566 papers
This paper introduces 3DSTokesFlow, a novel simulation-based inference framework using conditional flow matching to perform fast, scalable, and spatially correlated Bayesian inversion of 3D solar atmospheric parameters from Stokes profiles, enabling the generation of reliable posterior distributions and the computation of 3D physical quantities like electric currents and magnetic loop emergence.
The paper demonstrates that AESTRA, a generative spectral modeling framework applied to NEID Sun-as-a-star data, significantly outperforms traditional activity-indicator detrending methods by recovering 238 injected Earth-analog planets (including 13 with semi-amplitudes below 0.3 m/s) while maintaining zero spurious detections, thereby overcoming key limitations in extreme-precision radial velocity measurements.
Using quadruple optical tweezers and a Bayesian unmasking strategy, this study demonstrates that single actin and vimentin filaments form direct, force-bearing interactions independent of crosslinking proteins, with bond strengths that are robust to ionic strength variations but constrained by the mechanical limits of actin filaments.
This study demonstrates that the spatial organization of anionic lipids within lipid nanodiscs, driven by clustering and interactions with the membrane scaffold protein, critically regulates the conformational equilibria of the A2A adenosine receptor, a mechanism that can be modulated through targeted protein engineering.
This paper demonstrates that unsupervised nonlinear dimensionality reduction applied to the Inorganic Crystal Structure Database reveals a hidden low-dimensional geometric manifold that organizes crystalline materials, successfully segregating superconductors and enabling accurate prediction of critical temperatures without knowledge of the underlying pairing mechanism.
This paper introduces a temperature-modulated de Haas-van Alphen technique that utilizes the non-monotonic temperature dependence of oscillation amplitudes to selectively isolate and measure weak, heavy-orbit quantum oscillations in MoSi that are typically obscured by stronger signals in conventional measurements.
This paper establishes a rigorously self-consistent bidirectional magneto-thermal coupling model that integrates the stochastic Landau-Lifshitz-Gilbert equation with a generalized heat transfer equation to dynamically link magnetization dissipation and thermal fluctuations, thereby ensuring thermodynamic consistency and enabling the study of complex nonequilibrium spin-caloritronic phenomena.
This paper derives semiclassical equations of motion for composite bound states in insulators and semiconductors, revealing that their dynamics are governed by a distinct quantum geometric dipole and a carefully chosen Berry curvature dependent on the composite's spatial center, leading to unique phenomena such as transverse drift and internal dipole oscillations in trions within magic-angle twisted bilayer graphene.
This paper warns that machine-learning interatomic potentials trained on PBE-based DFT data incorrectly predict the ground-state structure of HfO due to the functional's tendency to over-stabilize low-density phases, a flaw that can be mitigated by using alternative functionals like PBEsol or LDA.
This paper demonstrates that combining cepstral analysis with Green-Kubo simulations and machine-learned potentials provides a robust, automated, and efficient framework for accurately predicting the thermal conductivity of metal-organic frameworks by overcoming the statistical noise and parameter sensitivity inherent in conventional methods.
This paper evaluates various lie detectors across model scales and reveals that while they scale with capability on prompted lying, most fail to detect lies in models with verified hidden beliefs, highlighting the current inability of these tools to reliably audit model truthfulness.
This paper reveals that Canada's algorithmic visa triage system creates significant asymmetries in accountability—specifically epistemic, jurisdictional, and temporal-relational gaps—between the institution's transparency-focused design and applicants' lived experiences of uncertainty, necessitating a shift in focus toward the uneven distribution of algorithmic governance impacts.
This paper introduces AfriSUD, the first large-scale collection of native-speaker verified syntactic treebanks for nine diverse African languages using the SUD framework, and demonstrates that current NLP models still face significant limitations in capturing the structural diversity of African-language syntax.
This paper introduces ArogyaSutra, a multi-agent framework and a large-scale multilingual multimodal medical dataset designed to enhance AI-driven healthcare reasoning in low-resource Indic languages by integrating tool grounding, dual-memory mechanisms, and actor-critic distillation.
This paper establishes the continuity of equilibrium correspondences on a dense subset of infinite-dimensional Bochner and Gel'fand economies modeled on Banach lattices, providing a unified framework that extends classical results to diverse economic models without requiring differentiability assumptions.
This paper introduces and analyzes the concept of Cross-Validation Equilibrium, a strategic framework where players delegate belief formation to machine learning agents that select predictive models based on endogenous training data to minimize out-of-sample error, thereby influencing equilibrium outcomes in games like jury voting and speculative betting.
This paper introduces **faust2clap**, a dual-mode compilation framework that enables both efficient ahead-of-time native binary generation and dynamic runtime interpretation of Faust DSP code within the CLAP format, specifically solving the friction of edit-compile-reload cycles through novel address-based parameter identity preservation.
This paper introduces a computationally efficient beam-membrane biomechanical model of vocal folds that incorporates muscle-driven posturing and glottal conformation to predict voice production dynamics and investigate voice disorders, offering a practical alternative to expensive high-fidelity simulations.
This paper investigates quantum steering harvesting between two Unruh-DeWitt detectors in a Lorentz-violating BTZ black hole, revealing a counterintuitive inversion where the detector in a hotter environment exhibits stronger steerability and demonstrating that Lorentz violation acts as a geometric constraint that suppresses maximal correlation extraction and alters the directionality of quantum correlations.
This paper argues that any viable theory of quantum gravity must satisfy an epistemological constraint requiring the recovery of objective geometrical measurability—ensuring the physical possibility of determining relational quantities through stable devices, causal access, and record formation—alongside the emergence of spacetime geometry itself.
This paper presents AgentRivet, an automated workflow utilizing Large Language Models to extract physics analysis details from journal publications and generate Rivet routines, thereby addressing the significant gap in coverage for model-independent measurements in particle physics.
MicroBooNE reports the first measurement of the charged-current coherent pion production cross section on argon at sub-GeV neutrino energies, yielding a flux-averaged value of that offers a valuable tool for constraining neutrino flux uncertainties in future oscillation experiments like DUNE.
This paper introduces an inclusive generalised- jet algorithm for Deep Inelastic Scattering defined in the Breit frame, investigating its phenomenological applications for identifying the struck quark jet, assessing its sensitivity to non-perturbative effects, and comparing its performance with the Centauro algorithm.
This paper challenges the prevailing view that Primordial Black Holes (PBHs) are generically fine-tuned dark matter candidates by demonstrating that, when evaluated across a broad landscape of production mechanisms and compared to particle dark matter benchmarks using uniform naturalness measures, PBHs span a full spectrum of naturalness tiers ranging from as natural as standard WIMPs to severely tuned, thereby proving that dismissing them as a whole conflates worst-case scenarios with a diverse landscape of viable models.
This paper presents remarkably simple, dimension-independent covariant expressions for graviton and ghost propagators in Anti-de Sitter spacetime, achieved by selecting a special gauge-fixing condition that ensures improved infrared behavior and is unattainable in flat spacetime.
This paper analyzes perturbations of strongly coupled Yang-Mills plasma by demonstrating that while classical spectral truncation methods are limited by convergence boundaries, an exact WKB analysis combined with Seiberg--Witten theory provides a systematic framework to resum quasinormal modes, yielding an accurate spectrum that remains valid from the large wave number regime all the way to zero.
This paper investigates how perturbations affect the hyperuniformity of stationary lattices, demonstrating that such properties are preserved in dimensions one and two under finite moment conditions but can be destroyed by arbitrarily small perturbations in dimensions three and higher.
This paper establishes a new communication complexity lower bound of for nonuniformly convex consensus optimization over time-varying networks, demonstrating that the round complexity achievable under uniform regularity cannot be matched in the nonuniform regime through a construction embedding time-rotating star gadgets into expander graphs.
This paper introduces a graphical coaction framework for Friedmann-Robertson-Walker (FRW) integrals at all loop orders using intersection theory in twisted (co)homology to decompose cosmological observables into graph-based building blocks, thereby revealing the combinatorial structure of their governing differential equations and providing open-source tools for their computation.
This paper generalizes the ZX calculus to two-dimensional Yang-Mills theory with a compact gauge group by leveraging a shared Hopf Frobenius algebraic structure, thereby establishing a foundation for applying this graphical formalism to low-dimensional gravity.
This study identifies and characterizes two distinct subsets of postnatal human neural stem cells (NINO and NAC) with specific differentiation biases, revealing that while their frequency declines exponentially during the first two decades of life, they persist into old age, providing a new framework for studying human brain development and aging.
This study demonstrates that the RAI1 gene acts as a critical brake on human neurodevelopment by suppressing mesodermal lineage programs and slowing the tempo of gene expression, thereby ensuring the fidelity and prolonged timeline necessary for advanced cognitive development.
This paper introduces a purely data-driven model-predictive control strategy based on adiabatic spectral submanifolds (aSSMs) that effectively manages the nonlinear dynamics of soft robots, achieving tracking performance up to 10 times superior to existing methods by leveraging low-dimensional invariant manifolds derived from high-dimensional simulations and experiments.
This paper demonstrates that in quantized non-KAM systems like the kicked harmonic oscillator, resonant frequency ratios induce a distinct quadratic growth in out-of-time-ordered correlators (OTOCs) driven by a number-theoretic structure involving the Euler totient function, revealing that resonances significantly influence information scrambling even in the absence of conventional chaos.
This paper proposes a data-constrained method using blast-wave fits to charged hadron spectra to predict the low- ratio, thereby significantly reducing background uncertainties for direct-photon and dilepton measurements in heavy-ion collisions.
This paper presents a large-scale shell-model investigation of two-neutrino double electron capture in Ba and Kr, providing updated nuclear matrix elements and half-life predictions based on validated effective interactions to support future experimental efforts.
This paper demonstrates that machine learning and deep learning models can accurately classify compact stars as neutron stars or quark stars based on observable macroscopic properties like mass, radius, and tidal deformability, offering a promising tool for probing dense matter composition while noting the need for further validation with hybrid and exotic matter scenarios.
This paper presents the first calculation of heavy quark transport and thermal dilepton production in a QGP using second-order viscous corrections derived from the Chapman-Enskog expansion, revealing that these corrections significantly suppress drag forces and enhance early-time dilepton yields while demonstrating that observable modifications depend on the complex interplay between correction magnitude, momentum dependence, and the specific momentum weighting of each observable.
This paper presents a low-cost, unified magnetic torsional pendulum equipped with a wireless gyroscope that enables undergraduate students to experimentally investigate and compare the dynamics of forced resonance, parametric resonance, and parametric amplification within a single physical system.
This paper proposes a unified mathematical theory of value as a logarithmic, frame-relative structural quantity analogous to information, deriving a coding theorem that links goal-progress to mutual information and misalignment to measurable waste, a framework empirically validated by its ability to predict language model performance and over-confidence across diverse tasks.
This study employs a novel approach combining photoluminescence imaging, drift-diffusion simulations, and Bayesian inference to map the spatially non-uniform degradation of perovskite solar cells, successfully distinguishing between bulk and interface defects and demonstrating that amino-silane passivation effectively suppresses interfacial degradation.
This paper demonstrates the first stable, bidirectional electro-optic microwave-to-optical transduction in thin-film lithium tantalate (TFLT), achieving high-efficiency coherent conversion with minimal added noise and superior long-term bias stability to enable scalable quantum interconnects.
This paper reports the successful sympathetic cooling and Coulomb crystallization of xenon highly charged ions within a laser-cooled calcium ion matrix, establishing a versatile platform for precision metrology, new physics searches, and quantum information science.
This review article outlines the theoretical foundations and summarizes recent experimental efforts to detect exotic spin-dependent interactions mediated by light particles like axions and Axion-Like Particles, which are proposed solutions to the strong CP problem, dark matter, and other physics beyond the Standard Model.
This paper introduces a computation-assisted strategy combining rotating-frame acquisition with frame-shift tracking to isolate weak higher-order signals, such as 7th-order responses, from dominant lower-order backgrounds in two-dimensional spectroscopy, thereby enabling the direct study of complex many-body quantum dynamics without sacrificing signal intensity.
This paper reports the experimental design and validation of ultra-low threshold hybrid III-V/Si quantum dot microring lasers that achieve record wall-plug efficiency, low threshold current density, high thermal stability, and 5 GHz bandwidth for 1.3 μm emission.
This paper introduces a likelihood-based extension to Biologically-Informed Neural Networks (BINNs) that simultaneously learns mechanistic growth dynamics and heteroscedastic noise structures from sparse data, thereby improving prediction accuracy over existing methods that assume homoscedastic Gaussian noise.
This paper demonstrates that in multi-label lipid metabolism studies, strategically modeling a subset of available labels (specifically three out of five) offers an optimal balance between computational efficiency and the accuracy of parameter estimation and trajectory recovery, preventing the biologically implausible predictions that can arise from overly simplified single-label models.
This paper demonstrates that implementing a Human-in-the-Loop Economic Research (HLER) framework, which enforces pre-commitment, deterministic data handling, and human decision gates, significantly reduces critical failures in AI-assisted social science research from 72% to 16% compared to unconstrained multi-agent baselines.
This study utilizes a large-scale agent-based model to demonstrate that adaptive rerouting in response to maritime chokepoint disruptions creates complex, dynamic loss patterns characterized by delayed vessel cycles and uneven regional impacts, proving that the true economic risk is driven by routing behavior and timing rather than static network topology.
This paper presents a quantum algorithm for random number generation that achieves a provable quadratic speedup over classical Markov chain mixing by integrating the Quantum Fourier Transform, controlled phase rotations, and the Grover diffusion operator, demonstrating improved efficiency on both qubit and qudit hardware.
This paper establishes a unified thermodynamic framework for nonsecular master equations by incorporating system-bath interaction energy and Lamb shifts into the energy balance, demonstrating that while these approximations lead to non-Gibbs steady states and distinct entropy production rates compared to the Spohn inequality, no work can be cyclically extracted from the steady state in a single thermal bath scenario.
This paper proposes a robust framework for forecasting complex dynamical systems that integrates Gaussian Process Ordinary Differential Equations with quadratic order model reduction and sphere projection to achieve accurate, stable, and uncertainty-quantified predictions that outperform existing reduced-order modeling methods.
This paper presents a simple, unified, and nearly tight convergence bound for sampling arbitrary logconcave distributions from a warm start using the In-and-Out algorithm with exponential lifting, achieved by establishing an improved Poincaré constant for the lifted distribution.