October 2025 arXiv papers — page 102

Aaron Liberman, Anton Golovanov, Sheroy Tata, Anda-Maria Talposi

Flying-focus wakefields, which can propagate with a tunable velocity along the optical axis, are promising solutions to electron dephasing in laser-wakefield accelerators. This is accomplished by a combination of spatio-temporal couplings and focusing with an axiparabola, a specialized optical element which produces a quasi-Bessel beam. If implemented, depha

Alexandros K. Angelidis, Georgios C. Makris, Evangelos Ioannidis, Ioannis E. Antoniou

Chaos reveals a fundamental paradox in the scientific understanding of Complex Systems. Although chaotic models may be mathematically deterministic, they are practically non-determinable due to the finite precision, which is inherent in all computational machines. Beyond the horizon of predictability, numerical computations accumulate errors, often undetecta

Ruiming Guo, Ayush Bhandari

The recovery of Dirac impulses, or spikes, from filtered measurements is a classical problem in signal processing. As the spikes lie in the continuous domain while measurements are discrete, this task is known as super-resolution or off-the-grid sparse recovery. Despite significant theoretical and algorithmic advances over the past decade, these developments

Pascal Jelinek

Gowers norms have been a key component in the proofs of many breakthrough results in connection to the sum of digits function. Spiegelhofer has used them to show that the Thue-Morse sequence has level of distribution 1 and also that it is equidistributed along cubes. Recently Gowers norms have been used to study the sum of digits function of the Zeckendorf e

Erfan Darzi, Aldo Pareja, Shreeanant Bharadwaj

Diagnosing GPU tail latency spikes in cloud and HPC infrastructure is critical for maintaining performance predictability and resource utilization, yet existing monitoring tools lack the granularity for root cause analysis in shared computing environments. We introduce an eBPF-based telemetry system that provides unified host-side monitoring of GPU workloads

Yacin Ameur

In connection with recent work on smallest gaps, C. Charlier proves that the 1-point function of a suitable planar Coulomb system $\{z_j\}_1^n$, in the determinantal case with respect to an external potential $Q(z)$, admits the expansion, as $n\to\infty$, $$R_n\bigg(z_0+\frac t {\sqrt{2n\partial\bar{\partial} Q(z_0)}}\nu(z_0)\bigg)=n\partial\bar{\partial} Q(

Spencer Rugaber, Scott Bunin, Andrew Hornback, Sungeun An

Conceptual modeling has been an important part of constructionist educational practices for many years, particularly in STEM (Science, Technology, Engineering and Mathematics) disciplines. What is not so common is using agent-based simulation to provide students feedback on model quality. This requires the capability of automatically compiling the concept mo

Dania Refai, Moataz Ahmed

Large language models (LLMs) are increasingly used to convert natural language descriptions into mathematical optimization formulations. Current evaluations often treat formulations as a whole, relying on coarse metrics like solution accuracy or runtime, which obscure structural or numerical errors. In this study, we present a comprehensive, component-level

Georgios Bouchouras, Georgios Sofianidis, Syragoula Charisi, Charalampos Pavlopoulos

Background. Knee Osteoarthritis (OA) is a common chronic joint condition, and its prevalence increases with age. This study aims to examine whether flattened vertical ground reaction force (vGRF) waveforms and reduced knee range of motion (RoM) occur together during gait as compensatory strategies to maintain gait speed. Methods. Twelve women with knee OA an

Qirong Yang

We prove that the Turaev--Viro invariants of the two surface bundles over the circle coincide for every spherical fusion category if the surface group is procongruently conjugacy separable and there exists a regular profinite isomorphism between the fundamental groups.

Cristian J. Vaca-Rubio, Roberto Pereira, Luis Blanco, Engin Zeydan

This work introduces Probabilistic Kolmogorov-Arnold Network (P-KAN), a novel probabilistic extension of Kolmogorov-Arnold Networks (KANs) for time series forecasting. By replacing scalar weights with spline-based functional connections and directly parameterizing predictive distributions, P-KANs offer expressive yet parameter-efficient models capable of cap

Sergey Pereverzev

Announcement of detection of Solar 8B neutrinos by XENONnT dual-phase detector come with a suite of unexplained detector effects. We expect accumulation of surface charges and charged surface instability resulting in maximal surface elevation and charge density at wire crossing regions, where anomalies were observed. Thou data from these regions were exclude

Pierre Coulombel, Fabian Denner

While it is well known that cavitation occurs in liquids under tension, no universally accepted criterion for its onset in transient pressure fields exists. We propose a precise definition of the critical tension for cavitation in transient pressure fields that bridges the gap between quasi-static and dynamic regimes, identifying cavitation as the transition

Alok Das, Kiseop Lee

Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time cha

Vikram Kher, Argyris Oikonomou, Manolis Zampetakis

The remarkable success of machine learning (ML) in predictive tasks has led scientists to incorporate ML predictions as a core component of the scientific discovery pipeline. This was exemplified by the landmark achievement of AlphaFold (Jumper et al. (2021)). In this paper, we study how ML predictions can be safely used in statistical analysis of data towar

Sagnik Dakshit, Sushmita Sinha Roy

Large Language Models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where multistep logic, symbolic reasoning, and conceptual clarity are critical. Conventional evaluation methods largely focus on fin

A. Foresi, G. Antonelli, C. Avanzini, G. Balestri

The ionization quenching factor, the fraction of an ion's initial kinetic energy lost through ionization in a medium, was measured for H$_2^+$ and He$^+$ ions within the 2.5-5 keV energy range in an Ar/CO$_2$ gas mixture at pressures between 75 and 150 mbar. The mixture was contained in the active volume of a MICROMEGAS type Micro Pattern Gaseous Detector (M

Hao Chen, Yan-Yue Fan, Yun-Hai Zhang, Cheng-Qun Pang

As members of the $J^{PC}=3^{++}$ light meson family, the assignments of the $a_3(1875)$, $a_3(2030)$, $a_3(2275)$, $f_3(2050)$, and $f_3(2300)$ states remain unclear. In this work, we investigate the mass spectra and the Okubo-Zweig-Iizuka-allowed two-body strong decays of the $3^{++}$ light meson family using the modified Godfrey-Isgur quark model and the

Wilson Arley Martinez, Samin Ingrid Ceron

In this paper, we construct Pell matrices, analogous to Fibonacci matrices, to study algebraic properties of Pell numbers via linear algebra. This framework yields identities involving the trace, inverse, and determinant, as well as matrix products that generate recurrence relations and closed-form expressions. Additionally, we classify all binary 3x3 matric

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

Recent leaps in large language models (LLMs) caused a revolution in programming tools (like GitHub Copilot) that can help with code generation, debugging, and even performance optimization. In this paper, we focus on the capabilities of the most recent reasoning models to generate optimized CUDA code for predefined, well-known tasks. Our objective is to dete

Emily Xiao, Yixiao Zeng, Ada Chen, Chin-Jou Li

A popular method to adapt large language models (LLMs) to new tasks is in-context learning (ICL), which is effective but incurs high inference costs as context length grows. In this paper we propose a method to perform instruction induction, where we take training examples and reduce them to a compact but descriptive prompt that can achieve performance compa

Zhaoliang Wan, Zida Zhou, Zetong Bi, Zehui Yang

This paper addresses the scarcity of affordable, fully-actuated five-fingered hands for dexterous teleoperation, which is crucial for collecting large-scale real-robot data within the "Learning from Demonstrations" paradigm. We introduce the prototype version of the RAPID Hand, the first low-cost, 20-degree-of-actuation (DoA) dexterous hand that integrates a

Yuchen Sun, Amit Bhat, Chunmei Wang, Kejun Huang

We propose a new algorithm called higher-order QR iteration (HOQRI) for computing low multilinear rank approximation (LMLRA), also known as the Tucker decomposition, of large and sparse tensors. Compared to the celebrated higher-order orthogonal iterations (HOOI), HOQRI relies on a simple orthogonalization step in each iteration rather than a more sophistica

Amit Kumar, Kalpataru Pradhan

We investigate the impact of random bond disorder on quantum skyrmions using a spin-half quantum Heisenberg model on the square lattice with Dzyaloshinskii-Moriya interaction, Heisenberg anisotropy, and boundary-pinned magnetic field. Utilizing the neural network quantum state technique, we explore the influence of disorder on spin textures, topological prop

Egor Petrov, Nikita Kiselev, Vladislav Meshkov, Andrey Grabovoy

The optimization landscape of Transformer models remains poorly understood despite their widespread adoption. While recent studies have derived curvature properties for isolated self-attention mechanisms, a comprehensive theoretical characterization of the full Transformer block, accounting for the interactions between Layer Normalization, Feed-Forward Netwo

Chenxu Li, Zhicai Wang, Yuan Sheng, Xingyu Zhu

Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking the critical question of resolution robustness - whether performance remains stable across varying input resolutions. To address this gap, we introduce \textbf{Res-Bench}, a comprehe

Zhiding Liu, Ben Chen, Mingyue Cheng, Enhong Chen

Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts--such as spatiotemporal factors, historical interactions, and current query's information--constitute an essential part of their decision-making, reflecting implicit preferences that complement explicit query terms. Modeling s

Jack Furby, Dan Cunnington, Dave Braines, Alun Preece

Deep Neural Networks (DNNs) are often considered black boxes due to their opaque decision-making processes. To reduce their opacity Concept Models (CMs), such as Concept Bottleneck Models (CBMs), were introduced to predict human-defined concepts as an intermediate step before predicting task labels. This enhances the interpretability of DNNs. In a human-mach

Zhihui Yang, Yupei Wang, Kaijie Mo, Zhe Zhao

Despite significant progress in multimodal language models (LMs), it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models. To address this question, we propose a novel embodied knowledge understanding benchmark based on the perceptual theory from psychology, encompassing visual, auditory, ta

Jorge Pinochet

British physicist Stephen Hawkings most important discovery was that black holes are not so black, as they possess a temperature and emit thermal radiation. In his popular science texts, Hawking offered a detailed explanation of this phenomenon. The aim of this work is to translate that explanation into mathematical language accessible to an advanced high sc

Justin Jose, Nikhil Kumar

Soil nutrients and water content are two crucial factors that significantly affect agricultural production yields. Hence, monitoring and measuring the water content and soil type are critical requirements. This study proposes a two-dimensional structure of photonic crystals centered around a symmetrical cross-shaped slot. The cross-slots act as resonators, a

V. K. Oikonomou

In this work we shall study the impact of a second order electroweak phase transition occurring at $\sim 150\,$GeV on the energy spectrum of the stochastic gravitational background. Specifically, we assume that the non-minimally coupled Higgs field controls the inflationary era, we find the reheating temperature for the Higgs inflationary model and we demons

Johannes Barcsay, Sara Neves Silva, Jordina Aviles Verdera, Charline Bradshaw

Purpose: To develop and evaluate a real-time method for automatic planning and measurement of fetal femur length - an important indicator of antenatal growth - during MRI. While routinely assessed by ultrasound, MRI-based femur length measurements remain challenging due to bone-slice misalignment, fetal motion, and the need for manual assessment. Methods: A

Christian Baer, Lashi Bandara

To empower the mathematical hitchhiker wishing to use operator methods in geometry and topology, we present this user's guide to first-order elliptic boundary value problems. Existence, regularity, and Fredholmness are discussed for general first-order elliptic operators on manifolds with compact boundary. The focus is on a very general class of elliptic bou

Chih-Kai Yang, Yen-Ting Piao, Tzu-Wen Hsu, Szu-Wei Fu

Knowledge editing enables targeted updates without retraining, but prior work focuses on textual or visual facts, leaving abstract auditory perceptual knowledge underexplored. We introduce SAKE, the first benchmark for editing perceptual auditory attribute knowledge in large audio-language models (LALMs), which requires modifying acoustic generalization rath

Dong Li, Xujiang Zhao, Linlin Yu, Yanchi Liu

Large Language Models (LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt engineering, which leads to poor generalization across problem types, or require costly supervised training. We introduce SolverLLM, a training-free framework that leverages test-time

Maria Jose Lozano Palacio, Hasan Nayfeh, Matthew Ware, David C. McKay

With the continued scaling of quantum processors, holistic benchmarks are essential for extensively evaluating device performance. Layer fidelity is a benchmark well-suited to assessing processor performance at scale. Key advantages of this benchmark include its natural alignment with randomized benchmarking (RB) procedures, crosstalk awareness, fast measure

Hongwei Yan, Guanglong Sun, Zhiqi Kang, Yi Zhong

To adapt effectively to dynamic real-world environments, intelligent systems must continually acquire new skills while generalizing them to diverse, unseen scenarios. Here, we introduce a novel and realistic setting named domain generalizable continual learning (DGCL): a model learns sequential tasks with each involving a single domain, aiming to perform wel

Akhila Kambhatla, Ahmed R Khaled

Thermal weapon segmentation is crucial for surveillance and security applications, enabling robust detection under lowlight and visually obscured conditions where RGB-based systems fail. While convolutional neural networks (CNNs) dominate thermal segmentation literature, their ability to capture long-range dependencies and fine structural details is limited.

Boris Bekker, Yuri G. Zarhin

Let $d\geq 2$ be an integer, $K_0$ a perfect field such that $char(K_0)$ does not divide $d$, $n > d$ an integer prime to $d$, $f(x)\in K_0[x]$ a degree $n$ monic polynomial without repeated roots, and $C_{f,d}$ a smooth projective model of the affine curve $y^d=f(x)$. Let $J(C_{f,d})$ be the Jacobian of the $K_0$-curve $C_{f,d} $. We identify $C_{f,d}$ with

Tsogt-Ochir Enkhbayar

We present a mechanistic analysis of literary style in GPT-2, identifying individual neurons that discriminate between exemplary prose and rigid AI-generated text. Using Herman Melville's Bartleby, the Scrivener as a corpus, we extract activation patterns from 355 million parameters across 32,768 neurons in late layers. We find 27,122 statistically significa

Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon, Khaleel Ahmad

How can short-term energy consumption be accurately forecasted when sensor data is noisy, incomplete, and lacks contextual richness? This question guided our participation in the \textit{2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics}, which challenged teams to predict next-day power demand using real-wor

Arkady Kitover, Mehmet Orhon

The well-known Kamowitz - Scheinberg theorem states that if $U$ is an automorphism of a commutative semi-simple Banach algebra and $U^n \neq I, n \in \mathds{N}$, then the spectrum of $U$ contains the unit circle. In this paper we present some results about the spectrum of weighted automorphisms of unital commutative semi-simple Banach algebras that consider

Giuseppe Zurlo, Lev Truskinovsky

We propose an extended kinematics of nominally elastic continuum solids allowing one to describe their mechanical interaction with micro-scale loading devices. The main new ingredient is the concept of a micro-displacement tensor which extends the conventional description of the deforming elastic solids in terms of macroscopic displacement vectors. We show t

Mykhailo Moklyachuk, Maria Sidei

The problem of the mean-square optimal linear estimation of the functional $A\xi=\ \int\limits_{R^s}a(t)\xi(-t)dt,$ which depends on the unknown values of stochastic stationary process $\xi(t)$ from observations of the process $\xi(t)+\eta(t)$ at points $t\in\mathbb{R} ^{-} \backslash S $, $S=\bigcup\limits_{l=1}^{s}[-M_{l}-N_{l}, \, \ldots, \, -M_{l} ],$ $R

Kangrui Wang, Pingyue Zhang, Zihan Wang, Yaning Gao

A key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, lies in the shift from textual states to complex visual observations. This transition introduces partial observability and demands robust world modeling. We ask: Can VLM agents construct internal world models through explicit visual state reasoning? To ad

Iryna Dubovets'ka, Mykhailo Moklyachuk

The aim of this article is to overview the problem of mean square optimal estimation of linear functionals which depend on unknown values of periodically correlated stochastic process. Estimates are based on observations of this process and noise. These problems are investigated under conditions of spectral certainty and spectral uncertainty. Formulas for ca

Yukang Cao, Rahul Moorthy, O. Goktug Poyrazoglu, Volkan Isler

Trajectory sampling is a key component of sampling-based control mechanisms. Trajectory samplers rely on control input samplers, which generate control inputs u from a distribution p(u | x) where x is the current state. We introduce the notion of Free Configuration Space Uniformity (C-Free-Uniform for short) which has two key features: (i) it generates a con

Markus Weyrauch, Moritz Linkmann, Jacob Page

Variational data assimilation and machine-learning based super-resolution are two alternative approaches to state estimation in turbulent flows. The former is an optimisation problem featuring a time series of coarse observations, the latter usually requires a library of high-resolution 'ground truth' data. We show that the classic '4DVar' data assimilation

Yuxin Yang, Changfu Shi, Yi-Ming Hu

We report evidence for nonlinear gravitational effects in the ringdown signal of gravitational wave event GW250114. Using Bayesian inference, we find that the inclusion of a nonlinear quasi-normal mode (220Q), a second-order harmonic predicted by general relativity, is statistically favored over the standard linear model (440 mode) when analyzing the post-me

I. I. Antokhin, E. A. Antokhina, A. M. Cherepashchuk

The article presents the results of the analysis of optical light curves of the massive binary system WR 20a (WN 6ha + WN 6ha). The analysis was performed with the binary system model, extending the standard Roche model for the case when both components of the system have powerful stellar winds. The model takes into account the collision of the winds and the

Shih-Yu Chang

Matrix functions extend scalar function concepts to linear operators, offering a unified framework with broad applications in mathematics, science, and engineering. Classical definitions--via power series, spectral calculus, or Jordan form--capture both diagonalizable and defective matrices, revealing insights into dynamics, stability, and modal interactions

Maksym Luz, Mykhailo Moklyachuk

The problem of optimal estimation of functionals $A\xi =\sum\nolimits_{k=0}^{\infty }{}a(k)\xi (k)$ and ${{A}_{N}}\xi =\sum\nolimits_{k=0}^{N}{}a(k)\xi (k)$ which depend on the unknown values of stochastic sequence $\xi (k)$ with stationary $n$th increments is considered. Estimates are based on observations of the sequence $\xi (m)$ at points of time $m=-1,-

Pin-Han Ho, Limei Peng, Yiming Miao, Yan Jiao

AI memory mechanisms primarily focus on preserving information content, often neglecting the validity conditions under which knowledge remains applicable, leading to semantic coordinate drift when agents move, change sensors, or encounter novel environments. This paper proposes epistemic memory as a validity-maintenance layer that governs when stored knowled

Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi

Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging Long Short-Term Memory (LSTM) networks to forecast day-ahead electricity prices in the California energy market. The model incorporates a v

Jose Siguenza, Bharath Ramsundar

Neural networks that incorporate geometric relationships respecting SE(3) group transformations (e.g. rotations and translations) are increasingly important in molecular applications, such as molecular property prediction, protein structure modeling, and materials design. These models, known as SE(3)-equivariant neural networks, ensure outputs transform pred

Yiming Hu

Two-Phase TMR conserves energy by partitioning redundancy operations into two stages and making the execution of the third task copy optional, yet it remains susceptible to permanent faults. Reactive-TMR (R-TMR) counters this by isolating faulty cores, handling both transient and permanent faults. However, the lightweight hardware required by R-TMR not only

Hatim A. Oujaa, Qiao Liu, Ebubechukwu O. Ilo-Okeke, Valentin Ivannikov

We investigate methods to broadcast timing information from a central clock to all other clocks by the use of multipartite entanglement. This task is a necessary step in establishing a coordinated universal time, currently performed using classical synchronization methods. Using an entanglement-based method has the advantage that the timing results are indep

Antonin Chodron de Courcel, Charles Elbar

We study a scalar conservation law on the torus in which the flux $\mathbf{j}$ is composed of a Coulomb interaction and a nonlinear mobility: $\mathbf{j} = -u^m\nabla\mathsf{g}\ast u$. We prove existence of entropy solutions and a weak-strong uniqueness principle. We also prove several properties shared among entropy solutions, in particular a lower barrier

Bo-Han Feng, Chien-Feng Liu, Yu-Hsuan Li Liang, Chih-Kai Yang

Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker

Ramon Dalmau, Gabriel Jarry, Philippe Very

Aviation's non-CO2 effects, particularly contrails, are a significant contributor to its climate impact. Persistent contrails can evolve into cirrus-like clouds that trap outgoing infrared radiation, with radiative forcing potentially comparable to or exceeding that of aviation's CO2 emissions. While physical models simulate contrail formation, evolution and

Jiří Klepl, Martin Kruliš, Matyáš Brabec

Message Passing Interface (MPI) has been a well-established technology in the domain of distributed high-performance computing for several decades. However, one of its greatest drawbacks is a rather ancient pure-C interface. It lacks many useful features of modern languages (namely C++), like basic type-checking or support for generic code design. In this pa

Zongjian Li, Zheyuan Liu, Qihui Zhang, Bin Lin

Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy

Nusrat Munia, Abdullah Imran

Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional class-conditioned generative models often struggle to generate images that accurately represent specific medical categories, limiting their usefulness for applications such as skin c

Hung Tran, Tien Mai, Minh Hoang Ha

The recursive logit (RL) model provides a flexible framework for modeling sequential decision-making in transportation and choice networks, with important applications in route choice analysis, multiple discrete choice problems, and activity-based travel demand modeling. Despite its versatility, estimation of the RL model typically relies on nested fixed-poi

Duo Wang, Yuan Zuo, Guangyue Lu, Junjie Wu

Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture graph structure. We introduce UniGTE, an instruction-tuned encoder-decoder framework that unifies structural and semantic reasoning. The encod

Giulia Giusti, Michele Pagani

Autodiff refers to the core of the automatic differentiation systems developed in projects like JAX and Dex. Autodiff has recently been formalised in a linear typed calculus by Radul et al in arXiv:2204.10923. Although this formalisation suffices to express the main program transformations of Autodiff, the calculus is very specific to this task, and it is no

Vedad Kunovac, Heather Cegla, Hritam Chakraborty, Cis Lagae

Stellar surface inhomogeneities such as spots and faculae introduce Doppler variations that challenge exoplanet detection via the radial velocity method. While their impact on disc-integrated spectra is well established, detailed studies of the underlying local line profiles have so far been limited to the Sun. We present an observational campaign targeting

Weida Wang, Benteng Chen, Di Zhang, Wanhao Liu

Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a generalizable Chemical Reasoning model designed to emulate the

Dang Vo Phuc

Let $P_h = \mathbb{F}_p[t_1,\dots,t_h]$ be the polynomial algebra over $\mathbb{F}_p$ ($p$ prime). We consider the hit problem: finding a minimal generating set for $P_h$ as a module over the mod $p$ Steenrod algebra $\mathscr{A}_p$, or equivalently, determining a basis for $\mathbb{F}_p \otimes_{\mathscr{A}_p} P_h$. This problem is related to the $\mathscr{

Martin Palmer, Xiaolei Wu

We show that labelled Thompson groups and twisted Brin--Thompson groups are all acyclic. This allows us to prove several new embedding results for groups. First, every group of type $F_n$ embeds quasi-isometrically as a subgroup of an acyclic group of type $F_n$ that has no proper finite-index subgroups. This improves results of Baumslag--Dyer--Heller ($n=1$

Zili Tang, Xiaoxin Xie, Guanwen Yao, Ligong Zhang

The non-equilibrium Green's function method combined with density functional theory (NEGF-DFT) provides a rigorous framework for simulating nanoscale electronic transport, but its computational cost scales steeply with system size. Recent artificial intelligence (AI) approaches have sought to accelerate such simulations, yet most rely on conventional machine

Heming Zou, Yunliang Zang, Wutong Xu, Xiangyang Ji

Using a nearly-frozen pretrained model, the continual representation learning paradigm reframes parameter updates as a similarity-matching problem to mitigate catastrophic forgetting. However, directly leveraging pretrained features for downstream tasks often suffers from multicollinearity in the similarity-matching stage, and more advanced methods can be co

Matej Brešar, Efim Zelmanov

The paper surveys the history and state-of-the-art of the study of Jordan homomorphisms.

Quanyu Tang

We prove Dual Smale's mean value conjecture for all odd polynomials with nonzero linear term. Precisely, if $P$ is an odd polynomial of degree $d\ge3$ with $P(0)=0$ and $P'(0)=1$, then there exists a critical point $\zeta$ of $P$ such that $$ \left|\frac{P(\zeta)}{\zeta}\right| \ge \frac1d. $$This result can be regarded as a dual counterpart of T. W. Ng's th

Jacob Leiken, Sunoo Park

Over time, cryptographically deniable systems have come to be associated in computer-science literature with the idea of "denying" evidence in court - specifically, with the ability to convincingly forge evidence in courtroom scenarios and an inability to authenticate evidence in such contexts. Evidentiary processes in courts, however, have been developed ov

Shaolei Zhang, Ju Fan, Meihao Fan, Guoliang Li

Autonomous data science, from raw data sources to analyst-grade deep research reports, has been a long-standing challenge, and is now becoming feasible with the emergence of powerful large language models (LLMs). Recent workflow-based data agents have shown promising results on specific data tasks but remain fundamentally limited in achieving fully autonomou

Anirban Chakraborty, Nimish Mishra, Sayandeep Saha, Sarani Bhattacharya

In the main text published at USENIX Security 2025, we presented a systematic analysis of the role of cache occupancy in the design considerations for randomized caches (from the perspectives of performance and security). On the performance front, we presented a uniform benchmarking strategy that allows for a fair comparison among different randomized cache

Yudan Ren, Xinlong Wang, Kexin Wang, Tian Xia

While brain-inspired artificial intelligence(AI) has demonstrated promising results, current understanding of the parallels between artificial neural networks (ANNs) and human brain processing remains limited: (1) unimodal ANN studies fail to capture the brain's inherent multimodal processing capabilities, and (2) multimodal ANN research primarily focuses on

Yuan Deng, Yilin Li, Wei Tang, Hanrui Zhang

Automated bidding to optimize online advertising with various constraints, e.g. ROI constraints and budget constraints, is widely adopted by advertisers. A key challenge lies in designing algorithms for non-truthful mechanisms with ROI constraints. While prior work has addressed truthful auctions or non-truthful auctions with weaker benchmarks, this paper pr

Beatriz Villarroel, Wesley A. Watters, Alina Streblyanska, Enrique Solano

For centuries, astronomers have discussed the possibility of inhabited worlds - from Herschel's 18th-century observations suggesting Mars may host life, to the systematic search for technosignatures that began in the 1960s using radio telescopes. Searching for artifacts in the solar system has received relatively little formal scientific interest and has fac

Alberto De Toni, Edoardo Bortolozzo, Alessandro Emanuele, Marco Venturini

Quantum Key Distribution (QKD) is a leading technology for enabling information-theoretic secure communication, with protocols such as BB84 and its variants already deployed in practical field implementations. As QKD evolves from point-to-point links to multi-node networks, scalability and cost-effectiveness become central challenges. Among the approaches to

Yuyang Yu, Zhengwei Chen, Xuemiao Xu, Lei Zhang

3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invari

Xinyu Wang, Yukang Wang, Xiao-Xiong Zeng

We investigate the imaging properties of spherically symmetric Konoplya-Zhidenko (KZ) black holes surrounded by geometrically thick accretion flows, adopting a phenomenological radiatively inefficient accretion flow (RIAF) model and an analytical ballistic approximation accretion flow (BAAF) model. General relativistic radiative transfer is employed to compu

Szilárd Csaba László

In this paper we study a second order dynamical system with variable coefficients in connection to the minimization problem of a smooth nonconvex function. The convergence of the trajectories generated by the dynamical system to a critical point of the objective function is assured, provided a regularization of the objective function satisfies the Kurdyka-{\

Shujian Gao, Yuan Wang, Zekuan Yu

Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-space consistency}, yet they overlook the equally critical \emph{representation-space alignment}. Without harmonizing latent features, models struggle to learn representations that

A. I. Sanzhur, S. Shlomo

The isobaric caloric curve is considered in subcritical states region. The energy fluctuations along the caloric curve are determined for small nuclear systems which consist of limited number of nucleons. The temperature dependence of heat capacity at fixed pressure is obtained. The calculated quantities of small nuclear system are discussed and checked agai

Helena M. Richie, Brandon S. Hensley

We present a new method for generating emission spectra from polycyclic aromatic hydrocarbons (PAHs) in arbitrary radiation fields. We utilize the single-photon limit for PAH heating and emission to treat individual photon absorptions as independent events. This allows the construction of a set of single-photon emission "basis spectra" that can be scaled to

Nan Zheng, Wenlong Pei, Qingguang Guan, Wenju Zhao

This paper addresses the divergence-free mixed finite element method (FEM) for nonlinear fourth-order Allen-Cahn phase field coupled active fluid equations. By introducing an auxiliary variable $w = \Delta u$, the original fourth-order problem is converted into a system of second-order equations, thereby easing the regularity constraints imposed on standard

Xianchao Zhou

In this paper, we investigate Riemannian curvature constraints on the Kodaira dimension of compact almost Hermitian manifolds. Specifically, for a compact almost Hermitian manifold $(M, J, g)$ in the Gray-Hervella class $\mathcal{W}_2\oplus\mathcal{W}_3\oplus \mathcal{W}_4$ with nonnegative Riemannian scalar curvature, we prove that its Kodaira dimension mus

Enes Ayalp

Computing Education faces significant challenges in equipping graduates with the resilience necessary to remain relevant amid rapid technological change. While existing curricula cultivate computing competencies, they often fail to integrate strategies for sustaining and adapting these skills, leading to reduced career resilience and recurrent industry layof

Jiyan Qiu, Lyulin Kuang, Guan Wang, Yichen Xu

Vehicle aerodynamics optimization has become critical for automotive electrification, where drag reduction directly determines electric vehicle range and energy efficiency. Traditional approaches face an intractable trade-off: computationally expensive Computational Fluid Dynamics (CFD) simulations requiring weeks per design iteration, or simplified models t

Liang Chen, Thomas W. Kephart

We examine high-density axion clusters under gravitational compression. These are transient events in which the majority of axions are rapidly converted into photons, with some configurations producing photon signals with distinctive and characteristic patterns. We estimated the mass of the remnant objects and note that some could be black holes while in som

Yasuhiro Tanaka, Hitoshi Seo

We theoretically investigate the transient spectral function during the photoinduced melting of charge order in a correlated electron system, to unravel the dynamical processes triggered by different initial excitations. We employ a one-dimensional interacting spinless fermion model introducing a pulsed laser light, and perform a comparative study by the Har

Akhila Kambhatla, Taminul Islam, Khaled R Ahmed

The escalating threat of weapon-related violence necessitates automated detection systems capable of pixel-level precision for accurate threat assessment in real-time security applications. Traditional weapon detection approaches rely on object detection frameworks that provide only coarse bounding box localizations, lacking the fine-grained segmentation req

Matthew Sharp, Omer Bilgin, Iason Gabriel, Lewis Hammond

Autonomous AI agents capable of complex planning and action mark a shift beyond today's generative tools. As these systems enter political and economic life, who can access them, how capable they are, and how many can be deployed will shape distributions of power and opportunity. We define this emerging challenge as "agentic inequality": disparities in power

Lixiang Yan, Yueqiao Jin, Linxuan Zhao, Roberto Martinez-Maldonado

Generative artificial intelligence (AI) agents are increasingly embedded in collaborative learning environments, yet their impact on the processes of argumentative knowledge construction remains insufficiently understood. Emerging conceptualisations of agentic AI and artificial agency suggest that such systems possess bounded autonomy, interactivity, and ada

Zhengqi Pei, Qingming Huang, Shuhui Wang

The ever-increasing scale of modern neural networks has brought unprecedented performance alongside daunting challenges in efficiency and interpretability. This paper addresses the core question of how to build large neural systems that learn efficient, modular, and interpretable representations. We propose Neuronal Group Communication (NGC), a theory-driven

Uday Gopan, Manjari Kulkarni, Lakshasri S, Kashish Mittal

DiRAC is a scalable, distributed framework designed to enable efficient task assignment and path planning in very large robotic swarms. It introduces a novel zone-partitioned architecture with dynamically elected leaders and a tick-synchronized consensus protocol that yields strong consistency and deterministic outcomes. For path planning, DiRAC uses a novel

Beyazit Bestami Yuksel

This study focuses on the connection of a development kit that enables real-time monitoring of electrocardiogram (ECG) signals using a mobile system. A software developed on the Visual Studio .NET platform reads real-time ECG signals from the human body through non invasive methods and displays them graphically on the mobile system. ECG electrodes placed on