2386 papers
This study employs a novel Randomization-Adjusted Overlap Correlation (RAOC) method to statistically demonstrate that LHAASO TeV gamma-ray sources are significantly associated with supernova remnants, pulsar wind nebulae, and microquasars, while revealing the critical role of molecular clouds in enhancing pulsar wind nebula emission and identifying systematic selection effects in existing catalogs.
This paper proposes a falsifiable timing test for the double-white-dwarf model of long-period transients like CHIME/ILT J1634+44, predicting that gravitational-wave losses and other interactions will cause a measurable joint drift in the burst and modulation periods over a single year.
This paper presents a fast direct solver within a Method-of-Moments framework that exploits rotational symmetry and multipole decomposition to efficiently compute mutual coupling and embedded element patterns for large reflector arrays, successfully enabling the first full-wave analysis of the 320-element HERA core.
This paper proposes a minimal two-population framework that consistently explains the complex spectral features of Galactic cosmic-ray protons and helium from GeV to PeV energies through a transition between two distinct source populations, eliminating the need for nearby sources or non-standard acceleration assumptions.
This paper proposes a multi-modal deep learning network combining ResNet-50 and BiLSTM to fuse image and catalog data from China Space Station Telescope (CSST) simulations, achieving over 99.6% recall for star-galaxy classification and demonstrating robust performance on faint and high-redshift objects.
By reconstructing the evolutionary histories of three black hole X-ray binaries, this study demonstrates that standard common envelope ejection models require unphysically high efficiency parameters (significantly greater than unity), implying that either additional energy sources are necessary or the current theoretical formalism must be revised.
This paper presents 43 GHz VLBI observations of the radio galaxy 3C 120 that link a record-breaking orphan -ray flare to a specific jet disturbance interacting with a stationary feature, providing the first direct observational evidence for the "Ring of Fire" scenario where inverse-Compton scattering of synchrotron photons explains the extreme emission without accompanying multi-wavelength variability.
This paper presents a multi-wavelength study of the Herbig-Haro object HH 270 using JWST, Subaru, and ALMA observations, which reveals a previously unseen collimated protostellar jet, newly identified knots, and the complex interaction between shock-excited jet emission and the surrounding molecular outflow.
This paper proposes a turbulence-driven magnetic reconnection model within the coronal region of the Seyfert II galaxy NGC 1068, where efficient first-order Fermi acceleration of protons produces high-energy neutrinos consistent with IceCube observations while the associated gamma-rays are suppressed by pair annihilation.
This paper reports on a bright X-ray flare from the black hole binary GRS 1915+105 during its obscured state, revealing that the event was caused by enhanced intrinsic emission and reduced obscuration within a stratified geometry following a failed disk wind, with a subsequent radio flare indicating accretion-jet coupling.
This paper introduces a minimal embedding-only model to analytically demonstrate how data frustration drives representation collapse through a slow time scale, while showing that stop-gradient mechanisms can stabilize non-collapsed solutions and preserve class separation.
This paper introduces an active-learning extension of adaptive-truncation configuration interaction (AL-ATCI) that overcomes the Hilbert-space scaling bottleneck in finite-Hamiltonian impurity solvers by systematically identifying relevant determinant manifolds, thereby enabling accurate, large-bath dynamical mean-field theory calculations for complex materials.
This paper introduces an improved soft penetrable sphere colloid model that accurately describes charge and excluded volume interactions in antibody solutions by incorporating the Y-shaped molecular structure and amino acid-level details, thereby enabling the quantitative prediction of thermodynamic and dynamic properties using directly derived structural parameters.
This paper demonstrates analytically and numerically that a local quantum quench in XX-spin chains with open boundary conditions can prevent thermalization and induce a strong violation of the eigenstate thermalization hypothesis, even when weaker versions of the hypothesis typically hold in integrable systems.
This paper demonstrates that the spatial overlap of successive spin configurations serves as a thermodynamic order parameter for Markov chain Monte Carlo dynamics in critical Ising and Potts models, revealing that cluster algorithms (Wolff and Swendsen-Wang) exhibit critical behavior linked to Fortuin-Kasteleins geometric clusters, whereas the local Metropolis algorithm's dynamics are governed solely by spin flip frequency.
This article provides an accessible, non-technical introduction to soft active matter physics for students and early-career researchers, explaining its fundamental differences from equilibrium systems and its relevance to biological processes through core concepts and everyday examples, while concluding with a discussion of experimental findings on organism migration.
This paper demonstrates that Anderson localization in a clean two-leg ladder system can be induced and controlled solely by engineering the Peierls phases of the hopping amplitudes, where random phases cause complete localization and quasiperiodic modulation drives a transition between delocalized, mixed, and localized phases.
Using an extended fermionic functional renormalization group method, this study maps the phase diagram of the infinite-U Hubbard model on a square lattice, revealing a progression from paramagnetic Fermi liquid to antiferromagnetic stripes and finally to an incoherent non-Fermi liquid Nagaoka ferromagnet that undergoes a Lifshitz transition between two distinct ferromagnetic regimes.
This paper develops an exact finite-time multispectral theory for a Brownian particle in a harmonic trap that characterizes the joint distribution of spectral estimators and their inter-frequency correlations, enabling more accurate parameter inference from single trajectories than traditional asymptotic methods.
This paper introduces a simple 2D spin-1/2 model that realizes a classical U(1) fracton spin liquid with extensive ground state degeneracy, but demonstrates that perturbative quantum effects fail to restore quantum fractonic behavior due to severe Hilbert space fragmentation, resulting in either magnetic long-range order or a classical spin liquid.
This study reports the crystal structure of a new helical InSeI polymorph and utilizes polarized Raman spectroscopy to map its lattice dynamics and chain orientation, revealing that despite its helical structure, the material lacks chiral phonons.
This paper proposes a scheme for generating self-sustained two-dimensional vibrations in a movable Cooper-pair box by harnessing inelastic Andreev tunneling within an adiabatic limit, where the system's nonlinearity naturally saturates the amplitude without requiring external feedback.
This paper employs the non-equilibrium Green's function framework to derive analytical formulas for the far-field radiated energy, linear momentum, and angular momentum from metallic thin films, demonstrating how an external magnetic field induces gyrotropy to enable torque radiation and establishing a unified connection between these radiative quantities and generalized Fresnel coefficients.
Using segment DMRG on infinite cylinders, this study demonstrates that gate-induced screening in fractional quantum Hall states can bind like-charged anyons into stable molecules across various filling factors and fusion channels by suppressing long-range repulsion and revealing intermediate-range attraction, with significant implications for addition spectra, interferometry, and anyon superconductivity.
This paper demonstrates that an asymmetrically driven hybrid magnon-photon system can function as a highly tunable quantum heat rectifier, where strong external driving of the magnonic subsystem in the weak hybridization regime enables precise control over both the magnitude and direction of thermal currents.
Using a charge self-consistent tight-binding method, this study reveals that junctions between Bernal and rhombohedral graphite half-crystals universally host localized electronic states, with most configurations involving rhombohedral stacking supporting flat bands that suggest the emergence of strongly correlated and topological phenomena.
This study demonstrates that applying approximately 3% tensile edge strain to zigzag CrI nanoribbons significantly enhances topological magnon-driven spin currents and their decay lengths by inducing strongly localized edge states, thereby establishing straintronics as a viable method for controlling topological magnons in two-dimensional magnetic materials.
This paper employs first-principles simulations of the full unitary dynamics in circuit QED to demonstrate that the observed drop in qubit and readout fidelity at high drive amplitudes is critically sensitive to the microscopic details of the bath spectrum, revealing limitations in standard Lindblad and effective master equation approaches.
This paper introduces a theoretical framework for bilayer planar cavities, demonstrating that the geometric placement of two magnetic films and their symmetry properties enable precise control over collective magnon-polariton excitations by modulating coupling strength, activating dark modes, and organizing multimode interactions through exchange-driven hierarchies.
This paper reports the discovery and theoretical verification of a giant longitudinal domain-wall Hall magnetoresistance in the magnetic Weyl semimetal Co3Sn2S2, which arises from an electric field distribution induced by the transverse giant anomalous Hall effect and offers potential for advanced multi-resistance-state modulation.
This paper introduces SevenNet-Nano, a lightweight universal machine-learning interatomic potential that leverages knowledge distillation from a large foundation model to achieve high accuracy and transferability while significantly reducing computational costs for large-scale atomistic simulations.
This paper demonstrates that incorporating quantum-chemical bonding descriptors into machine learning models significantly improves the prediction of elastic, vibrational, and thermodynamic properties of approximately 13,000 solid-state materials while also enabling the discovery of intuitive physical expressions for these properties.
This study demonstrates that while conventional GGA fails to predict the spin-density wave ground state of bcc chromium, various meta-GGA functionals exacerbate the issue by overestimating magnetic moments and destabilizing the SDW state, with the TPSS functional providing the most accurate description among those tested and highlighting the need for advanced non-local or hybrid functionals.
This paper establishes a unified, interpretable data-driven framework that decouples the governing mechanisms of hydrogen storage capacity and equilibrium pressure in interstitial hydrides, revealing that capacity is determined by geometric and thermal properties while pressure is controlled by elastic moduli, thereby enabling the rational design of materials with optimized performance.
This paper introduces a multi-fidelity contextual bandit strategy combined with a three-tier DFT validation funnel to efficiently screen oxide semiconductor dopants, successfully identifying optimal Cu-containing co-doped ZnO systems for visible-light applications while reducing computational costs by 81% and revealing that dopant performance is governed by just two latent chemical dimensions.
This paper proposes alternative relativistic corrections to the linear augmented plane wave (LAPW) method—specifically new basis functions, potential matrix corrections, and refined spin-orbit coupling calculations—which significantly improve the accuracy of predicted lattice constants and elastic moduli for actinides and reveal that uranium dioxide (UO2) is a semimetal with a small band gap.
This paper presents an experimentally validated, end-to-end computational framework that combines domain separation, machine-learning potentials, and in silico synthesis to enable quantitative operando atomistic modeling of complex amorphous metallosilicates, successfully reproducing key experimental observables and facilitating the analysis of catalytic processes.
This paper presents an autonomous system that utilizes model-free visual reinforcement learning to align single crystals directly from Laue diffraction patterns without requiring prior crystallographic knowledge, thereby enabling intelligent, human-like experimental workflows in materials science.
This study reveals that positive bias and high-temperature stress in amorphous IGZO TFTs induce a reversible negative threshold voltage shift caused by the broadening of conduction band tail states and increased hydrogen doping, rather than the generation of new dielectric traps.
This theoretical study demonstrates that the antiferromagnet FeTe exhibits a large, temperature- and field-sensitive intrinsic anomalous Hall effect driven by Berry curvature, establishing it as a promising platform for exploring quantum transport in low-dimensional correlated systems.
The paper introduces 3D-Anchored Lookahead Planning (3D-ALP), a System 2 reasoning engine that integrates Monte Carlo Tree Search with a persistent 3D-consistent world model to enable robotic manipulation tasks requiring spatial memory and accurate replanning under occlusion, significantly outperforming reactive baselines.
This paper introduces an executable and auditable benchmark for evaluating knowledge graph readiness in policy-like document analysis by aligning natural language contracts with a formal ontology to systematically compare text-only LLMs against ontology-driven pipelines for gap and overlap analysis.
This paper introduces PD36-C, a compact 1.25-million-parameter CNN model that achieves high accuracy (99.53% average test accuracy) in classifying 38 plant diseases across 87,000 images, offering a practical, edge-deployable solution for smart agriculture via a dedicated desktop application.
This paper proposes a dual cross-attention graph learning framework that effectively integrates structural and functional MRI data to achieve robust detection of Major Depressive Disorder, outperforming conventional feature concatenation methods on the REST-meta-MDD dataset with an accuracy of 84.71%.
This paper addresses critical limitations in reinforcement learning agent-based electricity market simulations by proposing a dual-positive monotone parameterization that ensures continuous differentiability and invertibility for multi-segment bid generation, alongside a validity assessment framework to rigorously evaluate convergence toward Nash equilibrium.
This paper proposes a Mamba-based multimodal network that integrates multi-scale blast-loading information with optical remote sensing images to achieve accurate and rapid structural damage assessment, demonstrating significant performance improvements over state-of-the-art methods on the 2020 Beirut explosion dataset.
This paper introduces a progressive training strategy utilizing a newly developed Chain-of-Thought dataset and a two-stage fine-tuning framework to significantly reduce spatio-temporal hallucinations and narrow the forward-backward performance gap in Vision-Language Models for embodied reasoning.
This paper proposes a multi-layered incentive framework for Indian healthcare that aligns academic promotion, institutional rankings, and revenue-sharing models with data curation and sharing to overcome economic and academic barriers, thereby enabling the nation's AI ambitions while complying with emerging data protection regulations.
This paper proposes a substrate-independent, quantitative definition of intelligence based on "intelligence density"—the ratio of a system's independent output logarithm to its description length—to distinguish genuine generalization from mere memorization, thereby resolving classic philosophical challenges like Putnam's triviality argument and Searle's Chinese Room.
The paper introduces MatBrain, a lightweight collaborative agent system featuring two synergistic models (a 30B analytical model and a 14B executive model) that decouples reasoning from tool orchestration to significantly outperform larger general-purpose models in autonomous crystal materials research while reducing hardware requirements by over 95%.
This paper introduces a structured clustering framework that jointly models events and characters to induce scalable, explainable narrative schemas, effectively bridging the gap between computational analysis and the nuanced storytelling structures emphasized in communication theory.
This paper presents the first comprehensive survey of inductive reasoning in large language models, systematically categorizing improvement methods, summarizing benchmarks with a new evaluation metric, and analyzing the sources of inductive ability to guide future research.
This paper demonstrates that inducing Big Five personality traits in Large Language Models via the Neuron-based Personality Trait Induction (NPTI) framework systematically alters their cognitive capabilities in task-dependent ways, revealing significant alignment with human personality-cognition relationships and enabling a more effective Dynamic Persona Routing (DPR) strategy.
This paper introduces Triadic Suffix Tokenization (TST), a deterministic scheme that partitions numbers into three-digit triads with explicit magnitude markers to resolve the inconsistent fragmentation of standard tokenization methods and improve numerical reasoning in large language models.
The paper introduces ACE-TA, an agentic framework that autonomously routes programming course queries to specialized modules for grounded conceptual Q&A, adaptive quiz generation, and interactive, sandboxed code tutoring using pre-trained Large Language Models.
This paper introduces AOP-Smart, a Retrieval-Augmented Generation framework that leverages official AOP-Wiki data to significantly reduce hallucinations and boost answer accuracy in Adverse Outcome Pathway analysis tasks across multiple large language models.
The paper introduces ASPIRin, a novel reinforcement learning framework that decouples speech timing from content generation via Action Space Projection to optimize turn-taking and backchanneling in full-duplex speech models while preventing the semantic degradation and repetition common in standard approaches.
The paper introduces Adaptive Multi-Expert Reasoning (AMR), a framework that enhances math reasoning robustness by dynamically routing problems to specialized experts based on predicted difficulty and uncertainty, ultimately achieving superior accuracy on GSM8K compared to similarly sized models trained on synthetic data.
This paper introduces the Bielik v3 7B and 11B models, which achieve significant performance improvements for the Polish language by replacing universal tokenizers with a dedicated, optimized vocabulary and employing a comprehensive training pipeline that includes FOCUS-based initialization, multi-stage pretraining, and advanced alignment techniques.
The paper introduces AggAgent, an aggregation agent that treats parallel agentic trajectories as an interactive environment with search tools to synthesize information on demand, thereby achieving superior performance and cost-efficiency in long-horizon tasks compared to existing aggregation methods.
This paper addresses the scarcity of high-quality datasets for detecting in-game toxicity by introducing the L2DTnH dataset created with expert League of Legends players, which enables the development of a specialized NLP-based detector that outperforms general-purpose models and is deployed via a privacy-preserving browser extension.
The paper presents 3DTV, a feedforward interpolation network that achieves real-time, high-quality sparse-view synthesis for interactive applications by combining Delaunay-based triplet selection with a pose-aware depth module, eliminating the need for scene-specific optimization or explicit proxies.
This paper presents a comparative theoretical analysis demonstrating that while traditional entropy regularization introduces persistent bias leading to suboptimal policies, covariance-based entropy control achieves asymptotic unbiasedness by selectively regularizing high-covariance tokens, thereby offering a principled solution to prevent premature convergence in reinforcement learning for large language models.
This paper introduces a deep graph self-supervised framework utilizing virtual nodes within a diffusion-contrastive graph neural network to accurately predict wind conditions in unobserved regions, achieving a 30% to 46% reduction in mean absolute error compared to traditional interpolation and regression methods.
This paper introduces C-Flat Turbo, an optimized continual learning algorithm that significantly accelerates training by eliminating redundant gradient computations and employing an adaptive scheduling strategy, thereby achieving comparable or superior accuracy to C-Flat with reduced computational overhead.
This paper introduces a Full Compression Pipeline (FCP) that integrates pruning, quantization, and Huffman encoding to significantly reduce communication and computational overhead in Federated Learning, achieving over an 11-fold reduction in model size and a 60% speedup in training with minimal accuracy loss.
This paper proposes PePNet, a workload prediction network that adaptively fuses changeable periodicity information and employs an Achilles' Heel Loss Function to significantly improve prediction accuracy for rare heavy workloads in cloud servers, thereby reducing Service Level Agreement violations.
This paper proposes and evaluates a hybrid intelligent framework that integrates data-driven learning with physics-informed residuals and temporal features, demonstrating through conformal prediction that such an approach significantly enhances both diagnostic accuracy and uncertainty-aware decision reliability in nonlinear industrial systems compared to single-source baselines.
This paper proposes a novel continuous mathematical framework that rigorously interprets the Transformer architecture as a discretization of a structured integro-differential equation, thereby characterizing self-attention as a non-local integral operator and layer normalization as a time-dependent projection to provide a unified, operator-theoretic foundation for understanding and designing these models.
This paper provides a mechanistic analysis of looped reasoning language models, demonstrating that their recurrent blocks converge to distinct cyclic fixed points in latent space where attention stabilizes and inference stages mirror those of standard feedforward models, thereby offering practical guidance for architectural design.
The paper introduces a ranked-candidate voting method based on Phragmen's procedure that generates a top-down proportional list satisfying the Droop proportionality criterion, house monotonicity, and coherence, while ensuring the top-ranked candidate is the Instant Runoff winner.
This paper demonstrates that for a specific six-state reversible lumpable Markov chain, the maximum determinant achievable through partition-constrained spectral compression using indicator vectors is strictly less than that of relaxed orthonormal spectral compression, proving that indicator-based frames are fundamentally weaker than general orthonormal frames even after global optimization.
This paper proposes a heuristic resolution to the St. Petersburg paradox by introducing a coarse-grained addition framework where repeated aggregation of rewards eventually reaches a state of "inertness," thereby preventing unbounded growth without relying on traditional assumptions like diminishing marginal utility.
This paper proposes a novel, easy-to-implement procedure using Reproducing Kernel Hilbert Space methods and a Bayesian bootstrap for inference to estimate and test average marginal effects in partially linear instrumental regressions, demonstrating its consistency, asymptotic normality, and strong finite-sample performance through simulations and empirical applications.
This paper introduces a new family of coupling designs that enhance estimation efficiency in randomized experiments with complex treatment spaces by matching units into homogeneous groups and assigning highly dispersed treatments within those groups, a mechanism shown to improve precision through the interplay of match quality and treatment dispersion.
This paper proposes a novel Double Machine Learning framework that utilizes general instrumental variables and uniform regular weighting functions to identify and estimate average dose-response functions for continuous treatments while mitigating bias from unobserved confounders.
This paper introduces a timing–entry–volume (TEV) framework to demonstrate that Dutch auctions can outperform posted prices and batch-clearing mechanisms in matching markets with waiting costs, with dominance conditions depending on specific earnings and timing gaps that are estimable from data.
This paper proposes a framework for partial identification and inference in general moment models that utilizes machine-learned proxies by treating them as linking variables between a downstream sample and an auxiliary validation sample, thereby enabling valid asymptotic inference without restrictive assumptions on the upstream machine learning procedure or the need for resampling.
This study analyzes data from the Survey of Health, Ageing and Retirement in Europe to demonstrate that interviewers' expectations regarding respondents' willingness to disclose financial information significantly predict actual item nonresponse, suggesting that these expectations offer valuable insights for improving survey design and interviewer training.
This paper formulates and analyzes an entropy-rate maximization problem for partially observed stochastic processes, proving the existence and uniqueness of the maximizer within feasible classes of hidden laws and characterizing its structural properties, optimality conditions, and geometric behavior.
This paper presents a low-power, TSMC 28-nm hardware accelerator for 3D Gaussian Splatting that achieves real-time 1080p rendering at 129 FPS with a 51.6× model-size reduction, significantly outperforming prior accelerators in area, throughput, and energy efficiency through optimized culling, comparison-free sorting, and a specialized compression pipeline.
This paper presents a theoretical study of a control-referenced tri-channel organic electrochemical transistor (OECT) receiver for brain organoid interfaces, demonstrating that incorporating a matched control pixel significantly improves the symbol error rate and limit of detection for hybrid molecular communication systems by effectively mitigating common-mode drift and baseline fluctuations.
This paper proposes "DeePConverters," a novel data-driven optimal control architecture based on DeePC that enables grid-connected power converters to implicitly learn grid characteristics from measured data and adaptively achieve robust, optimal performance without relying on simplified or inaccurate grid models.
This paper proposes a data-driven loss weighting (DLW) scheme that employs a bilevel optimization framework to train a neural network for predicting adaptive weights, thereby enhancing the performance and generalization of variational image denoising models across diverse and complex noise patterns.
This paper proposes a geometry map-based propagation channel model that leverages 3D building data and the Uniform Theory of Diffraction to accurately predict site-specific path loss and time-varying Doppler characteristics in urban environments, demonstrating superior performance over 3GPP and simplified models in non-line-of-sight scenarios.
This paper presents a novel modified boost converter topology that effectively accelerates and characterizes hot carrier and trap generation in GaN HEMTs, successfully validating the logarithmic increase in on-resistance and longitudinal optical phonon scattering energy parameters for improved reliability modeling.
This paper proposes a queueing-theoretic framework that models the temporal evolution of cyber-attack surfaces as a backlog of vulnerabilities, demonstrating how AI-driven automation amplifies exploit risks and validating a reinforcement learning-based adaptive defense strategy that reduces active vulnerabilities by over 90% while accounting for heavy-tailed patching times and resource constraints.
This paper provides a comprehensive review of hydrogen-enabled resilience enhancement for multi-energy systems by analyzing their advantages and challenges, proposing a systematic framework for metrics and contingency modeling, categorizing planning and operational strategies, and identifying future research directions.
This paper investigates the controllability of lithium-ion battery cells by linking the condition number of their controllability matrices to the required control effort, revealing how parameter variations and aging increase control demands and guiding the optimal assembly of mixed new and second-life cells.
This paper proposes a two-stage mixed-integer optimization framework linking infrastructure planning with grid-constrained operation to demonstrate that cost-minimal EV charging designs often fail to meet service needs due to spatial concentration, whereas uniformly distributed deployments significantly improve operational performance and reduce energy shortfalls.
This paper presents an analytic model of static semiclassical backreaction for a Schwarzschild black hole in the Hartle-Hawking state within a finite cavity, deriving explicit first-order corrections to the horizon location and Hawking temperature while demonstrating that the near-horizon geometry retains its universal Rindler structure, thereby confirming that semiclassical effects renormalize rather than fundamentally alter the geometric origin of Hawking radiation.
This paper proposes a regularization framework for black holes where a fundamental minimal length replaces the singularity with an "asymptotic throat" that avoids topological changes and multiple horizons while preserving standard surface gravity and Hawking temperature.
This paper establishes Birkhoff rigidity in four-dimensional spherical vacuum gravity by deriving a local seed-to-Kerr-Schild route that demonstrates Schwarzschild is the unique spherically symmetric stationary vacuum Kerr-Schild geometry generated by a nowhere-vanishing optical seed.
This paper derives an exact static, spherically symmetric black hole solution within Born-Infeld-f(R) gravity and analyzes its thermodynamic properties, revealing novel behaviors that significantly deviate from standard general relativity predictions.
This paper systematically investigates the optical, dynamical, and thermodynamic properties of electrically charged black holes in Kalb-Ramond gravity surrounded by perfect-fluid dark matter, analyzing key features such as photon spheres, shadows, ISCOs, epicyclic frequencies, and Hawking radiation to connect these signatures within a unified framework.
This paper utilizes the covariant phase space formalism to derive conserved charges for supergravity formulated as a constrained BF theory, demonstrating that while translational charges vanish on-shell due to super-torsion constraints, the remaining boundary charges successfully reproduce the expected superalgebra.
This paper demonstrates that classical-quantum gravity theories based on completely positive dynamics can be embedded into a fully quantum framework and, as a consequence, can violate fundamental conservation laws like angular momentum even when the underlying equations are rotationally symmetric.
This paper investigates a specific gravity model with effective curvature-matter interaction, demonstrating through statistical analysis of distance modulus data and a comparative study of early-Universe epochs that it predicts an earlier onset of structure formation and a higher matter-radiation equality redshift than standard CDM cosmology while remaining consistent with observed recombination data.
This paper advocates for comparing astrophysical population-synthesis models directly to gravitational-wave data in observable space rather than reconstructing the underlying population, demonstrating that this approach respects model validity domains and enables unbiased inference of the observable compact-binary population using LIGO-Virgo-KAGRA data.
This paper presents an exact analytical solution for geometrically perturbed strange stars using a sinusoidal spacetime deformation to generate anisotropy, demonstrating that such models can support massive pulsars up to while satisfying all physical stability and causality conditions consistent with observational data.
This paper introduces a computationally efficient Bayesian framework for Parton Distribution Function (PDF) determination that utilizes low-dimensional linear models derived from neural network bases to provide robust uncertainty estimates and transparent control over model selection, as validated through synthetic data and closure tests.
Using 138 fb⁻¹ of proton-proton collision data at 13 TeV collected by the CMS detector, this study presents model-independent and model-dependent searches that exclude semiclassical black holes and string balls with masses below approximately 8.4–11.4 TeV and 9.0–10.7 TeV, respectively, while setting a 95% confidence level upper limit of 0.0034 on the fraction of quark-quark interactions producing electroweak sphalerons above 9 TeV.
This paper characterizes the charge and timing responses, gain non-uniformity, and pulse artifacts of two 20-inch Hamamatsu R12860 photomultiplier tubes to support signal interpretation and systematic uncertainty estimation for the RENE neutrino detector.
This paper presents and characterizes a convolutional neural network-based algorithm designed to filter unnecessary detector hits, thereby accelerating online track reconstruction and mitigating the rising computational costs associated with high pile-up conditions at the High-Luminosity LHC.
Using 20.3 fb⁻¹ of collision data collected by the BESIII detector, this study presents the first amplitude analysis of singly Cabibbo-suppressed and decays, resulting in precise measurements of their absolute branching fractions and the first observation of the decay alongside the modes.
This paper investigates the minimal type II seesaw mechanism with two-zero neutrino mass textures, demonstrating how specific patterns of charged lepton flavor violation—particularly in tau decays—can be distinguished from other new physics scenarios and used to probe ultra-high flavor symmetry scales and doubly charged scalars at current and future experiments.
This paper reports the successful development and commissioning of an integrated experimental system at ELI-NP, utilizing a 0.1 PW laser to search for axion-like particles via Four-Wave Mixing, which has been validated for background studies and is ready for a stepwise energy scale-up from 20 mJ to 2.5 J.
Using collision data collected by the BESIII detector, this study presents an improved measurement of the branching fraction for the leptonic decay , which is subsequently used to determine the decay constant and the CKM matrix element with enhanced precision.
This paper proposes a machine-learning-based strategy that classifies neutrino interaction types prior to energy reconstruction to exploit intrinsic kinematic differences, thereby reducing modeling uncertainties and improving the accuracy and sensitivity of next-generation long-baseline oscillation experiments like DUNE by 10–20%.
This paper presents recent BESIII collaboration results on charmed purely leptonic and semileptonic decays, including measurements of branching fractions, CKM matrix elements, decay constants, form factors, and tests of lepton flavor universality derived from data collected at various center-of-mass energies.
This paper presents a variational approach to Initial Boundary Value Problems that treats coordinate maps as dynamical degrees of freedom, ensuring exact conservation of space-time symmetries and Noether charges even in discrete settings, which naturally drives an automatic adaptive mesh refinement process.
This paper compares the pion decay width in a uniform magnetic field calculated via chiral perturbation theory with lattice QCD results, finding consistency at large magnetic fields while attributing discrepancies at weak fields to differences in pion decay constants.
This paper provides a rigorous proof that generalized Ising models with interaction parameter exhibit "entropic order" at arbitrarily high temperatures and solve NP-hard graph packing problems, leading to the emergence of a novel "entropic glass" phase on arbitrary graphs.
This paper utilizes QCD sum rule methods to model the resonance as a all-charm tetraquark in a diquark-antidiquark configuration, calculating its mass and decay widths to demonstrate consistency with experimental data and support its interpretation as a tensor state.
This paper demonstrates that chiral-odd dimeson generalized distribution amplitudes, which encode the spin-orbit correlation in spin-zero mesons, can be experimentally accessed in reactions through the interference between leading one-photon and two-photon exchange amplitudes, offering a direct path to probe this previously unmeasured sector of meson structure at facilities like BES III.
This paper introduces a multiscale generative sampler that combines conditional Gaussian mixture models and masked continuous normalizing flows to overcome critical slowing down in lattice field theories, achieving significantly reduced autocorrelation times and enabling unbiased Multilevel Monte Carlo variance reduction for the two-dimensional scalar theory near criticality.
This paper presents a tensor-network formulation for the strong-coupling expansion of QCD with staggered quarks at nonzero chemical potential, deriving analytical results on small lattices and introducing an enhanced method for future large-scale computations.
This paper utilizes the instanton liquid model enhanced by correlated instanton-anti-instanton pairs to derive and analyze the transverse distribution of the color Lorentz force acting on quarks in pions and nucleons, demonstrating that these force form factors are intimately related to gravitational and transversity form factors and yield results for nucleons consistent with recent lattice QCD calculations.
This paper investigates the symmetries and 't Hooft anomalies of 2+1d lattice staggered fermions on sheared tori and Klein bottles, establishing a nontrivial mapping between lattice and continuum symmetries to successfully match their anomalies while developing a general formalism for Hamiltonian lattice models on compact flat spaces.
This paper utilizes approximately 4000 lattice QCD propagators on a heavy pion ensemble to analyze relativistic nucleon wavefunctions, revealing that the observed node structure in the spectrum arises from two distinct mechanisms: "superposition nodes" created by combining interpolating fields and novel "built-in nodes" inherent to the s-wave Dirac components of individual fields.
This paper presents a unified framework within the NuWro Monte Carlo generator that consistently treats final-state interactions in quasielastic lepton-nucleus scattering by combining convolution-based inclusive calculations with an event-level cascade classification, significantly improving agreement with both inclusive electron-scattering data and exclusive MicroBooNE measurements.
This paper proposes an analytic formalism for a non-minimally coupled inflationary model involving derivative interactions between gravity and a scalar field, demonstrating that it successfully generates scalar spectral index and tensor-to-scalar ratio values consistent with ACT and Planck observations across a wide range of inflaton potentials without encountering singularities in the slow-roll regime.
This paper demonstrates that by imposing specific momentum correlations among Volkov states, a charged lepton in an electromagnetic plane wave can form a structured wavepacket whose probability-density peak travels at an arbitrary, tailored velocity independent of the field amplitude and the standard expectation value.
This paper reviews constraints on baryon inhomogeneities derived from deuterium abundance measurements, concluding that while current and future data leave electroweak baryogenesis largely unconstrained, they significantly limit the viability of various alternative baryogenesis scenarios.
Using a soft-wall holographic QCD model with an AdS-RN metric, this study reveals that combined rotation and finite chemical potential induce spatially inhomogeneous chiral condensation and additively suppress the chiral phase transition temperature, creating a position-dependent phase structure relevant to non-central heavy-ion collisions.
This paper refines the theory of axion double level crossings in multi-axion systems, demonstrating that these phenomena—characterized by a high-temperature crossing followed by a QCD-induced crossing—are common in axion models but are constrained by the number of axions () in both light and heavy scenarios, with significant implications for axion cosmology.
The paper proposes DREAMuS, a fixed-target experiment at HIAF designed to search for GeV-scale muon-philic dark matter mediated by light flavor-violating bosons, demonstrating competitive sensitivity to couplings around and highlighting the enhanced discovery potential of a complementary beam option for sub-200 MeV dark matter.
This paper proposes a minimal radiative Dirac neutrino mass model stabilized by a non-invertible fusion rule from a gauging, which successfully generates one-loop neutrino masses, provides a viable bosonic dark matter candidate, and remains consistent with current lepton flavor violation and magnetic moment constraints.
This paper demonstrates that enhancing the sensitivity of the decay search at the LHC is achievable by combining a Boosted Decision Tree classifier with novel, physics-motivated kinematic observables derived from the plane to significantly improve the discrimination against the dominant Drell--Yan background.
This paper calculates the binding energies of excited positronium states using the two-body Dirac equations of constraint, comparing nonperturbative and perturbative results while correcting identified literature errors and validating the model against alternative formulations.
This paper establishes that the -algebra is isomorphic to the tight groupoid -algebra associated with a specific inverse semigroup, thereby providing a complete classification of its irreducible representations as four families parametrized by the circle group that correspond to Soibelman's representations.
This paper establishes that -metric graphs are -hyperbolic with a linear bound, proves that -metric graphs are exactly 1-hyperbolic, and demonstrates that the converse does not hold by constructing 1-hyperbolic graphs that are not -metric for any constant .
This paper establishes a one-dimensional variational reduction for the -variational capacity of interior condensers defined by sublevel and superlevel sets of a single phase, deriving an explicit optimal profile and energy weight while analyzing the conditions under which this fibered restriction coincides with or bounds the full geometric capacity.
This paper classifies all 2-blocks with abelian defect groups and an inertial quotient of prime order, thereby proving that Broué's abelian defect group conjecture holds for this specific family of blocks.
This paper introduces a custom monofidelity and a bi-fidelity proximal quasi-Newton method that significantly accelerates the resolution of dense rigid body suspension collisions by solving the underlying linear complementary problem in just three to four matrix-vector products, achieving speedups of up to 2x and reducing total simulation time for 216 particles from eight to five days.
This paper presents and analyzes a Discontinuous Petrov-Galerkin (DPG) method with optimal test functions for an elastic circular arch model, demonstrating through theoretical analysis and numerical experiments that the approach achieves optimal convergence rates and can be further improved by employing a suitably scaled test space norm.
This paper proposes a novel distributed bilevel optimization framework that enables large-scale multi-agent systems to achieve desired macroscopic behaviors by coordinating microscopic actions through local state estimation and hypergradient-based updates, with proven convergence to stationary points.
This paper establishes a fixed point theorem for random asymptotically pointwise contractions with linear contraction functions by integrating -stability techniques from random functional analysis with deterministic fixed point theory in spaces under the assumption of boundedness.
This paper establishes a structural upper bound on the -adic valuation of denominators for rationals in missing-digit sets, providing an effective finiteness criterion for reciprocals of various sequences (such as superfactorials and Fibonacci products) that generalizes and extends recent results on reciprocals of factorials.
This paper establishes the robust Stackelberg null controllability of a one-dimensional stochastic Kuramoto--Sivashinsky--Korteweg--de Vries equation under worst-case disturbances by formulating a hierarchical game with two leaders and a follower, and proving the result through duality techniques and novel Carleman estimates for coupled forward--backward stochastic systems.
This paper constructs the first scaling limit of a non-Abelian lattice Yang-Mills theory in dimensions higher than two by demonstrating that, under specific gauge fixing and coupling limits, the Yang-Mills-Higgs model converges to a massive Gaussian field, thereby providing the first rigorous proof of mass generation via the Higgs mechanism in such a setting.
This paper derives an explicit second-order upper bound for the free energy density of a two-dimensional Bose gas in the dilute regime below the Berezinskii-Kosterlitz-Thouless critical temperature, utilizing Bogoliubov theory to capture quasiparticle contributions with a specific dispersion relation dependent on the density and scattering length.
This paper investigates log-weighted quadrature domains (LQDs) defined by a singular weight, establishing a generalized Schwarz function characterization, proving that simply connected LQDs correspond to Riemann maps whose outer factors are exponentials of rational functions, and providing explicit formulae linking quadrature functions to Riemann maps via the Faber transform.
This paper establishes a new upper bound on the maximum balanced bipartite entanglement entropy of ground states in many-body systems with nontrivial automorphism groups, showing that the entropy is limited by the logarithm of a weighted sum of irreducible representation multiplicities and offering significant improvements over existing bounds, such as reducing scaling from linear to logarithmic for complete graphs.
This paper develops the direct and inverse spectral theory for finite Hermitian banded matrices using matrix orthogonal polynomials to explicitly reconstruct matrices from spectral data, establish conditions for valid spectral measures, and explore connections to block tridiagonalization algorithms and the Toda lattice evolution.
This paper establishes a central limit theorem for additive functionals of the determinantal point process with the confluent hypergeometric kernel as the scaling parameter tends to infinity, proving their convergence to a Gaussian distribution with a quantitative bound on the Kolmogorov-Smirnov distance derived from an exact identity for multiplicative functionals.
This paper employs a path-integral approach with a stationary-phase approximation to derive a Gaussian propagator for diffusion driven by fractional Gaussian noise with multiplicative coupling, revealing how the interplay between confinement and heterogeneous noise induces an effective drift that accumulates probability in regions of low noise amplitude.
This paper reviews recent non-perturbative developments in characterizing, classifying, and detecting crystalline symmetry-protected topological invariants in two-dimensional strongly interacting quantum many-body systems, with a focus on lattice translation and rotation symmetries in both integer and fractional Chern insulators.
This paper introduces a duality for Inönü-Wigner contractions of real symmetric Lie algebras, demonstrating that both the original contraction and its dual arise as real fibers within a single algebraic family of complex Lie algebras equipped with an anti-holomorphic involution.
This paper addresses J. R. Oppenheimer's question regarding the rigorous formulation of QED scattering operators without expansion in by constructing secondary generalized scattering operators for cases of linear divergence, thereby providing a positive solution through modified commutation relations.
This paper introduces a vector reformulation of Hirota's bilinear formalism to analyze the integrable structure and construct explicit multisoliton solutions for coupled modified Korteweg-de Vries systems with real symmetric coupling, revealing new ground states in indefinite coupling regimes and providing a unified framework for diverse nonlinear wave dynamics.
This paper constructs periodic orbits for Kaleidocycles, a linkage mechanism of tetrahedra, using elliptic theta functions to prove their existence for any number of tetrahedra greater than five while demonstrating their connection to integrable systems like the semi-discrete mKdV and sine-Gordon equations.
This paper mathematically justifies the physical view of the Toda lattice at thermal equilibrium as a dense collection of soliton-like quasiparticles by rigorously defining their locations, approximating local charges and currents, and proving an asymptotic scattering relation based on the spectral properties of the system's random Lax matrix.
This paper proposes two bio-inspired learning algorithms for one-dimensional time series based on the statistical-mechanical properties of the Loewner equation—specifically Gaussian process regression via driving force normality and a fluctuation-dissipation relation for sensitivity analysis—which are numerically validated on neuronal dynamics and discussed in the context of biological self-organization.
This paper investigates wide-range two-dimensional totalistic cellular automata with majority and frustrated majority vote rules, revealing that their dynamics deviate from mean-field predictions by exhibiting coarsening processes that halt at a specific curvature radius and active patterns with stable densities, alongside bifurcations in asymptotic behavior dependent on initial conditions and interaction ranges.
This exploratory paper investigates how reinforcement learning enables moving agents to modify a two-dimensional cellular automaton environment to achieve global goals, demonstrating that while agents can approximate these objectives in passive systems, the task becomes impossible when the environment exhibits active dynamics.
This paper constructs a new infinite class of deformed integrable cluster maps of type with the Laurent property and proven integrality for low ranks by utilizing a "local expansion" operation on quivers to lift lower-rank structures to higher dimensions.
This paper establishes that within a fixed connectivity class, the spectral support minimizing the average intensity of a focusing Nonlinear Schrödinger equation soliton condensate containing a prescribed anchor set is a compact set composed of critical trajectories of a quadratic differential derived from the associated hyperelliptic Riemann surface.
This paper extends the theory of the Kuramoto model to the Kuramoto-Sakaguchi model with uniform frequency distributions, demonstrating that the transition to synchrony remains discontinuous in the thermodynamic limit, even though the jump size becomes exponentially small for phase shifts near .
This study demonstrates through experiments and simulations that dynamic two-phase flow in porous media induces chaotic mixing via exponential fluid stretching, significantly enhancing solute mixing compared to steady single-phase flows by balancing shear deformation with flow reorientation at an optimal rate.
This paper presents the TQ4Q2.0 fragmentation functions, a high-precision, uncertainty-quantified framework for modeling the production of all-heavy -wave tetraquarks in hadronic collisions, which extends previous models by incorporating nonconstituent heavy-quark contributions and advanced evolution schemes to establish a definitive baseline for future collider phenomenology.
The NA61/SHINE Collaboration presents results from a comprehensive two-dimensional scan of collision energy and system size to investigate the onset of deconfinement and baryon transport mechanisms in strongly interacting matter, highlighting key findings such as the ratio and comparisons with previous NA49 data.
This paper extends perturbative Gubser flow solutions to derive analytic nonlinear response relations for flow harmonics and , revealing that a mismatch between participant and reaction planes introduces a critical angular factor that can significantly alter the magnitude and even the sign of effective nonlinear response coefficients.
This paper summarizes recent STAR collaboration results on heavy-ion collisions, covering topics such as jet and quarkonium modification in quark-gluon plasma, collective dynamics, low-energy and small-sized collisions, and vector meson production, while concluding with an outlook on the upcoming data-analysis era.
This paper introduces "SemiCharmTag," a novel tool for LHCb that utilizes secondary vertex hadron track tagging to effectively reject or isolate charm semileptonic backgrounds in dimuon Drell-Yan measurements at 13.6 TeV, significantly improving signal-to-background ratios while enabling the creation of unbiased charm-enriched samples.
This paper proposes that the anomalous suppression of ratios in neutron-deficient nuclei, which standard models fail to explain, arises from a low-lying mixed-symmetry collective excitation mode that bridges single-particle and collective dynamics.
This study validates olivine as a promising candidate for the "paleo-detector" technique by using STEM to characterize heavy-ion tracks at various depths without etching, confirming that observed changes in track continuity align with SRIM simulation predictions regarding the transition between electronic and nuclear stopping power.
This paper calculates the baryon number–electric charge susceptibility at finite baryon density using an S-matrix formalism for pion-nucleon interactions within a hadron gas model, revealing a significant increase with chemical potential and evaluating its behavior along the chemical freeze-out line and in a cooling fireball under Partial Chemical Equilibrium.
This paper challenges the conventional view that octupole deformation is driven solely by couplings by demonstrating, through Nilsson model analysis and particle-rotor calculations, that the often-overlooked coupling plays a synergistic and essential role in driving reflection asymmetry in octupole-deformed nuclei.
This review paper examines hadron structure within the covariant Nambu-Jona-Lasinio model as a chiral effective theory of QCD, demonstrating its ability to replicate spontaneous chiral symmetry breaking and confinement while validating consistency with parton distribution functions and electromagnetic form factors against experimental data and future collider prospects.
This study proposes that the rapid subsidence of surface bulges on post-scission fission fragments acts like a catapult, reflecting nucleons inward to generate high-energy neutrons at a few percent yield.
This paper presents a robust Bayesian framework that combines eight divergent mass-radius posteriors of PSR J0030+0451 by accounting for unknown model systematics, yielding a single conservative constraint that improves neutron star equation-of-state inference when integrated with data from PSR J0437–4715 and GW170817.
This paper introduces and validates a novel no-reaction-plane method for extracting directed and elliptic flows in heavy-ion collisions using simple count asymmetries, demonstrating through PHSD model simulations that it accurately captures flow fluctuations without the need for event-plane reconstruction.
This paper presents an updated Bayesian inference of a Skyrme energy density functional, enhanced by a flexible high-density meta-model and an efficient Gaussian emulator, to derive consistent neutron star crust and core properties that satisfy both *ab initio* nuclear matter constraints and recent astrophysical observations.
This paper presents a systematic extension of the variable phase method to calculate spin-1/2 correlation functions under both central and noncentral interactions, specifically evaluating nucleon-nucleon correlations with the Reid soft-core potential across various Gaussian source sizes.
This paper presents the first uncertainty-quantified neutron-capture cross section for Al, demonstrating that the USDBUQ500 shell model interaction yields nuclear level densities and radiative strength functions with constant uncertainties of 6% and 9%, respectively, which propagate to a 5–25% non-Gaussian uncertainty in the cross section.
This paper presents a systematic calculation of non-dissipative, quantum-origin second-order hydrodynamic corrections to the stress-energy tensor and currents for free boson and Dirac fields in thermal vorticity, deriving their Kubo formulae via equilibrium correlators and demonstrating that the axial current receives vorticity-proportional corrections independent of anomalous terms.
This study utilizes 2D and 3D Particle-in-Cell simulations to demonstrate that while 3D effects and strong guide fields delay the onset of driven reconnection in merging flux tubes, the system ultimately converges to a fast-merging phase with a consistent reconnection rate and produces similar nonthermal particle spectra characterized by an electric-field-limited acceleration process.
This paper presents a mathematical formalism for a -dimensional manifold featuring -correlators of -type objects with cross-correlations and contaminants, constructed using concepts from mathematical physics, field theory, topology, algebra, and statistics, and demonstrates its applicability across cosmological scales from astronomical to quantum levels.
This paper presents a scalable conjugate Bayesian framework that enables fast, closed-form 3D positronium lifetime estimation by restricting the system matrix to observed detector-time channels and utilizing a posterior-weighted Gamma-Exponential update, thereby reducing computational time from over a minute to just seconds while maintaining accuracy in both simulated and experimental data.
This study identifies a previously unrecognized Atlantic Convergence Divergence Mode (ACDM) that underwent a nonlinear, step-like regime shift in 2009, providing a dynamical explanation for the abrupt weakening of the Atlantic Meridional Overturning Circulation (AMOC) driven by a combination of low-frequency oceanic reorganization and episodic atmospheric shocks.
This paper introduces a sign-blocking method that mitigates the fermion sign problem by applying data blocking during post-processing to infer energy through correlations with sign factors, achieving exceptional agreement with state-of-the-art benchmarks in the 2D Fermi-Hubbard model.
This paper proposes a unified sharp-diffusive phase-field model that incorporates a localized interfacial source term to independently control interface toughness and accurately simulate both bulk and interfacial cohesive fracture without requiring complex corrections or fine mesh refinement.
This paper proposes a constrained convex optimization framework for reconstructing reaction-channel levels on fixed subgroup supports that guarantees physical nonnegativity by retaining low-order channel information exactly while fitting remaining conditions via weighted least squares, thereby resolving nonnegativity violations in cross-section-space probability tables at a manageable cost to response-level accuracy.
This paper characterizes Atmospheric Methane Removal (AMR) as a distinct climate intervention that offers non-durable warming reduction with a less abrupt termination rebound than Solar Radiation Management, while its impact on surface ozone air quality is modulated by background pollutant levels.
This paper introduces Geometric Filter Optimization (GFO), a method that leverages the intrinsic symmetry of triaxial diffusion encoding to generate optimal rotation sets that significantly improve the accuracy and precision of powder-averaged signals compared to existing spherical and electrostatic-repulsion-based designs.
This study numerically demonstrates that the length, shape, and stiffness of bicuspid valve leaflets critically determine their efficiency in preventing retrograde flow, explaining why shorter leaflets in abnormal or immature valves lead to reflux.
This work demonstrates that back-end-of-line compatible, indium-free thin-film transistors featuring sub-10 nm tungsten-doped tin oxide channels deposited via low-temperature atomic layer deposition achieve superior performance and stability, particularly after post-fabrication oxygen annealing, making them a promising platform for monolithic 3D integration.
This paper proposes and validates a model-free control strategy for managing oxygen stoichiometry in proton exchange membrane fuel cell air supply systems, demonstrating its effectiveness and robustness through numerical simulations under varying operating conditions and parameter uncertainties.
This paper proposes a method to emulate isotropic-to-anisotropic photonic time interfaces using isotropic-to-isotropic spacetime modulations in subwavelength multilayers, thereby enabling real-time control over wave propagation direction and effective permittivity tensors without requiring direct access to anisotropic material properties.
This paper presents a scalable method for geolocalizing buried fiber-optic cables with sub-meter accuracy by fusing Distributed Acoustic Sensing (DAS) measurements of traffic-induced seismic signals with vehicle trajectory data to estimate fiber geometry through physics-based optimization.
This paper reports that the layered diluted magnetic semiconductor (Ba,K)(Cd,Mn)As exhibits bulk ferromagnetism and a colossal negative magnetoresistance of approximately -100% at low fields, establishing it as a promising platform for low-temperature magnetoresistive applications.
This paper proposes a data-driven, unsupervised methodology for automatically identifying optimal defect-representative images in infrared thermography by utilizing three complementary statistical and morphological metrics—the Homogeneity Index of Mixture, Representative Elementary Area, and Total Variation Energy—to overcome the limitations of conventional evaluation methods that require prior knowledge of defect locations.
This study demonstrates that doping neopentyl glycol with 1 mol% pentaerythritol reduces thermal hysteresis and supercooling in barocaloric molecular crystals by increasing microstructural disorder and nucleation events, as revealed through combined infrared thermography and polarised light microscopy.
This paper presents an energy-efficient artificial neuron based on annealing-optimized Ag/HfZrO memristors that exhibits multiple spiking functionalities, including leaky integrate-and-fire behavior and various coding modes, using a simple circuit without additional electronic overhead.
This paper demonstrates that integrating nature-inspired hyperuniform nanohole patterning into the front glass of planar perovskite solar cells significantly enhances broadband light absorption and power conversion efficiency to 23.62% by optimizing light redistribution and angular tolerance while preserving electronic interfaces and maintaining robustness against fabrication-induced disorder.
Researchers successfully demonstrated the first observation of narrow-band 1.3 MeV radiation by channeling an 855 MeV electron beam through a boron-doped diamond superlattice acting as a crystalline micro-undulator, a breakthrough that paves the way for generating highly directional 14.5 MeV -ray beams with optimized doping profiles for future 3 GeV accelerators.
This paper presents a comprehensive investigation of He I line profiles for DB white dwarf spectroscopic analysis, utilizing SDSS DR17 data to compare traditional semi-analytical Stark profiles with new computer-simulated profiles while examining various physical effects such as frequency sampling, Doppler broadening, and 3D hydrodynamical corrections.
This paper presents a compact, dual-solenoid magnetic coil and control circuit capable of rapidly changing the magnetic field by up to 36 Gauss in 3 microseconds, enabling the study of non-equilibrium quantum dynamics in cold atom experiments via Feshbach resonances.
This paper presents a stable, phase-locking-free method for generating optical lattices by passing a single laser beam through a prism with n-fold symmetric facets, successfully demonstrating various configurations like triangular and ten-fold quasi-crystal lattices with minimal lattice constant variation and positional drift.
This paper presents a hybrid optical scheme combining fixed optics (axicons and prisms) with a digital micromirror device to efficiently generate high-quality, near-ideal hollow beams of various shapes, enabling the creation of highly homogeneous cold atom samples in customizable box potentials for advanced quantum many-body physics research.
This paper proposes a measurement-free, autonomous quantum error correction scheme for spin-oscillator hybrid qubits that utilizes engineered dissipation to stabilize the code space, offering a hardware-efficient path to noise-biased logical qubits compatible with existing experimental platforms like trapped ions.
This paper presents a new stripline measurement method combined with full-wave modeling to extract the complex permittivity and conductivity of commercially available Rydberg vapor cells across the 10–300 MHz range, thereby quantifying packaging-induced field distortions to enable precise corrections and optimized sensor designs.
This paper presents a detailed computer simulation method to investigate the non-monotonic evolution of K-shell ionization and characteristic x-ray radiation angular distributions from high-energy electrons and positrons traversing oriented silicon crystals across a wide energy range (1–1000 GeV), with a specific focus on the underlying physical mechanisms such as dechanneling.
This study experimentally demonstrates that dysprosium atoms can be prepared in a long-lived metastable state with a large induced electric dipole moment of over 1 Debye and a reduced transition dipole moment of 7.65 Debye, enabling efficient single-photon excitation from the ground state through a three-state Stark interaction.
This paper demonstrates that geometrically ordered, spatially extended 2D atom arrays with subwavelength spacing enable a new regime of collective light-matter interaction where photon-mediated interactions drive extensive superradiance and subradiance, revealing their underlying magnetic-like correlations and establishing a programmable platform for dissipative many-body quantum physics.
This paper reports the experimental observation and numerical verification of discrete one-dimensional solitons formed by balancing discrete diffraction and self-focusing in an optically induced photonic lattice within warm rubidium atomic vapor, offering a versatile platform for exploring non-Hermitian nonlinear dynamics and parity-time-symmetric systems.
This paper proposes a universal physics-informed neural network framework for electromagnetic scattering that leverages quasinormal mode expansions to ensure physical consistency, guarantee energy conservation and causality, and significantly improve data efficiency for modeling and inverse design of optical devices.
This paper proposes moiré superlattices as a novel, scalable solid-state platform for quantum optics, offering arrays of nearly identical artificial-atom emitters with tunable spacing and broad spectral coverage to overcome the fabrication challenges of traditional quantum dot arrays.
This paper introduces CV-HoloSR, a complex-valued deep learning framework that achieves physically consistent hologram super-resolution for 3D volume up-sampling by combining a depth-aware residual network, a specialized 4K dataset, and an efficient LoRA fine-tuning strategy to eliminate depth distortion and significantly improve perceptual realism.
This paper presents a hardware-implemented deep photonic reservoir computer that achieves ultra-fast, nanosecond-latency prediction of unmodeled UAV dynamics in confined spaces, significantly enhancing flight stability and training efficiency compared to traditional learning-based compensators.
This paper introduces direct volumetric X-ray fluorescence ghost tomography, a compressive sensing approach that reconstructs 3D elemental distributions from multiplexed measurements across all angles via a single inverse problem, achieving a 43-fold reduction in acquisition time compared to conventional raster scanning while preserving spatial resolution.
This paper presents the first integrated Huygens'-based microring resonators and directional/contradirectional couplers operating at S- and C-band wavelengths, demonstrating high-Q resonances with negative group index and near-zero dispersion to enable advanced applications in optical communications, quantum photonics, and sensing.
This paper reveals a novel form of topology in complex momentum bands driven by temporal variations, which exhibits unique localization at time interfaces and remains robust against arbitrarily strong disorder, a phenomenon experimentally verified in photonic quantum walks.
This paper demonstrates a compressive sensing-based imaging technique that utilizes random binary excitation patterns and GPSR-BB reconstruction to efficiently image sparse quantum emitters, such as NV centers in diamond, achieving high-fidelity intensity and maps with only 20% of the measurements required by conventional raster scanning.
This paper presents a unified theoretical framework demonstrating that voltage-controlled nematic liquid crystals can serve as tunable quantum photonic devices to manipulate the group velocity, temporal walk-off, and entanglement properties of orthogonally polarized photon wave packets for applications in quantum communication and information processing.
This paper presents a compact, all-fiber erbium-doped laser utilizing nonlinear polarization rotation to achieve continuously tunable and deterministically switchable multi-wavelength mode-locking, enabling flexible spectral control from single to seven wavelengths for applications in dense wavelength-division multiplexing and photonic signal processing.
This study introduces the Global Script Database to demonstrate that writing systems evolve according to a detectable molecular clock, a pattern significantly disrupted by imperial interventions that selectively rewrite deep structural features and accelerate script extinction, with the Spanish and Japanese empires identified as the most destructive forces in script replacement history.
This paper introduces a coupled astrocyte-neural field model demonstrating that astrocytic diffusion of a conserved resource pool, combined with synaptic replenishment, stabilizes persistent neural activity by suppressing drift instabilities and expanding the parameter regime for stationary bump solutions.
This paper introduces BadGraph, a novel backdoor attack that leverages textual triggers to poison latent diffusion models for text-guided graph generation, successfully inducing attacker-specified subgraphs during inference with high success rates and minimal performance degradation on clean data.
This paper introduces a deep state space modeling framework that leverages raw blood-oxygenation-level-dependent time series from resting-state fMRI to predict Alzheimer's disease behavior scores and identify key brain regions, outperforming traditional methods that rely on static functional connectivity matrices.
CAGenMol is a condition-aware discrete diffusion language model that integrates reinforcement learning to simultaneously optimize heterogeneous structural and property constraints for goal-directed molecular generation, effectively resolving conflicting objectives while maintaining chemical validity and diversity.
This study finds that while large language models generate semantically consistent exercise prescriptions across repeated trials, they exhibit significant variability in key quantitative components like intensity, indicating that expert validation and structural constraints are necessary before clinical deployment.
This paper introduces EvoIF, a lightweight model that unifies protein language model theory with evolutionary profiles and structural constraints to achieve state-of-the-art zero-shot fitness prediction for protein mutations while significantly reducing data and parameter requirements.
The paper proposes that a cycle of landscape fragmentation and reconnection acts as a "diversity ratchet" that systematically increases ecosystem diversity by triggering extinction events that preferentially eliminate highly connected species, ultimately resulting in higher diversity levels than a continuously connected landscape could sustain.
This paper demonstrates that incorporating nonlinear complex behavioural contagion into epidemic models can lead to multiple disease-free equilibria, where an initial epidemic triggers a self-sustaining increase in protective behaviours that ultimately eliminates the disease, even for high basic reproduction numbers.
This paper provides a comprehensive technical review of ancestral recombination graph (ARG) samplers developed over the past three decades, evaluating their performance, usability, and biological realism to assist researchers in creating scalable ancestry simulation and inference tools.
This paper proposes a standardized counterfactual diagnostic framework that systematically attributes Kolmogorov-Smirnov (KS) statistic deterioration in credit risk models to specific causes—ranging from sampling variability to model drift—thereby replacing ad hoc judgments with a transparent, defensible, and governance-relevant analytical process.
This paper proposes a micro-founded herding-based model of technological diffusion to explain economic convergence, demonstrating how peer effects and individual incentives drive Total Factor Productivity growth in Central and Eastern European economies as they adopt technologies from the global frontier.
This paper proposes a multilevel stochastic approximation algorithm that significantly improves the computational efficiency of estimating Value-at-Risk and Expected Shortfall for nested simulation problems, achieving near-optimal complexity orders of and respectively, compared to the standard complexity.
This paper introduces a high-precision AI patent classifier to reveal that while the United States and China exhibit converging AI patenting growth and market value premiums, they differ significantly in organizational structures—with the U.S. dominated by large private firms and China by diverse institutions—and remain technologically interdependent through cross-border knowledge flows.
This paper proposes and analyzes an adaptive multilevel stochastic approximation algorithm that mitigates the suboptimality of previous methods for computing Value-at-Risk by adaptively selecting inner samples, thereby achieving a near-optimal complexity of .
This paper introduces a novel concept of Aharonov-Bohm-type arbitrage in financial markets, demonstrating how global topological inconsistencies (non-trivial holonomy) in filtered systems can generate profitable, predictable trading strategies despite the absence of local price discrepancies.
This paper establishes central limit theorems for the renormalized estimation errors of a nested stochastic approximation algorithm and its multilevel acceleration, originally proposed by Crépey, Frikha, and Louzi (2025), for computing the value-at-risk and expected shortfall of random financial losses.
This paper proposes a hybrid Temporal Convolutional Network and Attention-based LSTM model using Chinese A-share data to outperform traditional static methods in predicting stock repurchases, while leveraging Explainable AI to reveal that prolonged undervaluation serves as a long-term motive and sharp cash flow increases act as short-term triggers for these decisions.
This paper utilizes stochastic reflected diffusion processes to analyze how trading fees and continuous-time arbitrage impact liquidity provider profitability in Geometric Mean Market Makers, extending previous findings on Uniswap v2 to broader models like Balancer to derive the long-term expected logarithmic growth of LP wealth.
This paper argues that investing is fundamentally a compression problem, demonstrating that maximizing long-term wealth via the Kelly criterion is equivalent to minimizing the divergence between an investor's chosen distribution and the unknown true distribution, thereby framing portfolio optimization as an information-theoretic task.
This paper demonstrates that a Hybrid Quantum Genetic Algorithm outperforms classical Genetic Algorithms in portfolio optimization by achieving faster convergence to the optimal solution, maintaining greater population diversity, and requiring significantly fewer evaluations than brute-force methods.
This paper proposes a detector-based inferential framework where quantum theory and spacetime geometry emerge from the information geometry of detector states, ultimately deriving the Einstein equations and an operational definition of matter from the consistency of measurement outcomes.
This paper proposes a framework for predicting the entanglement spectra of one-dimensional gapless symmetry-protected topological states by demonstrating that their reduced density matrices can be derived from trivial critical states via local quantum channels that modify conformal boundary conditions.
This paper presents a quantum algorithm for multiplying two -bit integers that achieves circuit and -depth using resources, representing the lowest known -depth for multiplication in the Clifford + model and significantly advancing the feasibility of large-scale fault-tolerant quantum computing.
This paper presents a systematic experimental study demonstrating that the combined presence of noisy classical inputs and quantum hardware noise significantly degrades the training stability and classification accuracy of variational quantum classifiers, highlighting the critical need to evaluate both noise sources simultaneously in NISQ-era machine learning.
This paper proposes a parallel fixed-point quantum search algorithm that leverages entanglement to process SAT clauses independently, thereby reducing circuit depth and mitigating Grover's "Souffle problem" to make it particularly suitable for the noisy intermediate-scale quantum (NISQ) era.
This paper proposes an efficient, deterministic algorithm that accelerates the inchworm method for simulating open quantum systems by approximating the bath influence functional as a tensor train, thereby achieving linear scaling with dimensionality and enabling accurate long-time dynamics simulations.
The paper proposes a broadly applicable fault-tolerant quantum computation scheme for good qLDPC codes that achieves constant qubit overhead and asymptotically faster time complexity of by utilizing parallelized code surgery and classical locally testable codes, significantly outperforming existing methods for codes with distance scaling where .
This paper demonstrates that applying a quantum eigenvalue estimation algorithm to non-Hermitian transcorrelated Hamiltonians with the xTC approximation can achieve chemical accuracy comparable to standard qubitization on significantly larger basis sets for small atoms, though the method's accuracy degrades for larger second-row elements.
This paper presents a hardware-validated reference dataset and analysis of H ground-state energy calculations on 2026 IBM Quantum processors, demonstrating that circuit simplification via tapered mappings offers the most consistent accuracy gains while resilience level 1 incurs substantial costs and session-based execution provides no systematic accuracy advantage despite higher billed time.
This paper establishes a systematic framework for -mutual information in additive Gaussian noise channels by proving key regularity properties, deriving a generalized -I-MMSE relationship that links information derivatives to estimation errors under tilted distributions, and characterizing asymptotic behaviors across low and high signal-to-noise regimes.
This study presents the first Bayesian Age-Period-Cohort analysis of Puerto Rican fertility from 1948 to 2022, revealing that cohort effects, particularly among those born from 1963 onward, are the primary driver of the island's record-low fertility rates rather than period effects or birth postponement.
This paper introduces MAHGenTa, an algorithm that leverages information geometry to decompose KL error and select sparse higher-order mode interactions, thereby enabling more data-efficient learning of probability distributions for both generative and discriminative tasks.
This paper proposes two deep generative frameworks—a dimension-aware mixture of variational autoencoders and a diffusion-based model—to effectively learn distributions on stratified spaces, while establishing theoretical convergence rates and providing algorithms for consistently estimating the number and dimensions of underlying strata.
This paper proposes a novel family-based graphical framework that unifies and visualizes diverse gatekeeping strategies for hierarchically structured hypothesis families in clinical trials, offering a flexible, interpretable, and statistically rigorous method for controlling the familywise error rate.
This paper introduces a privacy-preserving federated framework using threshold CKKS homomorphic encryption to enable multi-institutional Kaplan-Meier survival analysis that achieves high-fidelity results comparable to centralized data while preventing the reconstruction of sensitive individual records through encrypted aggregation and threshold decryption.
This paper proposes a unified Bayesian spatiotemporal framework using a flexible random effect that integrates SAR, DAGAR, and temporal autoregressive components to effectively model censored and missing areal data, demonstrating superior performance over ad hoc imputation methods and offering enhanced interpretability compared to traditional CAR approaches in air quality analysis.
This paper proposes a causal discovery strategy using degradation increments combined with non-temporal techniques to uncover dependencies between system parameters, demonstrating through numerical and engineering case studies that this approach outperforms raw data methods, with stable Peter-Clark and greedy equivalence search identified as the most robust algorithms.
This paper introduces a novel reference prior for Gaussian hierarchical models with intrinsic conditional autoregressive random effects that maintains equivalence to a previously published prior while drastically reducing computational complexity from to , thereby enabling efficient objective Bayesian variable selection for large datasets.
This paper refutes a conjecture by Arnold and Villasenor regarding the relationship between the sum and the maximum of independent and identically distributed half-normal random variables.