791 papers
This paper demonstrates that unconventional early-time relaxation dynamics in Rydberg atom chains, characterized by a specific decay rate of the survival probability, serve as a robust experimental signature for detecting quantum many-body scarring at time scales significantly shorter than thermalization.
This paper provides a comprehensive analysis of the dynamical behaviors, classification, and various forms of stability for all 39 possible two-node McCulloch-Pitts networks with self-loops and ternary weights, highlighting how model variants, value encodings, and regulatory structures fundamentally influence system dynamics and robustness.
This paper analytically and numerically investigates the non-equilibrium dynamics of a tracer particle driven through a disordered, confined lattice, revealing how confinement induces dimensional crossovers, qualitatively alters force-dependent diffusion, and sustains superdiffusive fluctuations even under external driving.
The Two-Time Relativistic Bohmian Model (TTBM) proposes that an extra unobservable time dimension restores determinism in quantum mechanics, explains phenomena like Zitterbewegung and static atomic orbitals through motion in this new dimension, and predicts a relativistic correction to the uncertainty principle.
This paper introduces a novel, scalable, and size-intensive "Double Configuration Interaction Singles" method that utilizes a perturbative treatment of the electronic Hessian to variationaly account for orbital relaxation, thereby significantly improving the accuracy of charge-transfer excitation energies and single bond dissociation descriptions while maintaining a mean-field computational cost.
This paper classifies low-order local Hamiltonian operators for two-component evolutionary differential-difference equations, including degenerate cases beyond previous scalar results, and computes the Poisson cohomology of a specific degenerate operator to elucidate its deformation theory and bi-Hamiltonian structure in integrable systems like the Toda lattice.
This paper provides two distinct derivations of the full nonlinear Schwarzian effective action governing the low-temperature soft mode of the large- SYK model and its chain generalization, demonstrating that these effective descriptions can be rigorously derived from microscopic dynamics without relying on additional assumptions or matching procedures.
This paper introduces a versatile, momentum-conserving agent-based model for active flexible rods that successfully reproduces key hallmarks of active turbulence, such as spontaneous flows and defect dynamics, while also capturing emergent density-orientation coupling and enabling the simulation of complex phenomena like 3D flows and tissue growth.
This paper numerically investigates a ring network of phase oscillators with frequency heterogeneity and phase lag, revealing how these factors influence the basin sizes of multistable synchronous states and proposing a control method to steer the system toward specific wavenumbers.
This study demonstrates that tabletop extreme ultraviolet (EUV) ptychography provides a powerful, label-free, and quantitative imaging platform with 44 nm resolution to reveal nanoscale morphological and compositional changes in *Escherichia coli* and *Bacillus subtilis* under physiological and antibiotic stress.
This paper introduces TBSCI, a fully distributed diagonalization framework utilizing a tensor-product bitstring representation and novel communication strategies that overcomes memory bottlenecks to enable scalable selected configuration interaction calculations on over 2.5 million cores while demonstrating the method's ability to achieve near-full configuration interaction accuracy with a compact wavefunction.
The paper introduces Noise2Ghost, a self-supervised deep learning method that achieves superior noise reduction and reconstruction quality in ghost imaging without requiring clean reference data, thereby enabling high-quality imaging in low-light scenarios such as dose-sensitive x-ray fluorescence and biological studies.
This paper demonstrates that third-generation ground-based gravitational wave detectors, such as the Einstein Telescope and Cosmic Explorer, can effectively distinguish between bare primordial black holes and dressed primordial black holes surrounded by dark matter using Bayesian model selection on lensed gravitational wave signals.
This paper introduces EnsAI, an AI-based emulator that generates atmospheric chemical ensembles 3,300 times faster than the traditional GEM-MACH model while accurately reproducing meteorology-dependent features and delivering comparable emissions inversion results.
This paper presents an exact frequency-domain formulation of pendulum-like systems that unifies oscillatory, separatrix, and rotational regimes under a single universal spectral kernel, revealing that regime transitions are symmetry-driven reorganizations in frequency space rather than changes in the underlying spectral structure.
This paper introduces a physics-embedded Bayesian neural network (PE-BNN) framework that integrates an energy-independent phenomenological shell factor and WAIC-based hyperparameter optimization to accurately predict energy-dependent fission product yields with fine structures and close agreement with known nuclear shell effects.
This paper derives a novel, local, and crossing-symmetric dispersion relation for 2-2 scattering amplitudes motivated by string theory, demonstrating its utility in bounding Wilson coefficients for gravitational effective field theories and providing convergent series representations for Veneziano, Virasoro-Shapiro, and multi-variable generalizations.
This study utilizes the Bogoliubov approach in a ring-shaped Bose-Einstein condensate to demonstrate that quantum depletion increases with Hawking temperature due to horizon dynamics, revealing a critical threshold where backreaction effects challenge the approximation's validity while identifying a viable parameter regime for experimental investigation.
This study introduces a factorized-implicit Fourier Neural Operator (F-IFNO) framework that enhances long-term stability and accuracy in predicting three-dimensional turbulence by integrating uncertainty quantification and error propagation analysis to overcome the limitations of traditional models and existing neural operators.
This paper utilizes two-dimensional molecular dynamics simulations to introduce a wavenumber-dependent viscosity that unifies renormalized and bare viscosity by demonstrating how equilibrium stress correlations diverge at small wavenumbers while determining bare viscosity at large wavenumbers, thereby bridging mesoscopic fluctuations and macroscopic transport through microscopic dynamics.