April 2026 arXiv papers — page 75

Ric Fulop, Neil Gershenfeld

Field-driven phenomena, from flash sintering to electromigration, exhibit threshold fields spanning six orders of magnitude. We show their product with the onset activation coherence length is a universal critical activation voltage, Vc =0.1-2.7 V. Vc represents the threshold electrical work required to resonantly couple to the universal phonon damping peak

Shanshan Zhong, Yi Lu, Jingjie Ning, Yibing Wan

Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, comprising 20 verified, skill-dependent tasks a

A. Carrillo-Monteverde, L. López-Lozano, F. San Juan-Villegas

In this work, we investigate a decaying dark matter scenario and its associated indirect detection signatures. The model consists of a scalar singlet with a lifetime exceeding the age of the Universe. Stability is ensured by a $Z_2$ symmetry imposed on the Lagrangian, allowing decay through a non-minimal gravitational coupling. The decay of dark matter produ

Jonathan S. Arnaud, Christopher J. McDevitt, Golo Wimmer, Xian-Zhu Tang

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any experimental or synthetic data. The initial study considered an ITER-like tokamak and found that a PINN, after careful treatment, was capable of learning the solu

Lihui Meng, Lu Xu, Xusheng Zhu, Lixin He

The generation of attosecond pulses (1 as=10-18 s) has enabled real-time observation and manipulation of coherent electron dynamics, yet their low peak power has hindered the development of advanced attosecond pump-probe spectroscopy and attosecond nonlinear metrology. Here we overcome this limitation by generating 1.64 uJ, 263 as isolated attosecond pulses

Zongyao Lyu, William J. Beksi

Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a pool of unlabeled data for annotation. Traditional AL operates under a closed-set assumption, where all classes in the dataset are known and consistent. However, real-world scenarios often present open-set con

Lucie Charlotte Magister, Pietro Lio

The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks via concept-based explanations extracted from the latent representations obtained after message passing. However, these explanations fall short of explaining the message passing process itself. To this aim, we

Srujan Kumar Gandla

AWS Lambda terminates containers with an uncatchable SIGKILL signal when a function exceeds its configured timeout. When a Spark-on-AWS-Lambda (SoAL) job is killed between Phase 1 (data upload) and Phase 2 (metadata commit) of a write, the result is silent data loss: orphaned Parquet files accumulate on S3 while the table's committed state remains unchanged

R. Slater, R. Amorín, J. A. Fernández-Ontiveros, F. J. Sáez-Ruiz

Detecting cold molecular gas in metal-poor starbursts remains a major challenge. Low carbon and oxygen abundances hinder CO formation, while low dust content reduces shielding against UV photodissociation. Consequently, CO, the main tracer of molecular gas, becomes faint or undetectable. We study the spatial distribution and kinematics of cold molecular gas

Aarav Gupta, Gururaj Deshpande, Chandreyi Chakraborty

Auto-regressive Large Language Models (LLMs) achieve strong performance on coding tasks, but incur high memory and inference costs. Diffusion-based language models (d-LLMs) offer bounded inference cost via iterative denoising, but their behavior under post-training quantization (PTQ) has been sparsely explored. We investigate the application and robustness o

Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko

Graph-based techniques and spectral graph theory have enriched the field of machine learning with a variety of critical advances. A central object in the analysis is the graph Laplacian L, which encodes the structure of the graph. We consider the problem of learning over this Laplacian in a distributed streaming setting, where new edges of the graph are obse

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Large-scale kernel ridge regression (KRR) is limited by the need to store a large kernel matrix K_t. To avoid storing the entire matrix K_t, Nystrom methods subsample a subset of columns of the kernel matrix, and efficiently find an approximate KRR solution on the reconstructed matrix. The chosen subsampling distribution in turn affects the statistical and c

F. Acero, R. Z. E. Alsaberi, M. Arias, J. Borowska-Naguszewska

We report the first detection of radio emission from the Type II supernova SN 2007it, located at a distance of 12.2 Mpc in NGC 5530. The observations were obtained with the Australian Telescope Compact Array (ATCA) more than 18 yr after the explosion as part of the Rebrightening in Interacting Supernova Emission (RISE) program, which monitors nearby core-col

Pedro Abdalla, Radu Balan, Junren Chen

While it is well known that the restricted isometry property (RIP) guarantees uniform sparse recovery from noisy linear measurements, uniform recovery of structured signals from nonlinear observations remains much less understood. This paper shows that the restricted approximate invertibility condition (RAIC) provides a unified approach to this end. Particul

Tim Merino, Sam Earle, Ryunosuke Iwai, Julian Togelius

We introduce Dream-Cubed, a large-scale dataset of Minecraft worlds at voxel resolution, and a family of models using cubes as powerful compositional units for efficient generation of interactive 3D environments. Dream-Cubed comprises tens of billions of tokens from a carefully curated mixture of procedural biome terrain and high-quality human-authored maps.

Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh

A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike its predecessors, it resolves the ambiguity arising from the fact that a possibly large number of policies could match the expert's behavio

Yihao Sun, Kunting Qi, Thomas Gilray, Sidharth Kumar

Datalog is a declarative logic-programming language used for complex analytic reasoning workloads such as program analysis and graph analytics. Datalog's popularity is due to its unique price-point, marrying logic-defined specification with the potential for massive data parallelism. While traditional engines are CPU-based, the memory-bound nature of Datalog

Tianyi Chen, Mohammad Sharifi Kiasari, Sijing Yu, Youngser Park

Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, however, such vertex alignments can be misspecified or unknown. We study the impact of vertex alignment on changepoint localization for dynamic networks through two illustrative models, each with a similar changep

Arka Majhi

Recent discoveries in VR have opened up scope for designing physical tools and controllers to enhance immersion, through perceived reality. In a virtually simulated sports scenario it is challenging to immerse user because most of the available controllers are unable to bridge the user experience in the real world to the actions in the virtual world. My rese

Sadra Sabouri, Zeinabsadat Saghi, Run Huang, Sujay Maladi

Advances in AI agent capabilities have outpaced users' ability to meaningfully oversee their execution. AI agents can perform sophisticated, multi-step knowledge work autonomously from start to finish, yet this process remains effectively inaccessible during execution, often buried within large volumes of intermediate reasoning and outputs: by the time users

Dylan J. Morris, Lauren Kennedy, Andrew J. Black

Infectious disease dynamics operate across multiple biological scales, with within-host viral dynamics being a key driver of between-host transmission. However, while models that explicitly link these scales exist, none have been developed with statistical inference as a primary goal. In this paper we propose a multiscale model that jointly captures heteroge

Masahiro Ono, Daniel Selva, Morgan L. Cable, Marie Ethvignot

The surface and subsurface of worlds beyond Mars remain largely unexplored. Yet these worlds hold keys to fundamental questions in planetary science - from potentially habitable subsurface oceans on icy moons to ancient records preserved in Kuiper Belt objects. NASA's success in Mars exploration was achieved through incrementalism: 22 progressively sophistic

Ethan Ratliff-Crain, Colin M. Van Oort, Matthew T. K. Koehler, Brian F. Tivnan

This study strengthens the foundations of multi-venue market modeling by attempting an independent replication of Wah and Wellman's 2016 model of latency arbitrage in a fragmented market. We find that faithful replication is hindered by missing implementation details in the original paper and limited quantitative reporting. We demonstrate that increasing the

Jiahao Liu, Mingzhe Han, Guanming Liu, Weihang Wang

Personalization has traditionally depended on platform-specific user models that are optimized for prediction but remain largely inaccessible to the people they describe. As LLM-based assistants increasingly mediate search, shopping, travel, and content access, this arrangement may be giving way to a new personalization stack in which user representation is

Yibo Liu, Jiale Wang, Jiexiang Wang, Ying Dai

While antiferromagnetic magnons in two-dimensional (2D) materials hold immense promise for high-frequency spintronics, achieving their efficient active control remains a critical challenge. Here, we propose a universal mechanism for the nonvolatile ferroelectric (FE) switching of antiferromagnetic magnon transport in 2D multiferroic lattices. Our mechanism r

Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world data often suffer from missing modalities due to transmission errors or user behavior, severely degrading model performance. Existing methods enhance robustness via cross-modal consistency learning but largely ig

Seyma Yaman Kayadibi

Artificial intelligence systems are increasingly expected to operate over repeated cycles of interaction, adaptation, and update rather than through isolated one-shot outputs. This raises a fundamental theoretical question: can an AI system persist indefinitely without incurring unbounded structural aging? This paper develops a long-run persistence framework

Su Lan, Xuefei Yin, Yanming Zhu, Alan Wee-Chung Liew

Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms. However, existing LLM-based methods often rely on implicit language-level reasoning, resulting in opaque causal assumptions, unverifiable reasoning paths,

Changqing Li, Shouwei Gao, Kai Zhao, Sheng Di

Large language model (LLM) coding agents are increasingly applied to code translation and optimization, yet their effectiveness in performance-critical high-performance computing (HPC) settings remains poorly characterized. This paper evaluates LLM-based coding workflows on SZ-family error-bounded lossy compression kernels, which combine numerical constraint

Mostafa Dahshan, Quazi Mamun, Tanmoy Debnath

Large language models (LLMs) provide strong performance across a wide range of tasks but are typically hosted on centralised cloud infrastructure, incurring significant bandwidth, latency, and privacy costs. In contrast, small language models (SLMs) can run on edge devices but have limited capability and robustness. This paper introduces SWARM-LLM, a routing

Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli

Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent. Prior machine learning (ML) and deep learning (DL) approaches reported strong within-dataset results, yet were rarely evaluated in cross-dataset settings, leaving real-world generalizability unc

Yucheng Zhou, Junwei Sheng, Qianning Wang, Jianbing Shen

Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization. Recent work attributes this issue to pathological gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level. However, DFT assumes all demonstrations are equ

Matthew James Buchan

Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses agreement with factually correct statements. We introduce dual-stance evaluation, which tests both stances of each topic, and apply it to centroid-difference steering on Llama-3-8B-Instruct. We find a dissociati

Jonas Grill, Thomas Bayer, Sören Berlinger

Accurate extraction of structured information from Safety Data Sheets (SDS) remains challenging in industrial safety due to heterogeneous document formats and the limitations of traditional rule-based methods. This study benchmarks state-of-the-art Large Language Models (LLMs) for automated SDS data extraction, comparing text-based and multimodal processing

Faruk Alpay, Bugra Kilictas

Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output. Standard decoding methods may assign high probability to fluent continuations while placing low mass on continuations that realize all required anchors jointly. We study this regime as a rare-event sequential inference problem. LatticeBridge

Shuyu Jiang, Kaiyu Xu, Xingshu Chen, Hao Ren

Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in dominant languages and has not progressed in parallel with multilingual capability, creating exploitable gaps for jailbreak attacks. Current jailbreak defenses are largely developed and evaluated in dominant langua

Jin Gan, Xin Li, Jun Luo

The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among different methods, inference-time alignment is often cheaper as it intervenes (i.e., offers guidances) only during output generation. Existing proposals apply guidances extracted from certain aligned models wi

Julie A. Vera, David W. McDonald, Mark Zachry

During severe weather events, people must interpret rapidly evolving information to make time-sensitive safety decisions. Broadcast meteorologists have traditionally served as credentialed intermediaries within established media organizations, while independent "weatherfluencers" on YouTube have emerged as prominent real-time interpreters for large a

Yinan Chen, Yang Zhou, Xiaoxia Huang, Pan Li

Existing collaborative WiFi sensing systems rely on perfect node synchronization and complete data availability. However, real-world edge deployments suffer from heterogeneous computing and network dropouts, leading to asynchronous and incomplete features. We propose CREWS, a robust collaborative sensing framework that inherently resists these network volati

Carlos J. Costa, Joao Tiago Aparício, Manuela Aparício

As generative artificial intelligence (GenAI) diffuses across industries and becomes broadly accessible, the locus of sustainable competitive advantage shifts from technology ownership toward the quality of employee-level adoption and use. This paper develops a cross-level conceptual framework linking firm-level GenAI investment and governance to individual-

Mingchun Sun, Rongqiang Zhao, Muhammad Abdul Munnaf, Jie Liu

In wireless sensor networks (WSNs), data augmentation is a novel method to improve sampling-frequency decision performance, thereby enabling energy optimization for IoT (Internet of Things) sensors. However, existing methods rely on a single generator and empirically determined quantities, failing to establish a mapping between dynamic information gaps and m

Zhiming Chang, Ziyang Li

The formal verification of operating system kernels requires precise specifications that capture the intended behavior of system calls. Writing these specifications manually demands deep domain expertise, motivating the use of large language models (LLMs) to automate the process. However, in OSV-Bench, a benchmark of 245 specification generation tasks derive

Saad Mankarious

We introduce \emph{Quantum Frog}, a two-player cooperative game built on a novel \emph{quantized-time} mechanic in which the environment advances only when a player acts. Inspired by the classic arcade game Frogger, Quantum Frog requires two frogs to cross an 8$\times$8 grid of traffic and reach the far side together. We use reinforcement learning (RL) as an

Diandian Gu, Jing Lin, Gaohong Liu, Jiahang Liu

We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and application coverage. For geometry, a coarse-to-fine two-stage pipeline decouples global structure learning from high-frequency detail recovery, while a locality-aware VAE achieves h

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, Seán R. Kavanagh

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent effor

Saptarshi G. Dastider, K. Prashant, P. Shruti, C. Sudheesh

Accurate modeling of ion-molecule reaction networks is essential for understanding the chemical evolution of planetary ionospheres, particularly for giant planets where proton-transfer chains drive atmospheric composition. However, predicting reaction rates in these ultracold environments remains a challenge due to the non-trivial interplay between vibration

Samuel T. Sebastian, Siyao Xu, Yue Hu, Luca Comisso

We use the 3D fully kinetic simulation to study different turbulence modes and turbulence anisotropy of relativistic turbulence in magnetically dominated collisionless plasmas. We extend the method developed by Cho & Lazarian (2002) for decomposing non-relativistic magnetohydrodynamic (MHD) turbulence into Alfvén, fast, and slow modes to the regime of collis

Paulo Luz, Sante Carloni

We present a covariant and gauge-invariant formulation of the theory of radial adiabatic linear perturbations of self-gravitating, non-dissipative imperfect fluids within the theory of general relativity. By codifying the thermodynamical properties of the source into an equation of state and an ansatz on anisotropic pressure that involves both matter and kin

G. Mountrichas, F. Shankar, F. J. Carrera, A. Georgakakis

We investigate the connection between supermassive black holes (SMBHs), their host galaxies, and large-scale dark-matter halos using broad-line X-ray AGN from the XMM--XXL and Stripe\,82X surveys, together with galaxies from VIPERS and SDSS/Stripe\,82. Building on the homogeneous host-galaxy catalogue presented in Paper~I, we test whether AGN with a given bl

Oem Trivedi, Alfredo Gurrola, Robert J. Scherrer

Building on recent work introducing the idea of Quasilocal Probability in curved spacetime, we develop its observational implications for black hole ringdown in detail. We show that horizon-induced probability flux leads to an effective non-Hermitian dynamics producing three distinctive signatures, which are correlated multi-mode deviations, weak amplitude d

Varvara O. Mikhnevich, Anastasiia Plotnikova, Giovanni Carraro, Anton F. Seleznev

This paper introduces a new method to search for unresolved binary stars in open star clusters. The work aims at improving the approach introduced previously, which employs the (H-W2)-W1 versus W2-(BP-K) photometric diagram. This diagram, in tandem with the Gaia Color Magnitude Diagram (CMD) and using theoretical isochrones as reference sequences, is used to

Byeongjin Kim, Ian Anderson, Tzu-Hsuan Hsu, Ruochen Lu

In this work, we experimentally investigate the gradient stress (sigma1) in 128 deg Y-cut transferred thin film lithium niobate (TFLN) films with thicknesses from 100 to 460 nm using cantilever curvature analysis. The results reveal a strong dependence of sigma1 on both crystallographic orientation and film thickness, with stress-free orientations at approxi

Madhubanti Mukherjee, Rampi Ramprasad, Harikrishna Sahu

Superhard materials are critical for wear-resistant and high-stress applications. Conventional approaches correlating hardness with elastic moduli derived from DFT calculations enable rapid screening but overlook the strong load dependence of hardness. In this work, machine learning (ML) models were developed using a large, curated dataset of load-dependent

Tian-Le Yang, Wen Ning, Zhen-Biao Yang, Shi-Biao Zheng

The fragility of quantum metrological advantages under loss remains a major barrier to practical quantum sensing. For a two-photon-driven (TPD) Kerr resonator (TPD-Kerr model) subject to unavoidable single-photon loss (SPL), both the quantum Fisher information gain and squeezing level exhibit hard-to-track long-lived damped oscillations, restricting useful s

Guanghui Cai, Zhen-Ye Huang, Weikang Wang, Hai-Jun Zhou

Lateral predictive coding (LPC) is a simple theoretical framework to appreciate feature detection in biological neural circuits. Recent theoretical work [Huang et al., Phys.Rev.E 112, 034304 (2025)] has successfully constructed optimal LPC networks capable of extracting non-Gaussian hidden input features by imposing the tradeoff between energetic cost and in

MAGIC collaboration, K. Abe, S. Abe, J. Abhir

Mrk421 displayed its highest flux state ever observed in February of 2010 with very high TeV fluxes and interesting cross-band correlations and a spectral energy distribution (SED) evolution not entirely consistent with the standard single zone leptonic synchrotron self-Compton model. The source was already in a high state in January 2010 and displayed stron

Iago López-Vázquez, Òscar Iglesias, David Serantes

We investigate the feasibility of the macrospin approximation to account for the actual shape of soft magnetic nanoparticles (MNPs) with realistic geometries. Specifically focusing on magnetite, we use the superellipsoidal parametrisation to account for a variety of shapes, with a continuous interpolation from spherical to cubic morphologies, as well as diff

Kaimin He, Ke Shi, Jun Toshikawa, Xianzhong Zheng

We present a comprehensive study of the relationship between star formation rate (SFR) and stellar mass (M_*) from z = 0.1 to z = 4 using a mass-complete sample of approximately 290,000 galaxies from the COSMOS2020 catalog. We find that the SFR-M_* relation exhibits a pronounced high-mass decline that becomes increasingly evident at lower redshifts. Examinin

Pramodt Srinivasula, Doyel Pandey

Gate-modulated nanopores have emerged as a promising platform for achieving ion selectivity and ionic current rectification (ICR) with the advantage of active field-based control. However, the mechanistic origin of these experimentally reported phenomena, arising from electrostatic coupling between the prescribed radial pore surface potential and the axial t

De-Cheng Zou, Xu Yang, Meng-Yun Lai, Hyat Huang

We investigate the thermodynamic properties of static nonlinearly scalarized black holes in Einstein-scalar-Gauss-Bonnet theory with polynomial coupling functions. Based on the scalarized solutions constructed previously, we compute thermodynamical quantities of these scalarized black holes. Moreover, we examine the first law of black hole thermodynamics and

Eleonora P. Kraus, Jamie M. Fitzgerald, Carlos Maciel-Escudero, Ermin Malic

Sub-wavelength thick photonic crystal (PhC) slabs coupled to 2D excitonic materials, such as transition metal dichalcogenides (TMDs), are a promising platform for highly tunable, room-temperature, on-chip optoelectronic devices. Unlike conventional Fabry-Perot microcavities, these compact open cavities exhibit non-trivial electric field profiles, leading to

Hideki T. Miyazaki, Takeshi Kasaya, Masahiro Saito, Kazuya Kimoto

We demonstrate noninvasive measurement of gas temperature based on the optical gas imaging. Gas flows containing carbon dioxide (CO2) appear as either bright or dark images, depending on the relative temperatures of the background and the gas, when using a narrowband mid-infrared camera tuned to the CO2 absorption wavelength at 4.3 micrometers. When the back

A. Lavail, F. Debras, B. Klein, E. Chabrol

Young transiting exoplanets provide unique opportunities to probe planetary atmospheres during the critical early phases of evolution. HIP 67522 b, a 17 Myr old hot Jupiter with an extraordinarily low bulk density, represents an ideal target for high-resolution transmission spectroscopy. We aim to characterize the atmospheric composition, thermal structure,

Yu-Hui Zhou, Hui-Hua Zhong, Zhi-Yong Zhou, Xian-Hui Zhong

We present a unified desciption of the low-lying $1P$- and $2S$-wave nucleon resonance within the framework of an extended Lee-Friedrichs scheme. By incorporating the coupled-channel dynamics between bare quark-model states and the $πN$, $πΔ$ and $ηN$ meson-baryon continua, we examine the mass shifts and structural properties of these excited states. We demo

Márton I. Nagy, Máté Csanád, Dániel Kincses

Particle correlations and femtoscopy are a rich subfield of high-energy physics. As the experimental data become more precise, there is an increasing need for the theoretical calculations to provide better and more general descriptions of the measurements. One of the important new directions is the investigation of the precise shape of the Bose-Einstein corr

Honglin Zhou, Xinman Ye, Gang Wang, Devashibhai Adroja

Spin fluctuations have been generally believed as the pairing glue of high-$T_c$ superconductivity. Recent inelastic neutron scattering (INS) studies have revealed a weak flat spin-fluctuation signal around 45 meV in the bilayer nickelate La$_3$Ni$_2$O$_{7-δ}$, suggesting strong interlayer and weak intralayer magnetic couplings ($SJ_{\perp}\approx$ 60 meV, $

Shivaksh Rawat, Samyobrata Mukherjee, Gennady Shvets

Time-varying dielectric metasurfaces that support sharp optical resonances with nontrivial electromagnetic field distributions constitute a unique platform for realizing temporal interfaces for metasurface-guided waves (MGWs). Rapidly changing metasurface resonance enables frequency conversion and temporal scattering of a concurrently propagating MGW. Using

Zhaofei Zheng, Yuxin Luo, Juan Chen, Yimin Luo

Cellular organization and mechanotransduction pathways are crucial regulators of tissue morphogenesis, whereas their dysregulation contributes to pathologies. Overactive myofibroblasts are key drivers of fibrosis, yet how their presence alters collective cellular ordering remains unclear. Owing to steric interactions, elongated cells exhibit local order. Top

Kimet Jusufi, Douglas Singleton, Francisco S. N. Lobo

We incorporate non-local gravitational self-energy, motivated by string-inspired T-duality, into the Schrödinger-Newton equation. In this framework spacetime has an intrinsic non-locality, rendering the standard linear superposition principle only an approximation valid in the absence of gravitational effects. We then invert the logic by assuming the validit

Franc Forstneric

We show that the universal Teichmüller family of n-punctured compact Riemann surfaces of genus g is a Stein manifold for any n>0. We describe its basic function theoretic properties and pose several challenging questions. We show in particular that the space of fibrewise algebraic functions on the universal family is dense in the space of holomorphic functio

Damien C. Ragavan, Unnikrishnan Sureshkumar, Matt Hilton, John P. Hughes

We probe the stellar mass growth in a sample of 568 Sunyaev-Zel&#39;dovich (SZ) selected galaxy clusters with masses greater than $2.9 \times 10^{14} \mathrm{M_{\odot}}$ and redshifts in the range $0.2 < z < 0.8$, drawn from the fifth data release of the Atacama Cosmology Telescope (ACT DR5). By utilising deep photometry from the tenth data release of the Da

Rintaro Nakabe, Clayton J. Auton, Shunsuke Endo, Hiroyuki Fujioka

We report the first constraint on time-reversal invariance violating (TRIV) effects in polarized neutron transmission through a transversely polarized $^{139}$La target. We formulate the transmission asymmetry within the density matrix formalism, explicitly incorporating the forward scattering amplitude of $^{139}$La including tensor polarization terms up to

Y. Fujisawa, P. Wu, R. Okuma, B. R. M. Smith

Following the discovery of graphene, interest in van der Waals (vdW) materials has surged; however, advancing physics beyond graphene requires quantum vdW materials platforms that host versatile, strongly interacting many-body states. Here, using scanning tunneling microscopy and spectroscopy at 300 mK, we uncover multiple competing electronic states in the

Jan Tristram Acuña, Danny Marfatia, Po-Yan Tseng

The formation of primordial black holes (PBHs) during a first-order phase transition (FOPT) in a dark sector has been of recent interest. A quantity that characterizes a black hole is its spin. We carry out the first step towards determining the spin of such PBHs, by calculating the spin of spherical false vacuum bubbles induced by cosmological perturbations

Jianjun Jin

In this paper we introduce and study a new kind of generalized Hilbert matrix operators, induced by a positive finite Borel measure on (0,1), acting on weighted sequence spaces. We establish a sufficient and necessary condition for the boundedness of these operators. These results extend some related ones obtained recently in [Bull. London Math. Soc., 55 (20

Biswajit Das, Sreekanth K Manikandan

Entropy production is a universal measure of irreversibility and energy dissipation in physical, chemical, and biological systems operating far from equilibrium. However, quantifying and spatiotemporally localising it in complex processes directly from experimental data remains a major open challenge. Here we address this issue through a data-driven approach

Lesley Gover, Vinul Wimalaweera, Fariha Azad, Matthew DeCross

We time-evolve a translationally invariant quantum state on the Quantinuum H1-1 trapped-ion quantum processor, studying the dynamical quantum phase transition of the transverse field Ising model. This physics requires a delicate cancellation of phases in the many-body wavefunction and presents a tough challenge for current quantum devices. We follow the dyna

Saurabh Gupta, Tiziano Guadagnino, Benedikt Mersch, Niklas Trekel

Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognition help to maintain accurate pose estimates by mitigating global drift, and are thus key for realizing an effective SLAM system. This paper presents a robust loop closure detection pipeline for outdoor SLAM with L

Benedikt Höltgen, Robert C. Williamson

Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distribution. The standard presumption is that since data points are &#39;sampled from&#39; such a distribution, one can learn from observed data about this distribution and, thus, predict future data points which are

J. Khatua, D. Tay, T. Shiroka, M. Pregelj

The synergistic interplay between spin correlations, spin-orbit coupling, and competing exchange interactions provides a promising route to realize exotic quantum states with nontrivial excitations in rare-earth based frustrated magnets. Here, by using thermodynamic and local-probe measurements down to 16 mK, we demonstrate the exotic magnetism and spin dyna

Nikolaos Roidos

We introduce an $R$-sectoriality perturbation technique for non-commuting operators defined in Bochner spaces. Based on this and on bounded $H^{\infty}$-functional calculus results for the Laplacian on manifolds with conical singularities, we show maximal $L^{q}$-regularity for the Laplacian on manifolds with edge type singularities in appropriate weighted S

Sotaro Sugishita, Seiji Terashima

We examine the bulk reconstruction in the AdS/CFT correspondence. We demonstrate that the subregion duality fails to hold, highlighting discrepancies between operators in causal wedge reconstruction and those in global reconstruction at the leading order in the large $N$ limit. We argue the invalidity of the entanglement wedge reconstruction based on the hol

Saloni Garg, Amit Sagtani, Kamal Kant Hiran

The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning models that require a large volume of data to be stored in a single location have therefore become more susceptible to data breaches, privacy violations, and regulatory non-compliance. This report presents a

Karthik Duraisamy

Scientific machine learning is increasingly being spoken of as universal emulators for classical numerical solvers for multi-scale partial differential equations, but most apparent successes can be explained by facts that also define their limits. Many successful benchmarks live on low-dimensional solution manifolds where any competent reduced model will int

Razieh Emami, James A. A. Trussler, Tiger Yu-Yang Hsiao, Kaley Brauer

Extreme emission-line galaxies (EELGs) probe chemical enrichment in low-mass, bursty systems where star formation, feedback, and gas accretion are poorly constrained. Using DESI DR1, we select 23 nearby EELGs with detections of 19 ionic species (S/N $\geq$ 4), stellar masses $ M_* \geq 10^7 M_{\odot}$, and extreme H$\alpha$ and [O III] 5007 equivalent widths

Yichen Xu, Susan Gruber, Mark J. van der Laan

Estimating average treatment effects from observational data is challenging under practical violations of the positivity assumption. Targeted Maximum Likelihood Estimators (TMLEs) are widely used because of their double robustness and efficiency, but they can remain sensitive to such violations. We conduct extensive simulation studies to examine how targetin

Aseel Farhat, Edriss S. Titi, Collin Victor

This work investigates the effectiveness of the Back-and-Forth Nudging (BFN) data assimilation algorithm, specifically its performance when employing the Azouani-Olson-Titi (AOT) continuous data assimilation downscaling nudging algorithm, for recovering initial conditions of dissipative dynamical systems. Contrary to previous reports in the literature, we sh

Adis Husić, Xinting Yu, Ryan C. Blase, Edward L. Patrick

The Cassini-Huygens mission detected large negative ions in Titan's ionosphere at pressures as low as $10^{-6}$ torr. These ions ultimately polymerize to form Titan's complex organic haze particles, which are observed throughout the atmosphere and potentially on the surface. Laboratory analogs of these hazes, known as tholins, have been used to study Titan's

Toby Brown, Luca Cortese, Barbara Catinella, A. Fraser-McKelvie

We present early science results from the MAUVE (Multiphase Astrophysics to Unveil the Virgo Environment) program which targets 40 Virgo Cluster galaxies to investigate the effect of environment on the interstellar medium (ISM) at ~100 pc scales. From 12 galaxies in the MAUVE-MUSE early sample, we find systematically elevated line ratios compared to PHANGS-M

Patrick Vossler, Jean Feng, Venkat Sivaraman, Robert Gallo

Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable solutions. A critical step of QI is to identify the key modifiable contributing factors, a process we call QI factor discovery, typically through expert-driven semi-structured qualitative tools like fishbone diagr

Michael Barz

Let $k$ be a field of characteristic $p,$ and $f : X \to S$ a smooth proper morphism of smooth $k$-schemes. Katz's formula gives a relationship between the Kodaira--Spencer map of $f,$ and an invariant called the $p$-curvature of the Gauss--Manin connection associated to $f.$ Recently, Lam--Litt proved a variant of Katz's formula in non-abelian Hodge theory,

Shirin Afzal, Amesh Kahloon, Shabir Barzanjeh

The realization of on-chip polarization beam splitters robust to fabrication imperfections remains a key challenge for polarization-sensitive photonic integration. We demonstrate a topologically protected polarization beam splitter based on a Floquet-engineered microring lattice implemented on a CMOS-compatible silicon nitride platform. By tailoring the disp

Hanwen Huang

We propose Annealed Langevin Monte Carlo for Flow ODE Sampling (ALMC-ODE), a method for generating samples from unnormalized target distributions, with a particular emphasis on multimodal densities that are challenging for standard Markov chain Monte Carlo methods. ALMC-ODE is based on a probability-flow ordinary differential equation (ODE) derived from stoc

Chengyu Huang, Sheng-Yen Chou, Zhengxin Zhang, Claire Cardie

Self-play has recently emerged as a promising paradigm for post-training Large Language Models (LLMs). In self-play, the target LLM creates the task input (e.g., a question), which it then addresses itself by producing a task output (e.g., an answer). A reward model evaluates the output, and the rewards are used to train the LLM, typically via Reinforcement

Sergio Andreozzi

This paper aims to provide a proof of concept of the accuracy of simulations for advanced networking study. The particular target technology is the Differentiated Services (DiffServ) architecture. The method has been to apply experimental activities conducted in a real network to a simulation environment, to gather the same performance parameters and to comp

Dazhuang Liu, Yanqi Qiao, Rui Wang, Kaitai Liang

Vision Transformers (ViTs) have achieved remarkable success across vision tasks, yet recent studies show they remain vulnerable to backdoor attacks. Existing patch-wise attacks typically assume a single fixed trigger location during inference to maximize trigger attention. However, they overlook the self-attention mechanism in ViTs, which captures long-range

Yangming Zhang, Jian Xu, Chaojian Li, Kunxiong Zhu

3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis with high-quality rendering through continuous aggregations of millions of 3D Gaussian primitives. However, it suffers from a substantial memory footprint, particularly during training due to uncontrolled densification, posing a critical bottleneck for deployment on memory-constrained edge

Albert Osom, Ali Shojaie, Aaron Hudson

We present a general nonparametric approach for testing whether a statistical parameter defined through conditional distributions is constant across the conditioning variables. Such hypotheses arise naturally in problems such as assessing treatment effect heterogeneity, conditional associational effects, and conditional mean dependence. Our framework studies

Efthymios N. Karatzas

We develop and analyze a posteriori error estimators for a proper orthogonal decomposition-discrete empirical interpolation method (Pod-Deim) reduced order model applied to a parametric Poisson equation posed on a parameter-dependent domain defined by a level-set function. The full-order discretisations employ a cut finite element method (Cutfem) with Nitsch

Ziyi Wang, Chen Zhang, Wenjun Peng, Qi Wu

Explainability for Large Language Model (LLM) agents is especially challenging in interactive, partially observable settings, where decisions depend on evolving beliefs and other agents. We present \textbf{TriEx}, a tri-view explainability framework that instruments sequential decision making with aligned artifacts: (i) structured first-person self-reasoning