December 2025 arXiv papers — page 12

Avraham Aizenbud, Nir Avni, Shachar Carmeli

We study the collection of measures obtained via push-forward along a map between smooth varieties over p-adic fields. We investigate when the stalks of this collection are finite-dimensional. We provide an algebro-geometric criterion ensuring this property. This criterion is formulated in terms of a canonical subvariety of the cotangent bundle of the source

Meena Sahai, Sachin Singh

Let $RG$ be the group ring of an arbitrary group $G$ over an associative non-commutative ring $R$ with identity. In this paper, we have obtained the necessary and sufficient conditions under which $RG$ is Jordan nilpotent of index $4$.

Bo Peng

We show that it is impossible to classify topological conjugacy relation of minimal homeomorphisms on the torus by countable structures.

Chih-Whi Chen, Shun-Jen Cheng

We study various categories of Whittaker modules over the queer Lie superalgebras $\mathfrak q(n)$. We formulate standard Whittaker modules and reduce the problem of composition factors of these standard Whittaker modules to that of Verma modules in the BGG categories $\mathcal O$ of $\mathfrak q(n)$. We also obtain an analogue of Losev-Shu-Xiao decompositio

Takashi Yamamoto, Hiroaki Yaguchi, Shohei Kato, Hiroyuki Okada

A single service robot can present two distinct agencies: its onboard autonomy and an operator-mediated agency, yet users experience them through one physical body. We formalize this dual-agency structure as a User-Robot-Operator triad in an autonomous remote-control setting that integrates teleoperation with autonomous execution and human-in-the-loop remote

Andrew Elvey Price, Emmanuel Humbert, Kilian Raschel

In the study of walks with small steps confined to multidimensional orthants, a certain group of transformations plays a central role. In particular, several techniques to potentially compute the generating function, including the orbit sum method, can only be applied when this group is finite. In this note, we present three new results concerning this group

Mustafa Munir, Md Mostafijur Rahman, Kartikeya Bhardwaj, Paul Whatmough

Long-form video editing poses unique challenges due to the exponential increase in the computational cost from joint editing and Denoising Diffusion Implicit Models (DDIM) inversion across extended sequences. To address these limitations, we propose PipeFlow, a scalable, pipelined video editing method that introduces three key innovations: First, based on a

Michael Usher

We develop the notion of a "filtered cospan" as an algebraic object that stands in the same relation to interlevel persistence modules as filtered chain complexes stand with respect to sublevel persistence modules. This relation is expressed via a functor from a category of filtered cospans to a category of persistence modules that arise in Bauer-Botnan-Fluh

Kohei Fujikura, Toshifumi Noumi

Motivated by the cosmological collider program, which aims to probe high-energy physics through inflation, we investigate the phase diagram of multi-flavor QCD in de Sitter spacetime with a flavor-universal axial chemical potential induced by a rolling inflaton coupled to fermions. We determine the first-order critical line and a critical point as functions

Xingqi He, Yujie Zhang, Shuyong Gao, Wenjie Li

Text-guided object segmentation requires both cross-modal reasoning and pixel grounding abilities. Most recent methods treat text-guided segmentation as one-shot grounding, where the model predicts pixel prompts in a single forward pass to drive an external segmentor, which limits verification, refocusing and refinement when initial localization is wrong. To

Yunkai Dang, Donghao Wang, Jiacheng Yang, Yifan Jiang

Large vision-language models (VLMs) exhibit strong performance across various tasks. However, these VLMs encounter significant challenges when applied to the remote sensing domain due to the inherent differences between remote sensing images and natural images. Existing remote sensing VLMs often fail to extract fine-grained visual features and suffer from vi

Emuna Rimon, Eytan Grosfeld, Yevgeny Bar Lev

We study the full distribution of the zero-temperature Hall conductivity in a lattice model of the IQHE using the Kubo formula across disorder realizations. Near the localization-delocalization transition, the conductivity exhibits heavy-tailed fluctuations characterized by a power-law decay with exponent $\alpha \approx 2.3$--$2.5$, indicating a finite mean

Haijian Shao, Wei Liu, Xing Deng, Yingtao Jiang

Quantum neural networks (QNNs) suffer from severe gate-level redundancy, which hinders their deployment on noisy intermediate-scale quantum (NISQ) devices. In this work, we propose q-iPrune, a one-shot structured pruning framework grounded in the algebraic structure of $q$-deformed groups and task-conditioned quantum geometry. Unlike prior heuristic or gradi

Huanxiong Liang, Yunuo Chen, Yicheng Pan, Sixian Wang

Recent advances in 2D Gaussian Splatting (2DGS) have demonstrated its potential as a compact image representation with millisecond-level decoding. However, existing 2DGS-based pipelines allocate representation capacity and parameter precision largely oblivious to image structure, limiting their rate-distortion (RD) efficiency at low bitrates. To address this

Giovanni Inchiostro, Junyan Zhao

This paper continues the study initiated in [ISZ25] on the moduli of surfaces admitting lc-trivial fibrations. Using the techniques developed in [ISZ25], we (1) provide a classification of the surfaces appearing on the boundary of the KSBA-moduli space of elliptic surfaces with a bisection (2) recover the results of a series of papers on the moduli stacks of

Lu Yang, Yicheng Liu, Yanan Li, Xiang Bai

Realistic virtual try-on (VTON) concerns not only faithful rendering of garment details but also coordination of the style. Prior art typically pursues the former, but neglects a key factor that shapes the holistic style -- garment fit. Garment fit delineates how a garment aligns with the body of a wearer and is a fundamental element in fashion design. In th

Zixiang Li, Yue Song, Jianing Peng, Ting Liu

Recent methods in flow-based diffusion editing have enabled direct transformations between source and target image distribution without explicit inversion. However, the latent trajectories in these methods often exhibit accumulated velocity errors, leading to semantic inconsistency and loss of structural fidelity. We propose Conditioned Velocity Correction (

Ziran Zhang

Detecting multipartite entanglement in many qubit systems is measurement-intensive, motivating protocols that estimate only selected observables with provable efficiency. In this work we use the classical shadow protocol to study the sample complexity required to estimate a family of subsystem $n$-partite entanglement witness embedded in an larger $N$-qubit

Sijia Chen, Di Niu

Large language models (LLMs), when guided by explicit textual plans, can perform reliable step-by-step reasoning during problem-solving. However, generating accurate and effective textual plans remains challenging due to LLM hallucinations and the high diversity of task-specific questions. To address this, we draw inspiration from human Implicit Cognition (I

Hao Wu, Hui Li, Yiyun Su

Recent studies suggest that Visual Language Models (VLMs) hold great potential for tasks such as automated medical diagnosis. However, processing complex three-dimensional (3D) multimodal medical images poses significant challenges - specifically, the effective integration of complementary information and the occasional oversight of subtle yet critical patho

Liwen Gao, Xuejun Guo

In this paper we resolve a conjecture of Zhi-Wei Sun concerning the integrality and arithmetic structure of certain trigonometric determinants. Our approach builds on techniques developed in our previous work, where trigonometric determinants were studied via special values of Dirichlet $L$-functions. The method is refined by establishing a connection betwee

Suman Mundlia, Ritesh Kumar, Anshika Mishra, Malavika Chandrasekhar

Layered magnetic transition-metal chalcogenides (TMCs) are a focal point of research, revealing a variety of intriguing magnetic and topological ground states. Within this family of TMCs, chromium telluride has garnered significant attention because of its excellent tunability in magnetic response, owing to the presence of competing magnetic exchange interac

Holmfridur S. Hannesdottir, Luke Lippstreu, Andrew J. McLeod, Maria Polackova

The Steinmann relations are known to be violated with respect to some -- but not all -- two-particle momentum channels in massless Feynman integrals. We trace the source of this Steinmann violation to a special class of singularities, which arise from partially-overlapping minimal cuts. This allows us to propose an efficient graphical test for predicting whi

Landon Hurley

This paper introduces a novel quasi-likelihood extension of the generalised Kendall \(\tau_{a}\) estimator, together with an extension of the Kemeny metric and its associated covariance and correlation forms. The central contribution is to show that the U-statistic structure of the proposed coefficient \(\tau_{\kappa}\) naturally induces a quasi-maximum like

Bulent Soykan, Sean Mondesire, Ghaith Rabadi

Simulation optimization (SO) is frequently challenged by noisy evaluations, high computational costs, and complex, multimodal search landscapes. This paper introduces Tabu-Enhanced Simulation Optimization (TESO), a novel metaheuristic framework integrating adaptive search with memory-based strategies. TESO leverages a short-term Tabu List to prevent cycling

Jia Liu, Mengjie Wang, Zishuo Wang, Haoyu Liu

We investigate charged Dirac quasinormal spectra on Reissner-Nordstr\"om black holes in a mirror-like cavity. For this purpose, we first derive charged Dirac equations, and \textit{two} sets of Robin boundary conditions following the vanishing energy flux principle. The Dirac spectra are then computed both analytically and numerically. Our results reveal a s

Arda Sevinc, Rama Alassadi, Mustafa Sarisaman

This paper investigates the unique properties of PT-symmetric Topological Weyl Semimetals (TWS) within the framework of non-Hermitian physics, focusing on their potential for generating topological lasers. By exploring the role of spectral singularities and their relationship to exceptional points, we examine how these materials, characterized by Weyl nodes

Brijesh Kumar Jha, Subhra Sankar Dhar, Akash Ashirbad Panda

This article investigates the least squares estimators (LSE) for the unknown parameters in stochastic differential equations (SDEs) that are affected by L\'evy noise, particularly when the sample paths are sparse. Specifically, given $n$ sparsely observed curves related to this model, we derive the least squares estimators for the unknown parameters: the dri

P. C. Hinton, D. A. Brain, N. R. Schnepf, R. Jarvinen

We explore ion escape from, and solar ion deposition to, \hll{an unmagnetized Earth-like planet}. We use RHybrid, an ion-kinetic electron-fluid code to simulate the global plasma interaction of unmagnetized Earth with the solar wind. We vary the global ionospheric emission rate, and quantify the resultant planetary ion escape rates ($O^+$ and $H^+$) and the

J. Ricardo de Sousa

We study the electrostatics of a thin, finite-length conducting cylindrical shell held at constant potential V0. Exploiting axial symmetry, we recast the problem as a one-dimensional singular integral equation for the axial surface-charge density, with a kernel written in terms of complete elliptic integrals. A Chebyshev-weighted collocation scheme that inco

Tan Pan, Yixuan Sun, Chen Jiang, Qiong Gao

The multi-lead electrocardiogram (ECG) stands as a cornerstone of cardiac diagnosis. Recent strides in electrocardiogram self-supervised learning (eSSL) have brightened prospects for enhancing representation learning without relying on high-quality annotations. Yet earlier eSSL methods suffer a key limitation: they focus on consistent patterns across leads a

Maidoun Mortada, Ayman El Zein

It was recently proved that every claw-free cubic graph admits a (1, 1, 2, 2)-packing coloring--that is, its vertex set can be partitioned into two 1-packings and two 2-packings. This result was established by Bre\v{s}ar, Kuenzel, and Rall [Discrete Mathematics 348 (8) (2025), 114477]. In this paper, we provide a simpler and shorter proof.

Mageshwaran Tamilan

We present Green's function solutions for a geometrically thin, one-dimensional Keplerian accretion disk that includes angular momentum extraction and mass loss due to magnetohydrodynamic (MHD) winds. The disk viscosity is assumed to vary radially as $\nu \propto r^{n}$. We derive solutions for three types of boundary conditions applied at the inner radius $

Haotang Li, Zhenyu Qi, Hao Qin, Huanrui Yang

Self-supervised semantic segmentation methods often suffer from structural errors, including merging distinct objects or fragmenting coherent regions, because they rely primarily on low-level appearance cues such as color and texture. These cues lack structural discriminability: they carry no information about the structural organization of a region, making

A. R. Balasubramanian, Mohammad Hossein Khoshechin Jorshari, Rupak Majumdar, Umang Mathur

We study the estimation problem for concurrent programs: given a bounded program $P$, estimate the number of Mazurkiewicz trace-equivalence classes induced by its interleavings. This quantity informs two practical questions for enumeration-based model checking: how long a model checking run is likely to take, and what fraction of the search space has been co

Weixi Lin

The integration of Large Language Models (LLMs) into enterprise knowledge management systems has been catalyzed by the Retrieval-Augmented Generation (RAG) paradigm, which augments parametric memory with non-parametric external data. However, the transition from proof-of-concept to production-grade RAG systems is hindered by three persistent challenges: low

Landon Hurley

Non-parametric correlation coefficients have been widely used for analysing arbitrary random variables upon common populations, when requiring an explicit error distribution to be known is an unacceptable assumption. We examine an \(\ell_{2}\) representation of a correlation coefficient (Emond and Mason, 2002) from the perspective of a statistical estimator

Hengyu Wang, Hanhua Cui, Yongfeng Zhu, Hao Liang

$b \to s\gamma$ transition is a critical flavor-changing neutral current (FCNC) process that could be used to probe CP violation (CPV) and new physics (NP). We quantify the anticipated precision for measuring $B_s^0 \to \phi\gamma$ at the CEPC Z pole operation, showing that the relative statistical uncertainty could be as low as 0.16\%, improved by approxima

Wael Itani, Katepalli R. Sreenivasan

We attempt the use of a unitary operator to approximate the lattice Boltzmann collision operator. We use a modified amplitude encoding to bypass the renormalization that would have required classical processing at every step (thus eroding any quantum advantage to be had). We describe the hard-wiring of the lattice Boltzmann symmetries into the quantum circui

Jun Ding, Shang Gao

Accurate segmentation of heterogeneous anatomical structures is pivotal for computer-aided diagnosis and subsequent clinical decision-making. Although U-Net based convolutional neural networks have achieved remarkable progress, their intrinsic locality and largely homogeneous attention formulations often limit the modeling of long-range contextual dependenci

J. Ricardo de Sousa

We develop an exact electrostatic formulation for a finite-length conducting cylindrical shell of finite thickness separating two dielectric media with arbitrary permittivity contrast. The boundary-value problem is reduced to a coupled system of singular integral equations with elliptic kernels governing the induced surface-charge densities on the inner and

Zhenyu Zhang, Shujian Zhang, John Lambert, Wenxuan Zhou

Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches often rely on human-defined concepts (e.g., overthinking, reflection) at the word level to analyze reasoning in a supervised manner. However, such methods are limited, as it is infeas

Ajvad Haneef K, Karan Kuwar Singh, Madhu Kumar S D

Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection (MeLeMaD), a novel framework leveraging the adaptability and generalization capabilities of Model-Agnostic Meta-Learning (MAML) for

Bertrand Rouet-Leduc, Claudia Hulbert

Detecting surface changes from satellite imagery is critical for rapid disaster response and environmental monitoring, yet remains challenging due to the complex interplay between atmospheric noise, seasonal variations, and sensor artifacts. Here we show that deep learning can leverage the temporal redundancy of satellite time series to detect anomalies at u

Seunghyeon Lee, Thao Nguyen, Darby J. Luscher, Saryu J. Fensin

Uncertainties in the high-dimensional space of material parameters pose challenges for the predictive modeling of bcc single crystals, especially under extreme loading conditions. In this work, we identify the key physical assumptions and associated uncertainties in constitutive models that describe the deformation behavior of bcc single crystal molybdenum s

R. N. Garifullin, I. T. Habibullin

This paper proposes a method for identifying and classifying integrable nonlinear equations with three independent variables, one of which is discrete and the other two are continuous. A characteristic property of this class of equations, called Toda-type chains, is that they admit finite-field reductions in the form of open chains with enhanced integrabilit

Yuang Jia, Jinlong Wang, Jiayi Zhao, Chunlam Li

This paper presents an effective solution for view extrapolation in autonomous driving scenarios. Recent approaches focus on generating shifted novel view images from given viewpoints using diffusion models. However, these methods heavily rely on priors such as LiDAR point clouds, 3D bounding boxes, and lane annotations, which demand expensive sensors or lab

Hung-Fu Chang, MohammadShokrolah Shirazi, Lizhou Cao, Supannika Koolmanojwong Mobasser

Recent advances in large language models (LLMs) have introduced new paradigms in software development, including vibe coding, AI-assisted coding, and agentic coding, fundamentally reshaping how software is designed, implemented, and maintained. Prior research has primarily examined AI-based coding at the individual level or in educational settings, leaving i

Sebastián Gutiérrez-Bernal, Hector Medel Cobaxin, Abiel Galindo González

In this work we study the quality of low-dimensional embeddings from an explicitly information-theoretic perspective. We begin by noting that classical evaluation metrics such as stress, rank-based neighborhood criteria, or Local Procrustes quantify distortions in distances or in local geometries, but do not directly assess how much information is preserved

Qirui Fang, Yu Teng, Yukun Xiao, Wen Zheng

We determine all premodular subcategories and modular tensor subcategories in the module categories of Virasoro vertex operator algebras $L(c_{p,q},0)$ and the module categories of the simple current extensions of $L(c_{p,p+1},0)$.

Sarvesh Ravichandran Iyer, Himadri Mandal, Dhruman Gupta, Rushil Gupta

Consider the task of generating samples from a tilted distribution of a random vector whose underlying distribution is unknown, but samples from it are available. This finds applications in fields such as finance and climate science, and in rare event simulation. In this article, we discuss the asymptotic efficiency of a self-normalized importance sampler of

Giacinto Paolo Saggese, Paul Smith

We present DataFlow, a computational framework for building, testing, and deploying high-performance machine learning systems on unbounded time-series data. Traditional data science workflows assume finite datasets and require substantial reimplementation when moving from batch prototypes to streaming production systems. This gap introduces causality violati

Pengcheng Wei, Yunqin Zheng

Non-trivial 't Hooft anomaly obstructs the existence of a simple symmetric conformal boundary condition in a CFT. Conversely, there is a common piece of lore that trivial 't Hooft anomaly promises the existence of a simple symmetry conformal boundary condition in a given CFT. Recently, counter examples to this lore was realized in tetracritical Ising CFT [1]

Youdong Zhang, Xu He, Xiaolin Meng

Although existing deep learning-based Ultra-Wide Band (UWB) channel estimation methods achieve high accuracy, their computational intensity clashes sharply with the resource constraints of low-cost edge devices. Motivated by this, this letter explores the potential of Spiking Neural Networks (SNNs) for this task and develops a fully unsupervised SNN solution

Eliot W. Robson, Abhishek K. Umrawal

Influence Maximization (IM) seeks to identify a small set of seed nodes in a social network to maximize expected information spread under a diffusion model. While community-based approaches improve scalability by exploiting modular structure, they typically assume independence between communities, overlooking inter-community influence$\unicode{x2014}$a limit

Liangtao Feng, Zhenchang Liu, Feng Zhang, Xuefeng Ren

This paper introduces SHIELD, a Spherical-Projection Hybrid-Frontier Integration for Efficient LiDAR-based Drone exploration method. Although laser LiDAR offers the advantage of a wide field of view, its application in UAV exploration still faces several challenges. The observation quality of LiDAR point clouds is generally inferior to that of depth cameras.

Zhiming Lin, Kai Zhao, Sophie Zhang, Peilai Yu

Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zero-supervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesi

Yaoyun Zhou, Qian Wang

SPHINCS+ is a stateless hash-based signature scheme that provides strong post quantum security, but its signature generation is slow due to intensive hash computations. GPUs offer massive parallelism that can potentially accelerate SPHINCS+ signatures. However, existing GPU-based optimizations either fail to fully exploit the inherent parallelism of SPHINCS+

Lukas Abrie Nel

Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, policy gradient methods such as PPO suffer from high variance gradient estimates, requiring careful hyperparameter tuning and extensive computational resources. We introduce GRADE (Gumbel-softmax Relaxation fo

Yao Li

Following the idea of Refs.[1,2], the double-copy-like decomposition of exchanged internal states in the world-line limit of one-loop string amplitudes is systematically formulated and generalized to both bosonic and heterotic string theories. As an application, the one-loop beta functions for the gauge and gravitational coupling constants are investigated b

Peimo Sun, Yuhan Qin, Zheng Li

Entangled knots form spontaneously in flexible filaments, yet the influence of the surrounding environment on this process is poorly understood. Here we demonstrate that free-moving particles act as kinetic catalysts for spontaneous knotting. Through controlled agitation experiments, we find that a small number of inert beads substantially enhance the probab

Chen Zhang, Yang Bai, Jiahuan Li, Anchun Gui

We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited compute budget. In long-context scenarios, LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-

Carlo Malapad Acosta, Herath Mudiyanselage Viraj Vidura Herath, Jia Yu Lim, Abhishek Saha

Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and acc

Haiyan Lu

We investigate the electronic structure of americium (Am) across its four experimentally confirmed high-pressure phases Am-I (P63/mmc), Am-II (Fm-3m), Am-III (Fddd), and Am-IV (Pnma) up to 100 GPa, using density functional theory combined with embedded dynamical mean-field theory. Our results successfully reproduce the prominent localized 5f peak observed in

Junjie H. Xu

This research presents a cutting-edge recommendation system utilizing agentic AI for KYC (Know Your Customer in the financial domain), and its evaluation across five distinct content verticals: Advertising (Ad), News, Gossip, Sharing (User-Generated Content), and Technology (Tech). The study compares the performance of four experimental groups, grouping by t

Zonglin Mo, Yi Yin

We develop a bottom-up formulation of spin-kinetic theory for hot and/or dense plasmas. We introduce scalar and axial-vector phase-space functions as dynamical variables that parametrize both spin-averaged and spin-dependent distribution functions. Using spin-dependent Poisson brackets, we derive the corresponding kinetic equations and construct the associat

Bing Yan, Shu Wang, Jian Gao, Yuxi Wang

Extinction curves contain key information on interstellar dust composition and size distribution, with the 2175 {\AA} bump being the most prominent feature. We analyze 20 sightlines toward M31 using HST/STIS UV spectroscopy combined with multi-band photometry to characterize this feature. The extinction curves show substantial diversity, from MW-like shapes

Kihoon Seong, Hao Shen, Philippe Sosoe

We study the sine-Gordon measure defined on each homotopy class. The energy space decomposes into infinitely many such classes indexed by the topological degree $Q \in \mathbf{Z}$. Even though the sine-Gordon action admits no minimizer in homotopy classes with $|Q| \ge 2$, we prove that the Gibbs measure on each class nevertheless concentrates and exhibits O

Yuexing Xia, Yin Liang, Zhiyong Zhang, Tian Lan

Halide perovskites offer a great platform for room-temperature exciton-polaritons (EPs) due to their strong oscillator strength and large exciton binding energy, promising applications in next-generation photonic and polaritonic devices. Efficient manipulation of EP transport and relaxation is critical for device performance, yet their spatiotemporal dynamic

A. Rafique, A. Singh, R. Srinivas

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that will generate an unprecedented volume of heterogeneous information-from documentation and technical notes to experimental data and reconstruction pipelines. Efficient knowledge retrieval and contextual understanding are increasingly critical for collaboration-wide p

Yongmin Zhang, Pengyu Huang, Mingyi Dong, Jing Yao

Edge computing enables latency-critical applications to process data close to end devices, yet task heterogeneity and limited resources pose significant challenges to efficient orchestration. This paper presents a measurement-driven, container-based resource management framework for intra-node optimization on a single edge server hosting multiple heterogeneo

Huibin Li, Haoran Liu, Mingzhe Liu, Yulong Xiao

Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. Howe

Maxim R. Burke

A theorem of Hoischen states that given a positive continuous function $\varepsilon:\mathbb{R}\to\mathbb{R}$, an integer $n\geq 0$, and a closed discrete set $E\subseteq\mathbb{R}$, any $C^n$ function $f:\mathbb{R}\to\mathbb{R}$ can be approximated by an entire function $g$ so that for $k=0,\dots,n$, and $x\in\mathbb{R}$, $|D^{k}g(x)-D^{k}f(x)|<\varepsilon(x

Kacem Khaled, Felipe Gohring de Magalhães, Gabriela Nicolescu

Convolutional Neural Networks (CNNs) and their quantized counterparts are vulnerable to extraction attacks, posing a significant threat of IP theft. Yet, the robustness of quantized models against these attacks is little studied compared to large models. Previous defenses propose to inject calculated noise into the prediction probabilities. However, these de

Corinna Cortes, Mehryar Mohri, Yutao Zhong

The balanced loss is a widely adopted objective for multi-class classification under class imbalance. By assigning equal importance to all classes, regardless of their frequency, it promotes fairness and ensures that minority classes are not overlooked. However, directly minimizing the balanced classification loss is typically intractable, which makes the de

Tien-Tai Nguyen

In this paper, we investigate the instability of the trivial steady states to the incompressible viscous fluid with Navier-slip boundary conditions. For the linear instability, the existence of infinitely many normal mode solutions to the linearized equations is shown via the operator method of Lafitte and Nguyen (2022). Hence, we prove the nonlinear instabi

Eric Marberg

Yeliussizov has classified the positive specializations of symmetric Grothendieck functions, defined in several different ways, providing a K-theoretic lift of the classical Edrei-Thoma theorem. This note studies the analogous classification problem for Ikeda and Naruse's K-theoretic Schur P- and Q-functions, which are the shifted versions of symmetric Groth

Yan Meng, Daniel Donoho, Marcelle Altshuler, Omar Arnaout

Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhanc

Conrad Borchers, Manit Patel, Seiyon M. Lee, Anthony F. Botelho

Open-ended responses are central to learning, yet automated scoring often conflates what students wrote with how teachers grade. We present an analytics-first framework that separates content signals from rater tendencies, making judgments visible and auditable via analytics. Using de-identified ASSISTments mathematics responses, we model teacher histories a

Yifan Li, Bingjia Yang, Chunyi Zhang, Axel Gomez

First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of

Yifan Li, Bingjia Yang, Chunyi Zhang, Axel Gomez

Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature ($T_{\mathrm{m}}$) of ice Ih, including a higher density than ice and a density maximum at 4~$^{\circ}$C. Experimentally, an isotope effect on $T_{\mathrm{m}}$ is observed: the melting temperature of H$_2$O is approximately 4~K lower than that of D$_2$O. T

N. Esmati, T. Khodaei, E. Salahinejad, E. Sharifi

In this research, for the first time, the structure, bioactivity, biodegradation and biocompatibility of SiO2-MgO-CaO glasses doped with different levels of fluoride were studied. The glassy powder samples were synthesized by a coprecipitation method followed by calcination at 500 C, where amorphicity and fluoride incorporation were verified by X-ray diffrac

Hualin Ye, Bingxi Liu, Jixiang Du, Yu Qin

Cross-view geo-localisation (CVGL) aims to estimate the geographic location of a query image by matching it with images from a large-scale database. However, the significant view-point discrepancies present considerable challenges for effective feature aggregation and alignment. To address these challenges, we propose a novel CVGL system that incorporates th

Jiaxin Ai, Yukang Feng, Fanrui Zhang, Jianwen Sun

Multimodal agents are making rapid progress on general computer-use tasks, yet existing benchmarks remain largely confined to browsers and basic desktop applications, falling short in professional software workflows that dominate real-world scientific and industrial practice. To close this gap, we introduce ProSoftArena, a benchmark and platform specifically

Mamiya Kawaguchi, Masayasu Harada, Yong-Liang Ma

Clarifying the origin of hadron mass is one of the fundamental problems in particle physics, relevant from hadronic scales to astrophysical observations. At low energies, this issue is reflected in the decomposition of the hadron mass into chiral-variant and -invariant components. In this letter, we propose a method to extract the chiral invariant mass from

Yulong Zou, Bo Liu, Cun-Jing Zheng, Yuan-ming Geng

Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multimodal MRI data are often incomplete, making it challenging to fully utilize the available information. Therefore, maximizing the utilization of this incomplete multimodal informati

Suat Koç

A multiplicative subset $S$ of a ring $R$ is called \textit{strongly multiplicative} if $(\bigcap_{i\in\Delta}s_iR)\cap S \neq \emptyset$ for each family $(s_i)_{i\in\Delta}$ of elements in $S$. In this paper, we investigate how these sets help stabilize localization and ideal operations. We show that localization and arbitrary intersections commute, meaning

He Liu, Peng Wu, Hong-Ming Liu, Peng-Cheng Chu

We investigate temperature fluctuations in hot QCD matter using a 3-flavor Polyakov-loop extended Nambu--Jona-Lasinio (PNJL) model. The high-order cumulant ratios $R_{n2}$ ($n>2$) exhibit non-monotonic variations across the chiral phase transition, characterized by slight fluctuations in the chiral crossover region and significant oscillations around the cri

Zhao Li, Shaoqi Hou, Wen Zhao

Gravitational-wave (GW) scattering in strong gravitational fields is a central problem in GW lensing. Yet, conventional treatments based on asymptotic expansions suffer from divergences and become unreliable near the optical axis. In this work, we present a rigorous calculation of GW scattering by a Schwarzschild black hole (BH) within the BH perturbation th

Ioanna Gemou, Evangelos Lamprou

Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with

Yu-Hao Wan, Cheng-Ming Miao, Peng-Yi Liu, Qing-Feng Sun

We investigate the topological properties of modified Dirac Hamiltonians with an altermagnetic mass term and reveal a novel mechanism for realizing altermagnetic Weyl semimetals. Unlike the conventional Wilson mass, the altermagnetic mass drives direct transitions between nontrivial Chern phases of opposite sign and fundamentally reshapes the band inversion

Maximilian Schebek, Jiajun He, Emil Hoffmann, Yuanqi Du

The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multiple intermediate states to ensure sufficient overlap in phase space and are, consequently, computationally expensive. Boltzmann Generators and related generative-model-based method

Yalong Cao, Andrei Okounkov, Yehao Zhou, Zijun Zhou

This is the first in a sequence of papers devoted to stable envelopes in critical cohomology and critical $K$-theory for symmetric GIT quotients with potentials and related geometries, and their applications to geometric representation theory and enumerative geometry. In this paper, we construct critical stable envelopes and establish their general propertie

Yuchi Ma, Yawen Shen, Anu Swatantran, David B. Lobell

Geospatial foundation models (GFMs) have emerged as a promising approach to overcoming the limitations in existing featurization methods. More recently, Google DeepMind has introduced AlphaEarth Foundation (AEF), a GFM pre-trained using multi-source EOs across continuous time. An annual and global embedding dataset is produced using AEF that is ready for ana

Tianyuan Yu, Adam Thieme, Junxiao Shi, Lan Wang

A 1995 SIGCOMM paper, "A Reliable Multicast Framework for Light-weight Sessions and Application-Level Framing", commonly known as SRM, explored a fundamentally new approach to reliable multiparty data delivery. Rather than adapting established sender-driven reliable unicast mechanisms to multicast, as most contemporaneous proposals did, SRM introduced a data

Lars van der Laan, Nathan Kallus

Fitted $Q$-iteration (FQI) and soft FQI are widely used value-based methods for offline reinforcement learning, but their standard stability guarantees often depend on Bellman completeness, a strong closure condition that can fail under function approximation. We analyze soft FQI without Bellman completeness and identify the stability mechanism that replaces

Charles Orioma, Josef Krivan, Rujeena Mathema, Pedro Lencastre

Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. Although distinguishing saccades from fixations is not trivial, experimentalists generally use common ad-hoc thresholds in detection algorithms. This neglects inter-task and inter-i

Amir Shaikhha

Modern data analytics pipelines increasingly combine relational queries, graph processing, and tensor computation within a single application, but existing systems remain fragmented across paradigms, execution models, and research communities. This fragmentation results in repeated optimization efforts, limited interoperability, and strict separation between

Yinglun Zhu

Machine learning has achieved remarkable success across a wide range of applications, yet many of its most effective methods rely on access to large amounts of labeled data or extensive online interaction. In practice, acquiring high-quality labels and making decisions through trial-and-error can be expensive, time-consuming, or risky, particularly in large-

Eren Volkan Küçük

We apply De Haro's Geometric View of Theories to one of the simplest quantum systems: a spinless particle on a line and on a circle. The classical phase space M = T*Q is taken as the base of a trivial Hilbert bundle E ~ M x H, and the familiar position and momentum representations are realised as different global trivialisations of this bundle. The Fourier t