November 2025 arXiv papers — page 193

Anais Pauchet, Devesh Nandal

Population III stars supplied the first light and metals in the Universe, setting the pace of re-ionisation and early chemical enrichment. In dense haloes their evolution can be strongly influenced by the energy released when WIMPs annihilate inside the stellar core. We follow the evolution of a \(20\,M_\odot\) Population III model with the \textsc{genec} co

Julia Ahlvind, Josefin Larsson, Dennis Alp

The properties of the population of compact objects created in core-collapse supernovae (SNe) are uncertain. X-ray observations years to decades after the explosions offer a way to gain insight into this, as hard X-ray emission from the central regions will emerge as the ejecta absorption decreases. Here we analyze and place upper limits on late-time X-ray e

Stefan Joka

In this paper, we prove that the existence of a complete set of mutually unbiased bases (MUBs) in N-dimensional Hilbert space implies the existence of a complete set of mutually orthogonal Latin squares (MOLSs) of order N. In particular, we prove that a complete set of MUBs does not exist in dimension six (the first dimension which is not a power of prime).

Francisco Caravaca, Ángel Cuevas, Rubén Cuevas

The rapid expansion of Large Language Models (LLMs) has introduced unprecedented energy demands, extending beyond training to large-scale inference workloads that often dominate total lifecycle consumption. Deploying these models requires energy-intensive GPU infrastructure, and in some cases has even prompted plans to power data centers with nuclear energy.

Kazuki Okamura

We study the maximum likelihood estimator of the location parameter of the Pearson Type VII distribution with known scale. We rigorously establish precise asymptotic properties such as strong consistency, asymptotic normality, Bahadur efficiency and asymptotic variance of the maximum likelihood estimator. Our focus is the heavy-tailed case, including the Cau

Zhaoxin Chang, Fusang Zhang, Jie Xiong, Ziyu Li

In recent years, the number of Internet of Things (IoT) devices in smart homes has rapidly increased. A key challenge affecting user experience is how to enable users to efficiently and intuitively select the devices they wish to control. This paper proposes PnPSelect, a plug-and-play IoT device selection solution utilizing Ultra-wideband (UWB) technology on

Nils Japke, Furat Hamdan, Diana Baumann, David Bermbach

Benchmarking in cloud environments suffers from performance variability from multi-tenant resource contention. Duet benchmarking mitigates this by running two workload versions concurrently on the same VM, exposing them to identical external interference. However, intra-VM contention between synchronized workloads necessitates additional isolation mechanisms

Marc Martinez-Gost, Sara Pepe, Ana Pérez-Neira, Miguel Ángel Lagunas

In this paper, we extend our previous work on the Expressive Neural Network (ENN), a multilayer perceptron with adaptive activation functions parametrized using the Discrete Cosine Transform (DCT). Building upon previous work that demonstrated the strong expressiveness of ENNs with compact architectures, we now emphasize their efficiency, interpretability an

Xin Fu, Wenhao Ou

In a previous paper, the orbifold Bogomolov-Gieseker inequality is proved for a stable reflexive sheaf on a compact K\"ahler variety with klt singularities. In this paper, we give a characterization on the stable reflexive sheaf when the Bogomolov-Gieseker equality holds.

Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram, Lucas W. Remedios

Traumatic brain injury (TBI) is intrinsically heterogeneous, and typical clinical outcome measures like the Glasgow Coma Scale complicate this diversity. The large variability in severity and patient outcomes render it difficult to link structural damage to functional deficits. The Federal Interagency Traumatic Brain Injury Research (FITBIR) repository conta

Bryan L. M. de Oliveira, Felipe V. Frujeri, Marcos P. C. M. Queiroz, Luana G. B. Martins

Group Relative Policy Optimization (GRPO) has emerged as a scalable alternative to Proximal Policy Optimization (PPO) by eliminating the learned critic and instead estimating advantages through group-relative comparisons of trajectories. This simplification raises fundamental questions about the necessity of learned baselines in policy-gradient methods. We p

Dávid R. Szabó

For every $d\geq 2$, we construct a subset $D\subseteq \{1,2,\dots,n\}^d$ of size $n-o(n)$ such that every affine hyperplane of $\mathbb{R}^d$ intersects $D$ in at most $d$ points, and every hypersphere of $\mathbb{R}^n$ intersects $D$ in at most $d+1$ points. This construction is the largest one currently known, and strongly builds on ideas of Dong, Xu, and

Marco D'Elia, Irene Finocchi, Maurizio Patrignani

Maximal cliques play a fundamental role in numerous application domains, where their enumeration can prove extremely useful. Yet their sheer number, even in sparse real-world graphs, can make them impractical to be exploited effectively. To address this issue, one approach is to enumerate $\ell$-isolated maximal cliques, whose vertices have (on average) less

Paul Bastide, Julien Duron, Jędrzej Hodor, Weichan Liu

A connected subgraph of a graph is isometric if it preserves distances. In this short note, we provide counterexamples to several variants of the following general question: When a graph $G$ is edge covered by connected isometric subgraphs $H_1,\dots,H_k$, which properties of $G$ can we infer from properties of $H_1,\dots,H_k$? For example, Dumas, Foucaud, P

Mohammad Ashraf, Mohammad Afajal Ansari, Md Shamim Akhter, Feng Wei

Let $\U$ be a von Neumann algebra with a projection $P\in \U$. For any $A_1,A_2,\ldots,A_n\in\U,$ define $p_1(A_1)=A_1,$ $p_n (A_1,A_2,\ldots,A_n)=[p_{n-1} (A_1,A_2,\ldots,A_{n-1}),A_n]$ for all integers $n\geq 2,$ where $[A,B]=AB-BA$ $(A,B\in\U)$ denotes the usual Lie product. Assume that $\phi:\U\to\U$ is an additive mapping satisfying \[\phi(p_n(A_1, A_2,

François Delarue, Mattia Martini, Giacomo Enrico Sodini

We study linear and nonlinear PDEs defined on the space of $\mathcal{P}(\mathbb{T}^d)$ over the flat torus $\mathbb{T}^d$, equipped with the Dirichlet-Ferguson measure $\mathcal{D}$. We first develop an analytic framework based on the Wasserstein-Sobolev space $H^{1,2}(\mathcal{P}(\mathbb{T}^d), W_2, \mathcal{D})$ associated with the Dirichlet form induced b

Yannik P. Wotte, Patrick Buchfink, Silke Glas, Federico Califano

Lie groups and their actions are ubiquitous in the description of physical systems, and we explore implications in the setting of model order reduction (MOR). We present a novel framework of MOR via Lie groups, called MORLie, in which high-dimensional dynamical systems on manifolds are approximated by low-dimensional dynamical systems on Lie groups. In compa

Ajay Gautam, Feiyang Lin, Shubham Sinha

We study the cohomology groups of tautological bundles on Quot schemes over the projective line, which parametrize rank $r$ quotients of a vector bundle $V$ on $\mathbb{P}^1$. Our main result is an analogue of the Borel--Weil--Bott theorem for Quot schemes. As a corollary, we prove recent conjectures of Marian, Oprea, and Sam on the exterior and symmetric po

Axel Potier, Raphaël Galicher, Pierre Baudoz, Johan Mazoyer

High-contrast imaging relies on advanced coronagraphs and adaptive optics (AO) to attenuate the starlight. However, residual aberrations, especially non-common path aberrations between the AO channel and the coronagraph channel, limit the instrument performance. While post-processing techniques such as spectral or angular differential imaging (ADI) can parti

Wencheng Ye, Yan Liu

Large language models (LLMs) have shown strong capabilities in software engineering tasks, yet most existing LLM-based SWE-Agents mainly tackle well-defined problems using conventional methods, often overlooking alternative or innovative solutions beyond their predefined frameworks. This limitation is evident in open-world software environments, where emergi

Ágnes Backhausz, Christian Kuehn, Sjoerd van der Niet, Giulio Zucal

In this work, we develop a spectral theory for hypergraph limits. We prove the convergence of the spectra of adjacency and Laplacian matrices for hypergraph sequences converging in the $1$-cut metric. On the other hand, we give examples of matrix operators associated with hypergraphs whose spectra are not continuous with respect to the $1$-cut metric. Furthe

Amir Bahador Javadi, Amin Kargarian

The increasing penetration of renewable generation introduces uncertainty into power systems, challenging traditional deterministic optimization methods. Chance-constrained optimization offers an approach to balancing cost and risk; however, incorporating joint chance constraints introduces computational challenges. This paper presents an ensemble support ve

Susanna Heikkilä, Ilmari Kangasniemi

We prove that the recently shown cohomological obstruction for quasiregular ellipticity has a generalization in the theory of quasiregular values. More specifically, if $M$ is a closed, connected, and oriented Riemannian $n$-manifold, and there exists a map $f \in C(\mathbb{R}^n, M) \cap W^{1,n}_{\mathrm{loc}}(\mathbb{R}^n, M)$ satisfying $\lvert Df(x) \rver

Alexis Marin, Dorothea Vienne-Pollak

A proof of the uniformization theorem of Riemann surface is given with only elementary properties of holomorphic functions and not using the paracompacity of the surface. This proof leans on an holomorphic version of the topological characterization, due to Brown, of the sphere as variety covered by two discs, a generalization of the construction of double o

Stefano Redaelli

Collimation systems in particle accelerators are designed to safely and efficiently dispose of unavoidable beam losses during operation. Their specific roles vary depending on the type of accelerator. The state of the art in hadron beam collimation for high-intensity, high-energy superconducting colliders is exemplified by the system implemented at the CERN

Maike Fahrensohn, R. Matthias Geilhufe

We derive a repulsive, charge-dipole-like interaction for a Dirac particle in a rotating frame, arising from a geometric $U(1)$ gauge symmetry associated with the Berry phase. The Lagrangian of this system includes a non-inertial correction due to centrifugal field coupling. By imposing gauge symmetry and treating it as a full gauge theory, the Lagrangian is

Davood Momeni, Ratbay Myrzakulov

We investigate black hole entropy in a broad class of modified Myrzakulov gravity theories defined by generalized Lagrangians of the form \( \mathcal{L} = \alpha R + F(T, Q, R_{\mu\nu}T^{\mu\nu}, R_{\mu\nu}Q^{\mu\nu}, \dots) \), where \( R \), \( T \), and \( Q \) represent curvature, torsion, and non-metricity scalars. Using the vielbein formalism, we deriv

Qi Jia, Ye Shen, Xiujie Song, Kaiwei Zhang

Evaluating LLMs' instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to saturation and failing to account for users' interactive experience. In this work, we propose a novel framework featuring a three-layer tracking mechanism and a query synthesis agent

Youjian Chen, Hamed Vakili, Md Golam Morshed, Avik W. Ghosh

Processing-in-memory (PIM) reduces data transfer latency by rolling memory and logic elements into one compute location. As an emergent material candidate for such an architecture, we propose a strained Weyl semimetal based spin-orbit-torque random-access memory (SWSM-SOTRAM) device. The spin-orbit torque (SOT) originates from two mechanisms: (1) the inverse

Ding Chen, Simin Niu, Kehang Li, Peng Liu

Memory systems are key components that enable AI systems such as LLMs and AI agents to achieve long-term learning and sustained interaction. However, during memory storage and retrieval, these systems frequently exhibit memory hallucinations, including fabrication, errors, conflicts, and omissions. Existing evaluations of memory hallucinations are primarily

Anthony M. Gaglione, Dennis Spellman

Let $p$ and $n$ be positive integers. Assume additionally that $p\neq 3$ is a prime and that $n>2$. Let $R$ be a field of characteristic $p$. A very special consequence of a result of Bunina and Kunyavskii (2023, arXiv:2308.10076) is that $SL_{n}(R)$ is co-Hopfian as a group if and only if $R$ is co-Hopfian as a ring. In this paper, we prove that if $k$ is t

Saskia Haegens, Julio Rodriguez-Larios, Elie Rassi

Recent evidence suggests that beta-band activity plays a key role in decision-making. Here we review our recent work in humans and non-human primates showing that beta-band frequency shifts in frontal cortex signal categorical decision outcomes. We revisit our previous proposal suggesting that content-specific beta reflects the flexible recruiting of transie

Mohammadreza Soltanipour, Stefan Treue, Fred Wolf

Cortical circuits exhibit high levels of response diversity, even across apparently uniform neuronal populations. While emerging data-driven approaches exploit this heterogeneity to infer effective models of cortical circuit computation (e.g. Genkin et al. Nature 2025), the power of response diversity to enable inference of mechanistic circuit models is larg

Siyao Li

We investigate the decay dynamics of oscillons through interactions with an external scalar field. To examine how robust the decay dynamics of oscillons via parametric resonance we previously found in Li et al. 2025 are to the specific form of the coupling, we extend the analysis to include a three-point interaction $g_3\phi\chi^2$. We compute the Floquet ex

Yannick Hoyer, Kristoffer Rank Rasmussen

Given a curved differential graded algebra $A$, we define a new model structure on the category of curved differential graded $A$-modules, called the injective Guan-Lazarev model structure. We prove that the category of CDG $A$-modules with this model structure is Quillen equivalent to the category of curved differential graded contramodules over the extende

Kazi Reyazul Hasan, Mubasshira Musarrat, A. B. M. Alim Al Islam, Muhammad Abdullah Adnan

Large language models work well for technical problem solving in English but perform poorly when the same questions are asked in Bangla. A simple solution would be to translate Bangla questions into English first and then use these models. However, existing Bangla-English translation systems struggle with technical terms. They often mistranslate specialized

Lei Fu, Sahar Salimpour, Leonardo Militano, Harry Edelman

Agentic AI systems and Physical or Embodied AI systems have been two key research verticals at the forefront of Artificial Intelligence and Robotics, with Model Context Protocol (MCP) increasingly becoming a key component and enabler of agentic applications. However, the literature at the intersection of these verticals, i.e., Agentic Embodied AI, remains sc

Federica Chiappetta, Giuseppe Nisticò, Massimo Chimenti, Andrea Larosa

Shocks driven by coronal mass ejections (CMEs) are the most powerful accelerators of gradual solar energetic particles (SEPs) in the inner heliosphere. On 2023 March 13, a halo CME, as seen from the Solar Heliospheric Observatory (SoHO) and the Sun TErrestrial Relations Observatory (STEREO), gave rise to a strong SEP event. In this work, we aim to analyze th

Jiwoon Park

In this article, we construct and analyse a renormalisation group (RG) map for the weakly coupled $n$-component $|\varphi|^4$ model under periodic boundary conditions in dimension $d \ge 4$. Both short-range and long-range interactions with upper critical dimension four are considered. This extends and refines the RG map constructed by Bauerschmidt, Brydges

Hyunseok Kwak, Kyeongwon Lee, Jae-Jin Lee, Woojoo Lee

On-device fine-tuning of CNNs is essential to withstand domain shift in edge applications such as Human Activity Recognition (HAR), yet full fine-tuning is infeasible under strict memory, compute, and energy budgets. We present LoRA-Edge, a parameter-efficient fine-tuning (PEFT) method that builds on Low-Rank Adaptation (LoRA) with tensor-train assistance. L

Amir Bahador Javadi, Philip Pong

As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics

Aramis Raiola, Fabio Acerbi, Cyril Alispach, Domenico della Volpe

The POSiCS camera is a handheld, small field-of-view gamma camera developed for multipurpose use in radio-guided surgery (RGS), with sentinel lymph node biopsy (SLNB) as its benchmark application. This compact and lightweight detector (weighing approximately 350 g) can map tissues labeled with Tc-99m nanocolloids and guide surgeons to the location of target

Elvis Dohmatob, Mohammad Pezeshki, Reyhane Askari-Hemmat

This paper introduces a theoretical framework to resolve a central paradox in modern machine learning: When is it better to use less data? This question has become critical as classical scaling laws suggesting ``more is more'' (Sun et al., 2025) are challenged by methods like LIMO (``less is more'') and s1 (Ye et al., 2025; Muenighoff et al., 2025), which ac

Etienne Bamas, Shi Li, Lars Rohwedder

We show that under mild assumptions for a problem whose solutions admit a dynamic programming-like recurrence relation, we can still find a solution under additional packing constraints, which need to be satisfied approximately. The number of additional constraints can be very large, for example, polynomial in the problem size. Technically, we reinterpret th

Saif Ur Rehman, Wajid Yousuf

The accurate solution of nonlinear hyperbolic partial differential equations (PDEs) remains challenging due to steep gradients, discontinuities, and multiscale structures that make conventional solvers computationally demanding. Physics-Informed Neural Networks (PINNs) embed the governing equations into the learning process, enabling mesh-free solution of PD

Daniel Kang

Unstructured data, in the form of text, images, video, and audio, is produced at exponentially higher rates. In tandem, machine learning (ML) methods have become increasingly powerful at analyzing unstructured data. Modern ML methods can now detect objects in images, understand actions in videos, and even classify complex legal texts based on legal intent. C

Alvise Dei Rossi, Julia van der Meer, Markus H. Schmidt, Claudio L. A. Bassetti

Physiological signals are inherently heterogeneous: they are collected under diverse acquisition setups, differ in the number and type of modalities and channels, varying in quality, reliability, and relevance across tasks. This variability poses a major challenge for machine learning models required to generalize across subjects, sensors, and clinical envir

Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Zhiqiang Yuan

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. A realistic, standard-compatible channel model is essential for ISAC system design. To characterize the impact of Sensing Targets (STs), 3GPP defines ISAC channel as a combination of target and background channels, comprising multipath components related

David Soler, Carlos Dafonte, Manuel Fernández-Veiga, Ana Fernández Vilas

Instant messaging has become one of the most used methods of communication online, which has attracted significant attention to its underlying cryptographic protocols and security guarantees. Techniques to increase privacy such as End-to-End Encryption and pseudonyms have been introduced. However, online spaces such as messaging groups still require moderati

Yutong Geng, Enze Sun, Zonghan Yang, Yuhao Zhang

This paper studies the online scheduling problem of minimizing total flow time for $n$ jobs on $m$ identical machines. A classical $\Omega(n)$ lower bound shows that no deterministic single-machine algorithm can beat the trivial greedy, even when $n$ is known in advance. However, this barrier is specific to deterministic algorithms on a single machine, leavi

Mahdi Ebrahimi

Given an positive integer $k$, let $n:=\binom{k+1}{2}$. In 2012, during a talk at UCLA, Jan Saxl conjectured that all irreducible representations of the symmetric group $S_n$ occur in the decomposition of the tensor square of the irreducible representation corresponding to the staircase partition. In this paper, we investigate two useful methods to obtain so

Vansh Sharma, Harish Jai Ganesh, Maryam Akram, Wanjiao Liu

This study presents a new high-fidelity multi-modal dataset containing 16000+ geometric variants of automotive hoods useful for machine learning (ML) applications such as engineering component design and process optimization, and multiphysics system surrogates. The dataset is centered on a practical multiphysics problem-hood deformation from fluid entrapment

Zhenfeng Ouyang, Jing Jiang, Jian-Feng Zhang, Miao Gao

A very recent report claims that ambient-pressure high-temperature ($T_c$) superconductivity was found in boron-doped three-dimensional networks of carbon nanotubes (CNTs). Here, we systematically study the electron-phonon coupling (EPC) of one-dimensional (1D) (3,0) CNT under ambient pressure. Our results show that the EPC constant $\lambda$ of the undoped

Giorgio Palma, Ivan Santic, Andrea Serani, Lorenzo Minno

This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and its Bayesian extension (BHDMDc). Experiments were carried out on a Codevintec CK-14e ASV in the towing tank of CNR-INM, under both irregular and regular head-sea wave conditions. T

Jianbo Yuan, Haohua Zhu, Jing Dai, Sheng Yi

The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation with robotic systems remains a formidable challenge. Developing a dexterous robotic hand requires balancing human-like agility with engineering constraints such as complexity, si

Yutong Feng, Xu Liu, Yutong Xia, Yuxuan Liang

Accurately modeling complex dynamic spatio-temporal systems requires capturing flow-mediated interdependencies and context-sensitive interaction dynamics. Existing methods, predominantly graph-based or attention-driven, rely on similarity-driven connectivity assumptions, neglecting asymmetric flow exchanges that govern system evolution. We propose Spatio-Tem

Khalid Belhajjame, Haroun Mezrioui, Yuyan Zhao

Data provenance has numerous applications in the context of data preparation pipelines. It can be used for debugging faulty pipelines, interpreting results, verifying fairness, and identifying data quality issues, which may affect the sources feeding the pipeline execution. In this paper, we present an indexing mechanism to efficiently capture and query pipe

Mukesh Kumar, Pramod Sharma, Karabo Mosala, Bruce Mellado

The observed indications for a new scalar resonance with a mass around 95\,GeV, initially reported by LEP and supported by CMS and ATLAS in di-photon, $\tau \tau$, and $W^+ W^-$ channels, motivate exploring its discovery potential at future electron-positron colliders. This study focuses on the production of the new scalar ($S$) via $e^+ e^- \rightarrow ZS $

Jeongah Lee, Ali Sarvghad

Large multimodal models (LMMs) are increasingly capable of interpreting visualizations, yet they continue to struggle with spatial reasoning. One proposed strategy is decomposition, which breaks down complex visualizations into structured components. In this work, we examine the efficacy of scalable vector graphics (SVGs) as a decomposition strategy for impr

Shubham Sharma, Alexey Aparin

We investigate inclusive $J/\psi$ production in proton-proton collisions at tens of GeV $\sqrt{s}$ energy, relevant for forthcoming measurements with the Spin Physics Detector (SPD) at NICA. Simulations are performed using the PEGASUS event generator with transverse-momentum-dependent (TMD) gluon densities, comparing the recent KMR-based KL$'2025$ and CCFM-b

Juheon Kim, Minki Jang, Junhyeok Park, Byungjo Kim

Plasma-activated bonding of SiCN films offers high bonding strength at the hybrid-bonding interface, thereby enhancing mechanical reliability. Although experimental studies have shown that the interfacial bonding properties of SiCN films vary with SiCN composition and plasma treatment parameters, a clear correlation between these parameters and the resulting

Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng

AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples include retrieval-augmented generation, agent memory layers, and multi-agent orchestration. As input contexts get longer, prefill latency becomes the main bottleneck. Yet today's prefill acceleration techniques

Emmanuel Gnabeyeu, Gilles Pagès

This paper investigate the properties of solutions to forward Stochastic Volterra Integral Equations (SVIEs for short) with affine drift, specifically their stationarity, both over a finite horizon and in the long run. We demonstrate that it is possible to induce a $\textit{fake stationary regime}$, in the sense that all marginal distributions share the same

Andreas Bærentzen

This paper is an introductory and informal exposition on the topology of polygonal meshes. We begin with a broad overview of topological notions and discuss how homeomorphisms, homotopy, and homology can be used to characterise topology. We move on to define polygonal meshes and make a distinction between intrinsic topology and extrinsic topology which depen

Alexandru Cioba, Aya Kayal, Laura Toni, Sattar Vakili

In many real-world reinforcement learning (RL) problems, the environment exhibits inherent symmetries that can be exploited to improve learning efficiency. This paper develops a theoretical and algorithmic framework for incorporating known group symmetries into kernel-based RL. We propose a symmetry-aware variant of optimistic least-squares value iteration (

Kunyu Zhang, Guang Yang, Fashun Shi, Shaoying He

The large-scale integration of renewable energy and power electronic devices has increased the complexity of power system stability, making transient stability assessment more challenging. Conventional methods are limited in both accuracy and computational efficiency. To address these challenges, this paper proposes MoE-GraphSAGE, a graph neural network fram

Pedro J. Chocano

Given a group retraction $r: G \rightarrow H $, we construct a finite topological space $ X_r $ of height 1, together with a topological retraction $\overline{r}: X_r \rightarrow X_r $, such that the group of automorphisms $ \mathrm{Aut}(X_r) $ (or the group of self-homotopy equivalences $ \mathcal{E}(X_r) $) of $X_r$ is isomorphic to $ G $, and $ \mathrm{Au

Ming Gu, Ziwei Wang, Sicen Lai, Zirui Gao

Ensuring web accessibility is crucial for advancing social welfare, justice, and equality in digital spaces, yet the vast majority of website user interfaces remain non-compliant, due in part to the resource-intensive and unscalable nature of current auditing practices. While WCAG-EM offers a structured methodology for site-wise conformance evaluation, it in

F. Nakanishi, K. Abe, S. Abe, Y. Asaoka

In 2024, a failed supernova candidate, M31-2014-DS1, was reported in the Andromeda galaxy (M31), located at a distance of approximately 770 kpc. In this paper, we search for neutrinos from this failed supernova using data from Super-Kamiokande (SK). Based on the estimated time of black hole formation inferred from optical and infrared observations, we define

Jiahuang Chen, Siqi He

Let $F$ be a non-Archimedean valued field, $\Sigma$ a closed Riemann surface of genus at least two, and $\Gamma$ its fundamental group. Building on the theory of equivariant harmonic maps into $\mathbb{R}$-trees, we study the non-Archimedean Hitchin map from the $\mathrm{SL}_2(F)$-character variety $\mathcal{X}_F(\Gamma)$, equipped with the non-Archimedean t

Chang Zhou, Charles P. Hong, Yifan Wang, Ehud J. Schmidt

Radiofrequency ablation is widely used to prevent ventricular tachycardia (VT) by creating lesions to inhibit arrhythmias; however, the current surface ablation catheters are limited in creating lesions that are deeper within the left ventricle (LV) wall. Intramyocardial needle ablation (INA) addresses this limitation by penetrating the myocardium and delive

Lapo Santi, Nial Friel

The Bradley-Terry model is widely used for the analysis of pairwise comparison data and, in essence, produces a ranking of the items under comparison. We embed the Bradley-Terry model within a stochastic block model, allowing items to cluster. The resulting Bradley-Terry SBM (BT-SBM) ranks clusters so that items within a cluster share the same tied rank. We

Ringwald Celian, Gandon Fabien, Faron Catherine, Michel Franck

RDF pattern-based extraction is a compelling approach for fine-tuning small language models (SLMs) by focusing a relation extraction task on a specified SHACL shape. This technique enables the development of efficient models trained on limited text and RDF data. In this article, we introduce Kastor, a framework that advances this approach to meet the demands

Javier Giménez, José A. Cortés, Francisco Javier Cañete, Eduardo Martos-Naya

Orthogonal frequency division multiplexing (OFDM) is a widespread modulation but suffers from high out-of-band emissions (OOBE). Spectral shaping strategies such as precoding, active interference cancellation (AIC) and time-domain methods are effective at reducing the OOBE but entail optimization procedures and real-time implementation costs which might be c

Mihriban Kocak Balik, Pekka Marttinen, Negar Safinianaini

Integrating different molecular layers, i.e., multiomics data, is crucial for unraveling the complexity of diseases; yet, most deep generative models either prioritize predictive performance at the expense of interpretability or enforce interpretability by linearizing the decoder, thereby weakening the network's nonlinear expressiveness. To overcome this tra

Danlei Yang, Yang Wang, Jihong Wang, Zhenhua Rui

Long-duration energy storage (LDES) is vital for decarbonizing the energy system but faces economic challenges, including high upfront costs, low trading frequency, and limited revenue in current electricity markets. Compressed Air Energy Storage (CAES) is a promising LDES solution, though its economic viability, especially for long storage durations beyond

Enzo Francisco, Julien Lambret, Sébastien Aumaître

We use Diffusing Wave Spectroscopy (DWS) to perform the first direct space- and time-resolved measurement of the dissipation rate~$\epsilon$ at the boundary of a turbulent flow. We have shown in a previous publication that this technique provides maps of the dissipation rate of Newtonian fluids~\cite{Francisco}. Here, we apply the technique at the boundary o

Christian Bertram, Deborah Haun, Mads Vestergaard Jensen, Tuukka Korhonen

Kernelization studies polynomial-time preprocessing algorithms. Over the last 20 years, the most celebrated positive results of the field have been linear kernels for classical NP-hard graph problems on sparse graph classes. In this paper, we lift these results to the dynamic setting. As the canonical example, Alber, Fellows, and Niedermeier [J. ACM 2004] ga

Kohei Fukai, Hosho Katsura

We establish the integrability of a family of Sachdev-Ye-Kitaev (SYK) models with uniform $p$-body interactions. We derive the R-matrix and mutually commuting transfer matrices that generate the Hamiltonians of these models, and obtain their exact eigenspectra and eigenstates. Remarkably, the R-matrix is that of the critical transverse-field Ising chain. Thi

Kevin Manogue, Tomasz M Schang, Dilara Kuş, Jonas Müller

Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. O

Xiaoyu Qu, Jiahong Yu

Consider a bounded prism $(A,I)$ and a bounded quasi-l.c.i algebra $R$ over $\overline{A}$. In this paper, for any prism $S/A$ with a surjection $S\to R$ such that $\widehat{\mathbb L}_{\overline{S}/\overline{A}}$ is a $p$-completely flat module over $\overline{S}$, we establish an equivalence of categories between rational Hodge-Tate crystals on $(R/A)_{\De

Vishal Jadoliya, Mayukh Pahari, Sudip Bhattacharyya, Shaswat Suresh Nair

Whether the accretion disc in the X-ray high-mode of transitional millisecond pulsars (tMSP) reaches near the neutron star surface by penetrating the magnetosphere is a crucial question with many implications, including for continuous gravitational wave emission from the pulsar. We attempt to answer this question for the tMSP PSR J1023+0038 by segregating hi

Xuan Zou, Shuai Yin, Zi-Xiang Li, Hong Yao

Deconfined quantum critical point (DQCP) characterizes the continuous transition beyond Landau-Ginzburg-Wilson paradigm, occurring between two phases that exhibit distinct symmetry breaking. The debate over whether genuine DQCP exists in physical SU(2) spin systems or the transition is weakly first-order has persisted for many years. In this letter, we const

Guido Ladenburger, Finn Schmolke, Eric Lutz

First-passage phenomena play a fundamental role in classical stochastic processes. We here exactly solve a quantum first-passage time problem for quantum diffusion driven by measurement noise, a generalization of classical Brownian motion. Such continuous monitoring may trap the measured quantum system in a decoherence-free subspace, a fraction of the availa

Ángel David Ríos Ortiz, Javier Sendra-Arranz

We investigate the Hilbert scheme of points on curves with n-fold singularities, that is curves that look locally around their singular points as the axis in an affine space. We describe the structure and number of its irreducible components, and provide a detailed analysis of their singularities, revealing rich combinatorial patterns governing its geometry.

Davor Dragicevic

In a recent paper (Math. Ann. 393 (2025), 1769--1795), Elorreaga et al. have obtained a complete characterization of the notion of a $h$-dichotomy for ordinary differential equations on a finite-dimensional space in terms of the notions of $h$-expansiveness and $h$-noncriticality. Their results extended the previous results of Coppel and Palmer, which dealt

Wendong Xu, Chujie Chen, He Xiao, Kuan Li

Large Language Model (LLM) inference services demand exceptionally high availability and low latency, yet multi-GPU Tensor Parallelism (TP) makes them vulnerable to single-GPU failures. We present AnchorTP, a state-preserving elastic TP framework for fast recovery. It (i) enables Elastic Tensor Parallelism (ETP) with unequal-width partitioning over any numbe

Shiqing Qiu

In the era of smart manufacturing, predictive maintenance (PdM) plays a pivotal role in improving equipment reliability and reducing operating costs. In this paper, we propose a novel Markov Decision Process (MDP) framework that integrates advanced soft computing techniques - Fourier Neural Operator (FNO), Denoising Autoencoder (DAE), Graph Neural Network (G

Wenchao Yang, Weidong Yan, Wenkang Liu, Yulan Ma

Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, the

Jordan Roulleau-Pasdeloup

There is no known explicit global closed form solution for the standard income fluctuation problem with a borrowing constraint and where wealth accumulates with a constant interest rate $r$. Using a continuous time formulation, I derive an explicit global closed form solution for the case $r=0$ using the Lambert W function. For the case $r>0$, I derive an ex

Haida Li, Xiangdong Zhang

The exact measurement of neutrino mass remains a longstanding issue. So far, there has been much success in providing an upper bound for the neutrino rest mass, both theoretically and experimentally. In this work, by exploring the critical radius of a beam of polarized quantum spin-$\frac{1}{2}$ particle deflecting around a classical Kerr black hole, we atte

A. R. Khisameeva, A. Shuvaev, I. M. Moiseenko, P. A. Gusikhin

We experimentally investigate the terahertz spectrum of plasma excitations in a plasmonic crystal based on AlGaN/GaN two-dimensional electron system (2DES). While screened plasmon modes with linear dispersion are readily observed in the plasmonic crystals, the existence of unscreened modes localized in the slots between the gates has remained unobserved unti

Tongge Wen, Xiaoyu Yang, Sudeshna Roy, Thorsten Pöschel

The extremely weak gravity on small bodies makes landers prone to rebound and uncontrolled drift. To mitigate this, the Hayabusa2 mission employed a particle-filled flexible shell, but the coupled dynamics of shell deformation and internal particle dissipation remain unclear. We develop a computational model representing the flexible shell as a spring-mass n

Dhaval Pujara, Ankur Sinha

This paper presents a comprehensive review of techniques proposed in the literature for solving bilevel optimization problems encountered in various real-life applications. Bilevel optimization is an appropriate choice for hierarchical decision-making situations, where a decision-maker needs to consider a possible response from stakeholder(s) for each of its

Arnaud Descours, Léonard Deroose, Jan Ramon

Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical bottleneck, particularly for large-scale models. In this work, we introduce two complementary algorithms: ProjFL, designed for unbiased compressors, and ProjFL+EF, tailored for bia

Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan, Wenqi Huang

Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent processes. Goal: Our goal is to produce segmentations directly from sparse k-space measurements without the need for intermediate image reconstruction. Approach: We employ a transformer a

Luís Almeida, Alexis Léculier, Nga Nguyen, Nicolas Vauchelet

Mosquitoes are vectors of numerous diseases; a strategy to fight the spread of these diseases is to control the vector population. In this article, we focus on the use of the sterile insect technique. Starting from a reaction-diffusion system, we show the existence of 'forced' traveling waves obtained by translating the intervention zone at constant speed. T

Haonan Yuan, Qingyun Sun, Junhua Shi, Xingcheng Fu

Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GFMs struggle with unstable few-shot fine-tuning, where both performance and adaptation efficiency exhibit significant fluctuations caused by

Anwesh Ray, Tanushree Shah

The classical analogy between knots and primes motivates the study of Alexander polynomials through an arithmetic perspective. In this article we study the two-parameter family of torus knots and links $T_{p,q}$ and analyze the asymptotic behaviour of the zeros of their Alexander polynomials $\Delta_{p,q}(t)$, defined with respect to the total linking number

Károly Seller, Zsolt Szép, Zoltán Trócsányi

We present in the conventional equilibrium approach to leptogenesis the complete leading order prediction for the CP asymmetry factor in finite-temperature decays involving Majorana neutrinos. As thermal effects are generally not negligible, the knowledge of the high temperature behavior of the underlying particle physics model, in particular that of mass ge