "id": "u-decoherence-timescales-warm-systems", "type": "unknown", "title": "What determines quantum decoherence timescales in warm, wet biological systems, and can coherence survive long enough to be functionally relevant?", "status": "open", "fields": [ "quantum-biology", "quantum-mechanics", "biophysics" ], "color": "gray" }, { "id": "u-entanglement-entropy-area-law-exceptions", "type": "unknown", "title": "What are the conditions under which quantum systems violate the entanglement entropy area law, and what do violations imply for classical simulability?", "status": "open", "fields": [ "quantum-information", "condensed-matter-physics", "quantum-computing" ], "color": "gray" }, { "id": "u-entanglement-tensor-network-complexity", "type": "unknown", "title": "What is the computational complexity of contracting arbitrary tensor networks, and which physical entanglement structures admit efficient classical simulation versus those that are fundamentally classically intractable?", "status": "open", "fields": [ "quantum-physics", "mathematics", "computer-science" ], "color": "gray" }, { "id": "u-holographic-entanglement-bulk-reconstruction-limits", "type": "unknown", "title": "What are the precise limits of bulk reconstruction from boundary entanglement data, and can the entanglement wedge reconstruction be extended beyond semiclassical gravity to full quantum gravity?", "status": "open", "fields": [ "quantum-physics", "information-theory", "mathematics" ], "color": "gray" }, { "id": "u-many-worlds-copenhagen-experimental", "type": "unknown", "title": "Is there any experiment that could empirically distinguish many-worlds from Copenhagen or other single-outcome interpretations of quantum mechanics?", "status": "open", "fields": [ "quantum-foundations", "quantum-mechanics", "philosophy-of-physics" ], "color": "gray" }, { "id": "u-measurement-problem-interpretation", "type": "unknown", "title": "What physical process constitutes quantum measurement, and which interpretation of quantum mechanics is empirically correct?", "status": "open", "fields": [ "quantum-foundations", "quantum-mechanics", "philosophy-of-physics" ], "color": "gray" }, { "id": "u-neutral-atom-qubit-fidelity", "type": "unknown", "title": "What limits neutral atom qubit gate fidelities, and can they reach the error thresholds required for fault-tolerant quantum computing?", "status": "open", "fields": [ "quantum-computing", "atomic-physics", "quantum-information" ], "color": "gray" }, { "id": "u-perturbation-series-borel-summability-qft", "type": "unknown", "title": "What is the complete non-perturbative structure of QED and QCD that makes their perturbation series Borel summable (or not), and can resurgence theory systematically reconstruct the full answer from the divergent perturbative expansion?\n", "status": "open", "fields": [ "quantum-field-theory", "mathematics", "perturbation-theory", "resurgence-theory" ], "color": "gray" }, { "id": "u-photon-antibunching-sub-poissonian", "type": "unknown", "title": "What limits g⁽²⁾(0) from reaching zero in solid-state single-photon emitters at room temperature, and can photon indistinguishability be simultaneously maximized with high brightness?", "status": "open", "fields": [ "quantum-physics", "quantum-information", "photonics" ], "color": "gray" }, { "id": "u-photonic-qc-scalability", "type": "unknown", "title": "Can photonic quantum computing achieve fault-tolerant scalability given current photon loss rates and deterministic gate requirements?", "status": "open", "fields": [ "quantum-computing", "quantum-optics", "photonics" ], "color": "gray" }, { "id": "u-post-quantum-cryptography-transition", "type": "unknown", "title": "What is the timeline and risk profile of the transition to post-quantum cryptography, and which current systems are most vulnerable to harvest-now-decrypt-later attacks?", "status": "open", "fields": [ "cryptography", "quantum-computing", "cybersecurity" ], "color": "gray" }, { "id": "u-qaoa-depth-generalization-vs-classical-baselines", "type": "unknown", "title": "Under realistic hardware noise, when do finite-depth QAOA instances generalize better than strong classical combinatorial baselines on matched problem ensembles?", "status": "open", "fields": [ "quantum-computing", "computer-science", "operations-research" ], "color": "gray" }, { "id": "u-qft-non-perturbative-regimes", "type": "unknown", "title": "What analytical or computational methods can access the non-perturbative regimes of quantum field theories, particularly strongly coupled gauge theories?", "status": "open", "fields": [ "quantum-field-theory", "theoretical-physics", "nuclear-physics" ], "color": "gray" }, { "id": "u-qkd-practical-limits", "type": "unknown", "title": "What are the practical security limits of quantum key distribution against quantum computer attacks, side channels, and photonic implementation imperfections?", "status": "open", "fields": [ "quantum-cryptography", "quantum-information", "information-security" ], "color": "gray" }, { "id": "u-quantum-advantage-classical-boundary", "type": "unknown", "title": "For which computational problems does quantum hardware achieve asymptotic speedup that is robust to realistic noise and classical simulation advances?", "status": "open", "fields": [ "quantum-computing", "computational-complexity", "algorithms" ], "color": "gray" }, { "id": "u-quantum-annealing-qaoa-comparison", "type": "unknown", "title": "Under what conditions does quantum annealing outperform QAOA or classical optimisation methods for structured combinatorial problems?", "status": "open", "fields": [ "quantum-computing", "algorithms", "optimisation" ], "color": "gray" }, { "id": "u-quantum-chaos-scrambling-rates", "type": "unknown", "title": "What determines quantum information scrambling rates in chaotic systems, and does the Maldacena-Shenker-Stanford bound saturate in black holes and quantum matter?", "status": "open", "fields": [ "quantum-chaos", "quantum-gravity", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-quantum-darwinism-evidence", "type": "unknown", "title": "Does quantum Darwinism explain the emergence of classical objectivity from quantum mechanics, and what experimental evidence would confirm it?", "status": "open", "fields": [ "quantum-foundations", "quantum-mechanics", "quantum-information" ], "color": "gray" }, { "id": "u-quantum-darwinism-redundancy-threshold-classicality", "type": "unknown", "title": "What minimum redundancy in environmental information encoding is required for a quantum system to appear classical, and has quantum Darwinism been experimentally verified in a controlled system?", "status": "open", "fields": [ "quantum-physics", "information-theory" ], "color": "gray" }, { "id": "u-quantum-dot-blinking-power-law-mechanism", "type": "unknown", "title": "What is the physical mechanism that generates strictly power-law (rather than log-normal or stretched-exponential) on/off time distributions in quantum dot blinking, and why are the exponents universal across QD compositions?\n", "status": "open", "fields": [ "quantum-physics", "statistics", "materials-science" ], "color": "gray" }, { "id": "u-quantum-error-correction-overhead", "type": "unknown", "title": "What is the minimum physical-to-logical qubit overhead required for fault-tolerant quantum computing, and can it be reduced below current theoretical estimates?", "status": "open", "fields": [ "quantum-error-correction", "quantum-computing", "information-theory" ], "color": "gray" }, { "id": "u-quantum-gravity-unification", "type": "unknown", "title": "What is the correct theory unifying quantum mechanics and general relativity, and what are its experimentally distinguishable predictions?", "status": "open", "fields": [ "quantum-gravity", "theoretical-physics", "cosmology" ], "color": "gray" }, { "id": "u-quantum-memory-coherence-limits", "type": "unknown", "title": "What are the fundamental limits on quantum memory coherence times in solid-state, atomic, and photonic systems, and how close are current implementations?", "status": "open", "fields": [ "quantum-computing", "quantum-information", "atomic-physics" ], "color": "gray" }, { "id": "u-quantum-metrology-heisenberg-limit", "type": "unknown", "title": "Can quantum metrology routinely achieve Heisenberg-limited sensitivity in practical measurement contexts, overcoming decoherence and photon loss?", "status": "open", "fields": [ "quantum-metrology", "quantum-optics", "quantum-sensing" ], "color": "gray" }, { "id": "u-quantum-random-number-true-randomness", "type": "unknown", "title": "Can quantum random number generators produce certifiably true randomness, and what are the practical limits of device-independent randomness certification?", "status": "open", "fields": [ "quantum-information", "cryptography", "quantum-foundations" ], "color": "gray" }, { "id": "u-quantum-repeater-distance-limit", "type": "unknown", "title": "What is the maximum distance over which quantum entanglement can be distributed using quantum repeaters, and what are the fundamental rate-distance tradeoffs?", "status": "open", "fields": [ "quantum-networks", "quantum-information", "quantum-optics" ], "color": "gray" }, { "id": "u-quantum-sensing-biological-limits", "type": "unknown", "title": "Can quantum sensing technologies achieve the sensitivity required to probe biological processes at the single-molecule level in living systems?", "status": "open", "fields": [ "quantum-sensing", "biophysics", "quantum-metrology" ], "color": "gray" }, { "id": "u-quantum-simulation-classical-hardness", "type": "unknown", "title": "For which quantum systems is classical simulation provably hard, and where does the quantum-classical hardness boundary lie in practice?", "status": "open", "fields": [ "quantum-computing", "computational-physics", "quantum-chemistry" ], "color": "gray" }, { "id": "u-quantum-speedup-optimization-np", "type": "unknown", "title": "Can quantum algorithms provide polynomial or superpolynomial speedups for NP-hard combinatorial optimisation problems?", "status": "open", "fields": [ "quantum-computing", "computational-complexity", "algorithms" ], "color": "gray" }, { "id": "u-quantum-thermodynamics-arrow", "type": "unknown", "title": "Does quantum thermodynamics provide a microscopic derivation of the second law, and does quantum coherence generate or suppress thermodynamic irreversibility?", "status": "open", "fields": [ "quantum-thermodynamics", "statistical-mechanics", "quantum-information" ], "color": "gray" }, { "id": "u-topological-qc-fault-tolerance-threshold", "type": "unknown", "title": "Can topological quantum error correction achieve fault-tolerant thresholds in physical systems, and what are the minimum hardware requirements?", "status": "open", "fields": [ "quantum-computing", "condensed-matter-physics", "quantum-error-correction" ], "color": "gray" }, { "id": "u-majorana-zero-mode-experimental-confirmation", "type": "unknown", "title": "Whether unambiguous Majorana zero modes have been experimentally confirmed in any solid-state system, and whether the retracted Microsoft InAs nanowire results reflect a fundamental challenge to the topological qubit programme\n", "status": "open", "fields": [ "quantum-physics", "condensed-matter-physics", "quantum-computing" ], "color": "gray" }, { "id": "u-energy-landscape-mismatch-indicators-for-lesion-segmentation-qc", "type": "unknown", "title": "What failure boundaries determine when `b-graph-cut-energy-minimization-x-radiology-lesion-segmentation-qc` remains decision-useful?", "status": "open", "fields": [ "computer-vision", "radiology" ], "color": "gray" }, { "id": "u-resnet-histology-domain-shift-failure-modes", "type": "unknown", "title": "Which histopathology domain shifts most strongly degrade residual-network diagnostic reliability?", "status": "open", "fields": [ "radiology", "pathology", "machine-learning" ], "color": "gray" }, { "id": "u-sampling-pattern-transferability-for-compressed-sensing-mri", "type": "unknown", "title": "Which acquisition patterns keep `b-compressed-sensing-x-accelerated-mri-protocol-design` robust across scanners and patient cohorts?", "status": "open", "fields": [ "radiology", "signal-processing" ], "color": "gray" }, { "id": "u-ransac-optimal-sampling-strategy-non-uniform-inlier-distribution", "type": "unknown", "title": "What is the optimal sampling strategy for RANSAC when the inlier distribution is non-uniform (spatially clustered, class-imbalanced, or structured) ΓÇö and can information-theoretic bounds on robust estimation be derived for heterogeneous inlier distributions?\n", "status": "open", "fields": [ "statistics", "computer-vision", "robotics", "machine-learning" ], "color": "gray" }, { "id": "u-earthquake-alert-threshold-sprt-under-correlated-noise", "type": "unknown", "title": "Under spatially correlated seismic noise and aftershock clustering, what sequential decision boundaries achieve bounded false-alarm rates for EEW while preserving detection probability targets — without Wald’s classic i.i.d. guarantees?\n", "status": "open", "fields": [ "seismology", "statistics" ], "color": "gray" }, { "id": "u-earthquake-nucleation-dislocation-slip-weakening", "type": "unknown", "title": "What is the earthquake nucleation process at the asperity scale, and does rate-and-state friction or slip-weakening dislocation mechanics better describe the transition from quasi-static creep to dynamic rupture?\n", "status": "open", "fields": [ "seismology", "solid-mechanics" ], "color": "gray" }, { "id": "u-pino-aftershock-forecasting-uncertainty-calibration", "type": "unknown", "title": "Are PINO aftershock forecasts uncertainty-calibrated for operational hazard use?", "status": "open", "fields": [ "seismology", "machine-learning", "geophysics" ], "color": "gray" }, { "id": "u-bci-non-stationarity-adaptation", "type": "unknown", "title": "How can brain-computer interface decoders adapt to non-stationary neural signals (caused by learning, electrode drift, and day-to-day variability) without requiring lengthy daily recalibration?", "status": "open", "fields": [ "neuroscience", "signal-processing", "machine-learning" ], "color": "gray" }, { "id": "u-oxytocin-parochial-altruism-policy-implications", "type": "unknown", "title": "Are the parochial altruism effects of oxytocin (increased in-group cooperation, decreased out-group trust) large enough, robust enough, and manipulable enough to have policy-relevant implications for inter-group conflict and diversity- cooperation tradeoffs, or are they small-effect laboratory phenomena that do not generalise to real social behaviour?\n", "status": "open", "fields": [ "social-neuroscience", "behavioural-economics", "political-science", "psychology" ], "color": "gray" }, { "id": "u-abm-calibration-empirical-social-science-validation", "type": "unknown", "title": "How should agent-based models be calibrated, validated, and falsified against empirical social science data, given that the mapping between ABM agent rules and measurable individual behaviors is underdetermined and emergent outcomes are sensitive to rule specification?\n", "status": "open", "fields": [ "social-science", "mathematics", "complexity-science", "statistics" ], "color": "gray" }, { "id": "u-attitude-behavior-gap-pro-environmental", "type": "unknown", "title": "Why does high environmental concern consistently fail to predict pro-environmental behavior, and which psychological and structural mechanisms most effectively close the attitude-behavior gap at population scale?", "status": "open", "fields": [ "conservation-psychology", "social-psychology", "behavioral-economics", "environmental-sociology" ], "color": "gray" }, { "id": "u-behavioral-immune-system-pathogen-xenophobia-mechanism", "type": "unknown", "title": "What is the specific cognitive-neural mechanism by which the behavioral immune system generalises from disease-avoidance disgust to anti-outgroup prejudice, and can this mechanism be modulated without impairing genuine pathogen detection — enabling interventions that reduce xenophobia while preserving public-health-relevant disgust?\n", "status": "open", "fields": [ "social-science", "evolutionary-biology", "psychology", "neuroscience" ], "color": "gray" }, { "id": "u-collective-action-without-authority", "type": "unknown", "title": "Under what conditions can groups solve collective action problems without central authority, and how do informal institutions maintain cooperation?", "status": "open", "fields": [ "social-science", "economics", "political-science" ], "color": "gray" }, { "id": "u-collective-memory-formation", "type": "unknown", "title": "How do collective memories of historical events form, persist, and distort across generations, and what factors determine their cultural salience?", "status": "open", "fields": [ "social-science", "sociology", "history" ], "color": "gray" }, { "id": "u-complex-contagion-threshold-distribution-estimation", "type": "unknown", "title": "How can adoption thresholds φ_i be estimated from observational social network data, and does the empirical threshold distribution predict cascade dynamics better than mean-field SIR models for real social behaviours?\n", "status": "open", "fields": [ "social-science", "epidemiology", "network-science" ], "color": "gray" }, { "id": "u-criminal-justice-deterrence", "type": "unknown", "title": "What is the evidence that criminal punishment deters crime, and which punishment characteristics (certainty, severity, speed) have the largest effects?", "status": "open", "fields": [ "social-science", "criminology", "public-policy" ], "color": "gray" }, { "id": "u-crowd-dynamics-panic-transitions", "type": "unknown", "title": "What are the measurable precursors of the transition from orderly evacuation to panic-driven crowd turbulence, and can social force model parameters be calibrated in real-time from LiDAR or video tracking to give advance warning of a crowd disaster?", "status": "open", "fields": [ "physics", "social-science", "complex-systems" ], "color": "gray" }, { "id": "u-cultural-drift-vs-selection-detection", "type": "unknown", "title": "How can we empirically distinguish cultural drift (neutral random variation) from cultural selection (adaptive adoption) in archaeological and digital cultural datasets, given that both produce power-law frequency distributions?\n", "status": "open", "fields": [ "cultural-anthropology", "evolutionary-biology", "social-science", "archaeology" ], "color": "gray" }, { "id": "u-cultural-evolution-rate-prediction", "type": "unknown", "title": "What determines the rate of cultural evolution, and can formal evolutionary models predict the speed and direction of cultural change?", "status": "open", "fields": [ "social-science", "cultural-evolution", "anthropology" ], "color": "gray" }, { "id": "u-dark-patterns-cognitive-bias-exploitation-measurement", "type": "unknown", "title": "Can the harm potential of dark pattern UI designs be quantified on a principled scale based on cognitive load theory and behavioral economics — and what is the causal effect of specific dark patterns on user decision quality and autonomy?\n", "status": "open", "fields": [ "social-science", "cognitive-psychology", "engineering", "behavioral-economics", "law" ], "color": "gray" }, { "id": "u-democracy-stability-conditions", "type": "unknown", "title": "Under what economic, social, and institutional conditions is democracy stable, and what early warning indicators predict democratic backsliding?", "status": "open", "fields": [ "political-science", "social-science", "economics" ], "color": "gray" }, { "id": "u-differential-privacy-utility-tight-bound", "type": "unknown", "title": "What is the tight tradeoff between privacy budget epsilon and statistical utility for high-dimensional queries, and can tight privacy-utility Pareto frontiers be computed efficiently for arbitrary query workloads beyond counting queries?\n", "status": "open", "fields": [ "information-theory", "statistics", "computer-science", "social-science" ], "color": "gray" }, { "id": "u-economic-inequality-tipping-points", "type": "unknown", "title": "At what level of economic inequality do self-reinforcing mechanisms kick in to produce permanently elevated inequality, and can policy reverse this?", "status": "open", "fields": [ "social-science", "economics", "political-science" ], "color": "gray" }, { "id": "u-econophysics-wealth-distribution-mechanism", "type": "unknown", "title": "Do kinetic exchange econophysics models correctly identify the microscopic mechanism generating Pareto wealth distributions, or are emergent power laws coincidental fits requiring different causal explanations?", "status": "open", "fields": [ "sociology", "statistical-physics", "economics" ], "color": "gray" }, { "id": "u-epigenetic-intergenerational-transmission-social-stress", "type": "unknown", "title": "To what extent are epigenetic modifications induced by social adversity (poverty, trauma, discrimination) transmitted to offspring via germline epigenetic inheritance in humans — and over how many generations do such marks persist?\n", "status": "open", "fields": [ "social-science", "epigenetics", "biology", "developmental-biology", "public-health" ], "color": "gray" }, { "id": "u-fairness-impossibility-optimal-tradeoff", "type": "unknown", "title": "Given the mathematical impossibility of simultaneously satisfying demographic parity, equalized odds, and calibration, what principles should determine how to navigate the fairness-accuracy tradeoff in high-stakes algorithmic decisions?", "status": "open", "fields": [ "machine-learning", "social-science", "philosophy-of-justice", "statistics", "law-and-policy" ], "color": "gray" }, { "id": "u-gender-gap-stem-causes", "type": "unknown", "title": "What explains persistent gender gaps in STEM participation, and why do gaps vary so widely across countries, disciplines, and historical periods?", "status": "open", "fields": [ "social-science", "psychology", "education-research" ], "color": "gray" }, { "id": "u-genocide-early-warning-validity", "type": "unknown", "title": "Do genocide early warning systems have sufficient predictive validity and response time to enable preventive intervention?", "status": "open", "fields": [ "political-science", "social-science", "international-relations" ], "color": "gray" }, { "id": "u-happiness-set-point", "type": "unknown", "title": "Do individuals have a hedonic set point that pulls subjective wellbeing back to baseline after life events, and can deliberate activities shift it permanently?", "status": "open", "fields": [ "social-science", "psychology", "economics" ], "color": "gray" }, { "id": "u-human-error-organizational-accident-boundary", "type": "unknown", "title": "Where exactly is the boundary between individual human error and organizational/ systemic causation in accidents, and can a quantitative model assign causal weight to each level (operator, team, organization, regulatory, societal) in a manner that is both empirically valid and legally operable?\n", "status": "open", "fields": [ "social-science", "engineering", "organizational-psychology", "safety-science", "law" ], "color": "gray" }, { "id": "u-inequality-health-pathway", "type": "unknown", "title": "Does income inequality causally harm population health beyond individual poverty effects, and what are the specific pathways?", "status": "open", "fields": [ "social-science", "public-health", "economics" ], "color": "gray" }, { "id": "u-institutional-trust-collapse", "type": "unknown", "title": "What mechanisms drive rapid collapses in institutional trust, and can early warning signs be identified before collapse occurs?", "status": "open", "fields": [ "social-science", "political-science", "sociology" ], "color": "gray" }, { "id": "u-intergroup-contact-prejudice-reduction", "type": "unknown", "title": "Under what conditions does intergroup contact reliably reduce prejudice, and can these conditions be engineered in diverse societies at scale?", "status": "open", "fields": [ "social-science", "psychology", "sociology" ], "color": "gray" }, { "id": "u-matching-markets-dynamic-stability", "type": "unknown", "title": "Does the Gale-Shapley stable matching persist under dynamic entry/exit of agents, and what mechanism design principles govern real-time matching platforms (gig economy, ride-sharing)?", "status": "open", "fields": [ "social-science", "mathematics", "economics", "mechanism-design" ], "color": "gray" }, { "id": "u-meme-channel-capacity-measurement", "type": "unknown", "title": "Can the channel capacity for cultural transmission be measured empirically for different media and idea types?", "status": "open", "fields": [ "social-science", "information-theory", "cultural-evolution", "communication-theory" ], "color": "gray" }, { "id": "u-misinformation-correction-asymmetry", "type": "unknown", "title": "Why is misinformation often more persistent and spreadable than corrections, and what interventions reliably reduce its effects?", "status": "open", "fields": [ "social-science", "psychology", "communication-science" ], "color": "gray" }, { "id": "u-moral-intuition-evolutionary-stability-mapping", "type": "unknown", "title": "Which moral intuitions (Haidt's foundations) map onto evolutionarily stable strategies in iterated social games, and what payoff structures predict the cross-cultural variation in their strength?\n", "status": "open", "fields": [ "moral-psychology", "evolutionary-biology", "game-theory" ], "color": "gray" }, { "id": "u-multilateral-cooperation-failure-modes", "type": "unknown", "title": "Why do multilateral cooperation arrangements fail for global commons problems, and what institutional designs are most robust to defection?", "status": "open", "fields": [ "political-science", "social-science", "international-relations" ], "color": "gray" }, { "id": "u-network-centrality-temporal-dynamics-influence", "type": "unknown", "title": "How does temporal variation in social network structure (edge rewiring, link decay, node activity bursts) affect the validity of static centrality measures as predictors of influence, and what dynamic centrality metrics correctly capture time-varying social power?", "status": "open", "fields": [ "social-science", "network-science", "mathematics", "epidemiology", "computational-social-science" ], "color": "gray" }, { "id": "u-network-formation-dynamic-stability-real-world", "type": "unknown", "title": "Do empirical social networks converge to the Nash-stable configurations predicted by Jackson-Wolinsky theory, and how does the Braess paradox manifest in real social and information networks?", "status": "open", "fields": [ "social-science", "mathematics", "network-science" ], "color": "gray" }, { "id": "u-opinion-dynamics-critical-homophily", "type": "unknown", "title": "Is there an empirically measurable critical homophily threshold in real social networks above which opinion distributions undergo a sharp transition from unimodal (pluralism) to bimodal (polarisation), and can this threshold be predicted from network topology using Ising model theory?\n", "status": "open", "fields": [ "social-science", "political-science", "network-science", "statistical-physics", "complexity-science" ], "color": "gray" }, { "id": "u-opinion-dynamics-empirical-calibration-social-media-networks", "type": "unknown", "title": "Can Ising/bounded-confidence opinion dynamics models be empirically calibrated to social media data (Twitter/X, Reddit), and do their predictions for polarization trajectories and consensus thresholds agree with observed political opinion distributions in longitudinal surveys?\n", "status": "open", "fields": [ "social-science", "physics", "computational-social-science", "political-science" ], "color": "gray" }, { "id": "u-opinion-dynamics-phase-transition-prediction", "type": "unknown", "title": "Can Ising-model-based opinion dynamics models predict real-world political polarization tipping points, and what empirical observables correspond to the social \"temperature\" and \"coupling constant\"?", "status": "open", "fields": [ "social-science", "statistical-physics", "complexity-science", "political-science" ], "color": "gray" }, { "id": "u-opioid-prescribing-policy-chemistry-disconnect", "type": "unknown", "title": "Why does pharmacological evidence about opioid receptor pharmacokinetics and tolerance mechanisms fail to translate into evidence-based prescribing policy, and how can this science-policy gap be closed?", "status": "open", "fields": [ "social-science", "pharmacology", "chemistry", "public-health" ], "color": "gray" }, { "id": "u-political-polarisation-dynamics", "type": "unknown", "title": "What drives increasing political polarisation in democracies, and are online filter bubbles a primary cause or a symptom?", "status": "open", "fields": [ "social-science", "political-science", "computational-social-science" ], "color": "gray" }, { "id": "u-prediction-market-thin-market-accuracy-limits", "type": "unknown", "title": "What are the fundamental accuracy limits of prediction markets with thin trading, and can LMSR automated market makers with subsidized liquidity overcome the no-trade theorem in practice?", "status": "open", "fields": [ "information-economics", "mechanism-design", "statistics" ], "color": "gray" }, { "id": "u-racial-achievement-gap-mechanisms", "type": "unknown", "title": "What are the causal mechanisms producing persistent racial achievement gaps in education, and which interventions most reliably close them?", "status": "open", "fields": [ "social-science", "education-research", "psychology" ], "color": "gray" }, { "id": "u-replicator-equation-cultural-norm-stability", "type": "unknown", "title": "Can replicator equation dynamics explain the stability and collapse of cultural norms and social conventions, and when do multiple Nash equilibria coexist as persistent alternative cultures?", "status": "open", "fields": [ "cultural-evolution", "social-science", "evolutionary-game-theory" ], "color": "gray" }, { "id": "u-social-capital-measurement", "type": "unknown", "title": "What is the best way to measure social capital, and do different definitions (bonding, bridging, linking) have different effects on economic and social outcomes?", "status": "open", "fields": [ "social-science", "economics", "sociology" ], "color": "gray" }, { "id": "u-social-contagion-vs-homophily", "type": "unknown", "title": "For any given social behaviour, how much of its clustering in social networks is due to social influence (contagion) versus homophily (assortative mixing)?", "status": "open", "fields": [ "social-science", "network-science", "sociology" ], "color": "gray" }, { "id": "u-social-critical-temperature-empirical", "type": "unknown", "title": "Can the effective social temperature (disorder parameter in opinion Ising models) be empirically measured from large-scale social media data, and does it predict proximity to opinion cascade tipping points?\n", "status": "open", "fields": [ "social-science", "physics", "statistical-mechanics", "network-science" ], "color": "gray" }, { "id": "u-social-learning-cultural-transmission-mechanisms", "type": "unknown", "title": "What cognitive and social mechanisms determine which learning biases (conformist, prestige, content-based) dominate cultural transmission in different contexts, and can population-genetic style models quantitatively predict cross-cultural variation in the stability of cooperative norms?", "status": "open", "fields": [ "social-science", "evolutionary-biology", "cognitive-science" ], "color": "gray" }, { "id": "u-social-media-mental-health-causality", "type": "unknown", "title": "Does social media use causally increase depression, anxiety, and loneliness in adolescents, and which usage patterns are most harmful?", "status": "open", "fields": [ "social-science", "psychology", "public-health" ], "color": "gray" }, { "id": "u-social-mobility-measurement", "type": "unknown", "title": "How should intergenerational social mobility be measured, and what policies reliably increase it across different national contexts?", "status": "open", "fields": [ "social-science", "economics", "sociology" ], "color": "gray" }, { "id": "u-social-norm-cascade-tipping-points", "type": "unknown", "title": "What are the tipping point conditions under which social norms rapidly cascade through populations, and can they be predicted in advance?", "status": "open", "fields": [ "social-science", "complex-systems", "sociology" ], "color": "gray" }, { "id": "u-stereotype-formation-persistence", "type": "unknown", "title": "How do group stereotypes form, persist, and spread, and under what conditions do accurate versus inaccurate stereotypes prevail?", "status": "open", "fields": [ "social-science", "psychology", "sociology" ], "color": "gray" }, { "id": "u-strategic-voting-frequency-real-elections-empirical-magnitude", "type": "unknown", "title": "What fraction of voters in real elections with plurality or ranked-choice voting vote strategically (not according to their true first preference), what is the net electoral effect of strategic voting on outcomes, and does the Gibbard-Satterthwaite theorem's prediction of universal manipulability manifest at empirically relevant rates?\n", "status": "open", "fields": [ "political-science", "economics", "social-choice-theory", "behavioral-economics" ], "color": "gray" }, { "id": "u-traffic-jam-phantom-formation-threshold", "type": "unknown", "title": "What is the exact critical density threshold rho_c at which phantom traffic jams spontaneously form from uniform flow, and how does this threshold depend on driver behavioral parameters (reaction time, following distance preferences)?\n", "status": "open", "fields": [ "social-science", "physics", "fluid-dynamics", "transportation-science" ], "color": "gray" }, { "id": "u-trust-network-scale", "type": "unknown", "title": "How does interpersonal trust scale from dyadic to community to institutional levels, and when does network structure facilitate or undermine trust formation?", "status": "open", "fields": [ "social-science", "sociology", "network-science" ], "color": "gray" }, { "id": "u-urban-segregation-self-reinforcement", "type": "unknown", "title": "To what degree does residential racial and economic segregation self-reinforce through school quality, social networks, and political power?", "status": "open", "fields": [ "social-science", "urban-economics", "sociology" ], "color": "gray" }, { "id": "u-voter-model-zealots-persistent-minorities", "type": "unknown", "title": "In voter models with zealots (committed agents who never change opinion), what fraction of committed minority advocates is sufficient to shift consensus in heterogeneous social networks, and how does network community structure modulate this tipping threshold?\n", "status": "open", "fields": [ "social-science", "mathematics", "network-science", "statistical-physics" ], "color": "gray" }, { "id": "u-war-onset-prediction", "type": "unknown", "title": "What variables most reliably predict the onset of interstate and civil wars, and do existing models generalise across historical periods and regions?", "status": "open", "fields": [ "political-science", "social-science", "international-relations" ], "color": "gray" }, { "id": "u-wisdom-of-crowds-condorcet", "type": "unknown", "title": "Under what conditions does social influence in online crowds irreversibly destroy collective accuracy, and is there a critical correlation threshold above which crowd wisdom collapses?", "status": "open", "fields": [ "social-science", "probability", "behavioral-economics" ], "color": "gray" }, { "id": "u-homophily-echo-chamber-causality", "type": "unknown", "title": "Does social network homophily causally produce echo chambers and political polarization, or do pre-existing attitude differences drive both homophily and polarization simultaneously?", "status": "open", "fields": [ "social-science", "network-science", "political-science", "computational-social-science" ], "color": "gray" }, { "id": "u-social-capital-causal-vs-correlational", "type": "unknown", "title": "Whether network centrality measures (betweenness, eigenvector) causally determine socioeconomic outcomes or merely correlate with them, and whether targeted network interventions can increase social mobility by changing individuals' centrality positions\n", "status": "open", "fields": [ "sociology", "network-science", "economics", "causal-inference" ], "color": "gray" }, { "id": "u-colloidal-glass-transition-mode-coupling-breakdown", "type": "unknown", "title": "Why does mode-coupling theory (MCT) predict the colloidal glass transition at phi_MCT ~ 0.516 while experiments show phi_glass ~ 0.58-0.64, and what physical mechanism — activated hopping, cooperative rearrangements, or structural heterogeneity — controls the glass transition above phi_MCT?\n", "status": "open", "fields": [ "soft-matter", "statistical-mechanics", "condensed-matter-physics", "colloid-science" ], "color": "gray" }, { "id": "u-jamming-transition-universality-class", "type": "unknown", "title": "Does the jamming transition in frictionless sphere packings belong to the same universality class as mean-field percolation, and how do friction, asphericity, and polydispersity alter the critical exponents and diverging length scale near phi_J?", "status": "open", "fields": [ "soft-matter", "statistical-physics", "condensed-matter-physics" ], "color": "gray" }, { "id": "u-spatial-autocorrelation-economic-convergence-causality", "type": "unknown", "title": "Does high Moran's I spatial autocorrelation in regional income cause income divergence (reinforcing inequality via agglomeration), or does divergence cause high autocorrelation (sorting of high-productivity workers into high-income regions) — and can the Krugman bifurcation point be identified empirically from the trajectory of spatial autocorrelation statistics?\n", "status": "open", "fields": [ "spatial-economics", "economic-geography", "statistics", "regional-science" ], "color": "gray" }, { "id": "u-bayesian-dropout-trial-decision-calibration-gap", "type": "unknown", "title": "Can dropout-based uncertainty be calibrated sufficiently for adaptive trial stopping decisions?", "status": "open", "fields": [ "statistics", "machine-learning", "medicine" ], "color": "gray" }, { "id": "u-bayesian-phase-transition-learning", "type": "unknown", "title": "Do real machine learning models undergo sharp phase transitions in generalization analogous to the thermodynamic underfitting-overfitting transition?", "status": "open", "fields": [ "machine-learning", "statistical-physics", "statistics" ], "color": "gray" }, { "id": "u-cryoem-bayesian-map-calibration-across-platforms", "type": "unknown", "title": "How comparable are Bayesian posterior widths reported by different cryo-EM software stacks when processing identical particle stacks — especially prior choices affecting MAP versus sampled volumes?", "status": "open", "fields": [ "structural-biology", "statistics" ], "color": "gray" }, { "id": "u-elastic-net-prior-calibration-under-correlated-designs", "type": "unknown", "title": "How should elastic-net mixing parameters be calibrated as composite Laplace-Gaussian prior scales when predictors are strongly correlated and scientific interpretation depends on selected groups?\n", "status": "open", "fields": [ "statistics", "machine-learning" ], "color": "gray" }, { "id": "u-posterior-landscape-multimodality", "type": "unknown", "title": "How does multimodality in the posterior distribution arise in Bayesian inference for high-dimensional models, and what sampling methods reliably explore it?", "status": "open", "fields": [ "statistics", "statistical-physics", "machine-learning" ], "color": "gray" }, { "id": "u-ridge-as-gaussian-map-prior-identifiability", "type": "unknown", "title": "When does cross-validated ridge λ carry a coherent Bayesian interpretation under model mismatch and heavy-tailed true coefficients?", "status": "open", "fields": [ "statistics", "computer-science" ], "color": "gray" }, { "id": "u-catalytic-converter-cold-start-mitigation-strategies", "type": "unknown", "title": "What are the most cost-effective strategies to eliminate the cold-start emission spike (responsible for ~80% of vehicle emissions per trip) for both conventional and hybrid powertrains as electrification penetrates the fleet?\n", "status": "open", "fields": [ "surface-chemistry", "catalysis", "automotive-engineering", "environmental-science" ], "color": "gray" }, { "id": "u-crispr-hdr-efficiency-post-mitotic-cells", "type": "unknown", "title": "Why is homology-directed repair (HDR) efficiency <1% in post-mitotic neurons, cardiomyocytes, and skeletal muscle cells following CRISPR-Cas9 DSBs, and can prime editing or base editing fully bypass this constraint to enable therapeutic gene correction in non-dividing tissues?\n", "status": "open", "fields": [ "synthetic-biology", "molecular-biology", "medicine", "cell-biology" ], "color": "gray" }, { "id": "u-genetic-circuit-crosstalk-noise", "type": "unknown", "title": "How does molecular crosstalk and stochastic gene expression noise limit the complexity and reliability of engineered genetic circuits in living cells?", "status": "open", "fields": [ "synthetic-biology", "systems-biology", "information-theory" ], "color": "gray" }, { "id": "u-retroactivity-insulation-genetic-circuits", "type": "unknown", "title": "Can retroactivity (impedance-mismatch loading effects between genetic circuit modules) be sufficiently attenuated by biological insulator modules to enable reliable large-scale genetic circuit composition?", "status": "open", "fields": [ "synthetic-biology", "engineering", "systems-biology" ], "color": "gray" }, { "id": "u-rna-folding-planar-pseudoknot-limits", "type": "unknown", "title": "Which biological RNAs require pseudoknot representations beyond planar DP, and how often do planar approximations distort functional predictions?", "status": "open", "fields": [ "biology", "computer-science" ], "color": "gray" }, { "id": "u-synthetic-biology-chassis-orthogonality", "type": "unknown", "title": "Can a fully orthogonal genetic chassis be engineered that runs synthetic gene circuits without crosstalk with host transcription/translation machinery?", "status": "open", "fields": [ "synthetic-biology", "biotechnology", "genetic-engineering" ], "color": "gray" }, { "id": "u-boolean-grn-criticality-empirical-tests", "type": "unknown", "title": "Do empirical mammalian gene regulatory networks operate near Kauffman-style critical points once asynchronous stochastic dynamics replace synchronous Booleans?", "status": "open", "fields": [ "systems-biology", "computational-biology" ], "color": "gray" }, { "id": "u-cost-matrix-misspecification-in-optimal-transport-lineage-maps", "type": "unknown", "title": "When does cost-function misspecification make `b-optimal-transport-x-single-cell-developmental-lineage-mapping` biologically misleading?", "status": "open", "fields": [ "systems-biology", "statistics" ], "color": "gray" }, { "id": "u-coupling-topology-thresholds-for-beta-cell-synchrony-collapse", "type": "unknown", "title": "What validation boundary conditions determine when `b-kuramoto-synchrony-x-beta-cell-islet-oscillations` remains decision-useful?", "status": "open", "fields": [ "electrical-engineering", "systems-biology" ], "color": "gray" }, { "id": "u-latent-space-collapse-risks-in-single-cell-vae-models", "type": "unknown", "title": "Under which noise regimes does `b-variational-autoencoders-x-single-cell-latent-state-denoising` suffer latent collapse?", "status": "open", "fields": [ "systems-biology", "statistics" ], "color": "gray" }, { "id": "u-mca-global-sensitivity-beyond-log-linear", "type": "unknown", "title": "When metabolic steady states leave log-linear neighborhoods of MCA elasticity definitions, which global sensitivity methods (Sobol, DGSM) recover comparable biological insights without elasticity breakdown artifacts?", "status": "open", "fields": [ "systems-biology", "applied-mathematics" ], "color": "gray" }, { "id": "u-simclr-multiomics-batch-effect-collapse-risk", "type": "unknown", "title": "Under what conditions does contrastive multi-omics alignment collapse biological signal into batch-driven structure?", "status": "open", "fields": [ "systems-biology", "machine-learning" ], "color": "gray" }, { "id": "u-spectral-cluster-stability-metabolomics-batch-effects", "type": "unknown", "title": "How stable are spectral-clustered metabolite modules to batch-effect perturbations of empirical similarity graphs across cohorts and instrument drift?", "status": "open", "fields": [ "mathematics", "medicine", "systems-biology" ], "color": "gray" }, { "id": "u-transport-cost-selection-in-cross-platform-multiomic-alignment", "type": "unknown", "title": "What failure boundaries determine when `b-optimal-transport-barycenters-x-multiomic-patient-alignment` remains decision-useful?", "status": "open", "fields": [ "statistics", "systems-biology" ], "color": "gray" }, { "id": "u-validity-limits-of-adiabatic-elimination-in-noisy-gene-circuits", "type": "unknown", "title": "What validation boundary conditions determine when `b-adiabatic-elimination-x-gene-circuit-model-reduction` remains decision-useful?", "status": "open", "fields": [ "physics", "systems-biology" ], "color": "gray" }, { "id": "u-persistent-homology-noise-thresholds", "type": "unknown", "title": "What statistical criteria distinguish topologically significant features from noise in persistent homology, and how do these criteria scale with dataset size?", "status": "open", "fields": [ "algebraic-topology", "statistics", "data-science" ], "color": "gray" }, { "id": "u-topological-order-non-abelian-anyons-fault-tolerant", "type": "unknown", "title": "Can non-Abelian anyons (e.g. Majorana zero modes in topological superconductors or ν=5/2 FQH states) be reliably braided to perform fault-tolerant quantum gates, and does the topological protection survive realistic disorder, finite-temperature effects, and leakage out of the anyon subspace?\n", "status": "open", "fields": [ "physics", "mathematics", "quantum-computing" ], "color": "gray" }, { "id": "u-hott-univalence-axiom-computational-interpretation", "type": "unknown", "title": "What is the computational interpretation of the Univalence Axiom in Homotopy Type Theory — does it have a constructive proof term, and can HoTT be given a complete computational semantics that makes the axiom provable rather than assumed?\n", "status": "open", "fields": [ "type-theory", "mathematics", "logic", "computer-science" ], "color": "gray" }, { "id": "u-urban-biodiversity-governance-regime-causality", "type": "unknown", "title": "Does polycentric urban governance causally increase urban biodiversity and ecosystem service delivery relative to centralised or fragmented governance, and what governance design features (scale-matching, monitoring, sanctions, polycentrism) drive differences in urban green infrastructure outcomes?\n", "status": "open", "fields": [ "urban-ecology", "governance-science", "sustainability-science", "political-science" ], "color": "gray" }, { "id": "u-smart-city-equity-algorithmic-routing", "type": "unknown", "title": "How can smart city traffic and resource optimization algorithms be designed to satisfy both system efficiency objectives and distributional equity constraints across neighborhoods?", "status": "open", "fields": [ "urban-science", "engineering", "social-science", "computer-science" ], "color": "gray" }, { "id": "u-urban-heat-islands", "type": "unknown", "title": "What is the relative contribution of albedo, green infrastructure, anthropogenic heat, and sky view factor to urban heat island intensity across different city morphologies and climate zones?", "status": "open", "fields": [ "urban-science", "atmospheric-physics", "climate-science" ], "color": "gray" }, { "id": "u-urban-scaling-law-exponent-inequality-cultural-variation", "type": "unknown", "title": "Does the urban superlinear scaling exponent β ≈ 1.15 vary systematically with national inequality (Gini coefficient), urban planning regime, or transportation technology, and can deviations from β = 1.15 be used to identify cities that are underperforming or overperforming their interaction-based potential?", "status": "open", "fields": [ "urban-science", "physics", "economics", "social-science", "network-science" ], "color": "gray" }, { "id": "u-urban-segregation-phase-diagram", "type": "unknown", "title": "What is the full phase diagram of the Schelling segregation model as a function of tolerance threshold, density, and 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"b-fermentation-thermodynamic-equilibrium", "target": "u-fermentation-thermodynamic-efficiency-limit", "relation": "related_unknown" }, { "source": "b-fermentation-thermodynamic-equilibrium", "target": "h-fermentation-nad-ratio-pathway-selection-thermodynamic", "relation": "related_hypothesis" }, { "source": "b-lichen-astrobiology", "target": "u-synthetic-lichen-biofabrication", "relation": "related_unknown" }, { "source": "b-lichen-astrobiology", "target": "h-lichen-consortium-metabolic-coupling", "relation": "related_hypothesis" }, { "source": "b-antibiotic-mechanisms-resistance", "target": "u-antibiotic-resistance-evolution-rate-clinical-deployment", "relation": "related_unknown" }, { "source": "b-antibiotic-mechanisms-resistance", "target": "h-phage-therapy-combination-delays-resistance-evolution-eskape", "relation": "related_hypothesis" }, { "source": "b-autophagy-cellular-recycling", "target": "u-autophagy-selectivity-cargo-receptor-hierarchy", "relation": "related_unknown" }, { "source": "b-autophagy-cellular-recycling", "target": "h-pink1-parkin-mitophagy-parkinsons-therapeutic-target", "relation": "related_hypothesis" }, { "source": "b-circadian-clock-molecular-oscillator", "target": "u-circadian-temperature-compensation-mechanism", "relation": "related_unknown" }, { "source": "b-circadian-clock-molecular-oscillator", "target": "h-circadian-hopf-bifurcation-period-mutation-prediction", "relation": "related_hypothesis" }, { "source": "b-lipid-bilayer-membrane-thermodynamics", "target": "u-anesthesia-consciousness", "relation": "related_unknown" }, { "source": "b-lipid-bilayer-membrane-thermodynamics", "target": "h-anesthesia-consciousness-thalamic-disruption", "relation": "related_hypothesis" }, { "source": "b-lipid-bilayer-membrane-transport", "target": "u-lipid-raft-functional-role-signaling", "relation": "related_unknown" }, { "source": "b-lipid-bilayer-membrane-transport", "target": "h-lipid-raft-phase-separation-receptor-clustering", "relation": "related_hypothesis" }, { "source": "b-protein-folding-energy-landscape", "target": "u-protein-folding-alphafold2-de-novo-design-limits", "relation": "related_unknown" }, { "source": "b-protein-folding-energy-landscape", "target": "h-protein-folding-funnel-alphafold2-contact-prediction-mechanism", "relation": "related_hypothesis" }, { "source": "b-rna-folding-partition-function", "target": "u-rna-folding-pseudoknot-partition-function", "relation": "related_unknown" }, { "source": "b-rna-folding-partition-function", "target": "h-rna-boltzmann-ensemble-functional-structure-selection", "relation": "related_hypothesis" }, { "source": "b-rna-world-origin-of-life", "target": "u-rna-world-nonenzymatic-replication-fidelity", "relation": "related_unknown" }, { "source": "b-rna-world-origin-of-life", "target": "h-rna-world-ribozyme-first-protein-emergence", "relation": "related_hypothesis" }, { "source": "b-secondary-metabolites-drug-discovery", "target": "u-silent-bgc-activation-novel-antibiotics", "relation": "related_unknown" }, { "source": "b-secondary-metabolites-drug-discovery", "target": "h-secondary-metabolites-pksnrps-combinatorial-evolution", "relation": "related_hypothesis" }, { "source": "b-ant-colony-distributed-computation", "target": "u-ant-colony-optimization-convergence-rate", "relation": "related_unknown" }, { "source": "b-ant-colony-optimization-x-gradient-free-metaheuristics", "target": "u-aco-convergence-routing-instances", "relation": "related_unknown" }, { "source": "b-ant-colony-optimization-x-gradient-free-metaheuristics", "target": "h-ant-colony-optimization-x-gradient-free-metaheuristics", "relation": "related_hypothesis" }, { "source": "b-dna-origami-scaffold-routing-x-staged-compilation-analogy", "target": "u-dna-origami-compiler-analogy-yield-prediction-limits", "relation": "related_unknown" }, { "source": "b-dna-origami-scaffold-routing-x-staged-compilation-analogy", "target": "h-scaffold-routing-constraint-metrics-predict-origami-yield", "relation": "related_hypothesis" }, { "source": "b-flocking-reynolds-boids-alignment", "target": "u-flocking-topological-interaction-mechanism", "relation": "related_unknown" }, { "source": "b-gene-regulatory-networks-boolean-logic", "target": "u-boolean-attractor-cell-fate-mapping", "relation": "related_unknown" }, { "source": "b-gene-regulatory-networks-boolean-logic", "target": "h-critical-boolean-network-cell-type-count", "relation": "related_hypothesis" }, { "source": "b-kauffman-boolean-x-gene-network-attractor-stability", "target": "u-boolean-grn-criticality-empirical-tests", "relation": "related_unknown" }, { "source": "b-kauffman-boolean-x-gene-network-attractor-stability", "target": "h-kauffman-boolean-x-gene-network-attractor-stability", "relation": "related_hypothesis" }, { "source": "b-regulatory-networks-boolean-sat", "target": "u-regulatory-network-attractor-enumeration", "relation": "related_unknown" }, { "source": "b-regulatory-networks-boolean-sat", "target": "h-kauffman-network-criticality-cell-types", "relation": "related_hypothesis" }, { "source": "b-rna-secondary-structure-x-planar-graphs", "target": "u-rna-folding-planar-pseudoknot-limits", "relation": "related_unknown" }, { "source": "b-rna-secondary-structure-x-planar-graphs", "target": "h-nussinov-energy-approximates-planar-graph-parsimony", "relation": "related_hypothesis" }, { "source": "b-transformer-attention-x-protein-language-model-fitness-prediction", "target": "u-attention-head-interpretability-in-protein-language-models", "relation": "related_unknown" }, { "source": "b-transformer-attention-x-protein-language-model-fitness-prediction", "target": "h-attention-regularized-protein-language-models-improve-fitness-ranking", "relation": "related_hypothesis" }, { "source": "b-bacterial-chemotaxis-x-gradient-descent", "target": "u-bacterial-chemotaxis-x-gradient-descent", "relation": "related_unknown" }, { "source": "b-biomechanics-x-soft-robotics", "target": "u-biomechanics-x-soft-robotics", "relation": "related_unknown" }, { "source": "b-circadian-clock-x-feedback-oscillator", "target": "u-circadian-clock-x-feedback-oscillator", "relation": "related_unknown" }, { "source": "b-circadian-clock-x-feedback-oscillator", "target": "h-circadian-clock-x-feedback-oscillator", "relation": "related_hypothesis" }, { "source": "b-crispr-base-editing-x-error-correction", "target": "u-crispr-base-editing-x-error-correction", "relation": "related_unknown" }, { "source": "b-crispr-x-search-and-replace", "target": "u-crispr-x-search-and-replace", "relation": "related_unknown" }, { "source": "b-gene-expression-noise-x-information-theory", "target": "u-gene-expression-noise-x-information-theory", "relation": "related_unknown" }, { "source": "b-gene-regulatory-network-x-boolean-circuit", "target": "u-gene-regulatory-network-x-boolean-circuit", "relation": "related_unknown" }, { "source": "b-gene-regulatory-network-x-boolean-circuit", "target": "h-gene-regulatory-network-x-boolean-circuit", "relation": "related_hypothesis" }, { "source": "b-immune-memory-x-long-term-potentiation", "target": "u-immune-memory-x-long-term-potentiation", "relation": "related_unknown" }, { "source": "b-immune-system-x-anomaly-detection", "target": "u-immune-system-x-anomaly-detection", "relation": "related_unknown" }, { "source": "b-information-theory-x-evolutionary-biology", "target": "u-channel-capacity-evolution-rate", "relation": "related_unknown" }, { "source": "b-neural-plasticity-x-hebbian-learning", "target": "u-neural-plasticity-x-hebbian-learning", "relation": "related_unknown" }, { "source": "b-neural-spike-coding-x-information-compression", "target": "u-neural-spike-coding-x-information-compression", "relation": "related_unknown" }, { "source": "b-swarm-intelligence-x-distributed-computing", "target": "u-swarm-intelligence-x-distributed-computing", "relation": "related_unknown" }, { "source": "b-microbiome-diversity-stability", "target": "u-microbiome-diversity-stability-causality", "relation": "related_unknown" }, { "source": "b-microbiome-diversity-stability", "target": "h-microbiome-functional-redundancy-antibiotic-resilience", "relation": "related_hypothesis" }, { "source": "b-biofilm-self-assembly", "target": "u-supramolecular-self-assembly-prediction", "relation": "related_unknown" }, { "source": "b-biofilm-self-assembly", "target": "u-nanotechnology-self-assembly-yield", "relation": "related_unknown" }, { "source": "b-crispr-cas9-gene-editing", "target": "u-crispr-hdr-efficiency-post-mitotic-cells", "relation": "related_unknown" }, { "source": "b-crispr-cas9-gene-editing", "target": "h-prime-editing-hdr-bypass-therapeutic-window", "relation": "related_hypothesis" }, { "source": "b-neuromuscular-control-biomechanics", "target": "u-neuromuscular-control-redundancy-resolution", "relation": "related_unknown" }, { "source": "b-neuromuscular-control-biomechanics", "target": "h-neuromuscular-size-principle-metabolic-optimality", "relation": "related_hypothesis" }, { "source": "b-optogenetics-neural-circuit-control", "target": "u-optogenetics-human-therapeutic-scale-delivery", "relation": "related_unknown" }, { "source": "b-optogenetics-neural-circuit-control", "target": "h-optogenetic-restoration-vision-scales-to-complex-percepts", "relation": "related_hypothesis" }, { "source": "b-synthetic-biology-circuit-design", "target": "u-retroactivity-insulation-genetic-circuits", "relation": "related_unknown" }, { "source": "b-synthetic-biology-circuit-design", "target": "h-synthetic-insulator-retroactivity-control", "relation": "related_hypothesis" }, { "source": "b-tissue-engineering-regenerative-medicine", "target": "u-tissue-engineering-vascularization-thick-constructs", "relation": "related_unknown" }, { "source": "b-tissue-engineering-regenerative-medicine", "target": "h-sacrificial-templating-vascular-network-bioprinting", "relation": "related_hypothesis" }, { "source": "b-crispr-multiplex-pooling-x-barcode-redundancy-intuition", "target": "u-crispr-multiplex-error-floor-vs-code-distance", "relation": "related_unknown" }, { "source": "b-crispr-multiplex-pooling-x-barcode-redundancy-intuition", "target": "h-barcode-spacing-heuristic-lowers-decoding-error-measured-in-negative-controls", "relation": "related_hypothesis" }, { "source": "b-genetic-regulatory-boolean-circuits", "target": "u-genetic-circuit-crosstalk-noise", "relation": "related_unknown" }, { "source": "b-genetic-regulatory-boolean-circuits", "target": "h-kauffman-critical-k2-attractor-cell-types", "relation": "related_hypothesis" }, { "source": "b-gnn-x-gene-regulatory-network-perturbation-priors", "target": "u-grn-gnn-perturbation-identifiability", "relation": "related_unknown" }, { "source": "b-gnn-x-gene-regulatory-network-perturbation-priors", "target": "h-grn-gnn-priors-improve-perturbation-response-prediction", "relation": "related_hypothesis" }, { "source": "b-cell-division-x-branching-process", "target": "u-cell-division-x-branching-process", "relation": "related_unknown" }, { "source": "b-cell-division-x-branching-process", "target": "h-cell-division-x-branching-process", "relation": "related_hypothesis" }, { "source": "b-developmental-gradient-x-pde", "target": "u-developmental-gradient-x-pde", "relation": "related_unknown" }, { "source": "b-ecological-succession-x-markov", "target": "u-ecological-succession-x-markov", "relation": "related_unknown" }, { "source": "b-ecology-x-coexistence-theory", "target": "u-ecology-x-coexistence-theory", "relation": "related_unknown" }, { "source": "b-ecology-x-coexistence-theory", "target": "h-ecology-x-coexistence-theory", "relation": "related_hypothesis" }, { "source": "b-epigenetic-landscape-x-attractor", "target": "u-epigenetic-landscape-x-attractor", "relation": "related_unknown" }, { "source": "b-game-theory-x-antibiotic-resistance", "target": "u-game-theory-x-antibiotic-resistance", "relation": "related_unknown" }, { "source": "b-gut-microbiome-x-lotka-volterra", "target": "u-gut-microbiome-x-lotka-volterra", "relation": "related_unknown" }, { "source": "b-neutral-theory-x-stochastic-sampling", "target": "u-neutral-theory-x-stochastic-sampling", "relation": "related_unknown" }, { "source": "b-neutral-theory-x-stochastic-sampling", "target": "h-neutral-theory-x-stochastic-sampling", "relation": "related_hypothesis" }, { "source": "b-phylogenetics-x-coalescent-theory", "target": "u-phylogenetics-x-coalescent-theory", "relation": "related_unknown" }, { "source": "b-population-genetics-x-random-matrix", "target": "u-rmt-selective-sweep-detection-power", "relation": "related_unknown" }, { "source": "b-protein-folding-x-energy-landscape", "target": "u-protein-folding-x-energy-landscape", "relation": "related_unknown" }, { "source": "b-scale-free-network-x-metabolic", "target": "u-scale-free-network-x-metabolic", "relation": "related_unknown" }, { "source": "b-sir-model-x-compartmental-ode", "target": "u-sir-model-x-compartmental-ode", "relation": "related_unknown" }, { "source": "b-synthetic-biology-x-circuit-design", "target": "u-synthetic-biology-x-circuit-design", "relation": "related_unknown" }, { "source": "b-allometric-scaling-metabolic-geometry", "target": "u-allometric-scaling-metabolic-universality", "relation": "related_unknown" }, { "source": "b-blood-coagulation-cascade-boolean", "target": "u-blood-coagulation-cascade", "relation": "related_unknown" }, { "source": "b-blood-coagulation-cascade-boolean", "target": "h-blood-coagulation-cascade-boolean", "relation": "related_hypothesis" }, { "source": "b-contact-map-sparsity-x-hessian-low-rank-folding-cooperativity", "target": "u-contact-graph-hessian-rank-native-basin-surrogate", "relation": "related_unknown" }, { "source": "b-contact-map-sparsity-x-hessian-low-rank-folding-cooperativity", "target": "h-low-rank-hessian-surrogate-predicts-two-state-phi-profile-class", "relation": "related_hypothesis" }, { "source": "b-cooperative-breeding-kin-selection-inclusive-fitness", "target": "u-cooperative-breeding-hamiltons-rule-limits", "relation": "related_unknown" }, { "source": "b-developmental-geometry-morphogenesis", "target": "u-diffeomorphic-growth-mechanical-constraints", "relation": "related_unknown" }, { "source": "b-developmental-geometry-morphogenesis", "target": "h-developmental-geometry-diffeomorphism-geodesic", "relation": "related_hypothesis" }, { "source": "b-game-theory-honest-signaling", "target": "u-zahavi-handicap-mechanism-multimodal", "relation": "related_unknown" }, { "source": "b-game-theory-honest-signaling", "target": "h-zahavi-handicap-single-crossing-stable-honest", "relation": "related_hypothesis" }, { "source": "b-game-theory-immune-evasion", "target": "u-immune-escape-ess-predictions", "relation": "related_unknown" }, { "source": "b-game-theory-immune-evasion", "target": "h-red-queen-ess-influenza-diversity-prediction", "relation": "related_hypothesis" }, { "source": "b-intestinal-crypt-stem-cell-moran-process", "target": "u-intestinal-crypt-stem-cell-moran-selection", "relation": "related_unknown" }, { "source": "b-invasion-biology-spreading-speeds", "target": "u-invasion-fat-tailed-dispersal-empirical-detection", "relation": "related_unknown" }, { "source": "b-invasion-biology-spreading-speeds", "target": "h-rlde-satellite-colony-invasion-acceleration-branching-process", "relation": "related_hypothesis" }, { "source": "b-metabolic-control-analysis-x-local-sensitivity", "target": "u-mca-global-sensitivity-beyond-log-linear", "relation": "related_unknown" }, { "source": "b-metabolic-control-analysis-x-local-sensitivity", "target": "h-metabolic-control-analysis-x-local-sensitivity", "relation": "related_hypothesis" }, { "source": "b-morphogen-turing-patterning", "target": "u-turing-patterning-3d-robustness", "relation": "related_unknown" }, { "source": "b-phylogenetics-maximum-likelihood", "target": "u-phylogenetic-placement-long-branch-attraction-correction", "relation": "related_unknown" }, { "source": "b-phylogenetics-maximum-likelihood", "target": "h-gtr-model-adequate-metazoan-divergence-estimation", "relation": "related_hypothesis" }, { "source": "b-protein-crystallography-space-groups", "target": "u-protein-crystal-packing-predictability", "relation": "related_unknown" }, { "source": "b-protein-crystallography-space-groups", "target": "h-space-group-frequency-evolution-bias", "relation": "related_hypothesis" }, { "source": "b-protein-folding-funnel-x-polyak-lojasiewicz-optimization-region", "target": "u-protein-folding-pl-constant-coarse-grained-surrogate", "relation": "related_unknown" }, { "source": "b-protein-folding-funnel-x-polyak-lojasiewicz-optimization-region", "target": "h-two-state-folders-admit-pl-like-surrogate-on-contact-order-parameter", "relation": "related_hypothesis" }, { "source": "b-replicator-equations-evolutionary-dynamics", "target": "u-replicator-dynamics-prebiotic-origin", "relation": "related_unknown" }, { "source": "b-replicator-equations-evolutionary-dynamics", "target": "h-replicator-rl-convergence", "relation": "related_hypothesis" }, { "source": "b-cellular-senescence-tumor-suppression", "target": "u-senescence-sasp-cancer-promotion-threshold", "relation": "related_unknown" }, { "source": "b-glymphatic-aging", "target": "u-amyloid-progression-trajectory", "relation": "related_unknown" }, { "source": "b-glymphatic-aging", "target": "h-glymphatic-amyloid-clearance-rate", "relation": "related_hypothesis" }, { "source": "b-protein-interaction-robustness", "target": "u-protein-protein-interaction-design", "relation": "related_unknown" }, { "source": "b-protein-interaction-robustness", "target": "h-ppi-hub-targeting-cancer", "relation": "related_hypothesis" }, { "source": "b-random-boolean-networks-cell-fate", "target": "u-boolean-network-attractor-landscape-reprogramming", "relation": "related_unknown" }, { "source": "b-circadian-rhythms-neural-oscillators", "target": "u-circadian-desynchrony-disease-mechanisms", "relation": "related_unknown" }, { "source": "b-circadian-rhythms-neural-oscillators", "target": "h-kuramoto-scn-resynchronization-rate", "relation": "related_hypothesis" }, { "source": "b-sleep-memory-consolidation", "target": "u-sleep-replay-causal-role-memory-specificity", "relation": "related_unknown" }, { "source": "b-sleep-memory-consolidation", "target": "h-targeted-memory-reactivation-during-sleep-enhances-consolidation", "relation": "related_hypothesis" }, { "source": "b-action-potential-x-soliton", "target": "u-axon-soliton-collision-dynamics", "relation": "related_unknown" }, { "source": "b-biofilm-x-active-nematic", "target": "u-biofilm-x-active-nematic", "relation": "related_unknown" }, { "source": "b-biofilm-x-active-nematic", "target": "h-biofilm-x-active-nematic", "relation": "related_hypothesis" }, { "source": "b-bioluminescence-quantum-yield", "target": "u-oxyluciferin-excited-state-mechanism-enol-vs-keto", "relation": "related_unknown" }, { "source": "b-bioluminescence-quantum-yield", "target": "h-bioluminescence-coevolution-visual-system-deep-sea", "relation": "related_hypothesis" }, { "source": "b-calcium-signaling-x-stochastic-resonance", "target": "u-calcium-signaling-x-stochastic-resonance", "relation": "related_unknown" }, { "source": "b-chromatin-loop-extrusion-polymer", "target": "u-chromatin-loop-extrusion-processivity", "relation": "related_unknown" }, { "source": "b-chromatin-loop-extrusion-polymer", "target": "h-ctcf-boundary-polymer-wall", "relation": "related_hypothesis" }, { "source": "b-circadian-clocks-nonlinear-oscillators", "target": "u-circadian-temperature-compensation-mechanism", "relation": "related_unknown" }, { "source": "b-circadian-clocks-nonlinear-oscillators", "target": "h-circadian-hopf-bifurcation-delay-oscillator", "relation": "related_hypothesis" }, { "source": "b-cochlear-mechanics-hearing-biophysics", "target": "u-cochlear-hopf-bifurcation-active-hair-bundle-vs-somatic-motility", "relation": "related_unknown" }, { "source": "b-cochlear-mechanics-hearing-biophysics", "target": "h-prestin-somatic-motility-primary-cochlear-amplification-mechanism-mammals", "relation": "related_hypothesis" }, { "source": "b-cytoskeleton-x-active-matter", "target": "u-cytoskeletal-active-matter-defect-dynamics", "relation": "related_unknown" }, { "source": "b-developmental-turing-instability", "target": "u-turing-morphogen-identity-in-vivo-diffusion-measurement", "relation": "related_unknown" }, { "source": "b-developmental-turing-instability", "target": "h-turing-digit-count-bmp-gradient-wavelength-scaling", "relation": "related_hypothesis" }, { "source": "b-dna-mechanics-chromatin", "target": "u-chromatin-loop-extrusion-speed-processivity-in-vivo", "relation": "related_unknown" }, { "source": "b-dna-mechanics-chromatin", "target": "h-tad-boundary-disruption-ctcf-site-oncogene-activation-quantitative", "relation": "related_hypothesis" }, { "source": "b-hair-cell-bundle-x-hopf-bifurcation", "target": "u-hair-cell-bundle-x-hopf-bifurcation", "relation": "related_unknown" }, { "source": "b-hair-cells-mechanosensory-biophysics", "target": "u-met-channel-molecular-identity-pore-forming-subunit", "relation": "related_unknown" }, { "source": "b-hair-cells-mechanosensory-biophysics", "target": "h-hopf-bifurcation-universal-mechanism-vertebrate-hair-cell-amplification", "relation": "related_hypothesis" }, { "source": "b-intrinsically-disordered-proteins-polymer-physics", "target": "u-intrinsically-disordered-proteins", "relation": "related_unknown" }, { "source": "b-intrinsically-disordered-proteins-polymer-physics", "target": "h-intrinsically-disordered-proteins-polymer-physics", "relation": "related_hypothesis" }, { "source": "b-mechanosensing-x-force-transduction", "target": "u-mechanosensing-x-force-transduction", "relation": "related_unknown" }, { "source": "b-mechanosensing-x-force-transduction", "target": "h-mechanosensing-x-force-transduction", "relation": "related_hypothesis" }, { "source": "b-membrane-tension-x-laplace-pressure", "target": "u-membrane-tension-x-laplace-pressure", "relation": "related_unknown" }, { "source": "b-metabolic-scaling-fractal-vasculature", "target": "u-metabolic-scaling-deviations-non-mammalian", "relation": "related_unknown" }, { "source": "b-metabolic-scaling-fractal-vasculature", "target": "h-metabolic-exponent-network-dimension-prediction", "relation": "related_hypothesis" }, { "source": "b-metabolic-scaling-x-fractal-transport", "target": "u-metabolic-scaling-fractal-transport-unification", "relation": "related_unknown" }, { "source": "b-metabolic-scaling-x-fractal-transport", "target": "h-kleiber-exponent-from-fractal-like-transport-networks", "relation": "related_hypothesis" }, { "source": "b-morphogenesis-mechanical-forces", "target": "u-gyrification-mechanics-developmental-timing", "relation": "related_unknown" }, { "source": "b-morphogenesis-mechanical-forces", "target": "h-vertex-model-cortical-gyrification-mechanics", "relation": "related_hypothesis" }, { "source": "b-morphogenesis-x-mechanical-instability", "target": "u-morphogenesis-x-mechanical-instability", "relation": "related_unknown" }, { "source": "b-morphogenesis-x-mechanical-instability", "target": "h-morphogenesis-x-mechanical-instability", "relation": "related_hypothesis" }, { "source": "b-muscle-crossbridge-sliding-filament", "target": "u-muscle-crossbridge-kinetics", "relation": "related_unknown" }, { "source": "b-muscle-crossbridge-sliding-filament", "target": "h-muscle-crossbridge-sliding-filament", "relation": "related_hypothesis" }, { "source": "b-muscle-mechanics-x-crossbridge-theory", "target": "u-muscle-mechanics-x-crossbridge-theory", "relation": "related_unknown" }, { "source": "b-myosin-motor-x-brownian-ratchet", "target": "u-myosin-motor-x-brownian-ratchet", "relation": "related_unknown" }, { "source": "b-osmotic-pressure-x-viral-capsid", "target": "u-phage-ejection-force-osmotic-mechanism", "relation": "related_unknown" }, { "source": "b-photoreceptor-quantum-efficiency-x-photon-statistics", "target": "u-photoreceptor-quantum-efficiency-x-photon-statistics", "relation": "related_unknown" }, { "source": "b-plant-hydraulics-fluid-mechanics", "target": "u-xylem-cavitation-repair-mechanism", "relation": "related_unknown" }, { "source": "b-plant-hydraulics-fluid-mechanics", "target": "h-hydraulic-failure-drives-tree-mortality-drought", "relation": "related_hypothesis" }, { "source": "b-prion-misfolding-nucleation", "target": "u-prion-nucleation-spontaneous-rate-physiological", "relation": "related_unknown" }, { "source": "b-protein-aggregation-x-nucleation-growth", "target": "u-protein-aggregation-x-nucleation-growth", "relation": "related_unknown" }, { "source": "b-protein-aggregation-x-nucleation-growth", "target": "h-protein-aggregation-x-nucleation-growth", "relation": "related_hypothesis" }, { "source": "b-protein-folding-energy-landscape", "target": "u-protein-misfolding-disease-mechanism", "relation": "related_unknown" }, { "source": "b-protein-folding-energy-landscape", "target": "h-alphafold-energy-landscape-implicit-learning", "relation": "related_hypothesis" }, { "source": "b-viral-self-assembly-capsid-physics", "target": "u-capsid-assembly-kinetic-intermediates", "relation": "related_unknown" }, { "source": "b-viral-self-assembly-capsid-physics", "target": "h-rna-electrostatic-packaging-signal-design", "relation": "related_hypothesis" }, { "source": "b-wound-healing-cell-migration-chemotaxis", "target": "u-wound-healing-collective-migration-coordination", "relation": "related_unknown" }, { "source": "b-wound-healing-cell-migration-chemotaxis", "target": "h-active-matter-wound-closure-optimization", "relation": "related_hypothesis" }, { "source": "b-animal-cognition-theory-of-mind", "target": "u-great-ape-false-belief-implicit-explicit", "relation": "related_unknown" }, { "source": "b-animal-cognition-theory-of-mind", "target": "h-tom-implicit-explicit-dissociation", "relation": "related_hypothesis" }, { "source": "b-behavioral-economics-evolutionary-psychology", "target": "u-loss-aversion-cross-cultural-evolutionary-universality", "relation": "related_unknown" }, { "source": "b-behavioral-economics-evolutionary-psychology", "target": "h-prospect-theory-lambda-fitness-landscape-ancestral-environment", "relation": "related_hypothesis" }, { "source": "b-epigenetics-transgenerational-trauma", "target": "u-epigenetic-escape-loci-mechanisms-transgenerational-scope", "relation": "related_unknown" }, { "source": "b-epigenetics-transgenerational-trauma", "target": "h-sperm-small-rna-mediates-paternal-trauma-epigenetic-inheritance", "relation": "related_hypothesis" }, { "source": "b-quorum-sensing-x-game-theory", "target": "u-quorum-signaling-as-multiplayer-game", "relation": "related_unknown" }, { "source": "b-quorum-sensing-x-game-theory", "target": "h-quorum-thresholds-are-ess-under-stochastic-demography", "relation": "related_hypothesis" }, { "source": "b-sociobiology-kin-selection", "target": "u-multilevel-selection-kin-equivalence", "relation": "related_unknown" }, { "source": "b-sociobiology-kin-selection", "target": "h-cultural-multilevel-selection-dominates-genetic", "relation": "related_hypothesis" }, { "source": "b-dna-replication-fork-x-asymmetric-exclusion-traffic-jam", "target": "u-replication-fork-tasep-parameter-identifiability-from-seq-stalling-assays", "relation": "related_unknown" }, { "source": "b-dna-replication-fork-x-asymmetric-exclusion-traffic-jam", "target": "h-stalling-density-wave-speed-correlates-with-seq-measured-pause-density-peaks", "relation": "related_hypothesis" }, { "source": "b-epithelial-jamming-x-colloidal-glass-rheology", "target": "u-jamming-exponent-universality-epithelium-versus-colloid", "relation": "related_unknown" }, { "source": "b-epithelial-jamming-x-colloidal-glass-rheology", "target": "h-shared-shape-index-scaling-near-jamming-across-donors", "relation": "related_hypothesis" }, { "source": "b-cryoem-bayesian-x-single-particle-reconstruction", "target": "u-cryoem-bayesian-map-calibration-across-platforms", "relation": "related_unknown" }, { "source": "b-cryoem-bayesian-x-single-particle-reconstruction", "target": "h-cryoem-bayesian-x-single-particle-reconstruction", "relation": "related_hypothesis" }, { "source": "b-lasso-sparsity-x-biomarker-panel-design", "target": "u-measurement-drift-effects-on-lasso-biomarker-sparsity", "relation": "related_unknown" }, { "source": "b-lasso-sparsity-x-biomarker-panel-design", "target": "h-stability-selected-lasso-panels-outperform-fixed-biomarkers-under-assay-noise", "relation": "related_hypothesis" }, { "source": "b-microplate-absorbance-x-inverse-beer-lambert-calibration", "target": "u-microplate-inverse-beer-lambert-conditioning", "relation": "related_unknown" }, { "source": "b-microplate-absorbance-x-inverse-beer-lambert-calibration", "target": "h-multi-wavelength-beer-lambert-inverse-improves-plate-precision", "relation": "related_hypothesis" }, { "source": "b-random-matrix-denoising-x-single-cell-covariance-cleaning", "target": "u-random-matrix-eigenvalue-cleaning-single-cell-batch-effects", "relation": "related_unknown" }, { "source": "b-random-matrix-denoising-x-single-cell-covariance-cleaning", "target": "h-rmt-covariance-cleaning-improves-single-cell-state-clustering", "relation": "related_hypothesis" }, { "source": "b-molecular-motors-thermodynamic-efficiency", "target": "u-molecular-motor-efficiency-limit-biological", "relation": "related_unknown" }, { "source": "b-molecular-motors-thermodynamic-efficiency", "target": "h-molecular-motor-near-equilibrium-operation", "relation": "related_hypothesis" }, { "source": "b-stochastic-resonance-biosignaling-x-information-detection", "target": "u-stochastic-resonance-cell-signaling-bandwidth", "relation": "related_unknown" }, { "source": "b-stochastic-resonance-biosignaling-x-information-detection", "target": "h-stochastic-resonance-matches-information-peak-in-cell-signaling", "relation": "related_hypothesis" }, { "source": "b-mitochondrial-membrane-potential-pmf", "target": "u-mitochondrial-pmf-efficiency-carnot-bound", "relation": "related_unknown" }, { "source": "b-bayesian-dropout-x-adaptive-trial-stopping", "target": "u-bayesian-dropout-trial-decision-calibration-gap", "relation": "related_unknown" }, { "source": "b-bayesian-dropout-x-adaptive-trial-stopping", "target": "h-bayesian-dropout-uncertainty-improves-adaptive-trial-decisions", "relation": "related_hypothesis" }, { "source": "b-microbial-fuel-cells-bioelectrochemistry", "target": "u-microbial-fuel-cell-electron-transfer-limits", "relation": "related_unknown" }, { "source": "b-microbial-fuel-cells-bioelectrochemistry", "target": "h-microbial-fuel-cell-anodic-electron-transfer", "relation": "related_hypothesis" }, { "source": "b-plant-tropisms-auxin-reaction-diffusion", "target": "u-plant-tropism-auxin-gradient-mechanism", "relation": "related_unknown" }, { "source": "b-plant-tropisms-auxin-reaction-diffusion", "target": "h-auxin-turing-pattern-shoot-branching", "relation": "related_hypothesis" }, { "source": "b-protein-ubiquitination-proteostasis-network", "target": "u-ubiquitin-proteasome-proteostasis-collapse-threshold", "relation": "related_unknown" }, { "source": "b-protein-ubiquitination-proteostasis-network", "target": "h-proteasome-saturation-bistability-neurodegeneration", "relation": "related_hypothesis" }, { "source": "b-riboswitch-rna-aptamer-allosteric", "target": "u-riboswitch-cotranscriptional-folding-kinetics", "relation": "related_unknown" }, { "source": "b-riboswitch-rna-aptamer-allosteric", "target": "h-riboswitch-kinetic-proofreading-cotranscriptional", "relation": "related_hypothesis" }, { "source": "b-stress-granules-liquid-liquid-phase-separation", "target": "u-stress-granule-phase-separation-pathology", "relation": "related_unknown" }, { "source": "b-stress-granules-liquid-liquid-phase-separation", "target": "h-stress-granule-binodal-concentration-prediction", "relation": "related_hypothesis" }, { "source": "b-debye-length-x-membrane-electrical-double-layer", "target": "u-debye-length-ion-specificity-membrane-binding", "relation": "related_unknown" }, { "source": "b-debye-length-x-membrane-electrical-double-layer", "target": "h-ion-specific-double-layer-competition-modulates-permeation", "relation": "related_hypothesis" }, { "source": "b-electrochemical-impedance-x-cell-membrane", "target": "u-eis-membrane-hodgkin-huxley-identification", "relation": "related_unknown" }, { "source": "b-electrochemical-impedance-x-cell-membrane", "target": "h-eis-spectra-constrain-gating-substates", "relation": "related_hypothesis" }, { "source": "b-enzyme-engineering-directed-evolution", "target": "u-protein-fitness-landscape-epistasis-ruggedness", "relation": "related_unknown" }, { "source": "b-enzyme-engineering-directed-evolution", "target": "h-ml-directed-evolution-navigates-epistatic-fitness-landscape", "relation": "related_hypothesis" }, { "source": "b-enzyme-kinetics-metabolic-network", "target": "u-metabolic-flux-control-redistribution-disease", "relation": "related_unknown" }, { "source": "b-enzyme-kinetics-metabolic-network", "target": "h-mca-summation-theorem-distributed-cancer-target", "relation": "related_hypothesis" }, { "source": "b-enzyme-kinetics-x-michaelis-menten", "target": "u-enzyme-kinetics-x-michaelis-menten", "relation": "related_unknown" }, { "source": "b-marcus-tunneling-x-enzyme-reaction-coordinate", "target": "u-marcus-tunneling-reaction-coordinate-biochemistry", "relation": "related_unknown" }, { "source": "b-marcus-tunneling-x-enzyme-reaction-coordinate", "target": "h-marcus-tunneling-x-enzyme-reaction-coordinate", "relation": "related_hypothesis" }, { "source": "b-metabolic-flux-x-linear-programming", "target": "u-metabolic-flux-x-linear-programming", "relation": "related_unknown" }, { "source": "b-photosynthesis-quantum-energy-transfer", "target": "u-photosynthesis-quantum-coherence-physiological-function", "relation": "related_unknown" }, { "source": "b-photosynthesis-quantum-energy-transfer", "target": "h-vibronic-coupling-fmo-coherence-functional-enhancement", "relation": "related_hypothesis" }, { "source": "b-prion-fold-x-protein-phase-separation", "target": "u-prion-llps-nucleation-kinetics", "relation": "related_unknown" }, { "source": "b-protein-post-translational-modifications", "target": "u-ptm-crosstalk-code-histone-combinatorial-regulation", "relation": "related_unknown" }, { "source": "b-protein-post-translational-modifications", "target": "h-histone-code-combinatorial-specificity-exceeds-single-mark-models", "relation": "related_hypothesis" }, { "source": "b-alphafold-structure-priors-x-enzyme-engineering-screen-pruning", "target": "u-structure-uncertainty-propagation-from-alphafold-to-enzyme-design", "relation": "related_unknown" }, { "source": "b-alphafold-structure-priors-x-enzyme-engineering-screen-pruning", "target": "h-alphafold-confidence-weighted-screening-improves-enzyme-hit-rates", "relation": "related_hypothesis" }, { "source": "b-energy-landscape-funnels-x-protein-ligand-docking-search", "target": "u-funnel-ruggedness-docking-false-minima", "relation": "related_unknown" }, { "source": "b-energy-landscape-funnels-x-protein-ligand-docking-search", "target": "h-funnel-aware-search-reduces-docking-decoy-traps", "relation": "related_hypothesis" }, { "source": "b-reaction-networks-x-petri-nets", "target": "u-reaction-networks-x-petri-nets", "relation": "related_unknown" }, { "source": "b-chemical-ecology-signaling-networks", "target": "u-ocean-acidification-ecosystem-effects", "relation": "related_unknown" }, { "source": "b-chemical-ecology-signaling-networks", "target": "u-hydrothermal-vent-biodiversity", "relation": "related_unknown" }, { "source": "b-catalysis-reactor-design", "target": "u-sabatier-volcano-principle-multi-step-cascade-reaction-design", "relation": "related_unknown" }, { "source": "b-catalysis-reactor-design", "target": "h-dft-bep-relationship-enables-quantitative-catalyst-design-before-synthesis", "relation": "related_hypothesis" }, { "source": "b-corrosion-science-materials-protection", "target": "u-corrosion-inhibitor-molecular-mechanism", "relation": "related_unknown" }, { "source": "b-corrosion-science-materials-protection", "target": "h-ml-accelerated-corrosion-inhibitor-discovery", "relation": "related_hypothesis" }, { "source": "b-electrochemistry-battery-technology", "target": "u-solid-state-battery-sei-interface-resistance-origin", "relation": "related_unknown" }, { "source": "b-electrochemistry-battery-technology", "target": "h-llzo-single-ion-conductor-eliminates-dendrite-nucleation", "relation": "related_hypothesis" }, { "source": "b-green-chemistry-atom-economy", "target": "u-green-chemistry-pharmaceutical-e-factor-continuous-flow", "relation": "related_unknown" }, { "source": "b-green-chemistry-atom-economy", "target": "h-co2-feedstock-polycarbonate-cascade-net-carbon-neutral", "relation": "related_hypothesis" }, { "source": "b-nuclear-chemistry-reactor-physics", "target": "u-nuclear-waste-transmutation-accelerator-driven-systems", "relation": "related_unknown" }, { "source": "b-nuclear-chemistry-reactor-physics", "target": "h-thorium-msr-achieves-baseload-carbon-free-power-lower-waste", "relation": "related_hypothesis" }, { "source": "b-pem-hydrogen-economy", "target": "u-nafion-degradation-mechanism-longevity", "relation": "related_unknown" }, { "source": "b-pem-hydrogen-economy", "target": "h-pem-membrane-beyond-nafion-high-temperature", "relation": "related_hypothesis" }, { "source": "b-chemical-garden-osmotic-precipitation", "target": "u-chemical-garden-membrane-self-organization", "relation": "related_unknown" }, { "source": "b-chemical-garden-osmotic-precipitation", "target": "h-chemical-garden-osmotic-pressure-tube-morphology", "relation": "related_hypothesis" }, { "source": "b-vae-x-catalyst-latent-space-screening", "target": "u-vae-catalyst-latent-disentanglement-validity", "relation": "related_unknown" }, { "source": "b-vae-x-catalyst-latent-space-screening", "target": "h-vae-latent-regularization-improves-catalyst-hit-rate", "relation": "related_hypothesis" }, { "source": "b-graph-theory-x-molecular-structure", "target": "u-graph-theory-x-molecular-structure", "relation": "related_unknown" }, { "source": "b-graph-theory-x-molecular-structure", "target": "h-graph-theory-x-molecular-structure", "relation": "related_hypothesis" }, { "source": "b-molecular-dynamics-statistical-sampling", "target": "u-md-force-field-transferability-accuracy-limit", "relation": "related_unknown" }, { "source": "b-molecular-dynamics-statistical-sampling", "target": "h-metadynamics-collective-variables-protein-allostery", "relation": "related_hypothesis" }, { "source": "b-molecular-dynamics-x-stochastic-thermostats", "target": "u-md-thermostat-sde-equivalence-and-ergodicity", "relation": "related_unknown" }, { "source": "b-molecular-dynamics-x-stochastic-thermostats", "target": "h-nose-hoover-chains-match-target-kinetic-spectra-when-tuned", "relation": "related_hypothesis" }, { "source": "b-molecular-spectroscopy-x-matrix-diagonalization", "target": "u-anharmonic-spectroscopy-matrix-models-convergence", "relation": "related_unknown" }, { "source": "b-molecular-spectroscopy-x-matrix-diagonalization", "target": "h-molecular-spectroscopy-x-matrix-diagonalization", "relation": "related_hypothesis" }, { "source": "b-reaction-diffusion-excitable-media-bz", "target": "u-bz-reaction-3d-scroll-wave-instability", "relation": "related_unknown" }, { "source": "b-reaction-diffusion-pattern-formation", "target": "u-atmospheric-chemistry-aerosol-nucleation", "relation": "related_unknown" }, { "source": "b-reaction-diffusion-pattern-formation", "target": "h-turing-instability-aerosol-nucleation", "relation": "related_hypothesis" }, { "source": "b-reaction-network-graph-theory", "target": "u-crn-multistability-biological", "relation": "related_unknown" }, { "source": "b-reaction-network-graph-theory", "target": "h-crn-oscillator-design", "relation": "related_hypothesis" }, { "source": "b-thermodynamics-convex-analysis", "target": "u-thermodynamics-convex-geometry-non-equilibrium", "relation": "related_unknown" }, { "source": "b-thermodynamics-convex-analysis", "target": "h-thermodynamics-non-convex-regions-phase-coexistence", "relation": "related_hypothesis" }, { "source": "b-topological-data-analysis-x-catalyst-state-space-screening", "target": "u-which-persistence-features-remain-stable-under-noisy-catalyst-screening-assays", "relation": "related_unknown" }, { "source": "b-topological-data-analysis-x-catalyst-state-space-screening", "target": "h-persistence-based-features-improve-active-catalyst-hit-rate-in-high-throughput-screening", "relation": "related_hypothesis" }, { "source": "b-fluorescence-lifetime-x-mri-t2-star-dephasing", "target": "u-flim-t2star-multiexp-phantom-transfer", "relation": "related_unknown" }, { "source": "b-fluorescence-lifetime-x-mri-t2-star-dephasing", "target": "h-shared-biexponential-fitting-bias-function-across-modalities-same-snr", "relation": "related_hypothesis" }, { "source": "b-ocean-acidification-carbonate-chemistry", "target": "u-ocean-acidification-carbonate-threshold", "relation": "related_unknown" }, { "source": "b-catalysis-x-transition-state-theory", "target": "u-enzyme-surface-catalyst-design-principles", "relation": "related_unknown" }, { "source": "b-colloidal-systems-soft-matter", "target": "u-colloidal-glass-transition-mode-coupling-breakdown", "relation": "related_unknown" }, { "source": "b-colloidal-systems-soft-matter", "target": "h-dlvo-failure-short-range-attractions-gels", "relation": "related_hypothesis" }, { "source": "b-electrochemical-impedance-membranes", "target": "u-eis-channel-gating-mechanistic-link", "relation": "related_unknown" }, { "source": "b-electrochemical-impedance-membranes", "target": "h-eis-hodgkin-huxley-parameter-extraction", "relation": "related_hypothesis" }, { "source": "b-nmr-rotating-frame-x-effective-hamiltonian", "target": "u-nmr-effective-hamiltonian-calibration-open-system", "relation": "related_unknown" }, { "source": "b-nmr-rotating-frame-x-effective-hamiltonian", "target": "h-nmr-rotating-frame-x-effective-hamiltonian", "relation": "related_hypothesis" }, { "source": "b-nucleation-x-first-passage", "target": "u-nucleation-x-first-passage", "relation": "related_unknown" }, { "source": "b-percolation-threshold-x-polymer-gelation", "target": "u-percolation-mapping-quantitative-gel-chemistry", "relation": "related_unknown" }, { "source": "b-percolation-threshold-x-polymer-gelation", "target": "h-percolation-threshold-x-polymer-gelation", "relation": "related_hypothesis" }, { "source": "b-photocatalysis-x-semiconductor-physics", "target": "u-photocatalysis-x-semiconductor-physics", "relation": "related_unknown" }, { "source": "b-polymer-glass-x-jamming-transition", "target": "u-polymer-glass-x-jamming-transition", "relation": "related_unknown" }, { "source": "b-polymer-physics-scaling-laws", "target": "u-polymer-entanglement-topology", "relation": "related_unknown" }, { "source": "b-polymer-physics-scaling-laws", "target": "h-reptation-tube-model-constraint-release", "relation": "related_hypothesis" }, { "source": "b-quantum-chemistry-electronic-structure", "target": "u-dft-exchange-correlation-exact-functional", "relation": 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"b-reinforcement-learning-x-foraging-patch-models", "target": "h-reinforcement-learning-x-foraging-patch-models", "relation": "related_hypothesis" }, { "source": "b-approximation-algorithms-sdp", "target": "u-unique-games-conjecture-sdp-approximation-tight-gap", "relation": "related_unknown" }, { "source": "b-approximation-algorithms-sdp", "target": "h-sdp-rounding-universal-approximation-ratio-tight-ugc", "relation": "related_hypothesis" }, { "source": "b-cellular-automata-computational-universality", "target": "u-cellular-automata-complexity-classification", "relation": "related_unknown" }, { "source": "b-complexity-phase-transitions", "target": "u-p-vs-np-geometric-barriers", "relation": "related_unknown" }, { "source": "b-complexity-phase-transitions", "target": "h-rsg-transition-separates-polynomial-exponential-regimes", "relation": "related_hypothesis" }, { "source": "b-computational-irreducibility-wolfram-rule110", "target": 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"b-sat-phase-transition", "target": "h-spin-glass-p-np-separation", "relation": "related_hypothesis" }, { "source": "b-type-theory-logic-curry-howard", "target": "u-homotopy-type-theory-foundations", "relation": "related_unknown" }, { "source": "b-type-theory-logic-curry-howard", "target": "u-constructive-incompleteness", "relation": "related_unknown" }, { "source": "b-softmax-attention-x-cortical-divisive-normalization", "target": "u-softmax-attention-cortical-normalization-mapping", "relation": "related_unknown" }, { "source": "b-softmax-attention-x-cortical-divisive-normalization", "target": "h-softmax-attention-x-cortical-divisive-normalization", "relation": "related_hypothesis" }, { "source": "b-transformer-attention-neural-attention", "target": "u-transformer-attention-biological-plausibility", "relation": "related_unknown" }, { "source": "b-transformer-attention-neural-attention", "target": "h-transformer-neural-attention-alignment", "relation": "related_hypothesis" }, { 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"u-quantum-speedup-optimization-np", "relation": "related_unknown" }, { "source": "b-quantum-complexity-circuit-depth", "target": "h-random-circuit-sampling-classical-boundary-fidelity", "relation": "related_hypothesis" }, { "source": "b-quantum-supremacy-complexity", "target": "u-quantum-simulation-classical-hardness", "relation": "related_unknown" }, { "source": "b-quantum-supremacy-complexity", "target": "h-quantum-error-correction-surface-code-overhead-v2", "relation": "related_hypothesis" }, { "source": "b-self-supervised-learning-x-statistical-mechanics", "target": "u-contrastive-ssl-energy-model-bridge", "relation": "related_unknown" }, { "source": "b-self-supervised-learning-x-statistical-mechanics", "target": "h-contrastive-loss-implements-high-temperature-energy-comparison", "relation": "related_hypothesis" }, { "source": "b-simulated-annealing-stat-mech", "target": "u-optimal-cooling-schedule-convergence", "relation": "related_unknown" }, { "source": "b-replica-exchange-tempering-x-bayesian-neural-posteriors", "target": "u-parallel-tempering-cost-benefit-large-language-model-posteriors", "relation": "related_unknown" }, { "source": "b-replica-exchange-tempering-x-bayesian-neural-posteriors", "target": "h-adaptive-temperature-ladders-improve-posterior-mixing", "relation": "related_hypothesis" }, { "source": "b-ridge-regression-x-shrinkage", "target": "u-ridge-as-gaussian-map-prior-identifiability", "relation": "related_unknown" }, { "source": "b-ridge-regression-x-shrinkage", "target": "h-ridge-penalty-matches-bayesian-width-in-neural-decoding", "relation": "related_hypothesis" }, { "source": "b-compressed-sensing-x-accelerated-mri-protocol-design", "target": "u-sampling-pattern-transferability-for-compressed-sensing-mri", "relation": "related_unknown" }, { "source": "b-compressed-sensing-x-accelerated-mri-protocol-design", "target": "h-adaptive-kspace-schedules-preserve-diagnostic-mri-quality-at-higher-acceleration", "relation": "related_hypothesis" }, { "source": "b-graph-cut-energy-minimization-x-radiology-lesion-segmentation-qc", "target": "u-energy-landscape-mismatch-indicators-for-lesion-segmentation-qc", "relation": "related_unknown" }, { "source": "b-graph-cut-energy-minimization-x-radiology-lesion-segmentation-qc", "target": "h-graph-cut-energy-residuals-detect-lesion-segmentation-failure-modes-earlier", "relation": "related_hypothesis" }, { "source": "b-residual-learning-x-automated-retinal-screening-robustness", "target": "u-domain-shift-calibration-for-resnet-retinal-screening", "relation": "related_unknown" }, { "source": "b-residual-learning-x-automated-retinal-screening-robustness", "target": "h-self-supervised-residual-pretraining-reduces-retinal-screening-false-negatives", "relation": "related_hypothesis" }, { "source": "b-unet-segmentation-x-histopathology-quantification-workflows", "target": "u-stain-variation-failure-modes-for-unet-histopathology-segmentation", "relation": "related_unknown" }, { "source": "b-unet-segmentation-x-histopathology-quantification-workflows", "target": "h-stain-normalized-unet-training-improves-cross-site-pathology-consistency", "relation": "related_hypothesis" }, { "source": "b-liquid-crystal-cell-membranes", "target": "u-lipid-raft-protein-sorting-mechanism", "relation": "related_unknown" }, { "source": "b-liquid-crystal-cell-membranes", "target": "h-membrane-defects-protein-clustering", "relation": "related_hypothesis" }, { "source": "b-rock-magnetism-spin-ordering-domains", "target": "u-rock-magnetism-paleomagnetic-reversal-mechanism", "relation": "related_unknown" }, { "source": "b-rock-magnetism-spin-ordering-domains", "target": "h-rock-magnetism-single-domain-blocking-temperature", "relation": "related_hypothesis" }, { "source": "b-symmetry-breaking-goldstone-bosons", "target": "u-symmetry-breaking-goldstone", "relation": "related_unknown" }, { "source": "b-symmetry-breaking-goldstone-bosons", "target": "h-symmetry-breaking-goldstone-bosons", "relation": "related_hypothesis" }, { "source": "b-topological-insulators-bulk-boundary", "target": "u-topological-insulator-surface-state-interactions", "relation": "related_unknown" }, { "source": "b-topological-insulators-bulk-boundary", "target": "h-topological-insulator-disorder-robustness", "relation": "related_hypothesis" }, { "source": "b-symplectic-integrators-x-long-horizon-control", "target": "u-symplectic-discretization-bias-long-horizon-control", "relation": "related_unknown" }, { "source": "b-symplectic-integrators-x-long-horizon-control", "target": "h-symplectic-controllers-preserve-energy-bounds-long-horizon", "relation": "related_hypothesis" }, { "source": "b-control-barrier-functions-x-safe-artificial-pancreas", "target": "u-control-barrier-formal-safety-under-sensor-lag", "relation": "related_unknown" }, { "source": "b-control-barrier-functions-x-safe-artificial-pancreas", "target": "h-cbf-enforced-insulin-constraints-prevent-severe-lows", "relation": "related_hypothesis" }, { "source": "b-control-lyapunov-functions-x-antibiotic-cycling-policies", "target": "u-lyapunov-guided-antibiotic-cycling-resistance-ecology", "relation": "related_unknown" }, { "source": "b-control-lyapunov-functions-x-antibiotic-cycling-policies", "target": "h-lyapunov-constrained-antibiotic-cycling-reduces-resistance-and-relapse", "relation": "related_hypothesis" }, { "source": "b-hamilton-jacobi-bellman-x-adaptive-radiotherapy", "target": "u-state-representation-gaps-for-hjb-guided-adaptive-radiotherapy", "relation": "related_unknown" }, { "source": "b-hamilton-jacobi-bellman-x-adaptive-radiotherapy", "target": "h-hjb-derived-adaptive-fractionation-improves-tumor-control-toxicity-tradeoff", "relation": "related_hypothesis" }, { "source": "b-variational-data-assimilation-x-personalized-glucose-forecasting", "target": "u-assimilation-window-stability-for-patient-specific-glucose-dynamics", "relation": "related_unknown" }, { "source": "b-variational-data-assimilation-x-personalized-glucose-forecasting", "target": "h-variational-assimilation-derived-glucose-predictions-outperform-sliding-window-baselines", "relation": "related_hypothesis" }, { "source": "b-phase-response-curves-x-adaptive-deep-brain-stimulation-timing", "target": "u-state-dependent-phase-response-model-drift-in-adaptive-dbs", "relation": "related_unknown" }, { "source": "b-phase-response-curves-x-adaptive-deep-brain-stimulation-timing", "target": "h-phase-response-adaptive-dbs-reduces-off-target-neural-entrainment", "relation": "related_hypothesis" }, { "source": "b-kibble-zurek-morphogenesis", "target": "u-kibble-zurek-embryo", "relation": "related_unknown" }, { "source": "b-kibble-zurek-morphogenesis", "target": "u-topological-morphogenesis", "relation": "related_unknown" }, { "source": "b-kibble-zurek-morphogenesis", "target": "h-kibble-zurek-polarity-scaling", "relation": "related_hypothesis" }, { "source": "b-cosmic-inflation-x-epidemic-phase-plane-expansion", "target": "u-inflation-epidemic-analogy-falsifiability-limits", "relation": "related_unknown" }, { "source": "b-cosmic-inflation-x-epidemic-phase-plane-expansion", "target": "h-shared-tangent-field-exponential-region-only-logarithmic-visual-overlap", "relation": "related_hypothesis" }, { "source": "b-neural-cde-x-irregular-icu-trajectory-modeling", "target": "u-neural-cde-icu-robustness-to-missingness-patterns", "relation": "related_unknown" }, { "source": "b-neural-cde-x-irregular-icu-trajectory-modeling", "target": "h-neural-cde-models-improve-icu-event-lead-time", "relation": "related_hypothesis" }, { "source": "b-dna-replication-x-error-correction", "target": "u-dna-replication-x-error-correction", "relation": "related_unknown" }, { "source": "b-genetic-algorithm-x-natural-selection", "target": "u-genetic-algorithm-x-natural-selection", "relation": "related_unknown" }, { "source": "b-neural-architecture-search-x-evolutionary-biology", "target": "u-neural-architecture-search-x-evolutionary-biology", "relation": "related_unknown" }, { "source": "b-sparse-coding-x-neural-basis", "target": "u-sparse-coding-x-neural-basis", "relation": "related_unknown" }, { "source": "b-game-theory-x-cryptography", "target": "u-game-theory-x-cryptography", "relation": "related_unknown" }, { "source": "b-mechanism-design-x-market-equilibrium", "target": "u-mechanism-design-algorithmic-markets", "relation": "related_unknown" }, { "source": "b-boolean-satisfiability-x-spin-glass", "target": "u-sat-spin-glass-algorithm-design", "relation": "related_unknown" }, { "source": "b-compressed-sensing-x-sparse-recovery", "target": "u-compressed-sensing-x-sparse-recovery", "relation": "related_unknown" }, { "source": "b-graph-neural-network-x-spectral-graph-theory", "target": "u-graph-neural-network-x-spectral-graph-theory", "relation": "related_unknown" }, { "source": "b-pagerank-x-markov-chain", "target": "u-pagerank-x-markov-chain", "relation": "related_unknown" }, { "source": "b-reinforcement-learning-x-bellman-equation", "target": "u-reinforcement-learning-x-bellman-equation", "relation": "related_unknown" }, { "source": "b-satisfiability-x-constraint-propagation", "target": "u-satisfiability-x-constraint-propagation", "relation": "related_unknown" }, { "source": "b-satisfiability-x-constraint-propagation", "target": "h-satisfiability-x-constraint-propagation", "relation": "related_hypothesis" }, { "source": "b-social-network-centrality-x-eigenvector", "target": "u-social-network-centrality-x-eigenvector", "relation": "related_unknown" }, { "source": "b-spectral-clustering-x-graph-laplacian", "target": "u-spectral-clustering-x-graph-laplacian", "relation": "related_unknown" }, { "source": "b-spectral-clustering-x-graph-laplacian", "target": "h-spectral-clustering-x-graph-laplacian", "relation": "related_hypothesis" }, { "source": "b-cellular-automata-x-computational-universality", "target": "u-cellular-automata-x-computational-universality", "relation": "related_unknown" }, { "source": "b-neural-ode-x-dynamical-systems", "target": "u-neural-ode-x-dynamical-systems", "relation": "related_unknown" }, { "source": "b-tensor-networks-x-quantum-states", "target": "u-tensor-networks-x-quantum-states", "relation": "related_unknown" }, { "source": "b-tensor-networks-x-quantum-states", "target": "h-tensor-networks-x-quantum-states", "relation": "related_hypothesis" }, { "source": "b-epidemiological-demographic-transition", "target": "u-epidemiological-demographic-transition-timing", "relation": "related_unknown" }, { "source": "b-regenerative-medicine-morphogenetic-fields", "target": "u-morphogenetic-field-bioelectric-code", "relation": "related_unknown" }, { "source": "b-regenerative-medicine-morphogenetic-fields", "target": "h-bioelectric-pattern-regeneration-control", "relation": "related_hypothesis" }, { "source": "b-gene-networks-waddington-landscape", "target": "u-waddington-canalization-mechanism", "relation": "related_unknown" }, { "source": "b-morphogen-gradients-diffusion", "target": "u-morphogen-gradient-robustness-scaling", "relation": "related_unknown" }, { "source": "b-morphogen-gradients-diffusion", "target": "h-turing-pattern-wavelength-experimental-test", "relation": "related_hypothesis" }, { "source": "b-topological-defects-tissue-morphogenesis", "target": "u-topological-defect-morphogenesis-3d-tissue", "relation": "related_unknown" }, { "source": "b-delay-embedding-x-icu-deterioration-early-warning", "target": "u-embedding-dimension-selection-for-icu-trajectory-instability-detection", "relation": "related_unknown" }, { "source": "b-delay-embedding-x-icu-deterioration-early-warning", "target": "h-delay-embedding-indicators-improve-icu-deterioration-lead-time", "relation": "related_hypothesis" }, { "source": "b-lstm-sequence-memory-x-icu-physiology-forecasting", "target": "u-missingness-aware-lstm-training-for-icu-forecasts", "relation": "related_unknown" }, { "source": "b-lstm-sequence-memory-x-icu-physiology-forecasting", "target": "h-missingness-augmented-lstm-models-improve-icu-decompensation-horizon-accuracy", "relation": "related_hypothesis" }, { "source": "b-agricultural-biodiversity-ecosystem", "target": "u-bef-relationship-agricultural-context", "relation": "related_unknown" }, { "source": "b-coevolution-arms-races", "target": "u-red-queen-molecular-clock-arms-race", "relation": "related_unknown" }, { "source": "b-coevolution-arms-races", "target": "h-geographic-mosaic-coevolution-trait-variance", "relation": "related_hypothesis" }, { "source": "b-microbiome-ecology-host-health", "target": "u-gut-brain-axis-causal-mechanism", "relation": "related_unknown" }, { "source": "b-microbiome-ecology-host-health", "target": "h-microbiome-diversity-host-resilience", "relation": "related_hypothesis" }, { "source": "b-allelopathy-chemical-ecology", "target": "u-allelopathy-invasive-plant-mycorrhizal-disruption", "relation": "related_unknown" }, { "source": "b-allelopathy-chemical-ecology", "target": "h-allelopathy-glucosinolate-diversity-coevolution-ratchet", "relation": "related_hypothesis" }, { "source": "b-redfield-ratio-ocean-stoichiometry", "target": "u-redfield-ratio-variability-drivers", "relation": "related_unknown" }, { "source": "b-soil-microbiome-carbon-cycling", "target": "u-soil-cue-temperature-sensitivity-warming-feedback", "relation": "related_unknown" }, { "source": "b-soil-microbiome-carbon-cycling", "target": "h-mems-high-cue-fungi-mineral-soc-stabilization-warming", "relation": "related_hypothesis" }, { "source": "b-stoichiometry-liebig-minimum", "target": "u-redfield-ratio-evolutionary-constraint", "relation": "related_unknown" }, { "source": "b-vicsek-flocking-x-consensus-raft-leader-stability", "target": "u-vicsek-noise-raft-jitter-quantitative-mapping", "relation": "related_unknown" }, { "source": "b-vicsek-flocking-x-consensus-raft-leader-stability", "target": "h-critical-noise-sweep-scaling-parallels-election-timeout-sweep-phenomenologically", "relation": "related_hypothesis" }, { "source": "b-control-lyapunov-ecological-harvest-management", "target": "u-control-lyapunov-safe-harvest-policy-ecology", "relation": "related_unknown" }, { "source": "b-control-lyapunov-ecological-harvest-management", "target": "h-clf-constrained-harvest-stabilizes-biomass-under-shocks", "relation": "related_hypothesis" }, { "source": "b-bet-hedging-x-portfolio-diversification", "target": "u-bet-hedging-correlation-structure-across-taxa", "relation": "related_unknown" }, { "source": "b-bet-hedging-x-portfolio-diversification", "target": "h-bet-hedging-x-portfolio-diversification", "relation": "related_hypothesis" }, { "source": "b-biomimicry-sustainable-design", "target": "u-spider-silk-recombinant-production-mechanical-parity", "relation": "related_unknown" }, { "source": "b-biomimicry-sustainable-design", "target": "h-biomimicry-design-convergence-performance-ceiling", "relation": "related_hypothesis" }, { "source": "b-climate-tick-range-lyme", "target": "u-lyme-ohio-surveillance-gap", "relation": "related_unknown" }, { "source": "b-climate-tick-range-lyme", "target": "h-ohio-lyme-deer-management-intervention", "relation": "related_hypothesis" }, { "source": "b-metapopulation-sir-patch-occupancy", "target": "u-metapopulation-epidemic-threshold-fragmented-landscape", "relation": "related_unknown" }, { "source": "b-niche-construction-extended-evolutionary-synthesis", "target": "u-niche-construction-feedback-tempo", "relation": "related_unknown" }, { "source": "b-niche-construction-extended-evolutionary-synthesis", "target": "h-niche-construction-accelerated-local-adaptation", "relation": "related_hypothesis" }, { "source": "b-phenotypic-plasticity-reaction-norms", "target": "u-phenotypic-plasticity-adaptive-limits-speed", "relation": "related_unknown" }, { "source": "b-phenotypic-plasticity-reaction-norms", "target": "h-reaction-norm-slope-predicts-climate-tracking", "relation": "related_hypothesis" }, { "source": "b-red-queen-coevolutionary-cycles", "target": "u-red-queen-cycle-period-determinants", "relation": "related_unknown" }, { "source": "b-red-queen-coevolutionary-cycles", "target": "h-red-queen-cycling-sustained-by-spatial-structure", "relation": "related_hypothesis" }, { "source": "b-ess-ecosystem-dynamics", "target": "u-ess-higher-order-interactions-ecosystem", "relation": "related_unknown" }, { "source": "b-ess-ecosystem-dynamics", "target": "h-cyclic-dominance-spatial-heterogeneity-biodiversity", "relation": "related_hypothesis" }, { "source": "b-biodiversity-entropy-measures", "target": "u-maxent-species-abundance-prediction", "relation": "related_unknown" }, { "source": "b-vision-transformer-x-crop-stress-phenotyping", "target": "u-vit-crop-stress-generalization-across-sensors", "relation": "related_unknown" }, { "source": "b-vision-transformer-x-crop-stress-phenotyping", "target": "h-vit-based-phenotyping-improves-early-crop-stress-detection", "relation": "related_hypothesis" }, { "source": "b-chaos-population-cycles", "target": "u-chaotic-population-cycles-detection-noise", "relation": "related_unknown" }, { "source": "b-chaos-population-cycles", "target": "h-logistic-map-feigenbaum-ecology-universality", "relation": "related_hypothesis" }, { "source": "b-forest-gap-dynamics-x-neutral-theory-sampling", "target": "u-gap-recruitment-neutral-theory-goodness-of-fit", "relation": "related_unknown" }, { "source": "b-forest-gap-dynamics-x-neutral-theory-sampling", "target": "h-neutral-theta-estimates-converge-pre-post-gap-chronosequence", "relation": "related_hypothesis" }, { "source": "b-forest-succession-intermediate-disturbance", "target": "u-intermediate-disturbance-diversity-peak", "relation": "related_unknown" }, { "source": "b-invasive-species-reaction-diffusion", "target": "u-invasive-species-reaction-diffusion", "relation": "related_unknown" }, { "source": "b-invasive-species-reaction-diffusion", "target": "h-invasive-species-reaction-diffusion", "relation": "related_hypothesis" }, { "source": "b-landscape-ecology-graph-theory", "target": "u-wildlife-corridor-percolation-threshold", "relation": "related_unknown" }, { "source": "b-landscape-ecology-graph-theory", "target": "h-circuit-theory-outperforms-lcp-gene-flow-prediction", "relation": "related_hypothesis" }, { "source": "b-metapopulation-dynamics-patch-theory", "target": "u-metapopulation-climate-velocity-extinction-debt", "relation": "related_unknown" }, { "source": "b-metapopulation-dynamics-patch-theory", "target": "h-metapopulation-capacity-climate-refugia-network", "relation": "related_hypothesis" }, { "source": "b-neutral-theory-random-matrix", "target": "u-neutral-vs-niche-ecology-partitioning", "relation": "related_unknown" }, { "source": "b-neutral-theory-random-matrix", "target": "h-may-stability-real-ecosystem-applicability", "relation": "related_hypothesis" }, { "source": "b-phylogeography-coalescent-theory", "target": "u-human-expansion-routes-coalescent-ancient-dna", "relation": "related_unknown" }, { "source": "b-phylogeography-coalescent-theory", "target": "h-lgm-refugia-predict-phylogeographic-breaks-globally", "relation": "related_hypothesis" }, { "source": "b-predator-prey-hopf-bifurcation", "target": "u-predator-prey-cycle-amplitude-stochastic", "relation": "related_unknown" }, { "source": "b-predator-prey-lotka-volterra-hamiltonian", "target": "u-lotka-volterra-hamiltonian-real-ecosystem-conservation", "relation": "related_unknown" }, { "source": "b-reaction-diffusion-spatial-ecology", "target": "u-turing-pattern-selection-ecology", "relation": "related_unknown" }, { "source": "b-reaction-diffusion-spatial-ecology", "target": "h-vegetation-stripe-turing-instability", "relation": "related_hypothesis" }, { "source": "b-replicator-dynamics-x-evolutionarily-stable-strategy-field-data", "target": "u-replicator-model-identifiability-multispecies-field-data", "relation": "related_unknown" }, { "source": "b-replicator-dynamics-x-evolutionarily-stable-strategy-field-data", "target": "h-replicator-residual-tests-improve-ess-prediction-under-competition", "relation": "related_hypothesis" }, { "source": "b-stochastic-population-extinction", "target": "u-extinction-debt-lag-time-empirical-quantification-fragmented-landscapes", "relation": "related_unknown" }, { "source": "b-stochastic-population-extinction", "target": "h-extinction-time-exponential-k-demographic-stochasticity-confirmed", "relation": "related_hypothesis" }, { "source": "b-stochastic-population-master-equation", "target": "u-demographic-stochasticity-extinction-threshold", "relation": "related_unknown" }, { "source": "b-stochastic-population-master-equation", "target": "h-extinction-debt-master-equation-prediction", "relation": "related_hypothesis" }, { "source": "b-vegetation-patterns-klausmeier-model", "target": "u-dryland-vegetation-pattern-formation", "relation": "related_unknown" }, { "source": "b-mutualistic-nestedness-robustness", "target": "u-nestedness-mutualistic-network-robustness", "relation": "related_unknown" }, { "source": "b-mutualistic-networks-nestedness", "target": "u-nestedness-stability-causal-mechanism", "relation": "related_unknown" }, { "source": "b-mutualistic-networks-nestedness", "target": "h-nestedness-robustness-degree-heterogeneity-mediation", "relation": "related_hypothesis" }, { "source": "b-openalex-percolation-habitat-connectivity", "target": "u-percolation-threshold-habitat-connectivity", "relation": "related_unknown" }, { "source": "b-openalex-percolation-habitat-connectivity", "target": "h-habitat-percolation-species-persistence", "relation": "related_hypothesis" }, { "source": "b-soil-food-webs-network-trophic-theory", "target": "u-soil-food-web-stability-topology", "relation": "related_unknown" }, { "source": "b-soil-food-webs-network-trophic-theory", "target": "h-soil-food-web-connectance-stability", "relation": "related_hypothesis" }, { "source": "b-trophic-cascades-network-motifs", "target": "u-trophic-cascade-motif-universality", "relation": "related_unknown" }, { "source": "b-trophic-cascades-network-motifs", "target": "h-food-web-motif-frequency-predicts-cascade-strength", "relation": "related_hypothesis" }, { "source": "b-advection-diffusion-x-odor-plume-search", "target": "u-plume-intermittency-foraging-optimal-rules", "relation": "related_unknown" }, { "source": "b-advection-diffusion-x-odor-plume-search", "target": "h-advection-diffusion-x-odor-plume-search", "relation": "related_hypothesis" }, { "source": "b-ecosystem-resilience-bifurcation", "target": "u-ecosystem-tipping-point-early-warning-false-positive-rate", "relation": "related_unknown" }, { "source": "b-ecosystem-resilience-bifurcation", "target": "h-critical-slowing-down-universal-ews-ecosystem-tipping-fold-bifurcation", "relation": "related_hypothesis" }, { "source": "b-forest-canopy-beer-lambert-radiative", "target": "u-forest-canopy-clumping-beer-lambert-deviation", "relation": "related_unknown" }, { "source": "b-forest-canopy-beer-lambert-radiative", "target": "h-clumping-index-primary-productivity-underestimate", "relation": "related_hypothesis" }, { "source": "b-forest-fire-self-organized-criticality", "target": "u-forest-fire-soc-climate-change-modification", "relation": "related_unknown" }, { "source": "b-forest-fire-self-organized-criticality", "target": "h-forest-fire-soc-beta-exponent-climate-invariance", "relation": "related_hypothesis" }, { "source": "b-island-biogeography-percolation", "target": "u-maxent-species-abundance-prediction", "relation": "related_unknown" }, { "source": "b-island-biogeography-percolation", "target": "h-habitat-percolation-z-exponent", "relation": "related_hypothesis" }, { "source": "b-neutral-theory-random-walks", "target": "u-nestedness-stability-causal-mechanism", "relation": "related_unknown" }, { "source": "b-neutral-theory-random-walks", "target": "h-nestedness-robustness-degree-heterogeneity-mediation", "relation": "related_hypothesis" }, { "source": "b-nutrient-cycling-stoichiometry", "target": "u-redfield-ratio-evolutionary-origin-mechanism", "relation": "related_unknown" }, { "source": "b-nutrient-cycling-stoichiometry", "target": "h-growth-rate-hypothesis-ribosome-phosphorus-universality", "relation": "related_hypothesis" }, { "source": "b-oceanic-turbulence-mixing", "target": "u-ocean-mixing-parameterization-climate-models", "relation": "related_unknown" }, { "source": "b-oceanic-turbulence-mixing", "target": "h-tidal-internal-wave-mixing-abyssal-hotspots", "relation": "related_hypothesis" }, { "source": "b-seed-dispersal-levy-flight", "target": "u-seed-dispersal-levy-flight", "relation": "related_unknown" }, { "source": "b-seed-dispersal-levy-flight", "target": "h-seed-dispersal-levy-flight", "relation": "related_hypothesis" }, { "source": "b-trophic-cascades-phase-transitions", "target": "u-ocean-acidification-ecosystem-effects", "relation": "related_unknown" }, { "source": "b-trophic-cascades-phase-transitions", "target": "u-soil-moisture-atmosphere-feedback", "relation": "related_unknown" }, { "source": "b-wildfire-dynamics-reaction-diffusion", "target": "u-pyroconvection-prediction-coupled-fire-atmosphere-models", "relation": "related_unknown" }, { "source": "b-wildfire-dynamics-reaction-diffusion", "target": "h-climate-fire-feedback-accelerates-beyond-linear-projections", "relation": "related_hypothesis" }, { "source": "b-common-pool-resources-game-theory", "target": "u-multilateral-cooperation-failure-modes", "relation": "related_unknown" }, { "source": "b-common-pool-resources-game-theory", "target": "h-ostrom-commons-multilateral-failure", "relation": "related_hypothesis" }, { "source": "b-common-pool-resources-institutional-design", "target": "u-digital-commons-governance-principles", "relation": "related_unknown" }, { "source": "b-common-pool-resources-institutional-design", "target": "h-ostrom-design-principles-digital-commons", "relation": "related_hypothesis" }, { "source": "b-conservation-psychology-pro-environmental", "target": "u-attitude-behavior-gap-pro-environmental", "relation": "related_unknown" }, { "source": "b-conservation-psychology-pro-environmental", "target": "h-place-attachment-mediates-conservation-behavior-more-than-vbn", "relation": "related_hypothesis" }, { "source": "b-political-ecology-environmental-justice", "target": "u-environmental-justice-cumulative-impact-assessment-methodology", "relation": "related_unknown" }, { "source": "b-political-ecology-environmental-justice", "target": "h-pes-elite-capture-indigenous-displacement-monitoring-prevention", "relation": "related_hypothesis" }, { "source": "b-resilience-theory-adaptive-management", "target": "u-collective-action-without-authority", "relation": "related_unknown" }, { "source": "b-resilience-theory-adaptive-management", "target": "h-collective-action-ostrom-design-principles-v2", "relation": "related_hypothesis" }, { "source": "b-species-distribution-maxent", "target": "u-maxent-species-range-shift-climate", "relation": "related_unknown" }, { "source": "b-species-distribution-maxent", "target": "h-maxent-invasive-species-prediction", "relation": "related_hypothesis" }, { "source": "b-ecosystem-metabolic-scaling", "target": "u-metabolic-scaling-exponent-deviation-extremes", "relation": "related_unknown" }, { "source": "b-soil-carbon-microbial-thermodynamics", "target": "u-soil-carbon-cue-temperature-response", "relation": "related_unknown" }, { "source": "b-soil-carbon-microbial-thermodynamics", "target": "h-microbial-cue-warming-feedback-carbon-cycle", "relation": "related_hypothesis" }, { "source": "b-ellsberg-paradox-ambiguity-aversion", "target": "u-ellsberg-ambiguity-aversion-neural-circuit", "relation": "related_unknown" }, { "source": "b-ellsberg-paradox-ambiguity-aversion", "target": "h-maxmin-eu-ambiguity-aversion-amygdala", "relation": "related_hypothesis" }, { "source": "b-prospect-theory-loss-aversion", "target": "u-loss-aversion-neural-substrate", "relation": "related_unknown" }, { "source": "b-prospect-theory-loss-aversion", "target": "h-prospect-theory-neural-value-coding", "relation": "related_hypothesis" }, { "source": "b-optimal-transport-x-machine-learning", "target": "u-optimal-transport-x-machine-learning", "relation": "related_unknown" }, { "source": "b-optimal-transport-x-machine-learning", "target": "h-optimal-transport-x-machine-learning", "relation": "related_hypothesis" }, { "source": "b-collective-risk-social-dilemma-x-insurance", "target": "u-collective-risk-pool-stability-evolution", "relation": "related_unknown" }, { "source": "b-collective-risk-social-dilemma-x-insurance", "target": "h-risk-pooling-institutions-shift-evolutionary-stable-cooperation", "relation": "related_hypothesis" }, { "source": "b-lotka-volterra-market-dynamics", "target": "u-predator-prey-market-oscillations", "relation": "related_unknown" }, { "source": "b-lotka-volterra-market-dynamics", "target": "h-lotka-volterra-semiconductor-capex-cycle", "relation": "related_hypothesis" }, { "source": "b-natural-capital-ecosystem-services", "target": "u-ecosystem-services-valuation-market-failure", "relation": "related_unknown" }, { "source": "b-natural-capital-ecosystem-services", "target": "h-ecosystem-services-pigouvian-subsidy-biodiversity-market", "relation": "related_hypothesis" }, { "source": "b-price-elasticity-x-elastic-stiffness-tensor-analogy", "target": "u-slutsky-vs-mechanical-reciprocity-operational-mapping", "relation": "related_unknown" }, { "source": "b-price-elasticity-x-elastic-stiffness-tensor-analogy", "target": "h-local-equilibrium-jacobian-best-conditioned-axis-aligns-with-principal-strain-demo-only", "relation": "related_hypothesis" }, { "source": "b-game-theoretic-vaccination-x-herd-immunity-threshold", "target": "u-vaccination-game-equilibrium-gaps-versus-measured-coverage", "relation": "related_unknown" }, { "source": "b-game-theoretic-vaccination-x-herd-immunity-threshold", "target": "h-price-subsidy-closes-nash-herd-gap-in-agent-based-metapopulations", "relation": "related_hypothesis" }, { "source": "b-signaling-theory-handicap-principle", "target": "u-handicap-principle-signal-cost-measurement", "relation": "related_unknown" }, { "source": "b-efficient-markets-martingale", "target": "h-martingale-ecological-pricing", "relation": "related_hypothesis" }, { "source": "b-causal-forest-x-policy-elasticity-heterogeneity", "target": "u-causal-forest-policy-effect-transportability", "relation": "related_unknown" }, { "source": "b-causal-forest-x-policy-elasticity-heterogeneity", "target": "h-causal-forest-heterogeneity-improves-policy-targeting-efficiency", "relation": "related_hypothesis" }, { "source": "b-auction-design-x-complexity-theory", "target": "u-auction-design-x-complexity-theory", "relation": "related_unknown" }, { "source": "b-arrow-impossibility-social-choice", "target": "u-behavioral-economics-policy-effectiveness", "relation": "related_unknown" }, { "source": "b-arrow-impossibility-social-choice", "target": "h-arrow-impossibility-voting-nudges", "relation": "related_hypothesis" }, { "source": "b-auction-theory-mechanism-design", "target": "u-vcg-combinatorial-auction-scalability", "relation": "related_unknown" }, { "source": "b-auction-theory-mechanism-design", "target": "h-vcg-regretnet-combinatorial-approximation", "relation": "related_hypothesis" }, { "source": "b-price-theory-walrasian-tatonnement", "target": "u-walrasian-tatonnement-convergence-without-gs", "relation": "related_unknown" }, { "source": "b-price-theory-walrasian-tatonnement", "target": "h-tatonnement-convergence-diagonal-dominance", "relation": "related_hypothesis" }, { "source": "b-supply-chain-network-x-bond-percolation-disruption", "target": "u-supply-chain-correlated-failure-calibration", "relation": "related_unknown" }, { "source": "b-supply-chain-network-x-bond-percolation-disruption", "target": "h-supply-chain-network-x-bond-percolation-disruption", "relation": "related_hypothesis" }, { "source": "b-inequality-health-gradient", "target": "u-inequality-health-phase-transition-threshold", "relation": "related_unknown" }, { "source": "b-inequality-health-gradient", "target": "h-polarisation-ising-phase-transition", "relation": "related_hypothesis" }, { "source": "b-inequality-health-gradient", "target": "h-norm-cascade-ising-ew", "relation": "related_hypothesis" }, { "source": "b-trade-network-leontief-shock-propagation", "target": "u-global-trade-leontief-systemic-shock-threshold", "relation": "related_unknown" }, { "source": "b-contagion-models-x-financial-crises", "target": "u-financial-contagion-epidemic-threshold-mapping", "relation": "related_unknown" }, { "source": "b-contagion-models-x-financial-crises", "target": "h-interbank-default-cascades-exhibit-epidemic-thresholds", "relation": "related_hypothesis" }, { "source": "b-dissipative-structures-economic-cycles", "target": "u-economic-dissipation-entropy-measure", "relation": "related_unknown" }, { "source": "b-dissipative-structures-economic-cycles", "target": "h-kondratiev-dissipative-entropy", "relation": "related_hypothesis" }, { "source": "b-financial-markets-nonequilibrium", "target": "u-financial-market-impact-model-universal-mechanism", "relation": "related_unknown" }, { "source": "b-financial-markets-nonequilibrium", "target": "h-inverse-cubic-law-agent-heterogeneity-mechanism", "relation": "related_hypothesis" }, { "source": "b-wealth-distribution-statistical-mechanics", "target": "u-statistical-mechanics-income-wealth", "relation": "related_unknown" }, { "source": "b-wealth-distribution-statistical-mechanics", "target": "u-pareto-exponent-redistribution-mechanism", "relation": "related_unknown" }, { "source": "b-wealth-distribution-statistical-mechanics", "target": "h-pareto-exponent-growth-redistribution-ratio", "relation": "related_hypothesis" }, { "source": "b-causal-inference-instrumental-variables", "target": "u-causal-inference-heterogeneous-treatment-effects-identification", "relation": "related_unknown" }, { "source": "b-causal-inference-instrumental-variables", "target": "h-iv-late-external-validity-population-representativeness", "relation": "related_hypothesis" }, { "source": "b-graph-signal-processing-x-power-grid-pmu-anomaly-localization", "target": "u-graph-spectral-leakage-pmu-event-localization", "relation": "related_unknown" }, { "source": "b-graph-signal-processing-x-power-grid-pmu-anomaly-localization", "target": "h-graph-wavelet-energy-localizes-pmu-grid-disturbances-better-than-scada", "relation": "related_hypothesis" }, { "source": "b-kuramoto-synchrony-x-beta-cell-islet-oscillations", "target": "u-coupling-topology-thresholds-for-beta-cell-synchrony-collapse", "relation": "related_unknown" }, { "source": "b-kuramoto-synchrony-x-beta-cell-islet-oscillations", "target": "h-grid-inspired-phase-coherence-metrics-predict-beta-cell-dysfunction-earlier", "relation": "related_hypothesis" }, { "source": "b-catalyst-sabatier-principle", "target": "u-oer-scaling-relation-break", "relation": "related_unknown" }, { "source": "b-catalyst-sabatier-principle", "target": "h-dual-site-catalyst-breaks-oer-scaling", "relation": "related_hypothesis" }, { "source": "b-nonhelical-landauer-reversible-em", "target": "u-landauer-limit-nonhelical-resonator", "relation": "related_unknown" }, { "source": "b-nonhelical-landauer-reversible-em", "target": "u-reversible-em-logic-gate-design", "relation": "related_unknown" }, { "source": "b-nonhelical-landauer-reversible-em", "target": "h-nonhelical-resonator-adiabatic-quantum-memory", "relation": "related_hypothesis" }, { "source": "b-maxwell-equations-wave-encoding", "target": "u-maxwell-shannon-channel-near-capacity", "relation": "related_unknown" }, { "source": "b-maxwell-equations-wave-encoding", "target": "h-maxwell-wave-channel-capacity-limit", "relation": "related_hypothesis" }, { "source": "b-bound-states-continuum-x-dielectric-metasurface-q", "target": "u-bic-metasurface-q-factor-radiative-disorder-limit", "relation": "related_unknown" }, { "source": "b-bound-states-continuum-x-dielectric-metasurface-q", "target": "h-bic-protected-metasurfaces-maintain-high-q-under-fabrication-noise", "relation": "related_hypothesis" }, { "source": "b-epsilon-near-zero-metamaterial-x-field-confinement-quality-factor", "target": "u-epsilon-near-zero-loss-radiation-q-tradeoff", "relation": "related_unknown" }, { "source": "b-epsilon-near-zero-metamaterial-x-field-confinement-quality-factor", "target": "h-enz-crossover-curvature-predicts-local-q-maximum-thin-film-cavity", "relation": "related_hypothesis" }, { "source": "b-fano-asymmetric-lineshape-x-metamaterial-dark-mode-quality-factor", "target": "u-fano-metamaterial-dark-mode-q-engineering", "relation": "related_unknown" }, { "source": "b-fano-asymmetric-lineshape-x-metamaterial-dark-mode-quality-factor", "target": "h-fano-q-factor-tracks-radiative-darkness-order-parameter", "relation": "related_hypothesis" }, { "source": "b-floquet-metamaterials-x-nonreciprocal-wave-mixing", "target": "u-floquet-metamaterial-nonreciprocity-passivity-limit", "relation": "related_unknown" }, { "source": "b-floquet-metamaterials-x-nonreciprocal-wave-mixing", "target": "h-floquet-metasurface-achieves-isolation-without-magnets-under-passive-bias", "relation": "related_hypothesis" }, { "source": "b-floquet-time-modulated-metamaterial-x-nonreciprocal-electromagnetic-response", "target": "u-floquet-metamaterial-isolation-bandwidth-loss-tradeoff", "relation": "related_unknown" }, { "source": "b-floquet-time-modulated-metamaterial-x-nonreciprocal-electromagnetic-response", "target": "h-staggered-commutation-frequency-threshold-for-target-isolation-db", "relation": "related_hypothesis" }, { "source": "b-nonhelical-turing-electromagnetic", "target": "u-nonhelical-turing-wavelength-scaling", "relation": "related_unknown" }, { "source": "b-nonhelical-turing-electromagnetic", "target": "u-metamaterial-self-assembly-dynamics", "relation": "related_unknown" }, { "source": "b-nonhelical-turing-electromagnetic", "target": "h-nonhelical-turing-cloaking-adaptation", "relation": "related_hypothesis" }, { "source": "b-biomimetic-robotics-locomotion", "target": "u-slip-model-biological-accuracy-multi-legged-running", "relation": "related_unknown" }, { "source": "b-biomimetic-robotics-locomotion", "target": "h-biomimetic-slip-locomotion-minimal-energy-cost-robots", "relation": "related_hypothesis" }, { "source": "b-droplet-splitting-microfluidics-x-binary-fission-metaphor", "target": "u-droplet-splitting-variance-biology-alignment", "relation": "related_unknown" }, { "source": "b-droplet-splitting-microfluidics-x-binary-fission-metaphor", "target": "h-droplet-split-binomial-partition-fission-alignment", "relation": "related_hypothesis" }, { "source": "b-engineering-reliability-extreme-value", "target": "u-offshore-wind-fatigue", "relation": "related_unknown" }, { "source": "b-engineering-reliability-extreme-value", "target": "u-3d-printed-metal-fatigue", "relation": "related_unknown" }, { "source": "b-feedback-control-homeostasis", "target": "u-homeostasis-integral-feedback-synthetic-design", "relation": "related_unknown" }, { "source": "b-feedback-control-homeostasis", "target": "h-integral-feedback-sufficient-perfect-adaptation-living-cells", "relation": "related_hypothesis" }, { "source": "b-organ-on-chip-microfluidics", "target": "u-organ-chip-vascularization-long-term-viability", "relation": "related_unknown" }, { "source": "b-organ-on-chip-microfluidics", "target": "h-organ-chip-multi-organ-body-on-chip-systemic-toxicity", "relation": "related_hypothesis" }, { "source": "b-robustness-evolvability-modularity", "target": "u-modularity-robustness-evolvability-tradeoff", "relation": "related_unknown" }, { "source": "b-robustness-evolvability-modularity", "target": "u-earthquake-soc-universality-class", "relation": "related_unknown" }, { "source": "b-robustness-evolvability-modularity", "target": "h-modular-architecture-robustness-evolvability", "relation": "related_hypothesis" }, { "source": "b-robustness-evolvability-modularity", "target": "h-gompertz-weibull-aging-unification", "relation": "related_hypothesis" }, { "source": "b-swarm-robotics-stigmergy", "target": "u-stigmergy-optimality-gap-real-environments", "relation": "related_unknown" }, { "source": "b-swarm-robotics-stigmergy", "target": "h-swarm-pheromone-convergence-rate", "relation": "related_hypothesis" }, { "source": "b-synthetic-biology-genetic-circuits", "target": "u-genetic-circuit-crosstalk-noise", "relation": "related_unknown" }, { "source": "b-tensegrity-cytoskeleton", "target": "h-tensegrity-cancer-mechanics", "relation": "related_hypothesis" }, { "source": "b-electron-microscopy-materials-characterization", "target": "u-cryo-em-resolution-limit-radiation-damage-versus-detector-efficiency", "relation": "related_unknown" }, { "source": "b-electron-microscopy-materials-characterization", "target": "h-cryo-em-membrane-protein-structures-without-detergent-native-lipid-bilayer", "relation": "related_hypothesis" }, { "source": "b-distributed-systems-consensus", "target": "u-byzantine-fault-tolerance-practical", "relation": "related_unknown" }, { "source": "b-distributed-systems-consensus", "target": "h-cap-theorem-pacelc-extension", "relation": "related_hypothesis" }, { "source": "b-skin-depth-shielding-x-financial-firewall-layers", "target": "u-em-skin-depth-financial-firewall-mapping-limits", "relation": "related_unknown" }, { "source": "b-skin-depth-shielding-x-financial-firewall-layers", "target": "h-layered-em-shielding-financial-firewall-depth-ratio-analogy", "relation": "related_hypothesis" }, { "source": "b-wind-turbine-betz-limit-actuator-disk", "target": "u-betz-limit-exceeded-unsteady-flow", "relation": "related_unknown" }, { "source": "b-wind-turbine-betz-limit-actuator-disk", "target": "h-betz-limit-array-cooperation-exceeds-individual", "relation": "related_hypothesis" }, { "source": "b-graph-transformer-x-grid-contingency-screening", "target": "u-graph-transformer-grid-contingency-false-negative-risk", "relation": "related_unknown" }, { "source": "b-graph-transformer-x-grid-contingency-screening", "target": "h-graph-transformer-improves-grid-contingency-screening-recall", "relation": "related_hypothesis" }, { "source": "b-control-theory-differential-geometry", "target": "u-subriemannian-geodesic-abnormal-optimality", "relation": "related_unknown" }, { "source": "b-control-theory-differential-geometry", "target": "h-lie-bracket-depth-complexity-robot-planning", "relation": "related_hypothesis" }, { "source": "b-control-theory-lie-groups", "target": "u-lie-group-nonholonomic-robot-optimality", "relation": "related_unknown" }, { "source": "b-control-theory-lie-groups", "target": "h-geometric-control-se3-optimal-robotic-grasping", "relation": "related_hypothesis" }, { "source": "b-finite-element-method-pde", "target": "u-fem-adaptivity-optimal-mesh-refinement", "relation": "related_unknown" }, { "source": "b-finite-element-method-pde", "target": "h-isogeometric-analysis-superior-convergence-thin-shells", "relation": "related_hypothesis" }, { "source": "b-finite-element-x-discrete-exterior-calculus", "target": "u-fem-dec-mixed-form-equivalence-limits", "relation": "related_unknown" }, { "source": "b-finite-element-x-discrete-exterior-calculus", "target": "h-mixed-fem-for-hodge-laplace-matches-dec-upwind-schemes", "relation": "related_hypothesis" }, { "source": "b-graph-algorithms-network-optimization", "target": "u-tsp-approximation-barrier-metric", "relation": "related_unknown" }, { "source": "b-graph-algorithms-network-optimization", "target": "h-christofides-tight-example-construction", "relation": "related_hypothesis" }, { "source": "b-information-theory-data-compression", "target": "u-rate-distortion-optimal-neural-codes", "relation": "related_unknown" }, { "source": "b-information-theory-data-compression", "target": "h-shannon-optimal-compression-biological-codes", "relation": "related_hypothesis" }, { "source": "b-lidar-x-inverse-problems", "target": "u-lidar-scene-reconstruction-nonuniqueness", "relation": "related_unknown" }, { "source": "b-lidar-x-inverse-problems", "target": "h-sparsity-priors-stabilize-lidar-surface-recovery", "relation": "related_hypothesis" }, { "source": "b-optimization-algorithms-convex-analysis", "target": "u-cache-efficient-algorithm-design", "relation": "related_unknown" }, { "source": "b-optimization-algorithms-convex-analysis", "target": "h-distribution-shift-invariant-risk-minimization", "relation": "related_hypothesis" }, { "source": "b-signal-processing-fourier-analysis", "target": "u-optimal-cooling-schedule-convergence", "relation": "related_unknown" }, { "source": "b-signal-processing-fourier-analysis", "target": "h-compressed-sensing-mri-fourier-sparsity", "relation": "related_hypothesis" }, { "source": "b-traffic-flow-lwr-pde", "target": "u-traffic-phantom-jam-nucleation-mechanism", "relation": "related_unknown" }, { "source": "b-aeroelastic-flutter-x-hopf-galloping-bifurcation", "target": "u-aeroelastic-hopf-normal-form-transfer-limits", "relation": "related_unknown" }, { "source": "b-aeroelastic-flutter-x-hopf-galloping-bifurcation", "target": "h-hopf-reduced-order-predicts-galloping-onset-threshold", "relation": "related_hypothesis" }, { "source": "b-chaos-control-systems", "target": "u-chaos-transition-engineering-systems", "relation": "related_unknown" }, { "source": "b-chaos-control-systems", "target": "h-hopf-bifurcation-power-grid-stability", "relation": "related_hypothesis" }, { "source": "b-hertz-contact-x-spherical-indentation", "target": "u-hertz-adhesion-crossover-biological-tissues", "relation": "related_unknown" }, { "source": "b-hertz-contact-x-spherical-indentation", "target": "h-hertz-contact-x-spherical-indentation", "relation": "related_hypothesis" }, { "source": "b-kelvin-wake-angle-x-ship-wave-dispersion-design", "target": "u-finite-depth-kelvin-wake-angle-design-transfer", "relation": "related_unknown" }, { "source": "b-kelvin-wake-angle-x-ship-wave-dispersion-design", "target": "h-dispersion-aware-wake-visualization-improves-hull-wave-interpretation", "relation": "related_hypothesis" }, { "source": "b-metamaterials-negative-refraction", "target": "u-optical-frequency-metamaterial-loss-limits-superlens", "relation": "related_unknown" }, { "source": "b-metamaterials-negative-refraction", "target": "h-metasurface-flat-lens-diffraction-limited-visible", "relation": "related_hypothesis" }, { "source": "b-metamaterials-negative-refractive-index", "target": "u-metamaterial-acoustic-cloaking", "relation": "related_unknown" }, { "source": "b-metamaterials-negative-refractive-index", "target": "h-acoustic-metamaterial-cloaking-bandwidth-thickness-tradeoff", "relation": "related_hypothesis" }, { "source": "b-microelectronics-quantum-confinement", "target": "u-2d-material-fet-contact-resistance-scaling-below-1nm", "relation": "related_unknown" }, { "source": "b-microelectronics-quantum-confinement", "target": "h-gaa-nanosheet-ballistic-transport-regime-room-temperature-3nm", "relation": "related_hypothesis" }, { "source": "b-openalex-topology-electrical-circuits-x-condensed-matter-physics", "target": "u-topoelectrical-circuit-disorder-robustness-limit", "relation": "related_unknown" }, { "source": "b-openalex-topology-electrical-circuits-x-condensed-matter-physics", "target": "h-topoelectrical-circuit-edge-mode-disorder-threshold", "relation": "related_hypothesis" }, { "source": "b-optical-fiber-nonlinear-optics", "target": "u-fiber-nonlinearity-capacity-limit-shannon", "relation": "related_unknown" }, { "source": "b-optical-fiber-nonlinear-optics", "target": "h-soliton-basis-transmission-optimal-nonlinear-channel-capacity", "relation": "related_hypothesis" }, { "source": "b-phased-array-beamforming-x-multi-coil-wireless-power-interference-lobes", "target": "u-multi-coil-wpt-array-grating-lobes-cross-talk", "relation": "related_unknown" }, { "source": "b-phased-array-beamforming-x-multi-coil-wireless-power-interference-lobes", "target": "h-half-wavelength-coil-spacing-bound-suppresses-near-field-grating-analogs", "relation": "related_hypothesis" }, { "source": "b-resonant-wireless-power-transfer-x-coupled-mode-q-bandwidth-limit", "target": "u-wireless-power-transfer-q-bandwidth-coupling-limit", "relation": "related_unknown" }, { "source": "b-resonant-wireless-power-transfer-x-coupled-mode-q-bandwidth-limit", "target": "h-critical-coupling-tracking-improves-mid-range-wireless-power-efficiency", "relation": "related_hypothesis" }, { "source": "b-skin-friction-x-boundary-layer", "target": "u-skin-friction-scaling-across-roughness-regimes", "relation": "related_unknown" }, { "source": "b-skin-friction-x-boundary-layer", "target": "h-law-of-wall-predicts-local-skin-friction-when-roughness-scaled", "relation": "related_hypothesis" }, { "source": "b-soft-ferrite-hysteresis-eddy-current-x-wpt-coil-core-losses", "target": "u-soft-ferrite-nonlinear-permeability-wpt-harmonic-loss", "relation": "related_unknown" }, { "source": "b-soft-ferrite-hysteresis-eddy-current-x-wpt-coil-core-losses", "target": "h-gapped-ferrite-bias-point-maximizes-wpt-q-under-saturation-margin", "relation": "related_hypothesis" }, { "source": "b-soft-robotics-hyperelastic-continuum", "target": "u-soft-robotics-hyperelastic-inverse-design", "relation": "related_unknown" }, { "source": "b-soft-robotics-hyperelastic-continuum", "target": "h-neo-hookean-model-predicts-soft-actuator-90pct", "relation": "related_hypothesis" }, { "source": "b-tesla-resonant-wireless-power", "target": "u-wpt-efficiency-biological-tissue-interaction", "relation": "related_unknown" }, { "source": "b-tesla-resonant-wireless-power", "target": "h-resonant-wpt-ev-charging-grid-integration", "relation": "related_hypothesis" }, { "source": "b-thermal-management-heat-transfer", "target": "u-heat-pipe-limit-miniaturization", "relation": "related_unknown" }, { "source": "b-thermal-management-heat-transfer", "target": "h-pcm-microencapsulation-enables-chiplet-thermal-buffering", "relation": "related_hypothesis" }, { "source": "b-wpt-resonator-q-bandwidth-tradeoff-x-matching-network-coexistence", "target": "u-wpt-narrowband-q-bandwidth-multi-standard-coexistence", "relation": "related_unknown" }, { "source": "b-wpt-resonator-q-bandwidth-tradeoff-x-matching-network-coexistence", "target": "h-wpt-coexistence-requires-q-bandwidth-renegotiation-per-standard", "relation": "related_hypothesis" }, { "source": "b-cybersecurity-adversarial-systems", "target": "u-optimal-cybersecurity-investment-under-adversarial-uncertainty", "relation": "related_unknown" }, { "source": "b-cybersecurity-adversarial-systems", "target": "h-stackelberg-equilibrium-predicts-security-market-underinvestment", "relation": "related_hypothesis" }, { "source": "b-infrastructure-cascade-failures", "target": "u-interdependent-network-early-warning-cascade", "relation": "related_unknown" }, { "source": "b-infrastructure-cascade-failures", "target": "h-infrastructure-interdependence-discontinuous-collapse-empirical", "relation": "related_hypothesis" }, { "source": "b-operations-research-market-design", "target": "u-combinatorial-auction-computational-complexity", "relation": "related_unknown" }, { "source": "b-operations-research-market-design", "target": "h-gale-shapley-deferred-acceptance-stability-uniqueness", "relation": "related_hypothesis" }, { "source": "b-smart-cities-urban-data-analytics", "target": "u-smart-city-equity-algorithmic-routing", "relation": "related_unknown" }, { "source": "b-smart-cities-urban-data-analytics", "target": "h-differential-privacy-urban-analytics-accuracy-threshold", "relation": "related_hypothesis" }, { "source": "b-next-generation-matrix-control-epidemic-interventions", "target": "u-epidemic-mpc-next-generation-matrix-robustness", "relation": "related_unknown" }, { "source": "b-next-generation-matrix-control-epidemic-interventions", "target": "h-mpc-with-ngm-constraints-reduces-epidemic-overshoot", "relation": "related_hypothesis" }, { "source": "b-federated-averaging-x-multisite-epidemic-forecasting", "target": "u-federated-epidemic-model-drift-across-sites", "relation": "related_unknown" }, { "source": "b-federated-averaging-x-multisite-epidemic-forecasting", "target": "h-federated-ensembles-improve-cross-site-epidemic-generalization", "relation": "related_hypothesis" }, { "source": "b-epidemic-ensemble-kalman-filter", "target": "u-epidemic-kalman-filter", "relation": "related_unknown" }, { "source": "b-epidemic-ensemble-kalman-filter", "target": "h-epidemic-ensemble-kalman-filter", "relation": "related_hypothesis" }, { "source": "b-floquet-stability-x-seasonal-epidemic-forcing-windows", "target": "u-floquet-instability-thresholds-seasonal-epidemic-control", "relation": "related_unknown" }, { "source": "b-floquet-stability-x-seasonal-epidemic-forcing-windows", "target": "h-floquet-instability-metrics-improve-seasonal-epi-intervention-timing", "relation": "related_hypothesis" }, { "source": "b-mori-zwanzig-memory-kernels-x-epidemic-model-reduction", "target": "u-memory-kernel-identifiability-from-case-time-series", "relation": "related_unknown" }, { "source": "b-mori-zwanzig-memory-kernels-x-epidemic-model-reduction", "target": "h-memory-augmented-seir-improves-forecast-turning-points", "relation": "related_hypothesis" }, { "source": "b-pandemic-optimal-stopping", "target": "u-pandemic-intervention-timing-optimal-uncertainty", "relation": "related_unknown" }, { "source": "b-sir-network-percolation-threshold", "target": "u-sir-percolation-temporal-network-threshold", "relation": "related_unknown" }, { "source": "b-sir-network-percolation-threshold", "target": "h-targeted-vaccination-percolation-optimality", "relation": "related_hypothesis" }, { "source": "b-openalex-network-epidemic-percolation", "target": "u-network-threshold-epidemic-spread", "relation": "related_unknown" }, { "source": "b-openalex-network-epidemic-percolation", "target": "h-sars-cov2-network-percolation", "relation": "related_hypothesis" }, { "source": "b-percolation-thresholds-x-antimicrobial-combination-therapy-networks", "target": "u-network-fragmentation-thresholds-for-combination-antibiotic-coverage", "relation": "related_unknown" }, { "source": "b-percolation-thresholds-x-antimicrobial-combination-therapy-networks", "target": "h-percolation-aware-combination-selection-delays-resistance-network-percolation", "relation": "related_hypothesis" }, { "source": "b-sir-percolation", "target": "u-network-epidemic-threshold-heterogeneity", "relation": "related_unknown" }, { "source": "b-sir-percolation", "target": "h-scale-free-epidemic-threshold-vaccination", "relation": "related_hypothesis" }, { "source": "b-cultural-transmission-sir-models", "target": "u-cultural-transmission-network-effects", "relation": "related_unknown" }, { "source": "b-cultural-transmission-sir-models", "target": "h-cultural-sir-meme-herd-immunity", "relation": "related_hypothesis" }, { "source": "b-causal-inference-negative-controls-x-observational-pharmacovigilance", "target": "u-negative-control-selection-bias-pharmacovigilance-target-trials", "relation": "related_unknown" }, { "source": "b-causal-inference-negative-controls-x-observational-pharmacovigilance", "target": "h-negative-control-calibrated-estimators-reduce-pharmacovigilance-signal-bias", "relation": "related_hypothesis" }, { "source": "b-extreme-value-theory-x-antimicrobial-resistance-surveillance", "target": "u-threshold-selection-bias-in-evt-based-amr-early-warning", "relation": "related_unknown" }, { "source": "b-extreme-value-theory-x-antimicrobial-resistance-surveillance", "target": "h-peaks-over-threshold-models-improve-amr-outbreak-early-warning", "relation": "related_hypothesis" }, { "source": "b-sequential-probability-ratio-test-x-pathogen-genomic-surveillance", "target": "u-drift-robust-sprt-thresholding-for-streaming-pathogen-variant-alerts", "relation": "related_unknown" }, { "source": "b-sequential-probability-ratio-test-x-pathogen-genomic-surveillance", "target": "h-adaptive-sprt-alerting-detects-concerning-pathogen-variants-earlier-than-fixed-window-rules", "relation": "related_hypothesis" }, { "source": "b-epigenetic-clocks-aging-biomarkers", "target": "u-epigenetic-clock-causal-mechanism", "relation": "related_unknown" }, { "source": "b-epigenetic-clocks-aging-biomarkers", "target": "h-epigenetic-reprogramming-lifespan-extension", "relation": "related_hypothesis" }, { "source": "b-predator-detection-signal-detection-theory", "target": "u-predator-vigilance-roc-optimal-threshold", "relation": "related_unknown" }, { "source": "b-market-liquidity-hawkes-processes", "target": "u-market-microstructure-hawkes-calibration", "relation": "related_unknown" }, { "source": "b-market-liquidity-hawkes-processes", "target": "h-hawkes-process-liquidity-flash-crash", "relation": "related_hypothesis" }, { "source": "b-capillary-length-x-droplet-contact-line-dynamics", "target": "u-capillary-length-contact-line-hysteresis-unified-model", "relation": "related_unknown" }, { "source": "b-capillary-length-x-droplet-contact-line-dynamics", "target": "h-capillary-wetting-pinning-length-universality-class", "relation": "related_hypothesis" }, { "source": "b-finite-time-lyapunov-exponents-x-intracardiac-flow-mixing", "target": "u-ftle-derived-thrombosis-risk-threshold-transferability", "relation": "related_unknown" }, { "source": "b-finite-time-lyapunov-exponents-x-intracardiac-flow-mixing", "target": "h-ftle-ridge-persistence-predicts-left-atrial-appendage-stasis", "relation": "related_hypothesis" }, { "source": "b-atmospheric-blocking-rossby-waves", "target": "u-atmospheric-blocking-climate-change-frequency", "relation": "related_unknown" }, { "source": "b-silicate-weathering-geocarb-carbon-cycle", "target": "u-silicate-weathering-temperature-sensitivity-field", "relation": "related_unknown" }, { "source": "b-silicate-weathering-geocarb-carbon-cycle", "target": "h-silicate-weathering-feedback-stabilizes-hothouse", "relation": "related_hypothesis" }, { "source": "b-plate-tectonics-mantle-convection", "target": "h-plate-tectonics-initiated-by-bolide-impacts", "relation": "related_hypothesis" }, { "source": "b-plate-tectonics-mantle-convection", "target": "h-subduction-initiation-passive-margin-collapse", "relation": "related_hypothesis" }, { "source": "b-earthquake-soc", "target": "u-earthquake-soc-predictability", "relation": "related_unknown" }, { "source": "b-earthquake-soc", "target": "h-bvalue-stress-criticality-forecast", "relation": "related_hypothesis" }, { "source": "b-adjoint-state-seismic-inversion-x-backprop-gradient-learning", "target": "u-adjoint-seismic-backprop-gradient-stability", "relation": "related_unknown" }, { "source": "b-adjoint-state-seismic-inversion-x-backprop-gradient-learning", "target": "h-adjoint-preconditioning-improves-seismic-inversion-convergence", "relation": "related_hypothesis" }, { "source": "b-microseismic-acoustic-emission-fracture", "target": "u-microseismic-acoustic-emission-b-value-failure", "relation": "related_unknown" }, { "source": "b-geomagnetic-reversal-dynamo", "target": "u-geomagnetic-reversal-trigger-mechanism", "relation": "related_unknown" }, { "source": "b-seismic-tomography-inverse-problems", "target": "u-seismic-tomography-null-space-resolution", "relation": "related_unknown" }, { "source": "b-seismic-tomography-inverse-problems", "target": "h-seismic-adjoint-tomography-resolves-mantle-plumes", "relation": "related_hypothesis" }, { "source": "b-tectonic-stress-coulomb-failure", "target": "u-tectonic-coulomb-failure", "relation": "related_unknown" }, { "source": "b-tectonic-stress-coulomb-failure", "target": "h-tectonic-stress-coulomb-failure", "relation": "related_hypothesis" }, { "source": "b-earthquake-early-warning-x-recursive-bayesian-source-estimation", "target": "u-earthquake-early-warning-bayesian-latency-magnitude-error", "relation": "related_unknown" }, { "source": "b-earthquake-early-warning-x-recursive-bayesian-source-estimation", "target": "h-eew-kalman-style-updates-tighten-magnitude-posterior-faster-with-dense-networks", "relation": "related_hypothesis" }, { "source": "b-kalman-state-estimation-x-nwp-data-assimilation", "target": "u-ensemble-kalman-assimilation-nonlinear-localization-errors", "relation": "related_unknown" }, { "source": "b-kalman-state-estimation-x-nwp-data-assimilation", "target": "h-adaptive-inflation-ensemble-kalman-corrects-extreme-events", "relation": "related_hypothesis" }, { "source": "b-plate-boundary-slip-x-fracture-mechanics", "target": "u-plate-boundary-fracture-scale-bridging", "relation": "related_unknown" }, { "source": "b-plate-boundary-slip-x-fracture-mechanics", "target": "h-plate-boundary-slip-x-fracture-mechanics", "relation": "related_hypothesis" }, { "source": "b-tsunami-shallow-water-x-dispersive-soliton-bore", "target": "u-tsunami-dispersive-nonlinearity-regime-classification", "relation": "related_unknown" }, { "source": "b-tsunami-shallow-water-x-dispersive-soliton-bore", "target": "h-tsunami-front-regime-classifier-nonlinear-dispersive-bore", "relation": "related_hypothesis" }, { "source": "b-unet-x-satellite-flood-extent-mapping", "target": "u-unet-satellite-flood-generalization-under-cloud-noise", "relation": "related_unknown" }, { "source": "b-unet-x-satellite-flood-extent-mapping", "target": "h-unet-domain-randomization-improves-flood-mapping-recall", "relation": "related_hypothesis" }, { "source": "b-biogeochemical-box-models-x-attractor-stability", "target": "u-biogeochemical-multistability-empirical-identification", "relation": "related_unknown" }, { "source": "b-biogeochemical-box-models-x-attractor-stability", "target": "h-biogeochemical-box-models-x-attractor-stability", "relation": "related_hypothesis" }, { "source": "b-coastal-erosion-x-diffusive-interface", "target": "u-coastline-roughness-effective-surface-tension", "relation": "related_unknown" }, { "source": "b-coastal-erosion-x-diffusive-interface", "target": "h-diffusive-interface-models-predict-shoreline-roughening-exponents", "relation": "related_hypothesis" }, { "source": "b-nitrogen-cycle-reservoirs-x-coupled-oscillator-stability", "target": "u-nitrogen-cycle-jacobian-eigenstructure-versus-observed-anomalies", "relation": "related_unknown" }, { "source": "b-nitrogen-cycle-reservoirs-x-coupled-oscillator-stability", "target": "h-linearized-n-cycle-models-predict-chlorophyll-mode-timescales", "relation": "related_hypothesis" }, { "source": "b-soil-aggregate-fractal-pore-stability", "target": "u-soil-aggregate-fractal-dimension-stability-link", "relation": "related_unknown" }, { "source": "b-eikonal-wavefronts-x-cardiac-activation-mapping", "target": "u-eikonal-anisotropy-identifiability-in-cardiac-activation-inverse-problems", "relation": "related_unknown" }, { "source": "b-eikonal-wavefronts-x-cardiac-activation-mapping", "target": "h-eikonal-regularized-inversion-improves-cardiac-activation-map-fidelity", "relation": "related_hypothesis" }, { "source": "b-ensemble-kalman-smoothing-x-icu-latent-state-estimation", "target": "u-ensemble-kalman-icu-parameter-identifiability", "relation": "related_unknown" }, { "source": "b-ensemble-kalman-smoothing-x-icu-latent-state-estimation", "target": "h-localized-enkf-reduces-icu-forecast-error", "relation": "related_hypothesis" }, { "source": "b-ensemble-smoother-x-precision-oncology-state-estimation", "target": "u-observation-operator-misspecification-in-ensemble-smoother-oncology-models", "relation": "related_unknown" }, { "source": "b-ensemble-smoother-x-precision-oncology-state-estimation", "target": "h-ensemble-smoothers-improve-precision-oncology-trajectory-calibration", "relation": "related_hypothesis" }, { "source": "b-earthquake-self-organized-criticality", "target": "u-earthquake-soc-universality-class", "relation": "related_unknown" }, { "source": "b-earthquake-self-organized-criticality", "target": "u-earthquake-nucleation", "relation": "related_unknown" }, { "source": "b-earthquake-self-organized-criticality", "target": "h-gutenberg-richter-soc-btw-exponent", "relation": "related_hypothesis" }, { "source": "b-earthquake-self-organized-criticality", "target": "h-gutenberg-richter-percolation-threshold", "relation": "related_hypothesis" }, { "source": "b-lithospheric-planform-x-rayleigh-benard-wavelength-scaling", "target": "u-mantle-horizontal-spectrum-versus-rb-wavelength-law", "relation": "related_unknown" }, { "source": "b-lithospheric-planform-x-rayleigh-benard-wavelength-scaling", "target": "h-numerical-mantle-spectral-peaks-track-effective-rb-wavenumber-branches", "relation": "related_hypothesis" }, { "source": "b-mantle-convection-rayleigh-benard", "target": "u-mantle-convection-plate-tectonic-onset", "relation": "related_unknown" }, { "source": "b-mantle-convection-rayleigh-benard", "target": "h-plate-tectonics-ra-viscosity-threshold", "relation": "related_hypothesis" }, { "source": "b-river-braiding-x-soc-like-morphodynamics", "target": "u-braided-river-scaling-criticality-test", "relation": "related_unknown" }, { "source": "b-river-braiding-x-soc-like-morphodynamics", "target": "h-river-braiding-x-soc-like-morphodynamics", "relation": "related_hypothesis" }, { "source": "b-glacier-dynamics-glens-law", "target": "u-glacier-basal-sliding-uncertainty", "relation": "related_unknown" }, { "source": "b-glacier-dynamics-glens-law", "target": "h-marine-ice-sheet-instability-threshold", "relation": "related_hypothesis" }, { "source": "b-glacier-calving-fracture-mechanics", "target": "u-glacier-calving-crack-propagation-threshold", "relation": "related_unknown" }, { "source": "b-glacier-calving-fracture-mechanics", "target": "h-glacier-calving-fracture-toughness-prediction", "relation": "related_hypothesis" }, { "source": "b-neural-operator-surrogates-x-groundwater-inverse-modeling", "target": "u-neural-operator-generalization-groundwater-boundary-shift", "relation": "related_unknown" }, { "source": "b-neural-operator-surrogates-x-groundwater-inverse-modeling", "target": "h-fourier-neural-operator-surrogates-accelerate-groundwater-inversion-with-calibrated-uncertainty", "relation": "related_hypothesis" }, { "source": "b-river-network-hacks-law-fractal", "target": "u-river-network-hacks-law-variability", "relation": "related_unknown" }, { "source": "b-immune-regulation-feedback", "target": "u-immune-treg-pi-control-quantitative", "relation": "related_unknown" }, { "source": "b-immune-regulation-feedback", "target": "h-autoimmune-pi-gain-deficiency", "relation": "related_hypothesis" }, { "source": "b-foundation-model-x-tcr-antigen-specificity-transfer", "target": "u-tcr-foundation-model-ood-binding-generalization", "relation": "related_unknown" }, { "source": "b-foundation-model-x-tcr-antigen-specificity-transfer", "target": "h-tcr-foundation-pretraining-improves-antigen-specificity-recall", "relation": "related_hypothesis" }, { "source": "b-lipid-nanoparticle-mrna-delivery", "target": "u-lnp-tissue-targeting-mechanism", "relation": "related_unknown" }, { "source": "b-lipid-nanoparticle-mrna-delivery", "target": "h-ionizable-lipid-pka-endosomal-escape", "relation": "related_hypothesis" }, { "source": "b-borrelia-immune-evasion", "target": "u-borrelia-persister-cell-eradication", "relation": "related_unknown" }, { "source": "b-borrelia-immune-evasion", "target": "h-borrelia-triple-combo-persister-eradication", "relation": "related_hypothesis" }, { "source": "b-immune-network-idiotypic", "target": "u-idiotypic-network-clinical-validation", "relation": "related_unknown" }, { "source": "b-immune-network-idiotypic", "target": "h-autoimmune-disease-idiotypic-attractor-bifurcation", "relation": "related_hypothesis" }, { "source": "b-immune-recognition-statistical-pattern-detection", "target": "u-tcr-repertoire-pathogen-space-coverage", "relation": "related_unknown" }, { "source": "b-immune-recognition-statistical-pattern-detection", "target": "h-tcr-repertoire-percolation-threshold-pathogen-coverage", "relation": "related_hypothesis" }, { "source": "b-masked-autoencoding-x-cryo-em-denoising-priors", "target": "u-mae-cryo-em-prior-induced-hallucination-risk", "relation": "related_unknown" }, { "source": "b-masked-autoencoding-x-cryo-em-denoising-priors", "target": "h-masked-autoencoder-pretraining-improves-cryo-em-low-snr-reconstruction", "relation": "related_hypothesis" }, { "source": "b-error-threshold-information", "target": "u-error-threshold-genome-size", "relation": "related_unknown" }, { "source": "b-error-threshold-information", "target": "h-viral-proofreading-shannon-optimality", "relation": "related_hypothesis" }, { "source": "b-knowledge-overload-findability", "target": "u-optimal-bridge-density-knowledge-graph", "relation": "related_unknown" }, { "source": "b-knowledge-overload-findability", "target": "h-bridge-catalog-reduces-rediscovery-lag", "relation": "related_hypothesis" }, { "source": "b-belief-propagation-x-genotype-phasing-linkage-graphs", "target": "u-bp-convergence-loopy-genetic-linkage-graphs", "relation": "related_unknown" }, { "source": "b-belief-propagation-x-genotype-phasing-linkage-graphs", "target": "h-damped-bp-calibration-improves-phasing-accuracy", "relation": "related_hypothesis" }, { "source": "b-shannon-entropy-genetic-information", "target": "u-genetic-code-information-optimality", "relation": "related_unknown" }, { "source": "b-shannon-entropy-genetic-information", "target": "h-genetic-code-error-correcting-design", "relation": "related_hypothesis" }, { "source": "b-language-evolution-cultural-selection", "target": "u-language-evolution-selection-neutrality", "relation": "related_unknown" }, { "source": "b-language-evolution-cultural-selection", "target": "h-language-change-replicator-conformity", "relation": "related_hypothesis" }, { "source": "b-entropy-rate-x-language-model-perplexity", "target": "u-entropy-rate-nonstationary-language-data", "relation": "related_unknown" }, { "source": "b-entropy-rate-x-language-model-perplexity", "target": "h-entropy-rate-x-language-model-perplexity", "relation": "related_hypothesis" }, { "source": "b-zipf-optimal-coding", "target": "h-zipf-optimal-coding-universality", "relation": "related_hypothesis" }, { "source": "b-zipf-optimal-coding", "target": "h-zipf-critical-point-communication-efficiency", "relation": "related_hypothesis" }, { "source": "b-formal-grammar-automata-theory", "target": "u-natural-language-complexity-class", "relation": "related_unknown" }, { "source": "b-formal-grammar-automata-theory", "target": "h-natural-language-mildly-context-sensitive-transformer-approximation", "relation": "related_hypothesis" }, { "source": "b-language-contact-x-graph-interpolation", "target": "u-dialect-contact-as-graph-diffusion", "relation": "related_unknown" }, { "source": "b-language-contact-x-graph-interpolation", "target": "h-lexical-diffusion-on-geographic-graphs-predicts-isoglosses", "relation": "related_hypothesis" }, { "source": "b-language-biomarker-diagnosis", "target": "u-language-biomarker-clinical-validity", "relation": "related_unknown" }, { "source": "b-language-biomarker-diagnosis", "target": "u-alzheimer-causal-biomarkers", "relation": "related_unknown" }, { "source": "b-language-biomarker-diagnosis", "target": "h-glymphatic-amyloid-clearance-rate", "relation": "related_hypothesis" }, { "source": "b-language-biomarker-diagnosis", "target": "h-linguistic-relativity-neural-boundary", "relation": "related_hypothesis" }, { "source": "b-birdsong-syntax-formal-language-theory", "target": "u-birdsong-syntax-generative-grammar-limits", "relation": "related_unknown" }, { "source": "b-birdsong-syntax-formal-language-theory", "target": "h-birdsong-context-free-grammar-test", "relation": "related_hypothesis" }, { "source": "b-linguistic-relativity-quantum-basis", "target": "u-linguistic-relativity-cognition", "relation": "related_unknown" }, { "source": "b-linguistic-relativity-quantum-basis", "target": "u-many-worlds-copenhagen-experimental", "relation": "related_unknown" }, { "source": "b-linguistic-relativity-quantum-basis", "target": "u-quantum-darwinism-evidence", "relation": "related_unknown" }, { "source": "b-fish-schooling-collective-motion", "target": "u-fish-schooling-topological-interaction", "relation": "related_unknown" }, { "source": "b-fish-schooling-collective-motion", "target": "h-topological-flocking-predator-evasion", "relation": "related_hypothesis" }, { "source": "b-antifreeze-proteins-ice-crystal", "target": "u-antifreeze-protein-ice-binding", "relation": "related_unknown" }, { "source": "b-biomineralization-crystal-growth", "target": "u-biomineralization-polymorph-control", "relation": "related_unknown" }, { "source": "b-biomineralization-crystal-growth", "target": "h-organic-template-polymorph-selection", "relation": "related_hypothesis" }, { "source": "b-biomineralization-crystal-nucleation", "target": "u-protein-matrix-nucleation-control", "relation": "related_unknown" }, { "source": "b-gecko-adhesion-van-der-waals", "target": "u-vdw-heterostructure-emergent-phases", "relation": "related_unknown" }, { "source": "b-phase-diagrams-alloy-design", "target": "u-high-entropy-alloy-phase-stability-prediction", "relation": "related_unknown" }, { "source": "b-phase-diagrams-alloy-design", "target": "h-high-entropy-alloy-configurational-entropy-stabilization", "relation": "related_hypothesis" }, { "source": "b-fracture-mechanics-griffith", "target": "u-2d-material-defect-transport", "relation": "related_unknown" }, { "source": "b-fracture-mechanics-griffith", "target": "h-griffith-crack-2d-material-defects", "relation": "related_hypothesis" }, { "source": "b-active-learning-x-bayesian-optimization-alloy-discovery", "target": "u-active-learning-bias-in-alloy-discovery-loops", "relation": "related_unknown" }, { "source": "b-active-learning-x-bayesian-optimization-alloy-discovery", "target": "h-active-learning-bayesian-optimization-improves-alloy-hit-rate", "relation": "related_hypothesis" }, { "source": "b-crystallography-group-theory", "target": "h-crystallographic-protein-folding", "relation": "related_hypothesis" }, { "source": "b-piezoelectricity-symmetry-breaking", "target": "u-piezoelectricity-symmetry-breaking", "relation": "related_unknown" }, { "source": "b-piezoelectricity-symmetry-breaking", "target": "h-piezoelectricity-symmetry-breaking", "relation": "related_hypothesis" }, { "source": "b-preisach-hysteresis-model", "target": "u-preisach-model-physical-interpretation-density", "relation": "related_unknown" }, { "source": "b-topological-persistence-x-materials-microstructure-failure-forecast", "target": "u-topological-signatures-microcrack-coalescence-transferability", "relation": "related_unknown" }, { "source": "b-topological-persistence-x-materials-microstructure-failure-forecast", "target": "h-persistent-h1-betti-curves-predict-material-failure-earlier-than-stress-thresholds", "relation": "related_hypothesis" }, { "source": "b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling", "target": "u-peridynamic-horizon-calibration-for-cortical-bone-microcrack-prediction", "relation": "related_unknown" }, { "source": "b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling", "target": "h-peridynamic-models-predict-bone-microdamage-hotspots-before-radiographic-failure", "relation": "related_hypothesis" }, { "source": "b-biofilm-mechanics-viscoelastic-polymer", "target": "u-biofilm-viscoelasticity-dispersal-trigger", "relation": "related_unknown" }, { "source": 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"h-quantum-dot-emission-confinement-scaling", "relation": "related_hypothesis" }, { "source": "b-alloy-strengthening-dislocation-theory", "target": "u-dislocation-dynamics-alloy-high-entropy", "relation": "related_unknown" }, { "source": "b-alloy-strengthening-dislocation-theory", "target": "h-high-entropy-alloy-dislocation-cocktail-hardening", "relation": "related_hypothesis" }, { "source": "b-auxetic-materials-negative-poisson-ratio", "target": "u-auxetic-materials-scalable-fabrication-3d", "relation": "related_unknown" }, { "source": "b-auxetic-materials-negative-poisson-ratio", "target": "h-reentrant-geometry-auxetic-impact-resistance", "relation": "related_hypothesis" }, { "source": "b-fisher-information-design-x-autonomous-materials-experiments", "target": "u-fisher-optimal-experiment-policy-shift-drift", "relation": "related_unknown" }, { "source": "b-fisher-information-design-x-autonomous-materials-experiments", "target": "h-information-optimal-batching-accelerates-material-discovery", "relation": "related_hypothesis" }, { "source": "b-semiconductor-doping-fermi-level-chemical-potential", "target": "u-semiconductor-doping-fermi-level-pinning", "relation": "related_unknown" }, { "source": "b-thermoelectric-efficiency-seebeck-onsager", "target": "u-thermoelectric-zt-theoretical-limit", "relation": "related_unknown" }, { "source": "b-thermoelectric-efficiency-seebeck-onsager", "target": "h-thermoelectric-phonon-glass-electron-crystal", "relation": "related_hypothesis" }, { "source": "b-knot-invariants-x-dna-topology", "target": "u-knot-invariants-x-dna-topology", "relation": "related_unknown" }, { "source": "b-persistence-homology-x-protein-structure", "target": "u-persistence-homology-x-protein-structure", "relation": "related_unknown" }, { "source": "b-topological-data-analysis-x-cancer-genomics", "target": "u-topological-data-analysis-x-cancer-genomics", "relation": "related_unknown" }, { "source": "b-category-theory-x-functional-programming", "target": "u-category-theory-x-functional-programming", "relation": "related_unknown" }, { "source": "b-expander-graphs-x-error-correcting-codes", "target": "u-expander-graphs-x-error-correcting-codes", "relation": "related_unknown" }, { "source": "b-fourier-transform-x-signal-processing", "target": "u-fourier-transform-x-signal-processing", "relation": "related_unknown" }, { "source": "b-tda-x-shape-recognition", "target": "u-tda-x-shape-recognition", "relation": "related_unknown" }, { "source": "b-tropical-geometry-x-neural-networks", "target": "u-tropical-geometry-x-neural-networks", "relation": "related_unknown" }, { "source": "b-tropical-geometry-x-neural-networks", "target": "h-tropical-geometry-x-neural-networks", "relation": "related_hypothesis" }, { "source": "b-island-biogeography-x-percolation", "target": "u-island-biogeography-x-percolation", "relation": "related_unknown" }, { "source": "b-island-biogeography-x-percolation", "target": "h-island-biogeography-x-percolation", "relation": "related_hypothesis" }, { "source": "b-lotka-volterra-x-game-theory", "target": "u-lotka-volterra-x-game-theory", "relation": "related_unknown" }, { "source": "b-percolation-x-disease-spread", "target": "u-percolation-herd-immunity-heterogeneous-networks", "relation": "related_unknown" }, { "source": "b-auction-theory-x-mechanism-design", "target": "u-auction-theory-x-mechanism-design", "relation": "related_unknown" }, { "source": "b-extreme-value-theory-x-risk-modeling", "target": "u-extreme-value-theory-x-risk-modeling", "relation": "related_unknown" }, { "source": "b-voting-theory-x-social-choice", "target": "u-voting-theory-x-social-choice", "relation": "related_unknown" }, { "source": "b-chaos-x-ergodic-theory", "target": "u-chaos-x-ergodic-theory", "relation": "related_unknown" }, { "source": "b-ergodic-theory-x-statistical-mechanics", "target": "u-ergodic-theory-x-statistical-mechanics", "relation": "related_unknown" 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"b-fisher-kpp-fronts-x-wound-healing-closure-forecasting", "target": "u-patient-specific-front-speed-estimation-in-wound-healing-kpp-models", "relation": "related_unknown" }, { "source": "b-fisher-kpp-fronts-x-wound-healing-closure-forecasting", "target": "h-fisher-kpp-front-models-improve-wound-closure-time-forecasting", "relation": "related_hypothesis" }, { "source": "b-allometry-fractal-networks", "target": "u-allometry-fractal-networks-deviations", "relation": "related_unknown" }, { "source": "b-allometry-fractal-networks", "target": "u-renormalization-allometric", "relation": "related_unknown" }, { "source": "b-allometry-fractal-networks", "target": "h-kleiber-wave-physics", "relation": "related_hypothesis" }, { "source": "b-allometry-fractal-networks", "target": "h-allometric-rg-fixed-point", "relation": "related_hypothesis" }, { "source": "b-evolutionary-graph-fixation-probability", "target": "u-evolutionary-graph-amplifier-natural-populations", "relation": "related_unknown" }, { "source": "b-evolutionary-graph-fixation-probability", "target": "h-social-network-star-topology-innovation-fixation", "relation": "related_hypothesis" }, { "source": "b-graph-theory-phylogenetics", "target": "u-phylogenetic-network-horizontal-transfer", "relation": "related_unknown" }, { "source": "b-graph-theory-protein-networks", "target": "u-ppi-scale-free-topology-functional-necessity", "relation": "related_unknown" }, { "source": "b-graph-theory-protein-networks", "target": "h-hub-lethality-protein-network-drug-targets", "relation": "related_hypothesis" }, { "source": "b-information-geometry-evolutionary-fitness", "target": "u-natural-gradient-selection-convergence-rate-fitness-landscape", "relation": "related_unknown" }, { "source": "b-information-geometry-evolutionary-fitness", "target": "h-natural-gradient-selection-reaches-fitness-optimum-faster-than-euclidean", "relation": "related_hypothesis" }, { "source": "b-knot-theory-dna-topology", "target": "u-topoisomerase-knot-selection", "relation": "related_unknown" }, { "source": "b-knot-theory-dna-topology", "target": "h-dna-knot-complexity-aging", "relation": "related_hypothesis" }, { "source": "b-optimal-control-cancer-treatment", "target": "u-adaptive-therapy-evolutionary-trap-clinical-validation", "relation": "related_unknown" }, { "source": "b-optimal-control-cancer-treatment", "target": "h-pontryagin-adaptive-therapy-outperforms-mtd-solid-tumors", "relation": "related_hypothesis" }, { "source": "b-optimal-transport-cell-differentiation", "target": "u-wasserstein-cell-fate-noise-geodesic-uniqueness", "relation": "related_unknown" }, { "source": "b-optimal-transport-cell-differentiation", "target": "h-optimal-transport-waddington-landscape-riemannian-geodesic", "relation": "related_hypothesis" }, { "source": "b-optimal-transport-vasculature", "target": "u-optimal-transport-angiogenesis", "relation": "related_unknown" }, { "source": "b-optimal-transport-vasculature", "target": "u-renormalization-allometric", "relation": "related_unknown" }, { "source": "b-optimal-transport-vasculature", "target": "h-allometric-rg-fixed-point", "relation": "related_hypothesis" }, { "source": "b-renormalization-biological-scaling", "target": "u-kleiber-pulsatile-waves", "relation": "related_unknown" }, { "source": "b-renormalization-biological-scaling", "target": "u-renormalization-allometric", "relation": "related_unknown" }, { "source": "b-renormalization-biological-scaling", "target": "h-kleiber-wave-physics", "relation": "related_hypothesis" }, { "source": "b-renormalization-biological-scaling", "target": "h-allometric-rg-fixed-point", "relation": "related_hypothesis" }, { "source": "b-stochastic-gene-expression-noise", "target": "u-stochastic-gene-expression-bet-hedging-quantitative", "relation": "related_unknown" }, { "source": "b-stochastic-gene-expression-noise", "target": "h-stochastic-gene-expression-bet-hedging-optimal-noise", "relation": "related_hypothesis" }, { "source": "b-topology-morphogenesis", "target": "u-topological-morphogenesis", "relation": "related_unknown" }, { "source": "b-topology-morphogenesis", "target": "h-topological-defect-morphogenesis", "relation": "related_hypothesis" }, { "source": "b-turing-reaction-diffusion", "target": "u-turing-digit-wavelength-scaling", "relation": "related_unknown" }, { "source": "b-turing-reaction-diffusion", "target": "h-turing-zebrafish-diffusivity-ratio", "relation": "related_hypothesis" }, { "source": "b-voronoi-tessellation-cellular-architecture", "target": "u-biomineralisation-control", "relation": "related_unknown" }, { "source": "b-voronoi-tessellation-cellular-architecture", "target": "h-biomineralisation-voronoi-control", "relation": "related_hypothesis" }, { "source": "b-knot-theory-dna-topology", "target": "u-knot-invariants-rna-tertiary-structure-topology", "relation": "related_unknown" }, { "source": "b-knot-theory-dna-topology", "target": "h-topo-ii-inhibitor-transcription-coupled-dna-damage-selectivity", "relation": "related_hypothesis" }, { "source": "b-approximation-theory-deep-learning", "target": "u-deep-learning-approximation-sobolev-optimal", "relation": "related_unknown" }, { "source": "b-approximation-theory-deep-learning", "target": "h-depth-separation-compositional-function-approximation", "relation": "related_hypothesis" }, { "source": "b-bond-percolation-x-cyber-lateral-movement", "target": "u-graph-percolation-lateral-movement-detection-threshold", "relation": "related_unknown" }, { "source": "b-bond-percolation-x-cyber-lateral-movement", "target": "h-zero-trust-control-raises-effective-percolation-threshold", "relation": "related_hypothesis" }, { "source": "b-cahn-hilliard-phase-separation-x-diffuse-interface-segmentation", "target": "u-cahn-hilliard-segmentation-parameter-transfer-limits", "relation": "related_unknown" }, { "source": "b-cahn-hilliard-phase-separation-x-diffuse-interface-segmentation", "target": "h-interface-width-regularization-predicts-segmentation-stability", "relation": "related_hypothesis" }, { "source": "b-category-theory-functional-programming", "target": "u-homotopy-type-theory-computational-foundations", "relation": "related_unknown" }, { "source": "b-category-theory-functional-programming", "target": "h-univalence-axiom-proof-assistant-verification", "relation": "related_hypothesis" }, { "source": "b-complexity-proof-theory", "target": "u-p-vs-np-geometric-complexity", "relation": "related_unknown" }, { "source": "b-complexity-proof-theory", "target": "h-geometric-complexity-theory-p-np", "relation": "related_hypothesis" }, { "source": "b-compressed-sensing-sparse-recovery", "target": "u-rip-constant-optimal-measurement-matrix-deterministic-construction", "relation": "related_unknown" }, { "source": "b-compressed-sensing-sparse-recovery", "target": "h-compressed-sensing-mri-10x-scan-time-reduction-clinical-safety", "relation": "related_hypothesis" }, { "source": "b-convolution-x-convolutional-neural-nets", "target": "u-discrete-convolution-theorem-cnn-inductive-bias", "relation": "related_unknown" }, { "source": "b-convolution-x-convolutional-neural-nets", "target": "h-cnn-layers-approximate-localized-spectral-filters", "relation": "related_hypothesis" }, { "source": "b-cryptography-number-theory", "target": "u-lwe-hardness-proof-quantum-reduction", "relation": "related_unknown" }, { "source": "b-cryptography-number-theory", "target": "h-kyber-lwe-parameter-quantum-security-margin", "relation": "related_hypothesis" }, { "source": "b-elastic-net-map-x-laplace-gaussian-composite-prior", "target": "u-elastic-net-prior-calibration-under-correlated-designs", "relation": "related_unknown" }, { "source": "b-elastic-net-map-x-laplace-gaussian-composite-prior", "target": "h-mixing-parameter-matches-posterior-sparsity-stability-curves", "relation": "related_hypothesis" }, { "source": "b-elliptic-curve-complex-torus-x-finite-field-crypto-pedagogy", "target": "u-ecc-torus-intuition-misconception-rates", "relation": "related_unknown" }, { "source": "b-elliptic-curve-complex-torus-x-finite-field-crypto-pedagogy", "target": "h-sequence-complex-torus-first-ecc-exam-performance", "relation": "related_hypothesis" }, { "source": "b-gnn-weisfeiler-lehman-isomorphism", "target": "u-gnn-expressiveness-beyond-wl", "relation": "related_unknown" }, { "source": "b-gnn-weisfeiler-lehman-isomorphism", "target": "h-higher-order-gnn-practical-expressiveness", "relation": "related_hypothesis" }, { "source": "b-hyperbolic-geometry-x-network-embedding", "target": "u-hyperbolic-embeddings-hierarchy-identifiability", "relation": "related_unknown" }, { "source": "b-hyperbolic-geometry-x-network-embedding", "target": "h-real-hierarchies-embed-better-in-hyperbolic-space", "relation": "related_hypothesis" }, { "source": "b-information-geometry-machine-learning", "target": "u-neural-network-generalisation-theory", "relation": "related_unknown" }, { "source": "b-information-geometry-machine-learning", "target": "u-federated-learning-privacy-utility", "relation": "related_unknown" }, { "source": "b-linear-algebra-deep-learning", "target": "u-ntk-finite-width-corrections", "relation": "related_unknown" }, { "source": "b-linear-algebra-deep-learning", "target": "h-ntk-deep-learning-kernel-regression", "relation": "related_hypothesis" }, { "source": "b-ransac-robust-estimation-x-astronomical-source-matching", "target": "u-astronomical-source-matching-structured-outlier-robustness", "relation": "related_unknown" }, { "source": "b-ransac-robust-estimation-x-astronomical-source-matching", "target": "h-quality-ranked-ransac-improves-astrometric-crossmatch-precision", "relation": "related_hypothesis" }, { "source": "b-stone-weierstrass-x-universal-approximation-intuition", "target": "u-uap-stone-weierstrass-pedagogy-misconception-rate", "relation": "related_unknown" }, { "source": "b-stone-weierstrass-x-universal-approximation-intuition", "target": "h-compact-algebra-first-sequence-improves-uap-transfer", "relation": "related_hypothesis" }, { "source": "b-type-theory-functional-programming", "target": "u-hott-univalence-axiom-computational-interpretation", "relation": "related_unknown" }, { "source": "b-type-theory-functional-programming", "target": "h-dependent-types-industrial-systems-programming-feasibility", "relation": "related_hypothesis" }, { "source": "b-wasserstein-gan-gradient-penalty-x-kantorovich-lipschitz-stability", "target": "u-wgan-gp-tightness-versus-exact-lipschitz-projections", "relation": "related_unknown" }, { "source": "b-wasserstein-gan-gradient-penalty-x-kantorovich-lipschitz-stability", "target": "h-gradient-penalty-magnitude-tracks-dual-feasibility-proxy-metrics", "relation": "related_hypothesis" }, { "source": "b-optimal-foraging-x-explore-exploit", "target": "u-marginal-value-theorem-bandit-bridge", "relation": "related_unknown" }, { "source": "b-optimal-foraging-x-explore-exploit", "target": "h-charnov-marginal-value-maps-to-index-policy-budgeting", "relation": "related_hypothesis" }, { "source": "b-perron-frobenius-population-dynamics", "target": "u-leslie-matrix-density-dependence-extension", "relation": "related_unknown" }, { "source": "b-perron-frobenius-population-dynamics", "target": "h-elasticity-analysis-conservation-prioritisation", "relation": "related_hypothesis" }, { "source": "b-convex-optimization-economic-equilibrium", "target": "u-business-cycle-prediction-limits", "relation": "related_unknown" }, { "source": "b-convex-optimization-economic-equilibrium", "target": "u-inequality-growth-relationship", "relation": "related_unknown" }, { "source": "b-information-economics-mechanism", "target": "u-mechanism-design-ai-alignment", "relation": "related_unknown" }, { "source": "b-information-economics-mechanism", "target": "h-revelation-principle-ai-alignment-mechanism", "relation": "related_hypothesis" }, { "source": "b-preference-elicitation-x-vickrey-auction", "target": "u-truthful-elicitation-mechanism-duality", "relation": "related_unknown" }, { "source": "b-preference-elicitation-x-vickrey-auction", "target": "h-vickrey-clarke-groves-payments-improve-lab-truthful-reporting", "relation": "related_hypothesis" }, { "source": "b-bode-sensitivity-integral-x-waterbed-effect", "target": "u-bode-waterbed-multi-loop-multi-objective-tradeoffs", "relation": "related_unknown" }, { "source": "b-bode-sensitivity-integral-x-waterbed-effect", "target": "h-minimum-phase-plants-attain-tighter-bode-bounds", "relation": "related_hypothesis" }, { "source": "b-koopman-operator-x-data-driven-dmd", "target": "u-koopman-dmd-spectrum-convergence-navier-stokes", "relation": "related_unknown" }, { "source": "b-koopman-operator-x-data-driven-dmd", "target": "h-koopman-linear-dynamics-capture-coherent-structures-limited-window", "relation": "related_hypothesis" }, { "source": "b-lyapunov-stability-nonlinear-control", "target": "u-lyapunov-function-discovery-automation", "relation": "related_unknown" }, { "source": "b-lyapunov-stability-nonlinear-control", "target": "h-sos-lyapunov-global-nonpolynomial", "relation": "related_hypothesis" }, { "source": "b-optimization-theory-machine-learning", "target": "u-neural-network-loss-landscape-global-structure", "relation": "related_unknown" }, { "source": "b-optimization-theory-machine-learning", "target": "h-lottery-ticket-sparse-subnetwork-universality", "relation": "related_hypothesis" }, { "source": "b-origami-mathematics-computational-fold", "target": "u-origami-fold-design-complexity", "relation": "related_unknown" }, { "source": "b-robust-control-h-infinity", "target": "u-h-infinity-nonlinear-systems-computational-tractability", "relation": "related_unknown" }, { "source": "b-robust-control-h-infinity", "target": "h-robust-control-lmi-neural-network-stability-certificates", "relation": "related_hypothesis" }, { "source": "b-robust-statistics-outlier-detection", "target": "u-ransac-optimal-sampling-strategy-non-uniform-inlier-distribution", "relation": "related_unknown" }, { "source": "b-robust-statistics-outlier-detection", "target": "h-robust-statistics-deep-learning-improves-noisy-label-training", "relation": "related_hypothesis" }, { "source": "b-wavelet-theory-signal-compression", "target": "u-wavelet-optimal-basis-nonstationary-signal-adaptation", "relation": "related_unknown" }, { "source": "b-wavelet-theory-signal-compression", "target": "h-wavelet-shrinkage-minimax-optimal-natural-image-sparsity", "relation": "related_hypothesis" }, { "source": "b-game-theory-evolution", "target": "u-replicator-dynamics-llm-training", "relation": "related_unknown" }, { "source": "b-game-theory-evolution", "target": "h-gan-training-redqueen-dynamics", "relation": "related_hypothesis" }, { "source": "b-kin-selection-price-equation", "target": "u-kin-selection-price-equation-unification", "relation": 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"b-fisher-information-cramer-rao-x-dose-spacing-experimental-design", "target": "h-fisher-optimal-dose-grid-reduces-parameter-variance-simulation", "relation": "related_hypothesis" }, { "source": "b-hopf-bifurcation-x-cardiac-alternans", "target": "u-hopf-normal-form-cardiac-alternans-mapping", "relation": "related_unknown" }, { "source": "b-hopf-bifurcation-x-cardiac-alternans", "target": "h-bifurcation-continuation-predicts-alternans-onset-optical-mapping", "relation": "related_hypothesis" }, { "source": "b-persistent-homology-rr-intervals-x-arrhythmia-early-warning", "target": "u-topological-biomarker-robustness-across-wearables", "relation": "related_unknown" }, { "source": "b-persistent-homology-rr-intervals-x-arrhythmia-early-warning", "target": "h-persistent-h1-rise-precedes-afib-onset", "relation": "related_hypothesis" }, { "source": "b-spectral-clustering-x-metabolite-similarity-network-modules", "target": "u-spectral-cluster-stability-metabolomics-batch-effects", "relation": "related_unknown" }, { "source": "b-spectral-clustering-x-metabolite-similarity-network-modules", "target": "h-graph-laplacian-regularization-improves-module-replicability", "relation": "related_hypothesis" }, { "source": "b-topology-disease-progression", "target": "u-topological-data-analysis-phase-transitions", "relation": "related_unknown" }, { "source": "b-topology-disease-progression", "target": "u-tumor-evolution-topology-branching", "relation": "related_unknown" }, { "source": "b-topology-disease-progression", "target": "h-tda-cancer-subtype-prognosis-superiority", "relation": "related_hypothesis" }, { "source": "b-mycelial-networks-minimum-spanning-trees", "target": "u-mycelial-network-optimization-principle", "relation": "related_unknown" }, { "source": "b-mycelial-networks-minimum-spanning-trees", "target": "h-mycelial-network-mst-approximation", "relation": "related_hypothesis" }, { "source": "b-bayesian-brain-predictive-coding", "target": "u-predictive-coding-neural-implementation-evidence", "relation": "related_unknown" }, { "source": "b-bayesian-brain-predictive-coding", "target": "h-precision-weighting-schizophrenia-nmda-receptor", "relation": "related_hypothesis" }, { "source": "b-grid-cells-hexagonal-lattice-fourier", "target": "u-grid-cell-fourier-basis-navigation", "relation": "related_unknown" }, { "source": "b-grid-cells-hexagonal-lattice-fourier", "target": "h-grid-cell-torus-manifold-decoding", "relation": "related_hypothesis" }, { "source": "b-population-vector-motor-cortex", "target": "u-motor-cortex-population-dynamics-motor-programs", "relation": "related_unknown" }, { "source": "b-population-vector-motor-cortex", "target": "h-motor-cortex-rotational-dynamics-initial-condition-mechanism", "relation": "related_hypothesis" }, { "source": "b-reinforcement-learning-dopamine", "target": "u-td-learning-dopamine-biological-implementation", "relation": "related_unknown" }, { "source": "b-reinforcement-learning-dopamine", "target": "h-td-prediction-error-dopamine-burst-identity-schultz", "relation": "related_hypothesis" }, { "source": "b-catastrophe-theory-phase-transitions", "target": "u-catastrophe-normal-form-completeness", "relation": "related_unknown" }, { "source": "b-catastrophe-theory-phase-transitions", "target": "h-catastrophe-theory-first-order-transitions", "relation": "related_hypothesis" }, { "source": "b-chaos-theory-strange-attractors", "target": "u-chaos-quantum-correspondence-lyapunov-exponents", "relation": "related_unknown" }, { "source": "b-chaos-theory-strange-attractors", "target": "h-feigenbaum-universality-quantum-maps-period-doubling", "relation": "related_hypothesis" }, { "source": "b-differential-forms-maxwell", "target": "u-non-abelian-aharonov-bohm-observable-consequences", "relation": "related_unknown" }, { "source": "b-differential-forms-maxwell", "target": "h-chern-simons-theory-topological-quantum-computation", "relation": "related_hypothesis" }, { "source": "b-ergodic-theory-statistical-mechanics", "target": "u-ergodic-failure-quantum-thermalization", "relation": "related_unknown" }, { "source": "b-ergodic-theory-statistical-mechanics", "target": "u-quantum-thermodynamics-arrow", "relation": "related_unknown" }, { "source": "b-ergodic-theory-statistical-mechanics", "target": "h-kam-nonergodicity-many-body-localization", "relation": "related_hypothesis" }, { "source": "b-fiber-bundle-gauge-field-topology", "target": "u-fiber-bundle-gauge-field-quantum-gravity", "relation": "related_unknown" }, { "source": "b-fiber-bundle-gauge-field-topology", "target": "h-principal-bundle-chern-class-anomaly-cancellation", "relation": "related_hypothesis" }, { "source": "b-gauge-theory-x-connection-forms", "target": "u-gauge-fields-as-bundle-connections-pedagogy", "relation": "related_unknown" }, { "source": "b-gauge-theory-x-connection-forms", "target": "h-gauge-fixing-parallels-coordinate-choice-in-models", "relation": "related_hypothesis" }, { "source": "b-geometric-measure-minimal-surfaces", "target": "u-almgren-regularity-singular-set-sharp-dimension", "relation": "related_unknown" }, { "source": "b-geometric-measure-minimal-surfaces", "target": "h-willmore-energy-biological-membrane-morphogenesis-ground-state", "relation": "related_hypothesis" }, { "source": "b-group-theory-symmetry-breaking", "target": "u-goldstone-boson-higher-dimensional-systems", "relation": "related_unknown" }, { "source": "b-group-theory-symmetry-breaking", "target": "h-symmetry-breaking-universal-phase-transition-classifier", "relation": "related_hypothesis" }, { "source": "b-integrable-systems-solitons", "target": "u-integrability-breaking-perturbations-soliton-stability-realistic-systems", "relation": "related_unknown" }, { "source": "b-integrable-systems-solitons", "target": "h-quantum-solitons-bethe-ansatz-connection-quantum-inverse-scattering", "relation": "related_hypothesis" }, { "source": "b-measure-theory-probability", "target": "u-measure-theoretic-foundations-quantum-probability", "relation": "related_unknown" }, { "source": "b-measure-theory-probability", "target": "h-quantum-probability-gleason-measure-uniqueness", "relation": "related_hypothesis" }, { "source": "b-morse-homology-x-conley-index-isolated-invariants", "target": "u-conley-index-computable-verification-higher-dim", "relation": "related_unknown" }, { "source": "b-morse-homology-x-conley-index-isolated-invariants", "target": "h-morse-homology-x-conley-index-isolated-invariants", "relation": "related_hypothesis" }, { "source": "b-nonlinear-optics-soliton-propagation", "target": "u-nonlinear-optics-soliton-stability", "relation": "related_unknown" }, { "source": "b-percolation-network-robustness", "target": "u-percolation-phase-transition-interdependent-networks-cascading-failures", "relation": "related_unknown" }, { "source": "b-percolation-network-robustness", "target": "h-targeted-hub-vaccination-achieves-herd-immunity-fewer-doses-scale-free", "relation": "related_hypothesis" }, { "source": "b-perturbation-theory-quantum-corrections", "target": "u-perturbation-series-borel-summability-qft", "relation": "related_unknown" }, { "source": "b-perturbation-theory-quantum-corrections", "target": "h-resurgence-connects-perturbative-nonperturbative-qft", "relation": "related_hypothesis" }, { "source": "b-renormalization-group-scale-invariance", "target": "u-nonperturbative-rg-completeness", "relation": "related_unknown" }, { "source": "b-renormalization-group-scale-invariance", "target": "h-rg-universality-neural-network-criticality", "relation": "related_hypothesis" }, { "source": "b-ricci-flow-x-geometrization-program", "target": "u-ricci-flow-intuition-vs-rigorous-gr-correspondence", "relation": "related_unknown" }, { "source": "b-ricci-flow-x-geometrization-program", "target": "h-ricci-flow-x-geometrization-program", "relation": "related_hypothesis" }, { "source": "b-stochastic-de-quantum-field-theory", "target": "u-stochastic-quantization-non-equilibrium-regimes", "relation": "related_unknown" }, { "source": "b-stochastic-de-quantum-field-theory", "target": "h-onsager-machlup-loop-expansion-qft-thermal-field-theory", "relation": "related_hypothesis" }, { "source": "b-stochastic-quantization-qft", "target": "u-qft-non-perturbative-regimes", "relation": "related_unknown" }, { "source": "b-stochastic-quantization-qft", "target": "h-langlands-physics-electric-magnetic-duality", "relation": "related_hypothesis" }, { "source": "b-symplectic-geometry-hamiltonian", "target": "u-symplectic-topology-classical-quantum-correspondence-limits", "relation": "related_unknown" }, { "source": "b-symplectic-geometry-hamiltonian", "target": "h-gromov-nonsqueezing-quantum-uncertainty-derivation", "relation": "related_hypothesis" }, { "source": "b-symplectic-geometry-mechanics", "target": "u-symplectic-quantization-semiclassical", "relation": "related_unknown" }, { "source": "b-symplectic-geometry-mechanics", "target": "h-symplectic-quantization-new-prediction", "relation": "related_hypothesis" }, { "source": "b-topological-defects-homotopy-x-condensed-matter-order", "target": "u-topological-defect-classification-nonequilibrium-condensed-matter", "relation": "related_unknown" }, { "source": "b-topological-defects-homotopy-x-condensed-matter-order", "target": "h-defect-topology-predicts-coarsening-scaling-exponents", "relation": "related_hypothesis" }, { "source": "b-topology-condensed-matter", "target": "u-topological-order-non-abelian-anyons-fault-tolerant", "relation": "related_unknown" }, { "source": "b-topology-condensed-matter", "target": "h-topology-chern-number-predicts-edge-state-count", "relation": "related_hypothesis" }, { "source": "b-hopf-algebras-feynman-renormalization", "target": "u-hopf-algebra-qft-nonperturbative-extension", "relation": "related_unknown" }, { "source": "b-hopf-algebras-feynman-renormalization", "target": "h-motives-feynman-amplitudes-arithmetic", "relation": "related_hypothesis" }, { "source": "b-spectral-theory-quantum-mechanics", "target": "u-phi-measurement-neural-correlates", "relation": "related_unknown" }, { "source": "b-spectral-theory-quantum-mechanics", "target": "h-quantum-spectral-gap-computational-complexity", "relation": "related_hypothesis" }, { "source": "b-cooperative-game-theory-coalitions", "target": "u-shapley-value-practical-coalition-stability", "relation": "related_unknown" }, { "source": "b-cooperative-game-theory-coalitions", "target": "h-shapley-value-predicts-international-climate-burden-sharing", "relation": "related_hypothesis" }, { "source": "b-fair-division-combinatorics", "target": "u-collective-action-without-authority", "relation": "related_unknown" }, { "source": "b-fair-division-combinatorics", "target": "h-collective-action-ostrom-design-principles-v2", "relation": "related_hypothesis" }, { "source": "b-matching-theory-labor-markets", "target": "u-matching-markets-dynamic-stability", "relation": "related_unknown" }, { "source": "b-matching-theory-labor-markets", "target": "h-da-mechanism-welfare-improving-redesign", "relation": "related_hypothesis" }, { "source": "b-network-formation-games", "target": "u-network-formation-dynamic-stability-real-world", "relation": "related_unknown" }, { "source": "b-network-formation-games", "target": "h-braess-paradox-social-network-cascades", "relation": "related_hypothesis" }, { "source": "b-network-formation-graph-theory", "target": "u-social-capital-measurement", "relation": "related_unknown" }, { "source": "b-network-formation-graph-theory", "target": "u-trust-network-scale", "relation": "related_unknown" }, { "source": "b-optimal-transport-economic-geography", "target": "u-gravity-model-trade-structural-estimation-welfare", "relation": "related_unknown" }, { "source": "b-optimal-transport-economic-geography", "target": "h-optimal-transport-determines-city-structure-spatial-equilibrium", "relation": "related_hypothesis" }, { "source": "b-replicator-dynamics-ess", "target": "u-replicator-equation-cultural-norm-stability", "relation": "related_unknown" }, { "source": "b-replicator-dynamics-ess", "target": "h-replicator-dynamics-ess-institutional-design", "relation": "related_hypothesis" }, { "source": "b-spatial-statistics-geographic-inequality", "target": "u-spatial-autocorrelation-economic-convergence-causality", "relation": "related_unknown" }, { "source": "b-graph-laplacian-manifold-learning-x-cryoem-conformational-maps", "target": "u-cryoem-laplacian-eigenmode-physical-interpretability", "relation": "related_unknown" }, { "source": "b-graph-laplacian-manifold-learning-x-cryoem-conformational-maps", "target": "h-laplacian-eigenmodes-improve-cryoem-conformation-clustering", "relation": "related_hypothesis" }, { "source": "b-ddpm-x-accelerated-mri-inverse-reconstruction", "target": "u-ddpm-mri-prior-mismatch-artifact-risk", "relation": "related_unknown" }, { "source": "b-ddpm-x-accelerated-mri-inverse-reconstruction", "target": "h-ddpm-priors-reduce-mri-reconstruction-error-at-fixed-dose", "relation": "related_hypothesis" }, { "source": "b-electrical-impedance-tomography-x-fisher-information-design", "target": "u-eit-fisher-information-electrode-geometry-optimality", "relation": "related_unknown" }, { "source": "b-electrical-impedance-tomography-x-fisher-information-design", "target": "h-fisher-information-optimized-eit-electrodes-improve-lesion-detectability", "relation": "related_hypothesis" }, { "source": "b-persistent-homology-x-microscopy-noise-topology-qc", "target": "u-persistent-homology-parameter-stability-noisy-microscopy", "relation": "related_unknown" }, { "source": "b-persistent-homology-x-microscopy-noise-topology-qc", "target": "h-multiscale-filtration-persistence-improves-microscopy-segmentation-qc", "relation": "related_hypothesis" }, { "source": "b-bayesian-inverse-imaging-x-uncertainty-quantification", "target": "u-bayesian-imaging-inverse-problem-posterior-calibration", "relation": "related_unknown" }, { "source": "b-bayesian-inverse-imaging-x-uncertainty-quantification", "target": "h-hierarchical-bayesian-priors-improve-imaging-inverse-coverage", "relation": "related_hypothesis" }, { "source": "b-transformer-attention-x-longitudinal-ehr-reasoning", "target": "u-transformer-ehr-long-horizon-attribution-validity", "relation": "related_unknown" }, { "source": "b-transformer-attention-x-longitudinal-ehr-reasoning", "target": "h-transformer-temporal-attention-improves-ehr-risk-stratification", "relation": "related_hypothesis" }, { "source": "b-diffusion-mri-x-effective-medium-tortuosity", "target": "u-dmri-tortuosity-effective-medium-identifiability", "relation": "related_unknown" }, { "source": "b-diffusion-mri-x-effective-medium-tortuosity", "target": "h-multi-shell-dmri-estimates-track-phantom-tortuosity", "relation": "related_hypothesis" }, { "source": "b-renewal-processes-x-hospital-readmission-burst-modeling", "target": "u-renewal-kernel-selection-readmission-burst-identifiability", "relation": "related_unknown" }, { "source": "b-renewal-processes-x-hospital-readmission-burst-modeling", "target": "h-self-exciting-renewal-models-improve-readmission-burst-forecasting", "relation": "related_hypothesis" }, { "source": "b-atmospheric-convection-lorenz-chaos", "target": "u-atmospheric-predictability-limit-extended", "relation": "related_unknown" }, { "source": "b-atmospheric-convection-lorenz-chaos", "target": "h-lorenz-attractor-seasonal-forecast-skill", "relation": "related_hypothesis" }, { "source": "b-microbe-mineral-geochemical-cycling", "target": "u-microbial-mineral-weathering-rate-in-situ", "relation": "related_unknown" }, { "source": "b-microbe-mineral-geochemical-cycling", "target": "h-microbial-iron-reduction-sediment-carbon-preservation", "relation": "related_hypothesis" }, { "source": "b-antibiotic-tolerance-persister-switching", "target": "u-persister-cell-switching-rates-clinical", "relation": "related_unknown" }, { "source": "b-lotka-volterra-competition-x-phage-bacteria-chemostat-control", "target": "u-parameter-regimes-where-lotka-volterra-surrogates-fail-for-phage-bacteria-chemostats", "relation": "related_unknown" }, { "source": "b-lotka-volterra-competition-x-phage-bacteria-chemostat-control", "target": "h-lotka-volterra-informed-feedback-control-delays-phage-resistance-dominance", "relation": "related_hypothesis" }, { "source": "b-sindy-sparse-discovery-x-host-pathogen-dynamics", "target": "u-sindy-library-selection-bias-in-host-pathogen-inference", "relation": "related_unknown" }, { "source": "b-sindy-sparse-discovery-x-host-pathogen-dynamics", "target": "h-sindy-guided-control-policies-delay-phage-resistance-takeover", "relation": "related_hypothesis" }, { "source": "b-music-physics-resonance", "target": "u-music-universals", "relation": "related_unknown" }, { "source": "b-graph-convolution-x-transmission-network-inference", "target": "u-gcn-transmission-edge-direction-identifiability", "relation": "related_unknown" }, { "source": "b-graph-convolution-x-transmission-network-inference", "target": "h-graph-convolution-with-mobility-priors-improves-outbreak-link-recovery", "relation": "related_hypothesis" }, { "source": "b-glial-cells-brain-homeostasis", "target": "u-microglia-synapse-pruning-alzheimers-pathological-threshold", "relation": "related_unknown" }, { "source": "b-glial-cells-brain-homeostasis", "target": "h-complement-mediated-synapse-loss-drives-alzheimers-cognitive-decline", "relation": "related_hypothesis" }, { "source": "b-memory-reconsolidation-synaptic-plasticity", "target": "u-reconsolidation-synaptic-locus-ampa-receptor", "relation": "related_unknown" }, { "source": "b-memory-reconsolidation-synaptic-plasticity", "target": "h-reconsolidation-ampar-endocytosis-labilisation", "relation": "related_hypothesis" }, { "source": "b-neurodegeneration-protein-aggregation", "target": "u-prion-like-spread-neurodegeneration-circuit-specificity", "relation": "related_unknown" }, { "source": "b-neurodegeneration-protein-aggregation", "target": "h-tau-propagation-circuit-connectivity-determines-staging", "relation": "related_hypothesis" }, { "source": "b-retinal-waves-spontaneous-activity", "target": "u-retinal-wave-spatial-statistics-map-precision", "relation": "related_unknown" }, { "source": "b-retinal-waves-spontaneous-activity", "target": "h-retinal-wave-bandwidth-map-resolution-constraint", "relation": "related_hypothesis" }, { "source": "b-nociception-gate-control-spinal-circuit", "target": "u-spinal-gate-control-interneuron-identity", "relation": "related_unknown" }, { "source": "b-synaptic-vesicle-snare-fusion", "target": "u-snare-force-threshold-in-vivo", "relation": "related_unknown" }, { "source": "b-synaptic-vesicle-snare-fusion", "target": "h-snare-zippering-force-gates-fusion-rate", "relation": "related_hypothesis" }, { "source": "b-anesthesia-consciousness-suppression", "target": "u-neural-correlates-consciousness-anesthesia-mechanism", "relation": "related_unknown" }, { "source": "b-anesthesia-consciousness-suppression", "target": "h-ketamine-antidepressant-ampa-potentiation-mechanism", "relation": "related_hypothesis" }, { "source": "b-ion-channel-gating-x-metastable-rate-theory", "target": "u-ion-channel-barrier-heights-from-multiscale-md-posteriors", "relation": "related_unknown" }, { "source": "b-ion-channel-gating-x-metastable-rate-theory", "target": "h-markov-gating-graph-consistency-with-kramers-scaling-under-voltage-clamp-protocols", "relation": "related_hypothesis" }, { "source": "b-neurogenesis-growth-factor-signaling", "target": "u-adult-human-hippocampal-neurogenesis-existence-rate-controversy", "relation": "related_unknown" }, { "source": "b-neurogenesis-growth-factor-signaling", "target": "h-neurogenesis-requirement-ssri-antidepressant-human-evidence", "relation": "related_hypothesis" }, { "source": "b-neurotransmitter-pharmacology", "target": "u-snare-complex-partial-zippering-spontaneous-release-rate", "relation": "related_unknown" }, { "source": "b-neurotransmitter-pharmacology", "target": "h-snare-zippering-energy-controls-vesicle-fusion-probability", "relation": "related_hypothesis" }, { "source": "b-neural-criticality-climate-tipping", "target": "u-abrupt-climate-transitions", "relation": "related_unknown" }, { "source": "b-neural-criticality-climate-tipping", "target": "u-neural-criticality-tipping-shared-mathematics", "relation": "related_unknown" }, { "source": "b-neural-criticality-climate-tipping", "target": "h-neural-ew-indicators-climate-tipping-transfer", "relation": "related_hypothesis" }, { "source": "b-cortical-hierarchy-predictive-coding", "target": "u-predictive-coding-laminar-circuit-mechanism", "relation": "related_unknown" }, { "source": "b-cortical-hierarchy-predictive-coding", "target": "h-mismatch-negativity-bayesian-precision-prediction-error", "relation": "related_hypothesis" }, { "source": "b-hippocampal-replay-sharp-wave-ripples", "target": "u-hippocampal-replay-sequence-selection-criteria", "relation": "related_unknown" }, { "source": "b-hippocampal-replay-sharp-wave-ripples", "target": "h-sharp-wave-ripple-consolidation-reward-bias", "relation": "related_hypothesis" }, { "source": "b-backpropagation-synaptic-plasticity", "target": "u-biological-backpropagation-mechanism", "relation": "related_unknown" }, { "source": "b-contrastive-predictive-coding-x-multiview-self-supervised-learning", "target": "u-cpc-negative-sampling-bias-temporal-structure", "relation": "related_unknown" }, { "source": "b-contrastive-predictive-coding-x-multiview-self-supervised-learning", "target": "h-predictive-cpc-loss-improves-downstream-transfer-under-shift", "relation": "related_hypothesis" }, { "source": "b-efficient-coding-hypothesis-x-information-bottleneck-representation-learning", "target": "u-efficient-coding-bottleneck-tradeoff-measurability", "relation": "related_unknown" }, { "source": "b-efficient-coding-hypothesis-x-information-bottleneck-representation-learning", "target": "h-information-bottleneck-alignment-improves-neural-encoding-metrics", "relation": "related_hypothesis" }, { "source": "b-rl-intrinsic-motivation-x-novelty-information-gain-neuroscience", "target": "u-rl-novelty-bonus-information-gain-mapping", "relation": "related_unknown" }, { "source": "b-rl-intrinsic-motivation-x-novelty-information-gain-neuroscience", "target": "h-count-novelty-scales-bayesian-information-gain-proxy", "relation": "related_hypothesis" }, { "source": "b-synaptic-tagging-x-cache-coherence-writeback-analogy", "target": "u-synaptic-tag-cache-analogy-quantitative-test", "relation": "related_unknown" }, { "source": "b-synaptic-tagging-x-cache-coherence-writeback-analogy", "target": "h-tag-decay-timescale-vs-write-buffer-lifetime-correlation-classroom-only", "relation": "related_hypothesis" }, { "source": "b-hysteresis-loop-area-x-neural-fatigue-recovery-dynamics", "target": "u-identifiability-of-hysteresis-biomarkers-in-neurofatigue-monitoring", "relation": "related_unknown" }, { "source": "b-hysteresis-loop-area-x-neural-fatigue-recovery-dynamics", "target": "h-hysteresis-loop-biomarkers-predict-neurofatigue-recovery-lag", "relation": "related_hypothesis" }, { "source": "b-motor-control-internal-models", "target": "u-forward-model-cerebellum-learning-rule", "relation": "related_unknown" }, { "source": "b-motor-control-internal-models", "target": "h-cerebellum-lqr-forward-model-implementation", "relation": "related_hypothesis" }, { "source": "b-neural-diversity-ecosystem-stability", "target": "u-ei-balance-diversity-robustness", "relation": "related_unknown" }, { "source": "b-neural-diversity-ecosystem-stability", "target": "h-neural-diversity-stability-random-matrix-prediction", "relation": "related_hypothesis" }, { "source": "b-computational-psychiatry-digital-biomarkers", "target": "u-computational-psychiatry-treatment-response-prediction", "relation": "related_unknown" }, { "source": "b-computational-psychiatry-digital-biomarkers", "target": "h-computational-psychiatry-aberrant-precision-antipsychotic-mechanism", "relation": "related_hypothesis" }, { "source": "b-insect-navigation-path-integration", "target": "u-insect-navigation-path-integration", "relation": "related_unknown" }, { "source": "b-insect-navigation-path-integration", "target": "h-insect-navigation-path-integration", "relation": "related_hypothesis" }, { "source": "b-kalman-filter-x-brain-state-estimation", "target": "u-kalman-filter-neural-implementation-limits", "relation": "related_unknown" }, { "source": "b-kalman-filter-x-brain-state-estimation", "target": "h-sensory-cortex-implements-approximate-kalman-updates", "relation": 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"related_unknown" }, { "source": "b-spike-coding-neuromorphic", "target": "h-neuromorphic-chips-edge-ai-energy-advantage", "relation": "related_hypothesis" }, { "source": "b-glymphatic-cerebrospinal-fluid", "target": "u-glymphatic-flow-impairment-alzheimers", "relation": "related_unknown" }, { "source": "b-glymphatic-cerebrospinal-fluid", "target": "h-glymphatic-dysfunction-drives-amyloid-accumulation", "relation": "related_hypothesis" }, { "source": "b-neurolyme-neuroinflammation", "target": "u-ptlds-neuroinflammation-self-sustaining", "relation": "related_unknown" }, { "source": "b-neurolyme-neuroinflammation", "target": "h-ptlds-neuroinflammation-il6-blockade", "relation": "related_hypothesis" }, { "source": "b-openalex-info-theory-intrinsic-motivation", "target": "u-intrinsic-motivation-information-maximization", "relation": "related_unknown" }, { "source": "b-openalex-info-theory-intrinsic-motivation", "target": "h-autonomy-need-empowerment-maximization", "relation": 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"b-meg-inverse-source-localization", "target": "u-meg-inverse-source-localization", "relation": "related_unknown" }, { "source": "b-meg-squid-forward-x-em-inverse-source", "target": "u-meg-inverse-source-nonunique-regularization-bounds", "relation": "related_unknown" }, { "source": "b-meg-squid-forward-x-em-inverse-source", "target": "h-squid-array-regularization-improves-meg-source-localization", "relation": "related_hypothesis" }, { "source": "b-neuronal-avalanches-branching-process", "target": "u-neuronal-avalanches-branching-process", "relation": "related_unknown" }, { "source": "b-neuronal-avalanches-branching-process", "target": "h-neuronal-avalanches-branching-process", "relation": "related_hypothesis" }, { "source": "b-persistent-homology-neural-representation", "target": "u-phi-measurement-neural-correlates", "relation": "related_unknown" }, { "source": "b-persistent-homology-neural-representation", "target": "h-tda-cognitive-map-nontrivial-topology", "relation": "related_hypothesis" }, { "source": "b-persistent-homology-neural-topology", "target": "u-persistent-homology-neural-manifold-geometry-vs-topology-decoupling", "relation": "related_unknown" }, { "source": "b-persistent-homology-neural-topology", "target": "h-hippocampal-place-cell-population-topology-reflects-navigated-space-topology", "relation": "related_hypothesis" }, { "source": "b-spike-sorting-dimensionality-reduction", "target": "u-neural-manifold-dimensionality-behavior", "relation": "related_unknown" }, { "source": "b-spike-sorting-dimensionality-reduction", "target": "h-neural-manifold-geometry-encodes-cognitive-map", "relation": "related_hypothesis" }, { "source": "b-topological-neuroscience", "target": "u-neural-manifold-topology-cognitive-states", "relation": "related_unknown" }, { "source": "b-topological-neuroscience", "target": "h-betti-numbers-cognitive-complexity", "relation": "related_hypothesis" }, { "source": "b-connectome-neurodegeneration", "target": "u-connectome-neurodegeneration-spread-rate", "relation": "related_unknown" }, { "source": "b-connectome-neurodegeneration", "target": "u-alzheimer-causal-biomarkers", "relation": "related_unknown" }, { "source": "b-connectome-neurodegeneration", "target": "h-scale-free-criticality-brain-hub-vulnerability", "relation": "related_hypothesis" }, { "source": "b-connectome-neurodegeneration", "target": "h-hopfield-alzheimers-glass-transition", "relation": "related_hypothesis" }, { "source": "b-predictive-coding-phenomenal-consciousness", "target": "u-neural-correlates-self", "relation": "related_unknown" }, { "source": "b-predictive-coding-phenomenal-consciousness", "target": "u-perceptual-binding-problem", "relation": "related_unknown" }, { "source": "b-predictive-coding-phenomenal-consciousness", "target": "u-metacognition-substrate", "relation": "related_unknown" }, { "source": "b-eeg-dipole-source-maxwell-equations", "target": "u-eeg-source-localization-skull-conductivity", "relation": "related_unknown" }, { "source": "b-eeg-dipole-source-maxwell-equations", "target": "h-eeg-individualized-forward-model-epilepsy", "relation": "related_hypothesis" }, { "source": "b-hodgkin-huxley-conductance", "target": "u-hodgkin-huxley-channel-heterogeneity-neuron-diversity", "relation": "related_unknown" }, { "source": "b-holographic-memory-fourier-phase-encoding", "target": "u-holographic-memory-neural-phase-encoding-test", "relation": "related_unknown" }, { "source": "b-holographic-memory-fourier-phase-encoding", "target": "h-hippocampal-population-holographic-capacity", "relation": "related_hypothesis" }, { "source": "b-integrate-fire-stochastic-processes", "target": "u-decision-fatigue-neural", "relation": "related_unknown" }, { "source": "b-integrate-fire-stochastic-processes", "target": "h-lif-decision-fatigue-ornstein-uhlenbeck", "relation": "related_hypothesis" }, { "source": "b-neural-avalanches-criticality", "target": "u-neural-criticality-consciousness-substrate", "relation": "related_unknown" }, { "source": "b-neural-avalanches-criticality", "target": "h-neural-avalanche-criticality-dynamic-range", "relation": "related_hypothesis" }, { "source": "b-neural-binding-gamma-oscillations", "target": "u-neural-binding-mechanism-synchrony", "relation": "related_unknown" }, { "source": "b-neural-binding-gamma-oscillations", "target": "h-gamma-oscillations-binding-causal-test", "relation": "related_hypothesis" }, { "source": "b-neural-field-theory-brain-waves", "target": "u-neural-field-theory-empirical-connectome-validation", "relation": "related_unknown" }, { "source": "b-neural-field-theory-brain-waves", "target": "h-cortical-eigenmodes-universal-resting-state-basis", "relation": "related_hypothesis" }, { "source": "b-neuroplasticity-stdp", "target": "u-stdp-synaptic-weight-saturation", "relation": "related_unknown" }, { "source": "b-openalex-stat-mech-memory-gating", "target": "u-lstm-gating-biological-analogue", "relation": "related_unknown" }, { "source": "b-openalex-stat-mech-memory-gating", "target": "h-lstm-gating-stat-mech-phase-transition", "relation": "related_hypothesis" }, { "source": "b-sensory-adaptation-weber-fechner", "target": "u-weber-fechner-stevens-unifying-neural-mechanism", "relation": "related_unknown" }, { "source": "b-sensory-adaptation-weber-fechner", "target": "h-efficient-coding-natural-statistics-sensory-cortex-universality", "relation": "related_hypothesis" }, { "source": "b-synaptic-plasticity-hebbian-learning", "target": "u-stdp-natural-stimuli-in-vivo-plasticity-rules", "relation": "related_unknown" }, { "source": "b-synaptic-plasticity-hebbian-learning", "target": "h-bcm-sliding-threshold-homeostatic-metaplasticity-cortical-map", "relation": "related_hypothesis" }, { "source": "b-memory-consolidation-synaptic-tagging", "target": "u-engram-molecular-basis", "relation": "related_unknown" }, { "source": "b-memory-consolidation-synaptic-tagging", "target": "h-sleep-rem-creative-insight-memory", "relation": "related_hypothesis" }, { "source": "b-bci-optimal-decoding", "target": "u-bci-non-stationarity-adaptation", "relation": "related_unknown" }, { "source": "b-bci-optimal-decoding", "target": "h-bci-information-rate-fisher-bound", "relation": "related_hypothesis" }, { "source": "b-decision-neuroscience-neuroeconomics", "target": "u-vmpfc-reference-dependent-coding-mechanism", "relation": "related_unknown" }, { "source": "b-decision-neuroscience-neuroeconomics", "target": "h-beta-delta-neuroeconomics-dual-system", "relation": "related_hypothesis" }, { "source": "b-social-neuroscience-group-behavior", "target": "u-oxytocin-parochial-altruism-policy-implications", "relation": "related_unknown" }, { "source": "b-neuronal-avalanches-soc-power-law", "target": "u-neuronal-avalanche-soc-universality-class", "relation": "related_unknown" }, { "source": "b-bayesian-brain-predictive-processing", "target": "u-bayesian-brain-prior-encoding", "relation": "related_unknown" }, { "source": "b-bayesian-brain-predictive-processing", "target": "h-predictive-processing-psychosis", "relation": "related_hypothesis" }, { "source": "b-neuroimaging-connectivity-graphical-models", "target": "u-fmri-connectivity-graphical-model-validity", "relation": "related_unknown" }, { "source": "b-a-stability-region-x-time-stepping-reaction-diffusion", "target": "u-a-stability-region-operator-splitting-reaction-diffusion", "relation": "related_unknown" }, { "source": "b-a-stability-region-x-time-stepping-reaction-diffusion", "target": "h-imex-time-stepping-expands-stable-reaction-diffusion-cfl", "relation": "related_hypothesis" }, { "source": "b-symbolic-regression-x-sparse-sensor-pde-structure-discovery", "target": "u-sindy-noise-and-collinearity-under-limited-sensing", "relation": "related_unknown" }, { "source": "b-symbolic-regression-x-sparse-sensor-pde-structure-discovery", "target": "h-sparse-sensor-placement-improves-pde-structure-recovery", "relation": "related_hypothesis" }, { "source": "b-lcs-retention-zone-x-coastal-larval-supply", "target": "u-lcs-retention-coastal-recruitment-transfer", "relation": "related_unknown" }, { "source": "b-lcs-retention-zone-x-coastal-larval-supply", "target": "h-ftle-ridge-threshold-correlates-larval-retention-proxy", "relation": "related_hypothesis" }, { "source": "b-tidal-forcing-ocean-mixing", "target": "u-internal-tide-mixing-efficiency-spatial", "relation": "related_unknown" }, { "source": "b-tidal-forcing-ocean-mixing", "target": "h-tidal-mixing-overturning-circulation-control", "relation": "related_hypothesis" }, { "source": "b-neural-spectral-model-x-submesoscale-forecasting", "target": "u-neural-spectral-ocean-forecast-stability-horizon", "relation": "related_unknown" }, { "source": "b-neural-spectral-model-x-submesoscale-forecasting", "target": "h-neural-spectral-ocean-model-improves-submesoscale-forecast-skill", "relation": "related_hypothesis" }, { "source": "b-ocean-acoustic-tomography-x-ultrasound-transmission-tomography", "target": "u-ocean-ultrasound-shared-inverse-regularizers", "relation": "related_unknown" }, { "source": "b-ocean-acoustic-tomography-x-ultrasound-transmission-tomography", "target": "h-adjoint-base-resolution-operator-matches-ray-density-despite-scale-gap", "relation": "related_hypothesis" }, { "source": "b-ocean-color-radiative-transfer", "target": "u-ocean-color-phytoplankton-remote-sensing", "relation": "related_unknown" }, { "source": "b-ocean-color-radiative-transfer", "target": "h-ocean-color-chlorophyll-inversion-accuracy", "relation": "related_hypothesis" }, { "source": "b-ribosome-kinetics-queuing-theory", "target": "u-ribosome-kinetics-queuing", "relation": "related_unknown" }, { "source": "b-ribosome-kinetics-queuing-theory", "target": "h-ribosome-kinetics-queuing-theory", "relation": "related_hypothesis" }, { "source": "b-supply-chain-network-robustness", "target": "u-supply-chain-network-topology-resilience", "relation": "related_unknown" }, { "source": "b-supply-chain-network-robustness", "target": "h-supply-chain-percolation-threshold-dual-sourcing", "relation": "related_hypothesis" }, { "source": "b-multi-armed-bandits-x-sepsis-antibiotic-de-escalation", "target": "u-regret-aware-safety-constraints-for-antibiotic-de-escalation-bandits", "relation": "related_unknown" }, { "source": "b-multi-armed-bandits-x-sepsis-antibiotic-de-escalation", "target": "h-constrained-bandit-policies-reduce-sepsis-antibiotic-overtreatment-days", "relation": "related_hypothesis" }, { "source": "b-heavy-traffic-queueing-x-emergency-department-flow", "target": "u-heavy-traffic-thresholds-for-ed-crowding-intervention-policies", "relation": "related_unknown" }, { "source": "b-heavy-traffic-queueing-x-emergency-department-flow", "target": "h-diffusion-queueing-threshold-policies-reduce-ed-boarding-time-variance", "relation": "related_hypothesis" }, { "source": "b-chromatic-aberration-dispersion", "target": "u-chromatic-aberration-broadband-metalens", "relation": "related_unknown" }, { "source": "b-drug-resistance-fitness-landscapes", "target": "u-fitness-landscape-drug-resistance-prediction", "relation": "related_unknown" }, { "source": "b-drug-resistance-fitness-landscapes", "target": "h-collateral-sensitivity-cycling-drug-resistance", "relation": "related_hypothesis" }, { "source": "b-neural-ode-x-pharmacokinetic-state-space-modeling", "target": "u-neural-ode-pk-identifiability-under-sparse-sampling", "relation": "related_unknown" }, { "source": "b-neural-ode-x-pharmacokinetic-state-space-modeling", "target": "h-neural-ode-priors-improve-pk-state-forecasting", "relation": "related_hypothesis" }, { "source": "b-pharmacokinetics-compartmental-ode", "target": "u-pharmacokinetic-interindividual-variability", "relation": "related_unknown" }, { "source": "b-antibiotic-synergy-pharmacodynamic-surfaces", "target": "u-antibiotic-synergy-surfaces", "relation": "related_unknown" }, { "source": "b-antibiotic-synergy-pharmacodynamic-surfaces", "target": "h-antibiotic-synergy-pharmacodynamic-surfaces", "relation": "related_hypothesis" }, { "source": "b-kolmogorov-complexity-explanation", "target": "u-kolmogorov-complexity-computable-approximation", "relation": "related_unknown" }, { "source": "b-kolmogorov-complexity-explanation", "target": "h-mdl-scientific-theory-selection", "relation": "related_hypothesis" }, { "source": "b-bayesian-scientific-inference", "target": "u-bayesian-prior-objectivity", "relation": "related_unknown" }, { "source": "b-bayesian-scientific-inference", "target": "u-model-selection-validity", "relation": "related_unknown" }, { "source": "b-bayesian-scientific-inference", "target": "h-bayes-factor-theory-selection", "relation": "related_hypothesis" }, { "source": "b-bayesian-scientific-inference", "target": "h-preregistration-field-replication-rate", "relation": "related_hypothesis" }, { "source": "b-bayesian-inference-scientific-confirmation", "target": "u-bayesian-old-evidence-problem", "relation": "related_unknown" }, { "source": "b-bayesian-inference-scientific-confirmation", "target": "h-bayesian-marginal-likelihood-occam-razor-automatic", "relation": "related_hypothesis" }, { "source": "b-induction-bayesian-convergence", "target": "u-bayesian-convergence-prior-dependence", "relation": "related_unknown" }, { "source": "b-induction-bayesian-convergence", "target": "h-doob-convergence-rate-scientific-inference", "relation": "related_hypothesis" }, { "source": "b-philosophy-underdetermination-quantum", "target": "u-demarcation-problem", "relation": "related_unknown" }, { "source": "b-philosophy-underdetermination-quantum", "target": "u-underdetermination-theory", "relation": "related_unknown" }, { "source": "b-philosophy-underdetermination-quantum", "target": "u-scientific-realism-debate", "relation": "related_unknown" }, { "source": "b-phase-transitions-ml-grokking", "target": "u-grokking-criticality-universality-class", "relation": "related_unknown" }, { "source": "b-phase-transitions-ml-grokking", "target": "h-grokking-criticality-universality", "relation": "related_hypothesis" }, { "source": "b-active-brownian-motion-x-cell-migration", "target": "u-active-brownian-motion-x-cell-migration", "relation": "related_unknown" }, { "source": "b-active-matter-collective-locomotion", "target": "u-active-matter-topological-defect-biology", "relation": "related_unknown" }, { "source": "b-active-matter-collective-locomotion", "target": "h-active-nematic-defect-tissue-extrusion", "relation": "related_hypothesis" }, { "source": "b-allosteric-regulation-x-conformational-dynamics", "target": "u-allosteric-regulation-x-conformational-dynamics", "relation": "related_unknown" }, { "source": "b-bioacoustics-sound-production", "target": "u-whale-song-information-content-localization", "relation": "related_unknown" }, { "source": "b-bioacoustics-sound-production", "target": "h-bat-echolocation-neural-matched-filter-implementation", "relation": "related_hypothesis" }, { "source": "b-bioenergetics-proton-motive-force", "target": "u-atp-synthase-torque-slip-mechanism", "relation": "related_unknown" }, { "source": "b-bioenergetics-proton-motive-force", "target": "h-pmf-bacterial-flagella-atp-synthase-evolutionary-homology", "relation": "related_hypothesis" }, { "source": "b-brownian-motion-cell-diffusion", "target": "u-anomalous-diffusion-cytoplasm", "relation": "related_unknown" }, { "source": "b-brownian-motion-molecular-motors", "target": "u-kinesin-thermal-noise-efficiency", "relation": "related_unknown" }, { "source": "b-brownian-motion-molecular-motors", "target": "h-molecular-motor-efficiency-fluctuation-theorem", "relation": "related_hypothesis" }, { "source": "b-cochlear-mechanics-hearing", "target": "u-cochlear-amplifier-molecular-mechanism-prestin", "relation": "related_unknown" }, { "source": "b-cochlear-mechanics-hearing", "target": "h-cochlear-active-amplification-hopf-bifurcation", "relation": "related_hypothesis" }, { "source": "b-diffusion-limited-aggregation-x-fractal-growth", "target": "u-diffusion-limited-aggregation-x-fractal-growth", "relation": "related_unknown" }, { "source": "b-electrophysiology-action-potential", "target": "u-myelination-conduction-velocity-optimality", "relation": "related_unknown" }, { "source": "b-electrophysiology-action-potential", "target": "h-myelination-optimal-axon-diameter-conduction-velocity", "relation": "related_hypothesis" }, { "source": "b-entropy-production-x-living-systems", "target": "u-entropy-production-x-living-systems", "relation": "related_unknown" }, { "source": "b-entropy-production-x-living-systems", "target": "h-entropy-production-x-living-systems", "relation": "related_hypothesis" }, { "source": "b-flagellar-motor-rotary-machines", "target": "u-flagellar-motor-stator-number-regulation-pmf", "relation": "related_unknown" }, { "source": "b-flagellar-motor-rotary-machines", "target": "h-flagellar-motor-stator-assembly-pmf-dependent-mechanosensing", "relation": "related_hypothesis" }, { "source": "b-flagellar-motor-rotary-mechanics", "target": "u-optimal-stopping-biological-decisions", "relation": "related_unknown" }, { "source": "b-flagellar-motor-x-rotary-engine", "target": "u-flagellar-motor-x-rotary-engine", "relation": "related_unknown" }, { "source": "b-liquid-crystal-x-cell-membrane", "target": "u-liquid-crystal-x-cell-membrane", "relation": "related_unknown" }, { "source": "b-mechanobiology-cellular-force-sensing", "target": "u-yap-taz-stiffness-sensing-mechanism-molecular", "relation": "related_unknown" }, { "source": "b-mechanobiology-cellular-force-sensing", "target": "h-ecm-stiffness-cancer-invasion-threshold", "relation": "related_hypothesis" }, { "source": "b-mechanobiology-continuum-mechanics", "target": "u-cell-jamming-tissue-development", "relation": "related_unknown" }, { "source": "b-mechanobiology-continuum-mechanics", "target": "h-durotaxis-cancer-metastasis", "relation": "related_hypothesis" }, { "source": "b-mechanosensing-piezoelectric", "target": "u-piezoelectric-biopolymers", "relation": "related_unknown" }, { "source": "b-mechanosensing-piezoelectric", "target": "u-self-healing-polymer-mechanism", "relation": "related_unknown" }, { "source": "b-neurovascular-coupling-x-fluid-dynamics", "target": "u-neurovascular-coupling-x-fluid-dynamics", "relation": "related_unknown" }, { "source": "b-nonequilibrium-statistical-mechanics-metabolism", "target": "u-metabolic-flux-entropy-production-cancer-cells", "relation": "related_unknown" }, { "source": "b-nonequilibrium-statistical-mechanics-metabolism", "target": "h-jarzynski-equality-molecular-motor-efficiency-measurement", "relation": "related_hypothesis" }, { "source": "b-optogenetics-x-control-theory", "target": "u-optogenetics-x-control-theory", "relation": "related_unknown" }, { "source": "b-optogenetics-x-control-theory", "target": "h-optogenetics-x-control-theory", "relation": "related_hypothesis" }, { "source": "b-osmosis-cell-volume-regulation", "target": "u-aqp-gating-osmosensing-mechanism", "relation": "related_unknown" }, { "source": "b-osmosis-cell-volume-regulation", "target": "h-aqp2-trafficking-as-osmotic-valve", "relation": "related_hypothesis" }, { "source": "b-vicsek-active-matter-flocking", "target": "u-vicsek-transition-order-finite-systems", "relation": "related_unknown" }, { "source": "b-casimir-polder-retardation-x-lifshitz-vdw-crossover", "target": "u-unified-spectral-epsilon-model-across-vdw-casimir-length-scales", "relation": "related_unknown" }, { "source": "b-casimir-polder-retardation-x-lifshitz-vdw-crossover", "target": "h-joint-fit-lifshitz-hamaker-colloid-force-curves", "relation": "related_hypothesis" }, { "source": "b-catalytic-converter-surface-chemistry", "target": "u-catalytic-converter-cold-start-mitigation-strategies", "relation": "related_unknown" }, { "source": "b-catalytic-converter-surface-chemistry", "target": "h-electric-catalyst-preheating-eliminates-cold-start-emissions", "relation": "related_hypothesis" }, { "source": "b-electrochemical-energy-storage-conversion", "target": "u-pem-fuel-cell-pt-catalyst-degradation-mechanism", "relation": "related_unknown" }, { "source": "b-electrochemical-energy-storage-conversion", "target": "h-single-atom-catalyst-orr-selectivity-4e", "relation": "related_hypothesis" }, { "source": "b-kramers-escape-rate-x-drift-diffusion-decision-threshold", "target": "u-kramers-drift-diffusion-barrier-mapping-neural-decisions", "relation": "related_unknown" }, { "source": "b-kramers-escape-rate-x-drift-diffusion-decision-threshold", "target": "h-reaction-time-tail-scales-with-effective-barrier-height", "relation": "related_hypothesis" }, { "source": "b-maxwell-boltzmann-chemical-kinetics", "target": "u-arrhenius-prefactor-molecular-basis", "relation": "related_unknown" }, { "source": "b-maxwell-boltzmann-chemical-kinetics", "target": "h-activation-energy-mb-tail-universality", "relation": "related_hypothesis" }, { "source": "b-nmr-quantum-coherence", "target": "u-quantum-coherence-biological-systems-nmr-detectable", "relation": "related_unknown" }, { "source": "b-nmr-quantum-coherence", "target": "h-solid-state-nmr-amyloid-structure-mechanism", "relation": "related_hypothesis" }, { "source": "b-reaction-rate-transition-state", "target": "u-kramers-turnover-solvent-friction", "relation": "related_unknown" }, { "source": "b-reaction-rate-transition-state", "target": "h-marcus-inverted-region-biological-electron-transfer", "relation": "related_hypothesis" }, { "source": "b-statistical-thermodynamics-equilibrium", "target": "u-partition-function-anharmonic-correction", "relation": "related_unknown" }, { "source": "b-statistical-thermodynamics-equilibrium", "target": "h-statistical-thermodynamics-equilibrium-partition-function", "relation": "related_hypothesis" }, { "source": "b-superconductivity-cooper-pairs", "target": "u-high-tc-superconductor-pairing-mechanism", "relation": "related_unknown" }, { "source": "b-superconductivity-cooper-pairs", "target": "h-cuprate-pairing-spin-fluctuation-glue", "relation": "related_hypothesis" }, { "source": "b-transition-state-theory-kinetics", "target": "u-crystallization-kinetics-nucleation", "relation": "related_unknown" }, { "source": "b-transition-state-theory-kinetics", "target": "u-reaction-mechanism-automated-discovery", "relation": "related_unknown" }, { "source": "b-transition-state-x-saddle-point", "target": "u-transition-state-x-saddle-point", "relation": "related_unknown" }, { "source": "b-van-der-waals-phase-transitions", "target": "u-posterior-landscape-multimodality", "relation": "related_unknown" }, { "source": "b-van-der-waals-phase-transitions", "target": "h-van-der-waals-free-energy-double-well", "relation": "related_hypothesis" }, { "source": "b-xray-crystallography-structure", "target": "u-crystallography-phase-problem-ab-initio", "relation": "related_unknown" }, { "source": "b-xray-crystallography-structure", "target": "h-cryo-em-supersedes-xray-membrane-proteins", "relation": "related_hypothesis" }, { "source": "b-climate-tipping-percolation", "target": "u-permafrost-tipping-point", "relation": "related_unknown" }, { "source": "b-climate-tipping-percolation", "target": "u-amoc-collapse-threshold", "relation": "related_unknown" }, { "source": "b-climate-tipping-percolation", "target": "u-abrupt-climate-transitions", "relation": "related_unknown" }, { "source": "b-tipping-points-phase-transitions", "target": "u-climate-ew-indicator-universality", "relation": "related_unknown" }, { "source": "b-tipping-points-phase-transitions", "target": "h-amoc-fold-bifurcation-ew", "relation": "related_hypothesis" }, { "source": "b-entropy-conscious-experience", "target": "u-phi-measurement-neural-correlates", "relation": "related_unknown" }, { "source": "b-self-organized-criticality", "target": "u-brain-criticality-function", "relation": "related_unknown" }, { "source": "b-self-organized-criticality", "target": "u-soc-universality-class-brain", "relation": "related_unknown" }, { "source": "b-self-organized-criticality", "target": "h-criticality-conscious-integration", "relation": "related_hypothesis" }, { "source": "b-self-organized-criticality", "target": "h-grokking-criticality-universality", "relation": "related_hypothesis" }, { "source": "b-ising-model-x-hopfield-network", "target": "u-hopfield-capacity-modern-architectures", "relation": "related_unknown" }, { "source": "b-quantum-annealing-optimization", "target": "u-quantum-annealing-qaoa-comparison", "relation": "related_unknown" }, { "source": "b-quantum-annealing-optimization", "target": "h-quantum-annealing-qaoa-comparison", "relation": "related_hypothesis" }, { "source": "b-quantum-zeno-x-watchdog-sampling-analogy", "target": "u-quantum-zeno-watchdog-quantitative-mapping", "relation": "related_unknown" }, { "source": "b-quantum-zeno-x-watchdog-sampling-analogy", "target": "h-student-transfer-zeno-curve-to-sampling-stability-drills", "relation": "related_hypothesis" }, { "source": "b-renormalization-group-flow-x-deep-network-layer-coarse-graining", "target": "u-rg-layerwise-flow-identifiability-across-architectures", "relation": "related_unknown" }, { "source": "b-renormalization-group-flow-x-deep-network-layer-coarse-graining", "target": "h-beta-scheduled-layer-wise-training-mimics-rg-stability", "relation": "related_hypothesis" }, { "source": "b-restricted-boltzmann-machine-x-ising-energy-based-models", "target": "u-rbm-training-critical-slowdown-near-phase-boundaries", "relation": "related_unknown" }, { "source": "b-restricted-boltzmann-machine-x-ising-energy-based-models", "target": "h-annealed-rbm-pretraining-improves-phase-diagram-discovery", "relation": "related_hypothesis" }, { "source": "b-spin-glass-replica-optimization", "target": "u-replica-symmetry-breaking-algorithmic-hardness", "relation": "related_unknown" }, { "source": "b-quantum-error-correction-topology", "target": "u-topological-qec-physical-realization", "relation": "related_unknown" }, { "source": "b-quantum-error-correction-topology", "target": "h-topological-phase-qec-threshold-correspondence", "relation": "related_hypothesis" }, { "source": "b-spin-glass-neural-networks", "target": "u-hopfield-capacity-cortex", "relation": "related_unknown" }, { "source": "b-spin-glass-neural-networks", "target": "h-hopfield-alzheimers-glass-transition", "relation": "related_hypothesis" }, { "source": "b-boltzmann-machine-x-ising-model", "target": "u-boltzmann-machine-x-ising-model", "relation": "related_unknown" }, { "source": "b-cavity-method-x-belief-propagation", "target": "u-cavity-method-x-belief-propagation", "relation": "related_unknown" }, { "source": "b-cavity-method-x-belief-propagation", "target": "h-cavity-method-x-belief-propagation", "relation": "related_hypothesis" }, { "source": "b-diffusion-models-x-stochastic-processes", "target": "u-diffusion-models-x-stochastic-processes", "relation": "related_unknown" }, { "source": "b-mean-field-theory-x-neural-networks", "target": "u-mean-field-theory-x-neural-networks", "relation": "related_unknown" }, { "source": "b-quantum-error-correction-x-topological-codes", "target": "u-quantum-error-correction-x-topological-codes", "relation": "related_unknown" }, { "source": "b-quantum-walk-x-classical-random-walk", "target": "u-quantum-walk-x-classical-random-walk", "relation": "related_unknown" }, { "source": "b-renormalization-group-x-machine-learning", "target": "u-rg-ml-universality-classes", "relation": "related_unknown" }, { "source": "b-renormalization-x-compression", "target": "u-renormalization-x-compression", "relation": "related_unknown" }, { "source": "b-reservoir-computing-x-dynamical-systems", "target": "u-reservoir-computing-x-dynamical-systems", "relation": "related_unknown" }, { "source": "b-reservoir-computing-x-dynamical-systems", "target": "h-reservoir-computing-x-dynamical-systems", "relation": "related_hypothesis" }, { "source": "b-simulated-annealing-x-statistical-mechanics", "target": "u-simulated-annealing-x-statistical-mechanics", "relation": "related_unknown" }, { "source": "b-thermodynamics-x-information-theory", "target": "u-landauer-limit-neuronal-computation", "relation": "related_unknown" }, { "source": "b-topological-insulator-x-band-theory", "target": "u-topological-insulator-x-band-theory", "relation": "related_unknown" }, { "source": "b-variational-inference-x-free-energy", "target": "u-variational-inference-x-free-energy", "relation": "related_unknown" }, { "source": "b-ecological-stoichiometry-redfield", "target": "u-redfield-ratio-evolution-optimality", "relation": "related_unknown" }, { "source": "b-ecological-stoichiometry-redfield", "target": "u-stoichiometry-food-web-stability", "relation": "related_unknown" }, { "source": "b-ecological-stoichiometry-redfield", "target": "h-redfield-growth-rate-rg-fixed-point", "relation": "related_hypothesis" }, { "source": "b-habitat-percolation-ecology", "target": "u-habitat-fragmentation-threshold", "relation": "related_unknown" }, { "source": "b-habitat-percolation-ecology", "target": "h-habitat-percolation-critical-density", "relation": "related_hypothesis" }, { "source": "b-maximum-entropy-ecology", "target": "u-maxent-ecology-failure-modes", "relation": "related_unknown" }, { "source": "b-maximum-entropy-ecology", "target": "h-mete-non-equilibrium-deviations", "relation": "related_hypothesis" }, { "source": "b-turing-patterns-ecosystem-tipping", "target": "u-turing-ms-demyelination-pattern", "relation": "related_unknown" }, { "source": "b-agent-based-models-x-emergent-markets", "target": "u-agent-based-models-x-emergent-markets", "relation": "related_unknown" }, { "source": "b-blackscholes-x-diffusion-equation", "target": "u-blackscholes-x-diffusion-equation", "relation": "related_unknown" }, { "source": "b-chemical-potential-utility-maximization", "target": "u-chemical-potential-utility-non-equilibrium-markets", "relation": "related_unknown" }, { "source": "b-entropy-maximization-x-income-distribution", "target": "u-entropy-maximization-x-income-distribution", "relation": "related_unknown" }, { "source": "b-green-kubo-correlations-x-return-volatility-memory", "target": "u-fluctuation-dissipation-stationary-market-assumption-breakdown", "relation": "related_unknown" }, { "source": "b-green-kubo-correlations-x-return-volatility-memory", "target": "h-volatility-autocorrelation-satisfies-effective-fd-response", "relation": "related_hypothesis" }, { "source": "b-kinetic-theory-wealth-distribution", "target": "u-wealth-distribution-universality", "relation": "related_unknown" }, { "source": "b-kinetic-theory-wealth-distribution", "target": "h-two-class-economy-boltzmann-pareto-transition", "relation": "related_hypothesis" }, { "source": "b-lyapunov-divergence-x-bank-run-amplification", "target": "u-financial-lyapunov-time-versus-policy-interventions", "relation": "related_unknown" }, { "source": "b-lyapunov-divergence-x-bank-run-amplification", "target": "h-bank-run-lyapunov-time-shrinks-with-public-information-leaks", "relation": "related_hypothesis" }, { "source": "b-minority-game-market-microstructure", "target": "u-market-microstructure-price-formation", "relation": "related_unknown" }, { "source": "b-minority-game-market-microstructure", "target": "h-minority-game-hft-phase-transition", "relation": "related_hypothesis" }, { "source": "b-minority-game-x-market-microstructure", "target": "u-minority-game-x-market-microstructure", "relation": "related_unknown" }, { "source": "b-minority-game-x-market-microstructure", "target": "h-minority-game-x-market-microstructure", "relation": "related_hypothesis" }, { "source": "b-rational-inattention-x-entropy", "target": "u-rational-inattention-x-entropy", "relation": "related_unknown" }, { "source": "b-schawlow-townes-linewidth-x-leeson-oscillator-phase-noise", "target": "u-quantum-linewidth-vs-leeson-corner-crossover-measurement-protocol", "relation": "related_unknown" }, { "source": "b-schawlow-townes-linewidth-x-leeson-oscillator-phase-noise", "target": "h-identical-analyzer-method-noise-floor-dominated-regimes-match-at-mm-wave-carriers", "relation": "related_hypothesis" }, { "source": "b-acoustics-room-design", "target": "u-concert-hall-acoustic-quality-metrics", "relation": "related_unknown" }, { "source": "b-acoustics-room-design", "target": "h-room-acoustic-quality-predictable-from-geometry", "relation": "related_hypothesis" }, { "source": "b-chaos-synchronization-pecora-carroll", "target": "u-chaos-synchronization-noise-robustness-threshold", "relation": "related_unknown" }, { "source": "b-chaos-synchronization-pecora-carroll", "target": "h-pecora-carroll-synchronization-noise-tolerance-lyapunov", "relation": "related_hypothesis" }, { "source": "b-compressible-shock-x-traffic-shock-wave", "target": "u-traffic-shock-microscopic-validation", "relation": "related_unknown" }, { "source": "b-compressible-shock-x-traffic-shock-wave", "target": "h-compressible-shock-x-traffic-shock-wave", "relation": "related_hypothesis" }, { "source": "b-johnson-nyquist-equilibrium-fluctuations-x-rf-noise-figure-definition", "target": "u-rf-noise-figure-two-port-correlation-matrix-room-temperature", "relation": "related_unknown" }, { "source": "b-johnson-nyquist-equilibrium-fluctuations-x-rf-noise-figure-definition", "target": "h-correlated-port-noise-matrix-lowers-effective-nf-two-port", "relation": "related_hypothesis" }, { "source": "b-microfluidics-lab-on-chip", "target": "u-droplet-microfluidics-cell-viability-encapsulation-efficiency", "relation": "related_unknown" }, { "source": "b-microfluidics-lab-on-chip", "target": "h-organ-on-chip-predicts-drug-toxicity-better-than-animal-models", "relation": "related_hypothesis" }, { "source": "b-plasma-physics-fusion-energy", "target": "u-plasma-turbulence-transport-barrier-formation-mechanism", "relation": "related_unknown" }, { "source": "b-plasma-physics-fusion-energy", "target": "h-iter-q10-ignition-margin-sufficient-commercial-fusion", "relation": "related_hypothesis" }, { "source": "b-quantum-limited-amplification-x-heisenberg-noise-figure-bound", "target": "u-quantum-noise-figure-silicon-mm-wave-cryo-vs-room", "relation": "related_unknown" }, { "source": "b-quantum-limited-amplification-x-heisenberg-noise-figure-bound", "target": "h-josephson-paramp-nears-quantum-noise-floor-with-rimp-matched-array", "relation": "related_hypothesis" }, { "source": "b-quantum-sensing-fundamental-limits", "target": "u-genetic-circuit-crosstalk-noise", "relation": "related_unknown" }, { "source": "b-quantum-sensing-fundamental-limits", "target": "h-heisenberg-limited-sensing-biological", "relation": "related_hypothesis" }, { "source": "b-semiconductor-lasers-photonics", "target": "u-vcsel-silicon-photonics-integration-limit", "relation": "related_unknown" }, { "source": "b-semiconductor-lasers-photonics", "target": "h-silicon-photonics-dfb-laser-integration", "relation": "related_hypothesis" }, { "source": "b-shockley-queisser-thermodynamic-limit", "target": "u-solar-cell-efficiency-practical-loss-mechanisms", "relation": "related_unknown" }, { "source": "b-shockley-queisser-thermodynamic-limit", "target": "h-tandem-cell-thermodynamic-optimum-bandgap-pairing", "relation": "related_hypothesis" }, { "source": "b-thermoacoustics-heat-engines", "target": "u-thermoacoustic-engine-efficiency-scaling", "relation": "related_unknown" }, { "source": "b-thermoacoustics-heat-engines", "target": "h-thermoacoustic-travelling-wave-carnot-approach", "relation": "related_hypothesis" }, { "source": "b-kuramoto-synchronization", "target": 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"target": "u-turbulence-market-reynolds-analogue", "relation": "related_unknown" }, { "source": "b-minority-game-economics", "target": "h-market-crash-turbulent-transition", "relation": "related_hypothesis" }, { "source": "b-minority-game-economics", "target": "h-minority-game-quasispecies-duality", "relation": "related_hypothesis" }, { "source": "b-openalex-stat-mech-agency-costs", "target": "u-agency-cost-entropy-maximization", "relation": "related_unknown" }, { "source": "b-openalex-stat-mech-agency-costs", "target": "h-firm-equilibrium-stat-mech-analogy", "relation": "related_hypothesis" }, { "source": "b-spin-glass-replica-x-factor-covariance-clustering-finance", "target": "u-spin-glass-rmt-factor-clustering-limits", "relation": "related_unknown" }, { "source": "b-spin-glass-replica-x-factor-covariance-clustering-finance", "target": "h-replica-sparsity-predicts-factor-eigenvalue-noise-bulk", "relation": "related_hypothesis" }, { "source": "b-turbulence-financial-markets", 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"u-sonoluminescence-emission-mechanism-state-resolved", "relation": "related_unknown" }, { "source": "b-sonoluminescence-x-acoustic-cavitation-collapse", "target": "h-spectral-linewidth-scales-with-collapse-shock-mach-estimate", "relation": "related_hypothesis" }, { "source": "b-atmospheric-convection-x-rayleigh-benard", "target": "u-atmospheric-convection-x-rayleigh-benard", "relation": "related_unknown" }, { "source": "b-mantle-rheology-x-viscoelasticity", "target": "u-mantle-rheology-x-viscoelasticity", "relation": "related_unknown" }, { "source": "b-plate-tectonics-x-convection", "target": "u-plate-tectonics-x-convection", "relation": "related_unknown" }, { "source": "b-seismic-wave-x-elastic-wave", "target": "u-seismic-wave-x-elastic-wave", "relation": "related_unknown" }, { "source": "b-seismic-wave-x-elastic-wave", "target": "h-seismic-wave-x-elastic-wave", "relation": "related_hypothesis" }, { "source": "b-self-organized-criticality-x-earthquake", "target": "u-soc-earthquake-precursor-detection", "relation": "related_unknown" }, { "source": "b-thermohaline-circulation-x-buoyancy-flow", "target": "u-thermohaline-circulation-x-buoyancy-flow", "relation": "related_unknown" }, { "source": "b-entropy-arrow-of-time", "target": "u-arrow-of-time-low-entropy-origin", "relation": "related_unknown" }, { "source": "b-entropy-arrow-of-time", "target": "h-landauer-cosmological-arrow", "relation": "related_hypothesis" }, { "source": "b-landauer-information-thermodynamics", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "b-landauer-information-thermodynamics", "target": "h-brain-landauer-efficiency", "relation": "related_hypothesis" }, { "source": "b-defects-mechanical-strength", "target": "u-dislocation-avalanche-statistical-mechanics-plasticity", "relation": "related_unknown" }, { "source": "b-defects-mechanical-strength", "target": "h-dislocation-density-taylor-hardening-md-validation", "relation": "related_hypothesis" }, { "source": "b-topological-materials-band-theory", "target": "u-topological-insulator-majorana-qubit-scalability", "relation": "related_unknown" }, { "source": "b-topological-materials-band-theory", "target": "h-topological-insulator-majorana-fault-tolerant-qubit", "relation": "related_hypothesis" }, { "source": "b-acoustic-metamaterials-x-negative-refraction", "target": "u-acoustic-metamaterials-x-negative-refraction", "relation": "related_unknown" }, { "source": "b-conformal-field-theory-x-critical-phenomena", "target": "u-conformal-field-theory-x-critical-phenomena", "relation": "related_unknown" }, { "source": "b-crystallography-x-group-theory", "target": "u-crystallography-x-group-theory", "relation": "related_unknown" }, { "source": "b-neutron-star-x-nuclear-matter", "target": "u-neutron-star-x-nuclear-matter", "relation": "related_unknown" }, { "source": "b-quantum-decoherence-x-classical-emergence", "target": "u-quantum-decoherence-x-classical-emergence", "relation": "related_unknown" }, { "source": "b-quantum-field-theory-x-combinatorics", "target": "u-quantum-field-theory-x-combinatorics", "relation": "related_unknown" }, { "source": "b-renyi-entropy-x-multifractal", "target": "u-renyi-entropy-x-multifractal", "relation": "related_unknown" }, { "source": "b-solid-mechanics-x-topology-optimization", "target": "u-solid-mechanics-x-topology-optimization", "relation": "related_unknown" }, { "source": "b-soliton-x-integrable-systems", "target": "u-soliton-x-integrable-systems", "relation": "related_unknown" }, { "source": "b-soliton-x-integrable-systems", "target": "h-soliton-x-integrable-systems", "relation": "related_hypothesis" }, { "source": "b-spin-waves-x-magnons", "target": "u-spin-waves-x-magnons", "relation": "related_unknown" }, { "source": "b-topological-defects-x-homotopy", "target": "u-topological-defects-x-homotopy", "relation": "related_unknown" }, { "source": "b-black-holes-information-theory", "target": "u-black-hole-information-paradox-bulk-reconstruction", "relation": "related_unknown" }, { "source": "b-black-holes-information-theory", "target": "h-island-formula-entanglement-wedge-quantum-error-correction", "relation": "related_hypothesis" }, { "source": "b-fluid-instabilities-bifurcation", "target": "u-lorenz-attractor-universality-class", "relation": "related_unknown" }, { "source": "b-fluid-instabilities-bifurcation", "target": "h-rayleigh-benard-turbulence-bifurcation-cascade", "relation": "related_hypothesis" }, { "source": "b-noether-theorem-conservation-laws", "target": "u-noether-quantum-gravity-symmetry", "relation": "related_unknown" }, { "source": "b-noether-theorem-conservation-laws", "target": "h-noether-symmetry-breaking-new-physics", "relation": "related_hypothesis" }, { "source": "b-radiocarbon-dating-exponential-decay", "target": "u-radiocarbon-calibration-plateau-dating-precision", "relation": "related_unknown" }, { "source": "b-random-matrix-quantum-chaos", "target": "u-bgs-conjecture-general-proof", "relation": "related_unknown" }, { "source": "b-random-matrix-quantum-chaos", "target": "h-sieber-richter-pairs-bgs-proof", "relation": "related_hypothesis" }, { "source": "b-renormalization-group-fixed-points", "target": "u-rg-fixed-points-non-wilson-fisher-universality-classes", "relation": "related_unknown" }, { "source": "b-renormalization-group-fixed-points", "target": "h-rg-epsilon-expansion-convergence-nonperturbative-corrections", "relation": "related_hypothesis" }, { "source": "b-renormalization-wilson-wavelets-x-wavelet-shrinkage-denoising", "target": "u-rg-wavelet-beta-function-quantitative-map", "relation": "related_unknown" }, { "source": "b-renormalization-wilson-wavelets-x-wavelet-shrinkage-denoising", "target": "h-wavelet-subband-energy-tracks-rg-relevant-flux", "relation": "related_hypothesis" }, { "source": "b-topology-condensed-matter-tqft", "target": "u-topological-insulator-surface-states", "relation": "related_unknown" }, { "source": "b-topology-condensed-matter-tqft", "target": "h-chern-number-tis-robustness", "relation": "related_hypothesis" }, { "source": "b-topology-knot-invariants-physics", "target": "u-3manifold-invariants-completeness", "relation": "related_unknown" }, { "source": "b-topology-knot-invariants-physics", "target": "h-3manifold-invariants-topological-completeness", "relation": "related_hypothesis" }, { "source": "b-turbulence-renormalization-group", "target": "u-turbulence-anomalous-scaling-intermittency-origin", "relation": "related_unknown" }, { "source": "b-turbulence-renormalization-group", "target": "h-navier-stokes-rg-fixed-point-intermittency-exponents", "relation": "related_hypothesis" }, { "source": "b-zeeman-multiplet-x-rmt-level-spacing", "target": "u-zeeman-spectrum-unfolding-rmt-quantitative-test", "relation": "related_unknown" }, { "source": "b-zeeman-multiplet-x-rmt-level-spacing", "target": "h-zeeman-multiplet-spacing-shows-quantum-chaos-statistics", "relation": "related_hypothesis" }, { "source": "b-scale-free-networks-criticality", "target": "u-scale-free-brain-connectome-criticality", "relation": "related_unknown" }, { "source": "b-scale-free-networks-criticality", "target": "u-pathogen-coevolution-network-percolation", "relation": "related_unknown" }, { "source": "b-scale-free-networks-criticality", "target": "h-scale-free-criticality-brain-hub-vulnerability", "relation": "related_hypothesis" }, { "source": "b-criticality-neuroscience", "target": "u-brain-criticality-function", "relation": "related_unknown" }, { "source": "b-criticality-neuroscience", "target": "h-criticality-conscious-integration", "relation": "related_hypothesis" }, { "source": "b-fluid-dynamics-glymphatic", "target": "u-glymphatic-csf-clearance-sleep-deprivation-rate", "relation": "related_unknown" }, { "source": "b-fluid-dynamics-glymphatic", "target": "h-csf-pulsatile-flow-amyloid-clearance-sleep-deprivation", "relation": "related_hypothesis" }, { "source": "b-hopfield-spin-glass", "target": "u-hopfield-modern-attention-biological-plausibility", "relation": "related_unknown" }, { "source": "b-hopfield-spin-glass", "target": "h-dense-hopfield-transformer-attention-unified", "relation": "related_hypothesis" }, { "source": "b-materials-consciousness-criticality", "target": "u-hard-problem-consciousness", "relation": "related_unknown" }, { "source": "b-materials-consciousness-criticality", "target": "u-amorphous-metal-magnetism", "relation": "related_unknown" }, { "source": "b-materials-consciousness-criticality", "target": "h-recurrent-processing-consciousness", "relation": "related_hypothesis" }, { "source": "b-poisson-counting-process-x-decay-spike-train-likelihood", "target": "u-neural-decay-poisson-deviation-shared-overdispersion-tests", "relation": "related_unknown" }, { "source": "b-poisson-counting-process-x-decay-spike-train-likelihood", "target": "h-time-rescaled-residuals-separate-poisson-from-bursty-counting-systems", "relation": "related_hypothesis" }, { "source": "b-quantum-biology-neural-computation", "target": "u-quantum-decoherence-microtubule-physiological-temperature-measured", "relation": "related_unknown" }, { "source": "b-quantum-biology-neural-computation", "target": "h-orch-or-quantum-consciousness-decoherence-timescale-refutes", "relation": "related_hypothesis" }, { "source": "b-quantum-zeno-x-measurement", "target": "u-quantum-zeno-measurement-neural-interruption", "relation": "related_unknown" }, { "source": "b-quantum-zeno-x-measurement", "target": "h-quantum-zeno-like-slowing-in-attention-networks", "relation": "related_hypothesis" }, { "source": "b-spin-waves-neural-synchronization", "target": "u-perceptual-binding-problem", "relation": "related_unknown" }, { "source": "b-spin-waves-neural-synchronization", "target": "u-neural-correlates-self", "relation": "related_unknown" }, { "source": "b-stochastic-resonance", "target": "u-stochastic-resonance-neural-tuning", "relation": "related_unknown" }, { "source": "b-stochastic-resonance", "target": "h-sensory-noise-sr-optimality", "relation": "related_hypothesis" }, { "source": "b-synchronization-circadian", "target": "u-circadian-kuramoto-jet-lag-dynamics", "relation": "related_unknown" }, { "source": "b-synchronization-circadian", "target": "u-soc-universality-class-brain", "relation": "related_unknown" }, { "source": "b-synchronization-circadian", "target": "h-circadian-synchrony-kuramoto-critical-coupling", "relation": "related_hypothesis" }, { "source": "b-percolation-oncology", "target": "u-tumor-containment-percolation", "relation": "related_unknown" }, { "source": "b-percolation-oncology", "target": "h-adaptive-therapy-percolation-threshold", "relation": "related_hypothesis" }, { "source": "b-higgs-superconductivity", "target": "u-higgs-mode-high-tc-superconductors", "relation": "related_unknown" }, { "source": "b-landau-theory-universality", "target": "u-landau-theory-neural-criticality-order-parameter", "relation": "related_unknown" }, { "source": "b-landau-theory-universality", "target": "u-higgs-mode-high-tc-superconductors", "relation": "related_unknown" }, { "source": "b-landau-theory-universality", "target": "h-criticality-conscious-integration", "relation": "related_hypothesis" }, { "source": "b-landau-theory-universality", "target": "h-landau-neural-transition-measurability", "relation": "related_hypothesis" }, { "source": "b-ising-social-dynamics", "target": "u-social-ising-universality", "relation": "related_unknown" }, { "source": "b-ising-social-dynamics", "target": "h-norm-cascade-ising-ew", "relation": "related_hypothesis" }, { "source": "b-statistical-physics-x-social-science", "target": "u-social-critical-temperature-empirical", "relation": "related_unknown" }, { "source": "b-crowd-dynamics-social-force-model", "target": "u-crowd-dynamics-panic-transitions", "relation": "related_unknown" }, { "source": "b-crowd-dynamics-social-force-model", "target": "h-crowd-dynamics-lane-formation-critical-density", "relation": "related_hypothesis" }, { "source": "b-order-book-market-microstructure", "target": "u-order-book-flash-crash-phase-transition-mechanism", "relation": "related_unknown" }, { "source": "b-order-book-market-microstructure", "target": "h-order-book-square-root-impact-universal-liquidity", "relation": "related_hypothesis" }, { "source": "b-percolation-rumor-spreading", "target": "u-misinformation-correction-asymmetry", "relation": "related_unknown" }, { "source": "b-percolation-rumor-spreading", "target": "h-misinformation-emotional-valence-persistence", "relation": "related_hypothesis" }, { "source": "b-schelling-phase-separation", "target": "u-cascade-threshold-infrastructure", "relation": "related_unknown" }, { "source": "b-schelling-phase-separation", "target": "h-schelling-spinodal-coarsening", "relation": "related_hypothesis" }, { "source": "b-statistical-mechanics-opinion", "target": "u-opinion-dynamics-empirical-calibration-social-media-networks", "relation": "related_unknown" }, { "source": "b-statistical-mechanics-opinion", "target": "h-bounded-confidence-epsilon-polarization-social-media-filter-bubbles", "relation": "related_hypothesis" }, { "source": "b-urban-scaling-statistical-physics", "target": "u-urban-scaling-law-exponent-inequality-cultural-variation", "relation": "related_unknown" }, { "source": "b-urban-scaling-statistical-physics", "target": "h-urban-superlinear-scaling-social-interaction-fractal-road-network", "relation": "related_hypothesis" }, { "source": "b-adiabatic-elimination-x-gene-circuit-model-reduction", "target": "u-validity-limits-of-adiabatic-elimination-in-noisy-gene-circuits", "relation": "related_unknown" }, { "source": "b-adiabatic-elimination-x-gene-circuit-model-reduction", "target": "h-adiabatic-elimination-preserves-switching-time-statistics-in-gene-circuit-surrogates", "relation": "related_hypothesis" }, { "source": "b-hawking-radiation-unruh-effect", "target": "u-hawking-unruh-experimental-detection", "relation": "related_unknown" }, { "source": "b-laser-cooling-doppler-optical-molasses", "target": "u-laser-cooling-sub-doppler-quantum-limit", "relation": "related_unknown" }, { "source": "b-lymphatic-drainage-interstitial-fluid", "target": "u-lymphatic-valve-gating-pressure-threshold", "relation": "related_unknown" }, { "source": "b-lymphatic-drainage-interstitial-fluid", "target": "h-starling-oncotic-reversal-lymphatic-dependence", "relation": "related_hypothesis" }, { "source": "b-prospect-theory-loss-aversion", "target": "h-prospect-theory-neural-encoding", "relation": "related_hypothesis" }, { "source": "b-agent-based-surrogate-x-intervention-optimization", "target": "u-agent-surrogate-policy-optimization-behavioral-fidelity", "relation": "related_unknown" }, { "source": "b-agent-based-surrogate-x-intervention-optimization", "target": "h-agent-surrogate-optimization-reduces-intervention-regret", "relation": "related_hypothesis" }, { "source": "b-epidemiological-aging-demographic-frailty", "target": "u-frailty-model-biological-age-calibration", "relation": "related_unknown" }, { "source": "b-epidemiological-aging-demographic-frailty", "target": "h-frailty-model-mortality-deceleration-test", "relation": "related_hypothesis" }, { "source": "b-quantum-biology-navigation", "target": "u-quantum-biology-decoherence", "relation": "related_unknown" }, { "source": "b-qaoa-x-classical-surrogate-combinatorial-optimization", "target": "u-qaoa-depth-generalization-vs-classical-baselines", "relation": "related_unknown" }, { "source": "b-qaoa-x-classical-surrogate-combinatorial-optimization", "target": "h-qaoa-parameter-transfer-improves-surrogate-warm-starts", "relation": "related_hypothesis" }, { "source": "b-qkd-information-theoretic-security", "target": "u-qkd-practical-implementation-side-channels", "relation": "related_unknown" }, { "source": "b-qkd-information-theoretic-security", "target": "h-qkd-satellite-global-scale-feasibility", "relation": "related_hypothesis" }, { "source": "b-stabilizer-codes-fault-tolerance", "target": "u-quantum-error-correction-overhead-reduction", "relation": "related_unknown" }, { "source": "b-stabilizer-codes-fault-tolerance", "target": "h-surface-code-practical-threshold-2030", "relation": "related_hypothesis" }, { "source": "b-continuous-time-qwalk-x-grover-spatial-search-geometry", "target": "u-ctqw-grover-geometry-transferability", "relation": "related_unknown" }, { "source": "b-continuous-time-qwalk-x-grover-spatial-search-geometry", "target": "h-johnson-graph-spectral-gap-predicts-ctqw-search-plateau", "relation": "related_hypothesis" }, { "source": "b-quantum-annealing-simulated-annealing", "target": "u-quantum-annealing-simulated", "relation": "related_unknown" }, { "source": "b-quantum-annealing-simulated-annealing", "target": "h-quantum-annealing-simulated-annealing", "relation": "related_hypothesis" }, { "source": "b-quantum-walks-random-walks", "target": "u-quantum-walk-decoherence-practical-speedup", "relation": "related_unknown" }, { "source": "b-quantum-walks-random-walks", "target": "h-quantum-walk-spatial-search-optimal", "relation": "related_hypothesis" }, { "source": "b-topological-quantum-computing-anyons", "target": "u-non-abelian-anyons-topological-qc", "relation": "related_unknown" }, { "source": "b-quantum-coherence-photosynthesis", "target": "u-quantum-coherence-physiological-role", "relation": "related_unknown" }, { "source": "b-quantum-coherence-photosynthesis", "target": "h-fmo-enaqt-efficiency", "relation": "related_hypothesis" }, { "source": "b-quantum-tunneling-enzyme-catalysis", "target": "u-quantum-tunneling-enzyme-room-temperature-scope", "relation": "related_unknown" }, { "source": "b-quantum-tunneling-enzyme-catalysis", "target": "h-protein-dynamics-optimize-quantum-tunneling", "relation": "related_hypothesis" }, { "source": "b-quantum-decoherence-einselection", "target": "u-quantum-darwinism-redundancy-threshold-classicality", "relation": "related_unknown" }, { "source": "b-quantum-gravity-holographic-entropy", "target": "u-holographic-entanglement-bulk-reconstruction-limits", "relation": "related_unknown" }, { "source": "b-topological-insulators-band-theory", "target": "u-majorana-zero-mode-experimental-confirmation", "relation": "related_unknown" }, { "source": "b-topological-insulators-band-theory", "target": "h-topological-qubit-fault-tolerance-threshold", "relation": "related_hypothesis" }, { "source": "b-entanglement-tensor-network-states", "target": "u-entanglement-tensor-network-complexity", "relation": "related_unknown" }, { "source": "b-representation-theory-particles", "target": "u-standard-model-representation-completeness", "relation": "related_unknown" }, { "source": "b-representation-theory-particles", "target": "h-lie-group-beyond-standard-model", "relation": "related_hypothesis" }, { "source": "b-berry-phase-x-polarization-parallel-transport-optics", "target": "u-geometric-phase-calibration-across-polarization-optics", "relation": "related_unknown" }, { "source": "b-berry-phase-x-polarization-parallel-transport-optics", "target": "h-pancharatnam-loop-area-predicts-interferometric-phase-shifts", "relation": "related_hypothesis" }, { "source": "b-photon-antibunching-sub-poissonian", "target": "u-photon-antibunching-sub-poissonian", "relation": "related_unknown" }, { "source": "b-photon-antibunching-sub-poissonian", "target": "h-photon-antibunching-sub-poissonian", "relation": "related_hypothesis" }, { "source": "b-quantum-dot-blinking-renewal-process", "target": "u-quantum-dot-blinking-power-law-mechanism", "relation": "related_unknown" }, { "source": "b-resnet-x-histopathology-domain-shift-robustness", "target": "u-resnet-histology-domain-shift-failure-modes", "relation": "related_unknown" }, { "source": "b-resnet-x-histopathology-domain-shift-robustness", "target": "h-residual-feature-normalization-reduces-histology-site-shift-error", "relation": "related_hypothesis" }, { "source": "b-physics-informed-neural-operator-x-aftershock-field-evolution", "target": "u-pino-aftershock-forecasting-uncertainty-calibration", "relation": "related_unknown" }, { "source": "b-physics-informed-neural-operator-x-aftershock-field-evolution", "target": "h-pino-aftershock-fields-improve-short-term-seismic-hazard-maps", "relation": "related_hypothesis" }, { "source": "b-hawkes-self-excitation-x-seizure-aftershock-clustering", "target": "u-hawkes-branching-ratio-seizure-cascade-threshold", "relation": "related_unknown" }, { "source": "b-hawkes-self-excitation-x-seizure-aftershock-clustering", "target": "h-hawkes-branching-threshold-predicts-seizure-clusters", "relation": "related_hypothesis" }, { "source": "b-earthquake-source-dislocation-theory", "target": "u-earthquake-nucleation-dislocation-slip-weakening", "relation": "related_unknown" }, { "source": "b-earthquake-source-dislocation-theory", "target": "h-dislocation-nucleation-length-predicts-mainshock-magnitude", "relation": "related_hypothesis" }, { "source": "b-seismology-percolation", "target": "u-earthquake-nucleation", "relation": "related_unknown" }, { "source": "b-seismology-percolation", "target": "u-earthquake-swarm-migration", "relation": "related_unknown" }, { "source": "b-earthquake-alarm-decision-x-wald-sequential-probability-ratio-test", "target": "u-earthquake-alert-threshold-sprt-under-correlated-noise", "relation": "related_unknown" }, { "source": "b-earthquake-alarm-decision-x-wald-sequential-probability-ratio-test", "target": "h-aftershock-clustering-inflates-sprt-false-alarm-rate-fixed-boundaries", "relation": "related_hypothesis" }, { "source": "b-phase-retrieval-x-cryoem-orientation-inference", "target": "u-when-does-alternating-projection-outperform-em-in-cryoem-orientation-inference", "relation": "related_unknown" }, { "source": "b-phase-retrieval-x-cryoem-orientation-inference", "target": "h-alternating-projection-warm-starts-reduce-cryoem-orientation-assignment-errors", "relation": "related_hypothesis" }, { "source": "b-social-ising-polarisation", "target": "u-political-polarisation-dynamics", "relation": "related_unknown" }, { "source": "b-social-ising-polarisation", "target": "u-social-norm-cascade-tipping-points", "relation": "related_unknown" }, { "source": "b-social-ising-polarisation", "target": "u-social-ising-universality", "relation": "related_unknown" }, { "source": "b-social-ising-polarisation", "target": "h-polarisation-ising-phase-transition", "relation": "related_hypothesis" }, { "source": "b-behavioral-immunology-pathogen-avoidance", "target": "u-behavioral-immune-system-pathogen-xenophobia-mechanism", "relation": "related_unknown" }, { "source": "b-behavioral-immunology-pathogen-avoidance", "target": "h-bis-disgust-threshold-pathogen-prevalence-calibration", "relation": "related_hypothesis" }, { "source": "b-chronobiology-social-jet-lag", "target": "u-chronotype-genetic-variants-full-population-distribution", "relation": "related_unknown" }, { "source": "b-chronobiology-social-jet-lag", "target": "h-delayed-school-start-improves-adolescent-outcomes-causally", "relation": "related_hypothesis" }, { "source": "b-cultural-evolution-darwinian", "target": "u-cultural-drift-vs-selection-detection", "relation": "related_unknown" }, { "source": "b-cultural-evolution-darwinian", "target": "h-price-equation-cultural-trait-frequency", "relation": "related_hypothesis" }, { "source": "b-cultural-evolution-dual-inheritance", "target": "u-gene-culture-coevolution-rate-modern", "relation": "related_unknown" }, { "source": "b-cultural-evolution-dual-inheritance", "target": "h-dual-inheritance-lactase-selection", "relation": "related_hypothesis" }, { "source": "b-cultural-evolution-memetics", "target": "u-cultural-evolution-rate-prediction", "relation": "related_unknown" }, { "source": "b-cultural-evolution-memetics", "target": "h-cultural-replicator-dynamics-rate", "relation": "related_hypothesis" }, { "source": "b-moral-psychology-cooperation-game-theory", "target": "u-moral-intuition-evolutionary-stability-mapping", "relation": "related_unknown" }, { "source": "b-moral-psychology-cooperation-game-theory", "target": "h-punishment-threshold-ess-moral-universality", "relation": "related_hypothesis" }, { "source": "b-social-learning-cultural-transmission", "target": "u-social-learning-cultural-transmission-mechanisms", "relation": "related_unknown" }, { "source": "b-stress-biology-social-determinants", "target": "u-epigenetic-intergenerational-transmission-social-stress", "relation": "related_unknown" }, { "source": "b-stress-biology-social-determinants", "target": "h-telomere-length-social-gradient-reversibility", "relation": "related_hypothesis" }, { "source": "b-drug-policy-pharmacoepidemiology", "target": "u-opioid-prescribing-policy-chemistry-disconnect", "relation": "related_unknown" }, { "source": "b-drug-policy-pharmacoepidemiology", "target": "h-psilocybin-rescheduling-neuroplasticity-evidence", "relation": "related_hypothesis" }, { "source": "b-urban-ecology-ses", "target": "u-urban-biodiversity-governance-regime-causality", "relation": "related_unknown" }, { "source": "b-urban-ecology-ses", "target": "h-green-infrastructure-urban-cooling-nonlinear-threshold", "relation": "related_hypothesis" }, { "source": "b-hci-cognitive-load", "target": "u-dark-patterns-cognitive-bias-exploitation-measurement", "relation": "related_unknown" }, { "source": "b-hci-cognitive-load", "target": "h-fitts-law-bci-pointer-information-bandwidth-limit", "relation": "related_hypothesis" }, { "source": "b-human-factors-system-safety", "target": "u-human-error-organizational-accident-boundary", "relation": "related_unknown" }, { "source": "b-human-factors-system-safety", "target": "h-swiss-cheese-alignment-accident-prediction", "relation": "related_hypothesis" }, { "source": "b-social-contagion-complex-threshold", "target": "u-complex-contagion-threshold-distribution-estimation", "relation": "related_unknown" }, { "source": "b-social-contagion-complex-threshold", "target": "h-social-movement-cascade-clustered-network-advantage", "relation": "related_hypothesis" }, { "source": "b-cultural-memes-shannon-entropy", "target": "u-meme-channel-capacity-measurement", "relation": "related_unknown" }, { "source": "b-cultural-memes-shannon-entropy", "target": "h-meme-channel-social-media-bias", "relation": "related_hypothesis" }, { "source": "b-privacy-differential-privacy", "target": "u-differential-privacy-utility-tight-bound", "relation": "related_unknown" }, { "source": "b-privacy-differential-privacy", "target": "h-differential-privacy-hypothesis-testing-connection", "relation": "related_hypothesis" }, { "source": "b-agent-based-modeling-emergent-institutions", "target": "u-abm-calibration-empirical-social-science-validation", "relation": "related_unknown" }, { "source": "b-agent-based-modeling-emergent-institutions", "target": "h-schelling-abm-segregation-threshold-real-world-preference-calibration", "relation": "related_hypothesis" }, { "source": "b-algorithmic-fairness-social-choice", "target": "u-fairness-impossibility-optimal-tradeoff", "relation": "related_unknown" }, { "source": "b-algorithmic-fairness-social-choice", "target": "h-causal-fairness-resolves-impossibility-tradeoffs", "relation": "related_hypothesis" }, { "source": "b-auction-theory-mechanism-design", "target": "u-cascade-threshold-infrastructure", "relation": "related_unknown" }, { "source": "b-auction-theory-mechanism-design", "target": "h-mechanism-design-spectrum-auctions-efficiency", "relation": "related_hypothesis" }, { "source": "b-bargaining-theory-negotiation", "target": "u-bargaining-power-measurement-real-world-negotiations", "relation": "related_unknown" }, { "source": "b-bargaining-theory-negotiation", "target": "h-outside-option-effect-causal-wage-effect", "relation": "related_hypothesis" }, { "source": "b-condorcet-paradox-voting-cycles", "target": "u-collective-action-without-authority", "relation": "related_unknown" }, { "source": "b-condorcet-paradox-voting-cycles", "target": "h-collective-action-ostrom-design-principles-v2", "relation": "related_hypothesis" }, { "source": "b-network-centrality-social-influence", "target": "u-network-centrality-temporal-dynamics-influence", "relation": "related_unknown" }, { "source": "b-network-centrality-social-influence", "target": "h-eigenvector-centrality-superspreader-epidemic-prediction", "relation": "related_hypothesis" }, { "source": "b-prediction-markets-information-aggregation", "target": "u-prediction-market-thin-market-accuracy-limits", "relation": "related_unknown" }, { "source": "b-prediction-markets-information-aggregation", "target": "h-lmsr-automated-market-maker-dominates-polls-epistemic-accuracy", "relation": "related_hypothesis" }, { "source": "b-structural-holes-brokerage", "target": "u-structural-holes-dynamics-network-evolution-brokerage-persistence", "relation": "related_unknown" }, { "source": "b-structural-holes-brokerage", "target": "h-brokerage-advantage-diminishes-with-organizational-transparency", "relation": "related_hypothesis" }, { "source": "b-voter-model-consensus", "target": "u-voter-model-zealots-persistent-minorities", "relation": "related_unknown" }, { "source": "b-voter-model-consensus", "target": "h-network-community-structure-drives-polarization", "relation": "related_hypothesis" }, { "source": "b-voting-theory-social-choice", "target": "u-strategic-voting-frequency-real-elections-empirical-magnitude", "relation": "related_unknown" }, { "source": "b-voting-theory-social-choice", "target": "h-approval-voting-reduces-strategic-manipulation-vs-plurality", "relation": "related_hypothesis" }, { "source": "b-wisdom-of-crowds-condorcet", "target": "u-wisdom-of-crowds-condorcet", "relation": "related_unknown" }, { "source": "b-wisdom-of-crowds-condorcet", "target": "h-wisdom-of-crowds-condorcet", "relation": "related_hypothesis" }, { "source": "b-homophily-structural-segregation", "target": "u-homophily-echo-chamber-causality", "relation": "related_unknown" }, { "source": "b-homophily-structural-segregation", "target": "h-network-assortativity-predicts-misinformation-spread-rate", "relation": "related_hypothesis" }, { "source": "b-social-capital-network-centrality", "target": "u-social-capital-causal-vs-correlational", "relation": "related_unknown" }, { "source": "b-social-capital-network-centrality", "target": "h-structural-holes-income-mobility-mediation", "relation": "related_hypothesis" }, { "source": "b-cascade-failures-interdependent-networks", "target": "u-cascade-threshold-infrastructure", "relation": "related_unknown" }, { "source": "b-complexity-economics-far-equilibrium", "target": "u-complexity-economics-policy-design-far-equilibrium", "relation": "related_unknown" }, { "source": "b-complexity-economics-far-equilibrium", "target": "h-complexity-economics-minority-game-market-ecology", "relation": "related_hypothesis" }, { "source": "b-econophysics-wealth-distribution", "target": "u-econophysics-pareto-index-cross-national-variation", "relation": "related_unknown" }, { "source": "b-econophysics-wealth-distribution", "target": "h-multiplicative-noise-pareto-exponent-capital-tax-rate", "relation": "related_hypothesis" }, { "source": "b-opinion-dynamics-ising", "target": "u-opinion-dynamics-critical-homophily", "relation": "related_unknown" }, { "source": "b-opinion-dynamics-ising", "target": "h-social-ising-polarization-transition", "relation": "related_hypothesis" }, { "source": "b-opinion-dynamics-ising", "target": "h-polarisation-ising-phase-transition", "relation": "related_hypothesis" }, { "source": "b-opinion-dynamics-ising", "target": "h-norm-cascade-ising-ew", "relation": "related_hypothesis" }, { "source": "b-social-stratification-statistical-mechanics", "target": "u-econophysics-wealth-distribution-mechanism", "relation": "related_unknown" }, { "source": "b-social-stratification-statistical-mechanics", "target": "h-wealth-distribution-boltzmann-savings-propensity", "relation": "related_hypothesis" }, { "source": "b-sociophysics-cultural-dynamics", "target": "u-axelrod-model-empirical-validation-cultural-diversity", "relation": "related_unknown" }, { "source": "b-sociophysics-cultural-dynamics", "target": "h-cultural-phase-transition-globalization-diversity-paradox", "relation": "related_hypothesis" }, { "source": "b-traffic-flow-fluid-dynamics", "target": "u-traffic-jam-phantom-formation-threshold", "relation": "related_unknown" }, { "source": "b-traffic-flow-fluid-dynamics", "target": "h-traffic-flow-turing-instability-stop-go", "relation": "related_hypothesis" }, { "source": "b-survey-causal-inference", "target": "u-natural-experiment-validity-economics", "relation": "related_unknown" }, { "source": "b-survey-causal-inference", "target": "u-business-cycle-prediction-limits", "relation": "related_unknown" }, { "source": "b-liquid-crystals-frank-elasticity", "target": "u-liquid-crystals-frank-elasticity", "relation": "related_unknown" }, { "source": "b-liquid-crystals-frank-elasticity", "target": "h-liquid-crystals-frank-elasticity", "relation": "related_hypothesis" }, { "source": "b-granular-matter-jamming-transition", "target": "u-jamming-transition-universality-class", "relation": "related_unknown" }, { "source": "b-granular-matter-jamming-transition", "target": "h-jamming-transition-critical-exponents", "relation": "related_hypothesis" }, { "source": "b-nematic-ordering-maier-saupe-mean-field", "target": "u-nematic-ordering-fluctuation-corrections-maier-saupe", "relation": "related_unknown" }, { "source": "b-boltzmann-shannon-entropy", "target": "u-boltzmann-shannon-nonequilibrium-bridge", "relation": "related_unknown" }, { "source": "b-boltzmann-shannon-entropy", "target": "h-maxent-nonequilibrium-statistical-mechanics", "relation": "related_hypothesis" }, { "source": "b-stochastic-thermodynamics-fluctuation-theorems", "target": "u-fluctuation-theorem-biological-motors", "relation": "related_unknown" }, { "source": "b-stochastic-thermodynamics-fluctuation-theorems", "target": "h-landauer-limit-biological-computation", "relation": "related_hypothesis" }, { "source": "b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling", "target": "u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models", "relation": "related_unknown" }, { "source": "b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling", "target": "h-kramers-moyal-surrogates-improve-tumor-state-transition-forecast-calibration", "relation": "related_hypothesis" }, { "source": "b-thermodynamic-uncertainty-relation-x-estimation-precision", "target": "u-thermodynamic-uncertainty-bound-biochemical-estimators", "relation": "related_unknown" }, { "source": "b-thermodynamic-uncertainty-relation-x-estimation-precision", "target": "h-tur-constrained-estimators-predict-atp-cost-precision-frontier", "relation": "related_hypothesis" }, { "source": "b-fisher-information-evolution", "target": "u-fisher-natural-gradient-evolution", "relation": "related_unknown" }, { "source": "b-fisher-information-evolution", "target": "h-fisher-speed-limit-selection", "relation": "related_hypothesis" }, { "source": "b-fisher-information-evolution", "target": "h-quantum-compass-precision", "relation": "related_hypothesis" }, { "source": "b-deseq2-shrinkage-estimation-x-low-count-clinical-biomarker-surveillance", "target": "u-dispersion-shrinkage-stability-under-clinical-batch-effects", "relation": "related_unknown" }, { "source": "b-deseq2-shrinkage-estimation-x-low-count-clinical-biomarker-surveillance", "target": "h-deseq2-style-shrinkage-reduces-false-alerts-in-low-count-clinical-monitoring", "relation": "related_hypothesis" }, { "source": "b-elastic-net-regularization-x-polygenic-risk-model-stability", "target": "u-ancestry-shift-sensitivity-of-elastic-net-prs", "relation": "related_unknown" }, { "source": "b-elastic-net-regularization-x-polygenic-risk-model-stability", "target": "h-elastic-net-prs-retraining-with-ancestry-balancing-reduces-calibration-drift", "relation": "related_hypothesis" }, { "source": "b-laplace-approximation-x-clinical-trial-adaptive-enrichment", "target": "u-prior-sensitivity-of-laplace-based-interim-decision-rules", "relation": "related_unknown" }, { "source": "b-laplace-approximation-x-clinical-trial-adaptive-enrichment", "target": "h-laplace-approximated-interim-rules-improve-enrichment-decision-efficiency", "relation": "related_hypothesis" }, { "source": "b-bayesian-inference-stat-mech", "target": "u-posterior-landscape-multimodality", "relation": "related_unknown" }, { "source": "b-optimal-transport-barycenters-x-multiomic-patient-alignment", "target": "u-transport-cost-selection-in-cross-platform-multiomic-alignment", "relation": "related_unknown" }, { "source": "b-optimal-transport-barycenters-x-multiomic-patient-alignment", "target": "h-ot-barycenter-alignment-improves-cross-cohort-multiomic-risk-stratification", "relation": "related_hypothesis" }, { "source": "b-optimal-transport-x-single-cell-developmental-lineage-mapping", "target": "u-cost-matrix-misspecification-in-optimal-transport-lineage-maps", "relation": "related_unknown" }, { "source": "b-optimal-transport-x-single-cell-developmental-lineage-mapping", "target": "h-optimal-transport-lineage-couplings-improve-fate-prediction-calibration", "relation": "related_hypothesis" }, { "source": "b-variational-autoencoders-x-single-cell-latent-state-denoising", "target": "u-latent-space-collapse-risks-in-single-cell-vae-models", "relation": "related_unknown" }, { "source": "b-variational-autoencoders-x-single-cell-latent-state-denoising", "target": "h-beta-vae-regularization-improves-single-cell-state-separability", "relation": "related_hypothesis" }, { "source": "b-markov-jump-processes-x-cell-state-switching-therapy-design", "target": "u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models", "relation": "related_unknown" }, { "source": "b-markov-jump-processes-x-cell-state-switching-therapy-design", "target": "h-markov-jump-therapy-policies-reduce-relapse-prone-cell-state-occupancy", "relation": "related_hypothesis" }, { "source": "b-synthetic-genetics-xna-alphabet", "target": "u-xna-expanded-genetic-alphabet-catalysis", "relation": "related_unknown" }, { "source": "b-synthetic-genetics-xna-alphabet", "target": "h-xna-ribozyme-catalytic-efficiency-backbone-independence", "relation": "related_hypothesis" }, { "source": "b-simclr-x-multiomics-latent-alignment", "target": "u-simclr-multiomics-batch-effect-collapse-risk", "relation": "related_unknown" }, { "source": "b-simclr-x-multiomics-latent-alignment", "target": "h-contrastive-pretraining-improves-multiomics-transfer-stability", "relation": "related_hypothesis" }, { "source": "b-carbon-capture-entropy-cost", "target": "u-dac-sorbent-entropy-production-mechanism", "relation": "related_unknown" }, { "source": "b-carbon-capture-entropy-cost", "target": "h-mof-sorbent-approaches-dac-thermodynamic-limit", "relation": "related_hypothesis" }, { "source": "b-maxwells-demon-computation", "target": "u-landauer-bound-experimental-verification", "relation": "related_unknown" }, { "source": "b-maxwells-demon-computation", "target": "h-reversible-computing-landauer-limit", "relation": "related_hypothesis" }, { "source": "b-viral-quasispecies-x-nk-rugged-landscape", "target": "u-quasispecies-nk-parameter-identifiability", "relation": "related_unknown" }, { "source": "b-viral-quasispecies-x-nk-rugged-landscape", "target": "h-viral-quasispecies-x-nk-rugged-landscape", "relation": "related_hypothesis" }, { "source": "b-viral-quasispecies-error-threshold", "target": "u-quasispecies-fitness-landscape-mapping", "relation": "related_unknown" }, { "source": "b-viral-quasispecies-error-threshold", "target": "h-lethal-mutagenesis-antiviral-threshold", "relation": "related_hypothesis" }, { "source": "b-protein-language-model-x-viral-escape-fitness-landscape", "target": "u-protein-language-model-viral-escape-epistasis-misspecification", "relation": "related_unknown" }, { "source": "b-protein-language-model-x-viral-escape-fitness-landscape", "target": "h-protein-language-model-priors-improve-viral-escape-forecasting", "relation": "related_hypothesis" }, { "source": "b-magma-fragmentation-rheology", "target": "u-magma-fragmentation-rheology-threshold", "relation": "related_unknown" }, { "source": "h-3manifold-invariants-topological-completeness", "target": "u-3manifold-invariants-completeness", "relation": "addresses_unknown" }, { "source": "h-abc-conjecture-iut-verification-path", "target": "u-abc-conjecture-verification", "relation": "addresses_unknown" }, { "source": "h-aco-convergence-rate-pheromone-evaporation", "target": "u-ant-colony-optimization-convergence-rate", "relation": "addresses_unknown" }, { "source": "h-acoustic-metamaterial-cloaking-bandwidth-thickness-tradeoff", "target": "u-metamaterial-acoustic-cloaking", "relation": "addresses_unknown" }, { "source": "h-acoustic-metamaterials-x-negative-refraction", "target": "u-acoustic-metamaterials-x-negative-refraction", "relation": "addresses_unknown" }, { "source": "h-acoustic-topological-insulator-surface-states", "target": "u-phononic-crystal-3d-complete-band-gap", "relation": 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"addresses_unknown" }, { "source": "h-cherenkov-mach-prerequisite-transfer-diagnostic", "target": "u-cherenkov-mach-cone-unified-demo-transfer", "relation": "addresses_unknown" }, { "source": "h-chern-number-tis-robustness", "target": "u-topological-insulator-surface-states", "relation": "addresses_unknown" }, { "source": "h-chern-simons-theory-topological-quantum-computation", "target": "u-non-abelian-aharonov-bohm-observable-consequences", "relation": "addresses_unknown" }, { "source": "h-cholesteric-lc-structural-color-biomimetic-photonic-applications", "target": "u-active-liquid-crystal-topology-flow-coupling", "relation": "addresses_unknown" }, { "source": "h-christofides-tight-example-construction", "target": "u-tsp-approximation-barrier-metric", "relation": "addresses_unknown" }, { "source": "h-chronic-pain-glial-sensitization", "target": "u-chronic-pain-sensitization", "relation": "addresses_unknown" }, { "source": "h-circadian-clock-feeding-entrainment", "target": "u-organ-clock-synchrony", "relation": "addresses_unknown" }, { "source": "h-circadian-clock-x-feedback-oscillator", "target": "u-circadian-clock-x-feedback-oscillator", "relation": "addresses_unknown" }, { "source": "h-circadian-hopf-bifurcation-delay-oscillator", "target": "u-circadian-temperature-compensation-mechanism", "relation": "addresses_unknown" }, { "source": "h-circadian-hopf-bifurcation-period-mutation-prediction", "target": "u-circadian-temperature-compensation-mechanism", "relation": "addresses_unknown" }, { "source": "h-circadian-per3-prc-amplitude-chronotype", "target": "u-circadian-prc-individual-variation-prediction", "relation": "addresses_unknown" }, { "source": "h-circadian-synchrony-kuramoto-critical-coupling", "target": "u-circadian-kuramoto-jet-lag-dynamics", "relation": "addresses_unknown" }, { "source": "h-circuit-theory-outperforms-lcp-gene-flow-prediction", "target": "u-wildlife-corridor-percolation-threshold", "relation": "addresses_unknown" }, { "source": "h-citizen-science-validation-training-protocols", "target": "u-citizen-science-quality", "relation": "addresses_unknown" }, { "source": "h-clf-constrained-harvest-stabilizes-biomass-under-shocks", "target": "u-control-lyapunov-safe-harvest-policy-ecology", "relation": "addresses_unknown" }, { "source": "h-climate-fire-feedback-accelerates-beyond-linear-projections", "target": "u-pyroconvection-prediction-coupled-fire-atmosphere-models", "relation": "addresses_unknown" }, { "source": "h-climate-sensitivity-emergent-constraint-water-vapor", "target": "u-climate-ecs-feedback-uncertainty", "relation": "addresses_unknown" }, { "source": "h-climate-sensitivity-fat-tail-cloud-convection", "target": "u-climate-sensitivity-tails", "relation": "addresses_unknown" }, { "source": "h-cloud-feedback-low-cloud-positive", "target": "u-cloud-feedback-sign", "relation": "addresses_unknown" }, { "source": "h-cloud-seeding-hygroscopic-efficacy-mechanism", "target": "u-cloud-seeding-efficacy", "relation": 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"h-collateral-sensitivity-cycling-drug-resistance", "target": "u-fitness-landscape-drug-resistance-prediction", "relation": "addresses_unknown" }, { "source": "h-collective-action-ostrom-design-principles-v2", "target": "u-collective-action-without-authority", "relation": "addresses_unknown" }, { "source": "h-collective-action-ostrom-design-principles", "target": "u-collective-action-without-authority", "relation": "addresses_unknown" }, { "source": "h-collective-memory-social-network-transmission", "target": "u-collective-memory-formation", "relation": "addresses_unknown" }, { "source": "h-color-emotion-universal-hue-valence", "target": "u-color-emotion-mapping", "relation": "addresses_unknown" }, { "source": "h-compact-algebra-first-sequence-improves-uap-transfer", "target": "u-uap-stone-weierstrass-pedagogy-misconception-rate", "relation": "addresses_unknown" }, { "source": "h-complement-mediated-synapse-loss-drives-alzheimers-cognitive-decline", "target": 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"relation": "addresses_unknown" }, { "source": "h-computational-irreducibility-turbulence-pspace", "target": "u-computational-irreducibility-physical-systems-scope", "relation": "addresses_unknown" }, { "source": "h-computational-psychiatry-aberrant-precision-antipsychotic-mechanism", "target": "u-computational-psychiatry-treatment-response-prediction", "relation": "addresses_unknown" }, { "source": "h-conformal-field-theory-x-critical-phenomena", "target": "u-conformal-field-theory-x-critical-phenomena", "relation": "addresses_unknown" }, { "source": "h-connectome-graph-laplacian-spectral", "target": "u-connectome-spectral-laplacian", "relation": "addresses_unknown" }, { "source": "h-connectome-hub-vulnerability-neurodegeneration", "target": "u-connectome-neurodegeneration-spread-rate", "relation": "addresses_unknown" }, { "source": "h-connectome-hub-vulnerability-neurodegeneration", "target": "u-alzheimer-causal-biomarkers", "relation": "addresses_unknown" }, { "source": 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"h-correlated-port-noise-matrix-lowers-effective-nf-two-port", "target": "u-rf-noise-figure-two-port-correlation-matrix-room-temperature", "relation": "addresses_unknown" }, { "source": "h-cortical-eigenmodes-universal-resting-state-basis", "target": "u-neural-field-theory-empirical-connectome-validation", "relation": "addresses_unknown" }, { "source": "h-cortical-sulcal-topological-conservation", "target": "u-cortical-folding-topology", "relation": "addresses_unknown" }, { "source": "h-cortical-sulcal-topological-conservation", "target": "u-topological-morphogenesis", "relation": "addresses_unknown" }, { "source": "h-cosmic-string-cmb-power-spectrum", "target": "u-cosmic-string-networks", "relation": "addresses_unknown" }, { "source": "h-cosmic-string-gwb-signature", "target": "u-cosmic-string-networks", "relation": "addresses_unknown" }, { "source": "h-count-novelty-scales-bayesian-information-gain-proxy", "target": "u-rl-novelty-bonus-information-gain-mapping", "relation": 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"h-deep-ocean-carbon-biological-pump-efficiency", "target": "u-deep-ocean-carbon-sequestration", "relation": "addresses_unknown" }, { "source": "h-deep-water-cycle-mantle-surface-coupling", "target": "u-deep-earth-water-cycle", "relation": "addresses_unknown" }, { "source": "h-default-mode-network-prospective-memory", "target": "u-default-mode-network-function", "relation": "addresses_unknown" }, { "source": "h-defect-topology-predicts-coarsening-scaling-exponents", "target": "u-topological-defect-classification-nonequilibrium-condensed-matter", "relation": "addresses_unknown" }, { "source": "h-deformation-quantization-symplectic-bridge", "target": "u-symplectic-quantization-gap", "relation": "addresses_unknown" }, { "source": "h-degrowth-wellbeing-decoupling", "target": "u-degrowth-economic-viability", "relation": "addresses_unknown" }, { "source": "h-delay-embedding-indicators-improve-icu-deterioration-lead-time", "target": 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"h-diffusion-downscaling-improves-extreme-precipitation-fidelity", "target": "u-diffusion-downscaling-physical-consistency-under-shift", "relation": "addresses_unknown" }, { "source": "h-diffusion-limited-aggregation-x-fractal-growth", "target": "u-diffusion-limited-aggregation-x-fractal-growth", "relation": "addresses_unknown" }, { "source": "h-diffusion-models-x-stochastic-processes", "target": "u-diffusion-models-x-stochastic-processes", "relation": "addresses_unknown" }, { "source": "h-diffusion-queueing-threshold-policies-reduce-ed-boarding-time-variance", "target": "u-heavy-traffic-thresholds-for-ed-crowding-intervention-policies", "relation": "addresses_unknown" }, { "source": "h-diffusive-interface-models-predict-shoreline-roughening-exponents", "target": "u-coastline-roughness-effective-surface-tension", "relation": "addresses_unknown" }, { "source": "h-dipole-weakening-precursor-reversal", "target": "u-geomagnetic-reversal-trigger-mechanism", "relation": "addresses_unknown" 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"h-dna-knot-complexity-aging", "target": "u-topoisomerase-knot-selection", "relation": "addresses_unknown" }, { "source": "h-dna-replication-optimal-mutation-rate", "target": "u-dna-replication-x-error-correction", "relation": "addresses_unknown" }, { "source": "h-doob-convergence-rate-scientific-inference", "target": "u-bayesian-convergence-prior-dependence", "relation": "addresses_unknown" }, { "source": "h-doppler-carry-yield-curve-steepness-speculative-parallels", "target": "u-doppler-redshift-option-carry-speculative-analogy", "relation": "addresses_unknown" }, { "source": "h-double-network-hydrogel-toughness-sacrificial-bond", "target": "u-hydrogel-fracture-toughness-network-structure", "relation": "addresses_unknown" }, { "source": "h-droplet-split-binomial-partition-fission-alignment", "target": "u-droplet-splitting-variance-biology-alignment", "relation": "addresses_unknown" }, { "source": "h-dual-inheritance-lactase-selection", "target": 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"h-extinction-debt-master-equation-prediction", "target": "u-demographic-stochasticity-extinction-threshold", "relation": "addresses_unknown" }, { "source": "h-extinction-time-exponential-k-demographic-stochasticity-confirmed", "target": "u-extinction-debt-lag-time-empirical-quantification-fragmented-landscapes", "relation": "addresses_unknown" }, { "source": "h-extreme-value-theory-x-risk-modeling", "target": "u-extreme-value-theory-x-risk-modeling", "relation": "addresses_unknown" }, { "source": "h-face-recognition-fusiform-holistic-coding", "target": "u-face-recognition-substrate", "relation": "addresses_unknown" }, { "source": "h-fano-q-factor-tracks-radiative-darkness-order-parameter", "target": "u-fano-metamaterial-dark-mode-q-engineering", "relation": "addresses_unknown" }, { "source": "h-federated-ensembles-improve-cross-site-epidemic-generalization", "target": "u-federated-epidemic-model-drift-across-sites", "relation": "addresses_unknown" }, { "source": "h-feigenbaum-universality-quantum-maps-period-doubling", "target": "u-chaos-quantum-correspondence-lyapunov-exponents", "relation": "addresses_unknown" }, { "source": "h-fermentation-nad-ratio-pathway-selection-thermodynamic", "target": "u-fermentation-thermodynamic-efficiency-limit", "relation": "addresses_unknown" }, { "source": "h-ferroelectric-fatigue-oxygen-vacancy-pinning", "target": "u-ferroelectric-fatigue-mechanism", "relation": "addresses_unknown" }, { "source": "h-fibrosis-reversibility-mechanosensing", "target": "u-organ-fibrosis-reversibility", "relation": "addresses_unknown" }, { "source": "h-financial-contagion-core-periphery-topology", "target": "u-financial-contagion-network-topology", "relation": "addresses_unknown" }, { "source": "h-financialisation-investment-crowding", "target": "u-financialisation-real-economy-effects", "relation": "addresses_unknown" }, { "source": "h-fire-regime-threshold-fuel-structure", "target": "u-fire-regime-ecological-threshold", "relation": "addresses_unknown" }, { "source": "h-firm-equilibrium-stat-mech-analogy", "target": "u-agency-cost-entropy-maximization", "relation": "addresses_unknown" }, { "source": "h-first-passage-hitting-time-models-extend-clinical-warning-lead-time", "target": "u-first-passage-warning-times-clinical-deterioration-model-shift", "relation": "addresses_unknown" }, { "source": "h-fiscal-multiplier-credit-constraints", "target": "u-multiplier-fiscal-policy", "relation": "addresses_unknown" }, { "source": "h-fisher-information-optimized-eit-electrodes-improve-lesion-detectability", "target": "u-eit-fisher-information-electrode-geometry-optimality", "relation": "addresses_unknown" }, { "source": "h-fisher-kpp-front-models-improve-wound-closure-time-forecasting", "target": "u-patient-specific-front-speed-estimation-in-wound-healing-kpp-models", "relation": "addresses_unknown" }, { "source": "h-fisher-optimal-dose-grid-reduces-parameter-variance-simulation", "target": "u-dose-spacing-fisher-information-design-trial-calibration", "relation": "addresses_unknown" }, { "source": "h-fisher-ricci-price-covariance-analogy-calibration", "target": "u-ricci-price-covariance-analogy-scope", "relation": "addresses_unknown" }, { "source": "h-fisher-speed-limit-selection", "target": "u-fisher-natural-gradient-evolution", "relation": "addresses_unknown" }, { "source": "h-fitts-law-bci-pointer-information-bandwidth-limit", "target": "u-dark-patterns-cognitive-bias-exploitation-measurement", "relation": "addresses_unknown" }, { "source": "h-flagellar-motor-proton-coupling-cryo", "target": "u-flagellar-motor-x-rotary-engine", "relation": "addresses_unknown" }, { "source": "h-flagellar-motor-stator-assembly-pmf-dependent-mechanosensing", "target": "u-flagellar-motor-stator-number-regulation-pmf", "relation": "addresses_unknown" }, { "source": "h-flexible-stoichiometry-p-limitation-gyre", "target": "u-redfield-ratio-variability-drivers", "relation": "addresses_unknown" 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"u-quantum-coherence-physiological-role", "relation": "addresses_unknown" }, { "source": "h-food-web-motif-frequency-predicts-cascade-strength", "target": "u-trophic-cascade-motif-universality", "relation": "addresses_unknown" }, { "source": "h-forest-fire-soc-beta-exponent-climate-invariance", "target": "u-forest-fire-soc-climate-change-modification", "relation": "addresses_unknown" }, { "source": "h-fourier-neural-operator-surrogates-accelerate-groundwater-inversion-with-calibrated-uncertainty", "target": "u-neural-operator-generalization-groundwater-boundary-shift", "relation": "addresses_unknown" }, { "source": "h-fourier-transform-x-signal-processing", "target": "u-fourier-transform-x-signal-processing", "relation": "addresses_unknown" }, { "source": "h-fracture-depinning-crackling-noise-exponent", "target": "u-fracture-avalanche-universality-class", "relation": "addresses_unknown" }, { "source": "h-frailty-model-mortality-deceleration-test", "target": "u-frailty-model-biological-age-calibration", "relation": "addresses_unknown" }, { "source": "h-frb-gue-universality-magnetar", "target": "u-fast-radio-burst-origin", "relation": "addresses_unknown" }, { "source": "h-frb-gue-universality-magnetar", "target": "u-frb-waiting-time-universality", "relation": "addresses_unknown" }, { "source": "h-ftle-ridge-persistence-predicts-left-atrial-appendage-stasis", "target": "u-ftle-derived-thrombosis-risk-threshold-transferability", "relation": "addresses_unknown" }, { "source": "h-ftle-ridge-threshold-correlates-larval-retention-proxy", "target": "u-lcs-retention-coastal-recruitment-transfer", "relation": "addresses_unknown" }, { "source": "h-funnel-aware-search-reduces-docking-decoy-traps", "target": "u-funnel-ruggedness-docking-false-minima", "relation": "addresses_unknown" }, { "source": "h-fusion-lawson-criterion-turbulent-transport-barrier", "target": "u-fusion-plasma-stability", "relation": "addresses_unknown" }, { "source": "h-gaa-nanosheet-ballistic-transport-regime-room-temperature-3nm", "target": "u-2d-material-fet-contact-resistance-scaling-below-1nm", "relation": "addresses_unknown" }, { "source": "h-galactic-magnetic-alpha-omega-dynamo", "target": "u-magnetic-field-origin-galaxies", "relation": "addresses_unknown" }, { "source": "h-galactic-magnetic-field-alpha-omega-dynamo", "target": "u-magnetic-field-origin-galaxies", "relation": "addresses_unknown" }, { "source": "h-galaxy-angular-momentum-tidal-torque-confirmed", "target": "u-galaxy-angular-momentum", "relation": "addresses_unknown" }, { "source": "h-galaxy-angular-momentum-tidal-torque", "target": "u-galaxy-angular-momentum", "relation": "addresses_unknown" }, { "source": "h-gale-shapley-deferred-acceptance-stability-uniqueness", "target": "u-combinatorial-auction-computational-complexity", "relation": "addresses_unknown" }, { "source": "h-game-signaling-costly-honest-equilibrium", "target": "u-handicap-principle-signal-cost-measurement", "relation": "addresses_unknown" }, { "source": "h-game-theory-x-antibiotic-resistance", "target": "u-game-theory-x-antibiotic-resistance", "relation": "addresses_unknown" }, { "source": "h-gamma-oscillations-binding-causal-test", "target": "u-neural-binding-mechanism-synchrony", "relation": "addresses_unknown" }, { "source": "h-gapped-ferrite-bias-point-maximizes-wpt-q-under-saturation-margin", "target": "u-soft-ferrite-nonlinear-permeability-wpt-harmonic-loss", "relation": "addresses_unknown" }, { "source": "h-gate-control-pkc-gamma-interneuron", "target": "u-spinal-gate-control-interneuron-identity", "relation": "addresses_unknown" }, { "source": "h-gauge-fixing-parallels-coordinate-choice-in-models", "target": "u-gauge-fields-as-bundle-connections-pedagogy", "relation": "addresses_unknown" }, { "source": "h-gender-gap-stem-social-role-expectancy", "target": "u-gender-gap-stem-causes", "relation": "addresses_unknown" }, { "source": "h-gender-gap-stem-social-role-theory", "target": "u-gender-gap-stem-causes", "relation": "addresses_unknown" }, { "source": "h-gene-expression-noise-x-information-theory", "target": "u-gene-expression-noise-x-information-theory", "relation": "addresses_unknown" }, { "source": "h-gene-regulatory-network-x-boolean-circuit", "target": "u-gene-regulatory-network-x-boolean-circuit", "relation": "addresses_unknown" }, { "source": "h-genetic-algorithm-x-natural-selection", "target": "u-genetic-algorithm-x-natural-selection", "relation": "addresses_unknown" }, { "source": "h-genetic-code-error-correcting-design", "target": "u-genetic-code-information-optimality", "relation": "addresses_unknown" }, { "source": "h-genocide-early-warning-machine-learning-validity", "target": "u-genocide-early-warning-validity", "relation": "addresses_unknown" }, { "source": "h-geographic-mosaic-coevolution-trait-variance", "target": "u-red-queen-molecular-clock-arms-race", "relation": "addresses_unknown" }, { "source": "h-geomagnetic-reversal-climate-null", "target": "u-geomagnetic-excursion-climate", "relation": "addresses_unknown" }, { "source": "h-geomagnetic-reversal-inner-core-crystallization", "target": "u-geomagnetic-reversal-prediction", "relation": "addresses_unknown" }, { "source": "h-geometric-complexity-theory-p-np", "target": "u-p-vs-np-geometric-complexity", "relation": "addresses_unknown" }, { "source": "h-geometric-control-se3-optimal-robotic-grasping", "target": "u-lie-group-nonholonomic-robot-optimality", "relation": "addresses_unknown" }, { "source": "h-geostrophic-balance-climate-change", "target": "u-rossby-wave-climate-tipping", "relation": "addresses_unknown" }, { "source": "h-geothermal-induced-seismicity-pore-pressure", "target": "u-geothermal-subsidence", "relation": "addresses_unknown" }, { "source": "h-gesture-speech-constitutive-integration", "target": "u-gesture-language-interface", "relation": "addresses_unknown" }, { "source": "h-gig-economy-welfare-net-negative", "target": "u-gig-economy-welfare-effects", "relation": "addresses_unknown" }, { "source": "h-gini-mortality-phase-transition", "target": "u-inequality-health-phase-transition-threshold", "relation": "addresses_unknown" }, { "source": "h-glacier-calving-fracture-toughness-prediction", "target": "u-glacier-calving-crack-propagation-threshold", "relation": "addresses_unknown" }, { "source": "h-glial-tripartite-synapse-gain-modulation", "target": "u-glial-cell-computation", "relation": "addresses_unknown" }, { "source": "h-globular-cluster-formation-high-redshift-merger", "target": "u-globular-cluster-formation", "relation": "addresses_unknown" }, { "source": "h-globular-cluster-multiple-populations-enrichment", "target": "u-globular-cluster-formation", "relation": "addresses_unknown" }, { "source": "h-glymphatic-amyloid-clearance-rate", "target": "u-amyloid-progression-trajectory", "relation": "addresses_unknown" }, { "source": "h-glymphatic-amyloid-clearance-rate", "target": "u-aging-interventions-translatability", "relation": "addresses_unknown" }, { "source": "h-glymphatic-dysfunction-drives-amyloid-accumulation", "target": "u-glymphatic-flow-impairment-alzheimers", "relation": "addresses_unknown" }, { "source": "h-glymphatic-sleep-aquaporin4-clearance", "target": "u-lymphatic-system-brain", "relation": "addresses_unknown" }, { "source": "h-gr-gauge-theory-fiber-bundle-unification", "target": "u-phi-measurement-neural-correlates", "relation": "addresses_unknown" }, { "source": "h-gradient-penalty-magnitude-tracks-dual-feasibility-proxy-metrics", "target": "u-wgan-gp-tightness-versus-exact-lipschitz-projections", "relation": "addresses_unknown" }, { "source": "h-graph-convolution-with-mobility-priors-improves-outbreak-link-recovery", "target": "u-gcn-transmission-edge-direction-identifiability", "relation": "addresses_unknown" }, { "source": "h-graph-cut-energy-residuals-detect-lesion-segmentation-failure-modes-earlier", "target": "u-energy-landscape-mismatch-indicators-for-lesion-segmentation-qc", "relation": "addresses_unknown" }, { "source": "h-graph-laplacian-regularization-improves-module-replicability", "target": "u-spectral-cluster-stability-metabolomics-batch-effects", "relation": "addresses_unknown" }, { "source": "h-graph-neural-network-x-spectral-graph-theory", "target": "u-graph-neural-network-x-spectral-graph-theory", "relation": "addresses_unknown" }, { "source": "h-graph-theory-x-molecular-structure", "target": "u-graph-theory-x-molecular-structure", "relation": "addresses_unknown" }, { "source": "h-graph-transformer-improves-grid-contingency-screening-recall", "target": "u-graph-transformer-grid-contingency-false-negative-risk", "relation": "addresses_unknown" }, { "source": "h-graph-wavelet-energy-localizes-pmu-grid-disturbances-better-than-scada", "target": "u-graph-spectral-leakage-pmu-event-localization", "relation": "addresses_unknown" }, { "source": "h-gravitational-lensing-caustic-classification-test", "target": "u-gravitational-lensing-caustic-topology", "relation": "addresses_unknown" }, { "source": "h-grb-cambrian-explosion-trigger", "target": "u-grb-mass-extinction-link", "relation": "addresses_unknown" }, { "source": "h-grb-cambrian-explosion-trigger", "target": "u-uhecr-origin", "relation": "addresses_unknown" }, { "source": "h-green-hydrogen-iridium-scarcity-pem-electrolysis", "target": "u-green-hydrogen-electrolysis", "relation": "addresses_unknown" }, { "source": "h-green-infrastructure-urban-cooling-nonlinear-threshold", "target": "u-urban-biodiversity-governance-regime-causality", "relation": "addresses_unknown" }, { "source": "h-grid-cell-torus-manifold-decoding", "target": "u-grid-cell-fourier-basis-navigation", "relation": "addresses_unknown" }, { "source": "h-grid-inspired-phase-coherence-metrics-predict-beta-cell-dysfunction-earlier", "target": "u-coupling-topology-thresholds-for-beta-cell-synchrony-collapse", "relation": "addresses_unknown" }, { "source": "h-griffith-crack-2d-material-defects", "target": "u-2d-material-defect-transport", "relation": "addresses_unknown" }, { "source": "h-grn-gnn-priors-improve-perturbation-response-prediction", "target": "u-grn-gnn-perturbation-identifiability", "relation": "addresses_unknown" }, { "source": "h-grokking-criticality-universality", "target": "u-grokking-criticality-universality-class", "relation": "addresses_unknown" }, { "source": "h-gromov-nonsqueezing-quantum-uncertainty-derivation", "target": "u-symplectic-topology-classical-quantum-correspondence-limits", "relation": "addresses_unknown" }, { "source": "h-gromov-witten-quantum-cohomology-counts", "target": "u-pseudo-holomorphic-curve-counts", "relation": "addresses_unknown" }, { "source": "h-group-creativity-cognitive-diversity-optimal", "target": "u-collective-vs-individual-creativity", "relation": "addresses_unknown" }, { "source": "h-group-creativity-cognitive-diversity", "target": "u-collective-vs-individual-creativity", "relation": "addresses_unknown" }, { "source": "h-growth-rate-hypothesis-ribosome-phosphorus-universality", "target": "u-redfield-ratio-evolutionary-origin-mechanism", "relation": "addresses_unknown" }, { "source": "h-gtr-model-adequate-metazoan-divergence-estimation", "target": "u-phylogenetic-placement-long-branch-attraction-correction", "relation": "addresses_unknown" }, { "source": "h-gut-microbiome-serotonin-depression", "target": "u-microbiome-mental-health", "relation": "addresses_unknown" }, { "source": "h-gut-microbiome-x-lotka-volterra", "target": "u-gut-microbiome-x-lotka-volterra", "relation": "addresses_unknown" }, { "source": "h-gutenberg-richter-percolation-threshold", "target": "u-earthquake-nucleation", "relation": "addresses_unknown" }, { "source": "h-gutenberg-richter-soc-btw-exponent", "target": "u-earthquake-soc-universality-class", "relation": "addresses_unknown" }, { "source": "h-gutenberg-richter-soc-btw-exponent", "target": "u-earthquake-nucleation", "relation": "addresses_unknown" }, { "source": "h-gwb-spectrum-supermassive-bh-binaries", "target": "u-gwb-spectrum", "relation": "addresses_unknown" }, { "source": "h-gwb-supermassive-bh-binary-origin", "target": "u-gwb-spectrum", "relation": "addresses_unknown" }, { "source": "h-habitat-percolation-critical-density", "target": "u-habitat-fragmentation-threshold", "relation": "addresses_unknown" }, { "source": "h-habitat-percolation-species-persistence", "target": "u-percolation-threshold-habitat-connectivity", "relation": "addresses_unknown" }, { "source": "h-habitat-percolation-z-exponent", "target": "u-maxent-species-abundance-prediction", "relation": "addresses_unknown" }, { "source": "h-hair-cell-bundle-x-hopf-bifurcation", "target": "u-hair-cell-bundle-x-hopf-bifurcation", "relation": "addresses_unknown" }, { "source": "h-hair-cell-regeneration-notch-atoh1", "target": "u-hearing-regeneration-mammals", "relation": "addresses_unknown" }, { "source": "h-half-wavelength-coil-spacing-bound-suppresses-near-field-grating-analogs", "target": "u-multi-coil-wpt-array-grating-lobes-cross-talk", "relation": "addresses_unknown" }, { "source": "h-happiness-set-point-heritability-deliberate-activity", "target": "u-happiness-set-point", "relation": "addresses_unknown" }, { "source": "h-hasselmann-red-noise-ocean-temperature-spectrum", "target": "u-hasselmann-stochastic-resonance-glacial-mechanism", "relation": "addresses_unknown" }, { "source": "h-hawkes-branching-threshold-predicts-seizure-clusters", "target": "u-hawkes-branching-ratio-seizure-cascade-threshold", "relation": "addresses_unknown" }, { "source": "h-hawkes-process-liquidity-flash-crash", "target": "u-market-microstructure-hawkes-calibration", "relation": "addresses_unknown" }, { "source": "h-hawking-radiation-analog-bec-entanglement", "target": "u-hawking-unruh-experimental-detection", "relation": "addresses_unknown" }, { "source": "h-healthcare-cost-baumol-spiral-mechanisms", "target": "u-healthcare-cost-spiral-mechanisms", "relation": "addresses_unknown" }, { "source": "h-healthcare-cost-baumol-technology-spiral", "target": "u-healthcare-cost-spiral-mechanisms", "relation": "addresses_unknown" }, { "source": "h-heisenberg-limited-sensing-biological", "target": "u-genetic-circuit-crosstalk-noise", "relation": "addresses_unknown" }, { "source": "h-helioseismology-x-inverse-eigenvalue-problems", "target": "u-helioseismic-inversion-uniqueness-depth", "relation": "addresses_unknown" }, { "source": "h-hertz-contact-x-spherical-indentation", "target": "u-hertz-adhesion-crossover-biological-tissues", "relation": "addresses_unknown" }, { "source": "h-hierarchical-bayesian-priors-improve-imaging-inverse-coverage", "target": "u-bayesian-imaging-inverse-problem-posterior-calibration", "relation": "addresses_unknown" }, { "source": "h-high-entropy-alloy-configurational-entropy-stabilization", "target": "u-high-entropy-alloy-phase-stability-prediction", "relation": "addresses_unknown" }, { "source": "h-high-entropy-alloy-dislocation-cocktail-hardening", "target": "u-dislocation-dynamics-alloy-high-entropy", "relation": "addresses_unknown" }, { "source": "h-higher-order-gnn-practical-expressiveness", "target": "u-gnn-expressiveness-beyond-wl", "relation": "addresses_unknown" }, { "source": "h-hippocampal-place-cell-population-topology-reflects-navigated-space-topology", "target": "u-persistent-homology-neural-manifold-geometry-vs-topology-decoupling", "relation": "addresses_unknown" }, { "source": "h-hippocampal-population-holographic-capacity", "target": "u-holographic-memory-neural-phase-encoding-test", "relation": "addresses_unknown" }, { "source": "h-hippocampal-remapping-abstract-maps", "target": "u-hippocampal-spatial-code", "relation": "addresses_unknown" }, { "source": "h-histone-code-combinatorial-specificity-exceeds-single-mark-models", "target": "u-ptm-crosstalk-code-histone-combinatorial-regulation", "relation": "addresses_unknown" }, { "source": "h-historical-reconstruction-phylogenetic-limit", "target": "u-historical-reconstruction-limit", "relation": "addresses_unknown" }, { "source": "h-hjb-derived-adaptive-fractionation-improves-tumor-control-toxicity-tradeoff", "target": "u-state-representation-gaps-for-hjb-guided-adaptive-radiotherapy", "relation": "addresses_unknown" }, { "source": "h-holographic-encoding-hawking-radiation", "target": "u-black-hole-information-paradox", "relation": "addresses_unknown" }, { "source": "h-holographic-encoding-hawking-radiation", "target": "u-hawking-channel-capacity", "relation": "addresses_unknown" }, { "source": "h-homochirality-first-order-transition", "target": "u-chirality-emergence-prebiotic", "relation": "addresses_unknown" }, { "source": "h-hopf-bifurcation-lynx-hare-10yr-cycle", "target": "u-predator-prey-cycle-amplitude-stochastic", "relation": "addresses_unknown" }, { "source": "h-hopf-bifurcation-power-grid-stability", "target": "u-chaos-transition-engineering-systems", "relation": "addresses_unknown" }, { "source": "h-hopf-bifurcation-universal-mechanism-vertebrate-hair-cell-amplification", "target": "u-met-channel-molecular-identity-pore-forming-subunit", "relation": "addresses_unknown" }, { "source": "h-hopf-reduced-order-predicts-galloping-onset-threshold", "target": "u-aeroelastic-hopf-normal-form-transfer-limits", "relation": "addresses_unknown" }, { "source": "h-hopfield-capacity-modern-architectures", "target": "u-hopfield-capacity-modern-architectures", "relation": "addresses_unknown" }, { "source": "h-housing-affordability-zoning-supply-constraint", "target": "u-housing-affordability-structural-causes", "relation": "addresses_unknown" }, { "source": "h-hub-lethality-protein-network-drug-targets", "target": "u-ppi-scale-free-topology-functional-necessity", "relation": "addresses_unknown" }, { "source": "h-humor-incongruity-resolution-dopamine", "target": "u-humor-cognitive-mechanism", "relation": "addresses_unknown" }, { "source": "h-hydraulic-failure-drives-tree-mortality-drought", "target": "u-xylem-cavitation-repair-mechanism", "relation": "addresses_unknown" }, { "source": "h-hypersonic-cmcs-thermal-protection-reuse", "target": "u-hypersonic-thermal-protection", "relation": "addresses_unknown" }, { "source": "h-hysteresis-loop-biomarkers-predict-neurofatigue-recovery-lag", "target": "u-identifiability-of-hysteresis-biomarkers-in-neurofatigue-monitoring", "relation": "addresses_unknown" }, { "source": "h-ice-sheet-basal-topography-instability", "target": "u-ice-sheet-basal-melting", "relation": "addresses_unknown" }, { "source": "h-identical-analyzer-method-noise-floor-dominated-regimes-match-at-mm-wave-carriers", "target": "u-quantum-linewidth-vs-leeson-corner-crossover-measurement-protocol", "relation": "addresses_unknown" }, { "source": "h-imex-time-stepping-expands-stable-reaction-diffusion-cfl", "target": "u-a-stability-region-operator-splitting-reaction-diffusion", "relation": "addresses_unknown" }, { "source": "h-imf-stellar-feedback-variation", "target": "u-stellar-initial-mass-function", "relation": "addresses_unknown" }, { "source": "h-immune-memory-x-long-term-potentiation", "target": "u-immune-memory-x-long-term-potentiation", "relation": "addresses_unknown" }, { "source": "h-immune-negative-selection-optimal-threshold", "target": "u-immune-system-x-anomaly-detection", "relation": "addresses_unknown" }, { "source": "h-implicit-explicit-memory-prediction-error", "target": "u-implicit-explicit-memory-boundary", "relation": "addresses_unknown" }, { "source": "h-improvisation-prefrontal-deactivation-hypothesis", "target": "u-improvisation-neural-correlates", "relation": "addresses_unknown" }, { "source": "h-improvisation-prefrontal-deactivation", "target": "u-improvisation-neural-correlates", "relation": "addresses_unknown" }, { "source": "h-income-boltzmann-condensation-threshold", "target": "u-entropy-maximization-x-income-distribution", "relation": "addresses_unknown" }, { "source": "h-inequality-health-psychosocial-status-pathway", "target": "u-inequality-health-pathway", "relation": "addresses_unknown" }, { "source": "h-infinity-category-presentability-limits", "target": "u-infinity-category-limits", "relation": "addresses_unknown" }, { "source": "h-information-bottleneck-alignment-improves-neural-encoding-metrics", "target": "u-efficient-coding-bottleneck-tradeoff-measurability", "relation": "addresses_unknown" }, { "source": "h-information-optimal-batching-accelerates-material-discovery", "target": "u-fisher-optimal-experiment-policy-shift-drift", "relation": "addresses_unknown" }, { "source": "h-infrastructure-interdependence-discontinuous-collapse-empirical", "target": "u-interdependent-network-early-warning-cascade", "relation": "addresses_unknown" }, { "source": "h-inner-core-solidification-texture", "target": "u-inner-core-anisotropy-origin", "relation": "addresses_unknown" }, { "source": "h-innovation-diffusion-network-topology", "target": "u-innovation-diffusion-s-curve", "relation": "addresses_unknown" }, { "source": "h-inoculation-theory-science-misinformation", "target": "u-science-communication-effectiveness", "relation": "addresses_unknown" }, { "source": "h-insect-navigation-path-integration", "target": "u-insect-navigation-path-integration", "relation": "addresses_unknown" }, { "source": "h-insight-dopamine-prefrontal-release", "target": "u-insight-problem-solving", "relation": "addresses_unknown" }, { "source": "h-institutional-trust-elite-cue-propagation", "target": "u-institutional-trust-collapse", "relation": "addresses_unknown" }, { "source": "h-instrumental-variables-causal-inference-validity", "target": "u-causation-vs-correlation", "relation": "addresses_unknown" }, { "source": "h-integral-feedback-sufficient-perfect-adaptation-living-cells", "target": "u-homeostasis-integral-feedback-synthetic-design", "relation": "addresses_unknown" }, { "source": "h-interbank-default-cascades-exhibit-epidemic-thresholds", "target": "u-financial-contagion-epidemic-threshold-mapping", "relation": "addresses_unknown" }, { "source": "h-interdisciplinary-barriers-epistemic-terminology-gap", "target": "u-interdisciplinary-barriers", "relation": "addresses_unknown" }, { "source": "h-interface-width-regularization-predicts-segmentation-stability", "target": "u-cahn-hilliard-segmentation-parameter-transfer-limits", "relation": "addresses_unknown" }, { "source": "h-intermediate-disturbance-competition-colonization", "target": "u-intermediate-disturbance-diversity-peak", "relation": "addresses_unknown" }, { "source": "h-interoception-allostatic-consciousness", "target": "u-interoception-consciousness-link", "relation": "addresses_unknown" }, { "source": "h-intestinal-crypt-apc-selection-coefficient", "target": "u-intestinal-crypt-stem-cell-moran-selection", "relation": "addresses_unknown" }, { "source": "h-intrinsically-disordered-proteins-polymer-physics", "target": "u-intrinsically-disordered-proteins", "relation": "addresses_unknown" }, { "source": "h-invasive-species-reaction-diffusion", "target": "u-invasive-species-reaction-diffusion", "relation": "addresses_unknown" }, { "source": "h-inverse-cubic-law-agent-heterogeneity-mechanism", "target": "u-financial-market-impact-model-universal-mechanism", "relation": "addresses_unknown" }, { "source": "h-ion-specific-double-layer-competition-modulates-permeation", "target": "u-debye-length-ion-specificity-membrane-binding", "relation": "addresses_unknown" }, { "source": "h-ionizable-lipid-pka-endosomal-escape", "target": "u-lnp-tissue-targeting-mechanism", "relation": "addresses_unknown" }, { "source": "h-island-biogeography-x-percolation", "target": "u-island-biogeography-x-percolation", "relation": "addresses_unknown" }, { "source": "h-island-formula-entanglement-wedge-quantum-error-correction", "target": "u-black-hole-information-paradox-bulk-reconstruction", "relation": "addresses_unknown" }, { "source": "h-island-formula-page-curve-tensor-network-model", "target": "u-holographic-entanglement-bulk-reconstruction-limits", "relation": "addresses_unknown" }, { "source": "h-isogeometric-analysis-superior-convergence-thin-shells", "target": "u-fem-adaptivity-optimal-mesh-refinement", "relation": "addresses_unknown" }, { "source": "h-iter-q10-ignition-margin-sufficient-commercial-fusion", "target": "u-plasma-turbulence-transport-barrier-formation-mechanism", "relation": "addresses_unknown" }, { "source": "h-iv-late-external-validity-population-representativeness", "target": "u-causal-inference-heterogeneous-treatment-effects-identification", "relation": "addresses_unknown" }, { "source": "h-jamming-transition-critical-exponents", "target": "u-jamming-transition-universality-class", "relation": "addresses_unknown" }, { "source": "h-jarzynski-equality-molecular-motor-efficiency-measurement", "target": "u-metabolic-flux-entropy-production-cancer-cells", "relation": "addresses_unknown" }, { "source": "h-jet-break-timescale-scales-with-entropy-and-opening-angle", "target": "u-grb-jet-breakout-shock-microphysics", "relation": "addresses_unknown" }, { "source": "h-johnson-graph-spectral-gap-predicts-ctqw-search-plateau", "target": "u-ctqw-grover-geometry-transferability", "relation": "addresses_unknown" }, { "source": "h-joint-fit-lifshitz-hamaker-colloid-force-curves", "target": "u-unified-spectral-epsilon-model-across-vdw-casimir-length-scales", "relation": "addresses_unknown" }, { "source": "h-josephson-paramp-nears-quantum-noise-floor-with-rimp-matched-array", "target": "u-quantum-noise-figure-silicon-mm-wave-cryo-vs-room", "relation": "addresses_unknown" }, { "source": "h-jwst-massive-galaxies-feedback-suppressed-smf", "target": "u-early-galaxy-formation-jwst", "relation": "addresses_unknown" }, { "source": "h-jwst-pop3-lensing-detection", "target": "u-population-iii-stars", "relation": "addresses_unknown" }, { "source": "h-kalman-smoother-outperforms-static-regression-for-tree-ring-temperature", "target": "u-kalman-smoothing-proxy-noise-tree-ring-reconstruction", "relation": "addresses_unknown" }, { "source": "h-kam-nonergodicity-many-body-localization", "target": "u-ergodic-failure-quantum-thermalization", "relation": "addresses_unknown" }, { "source": "h-kam-nonergodicity-many-body-localization", "target": "u-quantum-thermodynamics-arrow", "relation": "addresses_unknown" }, { "source": "h-karst-connectivity-geophysical-mapping", "target": "u-karst-aquifer-connectivity", "relation": "addresses_unknown" }, { "source": "h-kauffman-boolean-x-gene-network-attractor-stability", "target": "u-boolean-grn-criticality-empirical-tests", "relation": "addresses_unknown" }, { "source": "h-kauffman-critical-k2-attractor-cell-types", "target": "u-genetic-circuit-crosstalk-noise", "relation": "addresses_unknown" }, { "source": "h-kauffman-network-criticality-cell-types", "target": "u-regulatory-network-attractor-enumeration", "relation": "addresses_unknown" }, { "source": "h-kessler-cascade-altitude-band", "target": "u-space-debris-removal", "relation": "addresses_unknown" }, { "source": "h-ketamine-antidepressant-ampa-potentiation-mechanism", "target": "u-neural-correlates-consciousness-anesthesia-mechanism", "relation": "addresses_unknown" }, { "source": "h-kh-growth-rate-normalization-predicts-billow-plasma-onset", "target": "u-kelvin-helmholtz-growth-rate-transfer-cloud-plasma-shear", "relation": "addresses_unknown" }, { "source": "h-kibble-zurek-polarity-scaling", "target": "u-kibble-zurek-embryo", "relation": "addresses_unknown" }, { "source": "h-kibble-zurek-polarity-scaling", "target": "u-topological-morphogenesis", "relation": "addresses_unknown" }, { "source": "h-klausmeier-pattern-wavelength-rainfall-indicator", "target": "u-dryland-vegetation-pattern-formation", "relation": "addresses_unknown" }, { "source": "h-kleiber-exponent-from-fractal-like-transport-networks", "target": "u-metabolic-scaling-fractal-transport-unification", "relation": "addresses_unknown" }, { "source": "h-kleiber-wave-physics", "target": "u-kleiber-pulsatile-waves", "relation": "addresses_unknown" }, { "source": "h-knot-invariants-x-dna-topology", "target": "u-knot-invariants-x-dna-topology", "relation": "addresses_unknown" }, { "source": "h-knot-jones-polynomial-completeness", "target": "u-knot-theory-x-quantum-gravity", "relation": "addresses_unknown" }, { "source": "h-kondratiev-dissipative-entropy", "target": "u-economic-dissipation-entropy-measure", "relation": "addresses_unknown" }, { "source": "h-koopman-linear-dynamics-capture-coherent-structures-limited-window", "target": "u-koopman-dmd-spectrum-convergence-navier-stokes", "relation": "addresses_unknown" }, { "source": "h-kramers-moyal-surrogates-improve-tumor-state-transition-forecast-calibration", "target": "u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models", "relation": "addresses_unknown" }, { "source": "h-krugman-bifurcation-detectable-moran-i-trajectory", "target": "u-spatial-autocorrelation-economic-convergence-causality", "relation": "addresses_unknown" }, { "source": "h-kuramoto-af-spectral-gap", "target": "u-cardiac-criticality-synchronization", "relation": "addresses_unknown" }, { "source": "h-kuramoto-scn-resynchronization-rate", "target": "u-circadian-desynchrony-disease-mechanisms", "relation": "addresses_unknown" }, { "source": "h-kyber-lwe-parameter-quantum-security-margin", "target": "u-lwe-hardness-proof-quantum-reduction", "relation": "addresses_unknown" }, { "source": "h-land-sparing-bef-optimum-yield-threshold", "target": "u-bef-relationship-agricultural-context", "relation": "addresses_unknown" }, { "source": "h-landau-neural-transition-measurability", "target": "u-landau-theory-neural-criticality-order-parameter", "relation": "addresses_unknown" }, { "source": "h-landauer-cosmological-arrow", "target": "u-arrow-of-time-low-entropy-origin", "relation": "addresses_unknown" }, { "source": "h-landauer-limit-biological-computation", "target": "u-fluctuation-theorem-biological-motors", "relation": "addresses_unknown" }, { "source": "h-landauer-limit-neuronal-computation", "target": "u-landauer-limit-neuronal-computation", "relation": "addresses_unknown" }, { "source": "h-langlands-physics-electric-magnetic-duality", "target": "u-langlands-physics-connection", "relation": "addresses_unknown" }, { "source": "h-language-change-replicator-conformity", "target": "u-language-evolution-selection-neutrality", "relation": "addresses_unknown" }, { "source": "h-language-contact-convergence-universals", "target": "u-language-contact-convergence-limit", "relation": "addresses_unknown" }, { "source": "h-language-critical-period-myelination-pruning", "target": "u-language-critical-period", "relation": "addresses_unknown" }, { "source": "h-language-critical-period-myelination", "target": "u-language-critical-period", "relation": "addresses_unknown" }, { "source": "h-language-revitalisation-intergenerational-transmission", "target": "u-language-death-reversal-feasibility", "relation": "addresses_unknown" }, { "source": "h-laplace-approximated-interim-rules-improve-enrichment-decision-efficiency", "target": "u-prior-sensitivity-of-laplace-based-interim-decision-rules", "relation": "addresses_unknown" }, { "source": "h-laplacian-eigenmodes-improve-cryoem-conformation-clustering", "target": "u-cryoem-laplacian-eigenmode-physical-interpretability", "relation": "addresses_unknown" }, { "source": "h-laser-cooling-maxwell-demon-landauer", "target": "u-laser-cooling-sub-doppler-quantum-limit", "relation": "addresses_unknown" }, { "source": "h-lattice-based-pqc-nist-transition-timeline", "target": "u-post-quantum-cryptography-transition", "relation": "addresses_unknown" }, { "source": "h-law-of-wall-predicts-local-skin-friction-when-roughness-scaled", "target": "u-skin-friction-scaling-across-roughness-regimes", "relation": "addresses_unknown" }, { "source": "h-layered-em-shielding-financial-firewall-depth-ratio-analogy", "target": "u-em-skin-depth-financial-firewall-mapping-limits", "relation": "addresses_unknown" }, { "source": "h-leaky-if-neuron-x-rc-membrane-circuit", "target": "u-lif-parameter-identifiability-noisy-synapses", "relation": "addresses_unknown" }, { "source": "h-leontief-network-near-percolation-threshold", "target": "u-global-trade-leontief-systemic-shock-threshold", "relation": "addresses_unknown" }, { "source": "h-leptogenesis-sm-cp-insufficient", "target": "u-baryon-asymmetry-origin", "relation": "addresses_unknown" }, { "source": "h-leptogenesis-sm-cp-insufficient", "target": "u-leptogenesis-cp-scale", "relation": "addresses_unknown" }, { "source": "h-lethal-mutagenesis-antiviral-threshold", "target": "u-quasispecies-fitness-landscape-mapping", "relation": "addresses_unknown" }, { "source": "h-lexical-diffusion-on-geographic-graphs-predicts-isoglosses", "target": "u-dialect-contact-as-graph-diffusion", "relation": "addresses_unknown" }, { "source": "h-lgm-refugia-predict-phylogeographic-breaks-globally", "target": "u-human-expansion-routes-coalescent-ancient-dna", "relation": "addresses_unknown" }, { "source": "h-lichen-consortium-metabolic-coupling", "target": "u-synthetic-lichen-biofabrication", "relation": "addresses_unknown" }, { "source": "h-lie-bracket-depth-complexity-robot-planning", "target": "u-subriemannian-geodesic-abnormal-optimality", "relation": "addresses_unknown" }, { "source": "h-lie-group-beyond-standard-model", "target": "u-standard-model-representation-completeness", "relation": "addresses_unknown" }, { "source": "h-lie-groups-x-symmetry-conservation", "target": "u-lie-groups-x-symmetry-conservation", "relation": "addresses_unknown" }, { "source": "h-lif-decision-fatigue-ornstein-uhlenbeck", "target": "u-decision-fatigue-neural", "relation": "addresses_unknown" }, { "source": "h-linearized-n-cycle-models-predict-chlorophyll-mode-timescales", "target": "u-nitrogen-cycle-jacobian-eigenstructure-versus-observed-anomalies", "relation": "addresses_unknown" }, { "source": "h-linguistic-relativity-neural-boundary", "target": "u-linguistic-relativity-cognition", "relation": "addresses_unknown" }, { "source": "h-linguistic-relativity-neural-boundary", "target": "u-linguistic-relativity-color-perception", "relation": "addresses_unknown" }, { "source": "h-lipid-raft-phase-separation-receptor-clustering", "target": "u-lipid-raft-functional-role-signaling", "relation": "addresses_unknown" }, { "source": "h-liquid-crystal-x-cell-membrane", "target": "u-liquid-crystal-x-cell-membrane", "relation": "addresses_unknown" }, { "source": "h-liquid-crystals-frank-elasticity", "target": "u-liquid-crystals-frank-elasticity", "relation": "addresses_unknown" }, { "source": "h-llm-meaning-statistical-form-without-grounding", "target": "u-language-model-meaning-vs-human", "relation": "addresses_unknown" }, { "source": "h-llm-scaling-emergence-artifact", "target": "u-emergent-capabilities-llm-prediction", "relation": "addresses_unknown" }, { "source": "h-llm-scaling-emergence-artifact", "target": "u-transformer-scaling-law-limits", "relation": "addresses_unknown" }, { "source": "h-llzo-single-ion-conductor-eliminates-dendrite-nucleation", "target": "u-solid-state-battery-sei-interface-resistance-origin", "relation": "addresses_unknown" }, { "source": "h-lmsr-automated-market-maker-dominates-polls-epistemic-accuracy", "target": "u-prediction-market-thin-market-accuracy-limits", "relation": "addresses_unknown" }, { "source": "h-lnt-model-invalid-endocrine-disruptors", "target": "u-endocrine-disruptor-dose-response-nonmonotonic", "relation": "addresses_unknown" }, { "source": "h-local-equilibrium-jacobian-best-conditioned-axis-aligns-with-principal-strain-demo-only", "target": "u-slutsky-vs-mechanical-reciprocity-operational-mapping", "relation": "addresses_unknown" }, { "source": "h-localized-enkf-reduces-icu-forecast-error", "target": "u-ensemble-kalman-icu-parameter-identifiability", "relation": "addresses_unknown" }, { "source": "h-logistic-map-feigenbaum-ecology-universality", "target": "u-chaotic-population-cycles-detection-noise", "relation": "addresses_unknown" }, { "source": "h-long-covid-viral-reservoir-reactivation", "target": "u-long-covid-mechanism", "relation": "addresses_unknown" }, { "source": "h-lookahead-oed-reduces-experiments-to-target-yield", "target": "u-oed-utility-misspecification-under-nonstationary-chemistry", "relation": "addresses_unknown" }, { "source": "h-lorenz-attractor-seasonal-forecast-skill", "target": "u-atmospheric-predictability-limit-extended", "relation": "addresses_unknown" }, { "source": "h-lotka-volterra-hamiltonian-microcosm-conservation", "target": "u-lotka-volterra-hamiltonian-real-ecosystem-conservation", "relation": "addresses_unknown" }, { "source": "h-lotka-volterra-informed-feedback-control-delays-phage-resistance-dominance", "target": "u-parameter-regimes-where-lotka-volterra-surrogates-fail-for-phage-bacteria-chemostats", "relation": "addresses_unknown" }, { "source": "h-lotka-volterra-semiconductor-capex-cycle", "target": "u-predator-prey-market-oscillations", "relation": "addresses_unknown" }, { "source": "h-lotka-volterra-x-game-theory", "target": "u-lotka-volterra-x-game-theory", "relation": "addresses_unknown" }, { "source": "h-lottery-ticket-sparse-subnetwork-universality", "target": "u-neural-network-loss-landscape-global-structure", "relation": "addresses_unknown" }, { "source": "h-low-rank-hessian-surrogate-predicts-two-state-phi-profile-class", "target": "u-contact-graph-hessian-rank-native-basin-surrogate", "relation": "addresses_unknown" }, { "source": "h-lstm-gating-stat-mech-phase-transition", "target": "u-lstm-gating-biological-analogue", "relation": "addresses_unknown" }, { "source": "h-lyapunov-constrained-antibiotic-cycling-reduces-resistance-and-relapse", "target": "u-lyapunov-guided-antibiotic-cycling-resistance-ecology", "relation": "addresses_unknown" }, { "source": "h-magma-fragmentation-deborah-threshold", "target": "u-magma-fragmentation-rheology-threshold", "relation": "addresses_unknown" }, { "source": "h-magnetar-rapid-rotation-dynamo", "target": "u-magnetar-formation-mechanism", "relation": "addresses_unknown" }, { "source": "h-magnons-spin-wave-collective-excitations", "target": "u-magnons-collective-excitations", "relation": "addresses_unknown" }, { "source": "h-majorana-topological-qubit-decoherence", "target": "u-non-abelian-anyons-topological-qc", "relation": "addresses_unknown" }, { "source": "h-manifold-hypothesis-m1-latent-dynamics-decoder-generalisation", "target": "u-neuroprosthetic-decoder-long-term-stability-mechanisms", "relation": "addresses_unknown" }, { "source": "h-mantle-convection-670km-intermittent", "target": "u-mantle-convection-transitions", "relation": "addresses_unknown" }, { "source": "h-mantle-rheology-x-viscoelasticity", "target": "u-mantle-rheology-x-viscoelasticity", "relation": "addresses_unknown" }, { "source": "h-marcus-inverted-region-biological-electron-transfer", "target": "u-kramers-turnover-solvent-friction", "relation": "addresses_unknown" }, { "source": "h-marcus-tunneling-x-enzyme-reaction-coordinate", "target": "u-marcus-tunneling-reaction-coordinate-biochemistry", "relation": "addresses_unknown" }, { "source": "h-marine-ice-sheet-instability-threshold", "target": "u-glacier-basal-sliding-uncertainty", "relation": "addresses_unknown" }, { "source": "h-markov-gating-graph-consistency-with-kramers-scaling-under-voltage-clamp-protocols", "target": "u-ion-channel-barrier-heights-from-multiscale-md-posteriors", "relation": "addresses_unknown" }, { "source": "h-markov-jump-therapy-policies-reduce-relapse-prone-cell-state-occupancy", "target": "u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models", "relation": "addresses_unknown" }, { "source": "h-masked-autoencoder-pretraining-improves-cryo-em-low-snr-reconstruction", "target": "u-mae-cryo-em-prior-induced-hallucination-risk", "relation": "addresses_unknown" }, { "source": "h-maxent-invasive-species-prediction", "target": "u-maxent-species-range-shift-climate", "relation": "addresses_unknown" }, { "source": "h-maxent-nonequilibrium-statistical-mechanics", "target": "u-boltzmann-shannon-nonequilibrium-bridge", "relation": "addresses_unknown" }, { "source": "h-maxmin-eu-ambiguity-aversion-amygdala", "target": "u-ellsberg-ambiguity-aversion-neural-circuit", "relation": "addresses_unknown" }, { "source": "h-maxwell-wave-channel-capacity-limit", "target": "u-maxwell-shannon-channel-near-capacity", "relation": "addresses_unknown" }, { "source": "h-may-stability-real-ecosystem-applicability", "target": "u-neutral-vs-niche-ecology-partitioning", "relation": "addresses_unknown" }, { "source": "h-mca-summation-theorem-distributed-cancer-target", "target": "u-metabolic-flux-control-redistribution-disease", "relation": "addresses_unknown" }, { "source": "h-mdl-scientific-theory-selection", "target": "u-kolmogorov-complexity-computable-approximation", "relation": "addresses_unknown" }, { "source": "h-mdr-asymptote-polymer-mechanism", "target": "u-turbulent-drag-reduction-limit", "relation": "addresses_unknown" }, { "source": "h-mean-field-theory-x-neural-networks", "target": "u-mean-field-theory-x-neural-networks", "relation": "addresses_unknown" }, { "source": "h-measurement-problem-decoherence-einselection", "target": "u-measurement-problem-interpretation", "relation": "addresses_unknown" }, { "source": "h-measurement-representational-theory-psychometrics", "target": "u-measurement-theory-foundations", "relation": "addresses_unknown" }, { "source": "h-mechanism-design-algorithmic-markets", "target": "u-mechanism-design-algorithmic-markets", "relation": "addresses_unknown" }, { "source": "h-mechanism-design-spectrum-auctions-efficiency", "target": "u-cascade-threshold-infrastructure", "relation": "addresses_unknown" }, { "source": "h-mechanosensing-x-force-transduction", "target": "u-mechanosensing-x-force-transduction", "relation": "addresses_unknown" }, { "source": "h-meg-sparse-inverse-solution-epilepsy", "target": "u-meg-inverse-source-localization", "relation": "addresses_unknown" }, { "source": "h-membrane-defects-protein-clustering", "target": "u-lipid-raft-protein-sorting-mechanism", "relation": "addresses_unknown" }, { "source": "h-membrane-tension-x-laplace-pressure", "target": "u-membrane-tension-x-laplace-pressure", "relation": "addresses_unknown" }, { "source": "h-meme-channel-social-media-bias", "target": "u-meme-channel-capacity-measurement", "relation": "addresses_unknown" }, { "source": "h-memory-augmented-seir-improves-forecast-turning-points", "target": "u-memory-kernel-identifiability-from-case-time-series", "relation": "addresses_unknown" }, { "source": "h-mems-high-cue-fungi-mineral-soc-stabilization-warming", "target": "u-soil-cue-temperature-sensitivity-warming-feedback", "relation": "addresses_unknown" }, { "source": "h-mercury-orbit-chaotic-diffusion-eccentricity", "target": "u-solar-system-stability-billion-year-timescale", "relation": "addresses_unknown" }, { "source": "h-merger-tree-branching-matches-subhalo-statistics-scaling", "target": "u-halo-merger-tree-nbody-clustering-analogy", "relation": "addresses_unknown" }, { "source": "h-metabolic-control-analysis-x-local-sensitivity", "target": "u-mca-global-sensitivity-beyond-log-linear", "relation": "addresses_unknown" }, { "source": "h-metabolic-exponent-network-dimension-prediction", "target": "u-metabolic-scaling-deviations-non-mammalian", "relation": "addresses_unknown" }, { "source": "h-metabolic-flux-x-linear-programming", "target": "u-metabolic-flux-x-linear-programming", "relation": "addresses_unknown" }, { "source": "h-metabolic-network-hub-essentiality-scaling", "target": "u-scale-free-network-x-metabolic", "relation": "addresses_unknown" }, { "source": "h-metabolic-scaling-3-4-fractal-derivation", "target": "u-metabolic-scaling-exponent-deviation-extremes", "relation": "addresses_unknown" }, { "source": "h-metacognition-prefrontal-hierarchical", "target": "u-metacognition-substrate", "relation": "addresses_unknown" }, { "source": "h-metacommunity-intermediate-dispersal-diversity", "target": "u-metacommunity-dispersal-diversity", "relation": "addresses_unknown" }, { "source": "h-metadynamics-collective-variables-protein-allostery", "target": "u-md-force-field-transferability-accuracy-limit", "relation": "addresses_unknown" }, { "source": "h-metamaterial-sub-diffraction-limit", "target": "u-metamaterial-wave-control", "relation": "addresses_unknown" }, { "source": "h-metaphor-abstract-thought-embodied-simulation", "target": "u-metaphor-abstract-thought", "relation": "addresses_unknown" }, { "source": "h-metaphor-embodied-conceptual-grounding", "target": "u-metaphor-abstract-thought", "relation": "addresses_unknown" }, { "source": "h-metaphor-universality-embodied-grounding", "target": "u-metaphor-universality", "relation": "addresses_unknown" }, { "source": "h-metaphor-universality-spatial-embodiment-constraint", "target": "u-metaphor-universality", "relation": "addresses_unknown" }, { "source": "h-metapopulation-capacity-climate-refugia-network", "target": "u-metapopulation-climate-velocity-extinction-debt", "relation": "addresses_unknown" }, { "source": "h-metapopulation-connectivity-predicts-spillover-r0", "target": "u-metapopulation-epidemic-threshold-fragmented-landscape", "relation": "addresses_unknown" }, { "source": "h-metasurface-flat-lens-diffraction-limited-visible", "target": "u-optical-frequency-metamaterial-loss-limits-superlens", "relation": "addresses_unknown" }, { "source": "h-mete-non-equilibrium-deviations", "target": "u-maxent-ecology-failure-modes", "relation": "addresses_unknown" }, { "source": "h-methane-clathrate-destabilization-threshold", "target": "u-methane-clathrate-destabilization", "relation": "addresses_unknown" }, { "source": "h-microbial-cue-warming-feedback-carbon-cycle", "target": "u-soil-carbon-cue-temperature-response", "relation": "addresses_unknown" }, { "source": "h-microbial-fuel-cell-anodic-electron-transfer", "target": "u-microbial-fuel-cell-electron-transfer-limits", "relation": "addresses_unknown" }, { "source": "h-microbial-iron-reduction-sediment-carbon-preservation", "target": "u-microbial-mineral-weathering-rate-in-situ", "relation": "addresses_unknown" }, { "source": "h-microbiome-diversity-host-resilience", "target": "u-gut-brain-axis-causal-mechanism", "relation": "addresses_unknown" }, { "source": "h-microbiome-functional-redundancy-antibiotic-resilience", "target": "u-microbiome-diversity-stability-causality", "relation": "addresses_unknown" }, { "source": "h-microglial-synaptic-pruning-depression", "target": "u-neuroinflammation-psychiatric", "relation": "addresses_unknown" }, { "source": "h-microplastic-nanofiltration-membrane-fouling-tradeoff", "target": "u-microplastic-filtration", "relation": "addresses_unknown" }, { "source": "h-microseismic-b-value-universal-failure-precursor", "target": "u-microseismic-acoustic-emission-b-value-failure", "relation": "addresses_unknown" }, { "source": "h-milankovitch-nonlinear-resonance-100kyr", "target": "u-stellar-forcing-climate-sensitivity-scale", "relation": "addresses_unknown" }, { "source": "h-mind-wandering-episodic-simulation", "target": "u-mind-wandering-function", "relation": "addresses_unknown" }, { "source": "h-mineral-nucleation-prenucleation-clusters", "target": "u-mineral-nucleation-kinetics", "relation": "addresses_unknown" }, { "source": "h-minimax-regret-pandemic-intervention", "target": "u-pandemic-intervention-timing-optimal-uncertainty", "relation": "addresses_unknown" }, { "source": "h-minimum-phase-plants-attain-tighter-bode-bounds", "target": "u-bode-waterbed-multi-loop-multi-objective-tradeoffs", "relation": "addresses_unknown" }, { "source": "h-minority-game-hft-phase-transition", "target": "u-market-microstructure-price-formation", "relation": "addresses_unknown" }, { "source": "h-minority-game-quasispecies-duality", "target": "u-minority-game-market-microstructure-universality", "relation": "addresses_unknown" }, { "source": "h-minority-game-x-market-microstructure", "target": "u-minority-game-x-market-microstructure", "relation": "addresses_unknown" }, { "source": "h-mirror-neuron-dance-therapy", "target": "u-mirror-neuron-aesthetic-cross-cultural", "relation": "addresses_unknown" }, { "source": "h-misinformation-emotional-valence-persistence", "target": "u-misinformation-correction-asymmetry", "relation": "addresses_unknown" }, { "source": "h-mismatch-negativity-bayesian-precision-prediction-error", "target": "u-predictive-coding-laminar-circuit-mechanism", "relation": "addresses_unknown" }, { "source": "h-missingness-augmented-lstm-models-improve-icu-decompensation-horizon-accuracy", "target": "u-missingness-aware-lstm-training-for-icu-forecasts", "relation": "addresses_unknown" }, { "source": "h-mitochondrial-pmf-stochastic-efficiency", "target": "u-mitochondrial-pmf-efficiency-carnot-bound", "relation": "addresses_unknown" }, { "source": "h-mixed-fem-for-hodge-laplace-matches-dec-upwind-schemes", "target": "u-fem-dec-mixed-form-equivalence-limits", "relation": "addresses_unknown" }, { "source": "h-mixing-parameter-matches-posterior-sparsity-stability-curves", "target": "u-elastic-net-prior-calibration-under-correlated-designs", "relation": "addresses_unknown" }, { "source": "h-miyake-event-wiggle-matching-year-precision", "target": "u-radiocarbon-calibration-plateau-dating-precision", "relation": "addresses_unknown" }, { "source": "h-ml-accelerated-corrosion-inhibitor-discovery", "target": "u-corrosion-inhibitor-molecular-mechanism", "relation": "addresses_unknown" }, { "source": "h-ml-directed-evolution-navigates-epistatic-fitness-landscape", "target": "u-protein-fitness-landscape-epistasis-ruggedness", "relation": "addresses_unknown" }, { "source": "h-modern-hopfield-transformer-attention-equivalence", "target": "u-hopfield-capacity-cortical-memory", "relation": "addresses_unknown" }, { "source": "h-modular-architecture-robustness-evolvability", "target": "u-modularity-robustness-evolvability-tradeoff", "relation": "addresses_unknown" }, { "source": "h-mof-sorbent-approaches-dac-thermodynamic-limit", "target": "u-dac-sorbent-entropy-production-mechanism", "relation": "addresses_unknown" }, { "source": "h-molecular-motor-efficiency-fluctuation-theorem", "target": "u-kinesin-thermal-noise-efficiency", "relation": "addresses_unknown" }, { "source": "h-molecular-motor-near-equilibrium-operation", "target": "u-molecular-motor-efficiency-limit-biological", "relation": "addresses_unknown" }, { "source": "h-molecular-spectroscopy-x-matrix-diagonalization", "target": "u-anharmonic-spectroscopy-matrix-models-convergence", "relation": "addresses_unknown" }, { "source": "h-monsoon-aerosol-bifurcation-tipping-point", "target": "u-monsoon-bifurcation", "relation": "addresses_unknown" }, { "source": "h-monsoon-aerosol-bifurcation-tipping", "target": "u-monsoon-bifurcation", "relation": "addresses_unknown" }, { "source": "h-morphogenesis-x-mechanical-instability", "target": "u-morphogenesis-x-mechanical-instability", "relation": "addresses_unknown" }, { "source": "h-morse-homology-x-conley-index-isolated-invariants", "target": "u-conley-index-computable-verification-higher-dim", "relation": "addresses_unknown" }, { "source": "h-morse-theory-x-energy-landscape", "target": "u-morse-theory-x-energy-landscape", "relation": "addresses_unknown" }, { "source": "h-motives-feynman-amplitudes-arithmetic", "target": "u-hopf-algebra-qft-nonperturbative-extension", "relation": "addresses_unknown" }, { "source": "h-motivic-cohomology-algorithmic-computation", "target": "u-motivic-cohomology-calculations", "relation": "addresses_unknown" }, { "source": "h-motor-cortex-rotational-dynamics-initial-condition-mechanism", "target": "u-motor-cortex-population-dynamics-motor-programs", "relation": "addresses_unknown" }, { "source": "h-mpc-with-ngm-constraints-reduces-epidemic-overshoot", "target": "u-epidemic-mpc-next-generation-matrix-robustness", "relation": "addresses_unknown" }, { "source": "h-mri-turbulence-alpha-prediction", "target": "u-accretion-disk-mri-saturation", "relation": "addresses_unknown" }, { "source": "h-mrna-vaccine-lipid-nanoparticle-durability", "target": "u-mrna-vaccine-durability", "relation": "addresses_unknown" }, { "source": "h-multi-shell-dmri-estimates-track-phantom-tortuosity", "target": "u-dmri-tortuosity-effective-medium-identifiability", "relation": "addresses_unknown" }, { "source": "h-multi-wavelength-beer-lambert-inverse-improves-plate-precision", "target": "u-microplate-inverse-beer-lambert-conditioning", "relation": "addresses_unknown" }, { "source": "h-multiplicative-noise-pareto-exponent-capital-tax-rate", "target": "u-econophysics-pareto-index-cross-national-variation", "relation": "addresses_unknown" }, { "source": "h-multiscale-filtration-persistence-improves-microscopy-segmentation-qc", "target": "u-persistent-homology-parameter-stability-noisy-microscopy", "relation": "addresses_unknown" }, { "source": "h-muscle-crossbridge-sliding-filament", "target": "u-muscle-crossbridge-kinetics", "relation": "addresses_unknown" }, { "source": "h-muscle-mechanics-x-crossbridge-theory", "target": "u-muscle-mechanics-x-crossbridge-theory", "relation": "addresses_unknown" }, { "source": "h-mycelial-network-mst-approximation", "target": 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"h-place-attachment-mediates-conservation-behavior-more-than-vbn", "target": "u-attitude-behavior-gap-pro-environmental", "relation": "addresses_unknown" }, { "source": "h-placebo-endogenous-opioid-dlpfc-pad", "target": "u-placebo-neural-basis", "relation": "addresses_unknown" }, { "source": "h-placebo-endogenous-opioid-dlpfc", "target": "u-placebo-neural-basis", "relation": "addresses_unknown" }, { "source": "h-plate-boundary-slip-x-fracture-mechanics", "target": "u-plate-boundary-fracture-scale-bridging", "relation": "addresses_unknown" }, { "source": "h-plate-tectonics-initiated-by-bolide-impacts", "target": "u-continental-drift-initiation", "relation": "addresses_unknown" }, { "source": "h-plate-tectonics-ra-viscosity-threshold", "target": "u-mantle-convection-plate-tectonic-onset", "relation": "addresses_unknown" }, { "source": "h-plate-tectonics-water-yield-stress", "target": "u-plate-tectonics-x-convection", "relation": "addresses_unknown" }, { "source": "h-platform-monopoly-two-sided-market-welfare", "target": "u-platform-monopoly-welfare-effects", "relation": "addresses_unknown" }, { "source": "h-pmf-bacterial-flagella-atp-synthase-evolutionary-homology", "target": "u-atp-synthase-torque-slip-mechanism", "relation": "addresses_unknown" }, { "source": "h-polar-vortex-wave-resonance-disruption", "target": "u-polar-vortex-disruption", "relation": "addresses_unknown" }, { "source": "h-polarisation-ising-phase-transition", "target": "u-political-polarisation-dynamics", "relation": "addresses_unknown" }, { "source": "h-polarisation-ising-phase-transition", "target": "u-social-contagion-vs-homophily", "relation": "addresses_unknown" }, { "source": "h-political-polarization-ising-critical-slowing", "target": "u-opinion-dynamics-phase-transition-prediction", "relation": "addresses_unknown" }, { "source": "h-polymer-glass-jamming-rfot-transition", "target": "u-polymer-glass-x-jamming-transition", "relation": "addresses_unknown" }, { "source": "h-pontryagin-adaptive-therapy-outperforms-mtd-solid-tumors", "target": "u-adaptive-therapy-evolutionary-trap-clinical-validation", "relation": "addresses_unknown" }, { "source": "h-post-perovskite-d-double-prime-dynamics", "target": "u-post-perovskite-implications", "relation": "addresses_unknown" }, { "source": "h-post-scarcity-ubi-marginal-cost", "target": "u-post-scarcity-economics-feasibility", "relation": "addresses_unknown" }, { "source": "h-ppi-hub-targeting-cancer", "target": "u-protein-protein-interaction-design", "relation": "addresses_unknown" }, { "source": "h-pragmatic-inference-mentalizing-network", "target": "u-pragmatic-inference-neural-basis", "relation": "addresses_unknown" }, { "source": "h-precision-weighting-schizophrenia-nmda-receptor", "target": "u-predictive-coding-neural-implementation-evidence", "relation": "addresses_unknown" }, { "source": "h-predator-detection-optimal-sdt-threshold", "target": "u-predator-vigilance-roc-optimal-threshold", "relation": "addresses_unknown" }, { "source": "h-predator-prey-damping-stochastic-forcing", "target": "u-predator-prey-oscillation-damping", "relation": "addresses_unknown" }, { "source": "h-predictive-cpc-loss-improves-downstream-transfer-under-shift", "target": "u-cpc-negative-sampling-bias-temporal-structure", "relation": "addresses_unknown" }, { "source": "h-predictive-processing-psychosis", "target": "u-bayesian-brain-prior-encoding", "relation": "addresses_unknown" }, { "source": "h-preisach-density-grain-size-prediction", "target": "u-preisach-model-physical-interpretation-density", "relation": "addresses_unknown" }, { "source": "h-preregistration-field-replication-rate", "target": "u-reproducibility-crisis-causes", "relation": "addresses_unknown" }, { "source": "h-preregistration-field-replication-rate", "target": "u-preregistration-effectiveness", "relation": "addresses_unknown" }, { "source": "h-prestin-somatic-motility-primary-cochlear-amplification-mechanism-mammals", "target": "u-cochlear-hopf-bifurcation-active-hair-bundle-vs-somatic-motility", "relation": "addresses_unknown" }, { "source": "h-price-equation-cultural-group-selection", "target": "u-kin-selection-price-equation-unification", "relation": "addresses_unknown" }, { "source": "h-price-equation-cultural-trait-frequency", "target": "u-cultural-drift-vs-selection-detection", "relation": "addresses_unknown" }, { "source": "h-price-subsidy-closes-nash-herd-gap-in-agent-based-metapopulations", "target": "u-vaccination-game-equilibrium-gaps-versus-measured-coverage", "relation": "addresses_unknown" }, { "source": "h-prime-editing-hdr-bypass-therapeutic-window", "target": "u-crispr-hdr-efficiency-post-mitotic-cells", "relation": "addresses_unknown" }, { "source": "h-primordial-gw-inflation-energy-scale", "target": "u-primordial-gravitational-waves", "relation": "addresses_unknown" }, { "source": "h-primordial-nucleosynthesis-reaction-networks", "target": "u-primordial-nucleosynthesis-reaction-networks", "relation": "addresses_unknown" }, { "source": "h-principal-bundle-chern-class-anomaly-cancellation", "target": "u-fiber-bundle-gauge-field-quantum-gravity", "relation": "addresses_unknown" }, { "source": "h-prion-llps-nucleation-kinetics", "target": "u-prion-llps-nucleation-kinetics", "relation": "addresses_unknown" }, { "source": "h-prion-nucleation-rate-prnp-polymorphism", "target": "u-prion-nucleation-spontaneous-rate-physiological", "relation": "addresses_unknown" }, { "source": "h-prion-tunneling-nanotube-intercellular-spread", "target": "u-prion-spread-pathway", "relation": "addresses_unknown" }, { "source": "h-program-synthesis-deductive-inductive-limits", "target": "u-program-synthesis-completeness", "relation": "addresses_unknown" }, { "source": "h-projective-hierarchy-determinacy", "target": "u-descriptive-set-projective-hierarchy", "relation": "addresses_unknown" }, { "source": "h-prosodic-bootstrapping-edge-finding", "target": "u-prosodic-bootstrapping-acquisition", "relation": "addresses_unknown" }, { "source": "h-prospect-theory-lambda-fitness-landscape-ancestral-environment", "target": "u-loss-aversion-cross-cultural-evolutionary-universality", "relation": "addresses_unknown" }, { "source": "h-prospect-theory-neural-encoding", "target": "u-loss-aversion-neural-substrate", "relation": "addresses_unknown" }, { "source": "h-prospect-theory-neural-value-coding", "target": "u-loss-aversion-neural-substrate", "relation": "addresses_unknown" }, { "source": "h-proteasome-saturation-bistability-neurodegeneration", "target": "u-ubiquitin-proteasome-proteostasis-collapse-threshold", "relation": "addresses_unknown" }, { "source": "h-protein-aggregation-x-nucleation-growth", "target": "u-protein-aggregation-x-nucleation-growth", "relation": "addresses_unknown" }, { "source": "h-protein-dynamics-optimize-quantum-tunneling", "target": "u-quantum-tunneling-enzyme-room-temperature-scope", "relation": "addresses_unknown" }, { "source": "h-protein-folding-frustration-aggregation", "target": "u-protein-folding-x-energy-landscape", "relation": "addresses_unknown" }, { "source": "h-protein-folding-funnel-alphafold2-contact-prediction-mechanism", "target": "u-protein-folding-alphafold2-de-novo-design-limits", "relation": "addresses_unknown" }, { "source": "h-protein-language-model-priors-improve-viral-escape-forecasting", "target": "u-protein-language-model-viral-escape-epistasis-misspecification", "relation": "addresses_unknown" }, { "source": "h-psilocybin-rescheduling-neuroplasticity-evidence", "target": "u-opioid-prescribing-policy-chemistry-disconnect", "relation": "addresses_unknown" }, { "source": "h-ptlds-neuroinflammation-il6-blockade", "target": "u-ptlds-neuroinflammation-self-sustaining", "relation": "addresses_unknown" }, { "source": "h-publication-bias-p-curve-correction", "target": "u-publication-bias-correction", "relation": "addresses_unknown" }, { "source": "h-punishment-threshold-ess-moral-universality", "target": "u-moral-intuition-evolutionary-stability-mapping", "relation": "addresses_unknown" }, { "source": "h-qaoa-parameter-transfer-improves-surrogate-warm-starts", "target": "u-qaoa-depth-generalization-vs-classical-baselines", "relation": "addresses_unknown" }, { "source": "h-qkd-device-independent-practical-security", "target": "u-qkd-practical-limits", "relation": "addresses_unknown" }, { "source": "h-qkd-satellite-global-scale-feasibility", "target": "u-qkd-practical-implementation-side-channels", "relation": "addresses_unknown" }, { "source": "h-quality-ranked-ransac-improves-astrometric-crossmatch-precision", "target": "u-astronomical-source-matching-structured-outlier-robustness", "relation": "addresses_unknown" }, { "source": "h-quantum-advantage-noise-threshold", "target": "u-quantum-advantage-classical-boundary", "relation": "addresses_unknown" }, { "source": "h-quantum-advantage-noise-threshold", "target": "u-topological-qc-fault-tolerance-threshold", "relation": "addresses_unknown" }, { "source": "h-quantum-annealing-qaoa-comparison", "target": "u-quantum-annealing-qaoa-comparison", "relation": "addresses_unknown" }, { "source": "h-quantum-annealing-simulated-annealing", "target": "u-quantum-annealing-simulated", "relation": "addresses_unknown" }, { "source": "h-quantum-chaos-mss-bound-saturation-black-holes", "target": "u-quantum-chaos-scrambling-rates", "relation": "addresses_unknown" }, { "source": "h-quantum-compass-precision", "target": "u-quantum-biology-decoherence", "relation": "addresses_unknown" }, { "source": "h-quantum-darwinism-photon-redundancy-verification", "target": "u-quantum-darwinism-redundancy-threshold-classicality", "relation": "addresses_unknown" }, { "source": "h-quantum-decoherence-biological-timescale-phonon", "target": "u-decoherence-timescales-warm-systems", "relation": "addresses_unknown" }, { "source": "h-quantum-decoherence-x-classical-emergence", "target": "u-quantum-decoherence-x-classical-emergence", "relation": "addresses_unknown" }, { "source": "h-quantum-diamond-nv-single-molecule-biosensing", "target": "u-quantum-sensing-biological-limits", "relation": "addresses_unknown" }, { "source": "h-quantum-dot-blinking-surface-trap-levy", "target": "u-quantum-dot-blinking-power-law-mechanism", "relation": "addresses_unknown" }, { "source": "h-quantum-dot-emission-confinement-scaling", "target": "u-quantum-dot-confinement-size-tunability", "relation": "addresses_unknown" }, { "source": "h-quantum-error-correction-surface-code-overhead-v2", "target": "u-quantum-error-correction-overhead", "relation": "addresses_unknown" }, { "source": "h-quantum-error-correction-surface-code-overhead", "target": "u-quantum-error-correction-overhead", "relation": "addresses_unknown" }, { "source": "h-quantum-error-correction-x-topological-codes", "target": "u-quantum-error-correction-x-topological-codes", "relation": "addresses_unknown" }, { "source": "h-quantum-field-theory-x-combinatorics", "target": "u-quantum-field-theory-x-combinatorics", "relation": "addresses_unknown" }, { "source": "h-quantum-graph-bfs-speedup", "target": "u-graph-algorithm-quantum-speedup", "relation": "addresses_unknown" }, { "source": "h-quantum-gravity-holography-ads-cft", "target": "u-quantum-gravity-unification", "relation": "addresses_unknown" }, { "source": "h-quantum-group-roots-of-unity-modular", "target": "u-quantum-group-representation", "relation": "addresses_unknown" }, { "source": "h-quantum-probability-gleason-measure-uniqueness", "target": "u-measure-theoretic-foundations-quantum-probability", "relation": "addresses_unknown" }, { "source": "h-quantum-repeater-multimode-memory-distance", "target": "u-quantum-repeater-distance-limit", "relation": "addresses_unknown" }, { "source": "h-quantum-solitons-bethe-ansatz-connection-quantum-inverse-scattering", "target": "u-integrability-breaking-perturbations-soliton-stability-realistic-systems", "relation": "addresses_unknown" }, { "source": "h-quantum-spectral-gap-computational-complexity", "target": "u-optimal-cooling-schedule-convergence", "relation": "addresses_unknown" }, { "source": "h-quantum-walk-spatial-search-optimal", "target": "u-quantum-walk-decoherence-practical-speedup", "relation": "addresses_unknown" }, { "source": "h-quantum-walk-x-classical-random-walk", "target": "u-quantum-walk-x-classical-random-walk", "relation": "addresses_unknown" }, { "source": "h-quantum-zeno-like-slowing-in-attention-networks", "target": "u-quantum-zeno-measurement-neural-interruption", "relation": "addresses_unknown" }, { "source": "h-quasar-feedback-kinetic-mode-quenching-mechanism", "target": "u-quasar-feedback-mechanism", "relation": "addresses_unknown" }, { "source": "h-quasar-feedback-kinetic-mode-quenching", "target": "u-quasar-feedback-mechanism", "relation": "addresses_unknown" }, { "source": "h-quasicrystal-phason-strain-stability", "target": "u-quasicrystal-stability-origin", "relation": "addresses_unknown" }, { "source": "h-quorum-thresholds-are-ess-under-stochastic-demography", "target": "u-quorum-signaling-as-multiplayer-game", "relation": "addresses_unknown" }, { "source": "h-r-process-cgm-metal-enrichment", "target": "u-cgm-enrichment", "relation": "addresses_unknown" }, { "source": "h-r-process-neutron-star-merger-dominant", "target": "u-stellar-nucleosynthesis-r-process-site", "relation": "addresses_unknown" }, { "source": "h-radiation-damage-ballistic-annealing", "target": "u-radiation-damage-recovery", "relation": "addresses_unknown" }, { "source": "h-radio-axion-like-dm-constraints", "target": "u-dark-matter-microphysics", "relation": "addresses_unknown" }, { "source": "h-ramsey-optimal-carbon-price-tipping-points", "target": "u-social-cost-carbon-discount-rate", "relation": "addresses_unknown" }, { "source": "h-random-circuit-sampling-classical-boundary-fidelity", "target": "u-quantum-supremacy-hardness-noise-boundary", "relation": "addresses_unknown" }, { "source": "h-random-circuit-sampling-classical-boundary-fidelity", "target": "u-quantum-speedup-optimization-np", "relation": "addresses_unknown" }, { "source": "h-random-circuit-sampling-classical-boundary-fidelity", "target": "u-entanglement-entropy-area-law-exceptions", "relation": "addresses_unknown" }, { "source": "h-random-matrix-universality-log-gas", "target": "u-random-matrix-universality", "relation": "addresses_unknown" }, { "source": "h-random-walk-x-brownian-motion", "target": "u-random-walk-x-brownian-motion", "relation": "addresses_unknown" }, { "source": "h-range-shift-thermal-safety-margin", "target": "u-range-shift-pace-climate-change", "relation": "addresses_unknown" }, { "source": "h-rational-cryptography-blockchain-nash", "target": "u-game-theory-x-cryptography", "relation": "addresses_unknown" }, { "source": "h-rational-inattention-x-entropy", "target": "u-rational-inattention-x-entropy", "relation": "addresses_unknown" }, { "source": "h-rayleigh-benard-turbulence-bifurcation-cascade", "target": "u-lorenz-attractor-universality-class", "relation": "addresses_unknown" }, { "source": "h-reaction-networks-x-petri-nets", "target": "u-reaction-networks-x-petri-nets", "relation": "addresses_unknown" }, { "source": "h-reaction-norm-slope-predicts-climate-tracking", "target": "u-phenotypic-plasticity-adaptive-limits-speed", "relation": "addresses_unknown" }, { "source": "h-reaction-time-tail-scales-with-effective-barrier-height", "target": "u-kramers-drift-diffusion-barrier-mapping-neural-decisions", "relation": "addresses_unknown" }, { "source": "h-real-hierarchies-embed-better-in-hyperbolic-space", "target": "u-hyperbolic-embeddings-hierarchy-identifiability", "relation": "addresses_unknown" }, { "source": "h-reconsolidation-ampar-endocytosis-labilisation", "target": "u-reconsolidation-synaptic-locus-ampa-receptor", "relation": "addresses_unknown" }, { "source": "h-recurrent-processing-consciousness", "target": "u-hard-problem-consciousness", "relation": "addresses_unknown" }, { "source": "h-red-queen-cycling-sustained-by-spatial-structure", "target": "u-red-queen-cycle-period-determinants", "relation": "addresses_unknown" }, { "source": "h-red-queen-ess-influenza-diversity-prediction", "target": "u-immune-escape-ess-predictions", "relation": "addresses_unknown" }, { "source": "h-red-sequence-age-spreads-constrain-quenching-models", "target": "u-red-sequence-quenching-unified-timescales", "relation": "addresses_unknown" }, { "source": "h-redfield-growth-rate-rg-fixed-point", "target": "u-redfield-ratio-evolution-optimality", "relation": "addresses_unknown" }, { "source": "h-reentrant-geometry-auxetic-impact-resistance", "target": "u-auxetic-materials-scalable-fabrication-3d", "relation": "addresses_unknown" }, { "source": "h-regional-sea-level-acceleration-grd-fingerprint", "target": "u-regional-sea-level-acceleration", "relation": "addresses_unknown" }, { "source": "h-reinforcement-learning-x-foraging-patch-models", "target": "u-patch-foraging-partial-observability-wild", "relation": "addresses_unknown" }, { "source": "h-reionization-dominated-by-faint-galaxies", "target": "u-reionization-topology", "relation": "addresses_unknown" }, { "source": "h-renormalization-group-deep-learning-criticality", "target": "u-renormalization-group-ml-universality", "relation": "addresses_unknown" }, { "source": "h-renormalization-group-universal-emergence-laws-cross-domain", "target": "u-emergence-quantification-integrated-information-empirical-test", "relation": "addresses_unknown" }, { "source": "h-renormalization-x-compression", "target": "u-renormalization-x-compression", "relation": "addresses_unknown" }, { "source": "h-renyi-entropy-turbulence-universal-spectrum", "target": "u-renyi-entropy-x-multifractal", "relation": "addresses_unknown" }, { "source": "h-replica-sparsity-predicts-factor-eigenvalue-noise-bulk", "target": "u-spin-glass-rmt-factor-clustering-limits", "relation": "addresses_unknown" }, { "source": "h-replica-symmetry-breaking-algorithm-hardness", "target": "u-spin-glass-optimization-hardness-phase-transition", "relation": "addresses_unknown" }, { "source": "h-replication-rate-scientific-progress-indicator", "target": "u-scientific-progress-measure", "relation": "addresses_unknown" }, { "source": "h-replicator-dynamics-ess-institutional-design", "target": "u-replicator-equation-cultural-norm-stability", "relation": "addresses_unknown" }, { "source": "h-replicator-residual-tests-improve-ess-prediction-under-competition", "target": "u-replicator-model-identifiability-multispecies-field-data", "relation": "addresses_unknown" }, { "source": "h-replicator-rl-convergence", "target": "u-replicator-dynamics-prebiotic-origin", "relation": "addresses_unknown" }, { "source": "h-reptation-tube-model-constraint-release", "target": "u-polymer-entanglement-topology", "relation": "addresses_unknown" }, { "source": "h-reservoir-computing-x-dynamical-systems", "target": "u-reservoir-computing-x-dynamical-systems", "relation": "addresses_unknown" }, { "source": "h-residual-feature-normalization-reduces-histology-site-shift-error", "target": "u-resnet-histology-domain-shift-failure-modes", "relation": "addresses_unknown" }, { "source": "h-resonant-wpt-ev-charging-grid-integration", "target": "u-wpt-efficiency-biological-tissue-interaction", "relation": "addresses_unknown" }, { "source": "h-resurgence-connects-perturbative-nonperturbative-qft", "target": "u-perturbation-series-borel-summability-qft", "relation": "addresses_unknown" }, { "source": "h-retinal-wave-bandwidth-map-resolution-constraint", "target": "u-retinal-wave-spatial-statistics-map-precision", "relation": "addresses_unknown" }, { "source": "h-reusable-rocket-guidance-limits", "target": "u-reusable-rocket-precision", "relation": "addresses_unknown" }, { "source": "h-revelation-principle-ai-alignment-mechanism", "target": "u-mechanism-design-ai-alignment", "relation": "addresses_unknown" }, { "source": "h-reversible-computing-landauer-limit", "target": "u-landauer-bound-experimental-verification", "relation": "addresses_unknown" }, { "source": "h-rewilding-trophic-cascade-predictability", "target": "u-rewilding-ecosystem-outcomes", "relation": "addresses_unknown" }, { "source": "h-rg-epsilon-expansion-convergence-nonperturbative-corrections", "target": "u-rg-fixed-points-non-wilson-fisher-universality-classes", "relation": "addresses_unknown" }, { "source": "h-rg-ml-universality-classes", "target": "u-rg-ml-universality-classes", "relation": "addresses_unknown" }, { "source": "h-rg-universality-neural-network-criticality", "target": "u-nonperturbative-rg-completeness", "relation": "addresses_unknown" }, { "source": "h-ribosome-kinetics-queuing-theory", "target": "u-ribosome-kinetics-queuing", "relation": "addresses_unknown" }, { "source": "h-riboswitch-kinetic-proofreading-cotranscriptional", "target": "u-riboswitch-cotranscriptional-folding-kinetics", "relation": "addresses_unknown" }, { "source": "h-ricci-flow-x-geometrization-program", "target": "u-ricci-flow-intuition-vs-rigorous-gr-correspondence", "relation": "addresses_unknown" }, { "source": "h-ridge-penalty-matches-bayesian-width-in-neural-decoding", "target": "u-ridge-as-gaussian-map-prior-identifiability", "relation": "addresses_unknown" }, { "source": "h-riemann-zeros-random-matrix-gue", "target": "u-riemann-zero-distribution", "relation": "addresses_unknown" }, { "source": "h-risk-pooling-institutions-shift-evolutionary-stable-cooperation", "target": "u-collective-risk-pool-stability-evolution", "relation": "addresses_unknown" }, { "source": "h-river-braiding-x-soc-like-morphodynamics", "target": "u-braided-river-scaling-criticality-test", "relation": "addresses_unknown" }, { "source": "h-river-network-branching-optimality", "target": "u-river-network-branching", "relation": "addresses_unknown" }, { "source": "h-river-network-ocn-energy-minimisation-test", "target": "u-river-network-hacks-law-variability", "relation": "addresses_unknown" }, { "source": "h-rl-distributional-bellman-convergence", "target": "u-reinforcement-learning-x-bellman-equation", "relation": "addresses_unknown" }, { "source": "h-rlde-satellite-colony-invasion-acceleration-branching-process", "target": "u-invasion-fat-tailed-dispersal-empirical-detection", "relation": "addresses_unknown" }, { "source": "h-rmt-covariance-cleaning-improves-single-cell-state-clustering", "target": "u-random-matrix-eigenvalue-cleaning-single-cell-batch-effects", "relation": "addresses_unknown" }, { "source": "h-rmt-selective-sweep-detection-power", "target": "u-rmt-selective-sweep-detection-power", "relation": "addresses_unknown" }, { "source": "h-rna-aptamer-selex-ml-design", "target": "u-rna-aptamer-design-principles", "relation": "addresses_unknown" }, { "source": "h-rna-boltzmann-ensemble-functional-structure-selection", "target": "u-rna-folding-pseudoknot-partition-function", "relation": "addresses_unknown" }, { "source": "h-rna-electrostatic-packaging-signal-design", "target": "u-capsid-assembly-kinetic-intermediates", "relation": "addresses_unknown" }, { "source": "h-rna-world-ribozyme-first-protein-emergence", "target": "u-rna-world-nonenzymatic-replication-fidelity", "relation": "addresses_unknown" }, { "source": "h-robust-control-lmi-neural-network-stability-certificates", "target": "u-h-infinity-nonlinear-systems-computational-tractability", "relation": "addresses_unknown" }, { "source": "h-robust-statistics-deep-learning-improves-noisy-label-training", "target": "u-ransac-optimal-sampling-strategy-non-uniform-inlier-distribution", "relation": "addresses_unknown" }, { "source": "h-rock-magnetism-single-domain-blocking-temperature", "target": "u-rock-magnetism-paleomagnetic-reversal-mechanism", "relation": "addresses_unknown" }, { "source": "h-room-acoustic-quality-predictable-from-geometry", "target": "u-concert-hall-acoustic-quality-metrics", "relation": "addresses_unknown" }, { "source": "h-rsg-transition-separates-polynomial-exponential-regimes", "target": "u-p-vs-np-geometric-barriers", "relation": "addresses_unknown" }, { "source": "h-rule-110-minimal-universal-ca", "target": "u-cellular-automata-complexity-classification", "relation": "addresses_unknown" }, { "source": "h-sacrificial-templating-vascular-network-bioprinting", "target": "u-tissue-engineering-vascularization-thick-constructs", "relation": "addresses_unknown" }, { "source": "h-sai-regional-precipitation-monsoon-disruption", "target": "u-sai-side-effects", "relation": "addresses_unknown" }, { "source": "h-sars-cov2-network-percolation", "target": "u-network-threshold-epidemic-spread", "relation": "addresses_unknown" }, { "source": "h-sat-spin-glass-algorithm-design", "target": "u-sat-spin-glass-algorithm-design", "relation": "addresses_unknown" }, { "source": "h-satisfiability-x-constraint-propagation", "target": "u-satisfiability-x-constraint-propagation", "relation": "addresses_unknown" }, { "source": "h-saturn-ring-viscosity-self-gravity-dominated", "target": "u-saturn-ring-viscosity-radial-transport", "relation": "addresses_unknown" }, { "source": "h-scaffold-routing-constraint-metrics-predict-origami-yield", "target": "u-dna-origami-compiler-analogy-yield-prediction-limits", "relation": "addresses_unknown" }, { "source": "h-scale-free-criticality-brain-hub-vulnerability", "target": "u-scale-free-brain-connectome-criticality", "relation": "addresses_unknown" }, { "source": "h-scale-free-criticality-brain-hub-vulnerability", "target": "u-alzheimer-network-attractor-dynamics", "relation": "addresses_unknown" }, { "source": "h-scale-free-epidemic-threshold-vaccination", "target": "u-network-epidemic-threshold-heterogeneity", "relation": "addresses_unknown" }, { "source": "h-scc-convex-damages-fat-tails", "target": "u-climate-damage-function-high-temperature-regime", "relation": "addresses_unknown" }, { "source": "h-schelling-abm-segregation-threshold-real-world-preference-calibration", "target": "u-abm-calibration-empirical-social-science-validation", "relation": "addresses_unknown" }, { "source": "h-schelling-spinodal-coarsening", "target": "u-cascade-threshold-infrastructure", "relation": "addresses_unknown" }, { "source": "h-schema-theorem-replicator-equivalence", "target": "u-evolutionary-algorithms-replicator-dynamics", "relation": "addresses_unknown" }, { "source": "h-scientific-consensus-social-epistemology", "target": "u-scientific-consensus-formation", "relation": "addresses_unknown" }, { "source": "h-scientific-method-bridges-as-falsifiable-predictions", "target": "u-scientific-method-cross-domain-falsifiability", "relation": "addresses_unknown" }, { "source": "h-sdp-rounding-universal-approximation-ratio-tight-ugc", "target": "u-unique-games-conjecture-sdp-approximation-tight-gap", "relation": "addresses_unknown" }, { "source": "h-sea-level-fingerprint-attribution", "target": "u-sea-level-fingerprint", "relation": "addresses_unknown" }, { "source": "h-secondary-metabolites-pksnrps-combinatorial-evolution", "target": "u-silent-bgc-activation-novel-antibiotics", "relation": "addresses_unknown" }, { "source": "h-sediment-transport-stochastic-threshold", "target": "u-sediment-transport-nonlinearity", "relation": "addresses_unknown" }, { "source": "h-seed-dispersal-levy-flight", "target": "u-seed-dispersal-levy-flight", "relation": "addresses_unknown" }, { "source": "h-sei-organic-inorganic-layers", "target": "u-solid-electrolyte-interface", "relation": "addresses_unknown" }, { "source": "h-seismic-adjoint-tomography-resolves-mantle-plumes", "target": "u-seismic-tomography-null-space-resolution", "relation": "addresses_unknown" }, { "source": "h-seismic-wave-x-elastic-wave", "target": "u-seismic-wave-x-elastic-wave", "relation": "addresses_unknown" }, { "source": "h-self-exciting-renewal-models-improve-readmission-burst-forecasting", "target": "u-renewal-kernel-selection-readmission-burst-identifiability", "relation": "addresses_unknown" }, { "source": "h-self-healing-polymer-dynamic-covalent", "target": "u-self-healing-polymer-mechanism", "relation": "addresses_unknown" }, { "source": "h-self-supervised-residual-pretraining-reduces-retinal-screening-false-negatives", "target": "u-domain-shift-calibration-for-resnet-retinal-screening", "relation": "addresses_unknown" }, { "source": "h-semantic-compositionality-limits-construction-grammar", "target": "u-semantic-compositionality-limits", "relation": "addresses_unknown" }, { "source": "h-semantic-compositionality-type-logical-grammar", "target": "u-semantic-compositionality-limits", "relation": "addresses_unknown" }, { "source": "h-semiconductor-fermi-pinning-chemical-potential-control", "target": "u-semiconductor-doping-fermi-level-pinning", "relation": "addresses_unknown" }, { "source": "h-senolytic-therapy-reduces-cancer-risk-aged-tissue", "target": "u-senescence-sasp-cancer-promotion-threshold", "relation": "addresses_unknown" }, { "source": "h-sensory-cortex-implements-approximate-kalman-updates", "target": "u-kalman-filter-neural-implementation-limits", "relation": "addresses_unknown" }, { "source": "h-sensory-noise-sr-optimality", "target": "u-stochastic-resonance-neural-tuning", "relation": "addresses_unknown" }, { "source": "h-sepsis-endotype-genomic-immunophenotype", "target": "u-sepsis-heterogeneity", "relation": "addresses_unknown" }, { "source": "h-sequence-complex-torus-first-ecc-exam-performance", "target": "u-ecc-torus-intuition-misconception-rates", "relation": "addresses_unknown" }, { "source": "h-serpentinization-abiotic-hydrogen-flux", "target": "u-serpentinization-hydrogen", "relation": "addresses_unknown" }, { "source": "h-shannon-optimal-compression-biological-codes", "target": "u-rate-distortion-optimal-neural-codes", "relation": "addresses_unknown" }, { "source": "h-shapley-value-predicts-international-climate-burden-sharing", "target": "u-shapley-value-practical-coalition-stability", "relation": "addresses_unknown" }, { "source": "h-shared-biexponential-fitting-bias-function-across-modalities-same-snr", "target": "u-flim-t2star-multiexp-phantom-transfer", "relation": "addresses_unknown" }, { "source": "h-shared-shape-index-scaling-near-jamming-across-donors", "target": "u-jamming-exponent-universality-epithelium-versus-colloid", "relation": "addresses_unknown" }, { "source": "h-shared-tangent-field-exponential-region-only-logarithmic-visual-overlap", "target": "u-inflation-epidemic-analogy-falsifiability-limits", "relation": "addresses_unknown" }, { "source": "h-sharp-wave-ripple-consolidation-reward-bias", "target": "u-hippocampal-replay-sequence-selection-criteria", "relation": "addresses_unknown" }, { "source": "h-sieber-richter-pairs-bgs-proof", "target": "u-bgs-conjecture-general-proof", "relation": "addresses_unknown" }, { "source": "h-signed-language-same-substrate-spoken", "target": "u-signed-language-neural-substrate", "relation": "addresses_unknown" }, { "source": "h-silicate-weathering-feedback-stabilizes-hothouse", "target": "u-silicate-weathering-temperature-sensitivity-field", "relation": "addresses_unknown" }, { "source": "h-silicon-photonics-dfb-laser-integration", "target": "u-vcsel-silicon-photonics-integration-limit", "relation": "addresses_unknown" }, { "source": "h-silicon-vacancy-coherence-milliseconds", "target": "u-quantum-memory-coherence-limits", "relation": "addresses_unknown" }, { "source": "h-simulated-annealing-x-statistical-mechanics", "target": "u-simulated-annealing-x-statistical-mechanics", "relation": "addresses_unknown" }, { "source": "h-sindy-guided-control-policies-delay-phage-resistance-takeover", "target": "u-sindy-library-selection-bias-in-host-pathogen-inference", "relation": "addresses_unknown" }, { "source": "h-single-atom-catalyst-orr-selectivity-4e", "target": "u-pem-fuel-cell-pt-catalyst-degradation-mechanism", "relation": "addresses_unknown" }, { "source": "h-sir-model-x-compartmental-ode", "target": "u-sir-model-x-compartmental-ode", "relation": "addresses_unknown" }, { "source": "h-sleep-rem-associative-creative-insight", "target": "u-sleep-creative-insight", "relation": "addresses_unknown" }, { "source": "h-sleep-rem-creative-insight-memory", "target": "u-sleep-creative-insight", "relation": "addresses_unknown" }, { "source": "h-slow-roll-spectral-tilt-potential-discrimination", "target": "u-inflation-slow-roll-end-reheating-mechanism", "relation": "addresses_unknown" }, { "source": "h-slow-slip-seismic-loading", "target": "u-slow-slip-event-origin", "relation": "addresses_unknown" }, { "source": "h-slow-slip-seismic-loading", "target": "u-earthquake-nucleation", "relation": "addresses_unknown" }, { "source": "h-sma-fatigue-martensitic-slip-competition", "target": "u-shape-memory-alloy-fatigue", "relation": "addresses_unknown" }, { "source": "h-smart-grid-virtual-inertia-stability-v2", "target": "u-smart-grid-stability", "relation": "addresses_unknown" }, { "source": "h-smart-grid-virtual-inertia-stability", "target": "u-smart-grid-stability", "relation": "addresses_unknown" }, { "source": "h-smr-proliferation-risk-lower", "target": "u-advanced-fission-proliferation", "relation": "addresses_unknown" }, { "source": "h-snare-zippering-energy-controls-vesicle-fusion-probability", "target": "u-snare-complex-partial-zippering-spontaneous-release-rate", "relation": "addresses_unknown" }, { "source": "h-snare-zippering-force-gates-fusion-rate", "target": "u-snare-force-threshold-in-vivo", "relation": "addresses_unknown" }, { "source": "h-snowball-earth-escape-volcanic-co2-ice-albedo", "target": "u-snowball-earth-escape", "relation": "addresses_unknown" }, { "source": "h-soc-earthquake-precursor-detection", "target": "u-soc-earthquake-precursor-detection", "relation": "addresses_unknown" }, { "source": "h-social-cognition-mentalizing-mirror-dissociation", "target": "u-social-cognition-architecture", "relation": "addresses_unknown" }, { "source": "h-social-cognition-mentalizing-network", "target": "u-social-cognition-architecture", "relation": "addresses_unknown" }, { "source": "h-social-critical-temperature-empirical", "target": "u-social-critical-temperature-empirical", "relation": "addresses_unknown" }, { "source": "h-social-ising-polarization-transition", "target": "u-opinion-dynamics-critical-homophily", "relation": "addresses_unknown" }, { "source": "h-social-media-depression-passive-consumption-mechanism", "target": "u-social-media-mental-health-causality", "relation": "addresses_unknown" }, { "source": "h-social-mobility-place-childhood-effects", "target": "u-social-mobility-measurement", "relation": "addresses_unknown" }, { "source": "h-social-movement-cascade-clustered-network-advantage", "target": "u-complex-contagion-threshold-distribution-estimation", "relation": "addresses_unknown" }, { "source": "h-social-network-centrality-x-eigenvector", "target": "u-social-network-centrality-x-eigenvector", "relation": "addresses_unknown" }, { "source": "h-social-network-star-topology-innovation-fixation", "target": "u-evolutionary-graph-amplifier-natural-populations", "relation": "addresses_unknown" }, { "source": "h-social-pain-dacc-health-outcomes-mediation", "target": "u-oxytocin-parochial-altruism-policy-implications", "relation": "addresses_unknown" }, { "source": "h-soft-actuator-fatigue-mechanism", "target": "u-soft-robotics-actuator-lifespan", "relation": "addresses_unknown" }, { "source": "h-softmax-attention-x-cortical-divisive-normalization", "target": "u-softmax-attention-cortical-normalization-mapping", "relation": "addresses_unknown" }, { "source": "h-soil-aggregate-fractal-stability-mechanism", "target": "u-soil-aggregate-fractal-dimension-stability-link", "relation": "addresses_unknown" }, { "source": "h-soil-food-web-connectance-stability", "target": "u-soil-food-web-stability-topology", "relation": "addresses_unknown" }, { "source": "h-soil-microbiome-carbon-enhancement", "target": "u-soil-microbiome-carbon-cycling", "relation": "addresses_unknown" }, { "source": "h-solid-electrolyte-sei-thermodynamic-stability-window", "target": "u-battery-solid-electrolyte-stability", "relation": "addresses_unknown" }, { "source": "h-solid-mechanics-x-topology-optimization", "target": "u-solid-mechanics-x-topology-optimization", "relation": "addresses_unknown" }, { "source": "h-solid-state-battery-pressure-dendrite", "target": "u-solid-state-battery-failure", "relation": "addresses_unknown" }, { "source": "h-solid-state-nmr-amyloid-structure-mechanism", "target": "u-quantum-coherence-biological-systems-nmr-detectable", "relation": "addresses_unknown" }, { "source": "h-soliton-basis-transmission-optimal-nonlinear-channel-capacity", "target": "u-fiber-nonlinearity-capacity-limit-shannon", "relation": "addresses_unknown" }, { "source": "h-soliton-x-integrable-systems", "target": "u-soliton-x-integrable-systems", "relation": "addresses_unknown" }, { "source": "h-soluble-amyloid-oligomers-synaptic", "target": "u-alzheimer-causal-biomarkers", "relation": "addresses_unknown" }, { "source": "h-sos-lyapunov-global-nonpolynomial", "target": "u-lyapunov-function-discovery-automation", "relation": "addresses_unknown" }, { "source": "h-sovereign-debt-sustainability-fiscal-space", "target": "u-sovereign-debt-sustainability", "relation": "addresses_unknown" }, { "source": "h-space-group-frequency-evolution-bias", "target": "u-protein-crystal-packing-predictability", "relation": "addresses_unknown" }, { "source": "h-spacetime-emerges-from-entanglement", "target": "u-black-hole-information-paradox", "relation": "addresses_unknown" }, { "source": "h-sparse-coding-x-neural-basis", "target": "u-sparse-coding-x-neural-basis", "relation": "addresses_unknown" }, { "source": "h-sparse-sensor-placement-improves-pde-structure-recovery", "target": "u-sindy-noise-and-collinearity-under-limited-sensing", "relation": "addresses_unknown" }, { "source": "h-sparsity-priors-stabilize-lidar-surface-recovery", "target": "u-lidar-scene-reconstruction-nonuniqueness", "relation": "addresses_unknown" }, { "source": "h-species-abundance-maximum-entropy", "target": "u-species-abundance-distribution", "relation": "addresses_unknown" }, { "source": "h-spectral-clustering-x-graph-laplacian", "target": "u-spectral-clustering-x-graph-laplacian", "relation": "addresses_unknown" }, { "source": "h-spectral-linewidth-scales-with-collapse-shock-mach-estimate", "target": "u-sonoluminescence-emission-mechanism-state-resolved", "relation": "addresses_unknown" }, { "source": "h-speech-coherence-alzheimers-prediction", "target": "u-language-biomarker-clinical-validity", "relation": "addresses_unknown" }, { "source": "h-speech-coherence-alzheimers-prediction", "target": "u-alzheimer-causal-biomarkers", "relation": "addresses_unknown" }, { "source": "h-sperm-small-rna-mediates-paternal-trauma-epigenetic-inheritance", "target": "u-epigenetic-escape-loci-mechanisms-transgenerational-scope", "relation": "addresses_unknown" }, { "source": "h-spin-fluctuation-pairing-cuprates", "target": "u-high-tc-pairing-mechanism", "relation": "addresses_unknown" }, { "source": "h-spin-glass-p-np-separation", "target": "u-sat-phase-transition-p-np", "relation": "addresses_unknown" }, { "source": "h-spin-squeezed-states-heisenberg-limited-sensing", "target": "u-quantum-metrology-heisenberg-limit", "relation": "addresses_unknown" }, { "source": "h-spin-waves-x-magnons", "target": "u-spin-waves-x-magnons", "relation": "addresses_unknown" }, { "source": "h-spinal-cord-nogo-combinatorial-repair", "target": "u-spinal-cord-complete-repair", "relation": "addresses_unknown" }, { "source": "h-squid-array-regularization-improves-meg-source-localization", "target": "u-meg-inverse-source-nonunique-regularization-bounds", "relation": "addresses_unknown" }, { "source": "h-stability-selected-lasso-panels-outperform-fixed-biomarkers-under-assay-noise", "target": "u-measurement-drift-effects-on-lasso-biomarker-sparsity", "relation": "addresses_unknown" }, { "source": "h-stackelberg-equilibrium-predicts-security-market-underinvestment", "target": "u-optimal-cybersecurity-investment-under-adversarial-uncertainty", "relation": "addresses_unknown" }, { "source": "h-staggered-commutation-frequency-threshold-for-target-isolation-db", "target": "u-floquet-metamaterial-isolation-bandwidth-loss-tradeoff", "relation": "addresses_unknown" }, { "source": "h-stain-normalized-unet-training-improves-cross-site-pathology-consistency", "target": "u-stain-variation-failure-modes-for-unet-histopathology-segmentation", "relation": "addresses_unknown" }, { "source": "h-stalling-density-wave-speed-correlates-with-seq-measured-pause-density-peaks", "target": "u-replication-fork-tasep-parameter-identifiability-from-seq-stalling-assays", "relation": "addresses_unknown" }, { "source": "h-starling-murmuration-criticality-vicsek", "target": "u-vicsek-transition-order-finite-systems", "relation": "addresses_unknown" }, { "source": "h-starling-oncotic-reversal-lymphatic-dependence", "target": "u-lymphatic-valve-gating-pressure-threshold", "relation": "addresses_unknown" }, { "source": "h-statistical-thermodynamics-equilibrium-partition-function", "target": "u-partition-function-anharmonic-correction", "relation": "addresses_unknown" }, { "source": "h-stdp-homeostatic-scaling-weight-stability", "target": "u-stdp-synaptic-weight-saturation", "relation": "addresses_unknown" }, { "source": "h-stellar-bh-spin-tidal-synchronization", "target": "u-stellar-bh-spin-distribution", "relation": "addresses_unknown" }, { "source": "h-stellar-imf-turbulent-fragmentation-universal", "target": "u-stellar-initial-mass-function", "relation": "addresses_unknown" }, { "source": "h-stem-cell-niche-mechanotransduction-quiescence", "target": "u-stem-cell-niche-regulation", "relation": "addresses_unknown" }, { "source": "h-stereotype-kernel-of-truth-social-learning", "target": "u-stereotype-formation-persistence", "relation": "addresses_unknown" }, { "source": "h-stereotype-threat-racial-achievement-gap-mechanism", "target": "u-racial-achievement-gap-mechanisms", "relation": "addresses_unknown" }, { "source": "h-stochastic-gene-expression-bet-hedging-optimal-noise", "target": "u-stochastic-gene-expression-bet-hedging-quantitative", "relation": "addresses_unknown" }, { "source": "h-stochastic-resonance-matches-information-peak-in-cell-signaling", "target": "u-stochastic-resonance-cell-signaling-bandwidth", "relation": "addresses_unknown" }, { "source": "h-stochastic-resonance-neural-coding-optimality", "target": "u-stochastic-resonance-neural-coding-optimality", "relation": "addresses_unknown" }, { "source": "h-stratosphere-troposphere-annular-mode-coupling", "target": "u-stratosphere-troposphere-coupling", "relation": "addresses_unknown" }, { "source": "h-stratosphere-troposphere-annular-mode", "target": "u-stratosphere-troposphere-coupling", "relation": "addresses_unknown" }, { "source": "h-stress-granule-binodal-concentration-prediction", "target": "u-stress-granule-phase-separation-pathology", "relation": "addresses_unknown" }, { "source": "h-structural-holes-income-mobility-mediation", "target": "u-social-capital-causal-vs-correlational", "relation": "addresses_unknown" }, { "source": "h-student-transfer-zeno-curve-to-sampling-stability-drills", "target": "u-quantum-zeno-watchdog-quantitative-mapping", "relation": "addresses_unknown" }, { "source": "h-subduction-initiation-passive-margin-collapse", "target": "u-subduction-initiation", "relation": "addresses_unknown" }, { "source": "h-supersymmetry-electroweak-hierarchy-stabilization", "target": "u-standard-model-beyond-hierarchy-dark-matter-identity", "relation": "addresses_unknown" }, { "source": "h-supervolcano-uplift-precursor-timescale", "target": "u-supervolcano-eruption-forecasting", "relation": "addresses_unknown" }, { "source": "h-supply-chain-network-x-bond-percolation-disruption", "target": "u-supply-chain-correlated-failure-calibration", "relation": "addresses_unknown" }, { "source": "h-supply-chain-percolation-threshold-dual-sourcing", "target": "u-supply-chain-network-topology-resilience", "relation": "addresses_unknown" }, { "source": "h-supply-chain-resilience-modularity", "target": "u-supply-chain-resilience-efficiency", "relation": "addresses_unknown" }, { "source": "h-surface-code-practical-threshold-2030", "target": "u-quantum-error-correction-overhead-reduction", "relation": "addresses_unknown" }, { "source": "h-surprisal-n400-mismatch-equivalence", "target": "u-predictive-coding-grammar-neural-substrate", "relation": "addresses_unknown" }, { "source": "h-surprisal-n400-mismatch-equivalence", "target": "u-bilingual-cognitive-advantage-replication", "relation": "addresses_unknown" }, { "source": "h-survey-propagation-rsat-threshold-prediction", "target": "u-replica-symmetry-breaking-algorithmic-hardness", "relation": "addresses_unknown" }, { "source": "h-swarm-pheromone-convergence-rate", "target": "u-stigmergy-optimality-gap-real-environments", "relation": "addresses_unknown" }, { "source": "h-swiss-cheese-alignment-accident-prediction", "target": "u-human-error-organizational-accident-boundary", "relation": "addresses_unknown" }, { "source": "h-symmetry-breaking-goldstone-bosons", "target": "u-symmetry-breaking-goldstone", "relation": "addresses_unknown" }, { "source": "h-symmetry-breaking-universal-phase-transition-classifier", "target": "u-goldstone-boson-higher-dimensional-systems", "relation": "addresses_unknown" }, { "source": "h-symplectic-capacities-convex-equality", "target": "u-symplectic-capacities", "relation": "addresses_unknown" }, { "source": "h-symplectic-controllers-preserve-energy-bounds-long-horizon", "target": "u-symplectic-discretization-bias-long-horizon-control", "relation": "addresses_unknown" }, { "source": "h-symplectic-quantization-new-prediction", "target": "u-symplectic-quantization-semiclassical", "relation": "addresses_unknown" }, { "source": "h-synaesthesia-disinhibited-feedback-hyperconnectivity", "target": "u-synaesthesia-mechanism", "relation": "addresses_unknown" }, { "source": "h-synapse-heterogeneity-plasticity-code", "target": "u-synapse-heterogeneity-function", "relation": "addresses_unknown" }, { "source": "h-synchrony-prosociality-physiological", "target": "u-rhythm-synchronization-social", "relation": "addresses_unknown" }, { "source": "h-synthetic-biology-x-circuit-design", "target": "u-synthetic-biology-x-circuit-design", "relation": "addresses_unknown" }, { "source": "h-synthetic-insulator-retroactivity-control", "target": "u-retroactivity-insulation-genetic-circuits", "relation": "addresses_unknown" }, { "source": "h-t2d-reversal-hepatic-fat-beta-cell-recovery", "target": "u-type2-diabetes-reversal", "relation": "addresses_unknown" }, { "source": "h-t2d-reversal-hepatic-fat-beta-cell", "target": "u-type2-diabetes-reversal", "relation": "addresses_unknown" }, { "source": "h-tad-boundary-disruption-ctcf-site-oncogene-activation-quantitative", "target": "u-chromatin-loop-extrusion-speed-processivity-in-vivo", "relation": "addresses_unknown" }, { "source": "h-tag-decay-timescale-vs-write-buffer-lifetime-correlation-classroom-only", "target": "u-synaptic-tag-cache-analogy-quantitative-test", "relation": "addresses_unknown" }, { "source": "h-tandem-cell-thermodynamic-optimum-bandgap-pairing", "target": "u-solar-cell-efficiency-practical-loss-mechanisms", "relation": "addresses_unknown" }, { "source": "h-targeted-hub-vaccination-achieves-herd-immunity-fewer-doses-scale-free", "target": "u-percolation-phase-transition-interdependent-networks-cascading-failures", "relation": "addresses_unknown" }, { "source": "h-targeted-memory-reactivation-during-sleep-enhances-consolidation", "target": "u-sleep-replay-causal-role-memory-specificity", "relation": "addresses_unknown" }, { "source": "h-targeted-vaccination-percolation-optimality", "target": "u-sir-percolation-temporal-network-threshold", "relation": "addresses_unknown" }, { "source": "h-tatonnement-convergence-diagonal-dominance", "target": "u-walrasian-tatonnement-convergence-without-gs", "relation": "addresses_unknown" }, { "source": "h-tau-propagation-circuit-connectivity-determines-staging", "target": "u-prion-like-spread-neurodegeneration-circuit-specificity", "relation": "addresses_unknown" }, { "source": "h-tbi-neuroinflammation-microbiome-repair", "target": "u-tbi-repair-limits", "relation": "addresses_unknown" }, { "source": "h-tcr-foundation-pretraining-improves-antigen-specificity-recall", "target": "u-tcr-foundation-model-ood-binding-generalization", "relation": "addresses_unknown" }, { "source": "h-tcr-repertoire-percolation-threshold-pathogen-coverage", "target": "u-tcr-repertoire-pathogen-space-coverage", "relation": "addresses_unknown" }, { "source": "h-td-prediction-error-dopamine-burst-identity-schultz", "target": "u-td-learning-dopamine-biological-implementation", "relation": "addresses_unknown" }, { "source": "h-tda-cancer-subtype-prognosis-superiority", "target": "u-tumor-evolution-topology-branching", "relation": "addresses_unknown" }, { "source": "h-tda-cancer-subtype-prognosis-superiority", "target": "u-cancer-stem-cell-hierarchy", "relation": "addresses_unknown" }, { "source": "h-tda-cognitive-map-nontrivial-topology", "target": "u-phi-measurement-neural-correlates", "relation": "addresses_unknown" }, { "source": "h-tda-x-shape-recognition", "target": "u-tda-x-shape-recognition", "relation": "addresses_unknown" }, { "source": "h-tectonic-stress-coulomb-failure", "target": "u-tectonic-coulomb-failure", "relation": "addresses_unknown" }, { "source": "h-telomere-causality-partial-reprogramming", "target": "u-telomere-aging-causality", "relation": "addresses_unknown" }, { "source": "h-telomere-length-social-gradient-reversibility", "target": "u-epigenetic-intergenerational-transmission-social-stress", "relation": "addresses_unknown" }, { "source": "h-tensor-network-entanglement-phase-boundary", "target": "u-entanglement-tensor-network-complexity", "relation": "addresses_unknown" }, { "source": "h-tensor-networks-x-quantum-states", "target": "u-tensor-networks-x-quantum-states", "relation": "addresses_unknown" }, { "source": "h-theory-laden-observation-background-knowledge", "target": "u-theory-ladenness-observation", "relation": "addresses_unknown" }, { "source": "h-theory-of-mind-tpj-development-timing", "target": "u-theory-of-mind-substrate", "relation": "addresses_unknown" }, { "source": "h-theory-of-mind-tpj-development", "target": "u-theory-of-mind-substrate", "relation": "addresses_unknown" }, { "source": "h-thermoacoustic-travelling-wave-carnot-approach", "target": "u-thermoacoustic-engine-efficiency-scaling", "relation": "addresses_unknown" }, { "source": "h-thermodynamics-non-convex-regions-phase-coexistence", "target": "u-thermodynamics-convex-geometry-non-equilibrium", "relation": "addresses_unknown" }, { "source": "h-thermoelectric-phonon-glass-electron-crystal", "target": "u-thermoelectric-zt-theoretical-limit", "relation": "addresses_unknown" }, { "source": "h-thermoelectric-zt-phonon-glass-electron-crystal", "target": "u-thermoelectric-zt-limit", "relation": "addresses_unknown" }, { "source": "h-thermohaline-circulation-x-buoyancy-flow", "target": "u-thermohaline-circulation-x-buoyancy-flow", "relation": "addresses_unknown" }, { "source": "h-thorium-msr-achieves-baseload-carbon-free-power-lower-waste", "target": "u-nuclear-waste-transmutation-accelerator-driven-systems", "relation": "addresses_unknown" }, { "source": "h-thymic-rejuvenation-immunosenescence", "target": "u-immune-aging-rejuvenation", "relation": "addresses_unknown" }, { "source": "h-tidal-deformability-tightens-symmetry-energy-slope", "target": "u-neutron-star-core-qcd-constraints", "relation": "addresses_unknown" }, { "source": "h-tidal-internal-wave-mixing-abyssal-hotspots", "target": "u-ocean-mixing-parameterization-climate-models", "relation": "addresses_unknown" }, { "source": "h-tidal-mixing-overturning-circulation-control", "target": "u-internal-tide-mixing-efficiency-spatial", "relation": "addresses_unknown" }, { "source": "h-time-perception-striatal-beat-frequency-model", "target": "u-time-perception-mechanism", "relation": "addresses_unknown" }, { "source": "h-time-perception-striatal-beat-frequency", "target": "u-time-perception-mechanism", "relation": "addresses_unknown" }, { "source": "h-time-rescaled-residuals-separate-poisson-from-bursty-counting-systems", "target": "u-neural-decay-poisson-deviation-shared-overdispersion-tests", "relation": "addresses_unknown" }, { "source": "h-tissue-jamming-universality-class", "target": "u-jamming-transition-biological-tissues", "relation": "addresses_unknown" }, { "source": "h-tissue-jamming-universality-class", "target": "u-confluent-tissue-brownian-universality", "relation": "addresses_unknown" }, { "source": "h-tom-implicit-explicit-dissociation", "target": "u-great-ape-false-belief-implicit-explicit", "relation": "addresses_unknown" }, { "source": "h-topo-ii-inhibitor-transcription-coupled-dna-damage-selectivity", "target": "u-knot-invariants-rna-tertiary-structure-topology", "relation": "addresses_unknown" }, { "source": "h-topoelectrical-circuit-edge-mode-disorder-threshold", "target": "u-topoelectrical-circuit-disorder-robustness-limit", "relation": "addresses_unknown" }, { "source": "h-topological-data-analysis-x-cancer-genomics", "target": "u-topological-data-analysis-x-cancer-genomics", "relation": "addresses_unknown" }, { "source": "h-topological-defect-density-predicts-organoid-lumenogenesis", "target": "u-topological-defect-morphogenesis-3d-tissue", "relation": "addresses_unknown" }, { "source": "h-topological-defect-morphogenesis", "target": "u-topological-morphogenesis", "relation": "addresses_unknown" }, { "source": "h-topological-defects-x-homotopy", "target": "u-topological-defects-x-homotopy", "relation": "addresses_unknown" }, { "source": "h-topological-flocking-predator-evasion", "target": "u-fish-schooling-topological-interaction", "relation": "addresses_unknown" }, { "source": "h-topological-insulator-disorder-robustness", "target": "u-topological-insulator-surface-state-interactions", "relation": "addresses_unknown" }, { "source": "h-topological-insulator-majorana-fault-tolerant-qubit", "target": "u-topological-insulator-majorana-qubit-scalability", "relation": "addresses_unknown" }, { "source": "h-topological-insulator-x-band-theory", "target": "u-topological-insulator-x-band-theory", "relation": "addresses_unknown" }, { "source": "h-topological-phase-qec-threshold-correspondence", "target": "u-topological-qec-physical-realization", "relation": "addresses_unknown" }, { "source": "h-topological-qubit-fault-tolerance-threshold", "target": "u-majorana-zero-mode-experimental-confirmation", "relation": "addresses_unknown" }, { "source": "h-topology-chern-number-predicts-edge-state-count", "target": "u-topological-order-non-abelian-anyons-fault-tolerant", "relation": "addresses_unknown" }, { "source": "h-trade-war-tit-for-tat-equilibrium", "target": "u-trade-war-equilibrium", "relation": "addresses_unknown" }, { "source": "h-traffic-flow-turing-instability-stop-go", "target": "u-traffic-jam-phantom-formation-threshold", "relation": "addresses_unknown" }, { "source": "h-transactive-memory-network-topology-performance", "target": "u-collective-memory-network-structure", "relation": "addresses_unknown" }, { "source": "h-transcriptomic-conductance-firing-phenotype", "target": "u-hodgkin-huxley-channel-heterogeneity-neuron-diversity", "relation": "addresses_unknown" }, { "source": "h-transformer-embeddings-compositional-brain-alignment", "target": "u-distributional-semantics-compositionality", "relation": "addresses_unknown" }, { "source": "h-transformer-neural-attention-alignment", "target": "u-transformer-attention-biological-plausibility", "relation": "addresses_unknown" }, { "source": "h-transformer-temporal-attention-improves-ehr-risk-stratification", "target": "u-transformer-ehr-long-horizon-attribution-validity", "relation": "addresses_unknown" }, { "source": "h-transition-state-x-saddle-point", "target": "u-transition-state-x-saddle-point", "relation": "addresses_unknown" }, { "source": "h-trophic-cascade-metabolic-scaling", "target": "u-trophic-cascade-predictability", "relation": "addresses_unknown" }, { "source": "h-tropical-geometry-matroid-polytopes", "target": "u-tropical-geometry-combinatorics", "relation": "addresses_unknown" }, { "source": "h-tropical-geometry-x-neural-networks", "target": "u-tropical-geometry-x-neural-networks", "relation": "addresses_unknown" }, { "source": "h-tsunami-front-regime-classifier-nonlinear-dispersive-bore", "target": "u-tsunami-dispersive-nonlinearity-regime-classification", "relation": "addresses_unknown" }, { "source": "h-tsunami-submarine-slide-rheology", "target": "u-tsunami-submarine-slides", "relation": "addresses_unknown" }, { "source": "h-tur-constrained-estimators-predict-atp-cost-precision-frontier", "target": "u-thermodynamic-uncertainty-bound-biochemical-estimators", "relation": "addresses_unknown" }, { "source": "h-turbulence-directed-percolation", "target": "u-turbulence-onset-subcritical-transition", "relation": "addresses_unknown" }, { "source": "h-turbulence-energy-cascade-exact-scaling", "target": "u-turbulence-mathematical-formulation", "relation": "addresses_unknown" }, { "source": "h-turing-digit-count-bmp-gradient-wavelength-scaling", "target": "u-turing-morphogen-identity-in-vivo-diffusion-measurement", "relation": "addresses_unknown" }, { "source": "h-turing-instability-aerosol-nucleation", "target": "u-atmospheric-chemistry-aerosol-nucleation", "relation": "addresses_unknown" }, { "source": "h-turing-pattern-wavelength-experimental-test", "target": "u-morphogen-gradient-robustness-scaling", "relation": "addresses_unknown" }, { "source": "h-twistronics-magic-angle-correlated-states", "target": "u-twistronics-moire-phases", "relation": "addresses_unknown" }, { "source": "h-two-class-economy-boltzmann-pareto-transition", "target": "u-wealth-distribution-universality", "relation": "addresses_unknown" }, { "source": "h-two-compartment-pk-genotype-prediction", "target": "u-pharmacokinetic-interindividual-variability", "relation": "addresses_unknown" }, { "source": "h-two-state-folders-admit-pl-like-surrogate-on-contact-order-parameter", "target": "u-protein-folding-pl-constant-coarse-grained-surrogate", "relation": "addresses_unknown" }, { "source": "h-two-step-nucleation-density-liquid-precursor", "target": "u-classical-nucleation-theory-prefactor-discrepancy", "relation": "addresses_unknown" }, { "source": "h-type-ia-double-degenerate-dominant-channel", "target": "u-type-ia-supernova-progenitor", "relation": "addresses_unknown" }, { "source": "h-ubi-labour-supply-innovation-offset", "target": "u-universal-basic-income-macro-effects", "relation": "addresses_unknown" }, { "source": "h-unet-domain-randomization-improves-flood-mapping-recall", "target": "u-unet-satellite-flood-generalization-under-cloud-noise", "relation": "addresses_unknown" }, { "source": "h-univalence-axiom-proof-assistant-verification", "target": "u-homotopy-type-theory-computational-foundations", "relation": "addresses_unknown" }, { "source": "h-urban-heat-island-superlinear-density-scaling", "target": "u-urban-heat-island-nonlinearity", "relation": "addresses_unknown" }, { "source": "h-urban-heat-islands-energy-balance", "target": "u-urban-heat-islands", "relation": "addresses_unknown" }, { "source": "h-urban-segregation-school-quality-reinforcement", "target": "u-urban-segregation-self-reinforcement", "relation": "addresses_unknown" }, { "source": "h-urban-superlinear-scaling-social-interaction-fractal-road-network", "target": "u-urban-scaling-law-exponent-inequality-cultural-variation", "relation": "addresses_unknown" }, { "source": "h-v1-gabor-infomax-prediction", "target": "u-efficient-coding-metabolic-optimality", "relation": "addresses_unknown" }, { "source": "h-vae-latent-regularization-improves-catalyst-hit-rate", "target": "u-vae-catalyst-latent-disentanglement-validity", "relation": "addresses_unknown" }, { "source": "h-van-der-waals-free-energy-double-well", "target": "u-posterior-landscape-multimodality", "relation": "addresses_unknown" }, { "source": "h-variational-assimilation-derived-glucose-predictions-outperform-sliding-window-baselines", "target": "u-assimilation-window-stability-for-patient-specific-glucose-dynamics", "relation": "addresses_unknown" }, { "source": "h-variational-inference-free-energy-rg", "target": "u-variational-inference-x-free-energy", "relation": "addresses_unknown" }, { "source": "h-vcg-regretnet-combinatorial-approximation", "target": "u-vcg-combinatorial-auction-scalability", "relation": "addresses_unknown" }, { "source": "h-vegetation-stripe-turing-instability", "target": "u-turing-pattern-selection-ecology", "relation": "addresses_unknown" }, { "source": "h-vertex-model-cortical-gyrification-mechanics", "target": "u-gyrification-mechanics-developmental-timing", "relation": "addresses_unknown" }, { "source": "h-vibronic-coupling-fmo-coherence-functional-enhancement", "target": "u-photosynthesis-quantum-coherence-physiological-function", "relation": "addresses_unknown" }, { "source": "h-vickrey-clarke-groves-payments-improve-lab-truthful-reporting", "target": "u-truthful-elicitation-mechanism-duality", "relation": "addresses_unknown" }, { "source": "h-viral-proofreading-shannon-optimality", "target": "u-error-threshold-genome-size", "relation": "addresses_unknown" }, { "source": "h-viral-quasispecies-x-nk-rugged-landscape", "target": "u-quasispecies-nk-parameter-identifiability", "relation": "addresses_unknown" }, { "source": "h-virial-multicomponent-consistency-reduces-cluster-mass-bias", "target": "u-virial-cloud-cluster-multicomponent-bias", "relation": "addresses_unknown" }, { "source": "h-visual-art-fluency-arousal-valence-response", "target": "u-visual-art-emotional-response", "relation": "addresses_unknown" }, { "source": "h-visual-art-fluency-arousal-valence", "target": "u-visual-art-emotional-response", "relation": "addresses_unknown" }, { "source": "h-vit-based-phenotyping-improves-early-crop-stress-detection", "target": "u-vit-crop-stress-generalization-across-sensors", "relation": "addresses_unknown" }, { "source": "h-volatility-autocorrelation-satisfies-effective-fd-response", "target": "u-fluctuation-dissipation-stationary-market-assumption-breakdown", "relation": "addresses_unknown" }, { "source": "h-voting-theory-x-social-choice", "target": "u-voting-theory-x-social-choice", "relation": "addresses_unknown" }, { "source": "h-waddington-attractor-reprogramming-energy", "target": "u-waddington-canalization-mechanism", "relation": "addresses_unknown" }, { "source": "h-wais-mici-buttressing-stability", "target": "u-ice-sheet-instability-modes", "relation": "addresses_unknown" }, { "source": "h-wais-mici-buttressing-stability", "target": "u-marine-ice-cliff-instability", "relation": "addresses_unknown" }, { "source": "h-war-onset-grievance-greed-prediction", "target": "u-war-onset-prediction", "relation": "addresses_unknown" }, { "source": "h-wasserstein-dro-improves-tail-safe-adaptation-metrics", "target": "u-dro-ambiguity-set-specification-nonstationary-climate", "relation": "addresses_unknown" }, { "source": "h-water-llcp-femtosecond-detection", "target": "u-hydrogen-bond-network-water-anomalies", "relation": "addresses_unknown" }, { "source": "h-wavelet-shrinkage-minimax-optimal-natural-image-sparsity", "target": "u-wavelet-optimal-basis-nonstationary-signal-adaptation", "relation": "addresses_unknown" }, { "source": "h-wavelet-subband-energy-tracks-rg-relevant-flux", "target": "u-rg-wavelet-beta-function-quantitative-map", "relation": "addresses_unknown" }, { "source": "h-wealth-concentration-tipping-point-inequality", "target": "u-economic-inequality-tipping-points", "relation": "addresses_unknown" }, { "source": "h-wealth-distribution-boltzmann-savings-propensity", "target": "u-econophysics-wealth-distribution-mechanism", "relation": "addresses_unknown" }, { "source": "h-wildfire-aerosol-albedo-net-positive-feedback", "target": "u-wildfire-climate-feedback", "relation": "addresses_unknown" }, { "source": "h-willmore-energy-biological-membrane-morphogenesis-ground-state", "target": "u-almgren-regularity-singular-set-sharp-dimension", "relation": "addresses_unknown" }, { "source": "h-wireless-power-friis-near-field-tradeoff", "target": "u-wireless-power-transfer-limit", "relation": "addresses_unknown" }, { "source": "h-wisdom-of-crowds-condorcet", "target": "u-wisdom-of-crowds-condorcet", "relation": "addresses_unknown" }, { "source": "h-working-memory-alpha-suppression-capacity-limit", "target": "u-working-memory-capacity", "relation": "addresses_unknown" }, { "source": "h-wpt-coexistence-requires-q-bandwidth-renegotiation-per-standard", "target": "u-wpt-narrowband-q-bandwidth-multi-standard-coexistence", "relation": "addresses_unknown" }, { "source": "h-writing-system-phonological-awareness-route", "target": "u-writing-system-cognition-effects", "relation": "addresses_unknown" }, { "source": "h-xenolith-sampling-bias-kimberlite", "target": "u-xenolith-mantle-bias", "relation": "addresses_unknown" }, { "source": "h-xna-ribozyme-catalytic-efficiency-backbone-independence", "target": "u-xna-expanded-genetic-alphabet-catalysis", "relation": "addresses_unknown" }, { "source": "h-zahavi-handicap-single-crossing-stable-honest", "target": "u-zahavi-handicap-mechanism-multimodal", "relation": "addresses_unknown" }, { "source": "h-zeeman-multiplet-spacing-shows-quantum-chaos-statistics", "target": "u-zeeman-spectrum-unfolding-rmt-quantitative-test", "relation": "addresses_unknown" }, { "source": "h-zero-trust-control-raises-effective-percolation-threshold", "target": "u-graph-percolation-lateral-movement-detection-threshold", "relation": "addresses_unknown" }, { "source": "h-zipf-critical-point-communication-efficiency", "target": "u-zipf-law-mechanism-adaptive-vs-null", "relation": "addresses_unknown" }, { "source": "u-phononic-crystal-3d-complete-band-gap", "target": "b-acoustic-metamaterials-phononic-band-gaps", "relation": "related_bridge" }, { "source": "u-phononic-crystal-active-tunable-band-gap", "target": "b-phononic-crystals-acoustic-band-gap-bragg", "relation": "related_bridge" }, { "source": "u-neural-correlates-consciousness-anesthesia-mechanism", "target": "b-anesthesia-consciousness-suppression", "relation": "related_bridge" }, { "source": "u-antibiotic-resistance-evolution-rate-clinical-deployment", "target": "b-antibiotic-mechanisms-resistance", "relation": "related_bridge" }, { "source": "u-threshold-selection-bias-in-evt-based-amr-early-warning", "target": "b-extreme-value-theory-x-antimicrobial-resistance-surveillance", "relation": "related_bridge" }, { "source": "u-threshold-selection-bias-in-evt-based-amr-early-warning", "target": "h-peaks-over-threshold-models-improve-amr-outbreak-early-warning", "relation": "suggested_hypothesis" }, { "source": "u-astronomical-source-matching-structured-outlier-robustness", "target": "b-ransac-robust-estimation-x-astronomical-source-matching", "relation": "related_bridge" }, { "source": "u-astronomical-source-matching-structured-outlier-robustness", "target": "h-quality-ranked-ransac-improves-astrometric-crossmatch-precision", "relation": "suggested_hypothesis" }, { "source": "u-baryon-asymmetry-origin", "target": "u-leptogenesis-cp-scale", "relation": "related_unknown" }, { "source": "u-baryon-asymmetry-origin", "target": "b-baryon-asymmetry-cp-violation", "relation": "related_bridge" }, { "source": "u-black-hole-information-paradox", "target": "u-hawking-channel-capacity", "relation": "related_unknown" }, { "source": "u-black-hole-information-paradox", "target": "b-blackhole-information-paradox", "relation": "related_bridge" }, { "source": "u-dark-matter-particle-identity", "target": "u-qcd-ew-phase-transition-relics", "relation": "related_unknown" }, { "source": "u-dark-matter-particle-identity", "target": "b-dark-matter-phase-transition-relics", "relation": "related_bridge" }, { "source": "u-exoplanet-spectral-retrieval", "target": "b-exoplanet-spectral-retrieval-bayesian", "relation": "related_bridge" }, { "source": "u-fast-radio-burst-origin", "target": "u-frb-waiting-time-universality", "relation": "related_unknown" }, { "source": "u-fast-radio-burst-origin", "target": "b-frb-random-matrix", "relation": "related_bridge" }, { "source": "u-gravothermal-catastrophe-globular-cluster-timescale", "target": "b-stellar-structure-thermodynamics", "relation": "related_bridge" }, { "source": "u-gravothermal-catastrophe-globular-cluster-timescale", "target": "h-negative-heat-capacity-stellar-stability-criterion", "relation": "suggested_hypothesis" }, { "source": "u-halo-merger-tree-nbody-clustering-analogy", "target": "b-dark-matter-substructure-x-halo-merger-tree-algorithms", "relation": "related_bridge" }, { "source": "u-halo-merger-tree-nbody-clustering-analogy", "target": "h-merger-tree-branching-matches-subhalo-statistics-scaling", "relation": "suggested_hypothesis" }, { "source": "u-neural-operator-space-weather-extreme-event-calibration", "target": "b-neural-operator-x-space-weather-data-assimilation", "relation": "related_bridge" }, { "source": "u-neural-operator-space-weather-extreme-event-calibration", "target": "h-neural-operator-assimilation-improves-space-weather-lead-time", "relation": "suggested_hypothesis" }, { "source": "u-red-sequence-quenching-unified-timescales", "target": "b-red-sequence-x-galaxy-evolution", "relation": "related_bridge" }, { "source": "u-red-sequence-quenching-unified-timescales", "target": "h-red-sequence-age-spreads-constrain-quenching-models", "relation": "suggested_hypothesis" }, { "source": "u-saturn-ring-viscosity-radial-transport", "target": "b-planetary-rings-viscous-accretion-disk", "relation": "related_bridge" }, { "source": "u-stellar-forcing-climate-sensitivity-scale", "target": "b-stellar-forcing-paleoclimate", "relation": "related_bridge" }, { "source": "u-uhecr-origin", "target": "u-grb-mass-extinction-link", "relation": "related_unknown" }, { "source": "u-uhecr-origin", "target": "b-cosmic-rays-mutagenesis", "relation": "related_bridge" }, { "source": "u-virial-cloud-cluster-multicomponent-bias", "target": "b-virial-theorem-x-molecular-cloud-cluster-equilibrium", "relation": "related_bridge" }, { "source": "u-virial-cloud-cluster-multicomponent-bias", "target": "h-virial-multicomponent-consistency-reduces-cluster-mass-bias", "relation": "suggested_hypothesis" }, { "source": "u-accretion-disk-mri-saturation", "target": "b-accretion-disk-mhd-turbulence", "relation": "related_bridge" }, { "source": "u-black-hole-information-paradox", "target": "b-black-hole-entropy-holographic", "relation": "related_bridge" }, { "source": "u-black-hole-information-paradox", "target": "h-spacetime-emerges-from-entanglement", "relation": "suggested_hypothesis" }, { "source": "u-gravitational-lensing-caustic-topology", "target": "b-gravitational-lensing-optical-caustics", "relation": "related_bridge" }, { "source": "u-gravitational-wave-astrophysics-population", "target": "b-general-relativity-differential-geometry", "relation": "related_bridge" }, { "source": "u-grb-jet-breakout-shock-microphysics", "target": "b-gamma-ray-burst-jets-x-relativistic-hydrodynamics", "relation": "related_bridge" }, { "source": "u-grb-jet-breakout-shock-microphysics", "target": "h-jet-break-timescale-scales-with-entropy-and-opening-angle", "relation": "suggested_hypothesis" }, { "source": "u-helioseismic-inversion-uniqueness-depth", "target": "b-helioseismology-x-inverse-eigenvalue-problems", "relation": "related_bridge" }, { "source": "u-helioseismic-inversion-uniqueness-depth", "target": "h-helioseismology-x-inverse-eigenvalue-problems", "relation": "suggested_hypothesis" }, { "source": "u-neutron-star-eos-dense-matter-phase-transition", "target": "b-neutron-star-nuclear-eos", "relation": "related_bridge" }, { "source": "u-solar-wind-alfven-wave-dissipation-scale", "target": "b-solar-wind-alfven-wave-turbulence", "relation": "related_bridge" }, { "source": "u-bargaining-power-measurement-real-world-negotiations", "target": "b-bargaining-theory-negotiation", "relation": "related_bridge" }, { "source": "u-marcus-tunneling-reaction-coordinate-biochemistry", "target": "b-marcus-tunneling-x-enzyme-reaction-coordinate", "relation": "related_bridge" }, { "source": "u-marcus-tunneling-reaction-coordinate-biochemistry", "target": "h-marcus-tunneling-x-enzyme-reaction-coordinate", "relation": "suggested_hypothesis" }, { "source": "u-active-brownian-motion-x-cell-migration", "target": "b-active-brownian-motion-x-cell-migration", "relation": "related_bridge" }, { "source": "u-adaptive-therapy-evolutionary-trap-clinical-validation", "target": "b-optimal-control-cancer-treatment", "relation": "related_bridge" }, { "source": "u-aging-hallmarks-causal-hierarchy", "target": "u-epigenetic-inheritance-transgenerational", "relation": "related_unknown" }, { "source": "u-allometric-scaling-metabolic-universality", "target": "b-allometric-scaling-metabolic-geometry", "relation": "related_bridge" }, { "source": "u-allometry-fractal-networks-deviations", "target": "u-renormalization-allometric", "relation": "related_unknown" }, { "source": "u-allosteric-regulation-x-conformational-dynamics", "target": "b-allosteric-regulation-x-conformational-dynamics", "relation": "related_bridge" }, { "source": "u-alzheimer-network-attractor-dynamics", "target": "u-hopfield-capacity-cortex", "relation": "related_unknown" }, { "source": "u-alzheimer-network-attractor-dynamics", "target": "b-spin-glass-neural-networks", "relation": "related_bridge" }, { "source": "u-ant-colony-optimization-convergence-rate", "target": "b-ant-colony-distributed-computation", "relation": "related_bridge" }, { "source": "u-bacterial-chemotaxis-x-gradient-descent", "target": "b-bacterial-chemotaxis-x-gradient-descent", "relation": "related_bridge" }, { "source": "u-biofilm-x-active-nematic", "target": "b-biofilm-x-active-nematic", "relation": "related_bridge" }, { "source": "u-biomechanics-x-soft-robotics", "target": "b-biomechanics-x-soft-robotics", "relation": "related_bridge" }, { "source": "u-blood-coagulation-cascade", "target": "b-blood-coagulation-cascade-boolean", "relation": "related_bridge" }, { "source": "u-boolean-network-cancer-attractors", "target": "b-genetic-regulatory-boolean-circuits", "relation": "related_bridge" }, { "source": "u-bp-convergence-loopy-genetic-linkage-graphs", "target": "b-belief-propagation-x-genotype-phasing-linkage-graphs", "relation": "related_bridge" }, { "source": "u-bp-convergence-loopy-genetic-linkage-graphs", "target": "h-damped-bp-calibration-improves-phasing-accuracy", "relation": "suggested_hypothesis" }, { "source": "u-braess-paradox-biological-foraging", "target": "u-habitat-fragmentation-threshold", "relation": "related_unknown" }, { "source": "u-braess-paradox-biological-foraging", "target": "b-game-theory-evolution", "relation": "related_bridge" }, { "source": "u-calcium-signaling-x-stochastic-resonance", "target": "b-calcium-signaling-x-stochastic-resonance", "relation": "related_bridge" }, { "source": "u-cell-division-x-branching-process", "target": "b-cell-division-x-branching-process", "relation": "related_bridge" }, { "source": "u-chromatin-loop-extrusion-speed-processivity-in-vivo", "target": "b-dna-mechanics-chromatin", "relation": "related_bridge" }, { "source": "u-circadian-clock-x-feedback-oscillator", "target": "b-circadian-clock-x-feedback-oscillator", "relation": "related_bridge" }, { "source": "u-circadian-kuramoto-jet-lag-dynamics", "target": "u-soc-universality-class-brain", "relation": "related_unknown" }, { "source": "u-circadian-metabolism-coupling", "target": "u-circadian-kuramoto-jet-lag-dynamics", "relation": "related_unknown" }, { "source": "u-contact-graph-hessian-rank-native-basin-surrogate", "target": "b-contact-map-sparsity-x-hessian-low-rank-folding-cooperativity", "relation": "related_bridge" }, { "source": "u-contact-graph-hessian-rank-native-basin-surrogate", "target": "h-low-rank-hessian-surrogate-predicts-two-state-phi-profile-class", "relation": "suggested_hypothesis" }, { "source": "u-cortical-folding-topology", "target": "u-topological-morphogenesis", "relation": "related_unknown" }, { "source": "u-cortical-folding-topology", "target": "b-topology-morphogenesis", "relation": "related_bridge" }, { "source": "u-crispr-base-editing-x-error-correction", "target": "b-crispr-base-editing-x-error-correction", "relation": "related_bridge" }, { "source": "u-crispr-multiplex-error-floor-vs-code-distance", "target": "b-crispr-multiplex-pooling-x-barcode-redundancy-intuition", "relation": "related_bridge" }, { "source": "u-crispr-multiplex-error-floor-vs-code-distance", "target": "h-barcode-spacing-heuristic-lowers-decoding-error-measured-in-negative-controls", "relation": "suggested_hypothesis" }, { "source": "u-crispr-x-search-and-replace", "target": "b-crispr-x-search-and-replace", "relation": "related_bridge" }, { "source": "u-cryoem-laplacian-eigenmode-physical-interpretability", "target": "b-graph-laplacian-manifold-learning-x-cryoem-conformational-maps", "relation": "related_bridge" }, { "source": "u-cryoem-laplacian-eigenmode-physical-interpretability", "target": "h-laplacian-eigenmodes-improve-cryoem-conformation-clustering", "relation": "suggested_hypothesis" }, { "source": "u-cryptochrome-radical-pair-quantum-nav", "target": "u-quantum-biology-decoherence", "relation": "related_unknown" }, { "source": "u-cryptochrome-radical-pair-quantum-nav", "target": "b-spin-glass-neural-networks", "relation": "related_bridge" }, { "source": "u-developmental-gradient-x-pde", "target": "b-developmental-gradient-x-pde", "relation": "related_bridge" }, { "source": "u-dna-origami-compiler-analogy-yield-prediction-limits", "target": "b-dna-origami-scaffold-routing-x-staged-compilation-analogy", "relation": "related_bridge" }, { "source": "u-dna-origami-compiler-analogy-yield-prediction-limits", "target": "h-scaffold-routing-constraint-metrics-predict-origami-yield", "relation": "suggested_hypothesis" }, { "source": "u-dna-replication-x-error-correction", "target": "b-dna-replication-x-error-correction", "relation": "related_bridge" }, { "source": "u-ecology-resilience-spatial-indicator", "target": "u-climate-ew-indicator-universality", "relation": "related_unknown" }, { "source": "u-ecology-resilience-spatial-indicator", "target": "u-vegetation-pattern-tipping-universality", "relation": "related_unknown" }, { "source": "u-epigenetic-escape-loci-mechanisms-transgenerational-scope", "target": "b-epigenetics-transgenerational-trauma", "relation": "related_bridge" }, { "source": "u-epigenetic-escape-loci-mechanisms-transgenerational-scope", "target": "h-sperm-small-rna-mediates-paternal-trauma-epigenetic-inheritance", "relation": "suggested_hypothesis" }, { "source": "u-epigenetic-inheritance-transgenerational", "target": "u-fitness-landscape-overlapping-genes", "relation": "related_unknown" }, { "source": "u-epigenetic-landscape-x-attractor", "target": "b-epigenetic-landscape-x-attractor", "relation": "related_bridge" }, { "source": "u-error-threshold-genome-size", "target": "u-grokking-phase-transition", "relation": "related_unknown" }, { "source": "u-error-threshold-genome-size", "target": "b-error-threshold-information", "relation": "related_bridge" }, { "source": "u-evolution-undecidability-open-ended", "target": "u-error-threshold-genome-size", "relation": "related_unknown" }, { "source": "u-evolution-undecidability-open-ended", "target": "u-fisher-natural-gradient-evolution", "relation": "related_unknown" }, { "source": "u-evolution-undecidability-open-ended", "target": "b-game-theory-evolution", "relation": "related_bridge" }, { "source": "u-fisher-natural-gradient-evolution", "target": "b-fisher-information-evolution", "relation": "related_bridge" }, { "source": "u-fisher-natural-gradient-evolution", "target": "b-error-threshold-information", "relation": "related_bridge" }, { "source": "u-fitness-landscape-overlapping-genes", "target": "u-error-threshold-genome-size", "relation": "related_unknown" }, { "source": "u-flagellar-motor-x-rotary-engine", "target": "b-flagellar-motor-x-rotary-engine", "relation": "related_bridge" }, { "source": "u-flocking-topological-interaction-mechanism", "target": "b-flocking-reynolds-boids-alignment", "relation": "related_bridge" }, { "source": "u-funnel-ruggedness-docking-false-minima", "target": "b-energy-landscape-funnels-x-protein-ligand-docking-search", "relation": "related_bridge" }, { "source": "u-funnel-ruggedness-docking-false-minima", "target": "h-funnel-aware-search-reduces-docking-decoy-traps", "relation": "suggested_hypothesis" }, { "source": "u-game-theory-x-antibiotic-resistance", "target": "b-game-theory-x-antibiotic-resistance", "relation": "related_bridge" }, { "source": "u-gene-expression-noise-x-information-theory", "target": "b-gene-expression-noise-x-information-theory", "relation": "related_bridge" }, { "source": "u-gene-regulatory-network-x-boolean-circuit", "target": "b-gene-regulatory-network-x-boolean-circuit", "relation": "related_bridge" }, { "source": "u-grb-mass-extinction-link", "target": "u-uhecr-origin", "relation": "related_unknown" }, { "source": "u-grb-mass-extinction-link", "target": "b-cosmic-rays-mutagenesis", "relation": "related_bridge" }, { "source": "u-great-ape-false-belief-implicit-explicit", "target": "b-animal-cognition-theory-of-mind", "relation": "related_bridge" }, { "source": "u-great-ape-false-belief-implicit-explicit", "target": "h-tom-implicit-explicit-dissociation", "relation": "suggested_hypothesis" }, { "source": "u-grn-gnn-perturbation-identifiability", "target": "b-gnn-x-gene-regulatory-network-perturbation-priors", "relation": "related_bridge" }, { "source": "u-grn-gnn-perturbation-identifiability", "target": "h-grn-gnn-priors-improve-perturbation-response-prediction", "relation": "suggested_hypothesis" }, { "source": "u-gut-microbiome-x-lotka-volterra", "target": "b-gut-microbiome-x-lotka-volterra", "relation": "related_bridge" }, { "source": "u-habitat-fragmentation-threshold", "target": "u-percolation-epidemic-fss", "relation": "related_unknown" }, { "source": "u-habitat-fragmentation-threshold", "target": "u-tumor-containment-percolation", "relation": "related_unknown" }, { "source": "u-habitat-fragmentation-threshold", "target": "b-habitat-percolation-ecology", "relation": "related_bridge" }, { "source": "u-habitat-fragmentation-threshold", "target": "b-percolation-epidemiology", "relation": "related_bridge" }, { "source": "u-habitat-fragmentation-threshold", "target": "b-percolation-oncology", "relation": "related_bridge" }, { "source": "u-immune-memory-x-long-term-potentiation", "target": "b-immune-memory-x-long-term-potentiation", "relation": "related_bridge" }, { "source": "u-intestinal-crypt-stem-cell-moran-selection", "target": "b-intestinal-crypt-stem-cell-moran-process", "relation": "related_bridge" }, { "source": "u-jamming-exponent-universality-epithelium-versus-colloid", "target": "b-epithelial-jamming-x-colloidal-glass-rheology", "relation": "related_bridge" }, { "source": "u-jamming-exponent-universality-epithelium-versus-colloid", "target": "h-shared-shape-index-scaling-near-jamming-across-donors", "relation": "suggested_hypothesis" }, { "source": "u-lateral-gene-transfer-rate-limits", "target": "u-error-threshold-genome-size", "relation": "related_unknown" }, { "source": "u-lipid-raft-protein-sorting-mechanism", "target": "b-liquid-crystal-cell-membranes", "relation": "related_bridge" }, { "source": "u-lipid-raft-protein-sorting-mechanism", "target": "h-membrane-defects-protein-clustering", "relation": "suggested_hypothesis" }, { "source": "u-loss-aversion-cross-cultural-evolutionary-universality", "target": "b-behavioral-economics-evolutionary-psychology", "relation": "related_bridge" }, { "source": "u-maxent-ecology-failure-modes", "target": "b-maximum-entropy-ecology", "relation": "related_bridge" }, { "source": "u-maxent-ecology-failure-modes", "target": "h-mete-non-equilibrium-deviations", "relation": "suggested_hypothesis" }, { "source": "u-mechanical-bifurcation-morphogenesis", "target": "u-topological-morphogenesis", "relation": "related_unknown" }, { "source": "u-mechanical-bifurcation-morphogenesis", "target": "u-kibble-zurek-embryo", "relation": "related_unknown" }, { "source": "u-mechanical-bifurcation-morphogenesis", "target": "b-turing-reaction-diffusion", "relation": "related_bridge" }, { "source": "u-mechanosensing-x-force-transduction", "target": "b-mechanosensing-x-force-transduction", "relation": "related_bridge" }, { "source": "u-membrane-tension-x-laplace-pressure", "target": "b-membrane-tension-x-laplace-pressure", "relation": "related_bridge" }, { "source": "u-metabolic-flux-entropy-production-cancer-cells", "target": "b-nonequilibrium-statistical-mechanics-metabolism", "relation": "related_bridge" }, { "source": "u-metabolic-flux-x-linear-programming", "target": "b-metabolic-flux-x-linear-programming", "relation": "related_bridge" }, { "source": "u-metabolic-scaling-fractal-transport-unification", "target": "b-metabolic-scaling-x-fractal-transport", "relation": "related_bridge" }, { "source": "u-metabolic-scaling-fractal-transport-unification", "target": "h-kleiber-exponent-from-fractal-like-transport-networks", "relation": "suggested_hypothesis" }, { "source": "u-microbiome-brain-axis-mechanism", "target": "u-self-organized-criticality-consciousness", "relation": "related_unknown" }, { "source": "u-microplate-inverse-beer-lambert-conditioning", "target": "b-microplate-absorbance-x-inverse-beer-lambert-calibration", "relation": "related_bridge" }, { "source": "u-microplate-inverse-beer-lambert-conditioning", "target": "h-multi-wavelength-beer-lambert-inverse-improves-plate-precision", "relation": "suggested_hypothesis" }, { "source": "u-modularity-robustness-evolvability-tradeoff", "target": "u-earthquake-soc-universality-class", "relation": "related_unknown" }, { "source": "u-modularity-robustness-evolvability-tradeoff", "target": "b-robustness-evolvability-modularity", "relation": "related_bridge" }, { "source": "u-modularity-robustness-evolvability-tradeoff", "target": "b-engineering-reliability-extreme-value", "relation": "related_bridge" }, { "source": "u-morphogenesis-x-mechanical-instability", "target": "b-morphogenesis-x-mechanical-instability", "relation": "related_bridge" }, { "source": "u-muscle-mechanics-x-crossbridge-theory", "target": "b-muscle-mechanics-x-crossbridge-theory", "relation": "related_bridge" }, { "source": "u-myosin-motor-x-brownian-ratchet", "target": "b-myosin-motor-x-brownian-ratchet", "relation": "related_bridge" }, { "source": "u-neutral-theory-x-stochastic-sampling", "target": "b-neutral-theory-x-stochastic-sampling", "relation": "related_bridge" }, { "source": "u-pathogen-coevolution-network-percolation", "target": "u-percolation-epidemic-fss", "relation": "related_unknown" }, { "source": "u-pathogen-coevolution-network-percolation", "target": "u-habitat-fragmentation-threshold", "relation": "related_unknown" }, { "source": "u-percolation-epidemic-fss", "target": "u-tumor-containment-percolation", "relation": "related_unknown" }, { "source": "u-percolation-epidemic-fss", "target": "b-percolation-epidemiology", "relation": "related_bridge" }, { "source": "u-percolation-epidemic-fss", "target": "b-percolation-oncology", "relation": "related_bridge" }, { "source": "u-photoreceptor-quantum-efficiency-x-photon-statistics", "target": "b-photoreceptor-quantum-efficiency-x-photon-statistics", "relation": "related_bridge" }, { "source": "u-phylogenetics-x-coalescent-theory", "target": "b-phylogenetics-x-coalescent-theory", "relation": "related_bridge" }, { "source": "u-prion-nucleation-spontaneous-rate-physiological", "target": "b-prion-misfolding-nucleation", "relation": "related_bridge" }, { "source": "u-protein-aggregation-x-nucleation-growth", "target": "b-protein-aggregation-x-nucleation-growth", "relation": "related_bridge" }, { "source": "u-protein-folding-alphafold2-de-novo-design-limits", "target": "b-protein-folding-energy-landscape", "relation": "related_bridge" }, { "source": "u-protein-folding-pl-constant-coarse-grained-surrogate", "target": "b-protein-folding-funnel-x-polyak-lojasiewicz-optimization-region", "relation": "related_bridge" }, { "source": "u-protein-folding-pl-constant-coarse-grained-surrogate", "target": "h-two-state-folders-admit-pl-like-surrogate-on-contact-order-parameter", "relation": "suggested_hypothesis" }, { "source": "u-protein-folding-x-energy-landscape", "target": "b-protein-folding-x-energy-landscape", "relation": "related_bridge" }, { "source": "u-quantum-coherence-physiological-role", "target": "b-quantum-coherence-photosynthesis", "relation": "related_bridge" }, { "source": "u-quantum-coherence-physiological-role", "target": "h-fmo-enaqt-efficiency", "relation": "suggested_hypothesis" }, { "source": "u-quantum-tunneling-enzyme-room-temperature-scope", "target": "b-quantum-tunneling-enzyme-catalysis", "relation": "related_bridge" }, { "source": "u-quantum-tunneling-enzyme-room-temperature-scope", "target": "h-protein-dynamics-optimize-quantum-tunneling", "relation": "suggested_hypothesis" }, { "source": "u-quorum-signaling-as-multiplayer-game", "target": "b-quorum-sensing-x-game-theory", "relation": "related_bridge" }, { "source": "u-quorum-signaling-as-multiplayer-game", "target": "h-quorum-thresholds-are-ess-under-stochastic-demography", "relation": "suggested_hypothesis" }, { "source": "u-random-matrix-eigenvalue-cleaning-single-cell-batch-effects", "target": "b-random-matrix-denoising-x-single-cell-covariance-cleaning", "relation": "related_bridge" }, { "source": "u-random-matrix-eigenvalue-cleaning-single-cell-batch-effects", "target": "h-rmt-covariance-cleaning-improves-single-cell-state-clustering", "relation": "suggested_hypothesis" }, { "source": "u-redfield-ratio-evolution-optimality", "target": "u-stoichiometry-food-web-stability", "relation": "related_unknown" }, { "source": "u-regulatory-network-attractor-enumeration", "target": "b-regulatory-networks-boolean-sat", "relation": "related_bridge" }, { "source": "u-renormalization-allometric", "target": "u-kleiber-pulsatile-waves", "relation": "related_unknown" }, { "source": "u-renormalization-allometric", "target": "b-renormalization-biological-scaling", "relation": "related_bridge" }, { "source": "u-renormalization-allometric", "target": "b-topology-morphogenesis", "relation": "related_bridge" }, { "source": "u-replication-fork-tasep-parameter-identifiability-from-seq-stalling-assays", "target": "b-dna-replication-fork-x-asymmetric-exclusion-traffic-jam", "relation": "related_bridge" }, { "source": "u-replication-fork-tasep-parameter-identifiability-from-seq-stalling-assays", "target": "h-stalling-density-wave-speed-correlates-with-seq-measured-pause-density-peaks", "relation": "suggested_hypothesis" }, { "source": "u-replicator-dynamics-llm-training", "target": "h-gan-training-redqueen-dynamics", "relation": "suggested_hypothesis" }, { "source": "u-scale-free-network-x-metabolic", "target": "b-scale-free-network-x-metabolic", "relation": "related_bridge" }, { "source": "u-self-organized-criticality-consciousness", "target": "u-brain-criticality-function", "relation": "related_unknown" }, { "source": "u-self-organized-criticality-consciousness", "target": "u-soc-universality-class-brain", "relation": "related_unknown" }, { "source": "u-self-organized-criticality-consciousness", "target": "b-criticality-neuroscience", "relation": "related_bridge" }, { "source": "u-senescence-sasp-cancer-promotion-threshold", "target": "b-cellular-senescence-tumor-suppression", "relation": "related_bridge" }, { "source": "u-sir-model-x-compartmental-ode", "target": "b-sir-model-x-compartmental-ode", "relation": "related_bridge" }, { "source": "u-stochastic-gene-expression-bet-hedging-quantitative", "target": "b-stochastic-gene-expression-noise", "relation": "related_bridge" }, { "source": "u-stochastic-resonance-neural-tuning", "target": "b-stochastic-resonance", "relation": "related_bridge" }, { "source": "u-stochastic-resonance-neural-tuning", "target": "h-sensory-noise-sr-optimality", "relation": "suggested_hypothesis" }, { "source": "u-synthetic-biology-x-circuit-design", "target": "b-synthetic-biology-x-circuit-design", "relation": "related_bridge" }, { "source": "u-synthetic-lichen-biofabrication", "target": "h-lichen-consortium-metabolic-coupling", "relation": "suggested_hypothesis" }, { "source": "u-temporal-biosignature-information", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-topological-morphogenesis", "target": "b-topology-morphogenesis", "relation": "related_bridge" }, { "source": "u-tumor-containment-percolation", "target": "h-adaptive-therapy-percolation-threshold", "relation": "suggested_hypothesis" }, { "source": "u-turing-digit-wavelength-scaling", "target": "h-turing-zebrafish-diffusivity-ratio", "relation": "suggested_hypothesis" }, { "source": "u-turing-ms-demyelination-pattern", "target": "h-turing-zebrafish-diffusivity-ratio", "relation": "suggested_hypothesis" }, { "source": "u-vicsek-transition-order-finite-systems", "target": "b-vicsek-active-matter-flocking", "relation": "related_bridge" }, { "source": "u-lipid-raft-functional-role-signaling", "target": "b-lipid-bilayer-membrane-transport", "relation": "related_bridge" }, { "source": "u-protein-fitness-landscape-epistasis-ruggedness", "target": "b-enzyme-engineering-directed-evolution", "relation": "related_bridge" }, { "source": "u-rna-world-nonenzymatic-replication-fidelity", "target": "b-rna-world-origin-of-life", "relation": "related_bridge" }, { "source": "u-silent-bgc-activation-novel-antibiotics", "target": "b-secondary-metabolites-drug-discovery", "relation": "related_bridge" }, { "source": "u-oxyluciferin-excited-state-mechanism-enol-vs-keto", "target": "b-bioluminescence-quantum-yield", "relation": "related_bridge" }, { "source": "u-anomalous-diffusion-cytoplasm", "target": "b-brownian-motion-cell-diffusion", "relation": "related_bridge" }, { "source": "u-antifreeze-protein-ice-binding", "target": "b-antifreeze-proteins-ice-crystal", "relation": "related_bridge" }, { "source": "u-aqp-gating-osmosensing-mechanism", "target": "b-osmosis-cell-volume-regulation", "relation": "related_bridge" }, { "source": "u-aqp-gating-osmosensing-mechanism", "target": "h-aqp2-trafficking-as-osmotic-valve", "relation": "suggested_hypothesis" }, { "source": "u-atp-synthase-torque-slip-mechanism", "target": "b-bioenergetics-proton-motive-force", "relation": "related_bridge" }, { "source": "u-cochlear-hopf-bifurcation-active-hair-bundle-vs-somatic-motility", "target": "b-cochlear-mechanics-hearing-biophysics", "relation": "related_bridge" }, { "source": "u-cochlear-hopf-bifurcation-active-hair-bundle-vs-somatic-motility", "target": "h-prestin-somatic-motility-primary-cochlear-amplification-mechanism-mammals", "relation": "suggested_hypothesis" }, { "source": "u-cytoskeletal-active-matter-defect-dynamics", "target": "b-cytoskeleton-x-active-matter", "relation": "related_bridge" }, { "source": "u-cytoskeletal-active-matter-defect-dynamics", "target": "h-cytoskeletal-active-matter-defect-dynamics", "relation": "suggested_hypothesis" }, { "source": "u-debye-length-ion-specificity-membrane-binding", "target": "b-debye-length-x-membrane-electrical-double-layer", "relation": "related_bridge" }, { "source": "u-debye-length-ion-specificity-membrane-binding", "target": "h-ion-specific-double-layer-competition-modulates-permeation", "relation": "suggested_hypothesis" }, { "source": "u-eis-membrane-hodgkin-huxley-identification", "target": "b-electrochemical-impedance-x-cell-membrane", "relation": "related_bridge" }, { "source": "u-eis-membrane-hodgkin-huxley-identification", "target": "h-eis-spectra-constrain-gating-substates", "relation": "suggested_hypothesis" }, { "source": "u-hertz-adhesion-crossover-biological-tissues", "target": "b-hertz-contact-x-spherical-indentation", "relation": "related_bridge" }, { "source": "u-hertz-adhesion-crossover-biological-tissues", "target": "h-hertz-contact-x-spherical-indentation", "relation": "suggested_hypothesis" }, { "source": "u-intrinsically-disordered-proteins", "target": "b-intrinsically-disordered-proteins-polymer-physics", "relation": "related_bridge" }, { "source": "u-met-channel-molecular-identity-pore-forming-subunit", "target": "b-hair-cells-mechanosensory-biophysics", "relation": "related_bridge" }, { "source": "u-mitochondrial-pmf-efficiency-carnot-bound", "target": "b-mitochondrial-membrane-potential-pmf", "relation": "related_bridge" }, { "source": "u-muscle-crossbridge-kinetics", "target": "b-muscle-crossbridge-sliding-filament", "relation": "related_bridge" }, { "source": "u-phage-ejection-force-osmotic-mechanism", "target": "b-osmotic-pressure-x-viral-capsid", "relation": "related_bridge" }, { "source": "u-phage-ejection-force-osmotic-mechanism", "target": "h-phage-ejection-force-osmotic-mechanism", "relation": "suggested_hypothesis" }, { "source": "u-prion-llps-nucleation-kinetics", "target": "b-prion-fold-x-protein-phase-separation", "relation": "related_bridge" }, { "source": "u-prion-llps-nucleation-kinetics", "target": "h-prion-llps-nucleation-kinetics", "relation": "suggested_hypothesis" }, { "source": "u-wound-healing-collective-migration-coordination", "target": "b-wound-healing-cell-migration-chemotaxis", "relation": "related_bridge" }, { "source": "u-yap-taz-stiffness-sensing-mechanism-molecular", "target": "b-mechanobiology-cellular-force-sensing", "relation": "related_bridge" }, { "source": "u-flagellar-motor-stator-number-regulation-pmf", "target": "b-flagellar-motor-rotary-machines", "relation": "related_bridge" }, { "source": "u-microbial-fuel-cell-electron-transfer-limits", "target": "b-microbial-fuel-cells-bioelectrochemistry", "relation": "related_bridge" }, { "source": "u-plant-tropism-auxin-gradient-mechanism", "target": "b-plant-tropisms-auxin-reaction-diffusion", "relation": "related_bridge" }, { "source": "u-autophagy-selectivity-cargo-receptor-hierarchy", "target": "b-autophagy-cellular-recycling", "relation": "related_bridge" }, { "source": "u-boolean-network-attractor-landscape-reprogramming", "target": "b-random-boolean-networks-cell-fate", "relation": "related_bridge" }, { "source": "u-stress-granule-phase-separation-pathology", "target": "b-stress-granules-liquid-liquid-phase-separation", "relation": "related_bridge" }, { "source": "u-ubiquitin-proteasome-proteostasis-collapse-threshold", "target": "b-protein-ubiquitination-proteostasis-network", "relation": "related_bridge" }, { "source": "u-bz-reaction-3d-scroll-wave-instability", "target": "b-reaction-diffusion-excitable-media-bz", "relation": "related_bridge" }, { "source": "u-chemical-garden-membrane-self-organization", "target": "b-chemical-garden-osmotic-precipitation", "relation": "related_bridge" }, { "source": "u-dac-sorbent-entropy-production-mechanism", "target": "b-carbon-capture-entropy-cost", "relation": "related_bridge" }, { "source": "u-endocrine-disruptor-dose-response-nonmonotonic", "target": "b-toxicology-environmental-policy", "relation": "related_bridge" }, { "source": "u-endocrine-disruptor-dose-response-nonmonotonic", "target": "h-lnt-model-invalid-endocrine-disruptors", "relation": "suggested_hypothesis" }, { "source": "u-enzyme-kinetics-x-michaelis-menten", "target": "b-enzyme-kinetics-x-michaelis-menten", "relation": "related_bridge" }, { "source": "u-enzyme-surface-catalyst-design-principles", "target": "b-catalysis-x-transition-state-theory", "relation": "related_bridge" }, { "source": "u-enzyme-surface-catalyst-design-principles", "target": "h-enzyme-surface-catalyst-design-principles", "relation": "suggested_hypothesis" }, { "source": "u-enzyme-tunneling-quantum-mechanism", "target": "u-quantum-biology-decoherence", "relation": "related_unknown" }, { "source": "u-graph-theory-x-molecular-structure", "target": "b-graph-theory-x-molecular-structure", "relation": "related_bridge" }, { "source": "u-md-force-field-transferability-accuracy-limit", "target": "b-molecular-dynamics-statistical-sampling", "relation": "related_bridge" }, { "source": "u-metalloenzyme-design-principles", "target": "u-protein-folding-thermodynamics-kinetics", "relation": "related_unknown" }, { "source": "u-nmr-effective-hamiltonian-calibration-open-system", "target": "b-nmr-rotating-frame-x-effective-hamiltonian", "relation": "related_bridge" }, { "source": "u-nmr-effective-hamiltonian-calibration-open-system", "target": "h-nmr-rotating-frame-x-effective-hamiltonian", "relation": "suggested_hypothesis" }, { "source": "u-nucleation-x-first-passage", "target": "b-nucleation-x-first-passage", "relation": "related_bridge" }, { "source": "u-oed-utility-misspecification-under-nonstationary-chemistry", "target": "b-bayesian-oed-x-robotic-chemistry-optimization", "relation": "related_bridge" }, { "source": "u-oed-utility-misspecification-under-nonstationary-chemistry", "target": "h-lookahead-oed-reduces-experiments-to-target-yield", "relation": "suggested_hypothesis" }, { "source": "u-oer-scaling-relation-break", "target": "b-catalyst-sabatier-principle", "relation": "related_bridge" }, { "source": "u-percolation-mapping-quantitative-gel-chemistry", "target": "b-percolation-threshold-x-polymer-gelation", "relation": "related_bridge" }, { "source": "u-percolation-mapping-quantitative-gel-chemistry", "target": "h-percolation-threshold-x-polymer-gelation", "relation": "suggested_hypothesis" }, { "source": "u-photocatalysis-x-semiconductor-physics", "target": "b-photocatalysis-x-semiconductor-physics", "relation": "related_bridge" }, { "source": "u-polymer-glass-x-jamming-transition", "target": "b-polymer-glass-x-jamming-transition", "relation": "related_bridge" }, { "source": "u-reaction-networks-x-petri-nets", "target": "b-reaction-networks-x-petri-nets", "relation": "related_bridge" }, { "source": "u-self-replicating-molecules-minimal-criteria", "target": "u-chirality-emergence-prebiotic", "relation": "related_unknown" }, { "source": "u-structure-uncertainty-propagation-from-alphafold-to-enzyme-design", "target": "b-alphafold-structure-priors-x-enzyme-engineering-screen-pruning", "relation": "related_bridge" }, { "source": "u-structure-uncertainty-propagation-from-alphafold-to-enzyme-design", "target": "h-alphafold-confidence-weighted-screening-improves-enzyme-hit-rates", "relation": "suggested_hypothesis" }, { "source": "u-transition-state-x-saddle-point", "target": "b-transition-state-x-saddle-point", "relation": "related_bridge" }, { "source": "u-unified-spectral-epsilon-model-across-vdw-casimir-length-scales", "target": "b-casimir-polder-retardation-x-lifshitz-vdw-crossover", "relation": "related_bridge" }, { "source": "u-unified-spectral-epsilon-model-across-vdw-casimir-length-scales", "target": "h-joint-fit-lifshitz-hamaker-colloid-force-curves", "relation": "suggested_hypothesis" }, { "source": "u-vae-catalyst-latent-disentanglement-validity", "target": "b-vae-x-catalyst-latent-space-screening", "relation": "related_bridge" }, { "source": "u-vae-catalyst-latent-disentanglement-validity", "target": "h-vae-latent-regularization-improves-catalyst-hit-rate", "relation": "suggested_hypothesis" }, { "source": "u-which-persistence-features-remain-stable-under-noisy-catalyst-screening-assays", "target": "b-topological-data-analysis-x-catalyst-state-space-screening", "relation": "related_bridge" }, { "source": "u-which-persistence-features-remain-stable-under-noisy-catalyst-screening-assays", "target": "h-persistence-based-features-improve-active-catalyst-hit-rate-in-high-throughput-screening", "relation": "suggested_hypothesis" }, { "source": "u-green-chemistry-pharmaceutical-e-factor-continuous-flow", "target": "b-green-chemistry-atom-economy", "relation": "related_bridge" }, { "source": "u-nafion-degradation-mechanism-longevity", "target": "h-pem-membrane-beyond-nafion-high-temperature", "relation": "suggested_hypothesis" }, { "source": "u-sabatier-volcano-principle-multi-step-cascade-reaction-design", "target": "b-catalysis-reactor-design", "relation": "related_bridge" }, { "source": "u-sabatier-volcano-principle-multi-step-cascade-reaction-design", "target": "h-dft-bep-relationship-enables-quantitative-catalyst-design-before-synthesis", "relation": "suggested_hypothesis" }, { "source": "u-solid-state-battery-sei-interface-resistance-origin", "target": "b-electrochemistry-battery-technology", "relation": "related_bridge" }, { "source": "u-chronotype-genetic-variants-full-population-distribution", "target": "b-chronobiology-social-jet-lag", "relation": "related_bridge" }, { "source": "u-circadian-prc-individual-variation-prediction", "target": "b-circadian-entrainment-phase-response-curve", "relation": "related_bridge" }, { "source": "u-aerosol-cloud-nucleation-uncertainty", "target": "b-aerosol-nucleation-cloud-formation", "relation": "related_bridge" }, { "source": "u-climate-damage-function-high-temperature-regime", "target": "b-integrated-assessment-social-cost-carbon", "relation": "related_bridge" }, { "source": "u-climate-damage-function-high-temperature-regime", "target": "h-scc-convex-damages-fat-tails", "relation": "suggested_hypothesis" }, { "source": "u-climate-ecs-feedback-uncertainty", "target": "b-radiative-forcing-energy-balance", "relation": "related_bridge" }, { "source": "u-climate-health-tipping-threshold", "target": "b-climate-tipping-health", "relation": "related_bridge" }, { "source": "u-diffusion-downscaling-physical-consistency-under-shift", "target": "b-diffusion-model-x-ensemble-downscaling-bias-correction", "relation": "related_bridge" }, { "source": "u-diffusion-downscaling-physical-consistency-under-shift", "target": "h-diffusion-downscaling-improves-extreme-precipitation-fidelity", "relation": "suggested_hypothesis" }, { "source": "u-dro-ambiguity-set-specification-nonstationary-climate", "target": "b-distributionally-robust-optimization-x-deep-uncertainty-scenario-planning", "relation": "related_bridge" }, { "source": "u-dro-ambiguity-set-specification-nonstationary-climate", "target": "h-wasserstein-dro-improves-tail-safe-adaptation-metrics", "relation": "suggested_hypothesis" }, { "source": "u-hasselmann-stochastic-resonance-glacial-mechanism", "target": "b-stochastic-climate-hasselmann", "relation": "related_bridge" }, { "source": "u-kalman-smoothing-proxy-noise-tree-ring-reconstruction", "target": "b-kalman-smoothing-x-tree-ring-paleoclimate-reconstruction", "relation": "related_bridge" }, { "source": "u-kalman-smoothing-proxy-noise-tree-ring-reconstruction", "target": "h-kalman-smoother-outperforms-static-regression-for-tree-ring-temperature", "relation": "suggested_hypothesis" }, { "source": "u-online-change-point-detection-false-alarm-rate-under-trends", "target": "b-change-point-bayesian-online-detection-x-glacier-calving-regime-shifts", "relation": "related_bridge" }, { "source": "u-online-change-point-detection-false-alarm-rate-under-trends", "target": "h-bocd-with-hazard-adaptation-detects-glacier-regime-shifts-earlier", "relation": "suggested_hypothesis" }, { "source": "u-optimal-transport-shift-stability-under-extremes", "target": "b-optimal-transport-bias-correction-x-climate-downscaling", "relation": "related_bridge" }, { "source": "u-optimal-transport-shift-stability-under-extremes", "target": "h-ot-bias-correction-improves-tail-risk-calibration", "relation": "suggested_hypothesis" }, { "source": "u-collective-memory-network-structure", "target": "b-collective-memory-distributed-cognition", "relation": "related_bridge" }, { "source": "u-distributional-semantics-compositionality", "target": "b-semantic-memory-word-vectors", "relation": "related_bridge" }, { "source": "u-loss-aversion-neural-substrate", "target": "b-prospect-theory-loss-aversion", "relation": "related_bridge" }, { "source": "u-aco-convergence-routing-instances", "target": "b-ant-colony-optimization-x-gradient-free-metaheuristics", "relation": "related_bridge" }, { "source": "u-aco-convergence-routing-instances", "target": "h-ant-colony-optimization-x-gradient-free-metaheuristics", "relation": "suggested_hypothesis" }, { "source": "u-astrocyte-memory-replay-transformers", "target": "u-hopfield-capacity-cortex", "relation": "related_unknown" }, { "source": "u-astrocyte-memory-replay-transformers", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-astrocyte-memory-replay-transformers", "target": "b-spin-glass-neural-networks", "relation": "related_bridge" }, { "source": "u-byzantine-fault-tolerance-practical", "target": "b-distributed-systems-consensus", "relation": "related_bridge" }, { "source": "u-cahn-hilliard-segmentation-parameter-transfer-limits", "target": "b-cahn-hilliard-phase-separation-x-diffuse-interface-segmentation", "relation": "related_bridge" }, { "source": "u-cahn-hilliard-segmentation-parameter-transfer-limits", "target": "h-interface-width-regularization-predicts-segmentation-stability", "relation": "suggested_hypothesis" }, { "source": "u-cellular-automata-complexity-classification", "target": "b-cellular-automata-computational-universality", "relation": "related_bridge" }, { "source": "u-cellular-automata-x-computational-universality", "target": "b-cellular-automata-x-computational-universality", "relation": "related_bridge" }, { "source": "u-computational-irreducibility-physical-systems-scope", "target": "b-computational-irreducibility-wolfram-rule110", "relation": "related_bridge" }, { "source": "u-continuous-symmetry-neural-topology", "target": "u-grokking-phase-transition", "relation": "related_unknown" }, { "source": "u-contrastive-ssl-energy-model-bridge", "target": "b-self-supervised-learning-x-statistical-mechanics", "relation": "related_bridge" }, { "source": "u-contrastive-ssl-energy-model-bridge", "target": "h-contrastive-loss-implements-high-temperature-energy-comparison", "relation": "suggested_hypothesis" }, { "source": "u-cpc-negative-sampling-bias-temporal-structure", "target": "b-contrastive-predictive-coding-x-multiview-self-supervised-learning", "relation": "related_bridge" }, { "source": "u-cpc-negative-sampling-bias-temporal-structure", "target": "h-predictive-cpc-loss-improves-downstream-transfer-under-shift", "relation": "suggested_hypothesis" }, { "source": "u-cut-cell-segmentation-interface-consistency", "target": "b-cut-cell-finite-volume-x-voxel-medical-segmentation", "relation": "related_bridge" }, { "source": "u-cut-cell-segmentation-interface-consistency", "target": "h-cut-cell-conservative-flux-reduces-leakage-medical-seg", "relation": "suggested_hypothesis" }, { "source": "u-deq-solver-tolerance-versus-generalization-gap", "target": "b-deep-equilibrium-models-x-fixed-point-iteration", "relation": "related_bridge" }, { "source": "u-deq-solver-tolerance-versus-generalization-gap", "target": 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"u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-genetic-algorithm-x-natural-selection", "target": "b-genetic-algorithm-x-natural-selection", "relation": "related_bridge" }, { "source": "u-graph-neural-network-x-spectral-graph-theory", "target": "b-graph-neural-network-x-spectral-graph-theory", "relation": "related_bridge" }, { "source": "u-graph-percolation-lateral-movement-detection-threshold", "target": "b-bond-percolation-x-cyber-lateral-movement", "relation": "related_bridge" }, { "source": "u-graph-percolation-lateral-movement-detection-threshold", "target": "h-zero-trust-control-raises-effective-percolation-threshold", "relation": "suggested_hypothesis" }, { "source": "u-hopfield-capacity-modern-architectures", "target": "b-ising-model-x-hopfield-network", "relation": "related_bridge" }, { "source": "u-hopfield-capacity-modern-architectures", "target": "h-hopfield-capacity-modern-architectures", "relation": "suggested_hypothesis" }, { "source": "u-immune-system-x-anomaly-detection", "target": "b-immune-system-x-anomaly-detection", "relation": "related_bridge" }, { "source": "u-legal-argumentation-formal-completeness", "target": "b-legal-argumentation-formal-logic", "relation": "related_bridge" }, { "source": "u-lwe-hardness-proof-quantum-reduction", "target": "b-cryptography-number-theory", "relation": "related_bridge" }, { "source": "u-neural-architecture-search-x-evolutionary-biology", "target": "b-neural-architecture-search-x-evolutionary-biology", "relation": "related_bridge" }, { "source": "u-neural-ode-x-dynamical-systems", "target": "b-neural-ode-x-dynamical-systems", "relation": "related_bridge" }, { "source": "u-neuromorphic-thermodynamic-energy", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-neuromorphic-thermodynamic-energy", "target": "u-ion-pump-landauer-thermodynamics", "relation": "related_unknown" }, { "source": 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"b-stabilizer-codes-fault-tolerance", "relation": "related_bridge" }, { "source": "u-quantum-zeno-watchdog-quantitative-mapping", "target": "b-quantum-zeno-x-watchdog-sampling-analogy", "relation": "related_bridge" }, { "source": "u-quantum-zeno-watchdog-quantitative-mapping", "target": "h-student-transfer-zeno-curve-to-sampling-stability-drills", "relation": "suggested_hypothesis" }, { "source": "u-reinforcement-learning-x-bellman-equation", "target": "b-reinforcement-learning-x-bellman-equation", "relation": "related_bridge" }, { "source": "u-reservoir-computing-x-dynamical-systems", "target": "b-reservoir-computing-x-dynamical-systems", "relation": "related_bridge" }, { "source": "u-sat-phase-transition-p-np", "target": "b-sat-phase-transition", "relation": "related_bridge" }, { "source": "u-sat-spin-glass-algorithm-design", "target": "b-boolean-satisfiability-x-spin-glass", "relation": "related_bridge" }, { "source": "u-sat-spin-glass-algorithm-design", "target": "h-sat-spin-glass-algorithm-design", "relation": "suggested_hypothesis" }, { "source": "u-satisfiability-x-constraint-propagation", "target": "b-satisfiability-x-constraint-propagation", "relation": "related_bridge" }, { "source": "u-spectral-clustering-x-graph-laplacian", "target": "b-spectral-clustering-x-graph-laplacian", "relation": "related_bridge" }, { "source": "u-spin-glass-optimization-hardness-phase-transition", "target": "b-combinatorial-optimization-spin-glass", "relation": "related_bridge" }, { "source": "u-stdp-reward-modulation-rl-equivalence", "target": "u-replicator-dynamics-llm-training", "relation": "related_unknown" }, { "source": "u-stdp-reward-modulation-rl-equivalence", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-stdp-reward-modulation-rl-equivalence", "target": "b-game-theory-evolution", "relation": "related_bridge" }, { "source": "u-swarm-intelligence-x-distributed-computing", "target": "b-swarm-intelligence-x-distributed-computing", "relation": "related_bridge" }, { "source": "u-synaptic-tag-cache-analogy-quantitative-test", "target": "b-synaptic-tagging-x-cache-coherence-writeback-analogy", "relation": "related_bridge" }, { "source": "u-synaptic-tag-cache-analogy-quantitative-test", "target": "h-tag-decay-timescale-vs-write-buffer-lifetime-correlation-classroom-only", "relation": "suggested_hypothesis" }, { "source": "u-unique-games-conjecture-sdp-approximation-tight-gap", "target": "b-approximation-algorithms-sdp", "relation": "related_bridge" }, { "source": "u-variational-inference-x-free-energy", "target": "b-variational-inference-x-free-energy", "relation": "related_bridge" }, { "source": "u-wgan-gp-tightness-versus-exact-lipschitz-projections", "target": "b-wasserstein-gan-gradient-penalty-x-kantorovich-lipschitz-stability", "relation": "related_bridge" }, { "source": "u-wgan-gp-tightness-versus-exact-lipschitz-projections", "target": "h-gradient-penalty-magnitude-tracks-dual-feasibility-proxy-metrics", "relation": "suggested_hypothesis" }, { "source": "u-grokking-criticality-universality-class", "target": "h-grokking-criticality-universality", "relation": "suggested_hypothesis" }, { "source": "u-stdp-criticality-mechanism", "target": "h-criticality-conscious-integration", "relation": "suggested_hypothesis" }, { "source": "u-magnons-collective-excitations", "target": "b-magnons-spin-wave-collective-excitations", "relation": "related_bridge" }, { "source": "u-symmetry-breaking-goldstone", "target": "b-symmetry-breaking-goldstone-bosons", "relation": "related_bridge" }, { "source": "u-topological-insulator-surface-state-interactions", "target": "b-topological-insulators-bulk-boundary", "relation": "related_bridge" }, { "source": "u-inflation-epidemic-analogy-falsifiability-limits", "target": "b-cosmic-inflation-x-epidemic-phase-plane-expansion", "relation": "related_bridge" }, { "source": "u-inflation-epidemic-analogy-falsifiability-limits", "target": "h-shared-tangent-field-exponential-region-only-logarithmic-visual-overlap", "relation": "suggested_hypothesis" }, { "source": "u-inflation-slow-roll-end-reheating-mechanism", "target": "b-cosmological-inflation-slow-roll-scalar", "relation": "related_bridge" }, { "source": "u-embedding-dimension-selection-for-icu-trajectory-instability-detection", "target": "b-delay-embedding-x-icu-deterioration-early-warning", "relation": "related_bridge" }, { "source": "u-embedding-dimension-selection-for-icu-trajectory-instability-detection", "target": "h-delay-embedding-indicators-improve-icu-deterioration-lead-time", "relation": "suggested_hypothesis" }, { "source": "u-missingness-aware-lstm-training-for-icu-forecasts", "target": "b-lstm-sequence-memory-x-icu-physiology-forecasting", "relation": "related_bridge" }, { "source": "u-missingness-aware-lstm-training-for-icu-forecasts", "target": "h-missingness-augmented-lstm-models-improve-icu-decompensation-horizon-accuracy", "relation": "suggested_hypothesis" }, { "source": "u-neural-cde-icu-robustness-to-missingness-patterns", "target": "b-neural-cde-x-irregular-icu-trajectory-modeling", "relation": "related_bridge" }, { "source": "u-neural-cde-icu-robustness-to-missingness-patterns", "target": "h-neural-cde-models-improve-icu-event-lead-time", "relation": "suggested_hypothesis" }, { "source": "u-qkd-practical-implementation-side-channels", "target": "b-qkd-information-theoretic-security", "relation": "related_bridge" }, { "source": "u-axelrod-model-empirical-validation-cultural-diversity", "target": "b-sociophysics-cultural-dynamics", "relation": "related_bridge" }, { "source": "u-optimal-cybersecurity-investment-under-adversarial-uncertainty", "target": "b-cybersecurity-adversarial-systems", "relation": "related_bridge" }, { "source": "u-morphogen-gradient-robustness-scaling", "target": "b-morphogen-gradients-diffusion", "relation": "related_bridge" }, { "source": "u-topological-defect-morphogenesis-3d-tissue", "target": "b-topological-defects-tissue-morphogenesis", "relation": "related_bridge" }, { "source": "u-turing-morphogen-identity-in-vivo-diffusion-measurement", "target": "b-developmental-turing-instability", "relation": "related_bridge" }, { "source": "u-turing-patterning-3d-robustness", "target": "b-morphogen-turing-patterning", "relation": "related_bridge" }, { "source": "u-waddington-canalization-mechanism", "target": "b-gene-networks-waddington-landscape", "relation": "related_bridge" }, { "source": "u-allelopathy-invasive-plant-mycorrhizal-disruption", "target": "b-allelopathy-chemical-ecology", "relation": "related_bridge" }, { "source": "u-bef-relationship-agricultural-context", "target": "b-agricultural-biodiversity-ecosystem", "relation": "related_bridge" }, { "source": "u-bet-hedging-correlation-structure-across-taxa", "target": "b-bet-hedging-x-portfolio-diversification", "relation": "related_bridge" }, { "source": "u-bet-hedging-correlation-structure-across-taxa", "target": "h-bet-hedging-x-portfolio-diversification", "relation": "suggested_hypothesis" }, { "source": "u-biodiversity-productivity-relationship", "target": "u-trophic-cascade-predictability", "relation": "related_unknown" }, { "source": "u-coral-bleaching-thermal-stress", "target": "b-coral-bleaching-thermal-stress", "relation": "related_bridge" }, { "source": "u-digital-commons-governance-principles", "target": "b-common-pool-resources-institutional-design", "relation": "related_bridge" }, { "source": "u-dryland-vegetation-pattern-formation", "target": "b-vegetation-patterns-klausmeier-model", "relation": "related_bridge" }, { "source": "u-ecological-succession-x-markov", "target": "b-ecological-succession-x-markov", "relation": "related_bridge" }, { "source": "u-ecology-x-coexistence-theory", "target": "b-ecology-x-coexistence-theory", "relation": "related_bridge" }, { "source": "u-ecosystem-engineer-legacy-effects", "target": "u-trophic-cascade-predictability", "relation": "related_unknown" }, { "source": "u-ecosystem-engineer-legacy-effects", "target": "u-keystone-species-identification", "relation": "related_unknown" }, { "source": "u-ecosystem-services-valuation-market-failure", "target": "b-natural-capital-ecosystem-services", "relation": "related_bridge" }, { "source": "u-ecosystem-tipping-point-early-warning-false-positive-rate", "target": "b-ecosystem-resilience-bifurcation", "relation": "related_bridge" }, { "source": "u-environmental-justice-cumulative-impact-assessment-methodology", "target": "b-political-ecology-environmental-justice", "relation": "related_bridge" }, { "source": "u-ess-higher-order-interactions-ecosystem", "target": "b-ess-ecosystem-dynamics", "relation": "related_bridge" }, { "source": "u-ess-higher-order-interactions-ecosystem", "target": "h-cyclic-dominance-spatial-heterogeneity-biodiversity", "relation": "suggested_hypothesis" }, { "source": "u-forest-canopy-clumping-beer-lambert-deviation", "target": "b-forest-canopy-beer-lambert-radiative", "relation": "related_bridge" }, { "source": "u-gap-recruitment-neutral-theory-goodness-of-fit", "target": "b-forest-gap-dynamics-x-neutral-theory-sampling", "relation": "related_bridge" }, { "source": "u-gap-recruitment-neutral-theory-goodness-of-fit", "target": "h-neutral-theta-estimates-converge-pre-post-gap-chronosequence", "relation": "suggested_hypothesis" }, { "source": "u-gut-brain-axis-causal-mechanism", "target": "b-microbiome-ecology-host-health", "relation": "related_bridge" }, { "source": "u-gut-brain-axis-causal-mechanism", "target": "h-microbiome-diversity-host-resilience", "relation": "suggested_hypothesis" }, { "source": "u-intermediate-disturbance-diversity-peak", "target": "b-forest-succession-intermediate-disturbance", "relation": "related_bridge" }, { "source": "u-invasive-species-reaction-diffusion", "target": "b-invasive-species-reaction-diffusion", "relation": "related_bridge" }, { "source": "u-invasive-species-threshold", "target": "u-habitat-fragmentation-threshold", "relation": "related_unknown" }, { "source": "u-invasive-species-threshold", "target": "u-ecology-resilience-spatial-indicator", "relation": "related_unknown" }, { "source": "u-keystone-species-identification", "target": "u-trophic-cascade-predictability", "relation": "related_unknown" }, { "source": "u-lcs-retention-coastal-recruitment-transfer", "target": "b-lcs-retention-zone-x-coastal-larval-supply", "relation": "related_bridge" }, { "source": "u-lcs-retention-coastal-recruitment-transfer", "target": "h-ftle-ridge-threshold-correlates-larval-retention-proxy", "relation": "suggested_hypothesis" }, { "source": "u-lotka-volterra-hamiltonian-real-ecosystem-conservation", "target": "b-predator-prey-lotka-volterra-hamiltonian", "relation": "related_bridge" }, { "source": "u-lyme-ohio-surveillance-gap", "target": "u-borrelia-persister-cell-eradication", "relation": "related_unknown" }, { "source": "u-lyme-ohio-surveillance-gap", "target": "b-climate-tick-range-lyme", "relation": "related_bridge" }, { "source": "u-lyme-ohio-surveillance-gap", "target": "h-ohio-lyme-deer-management-intervention", "relation": "suggested_hypothesis" }, { "source": "u-maxent-species-abundance-prediction", "target": "b-biodiversity-entropy-measures", "relation": "related_bridge" }, { "source": "u-metabolic-scaling-exponent-deviation-extremes", "target": "b-ecosystem-metabolic-scaling", "relation": "related_bridge" }, { "source": "u-mutualism-network-robustness", "target": "u-trophic-cascade-predictability", "relation": "related_unknown" }, { "source": "u-mutualism-network-robustness", "target": "u-keystone-species-identification", "relation": "related_unknown" }, { "source": "u-nestedness-mutualistic-network-robustness", "target": "b-mutualistic-nestedness-robustness", "relation": "related_bridge" }, { "source": "u-niche-construction-feedback-tempo", "target": "b-niche-construction-extended-evolutionary-synthesis", "relation": "related_bridge" }, { "source": "u-ocean-mixing-parameterization-climate-models", "target": "b-oceanic-turbulence-mixing", "relation": "related_bridge" }, { "source": "u-patch-foraging-partial-observability-wild", "target": "b-reinforcement-learning-x-foraging-patch-models", "relation": "related_bridge" }, { "source": "u-patch-foraging-partial-observability-wild", "target": "h-reinforcement-learning-x-foraging-patch-models", "relation": "suggested_hypothesis" }, { "source": "u-percolation-threshold-habitat-connectivity", "target": "b-openalex-percolation-habitat-connectivity", "relation": "related_bridge" }, { "source": "u-plume-intermittency-foraging-optimal-rules", "target": "b-advection-diffusion-x-odor-plume-search", "relation": "related_bridge" }, { "source": "u-plume-intermittency-foraging-optimal-rules", "target": "h-advection-diffusion-x-odor-plume-search", "relation": "suggested_hypothesis" }, { "source": "u-predator-prey-cycle-amplitude-stochastic", "target": "b-predator-prey-hopf-bifurcation", "relation": "related_bridge" }, { "source": "u-redfield-ratio-evolutionary-constraint", "target": "b-stoichiometry-liebig-minimum", "relation": "related_bridge" }, { "source": "u-redfield-ratio-evolutionary-origin-mechanism", "target": "b-nutrient-cycling-stoichiometry", "relation": "related_bridge" }, { "source": "u-replicator-model-identifiability-multispecies-field-data", "target": "b-replicator-dynamics-x-evolutionarily-stable-strategy-field-data", "relation": "related_bridge" }, { "source": "u-replicator-model-identifiability-multispecies-field-data", "target": "h-replicator-residual-tests-improve-ess-prediction-under-competition", "relation": "suggested_hypothesis" }, { "source": "u-seed-dispersal-levy-flight", "target": "b-seed-dispersal-levy-flight", "relation": "related_bridge" }, { "source": "u-soil-carbon-cue-temperature-response", "target": "b-soil-carbon-microbial-thermodynamics", "relation": "related_bridge" }, { "source": "u-soil-cue-temperature-sensitivity-warming-feedback", "target": "b-soil-microbiome-carbon-cycling", "relation": "related_bridge" }, { "source": "u-soil-food-web-stability-topology", "target": "b-soil-food-webs-network-trophic-theory", "relation": "related_bridge" }, { "source": "u-species-area-exponent-prediction", "target": "b-island-biogeography-percolation", "relation": "related_bridge" }, { "source": "u-stoichiometry-food-web-stability", "target": "u-redfield-ratio-evolution-optimality", "relation": "related_unknown" }, { "source": "u-trophic-cascade-motif-universality", "target": "b-trophic-cascades-network-motifs", "relation": "related_bridge" }, { "source": "u-trophic-cascade-predictability", "target": "u-ecology-resilience-spatial-indicator", "relation": "related_unknown" }, { "source": "u-turing-pattern-selection-ecology", "target": "b-reaction-diffusion-spatial-ecology", "relation": "related_bridge" }, { "source": "u-vicsek-noise-raft-jitter-quantitative-mapping", "target": "b-vicsek-flocking-x-consensus-raft-leader-stability", "relation": "related_bridge" }, { "source": "u-vicsek-noise-raft-jitter-quantitative-mapping", "target": "h-critical-noise-sweep-scaling-parallels-election-timeout-sweep-phenomenologically", "relation": "suggested_hypothesis" }, { "source": "u-vit-crop-stress-generalization-across-sensors", "target": "b-vision-transformer-x-crop-stress-phenotyping", "relation": "related_bridge" }, { "source": "u-vit-crop-stress-generalization-across-sensors", "target": "h-vit-based-phenotyping-improves-early-crop-stress-detection", "relation": "suggested_hypothesis" }, { "source": "u-extinction-debt-lag-time-empirical-quantification-fragmented-landscapes", "target": "b-stochastic-population-extinction", "relation": "related_bridge" }, { "source": "u-extinction-debt-lag-time-empirical-quantification-fragmented-landscapes", "target": 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"u-auction-theory-x-mechanism-design", "target": "b-auction-theory-x-mechanism-design", "relation": "related_bridge" }, { "source": "u-blackscholes-x-diffusion-equation", "target": "b-blackscholes-x-diffusion-equation", "relation": "related_bridge" }, { "source": "u-causal-forest-policy-effect-transportability", "target": "b-causal-forest-x-policy-elasticity-heterogeneity", "relation": "related_bridge" }, { "source": "u-causal-forest-policy-effect-transportability", "target": "h-causal-forest-heterogeneity-improves-policy-targeting-efficiency", "relation": "suggested_hypothesis" }, { "source": "u-causal-inference-heterogeneous-treatment-effects-identification", "target": "b-causal-inference-instrumental-variables", "relation": "related_bridge" }, { "source": "u-chemical-potential-utility-non-equilibrium-markets", "target": "b-chemical-potential-utility-maximization", "relation": "related_bridge" }, { "source": "u-collective-risk-pool-stability-evolution", "target": "b-collective-risk-social-dilemma-x-insurance", "relation": "related_bridge" }, { "source": "u-collective-risk-pool-stability-evolution", "target": "h-risk-pooling-institutions-shift-evolutionary-stable-cooperation", "relation": "suggested_hypothesis" }, { "source": "u-complexity-economics-policy-design-far-equilibrium", "target": "b-complexity-economics-far-equilibrium", "relation": "related_bridge" }, { "source": "u-doppler-redshift-option-carry-speculative-analogy", "target": "b-doppler-redshift-x-option-adjusted-carry", "relation": "related_bridge" }, { "source": "u-doppler-redshift-option-carry-speculative-analogy", "target": "h-doppler-carry-yield-curve-steepness-speculative-parallels", "relation": "suggested_hypothesis" }, { "source": "u-ellsberg-ambiguity-aversion-neural-circuit", "target": "b-ellsberg-paradox-ambiguity-aversion", "relation": "related_bridge" }, { "source": "u-entropy-maximization-x-income-distribution", "target": "b-entropy-maximization-x-income-distribution", "relation": "related_bridge" }, { "source": "u-financial-contagion-epidemic-threshold-mapping", "target": "b-contagion-models-x-financial-crises", "relation": "related_bridge" }, { "source": "u-financial-contagion-epidemic-threshold-mapping", "target": "h-interbank-default-cascades-exhibit-epidemic-thresholds", "relation": "suggested_hypothesis" }, { "source": "u-financial-lyapunov-time-versus-policy-interventions", "target": "b-lyapunov-divergence-x-bank-run-amplification", "relation": "related_bridge" }, { "source": "u-financial-lyapunov-time-versus-policy-interventions", "target": "h-bank-run-lyapunov-time-shrinks-with-public-information-leaks", "relation": "suggested_hypothesis" }, { "source": "u-fluctuation-dissipation-stationary-market-assumption-breakdown", "target": "b-green-kubo-correlations-x-return-volatility-memory", "relation": "related_bridge" }, { "source": "u-fluctuation-dissipation-stationary-market-assumption-breakdown", "target": "h-volatility-autocorrelation-satisfies-effective-fd-response", "relation": "suggested_hypothesis" }, { "source": "u-game-theory-x-cryptography", "target": "b-game-theory-x-cryptography", "relation": "related_bridge" }, { "source": "u-global-trade-leontief-systemic-shock-threshold", "target": "b-trade-network-leontief-shock-propagation", "relation": "related_bridge" }, { "source": "u-inequality-health-phase-transition-threshold", "target": "b-inequality-health-gradient", "relation": "related_bridge" }, { "source": "u-mechanism-design-algorithmic-markets", "target": "b-mechanism-design-x-market-equilibrium", "relation": "related_bridge" }, { "source": "u-mechanism-design-algorithmic-markets", "target": "h-mechanism-design-algorithmic-markets", "relation": "suggested_hypothesis" }, { "source": "u-minority-game-x-market-microstructure", "target": "b-minority-game-x-market-microstructure", "relation": "related_bridge" }, { "source": "u-pareto-exponent-redistribution-mechanism", "target": 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"source": "u-social-cost-carbon-discount-rate", "target": "b-carbon-pricing-pigouvian", "relation": "related_bridge" }, { "source": "u-social-cost-carbon-discount-rate", "target": "h-ramsey-optimal-carbon-price-tipping-points", "relation": "suggested_hypothesis" }, { "source": "u-supply-chain-correlated-failure-calibration", "target": "b-supply-chain-network-x-bond-percolation-disruption", "relation": "related_bridge" }, { "source": "u-supply-chain-correlated-failure-calibration", "target": "h-supply-chain-network-x-bond-percolation-disruption", "relation": "suggested_hypothesis" }, { "source": "u-vaccination-game-equilibrium-gaps-versus-measured-coverage", "target": "b-game-theoretic-vaccination-x-herd-immunity-threshold", "relation": "related_bridge" }, { "source": "u-vaccination-game-equilibrium-gaps-versus-measured-coverage", "target": "h-price-subsidy-closes-nash-herd-gap-in-agent-based-metapopulations", "relation": "suggested_hypothesis" }, { "source": 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"target": "b-floquet-time-modulated-metamaterial-x-nonreciprocal-electromagnetic-response", "relation": "related_bridge" }, { "source": "u-floquet-metamaterial-isolation-bandwidth-loss-tradeoff", "target": "h-staggered-commutation-frequency-threshold-for-target-isolation-db", "relation": "suggested_hypothesis" }, { "source": "u-aeroelastic-hopf-normal-form-transfer-limits", "target": "b-aeroelastic-flutter-x-hopf-galloping-bifurcation", "relation": "related_bridge" }, { "source": "u-aeroelastic-hopf-normal-form-transfer-limits", "target": "h-hopf-reduced-order-predicts-galloping-onset-threshold", "relation": "suggested_hypothesis" }, { "source": "u-betz-limit-exceeded-unsteady-flow", "target": "b-wind-turbine-betz-limit-actuator-disk", "relation": "related_bridge" }, { "source": "u-bode-waterbed-multi-loop-multi-objective-tradeoffs", "target": "b-bode-sensitivity-integral-x-waterbed-effect", "relation": "related_bridge" }, { "source": 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"suggested_hypothesis" }, { "source": "u-em-skin-depth-financial-firewall-mapping-limits", "target": "b-skin-depth-shielding-x-financial-firewall-layers", "relation": "related_bridge" }, { "source": "u-em-skin-depth-financial-firewall-mapping-limits", "target": "h-layered-em-shielding-financial-firewall-depth-ratio-analogy", "relation": "suggested_hypothesis" }, { "source": "u-fem-dec-mixed-form-equivalence-limits", "target": "b-finite-element-x-discrete-exterior-calculus", "relation": "related_bridge" }, { "source": "u-fem-dec-mixed-form-equivalence-limits", "target": "h-mixed-fem-for-hodge-laplace-matches-dec-upwind-schemes", "relation": "suggested_hypothesis" }, { "source": "u-finite-depth-kelvin-wake-angle-design-transfer", "target": "b-kelvin-wake-angle-x-ship-wave-dispersion-design", "relation": "related_bridge" }, { "source": "u-finite-depth-kelvin-wake-angle-design-transfer", "target": "h-dispersion-aware-wake-visualization-improves-hull-wave-interpretation", "relation": "suggested_hypothesis" }, { "source": "u-graph-spectral-leakage-pmu-event-localization", "target": "b-graph-signal-processing-x-power-grid-pmu-anomaly-localization", "relation": "related_bridge" }, { "source": "u-graph-spectral-leakage-pmu-event-localization", "target": "h-graph-wavelet-energy-localizes-pmu-grid-disturbances-better-than-scada", "relation": "suggested_hypothesis" }, { "source": "u-graph-transformer-grid-contingency-false-negative-risk", "target": "b-graph-transformer-x-grid-contingency-screening", "relation": "related_bridge" }, { "source": "u-graph-transformer-grid-contingency-false-negative-risk", "target": "h-graph-transformer-improves-grid-contingency-screening-recall", "relation": "suggested_hypothesis" }, { "source": "u-interdependent-network-early-warning-cascade", "target": "b-infrastructure-cascade-failures", "relation": "related_bridge" }, { "source": "u-lidar-scene-reconstruction-nonuniqueness", "target": "b-lidar-x-inverse-problems", "relation": "related_bridge" }, { "source": "u-lidar-scene-reconstruction-nonuniqueness", "target": "h-sparsity-priors-stabilize-lidar-surface-recovery", "relation": "suggested_hypothesis" }, { "source": "u-molecular-motor-efficiency-limit-biological", "target": "b-molecular-motors-thermodynamic-efficiency", "relation": "related_bridge" }, { "source": "u-molecular-motor-efficiency-limit-biological", "target": "h-molecular-motor-near-equilibrium-operation", "relation": "suggested_hypothesis" }, { "source": "u-multi-coil-wpt-array-grating-lobes-cross-talk", "target": "b-phased-array-beamforming-x-multi-coil-wireless-power-interference-lobes", "relation": "related_bridge" }, { "source": "u-multi-coil-wpt-array-grating-lobes-cross-talk", "target": "h-half-wavelength-coil-spacing-bound-suppresses-near-field-grating-analogs", "relation": "suggested_hypothesis" }, { "source": "u-organ-chip-vascularization-long-term-viability", "target": "b-organ-on-chip-microfluidics", "relation": "related_bridge" }, { "source": "u-quantum-linewidth-vs-leeson-corner-crossover-measurement-protocol", "target": "b-schawlow-townes-linewidth-x-leeson-oscillator-phase-noise", "relation": "related_bridge" }, { "source": "u-quantum-linewidth-vs-leeson-corner-crossover-measurement-protocol", "target": "h-identical-analyzer-method-noise-floor-dominated-regimes-match-at-mm-wave-carriers", "relation": "suggested_hypothesis" }, { "source": "u-rate-distortion-optimal-neural-codes", "target": "b-information-theory-data-compression", "relation": "related_bridge" }, { "source": "u-rate-distortion-optimal-neural-codes", "target": "h-shannon-optimal-compression-biological-codes", "relation": "suggested_hypothesis" }, { "source": "u-reversible-em-logic-gate-design", "target": "b-nonhelical-landauer-reversible-em", "relation": "related_bridge" }, { "source": "u-reversible-em-logic-gate-design", "target": "h-nonhelical-resonator-adiabatic-quantum-memory", "relation": "suggested_hypothesis" }, { "source": "u-rf-noise-figure-two-port-correlation-matrix-room-temperature", "target": "b-johnson-nyquist-equilibrium-fluctuations-x-rf-noise-figure-definition", "relation": "related_bridge" }, { "source": "u-rf-noise-figure-two-port-correlation-matrix-room-temperature", "target": "h-correlated-port-noise-matrix-lowers-effective-nf-two-port", "relation": "suggested_hypothesis" }, { "source": "u-slip-model-biological-accuracy-multi-legged-running", "target": "b-biomimetic-robotics-locomotion", "relation": "related_bridge" }, { "source": "u-soft-robotics-hyperelastic-inverse-design", "target": "b-soft-robotics-hyperelastic-continuum", "relation": "related_bridge" }, { "source": "u-tissue-engineering-vascularization-thick-constructs", "target": "b-tissue-engineering-regenerative-medicine", "relation": "related_bridge" }, { "source": "u-topoelectrical-circuit-disorder-robustness-limit", "target": "b-openalex-topology-electrical-circuits-x-condensed-matter-physics", "relation": "related_bridge" }, { "source": "u-topoelectrical-circuit-disorder-robustness-limit", "target": "h-topoelectrical-circuit-edge-mode-disorder-threshold", "relation": "suggested_hypothesis" }, { "source": "u-traffic-phantom-jam-nucleation-mechanism", "target": "b-traffic-flow-lwr-pde", "relation": "related_bridge" }, { "source": "u-traffic-shock-microscopic-validation", "target": "b-compressible-shock-x-traffic-shock-wave", "relation": "related_bridge" }, { "source": "u-traffic-shock-microscopic-validation", "target": "h-compressible-shock-x-traffic-shock-wave", "relation": "suggested_hypothesis" }, { "source": "u-wpt-narrowband-q-bandwidth-multi-standard-coexistence", "target": "b-wpt-resonator-q-bandwidth-tradeoff-x-matching-network-coexistence", "relation": "related_bridge" }, { "source": "u-wpt-narrowband-q-bandwidth-multi-standard-coexistence", "target": "h-wpt-coexistence-requires-q-bandwidth-renegotiation-per-standard", "relation": "suggested_hypothesis" }, { "source": "u-2d-material-fet-contact-resistance-scaling-below-1nm", "target": "b-microelectronics-quantum-confinement", "relation": "related_bridge" }, { "source": "u-h-infinity-nonlinear-systems-computational-tractability", "target": "b-robust-control-h-infinity", "relation": "related_bridge" }, { "source": "u-plasma-turbulence-transport-barrier-formation-mechanism", "target": "b-plasma-physics-fusion-energy", "relation": "related_bridge" }, { "source": "u-plasma-turbulence-transport-barrier-formation-mechanism", "target": "h-iter-q10-ignition-margin-sufficient-commercial-fusion", "relation": "suggested_hypothesis" }, { "source": "u-skin-friction-scaling-across-roughness-regimes", "target": "b-skin-friction-x-boundary-layer", "relation": "related_bridge" }, { "source": "u-skin-friction-scaling-across-roughness-regimes", "target": "h-law-of-wall-predicts-local-skin-friction-when-roughness-scaled", "relation": "suggested_hypothesis" }, { "source": "u-wpt-efficiency-biological-tissue-interaction", "target": "h-resonant-wpt-ev-charging-grid-integration", "relation": "suggested_hypothesis" }, { "source": "u-cultural-transmission-network-effects", "target": "b-cultural-transmission-sir-models", "relation": "related_bridge" }, { "source": "u-dispersion-shrinkage-stability-under-clinical-batch-effects", "target": "b-deseq2-shrinkage-estimation-x-low-count-clinical-biomarker-surveillance", "relation": "related_bridge" }, { "source": "u-dispersion-shrinkage-stability-under-clinical-batch-effects", "target": "h-deseq2-style-shrinkage-reduces-false-alerts-in-low-count-clinical-monitoring", "relation": "suggested_hypothesis" }, { "source": "u-drift-robust-sprt-thresholding-for-streaming-pathogen-variant-alerts", "target": "b-sequential-probability-ratio-test-x-pathogen-genomic-surveillance", "relation": "related_bridge" }, { "source": "u-drift-robust-sprt-thresholding-for-streaming-pathogen-variant-alerts", "target": "h-adaptive-sprt-alerting-detects-concerning-pathogen-variants-earlier-than-fixed-window-rules", "relation": "suggested_hypothesis" }, { "source": "u-epidemic-kalman-filter", "target": "b-epidemic-ensemble-kalman-filter", "relation": "related_bridge" }, { "source": "u-epidemiological-demographic-transition-timing", "target": "b-epidemiological-demographic-transition", "relation": "related_bridge" }, { "source": "u-federated-epidemic-model-drift-across-sites", "target": "b-federated-averaging-x-multisite-epidemic-forecasting", "relation": "related_bridge" }, { "source": "u-federated-epidemic-model-drift-across-sites", "target": "h-federated-ensembles-improve-cross-site-epidemic-generalization", "relation": "suggested_hypothesis" }, { "source": "u-floquet-instability-thresholds-seasonal-epidemic-control", "target": "b-floquet-stability-x-seasonal-epidemic-forcing-windows", "relation": "related_bridge" }, { "source": "u-floquet-instability-thresholds-seasonal-epidemic-control", "target": "h-floquet-instability-metrics-improve-seasonal-epi-intervention-timing", "relation": "suggested_hypothesis" }, { "source": "u-memory-kernel-identifiability-from-case-time-series", "target": "b-mori-zwanzig-memory-kernels-x-epidemic-model-reduction", "relation": "related_bridge" }, { "source": "u-memory-kernel-identifiability-from-case-time-series", "target": "h-memory-augmented-seir-improves-forecast-turning-points", "relation": "suggested_hypothesis" }, { "source": "u-metapopulation-epidemic-threshold-fragmented-landscape", "target": "b-metapopulation-sir-patch-occupancy", "relation": "related_bridge" }, { "source": "u-network-epidemic-threshold-heterogeneity", "target": "b-sir-percolation", "relation": "related_bridge" }, { "source": "u-network-fragmentation-thresholds-for-combination-antibiotic-coverage", "target": "b-percolation-thresholds-x-antimicrobial-combination-therapy-networks", "relation": "related_bridge" }, { "source": "u-network-fragmentation-thresholds-for-combination-antibiotic-coverage", "target": "h-percolation-aware-combination-selection-delays-resistance-network-percolation", "relation": "suggested_hypothesis" }, { "source": "u-network-threshold-epidemic-spread", "target": "b-openalex-network-epidemic-percolation", "relation": "related_bridge" }, { "source": "u-pandemic-intervention-timing-optimal-uncertainty", "target": "b-pandemic-optimal-stopping", "relation": "related_bridge" }, { "source": "u-percolation-herd-immunity-heterogeneous-networks", "target": "b-percolation-x-disease-spread", "relation": "related_bridge" }, { "source": "u-percolation-herd-immunity-heterogeneous-networks", "target": "h-percolation-herd-immunity-heterogeneous-networks", "relation": "suggested_hypothesis" }, { "source": "u-ptm-crosstalk-code-histone-combinatorial-regulation", "target": "b-protein-post-translational-modifications", "relation": "related_bridge" }, { "source": "u-channel-capacity-evolution-rate", "target": "b-information-theory-x-evolutionary-biology", "relation": "related_bridge" }, { "source": "u-channel-capacity-evolution-rate", "target": "h-channel-capacity-evolution-rate", "relation": "suggested_hypothesis" }, { "source": "u-cooperative-breeding-hamiltons-rule-limits", "target": "b-cooperative-breeding-kin-selection-inclusive-fitness", "relation": "related_bridge" }, { "source": "u-cultural-group-selection-empirical-magnitude", "target": "b-cultural-group-selection-multilevel-theory", "relation": "related_bridge" }, { "source": "u-gene-culture-coevolution-rate-modern", "target": "b-cultural-evolution-dual-inheritance", "relation": "related_bridge" }, { "source": "u-handicap-principle-signal-cost-measurement", "target": "b-signaling-theory-handicap-principle", "relation": "related_bridge" }, { "source": "u-horizontal-gene-transfer-rate-estimation", "target": "b-graph-theory-phylogenetics", "relation": "related_bridge" }, { "source": "u-kin-selection-price-equation-unification", "target": "b-kin-selection-price-equation", "relation": "related_bridge" }, { "source": "u-phenotypic-plasticity-adaptive-limits-speed", "target": "b-phenotypic-plasticity-reaction-norms", "relation": "related_bridge" }, { "source": "u-phylogenetic-network-horizontal-transfer", "target": "b-graph-theory-phylogenetics", "relation": "related_bridge" }, { "source": "u-predator-vigilance-roc-optimal-threshold", "target": "b-predator-detection-signal-detection-theory", "relation": "related_bridge" }, { "source": "u-red-queen-cycle-period-determinants", "target": "b-red-queen-coevolutionary-cycles", "relation": "related_bridge" }, { "source": "u-rmt-selective-sweep-detection-power", "target": "b-population-genetics-x-random-matrix", "relation": "related_bridge" }, { "source": "u-rmt-selective-sweep-detection-power", "target": "h-rmt-selective-sweep-detection-power", "relation": "suggested_hypothesis" }, { "source": "u-zahavi-handicap-mechanism-multimodal", "target": "b-game-theory-honest-signaling", "relation": "related_bridge" }, { "source": "u-zahavi-handicap-mechanism-multimodal", "target": "h-zahavi-handicap-single-crossing-stable-honest", "relation": "suggested_hypothesis" }, { "source": "u-fiber-nonlinearity-capacity-limit-shannon", "target": "b-optical-fiber-nonlinear-optics", "relation": "related_bridge" }, { "source": "u-market-microstructure-hawkes-calibration", "target": "b-market-liquidity-hawkes-processes", "relation": "related_bridge" }, { "source": "u-spin-glass-rmt-factor-clustering-limits", "target": "b-spin-glass-replica-x-factor-covariance-clustering-finance", "relation": "related_bridge" }, { "source": "u-spin-glass-rmt-factor-clustering-limits", "target": "h-replica-sparsity-predicts-factor-eigenvalue-noise-bulk", "relation": "suggested_hypothesis" }, { "source": "u-rmt-noise-signal-separation-finance", "target": "b-random-matrix-portfolio-optimization", "relation": "related_bridge" }, { "source": "u-earthquake-soc-predictability", "target": "b-earthquake-soc", "relation": "related_bridge" }, { "source": "u-magma-fragmentation-rheology-threshold", "target": "b-magma-fragmentation-rheology", "relation": "related_bridge" }, { "source": "u-rock-magnetism-paleomagnetic-reversal-mechanism", "target": "b-rock-magnetism-spin-ordering-domains", "relation": "related_bridge" }, { "source": "u-seismic-tomography-null-space-resolution", "target": "b-seismic-tomography-inverse-problems", "relation": "related_bridge" }, { "source": "u-silicate-weathering-temperature-sensitivity-field", "target": "b-silicate-weathering-geocarb-carbon-cycle", "relation": "related_bridge" }, { "source": "u-tectonic-coulomb-failure", "target": "b-tectonic-stress-coulomb-failure", "relation": "related_bridge" }, { "source": "u-adjoint-seismic-backprop-gradient-stability", "target": "b-adjoint-state-seismic-inversion-x-backprop-gradient-learning", "relation": "related_bridge" }, { "source": "u-adjoint-seismic-backprop-gradient-stability", "target": "h-adjoint-preconditioning-improves-seismic-inversion-convergence", "relation": "suggested_hypothesis" }, { "source": "u-biogeochemical-multistability-empirical-identification", "target": "b-biogeochemical-box-models-x-attractor-stability", "relation": "related_bridge" }, { "source": "u-biogeochemical-multistability-empirical-identification", "target": "h-biogeochemical-box-models-x-attractor-stability", "relation": "suggested_hypothesis" }, { "source": "u-braided-river-scaling-criticality-test", "target": "b-river-braiding-x-soc-like-morphodynamics", "relation": "related_bridge" }, { "source": "u-braided-river-scaling-criticality-test", "target": "h-river-braiding-x-soc-like-morphodynamics", "relation": "suggested_hypothesis" }, { "source": "u-coastline-roughness-effective-surface-tension", "target": "b-coastal-erosion-x-diffusive-interface", "relation": "related_bridge" }, { "source": "u-coastline-roughness-effective-surface-tension", "target": "h-diffusive-interface-models-predict-shoreline-roughening-exponents", "relation": "suggested_hypothesis" }, { "source": "u-earthquake-early-warning-bayesian-latency-magnitude-error", "target": "b-earthquake-early-warning-x-recursive-bayesian-source-estimation", "relation": "related_bridge" }, { "source": "u-earthquake-early-warning-bayesian-latency-magnitude-error", "target": "h-eew-kalman-style-updates-tighten-magnitude-posterior-faster-with-dense-networks", "relation": "suggested_hypothesis" }, { "source": "u-earthquake-nucleation", "target": "u-slow-slip-event-origin", "relation": "related_unknown" }, { "source": "u-earthquake-soc-universality-class", "target": "u-earthquake-nucleation", "relation": "related_unknown" }, { "source": "u-earthquake-soc-universality-class", "target": "u-slow-slip-event-origin", "relation": "related_unknown" }, { "source": "u-earthquake-soc-universality-class", "target": "b-earthquake-self-organized-criticality", "relation": "related_bridge" }, { "source": "u-earthquake-soc-universality-class", "target": "b-self-organized-criticality", "relation": "related_bridge" }, { "source": "u-ensemble-kalman-assimilation-nonlinear-localization-errors", "target": "b-kalman-state-estimation-x-nwp-data-assimilation", "relation": "related_bridge" }, { "source": "u-ensemble-kalman-assimilation-nonlinear-localization-errors", "target": "h-adaptive-inflation-ensemble-kalman-corrects-extreme-events", "relation": "suggested_hypothesis" }, { "source": "u-geomagnetic-reversal-trigger-mechanism", "target": "b-geomagnetic-reversal-dynamo", "relation": "related_bridge" }, { "source": "u-mantle-horizontal-spectrum-versus-rb-wavelength-law", "target": "b-lithospheric-planform-x-rayleigh-benard-wavelength-scaling", "relation": "related_bridge" }, { "source": "u-mantle-horizontal-spectrum-versus-rb-wavelength-law", "target": "h-numerical-mantle-spectral-peaks-track-effective-rb-wavenumber-branches", "relation": "suggested_hypothesis" }, { "source": "u-mantle-rheology-x-viscoelasticity", "target": "b-mantle-rheology-x-viscoelasticity", "relation": "related_bridge" }, { "source": "u-microseismic-acoustic-emission-b-value-failure", "target": "b-microseismic-acoustic-emission-fracture", "relation": "related_bridge" }, { "source": "u-neural-operator-generalization-groundwater-boundary-shift", "target": "b-neural-operator-surrogates-x-groundwater-inverse-modeling", "relation": "related_bridge" }, { "source": "u-neural-operator-generalization-groundwater-boundary-shift", "target": "h-fourier-neural-operator-surrogates-accelerate-groundwater-inversion-with-calibrated-uncertainty", "relation": "suggested_hypothesis" }, { "source": "u-nitrogen-cycle-jacobian-eigenstructure-versus-observed-anomalies", "target": "b-nitrogen-cycle-reservoirs-x-coupled-oscillator-stability", "relation": "related_bridge" }, { "source": "u-nitrogen-cycle-jacobian-eigenstructure-versus-observed-anomalies", "target": "h-linearized-n-cycle-models-predict-chlorophyll-mode-timescales", "relation": "suggested_hypothesis" }, { "source": "u-ocean-ultrasound-shared-inverse-regularizers", "target": "b-ocean-acoustic-tomography-x-ultrasound-transmission-tomography", "relation": "related_bridge" }, { "source": "u-ocean-ultrasound-shared-inverse-regularizers", "target": "h-adjoint-base-resolution-operator-matches-ray-density-despite-scale-gap", "relation": "suggested_hypothesis" }, { "source": "u-plate-boundary-fracture-scale-bridging", "target": "b-plate-boundary-slip-x-fracture-mechanics", "relation": "related_bridge" }, { "source": "u-plate-boundary-fracture-scale-bridging", "target": "h-plate-boundary-slip-x-fracture-mechanics", "relation": "suggested_hypothesis" }, { "source": "u-plate-tectonics-x-convection", "target": "b-plate-tectonics-x-convection", "relation": "related_bridge" }, { "source": "u-river-network-hacks-law-variability", "target": "b-river-network-hacks-law-fractal", "relation": "related_bridge" }, { "source": "u-seismic-wave-x-elastic-wave", "target": "b-seismic-wave-x-elastic-wave", "relation": "related_bridge" }, { "source": "u-slow-slip-event-origin", "target": "u-earthquake-nucleation", "relation": "related_unknown" }, { "source": "u-soc-earthquake-precursor-detection", "target": "b-self-organized-criticality-x-earthquake", "relation": "related_bridge" }, { "source": "u-soc-earthquake-precursor-detection", "target": "h-soc-earthquake-precursor-detection", "relation": "suggested_hypothesis" }, { "source": "u-soil-aggregate-fractal-dimension-stability-link", "target": "b-soil-aggregate-fractal-pore-stability", "relation": "related_bridge" }, { "source": "u-thermohaline-circulation-x-buoyancy-flow", "target": "b-thermohaline-circulation-x-buoyancy-flow", "relation": "related_bridge" }, { "source": "u-tsunami-dispersive-nonlinearity-regime-classification", "target": "b-tsunami-shallow-water-x-dispersive-soliton-bore", "relation": "related_bridge" }, { "source": "u-tsunami-dispersive-nonlinearity-regime-classification", "target": "h-tsunami-front-regime-classifier-nonlinear-dispersive-bore", "relation": "suggested_hypothesis" }, { "source": "u-unet-satellite-flood-generalization-under-cloud-noise", "target": "b-unet-x-satellite-flood-extent-mapping", "relation": "related_bridge" }, { "source": "u-unet-satellite-flood-generalization-under-cloud-noise", "target": "h-unet-domain-randomization-improves-flood-mapping-recall", "relation": "suggested_hypothesis" }, { "source": "u-epigenetic-clock-causal-mechanism", "target": "b-epigenetic-clocks-aging-biomarkers", "relation": "related_bridge" }, { "source": "u-glacier-basal-sliding-uncertainty", "target": "b-glacier-dynamics-glens-law", "relation": "related_bridge" }, { "source": "u-glacier-calving-crack-propagation-threshold", "target": "b-glacier-calving-fracture-mechanics", "relation": "related_bridge" }, { "source": "u-black-hole-information-paradox-bulk-reconstruction", "target": "b-black-holes-information-theory", "relation": "related_bridge" }, { "source": "u-gauge-gravity-duality-mechanism", "target": "b-general-relativity-differential-geometry", "relation": "related_bridge" }, { "source": "u-quantum-gravity-semiclassical-limit", "target": "b-general-relativity-differential-geometry", "relation": "related_bridge" }, { "source": "u-aging-interventions-translatability", "target": "h-conserved-metabolic-bottlenecks-longevity", "relation": "suggested_hypothesis" }, { "source": "u-amyloid-progression-trajectory", "target": "h-glymphatic-amyloid-clearance-rate", "relation": "suggested_hypothesis" }, { "source": "u-brain-criticality-function", "target": "h-criticality-conscious-integration", "relation": "suggested_hypothesis" }, { "source": "u-dark-matter-microphysics", "target": "h-radio-axion-like-dm-constraints", "relation": "suggested_hypothesis" }, { "source": "u-borrelia-persister-cell-eradication", "target": "u-ptlds-neuroinflammation-self-sustaining", "relation": "related_unknown" }, { "source": "u-borrelia-persister-cell-eradication", "target": "b-borrelia-immune-evasion", "relation": "related_bridge" }, { "source": "u-borrelia-persister-cell-eradication", "target": "h-borrelia-triple-combo-persister-eradication", "relation": "suggested_hypothesis" }, { "source": "u-immune-treg-pi-control-quantitative", "target": "b-immune-regulation-feedback", "relation": "related_bridge" }, { "source": "u-lnp-tissue-targeting-mechanism", "target": "b-lipid-nanoparticle-mrna-delivery", "relation": "related_bridge" }, { "source": "u-tcr-foundation-model-ood-binding-generalization", "target": "b-foundation-model-x-tcr-antigen-specificity-transfer", "relation": "related_bridge" }, { "source": "u-tcr-foundation-model-ood-binding-generalization", "target": "h-tcr-foundation-pretraining-improves-antigen-specificity-recall", "relation": "suggested_hypothesis" }, { "source": "u-tcr-repertoire-pathogen-space-coverage", "target": "b-immune-recognition-statistical-pattern-detection", "relation": "related_bridge" }, { "source": "u-idiotypic-network-clinical-validation", "target": "h-autoimmune-disease-idiotypic-attractor-bifurcation", "relation": "suggested_hypothesis" }, { "source": "u-mae-cryo-em-prior-induced-hallucination-risk", "target": "b-masked-autoencoding-x-cryo-em-denoising-priors", "relation": "related_bridge" }, { "source": "u-mae-cryo-em-prior-induced-hallucination-risk", "target": "h-masked-autoencoder-pretraining-improves-cryo-em-low-snr-reconstruction", "relation": "suggested_hypothesis" }, { "source": "u-regret-aware-safety-constraints-for-antibiotic-de-escalation-bandits", "target": "b-multi-armed-bandits-x-sepsis-antibiotic-de-escalation", "relation": "related_bridge" }, { "source": "u-regret-aware-safety-constraints-for-antibiotic-de-escalation-bandits", "target": "h-constrained-bandit-policies-reduce-sepsis-antibiotic-overtreatment-days", "relation": "suggested_hypothesis" }, { "source": "u-entropy-rate-nonstationary-language-data", "target": "b-entropy-rate-x-language-model-perplexity", "relation": "related_bridge" }, { "source": "u-entropy-rate-nonstationary-language-data", "target": "h-entropy-rate-x-language-model-perplexity", "relation": "suggested_hypothesis" }, { "source": "u-genetic-code-information-optimality", "target": "h-genetic-code-error-correcting-design", "relation": "suggested_hypothesis" }, { "source": "u-birdsong-syntax-generative-grammar-limits", "target": "b-birdsong-syntax-formal-language-theory", "relation": "related_bridge" }, { "source": "u-creole-universals-origin", "target": "u-creole-genesis-mechanism", "relation": "related_unknown" }, { "source": "u-creole-universals-origin", "target": "u-language-evolution-emergence", "relation": "related_unknown" }, { "source": "u-creole-universals-origin", "target": "u-language-universals-typology", "relation": "related_unknown" }, { "source": "u-dialect-contact-as-graph-diffusion", "target": "b-language-contact-x-graph-interpolation", "relation": "related_bridge" }, { "source": "u-dialect-contact-as-graph-diffusion", "target": "h-lexical-diffusion-on-geographic-graphs-predicts-isoglosses", "relation": "suggested_hypothesis" }, { "source": "u-language-evolution-selection-neutrality", "target": "b-language-evolution-cultural-selection", "relation": "related_bridge" }, { "source": "u-language-thought-causality", "target": "u-language-thought-interface", "relation": "related_unknown" }, { "source": "u-language-thought-causality", "target": "u-linguistic-relativity-cognition", "relation": "related_unknown" }, { "source": "u-language-thought-causality", "target": "u-linguistic-relativity-color-perception", "relation": "related_unknown" }, { "source": "u-natural-language-complexity-class", "target": "b-formal-grammar-automata-theory", "relation": "related_bridge" }, { "source": "u-predictive-coding-grammar-neural-substrate", "target": "u-bilingual-cognitive-advantage-replication", "relation": "related_unknown" }, { "source": "u-predictive-coding-grammar-neural-substrate", "target": "u-attention-neural-mechanisms", "relation": "related_unknown" }, { "source": "u-prosody-meaning-interface", "target": "u-prosody-meaning-mapping", "relation": "related_unknown" }, { "source": "u-prosody-meaning-interface", "target": "u-tonal-language-cognitive-effects", "relation": "related_unknown" }, { "source": "u-semantic-shift-prediction", "target": "u-semantic-change-prediction", "relation": "related_unknown" }, { "source": "u-semantic-shift-prediction", "target": "u-language-evolution-emergence", "relation": "related_unknown" }, { "source": "u-universal-grammar-substrate", "target": "u-language-acquisition-poverty-stimulus", "relation": "related_unknown" }, { "source": "u-universal-grammar-substrate", "target": "u-recursion-uniquely-human", "relation": "related_unknown" }, { "source": "u-universal-grammar-substrate", "target": "u-syntax-innateness-evidence", "relation": "related_unknown" }, { "source": "u-active-liquid-crystal-topology-flow-coupling", "target": "b-soft-matter-liquid-crystal-order", "relation": "related_bridge" }, { "source": "u-liquid-crystals-frank-elasticity", "target": "b-liquid-crystals-frank-elasticity", "relation": "related_bridge" }, { "source": "u-nematic-ordering-fluctuation-corrections-maier-saupe", "target": "b-nematic-ordering-maier-saupe-mean-field", "relation": "related_bridge" }, { "source": "u-diffusion-models-x-stochastic-processes", "target": "b-diffusion-models-x-stochastic-processes", "relation": "related_bridge" }, { "source": "u-mean-field-theory-x-neural-networks", "target": "b-mean-field-theory-x-neural-networks", "relation": "related_bridge" }, { "source": "u-renormalization-group-ml-universality", "target": "b-openalex-renormalization-group-deep-learning", "relation": "related_bridge" }, { "source": "u-renormalization-group-ml-universality", "target": "b-grokking-criticality", "relation": "related_bridge" }, { "source": "u-rg-ml-universality-classes", "target": "b-renormalization-group-x-machine-learning", "relation": "related_bridge" }, { "source": "u-rg-ml-universality-classes", "target": "h-rg-ml-universality-classes", "relation": "suggested_hypothesis" }, { "source": "u-uap-stone-weierstrass-pedagogy-misconception-rate", "target": "b-stone-weierstrass-x-universal-approximation-intuition", "relation": "related_bridge" }, { "source": "u-uap-stone-weierstrass-pedagogy-misconception-rate", "target": "h-compact-algebra-first-sequence-improves-uap-transfer", "relation": "suggested_hypothesis" }, { "source": "u-fish-schooling-topological-interaction", "target": "b-fish-schooling-collective-motion", "relation": "related_bridge" }, { "source": "u-active-learning-bias-in-alloy-discovery-loops", "target": "b-active-learning-x-bayesian-optimization-alloy-discovery", "relation": "related_bridge" }, { "source": "u-active-learning-bias-in-alloy-discovery-loops", "target": "h-active-learning-bayesian-optimization-improves-alloy-hit-rate", "relation": "suggested_hypothesis" }, { "source": "u-amorphous-metal-fatigue-limit", "target": "u-metallic-glass-crystallisation", "relation": "related_unknown" }, { "source": "u-amorphous-metal-fatigue-limit", "target": "u-amorphous-metal-magnetism", "relation": "related_unknown" }, { "source": "u-auxetic-materials-scalable-fabrication-3d", "target": "b-auxetic-materials-negative-poisson-ratio", "relation": "related_bridge" }, { "source": "u-biomineralization-polymorph-control", "target": "b-biomineralization-crystal-nucleation", "relation": "related_bridge" }, { "source": "u-capillary-length-contact-line-hysteresis-unified-model", "target": "b-capillary-length-x-droplet-contact-line-dynamics", "relation": "related_bridge" }, { "source": "u-capillary-length-contact-line-hysteresis-unified-model", "target": "h-capillary-wetting-pinning-length-universality-class", "relation": "suggested_hypothesis" }, { "source": "u-classical-nucleation-theory-prefactor-discrepancy", "target": "b-classical-nucleation-theory", "relation": "related_bridge" }, { "source": "u-dislocation-avalanche-statistical-mechanics-plasticity", "target": "b-defects-mechanical-strength", "relation": "related_bridge" }, { "source": "u-fisher-optimal-experiment-policy-shift-drift", "target": "b-fisher-information-design-x-autonomous-materials-experiments", "relation": "related_bridge" }, { "source": "u-fisher-optimal-experiment-policy-shift-drift", "target": "h-information-optimal-batching-accelerates-material-discovery", "relation": "suggested_hypothesis" }, { "source": "u-grain-boundary-embrittlement-prediction", "target": "u-grain-boundary-segregation", "relation": "related_unknown" }, { "source": "u-grain-boundary-embrittlement-prediction", "target": "u-hydrogen-embrittlement-pathway", "relation": "related_unknown" }, { "source": "u-high-entropy-alloy-design-principles", "target": "u-entropy-stabilized-ceramics", "relation": "related_unknown" }, { "source": "u-high-entropy-alloy-phase-stability-prediction", "target": "b-phase-diagrams-alloy-design", "relation": "related_bridge" }, { "source": "u-hydrogel-fracture-toughness-network-structure", "target": "b-hydrogel-polymer-network-mechanics", "relation": "related_bridge" }, { "source": "u-lithium-dendrite-nucleation-control", "target": "u-battery-dendrite-nucleation", "relation": "related_unknown" }, { "source": "u-lithium-dendrite-nucleation-control", "target": "u-solid-electrolyte-interface", "relation": "related_unknown" }, { "source": "u-metamaterial-self-assembly-dynamics", "target": "b-nonhelical-turing-electromagnetic", "relation": "related_bridge" }, { "source": "u-metamaterial-self-assembly-dynamics", "target": "h-nonhelical-turing-cloaking-adaptation", "relation": "suggested_hypothesis" }, { "source": "u-peridynamic-horizon-calibration-for-cortical-bone-microcrack-prediction", "target": "b-peridynamics-nonlocal-fracture-x-bone-microdamage-remodeling", "relation": "related_bridge" }, { "source": "u-peridynamic-horizon-calibration-for-cortical-bone-microcrack-prediction", "target": "h-peridynamic-models-predict-bone-microdamage-hotspots-before-radiographic-failure", "relation": "suggested_hypothesis" }, { "source": "u-piezoelectricity-symmetry-breaking", "target": "b-piezoelectricity-symmetry-breaking", "relation": "related_bridge" }, { "source": "u-preisach-model-physical-interpretation-density", "target": "b-preisach-hysteresis-model", "relation": "related_bridge" }, { "source": "u-protein-matrix-nucleation-control", "target": "b-biomineralization-crystal-nucleation", "relation": "related_bridge" }, { "source": "u-quantum-dot-confinement-size-tunability", "target": "b-quantum-dots-particle-in-a-box", "relation": "related_bridge" }, { "source": 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"source": "u-category-theory-x-functional-programming", "target": "b-category-theory-x-functional-programming", "relation": "related_bridge" }, { "source": "u-causal-attribution-chain-rule-universality", "target": "u-fisher-natural-gradient-evolution", "relation": "related_unknown" }, { "source": "u-causal-attribution-chain-rule-universality", "target": "u-replicator-dynamics-llm-training", "relation": "related_unknown" }, { "source": "u-causal-attribution-chain-rule-universality", "target": "b-game-theory-evolution", "relation": "related_bridge" }, { "source": "u-chaos-x-ergodic-theory", "target": "b-chaos-x-ergodic-theory", "relation": "related_bridge" }, { "source": "u-compressed-sensing-x-sparse-recovery", "target": "b-compressed-sensing-x-sparse-recovery", "relation": "related_bridge" }, { "source": "u-conley-index-computable-verification-higher-dim", "target": "b-morse-homology-x-conley-index-isolated-invariants", "relation": "related_bridge" }, { "source": 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"u-expander-graphs-x-error-correcting-codes", "target": "b-expander-graphs-x-error-correcting-codes", "relation": "related_bridge" }, { "source": "u-extreme-value-theory-x-risk-modeling", "target": "b-extreme-value-theory-x-risk-modeling", "relation": "related_bridge" }, { "source": "u-fem-adaptivity-optimal-mesh-refinement", "target": "b-finite-element-method-pde", "relation": "related_bridge" }, { "source": "u-fiber-bundle-gauge-field-quantum-gravity", "target": "b-fiber-bundle-gauge-field-topology", "relation": "related_bridge" }, { "source": "u-fourier-analysis-non-euclidean-domains", "target": "b-signal-processing-fourier-analysis", "relation": "related_bridge" }, { "source": "u-fourier-transform-x-signal-processing", "target": "b-fourier-transform-x-signal-processing", "relation": "related_bridge" }, { "source": "u-frb-waiting-time-universality", "target": "u-fast-radio-burst-origin", "relation": "related_unknown" }, { "source": "u-frb-waiting-time-universality", "target": 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"u-optimal-transport-word-order-universals", "target": "u-riemann-zeta-biomedical-discoverability", "relation": "related_unknown" }, { "source": "u-optimal-transport-x-machine-learning", "target": "b-optimal-transport-x-machine-learning", "relation": "related_bridge" }, { "source": "u-origami-fold-design-complexity", "target": "b-origami-mathematics-computational-fold", "relation": "related_bridge" }, { "source": "u-origami-math-x-structural-engineering", "target": "b-origami-math-x-structural-engineering", "relation": "related_bridge" }, { "source": "u-p-vs-np-geometric-complexity", "target": "b-complexity-proof-theory", "relation": "related_bridge" }, { "source": "u-persistence-homology-x-protein-structure", "target": "b-persistence-homology-x-protein-structure", "relation": "related_bridge" }, { "source": "u-ppi-scale-free-topology-functional-necessity", "target": "b-graph-theory-protein-networks", "relation": "related_bridge" }, { "source": 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"source": "u-ricci-price-covariance-analogy-scope", "target": "h-fisher-ricci-price-covariance-analogy-calibration", "relation": "suggested_hypothesis" }, { "source": "u-rip-constant-optimal-measurement-matrix-deterministic-construction", "target": "b-compressed-sensing-sparse-recovery", "relation": "related_bridge" }, { "source": "u-rip-constant-optimal-measurement-matrix-deterministic-construction", "target": "h-compressed-sensing-mri-10x-scan-time-reduction-clinical-safety", "relation": "suggested_hypothesis" }, { "source": "u-sindy-noise-and-collinearity-under-limited-sensing", "target": "b-symbolic-regression-x-sparse-sensor-pde-structure-discovery", "relation": "related_bridge" }, { "source": "u-sindy-noise-and-collinearity-under-limited-sensing", "target": "h-sparse-sensor-placement-improves-pde-structure-recovery", "relation": "suggested_hypothesis" }, { "source": "u-social-network-centrality-x-eigenvector", "target": "b-social-network-centrality-x-eigenvector", "relation": "related_bridge" }, { "source": "u-spectral-geometry-phylogenetic-trees", "target": "u-riemann-zeta-biomedical-discoverability", "relation": "related_unknown" }, { "source": "u-statistical-mechanics-income-wealth", "target": "u-social-ising-universality", "relation": "related_unknown" }, { "source": "u-subriemannian-geodesic-abnormal-optimality", "target": "b-control-theory-differential-geometry", "relation": "related_bridge" }, { "source": "u-subriemannian-geodesic-abnormal-optimality", "target": "h-lie-bracket-depth-complexity-robot-planning", "relation": "suggested_hypothesis" }, { "source": "u-tda-x-shape-recognition", "target": "b-tda-x-shape-recognition", "relation": "related_bridge" }, { "source": "u-thermodynamics-convex-geometry-non-equilibrium", "target": "b-thermodynamics-convex-analysis", "relation": "related_bridge" }, { "source": "u-topological-data-analysis-phase-transitions", "target": "u-ising-exact-density-states-universality", "relation": "related_unknown" }, { "source": "u-topological-data-analysis-x-cancer-genomics", "target": "b-topological-data-analysis-x-cancer-genomics", "relation": "related_bridge" }, { "source": "u-tropical-geometry-x-neural-networks", "target": "b-tropical-geometry-x-neural-networks", "relation": "related_bridge" }, { "source": "u-tsp-approximation-barrier-metric", "target": "b-graph-algorithms-network-optimization", "relation": "related_bridge" }, { "source": "u-voting-theory-x-social-choice", "target": "b-voting-theory-x-social-choice", "relation": "related_bridge" }, { "source": "u-wasserstein-cell-fate-noise-geodesic-uniqueness", "target": "b-optimal-transport-cell-differentiation", "relation": "related_bridge" }, { "source": "u-wavelet-optimal-basis-nonstationary-signal-adaptation", "target": "b-wavelet-theory-signal-compression", "relation": "related_bridge" }, { "source": "u-zipf-law-mechanism-adaptive-vs-null", "target": "b-zipf-law-information-efficiency", "relation": "related_bridge" }, { "source": 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"relation": "related_bridge" }, { "source": "u-ergodic-failure-quantum-thermalization", "target": "u-quantum-thermodynamics-arrow", "relation": "related_unknown" }, { "source": "u-goldstone-boson-higher-dimensional-systems", "target": "h-symmetry-breaking-universal-phase-transition-classifier", "relation": "suggested_hypothesis" }, { "source": "u-integrability-breaking-perturbations-soliton-stability-realistic-systems", "target": "b-integrable-systems-solitons", "relation": "related_bridge" }, { "source": "u-measure-theoretic-foundations-quantum-probability", "target": "b-measure-theory-probability", "relation": "related_bridge" }, { "source": "u-percolation-phase-transition-interdependent-networks-cascading-failures", "target": "b-percolation-network-robustness", "relation": "related_bridge" }, { "source": "u-percolation-phase-transition-interdependent-networks-cascading-failures", "target": "h-targeted-hub-vaccination-achieves-herd-immunity-fewer-doses-scale-free", "relation": "suggested_hypothesis" }, { "source": "u-auction-market-design-kidney-exchange", "target": "b-auction-theory-mechanism-design", "relation": "related_bridge" }, { "source": "u-combinatorial-auction-computational-complexity", "target": "b-operations-research-market-design", "relation": "related_bridge" }, { "source": "u-mechanism-design-ai-alignment", "target": "b-information-economics-mechanism", "relation": "related_bridge" }, { "source": "u-mechanism-design-collusion-resistance", "target": "b-auction-theory-mechanism-design", "relation": "related_bridge" }, { "source": "u-truthful-elicitation-mechanism-duality", "target": "b-preference-elicitation-x-vickrey-auction", "relation": "related_bridge" }, { "source": "u-truthful-elicitation-mechanism-duality", "target": "h-vickrey-clarke-groves-payments-improve-lab-truthful-reporting", "relation": "suggested_hypothesis" }, { "source": "u-ddpm-mri-prior-mismatch-artifact-risk", "target": "b-ddpm-x-accelerated-mri-inverse-reconstruction", "relation": "related_bridge" }, { "source": "u-ddpm-mri-prior-mismatch-artifact-risk", "target": "h-ddpm-priors-reduce-mri-reconstruction-error-at-fixed-dose", "relation": "suggested_hypothesis" }, { "source": "u-persistent-homology-parameter-stability-noisy-microscopy", "target": "b-persistent-homology-x-microscopy-noise-topology-qc", "relation": "related_bridge" }, { "source": "u-persistent-homology-parameter-stability-noisy-microscopy", "target": "h-multiscale-filtration-persistence-improves-microscopy-segmentation-qc", "relation": "suggested_hypothesis" }, { "source": "u-ancestry-shift-sensitivity-of-elastic-net-prs", "target": "b-elastic-net-regularization-x-polygenic-risk-model-stability", "relation": "related_bridge" }, { "source": "u-ancestry-shift-sensitivity-of-elastic-net-prs", "target": "h-elastic-net-prs-retraining-with-ancestry-balancing-reduces-calibration-drift", "relation": "suggested_hypothesis" }, { "source": "u-assimilation-window-stability-for-patient-specific-glucose-dynamics", "target": "b-variational-data-assimilation-x-personalized-glucose-forecasting", "relation": "related_bridge" }, { "source": "u-assimilation-window-stability-for-patient-specific-glucose-dynamics", "target": "h-variational-assimilation-derived-glucose-predictions-outperform-sliding-window-baselines", "relation": "suggested_hypothesis" }, { "source": "u-control-barrier-formal-safety-under-sensor-lag", "target": "b-control-barrier-functions-x-safe-artificial-pancreas", "relation": "related_bridge" }, { "source": "u-control-barrier-formal-safety-under-sensor-lag", "target": "h-cbf-enforced-insulin-constraints-prevent-severe-lows", "relation": "suggested_hypothesis" }, { "source": "u-dmri-tortuosity-effective-medium-identifiability", "target": "b-diffusion-mri-x-effective-medium-tortuosity", "relation": "related_bridge" }, { "source": "u-dmri-tortuosity-effective-medium-identifiability", "target": "h-multi-shell-dmri-estimates-track-phantom-tortuosity", "relation": "suggested_hypothesis" }, { "source": "u-domain-shift-calibration-for-resnet-retinal-screening", "target": "b-residual-learning-x-automated-retinal-screening-robustness", "relation": "related_bridge" }, { "source": "u-domain-shift-calibration-for-resnet-retinal-screening", "target": "h-self-supervised-residual-pretraining-reduces-retinal-screening-false-negatives", "relation": "suggested_hypothesis" }, { "source": "u-dose-spacing-fisher-information-design-trial-calibration", "target": "b-fisher-information-cramer-rao-x-dose-spacing-experimental-design", "relation": "related_bridge" }, { "source": "u-dose-spacing-fisher-information-design-trial-calibration", "target": "h-fisher-optimal-dose-grid-reduces-parameter-variance-simulation", "relation": "suggested_hypothesis" }, { "source": "u-eikonal-anisotropy-identifiability-in-cardiac-activation-inverse-problems", "target": 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"suggested_hypothesis" }, { "source": "u-flim-t2star-multiexp-phantom-transfer", "target": "b-fluorescence-lifetime-x-mri-t2-star-dephasing", "relation": "related_bridge" }, { "source": "u-flim-t2star-multiexp-phantom-transfer", "target": "h-shared-biexponential-fitting-bias-function-across-modalities-same-snr", "relation": "suggested_hypothesis" }, { "source": "u-ftle-derived-thrombosis-risk-threshold-transferability", "target": "b-finite-time-lyapunov-exponents-x-intracardiac-flow-mixing", "relation": "related_bridge" }, { "source": "u-ftle-derived-thrombosis-risk-threshold-transferability", "target": "h-ftle-ridge-persistence-predicts-left-atrial-appendage-stasis", "relation": "suggested_hypothesis" }, { "source": "u-hopf-normal-form-cardiac-alternans-mapping", "target": "b-hopf-bifurcation-x-cardiac-alternans", "relation": "related_bridge" }, { "source": "u-hopf-normal-form-cardiac-alternans-mapping", "target": "h-bifurcation-continuation-predicts-alternans-onset-optical-mapping", 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"source": "u-adult-human-hippocampal-neurogenesis-existence-rate-controversy", "target": "b-neurogenesis-growth-factor-signaling", "relation": "related_bridge" }, { "source": "u-axon-soliton-collision-dynamics", "target": "b-action-potential-x-soliton", "relation": "related_bridge" }, { "source": "u-axon-soliton-collision-dynamics", "target": "h-axon-soliton-collision-dynamics", "relation": "suggested_hypothesis" }, { "source": "u-biological-backpropagation-mechanism", "target": "b-backpropagation-synaptic-plasticity", "relation": "related_bridge" }, { "source": "u-brain-criticality-universality-class", "target": "b-neural-avalanches-criticality", "relation": "related_bridge" }, { "source": "u-brain-criticality-universality-class", "target": "h-criticality-maximizes-neural-dynamic-range", "relation": "suggested_hypothesis" }, { "source": "u-cerebellar-prediction-coding", "target": "u-consciousness-binding-problem", "relation": "related_unknown" }, { "source": "u-circadian-desynchrony-disease-mechanisms", "target": "b-circadian-rhythms-neural-oscillators", "relation": "related_bridge" }, { "source": "u-connectome-neurodegeneration-spread-rate", "target": "u-alzheimer-causal-biomarkers", "relation": "related_unknown" }, { "source": "u-connectome-neurodegeneration-spread-rate", "target": "u-neuroplasticity-adult-limits", "relation": "related_unknown" }, { "source": "u-connectome-neurodegeneration-spread-rate", "target": "b-connectome-neurodegeneration", "relation": "related_bridge" }, { "source": "u-connectome-spectral-laplacian", "target": "b-connectome-graph-laplacian-spectral", "relation": "related_bridge" }, { "source": "u-consciousness-binding-problem", "target": "u-self-organized-criticality-consciousness", "relation": "related_unknown" }, { "source": "u-consciousness-information-integration-scale", "target": "b-entropy-conscious-experience", "relation": "related_bridge" }, { "source": "u-dopamine-prediction-error-temporal-credit", "target": "u-sleep-memory-consolidation-mechanism", "relation": "related_unknown" }, { "source": "u-eeg-source-localization-skull-conductivity", "target": "b-eeg-dipole-source-maxwell-equations", "relation": "related_bridge" }, { "source": "u-efficient-coding-bottleneck-tradeoff-measurability", "target": "b-efficient-coding-hypothesis-x-information-bottleneck-representation-learning", "relation": "related_bridge" }, { "source": "u-efficient-coding-bottleneck-tradeoff-measurability", "target": "h-information-bottleneck-alignment-improves-neural-encoding-metrics", "relation": "suggested_hypothesis" }, { "source": "u-efficient-coding-metabolic-optimality", "target": "u-predictive-coding-grammar-neural-substrate", "relation": "related_unknown" }, { "source": "u-efficient-coding-metabolic-optimality", "target": "b-efficient-coding-perception", "relation": "related_bridge" }, { "source": "u-fmri-connectivity-graphical-model-validity", "target": "b-neuroimaging-connectivity-graphical-models", "relation": "related_bridge" }, { "source": "u-glymphatic-flow-impairment-alzheimers", "target": "b-glymphatic-cerebrospinal-fluid", "relation": "related_bridge" }, { "source": "u-grid-cell-cognitive-map-geometry", "target": "u-sleep-memory-consolidation-mechanism", "relation": "related_unknown" }, { "source": "u-grid-cell-fourier-basis-navigation", "target": "b-grid-cells-hexagonal-lattice-fourier", "relation": "related_bridge" }, { "source": "u-hair-cell-bundle-x-hopf-bifurcation", "target": "b-hair-cell-bundle-x-hopf-bifurcation", "relation": "related_bridge" }, { "source": "u-hawkes-branching-ratio-seizure-cascade-threshold", "target": "b-hawkes-self-excitation-x-seizure-aftershock-clustering", "relation": "related_bridge" }, { "source": "u-hawkes-branching-ratio-seizure-cascade-threshold", "target": "h-hawkes-branching-threshold-predicts-seizure-clusters", "relation": "suggested_hypothesis" }, { "source": "u-hippocampal-replay-sequence-selection-criteria", "target": 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"related_bridge" }, { "source": "u-intrinsic-motivation-information-maximization", "target": "b-openalex-info-theory-intrinsic-motivation", "relation": "related_bridge" }, { "source": "u-ion-channel-barrier-heights-from-multiscale-md-posteriors", "target": "b-ion-channel-gating-x-metastable-rate-theory", "relation": "related_bridge" }, { "source": "u-ion-channel-barrier-heights-from-multiscale-md-posteriors", "target": "h-markov-gating-graph-consistency-with-kramers-scaling-under-voltage-clamp-protocols", "relation": "suggested_hypothesis" }, { "source": "u-kramers-drift-diffusion-barrier-mapping-neural-decisions", "target": "b-kramers-escape-rate-x-drift-diffusion-decision-threshold", "relation": "related_bridge" }, { "source": "u-kramers-drift-diffusion-barrier-mapping-neural-decisions", "target": "h-reaction-time-tail-scales-with-effective-barrier-height", "relation": "suggested_hypothesis" }, { "source": "u-landauer-limit-neuronal-computation", "target": "b-thermodynamics-x-information-theory", "relation": "related_bridge" }, { "source": "u-landauer-limit-neuronal-computation", "target": "h-landauer-limit-neuronal-computation", "relation": "suggested_hypothesis" }, { "source": "u-lstm-gating-biological-analogue", "target": "b-openalex-stat-mech-memory-gating", "relation": "related_bridge" }, { "source": "u-meg-inverse-source-localization", "target": "b-meg-inverse-source-localization", "relation": "related_bridge" }, { "source": "u-meg-inverse-source-nonunique-regularization-bounds", "target": "b-meg-squid-forward-x-em-inverse-source", "relation": "related_bridge" }, { "source": "u-meg-inverse-source-nonunique-regularization-bounds", "target": "h-squid-array-regularization-improves-meg-source-localization", "relation": "suggested_hypothesis" }, { "source": "u-mirror-neuron-function", "target": "u-consciousness-binding-problem", "relation": "related_unknown" }, { "source": "u-motor-cortex-population-dynamics-motor-programs", "target": "b-population-vector-motor-cortex", "relation": "related_bridge" }, { "source": "u-myelination-conduction-velocity-optimality", "target": "b-electrophysiology-action-potential", "relation": "related_bridge" }, { "source": "u-neural-binding-mechanism-synchrony", "target": "b-neural-binding-gamma-oscillations", "relation": "related_bridge" }, { "source": "u-neural-criticality-tipping-shared-mathematics", "target": "u-abrupt-climate-transitions", "relation": "related_unknown" }, { "source": "u-neural-criticality-tipping-shared-mathematics", "target": "u-amoc-collapse-threshold", "relation": "related_unknown" }, { "source": "u-neural-decay-poisson-deviation-shared-overdispersion-tests", "target": "b-poisson-counting-process-x-decay-spike-train-likelihood", "relation": "related_bridge" }, { "source": "u-neural-decay-poisson-deviation-shared-overdispersion-tests", "target": "h-time-rescaled-residuals-separate-poisson-from-bursty-counting-systems", "relation": "suggested_hypothesis" }, { "source": "u-neural-field-theory-empirical-connectome-validation", "target": "b-neural-field-theory-brain-waves", "relation": "related_bridge" }, { "source": "u-neural-optimal-control-noise-model", "target": "b-neural-control-theory", "relation": "related_bridge" }, { "source": "u-neural-optimal-control-noise-model", "target": "h-cerebellum-kalman-prediction-error", "relation": "suggested_hypothesis" }, { "source": "u-neural-plasticity-x-hebbian-learning", "target": "b-neural-plasticity-x-hebbian-learning", "relation": "related_bridge" }, { "source": "u-neural-spike-coding-x-information-compression", "target": "b-neural-spike-coding-x-information-compression", "relation": "related_bridge" }, { "source": "u-neuronal-avalanche-soc-universality-class", "target": "b-neuronal-avalanches-soc-power-law", "relation": "related_bridge" }, { "source": "u-neuronal-avalanches-branching-process", "target": "b-neuronal-avalanches-branching-process", "relation": "related_bridge" }, { "source": "u-neurovascular-coupling-x-fluid-dynamics", "target": "b-neurovascular-coupling-x-fluid-dynamics", "relation": "related_bridge" }, { "source": "u-optogenetics-x-control-theory", "target": "b-optogenetics-x-control-theory", "relation": "related_bridge" }, { "source": "u-persistent-homology-neural-manifold-geometry-vs-topology-decoupling", "target": "b-persistent-homology-neural-topology", "relation": "related_bridge" }, { "source": "u-persistent-homology-neural-manifold-geometry-vs-topology-decoupling", "target": "h-hippocampal-place-cell-population-topology-reflects-navigated-space-topology", "relation": "suggested_hypothesis" }, { "source": "u-phi-measurement-neural-correlates", "target": "b-entropy-conscious-experience", "relation": "related_bridge" }, { "source": "u-predictive-coding-neural-implementation-evidence", "target": "b-bayesian-brain-predictive-coding", "relation": "related_bridge" }, { "source": "u-prefrontal-working-memory-mechanism", "target": "u-consciousness-binding-problem", "relation": "related_unknown" }, { "source": "u-ptlds-neuroinflammation-self-sustaining", "target": "u-borrelia-persister-cell-eradication", "relation": "related_unknown" }, { "source": "u-ptlds-neuroinflammation-self-sustaining", "target": "b-neurolyme-neuroinflammation", "relation": "related_bridge" }, { "source": "u-ptlds-neuroinflammation-self-sustaining", "target": "h-ptlds-neuroinflammation-il6-blockade", "relation": "suggested_hypothesis" }, { "source": "u-rl-novelty-bonus-information-gain-mapping", "target": "b-rl-intrinsic-motivation-x-novelty-information-gain-neuroscience", "relation": "related_bridge" }, { "source": "u-rl-novelty-bonus-information-gain-mapping", "target": "h-count-novelty-scales-bayesian-information-gain-proxy", "relation": "suggested_hypothesis" }, { "source": "u-signal-sparsity-neural-coding", "target": "b-signal-processing-fourier-analysis", "relation": "related_bridge" }, { "source": "u-sleep-memory-consolidation-mechanism", "target": "u-consciousness-binding-problem", "relation": "related_unknown" }, { "source": "u-sleep-replay-causal-role-memory-specificity", "target": "b-sleep-memory-consolidation", "relation": "related_bridge" }, { "source": "u-snare-complex-partial-zippering-spontaneous-release-rate", "target": "b-neurotransmitter-pharmacology", "relation": "related_bridge" }, { "source": "u-snare-force-threshold-in-vivo", "target": "b-synaptic-vesicle-snare-fusion", "relation": "related_bridge" }, { "source": "u-softmax-attention-cortical-normalization-mapping", "target": "b-softmax-attention-x-cortical-divisive-normalization", "relation": "related_bridge" }, { "source": "u-softmax-attention-cortical-normalization-mapping", "target": "h-softmax-attention-x-cortical-divisive-normalization", "relation": "suggested_hypothesis" }, { "source": "u-sparse-coding-x-neural-basis", "target": "b-sparse-coding-x-neural-basis", "relation": "related_bridge" }, { "source": "u-spinal-gate-control-interneuron-identity", "target": "b-nociception-gate-control-spinal-circuit", "relation": "related_bridge" }, { "source": "u-stdp-natural-stimuli-in-vivo-plasticity-rules", "target": "b-synaptic-plasticity-hebbian-learning", "relation": "related_bridge" }, { "source": "u-stdp-synaptic-weight-saturation", "target": "b-neuroplasticity-stdp", "relation": "related_bridge" }, { "source": "u-stochastic-resonance-neural-coding-optimality", "target": "b-stochastic-resonance-x-signal-detection", "relation": "related_bridge" }, { "source": "u-stochastic-resonance-neural-coding-optimality", "target": "h-stochastic-resonance-neural-coding-optimality", "relation": "suggested_hypothesis" }, { "source": "u-td-learning-dopamine-biological-implementation", "target": "b-reinforcement-learning-dopamine", "relation": "related_bridge" }, { "source": "u-tda-brain-disease-biomarkers", "target": "b-persistent-homology-neural-representation", "relation": "related_bridge" }, { "source": "u-vmpfc-reference-dependent-coding-mechanism", "target": "b-decision-neuroscience-neuroeconomics", "relation": "related_bridge" }, { "source": "u-vmpfc-reference-dependent-coding-mechanism", "target": "h-beta-delta-neuroeconomics-dual-system", "relation": "suggested_hypothesis" }, { "source": "u-weber-fechner-stevens-unifying-neural-mechanism", "target": "b-sensory-adaptation-weber-fechner", "relation": "related_bridge" }, { "source": "u-computational-psychiatry-treatment-response-prediction", "target": "b-computational-psychiatry-digital-biomarkers", "relation": "related_bridge" }, { "source": "u-kalman-filter-neural-implementation-limits", "target": "b-kalman-filter-x-brain-state-estimation", "relation": "related_bridge" }, { "source": "u-kalman-filter-neural-implementation-limits", "target": "h-sensory-cortex-implements-approximate-kalman-updates", "relation": "suggested_hypothesis" }, { "source": "u-lif-parameter-identifiability-noisy-synapses", "target": "b-leaky-if-neuron-x-rc-membrane-circuit", "relation": "related_bridge" }, { "source": "u-lif-parameter-identifiability-noisy-synapses", "target": "h-leaky-if-neuron-x-rc-membrane-circuit", "relation": "suggested_hypothesis" }, { "source": "u-neural-spike-coding-rate-vs-temporal", "target": "h-neuromorphic-chips-edge-ai-energy-advantage", "relation": "suggested_hypothesis" }, { "source": "u-neuromuscular-control-redundancy-resolution", "target": "b-neuromuscular-control-biomechanics", "relation": "related_bridge" }, { "source": "u-microglia-synapse-pruning-alzheimers-pathological-threshold", "target": "b-glial-cells-brain-homeostasis", "relation": "related_bridge" }, { "source": "u-nuclear-waste-transmutation-accelerator-driven-systems", "target": "b-nuclear-chemistry-reactor-physics", "relation": "related_bridge" }, { "source": "u-stellar-nucleosynthesis-r-process-site", "target": "b-stellar-nucleosynthesis-network-flow", "relation": "related_bridge" }, { "source": "u-internal-tide-mixing-efficiency-spatial", "target": "b-tidal-forcing-ocean-mixing", "relation": "related_bridge" }, { "source": "u-neural-spectral-ocean-forecast-stability-horizon", "target": "b-neural-spectral-model-x-submesoscale-forecasting", "relation": "related_bridge" }, { "source": "u-neural-spectral-ocean-forecast-stability-horizon", "target": "h-neural-spectral-ocean-model-improves-submesoscale-forecast-skill", "relation": "suggested_hypothesis" }, { "source": "u-ocean-acidification-carbonate-threshold", "target": "b-ocean-acidification-carbonate-chemistry", "relation": "related_bridge" }, { "source": "u-ocean-color-phytoplankton-remote-sensing", "target": "b-ocean-color-radiative-transfer", "relation": "related_bridge" }, { "source": "u-redfield-ratio-variability-drivers", "target": "b-redfield-ratio-ocean-stoichiometry", "relation": "related_bridge" }, { "source": "u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models", "target": "b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling", "relation": "related_bridge" }, { "source": "u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models", "target": "h-kramers-moyal-surrogates-improve-tumor-state-transition-forecast-calibration", "relation": "suggested_hypothesis" }, { "source": "u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models", "target": "b-markov-jump-processes-x-cell-state-switching-therapy-design", "relation": "related_bridge" }, { "source": "u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models", "target": "h-markov-jump-therapy-policies-reduce-relapse-prone-cell-state-occupancy", "relation": "suggested_hypothesis" }, { "source": "u-heavy-traffic-thresholds-for-ed-crowding-intervention-policies", "target": "b-heavy-traffic-queueing-x-emergency-department-flow", "relation": "related_bridge" }, { "source": "u-heavy-traffic-thresholds-for-ed-crowding-intervention-policies", "target": "h-diffusion-queueing-threshold-policies-reduce-ed-boarding-time-variance", "relation": "suggested_hypothesis" }, { "source": "u-ribosome-kinetics-queuing", "target": "b-ribosome-kinetics-queuing-theory", "relation": "related_bridge" }, { "source": "u-supply-chain-network-topology-resilience", "target": "b-supply-chain-network-robustness", "relation": "related_bridge" }, { "source": "u-optogenetics-human-therapeutic-scale-delivery", "target": "b-optogenetics-neural-circuit-control", "relation": "related_bridge" }, { "source": "u-antibiotic-synergy-surfaces", "target": "b-antibiotic-synergy-pharmacodynamic-surfaces", "relation": "related_bridge" }, { "source": "u-cyp450-xenobiotic-metabolic-prediction", "target": "b-xenobiotic-metabolism-cyp450", "relation": "related_bridge" }, { "source": "u-fitness-landscape-drug-resistance-prediction", "target": "b-drug-resistance-fitness-landscapes", "relation": "related_bridge" }, { "source": "u-neural-ode-pk-identifiability-under-sparse-sampling", "target": "b-neural-ode-x-pharmacokinetic-state-space-modeling", "relation": "related_bridge" }, { "source": "u-neural-ode-pk-identifiability-under-sparse-sampling", "target": "h-neural-ode-priors-improve-pk-state-forecasting", "relation": "suggested_hypothesis" }, { "source": "u-pharmacokinetic-interindividual-variability", "target": "b-pharmacokinetics-compartmental-ode", "relation": "related_bridge" }, { "source": "u-bayesian-convergence-prior-dependence", "target": "b-induction-bayesian-convergence", "relation": "related_bridge" }, { "source": "u-bayesian-convergence-prior-dependence", "target": "h-doob-convergence-rate-scientific-inference", "relation": "suggested_hypothesis" }, { "source": "u-bayesian-old-evidence-problem", "target": "b-bayesian-inference-scientific-confirmation", "relation": "related_bridge" }, { "source": "u-bayesian-prior-objectivity", "target": "u-model-selection-validity", "relation": "related_unknown" }, { "source": "u-bayesian-prior-objectivity", "target": "u-peer-review-validity", "relation": "related_unknown" }, { "source": "u-bayesian-prior-objectivity", "target": "b-bayesian-scientific-inference", "relation": "related_bridge" }, { "source": "u-scientific-method-cross-domain-falsifiability", "target": "b-scientific-method-epistemological-foundations", "relation": "related_bridge" }, { "source": "u-optical-frequency-metamaterial-loss-limits-superlens", "target": "b-metamaterials-negative-refraction", "relation": "related_bridge" }, { "source": "u-vcsel-silicon-photonics-integration-limit", "target": "b-semiconductor-lasers-photonics", "relation": "related_bridge" }, { "source": "u-vcsel-silicon-photonics-integration-limit", "target": "h-silicon-photonics-dfb-laser-integration", "relation": "suggested_hypothesis" }, { "source": "u-phylogenetic-placement-long-branch-attraction-correction", "target": "b-phylogenetics-maximum-likelihood", "relation": "related_bridge" }, { "source": "u-human-expansion-routes-coalescent-ancient-dna", "target": "b-phylogeography-coalescent-theory", "relation": "related_bridge" }, { "source": "u-anharmonic-spectroscopy-matrix-models-convergence", "target": "b-molecular-spectroscopy-x-matrix-diagonalization", "relation": "related_bridge" }, { "source": "u-anharmonic-spectroscopy-matrix-models-convergence", "target": "h-molecular-spectroscopy-x-matrix-diagonalization", "relation": "suggested_hypothesis" }, { "source": "u-critical-exponents-non-mean-field", "target": "b-van-der-waals-phase-transitions", "relation": "related_bridge" }, { "source": "u-kramers-turnover-solvent-friction", "target": "b-reaction-rate-transition-state", "relation": "related_bridge" }, { "source": "u-md-thermostat-sde-equivalence-and-ergodicity", "target": "b-molecular-dynamics-x-stochastic-thermostats", "relation": "related_bridge" }, { "source": "u-md-thermostat-sde-equivalence-and-ergodicity", "target": "h-nose-hoover-chains-match-target-kinetic-spectra-when-tuned", "relation": "suggested_hypothesis" }, { "source": "u-acoustic-metamaterials-x-negative-refraction", "target": "b-acoustic-metamaterials-x-negative-refraction", "relation": "related_bridge" }, { "source": "u-active-matter-chiral-renormalization", "target": "u-active-matter-percolation", "relation": "related_unknown" }, { "source": "u-active-matter-percolation", "target": "u-tumor-containment-percolation", "relation": "related_unknown" }, { "source": "u-active-matter-percolation", "target": "b-percolation-oncology", "relation": "related_bridge" }, { "source": "u-anderson-localization-biological-systems", "target": "u-protein-folding-thermodynamics-kinetics", "relation": "related_unknown" }, { "source": "u-arrow-of-time-low-entropy-origin", "target": "b-entropy-arrow-of-time", "relation": "related_bridge" }, { "source": "u-arrow-of-time-low-entropy-origin", "target": "h-landauer-cosmological-arrow", "relation": "suggested_hypothesis" }, { "source": "u-atmospheric-convection-x-rayleigh-benard", "target": "b-atmospheric-convection-x-rayleigh-benard", "relation": "related_bridge" }, { "source": "u-bgs-conjecture-general-proof", "target": "b-random-matrix-quantum-chaos", "relation": "related_bridge" }, { "source": "u-boltzmann-machine-x-ising-model", "target": "b-boltzmann-machine-x-ising-model", "relation": "related_bridge" }, { "source": "u-boltzmann-shannon-nonequilibrium-bridge", "target": "b-boltzmann-shannon-entropy", "relation": "related_bridge" }, { "source": "u-cardiac-criticality-synchronization", "target": "u-brain-criticality-function", "relation": "related_unknown" }, { "source": "u-cardiac-criticality-synchronization", "target": "b-criticality-neuroscience", "relation": "related_bridge" }, { "source": "u-cardiomyocyte-synchronization-criticality", "target": "u-cardiac-criticality-synchronization", "relation": "related_unknown" }, { "source": "u-cardiomyocyte-synchronization-criticality", "target": "b-criticality-neuroscience", "relation": "related_bridge" }, { "source": "u-cavity-method-x-belief-propagation", "target": "b-cavity-method-x-belief-propagation", "relation": "related_bridge" }, { "source": "u-chaos-synchronization-noise-robustness-threshold", "target": "b-chaos-synchronization-pecora-carroll", "relation": "related_bridge" }, { "source": "u-cherenkov-mach-cone-unified-demo-transfer", "target": "b-cherenkov-radiation-x-mach-sonic-cone", "relation": "related_bridge" }, { "source": "u-cherenkov-mach-cone-unified-demo-transfer", "target": "h-cherenkov-mach-prerequisite-transfer-diagnostic", "relation": "suggested_hypothesis" }, { "source": "u-chromatic-aberration-broadband-metalens", "target": "b-chromatic-aberration-dispersion", "relation": "related_bridge" }, { "source": "u-climate-ew-indicator-universality", "target": "u-habitat-fragmentation-threshold", "relation": "related_unknown" }, { "source": "u-climate-ew-indicator-universality", "target": "u-kibble-zurek-embryo", "relation": "related_unknown" }, { "source": "u-climate-ew-indicator-universality", "target": "b-tipping-points-phase-transitions", "relation": "related_bridge" }, { "source": "u-climate-ew-indicator-universality", "target": "b-habitat-percolation-ecology", "relation": "related_bridge" }, { "source": "u-conformal-field-theory-x-critical-phenomena", "target": "b-conformal-field-theory-x-critical-phenomena", "relation": "related_bridge" }, { "source": "u-cosmological-constant-fine-tuning", "target": "u-arrow-of-time-low-entropy-origin", "relation": "related_unknown" }, { "source": "u-crystallography-x-group-theory", "target": "b-crystallography-x-group-theory", "relation": "related_bridge" }, { "source": "u-diffusion-limited-aggregation-x-fractal-growth", "target": "b-diffusion-limited-aggregation-x-fractal-growth", "relation": "related_bridge" }, { "source": "u-emergence-quantification-integrated-information-empirical-test", "target": "b-complex-systems-emergence", "relation": "related_bridge" }, { "source": "u-entropy-production-x-living-systems", "target": "b-entropy-production-x-living-systems", "relation": "related_bridge" }, { "source": "u-ergodic-theory-x-statistical-mechanics", "target": "b-ergodic-theory-x-statistical-mechanics", "relation": "related_bridge" }, { "source": "u-fluctuation-theorem-biological-motors", "target": "b-stochastic-thermodynamics-fluctuation-theorems", "relation": "related_bridge" }, { "source": "u-gauge-field-epidemic-nonlocality", "target": "b-percolation-epidemiology", "relation": "related_bridge" }, { "source": "u-geometric-phase-calibration-across-polarization-optics", "target": "b-berry-phase-x-polarization-parallel-transport-optics", "relation": "related_bridge" }, { "source": "u-geometric-phase-calibration-across-polarization-optics", "target": "h-pancharatnam-loop-area-predicts-interferometric-phase-shifts", "relation": "suggested_hypothesis" }, { "source": "u-gravitational-wave-memory-effect", "target": "u-arrow-of-time-low-entropy-origin", "relation": "related_unknown" }, { "source": "u-grokking-phase-transition", "target": "u-brain-criticality-function", "relation": "related_unknown" }, { "source": "u-grokking-phase-transition", "target": "b-grokking-criticality", "relation": "related_bridge" }, { "source": "u-grokking-phase-transition", "target": "b-criticality-neuroscience", "relation": "related_bridge" }, { "source": "u-hawking-channel-capacity", "target": "u-black-hole-information-paradox", "relation": "related_unknown" }, { "source": "u-hawking-channel-capacity", "target": "b-blackhole-information-paradox", "relation": "related_bridge" }, { "source": "u-hawking-unruh-experimental-detection", "target": "b-hawking-radiation-unruh-effect", "relation": "related_bridge" }, { "source": "u-high-tc-pairing-mechanism", "target": "b-bcs-superconductivity", "relation": "related_bridge" }, { "source": "u-hopfield-capacity-cortex", "target": "h-hopfield-alzheimers-glass-transition", "relation": "suggested_hypothesis" }, { "source": "u-ion-pump-landauer-thermodynamics", "target": "u-landauer-limit-biological-computation", "relation": "related_unknown" }, { "source": "u-ion-pump-landauer-thermodynamics", "target": "b-landauer-information-thermodynamics", "relation": "related_bridge" }, { "source": "u-jamming-transition-biological-tissues", "target": "u-mechanical-bifurcation-morphogenesis", "relation": "related_unknown" }, { "source": "u-jamming-transition-biological-tissues", "target": "u-confluent-tissue-brownian-universality", "relation": "related_unknown" }, { "source": "u-kelvin-helmholtz-growth-rate-transfer-cloud-plasma-shear", "target": "b-kelvin-helmholtz-cloud-billows-x-plasma-shear-instability", "relation": "related_bridge" }, { "source": "u-kelvin-helmholtz-growth-rate-transfer-cloud-plasma-shear", "target": "h-kh-growth-rate-normalization-predicts-billow-plasma-onset", "relation": "suggested_hypothesis" }, { "source": "u-kibble-zurek-embryo", "target": "u-topological-morphogenesis", "relation": "related_unknown" }, { "source": "u-kibble-zurek-embryo", "target": "u-cortical-folding-topology", "relation": "related_unknown" }, { "source": "u-kibble-zurek-embryo", "target": "b-kibble-zurek-morphogenesis", "relation": "related_bridge" }, { "source": "u-kibble-zurek-embryo", "target": "b-topology-morphogenesis", "relation": "related_bridge" }, { "source": "u-landau-theory-neural-criticality-order-parameter", "target": "u-soc-universality-class-brain", "relation": "related_unknown" }, { "source": "u-landau-theory-neural-criticality-order-parameter", "target": "u-self-organized-criticality-consciousness", "relation": "related_unknown" }, { "source": "u-landauer-bound-experimental-verification", "target": "b-maxwells-demon-computation", "relation": "related_bridge" }, { "source": "u-landauer-limit-biological-computation", "target": "h-brain-landauer-efficiency", "relation": "suggested_hypothesis" }, { "source": "u-landauer-limit-nonhelical-resonator", "target": "b-nonhelical-landauer-reversible-em", "relation": "related_bridge" }, { "source": "u-landauer-limit-nonhelical-resonator", "target": "h-nonhelical-resonator-adiabatic-quantum-memory", "relation": "suggested_hypothesis" }, { "source": "u-laser-cooling-sub-doppler-quantum-limit", "target": "b-laser-cooling-doppler-optical-molasses", "relation": "related_bridge" }, { "source": "u-leptogenesis-cp-scale", "target": "u-baryon-asymmetry-origin", "relation": "related_unknown" }, { "source": "u-leptogenesis-cp-scale", "target": "b-baryon-asymmetry-cp-violation", "relation": "related_bridge" }, { "source": "u-liquid-crystal-x-cell-membrane", "target": "b-liquid-crystal-x-cell-membrane", "relation": "related_bridge" }, { "source": "u-maxwell-shannon-channel-near-capacity", "target": "b-maxwell-equations-wave-encoding", "relation": "related_bridge" }, { "source": "u-minimum-dissipation-network-topology", "target": "u-optimal-transport-angiogenesis", "relation": "related_unknown" }, { "source": "u-minimum-dissipation-network-topology", "target": "u-kleiber-pulsatile-waves", "relation": "related_unknown" }, { "source": "u-minority-game-market-microstructure-universality", "target": "u-turbulence-market-reynolds-analogue", "relation": "related_unknown" }, { "source": "u-neutron-star-core-qcd-constraints", "target": "b-neutron-star-matter-x-qcd-phases", "relation": "related_bridge" }, { "source": "u-neutron-star-core-qcd-constraints", "target": "h-tidal-deformability-tightens-symmetry-energy-slope", "relation": "suggested_hypothesis" }, { "source": "u-neutron-star-x-nuclear-matter", "target": "b-neutron-star-x-nuclear-matter", "relation": "related_bridge" }, { "source": "u-noether-quantum-gravity-symmetry", "target": "b-noether-theorem-conservation-laws", "relation": "related_bridge" }, { "source": "u-nonextensive-entropy-turbulence", "target": "u-quantum-turbulence-simulation-limit", "relation": "related_unknown" }, { "source": "u-nonextensive-entropy-turbulence", "target": "u-turbulence-market-reynolds-analogue", "relation": "related_unknown" }, { "source": "u-nonhelical-turing-wavelength-scaling", "target": "b-nonhelical-turing-electromagnetic", "relation": "related_bridge" }, { "source": "u-nonhelical-turing-wavelength-scaling", "target": "h-nonhelical-turing-cloaking-adaptation", "relation": "suggested_hypothesis" }, { "source": "u-nonlinear-optics-soliton-stability", "target": "b-nonlinear-optics-soliton-propagation", "relation": "related_bridge" }, { "source": "u-primordial-nucleosynthesis-reaction-networks", "target": "b-primordial-nucleosynthesis-reaction-networks", "relation": "related_bridge" }, { "source": "u-qcd-ew-phase-transition-relics", "target": "u-dark-matter-particle-identity", "relation": "related_unknown" }, { "source": "u-qcd-ew-phase-transition-relics", "target": "b-dark-matter-phase-transition-relics", "relation": "related_bridge" }, { "source": "u-quantum-biology-decoherence", "target": "b-quantum-biology-navigation", "relation": "related_bridge" }, { "source": "u-quantum-decoherence-x-classical-emergence", "target": "b-quantum-decoherence-x-classical-emergence", "relation": "related_bridge" }, { "source": "u-quantum-error-correction-x-topological-codes", "target": "b-quantum-error-correction-x-topological-codes", "relation": "related_bridge" }, { "source": "u-quantum-field-theory-x-combinatorics", "target": "b-quantum-field-theory-x-combinatorics", "relation": "related_bridge" }, { "source": "u-quantum-glass-learning-efficiency", "target": "u-grokking-phase-transition", "relation": "related_unknown" }, { "source": "u-quantum-glass-learning-efficiency", "target": "u-replica-boltzmann-machine-glass", "relation": "related_unknown" }, { "source": "u-quantum-glass-learning-efficiency", "target": "b-spin-glass-neural-networks", "relation": "related_bridge" }, { "source": "u-quantum-noise-figure-silicon-mm-wave-cryo-vs-room", "target": "b-quantum-limited-amplification-x-heisenberg-noise-figure-bound", "relation": "related_bridge" }, { "source": "u-quantum-noise-figure-silicon-mm-wave-cryo-vs-room", "target": "h-josephson-paramp-nears-quantum-noise-floor-with-rimp-matched-array", "relation": "suggested_hypothesis" }, { "source": "u-quantum-turbulence-simulation-limit", "target": "u-turbulence-market-reynolds-analogue", "relation": "related_unknown" }, { "source": "u-quantum-walk-x-classical-random-walk", "target": "b-quantum-walk-x-classical-random-walk", "relation": "related_bridge" }, { "source": "u-radiocarbon-calibration-plateau-dating-precision", "target": "b-radiocarbon-dating-exponential-decay", "relation": "related_bridge" }, { "source": "u-rbm-training-critical-slowdown-near-phase-boundaries", "target": "b-restricted-boltzmann-machine-x-ising-energy-based-models", "relation": "related_bridge" }, { "source": "u-rbm-training-critical-slowdown-near-phase-boundaries", "target": "h-annealed-rbm-pretraining-improves-phase-diagram-discovery", "relation": "suggested_hypothesis" }, { "source": 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"suggested_hypothesis" }, { "source": "u-scale-free-brain-connectome-criticality", "target": "u-pathogen-coevolution-network-percolation", "relation": "related_unknown" }, { "source": "u-simulated-annealing-x-statistical-mechanics", "target": "b-simulated-annealing-x-statistical-mechanics", "relation": "related_bridge" }, { "source": "u-soc-universality-class-brain", "target": "h-criticality-conscious-integration", "relation": "suggested_hypothesis" }, { "source": "u-social-ising-universality", "target": "u-climate-ew-indicator-universality", "relation": "related_unknown" }, { "source": "u-social-ising-universality", "target": "u-grokking-phase-transition", "relation": "related_unknown" }, { "source": "u-social-ising-universality", "target": "b-ising-social-dynamics", "relation": "related_bridge" }, { "source": "u-social-ising-universality", "target": "b-tipping-points-phase-transitions", "relation": "related_bridge" }, { "source": "u-social-ising-universality", "target": 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"b-tensor-networks-x-quantum-states", "relation": "related_bridge" }, { "source": "u-topological-defects-x-homotopy", "target": "b-topological-defects-x-homotopy", "relation": "related_bridge" }, { "source": "u-topological-insulator-majorana-qubit-scalability", "target": "b-topological-materials-band-theory", "relation": "related_bridge" }, { "source": "u-topological-insulator-x-band-theory", "target": "b-topological-insulator-x-band-theory", "relation": "related_bridge" }, { "source": "u-topological-qec-physical-realization", "target": "b-quantum-error-correction-topology", "relation": "related_bridge" }, { "source": "u-topological-qec-physical-realization", "target": "h-topological-phase-qec-threshold-correspondence", "relation": "suggested_hypothesis" }, { "source": "u-turbulence-market-reynolds-analogue", "target": "h-market-crash-turbulent-transition", "relation": "suggested_hypothesis" }, { "source": "u-turbulence-onset-subcritical-transition", "target": 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"u-glymphatic-csf-clearance-sleep-deprivation-rate", "target": "b-fluid-dynamics-glymphatic", "relation": "related_bridge" }, { "source": "u-hopfield-modern-attention-biological-plausibility", "target": "h-dense-hopfield-transformer-attention-unified", "relation": "suggested_hypothesis" }, { "source": "u-quantum-zeno-measurement-neural-interruption", "target": "b-quantum-zeno-x-measurement", "relation": "related_bridge" }, { "source": "u-quantum-zeno-measurement-neural-interruption", "target": "h-quantum-zeno-like-slowing-in-attention-networks", "relation": "suggested_hypothesis" }, { "source": "u-lymphatic-valve-gating-pressure-threshold", "target": "b-lymphatic-drainage-interstitial-fluid", "relation": "related_bridge" }, { "source": "u-agent-surrogate-policy-optimization-behavioral-fidelity", "target": "b-agent-based-surrogate-x-intervention-optimization", "relation": "related_bridge" }, { "source": "u-agent-surrogate-policy-optimization-behavioral-fidelity", "target": 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"u-qaoa-depth-generalization-vs-classical-baselines", "target": "b-qaoa-x-classical-surrogate-combinatorial-optimization", "relation": "related_bridge" }, { "source": "u-qaoa-depth-generalization-vs-classical-baselines", "target": "h-qaoa-parameter-transfer-improves-surrogate-warm-starts", "relation": "suggested_hypothesis" }, { "source": "u-quantum-darwinism-redundancy-threshold-classicality", "target": "b-quantum-decoherence-einselection", "relation": "related_bridge" }, { "source": "u-quantum-dot-blinking-power-law-mechanism", "target": "b-quantum-dot-blinking-renewal-process", "relation": "related_bridge" }, { "source": "u-majorana-zero-mode-experimental-confirmation", "target": "h-topological-qubit-fault-tolerance-threshold", "relation": "suggested_hypothesis" }, { "source": "u-energy-landscape-mismatch-indicators-for-lesion-segmentation-qc", "target": "b-graph-cut-energy-minimization-x-radiology-lesion-segmentation-qc", "relation": "related_bridge" }, { "source": "u-energy-landscape-mismatch-indicators-for-lesion-segmentation-qc", "target": "h-graph-cut-energy-residuals-detect-lesion-segmentation-failure-modes-earlier", "relation": "suggested_hypothesis" }, { "source": "u-resnet-histology-domain-shift-failure-modes", "target": "b-resnet-x-histopathology-domain-shift-robustness", "relation": "related_bridge" }, { "source": "u-resnet-histology-domain-shift-failure-modes", "target": "h-residual-feature-normalization-reduces-histology-site-shift-error", "relation": "suggested_hypothesis" }, { "source": "u-sampling-pattern-transferability-for-compressed-sensing-mri", "target": "b-compressed-sensing-x-accelerated-mri-protocol-design", "relation": "related_bridge" }, { "source": "u-sampling-pattern-transferability-for-compressed-sensing-mri", "target": "h-adaptive-kspace-schedules-preserve-diagnostic-mri-quality-at-higher-acceleration", "relation": "suggested_hypothesis" }, { "source": "u-ransac-optimal-sampling-strategy-non-uniform-inlier-distribution", "target": "b-robust-statistics-outlier-detection", "relation": "related_bridge" }, { "source": "u-earthquake-alert-threshold-sprt-under-correlated-noise", "target": "b-earthquake-alarm-decision-x-wald-sequential-probability-ratio-test", "relation": "related_bridge" }, { "source": "u-earthquake-alert-threshold-sprt-under-correlated-noise", "target": "h-aftershock-clustering-inflates-sprt-false-alarm-rate-fixed-boundaries", "relation": "suggested_hypothesis" }, { "source": "u-earthquake-nucleation-dislocation-slip-weakening", "target": "b-earthquake-source-dislocation-theory", "relation": "related_bridge" }, { "source": "u-pino-aftershock-forecasting-uncertainty-calibration", "target": "b-physics-informed-neural-operator-x-aftershock-field-evolution", "relation": "related_bridge" }, { "source": "u-pino-aftershock-forecasting-uncertainty-calibration", "target": "h-pino-aftershock-fields-improve-short-term-seismic-hazard-maps", "relation": "suggested_hypothesis" }, { "source": "u-bci-non-stationarity-adaptation", "target": "b-bci-optimal-decoding", "relation": "related_bridge" }, { "source": "u-oxytocin-parochial-altruism-policy-implications", "target": "b-social-neuroscience-group-behavior", "relation": "related_bridge" }, { "source": "u-abm-calibration-empirical-social-science-validation", "target": "b-agent-based-modeling-emergent-institutions", "relation": "related_bridge" }, { "source": "u-behavioral-immune-system-pathogen-xenophobia-mechanism", "target": "b-behavioral-immunology-pathogen-avoidance", "relation": "related_bridge" }, { "source": "u-crowd-dynamics-panic-transitions", "target": "b-crowd-dynamics-social-force-model", "relation": "related_bridge" }, { "source": "u-cultural-drift-vs-selection-detection", "target": "b-cultural-evolution-darwinian", "relation": "related_bridge" }, { "source": "u-cultural-drift-vs-selection-detection", "target": "h-price-equation-cultural-trait-frequency", "relation": "suggested_hypothesis" }, { "source": "u-dark-patterns-cognitive-bias-exploitation-measurement", "target": "b-hci-cognitive-load", "relation": "related_bridge" }, { "source": "u-econophysics-wealth-distribution-mechanism", "target": "b-social-stratification-statistical-mechanics", "relation": "related_bridge" }, { "source": "u-epigenetic-intergenerational-transmission-social-stress", "target": "b-stress-biology-social-determinants", "relation": "related_bridge" }, { "source": "u-human-error-organizational-accident-boundary", "target": "b-human-factors-system-safety", "relation": "related_bridge" }, { "source": "u-human-error-organizational-accident-boundary", "target": "h-swiss-cheese-alignment-accident-prediction", "relation": "suggested_hypothesis" }, { "source": "u-network-centrality-temporal-dynamics-influence", "target": "b-network-centrality-social-influence", "relation": "related_bridge" }, { "source": "u-network-formation-dynamic-stability-real-world", "target": "b-network-formation-games", "relation": "related_bridge" }, { "source": "u-opinion-dynamics-critical-homophily", "target": "u-political-polarisation-dynamics", "relation": "related_unknown" }, { "source": "u-opinion-dynamics-critical-homophily", "target": "u-social-contagion-vs-homophily", "relation": "related_unknown" }, { "source": "u-opinion-dynamics-critical-homophily", "target": "b-opinion-dynamics-ising", "relation": "related_bridge" }, { "source": "u-opinion-dynamics-critical-homophily", "target": "b-social-ising-polarisation", "relation": "related_bridge" }, { "source": "u-opinion-dynamics-empirical-calibration-social-media-networks", "target": "b-statistical-mechanics-opinion", "relation": "related_bridge" }, { "source": "u-opinion-dynamics-phase-transition-prediction", "target": "b-opinion-dynamics-ising", "relation": "related_bridge" }, { "source": "u-prediction-market-thin-market-accuracy-limits", "target": "b-prediction-markets-information-aggregation", "relation": "related_bridge" }, { "source": "u-social-critical-temperature-empirical", "target": "b-statistical-physics-x-social-science", "relation": "related_bridge" }, { "source": "u-social-critical-temperature-empirical", "target": "h-social-critical-temperature-empirical", "relation": "suggested_hypothesis" }, { "source": "u-social-learning-cultural-transmission-mechanisms", "target": "b-social-learning-cultural-transmission", "relation": "related_bridge" }, { "source": "u-strategic-voting-frequency-real-elections-empirical-magnitude", "target": "b-voting-theory-social-choice", "relation": "related_bridge" }, { "source": "u-strategic-voting-frequency-real-elections-empirical-magnitude", "target": "h-approval-voting-reduces-strategic-manipulation-vs-plurality", "relation": "suggested_hypothesis" }, { "source": "u-voter-model-zealots-persistent-minorities", "target": "b-voter-model-consensus", "relation": "related_bridge" }, { "source": "u-voter-model-zealots-persistent-minorities", "target": "h-network-community-structure-drives-polarization", "relation": "suggested_hypothesis" }, { "source": "u-wisdom-of-crowds-condorcet", "target": "b-wisdom-of-crowds-condorcet", "relation": "related_bridge" }, { "source": "u-social-capital-causal-vs-correlational", "target": "h-structural-holes-income-mobility-mediation", "relation": "suggested_hypothesis" }, { "source": "u-colloidal-glass-transition-mode-coupling-breakdown", "target": "b-colloidal-systems-soft-matter", "relation": "related_bridge" }, { "source": "u-jamming-transition-universality-class", "target": "b-granular-matter-jamming-transition", "relation": "related_bridge" }, { "source": "u-spatial-autocorrelation-economic-convergence-causality", "target": "b-spatial-statistics-geographic-inequality", "relation": "related_bridge" }, { "source": "u-bayesian-dropout-trial-decision-calibration-gap", "target": "b-bayesian-dropout-x-adaptive-trial-stopping", "relation": "related_bridge" }, { "source": "u-bayesian-dropout-trial-decision-calibration-gap", "target": "h-bayesian-dropout-uncertainty-improves-adaptive-trial-decisions", "relation": "suggested_hypothesis" }, { "source": "u-bayesian-phase-transition-learning", "target": "b-bayesian-inference-stat-mech", "relation": "related_bridge" }, { "source": "u-cryoem-bayesian-map-calibration-across-platforms", "target": "b-cryoem-bayesian-x-single-particle-reconstruction", "relation": "related_bridge" }, { "source": "u-cryoem-bayesian-map-calibration-across-platforms", "target": "h-cryoem-bayesian-x-single-particle-reconstruction", "relation": "suggested_hypothesis" }, { "source": "u-elastic-net-prior-calibration-under-correlated-designs", "target": "b-elastic-net-map-x-laplace-gaussian-composite-prior", "relation": "related_bridge" }, { "source": "u-elastic-net-prior-calibration-under-correlated-designs", "target": "h-mixing-parameter-matches-posterior-sparsity-stability-curves", "relation": "suggested_hypothesis" }, { "source": "u-posterior-landscape-multimodality", "target": "b-bayesian-inference-stat-mech", "relation": "related_bridge" }, { "source": "u-ridge-as-gaussian-map-prior-identifiability", "target": "b-ridge-regression-x-shrinkage", "relation": "related_bridge" }, { "source": "u-ridge-as-gaussian-map-prior-identifiability", "target": "h-ridge-penalty-matches-bayesian-width-in-neural-decoding", "relation": "suggested_hypothesis" }, { "source": "u-catalytic-converter-cold-start-mitigation-strategies", "target": "b-catalytic-converter-surface-chemistry", "relation": "related_bridge" }, { "source": "u-crispr-hdr-efficiency-post-mitotic-cells", "target": "b-crispr-cas9-gene-editing", "relation": "related_bridge" }, { "source": "u-genetic-circuit-crosstalk-noise", "target": "b-synthetic-biology-genetic-circuits", "relation": "related_bridge" }, { "source": "u-retroactivity-insulation-genetic-circuits", "target": "b-synthetic-biology-circuit-design", "relation": "related_bridge" }, { "source": "u-rna-folding-planar-pseudoknot-limits", "target": "b-rna-secondary-structure-x-planar-graphs", "relation": "related_bridge" }, { "source": "u-rna-folding-planar-pseudoknot-limits", "target": "h-nussinov-energy-approximates-planar-graph-parsimony", "relation": "suggested_hypothesis" }, { "source": "u-synthetic-biology-chassis-orthogonality", "target": "b-synthetic-biology-genetic-circuits", "relation": "related_bridge" }, { "source": "u-boolean-grn-criticality-empirical-tests", "target": "b-kauffman-boolean-x-gene-network-attractor-stability", "relation": "related_bridge" }, { "source": "u-boolean-grn-criticality-empirical-tests", "target": "h-kauffman-boolean-x-gene-network-attractor-stability", "relation": "suggested_hypothesis" }, { "source": "u-cost-matrix-misspecification-in-optimal-transport-lineage-maps", "target": 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