October 2025 arXiv papers — page 107

Ewa Makowska-Tłumak, Sylwia Bedyńska, Kinga Skorupska, Radosław Nielek

Although information and communication technologies (ICT) solutions have positive outcomes for both companies and employees, the digital transformation (DT) could have an impact on the well-being of employees. The jobs of the employees became more demanding, and they were expected to learn ICT skills and cope with ICT workloads and hassles. Due to negative s

Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe

Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning behind annotators' decisions. One such approach is the LiTEx taxonomy, which categorizes free-text explanations in English into reasoning categories. However, previous work applyin

Peiran Xu, Xicheng Gong, Yadong MU

In this work we concentrate on the task of goal-oriented Vision-and-Language Navigation (VLN). Existing methods often make decisions based on historical information, overlooking the future implications and long-term outcomes of the actions. In contrast, we aim to develop a foresighted agent. Specifically, we draw upon Q-learning to train a Q-model using larg

Olga Aryasova, Franco Flandoli, Andrey Pilipenko

Motivated by the phenomenon of transport barriers in fusion plasma devices, we write a mathematical model of heat dispersion in a turbulent fluid with a transport barrier, properly idealized; in a scaling limit of the turbulence model with separation of scales we get a heat equation with space-dependent diffusion coefficient, poorly diffusing near the barrie

Deyi Ji, Yuekui Yang, Haiyang Wu, Shaoping Ma

Advertisement (Ad) video violation detection is critical for ensuring platform compliance, but existing methods struggle with precise temporal grounding, noisy annotations, and limited generalization. We propose RAVEN, a novel framework that integrates curriculum reinforcement learning with multimodal large language models (MLLMs) to enhance reasoning and co

Gregory Kucherov, Yakov Nekrich

The normalized substring complexity $\delta$ of a string is defined as $\max_k \{c[k]/k\}$, where $c[k]$ is the number of \textit{distinct} substrings of length $k$. This simply defined measure has recently attracted attention due to its established relationship to popular string compression algorithms. We consider the problem of computing $\delta$ online, w

J. Xiang, J. Zhao, Z. P. Li, D. Vretenar

We present a microscopic triaxial quadrupole-octupole collective Hamiltonian (TQOCH) that unifies collective rotations, quadrupole-octupole vibrations, and their couplings to model low-lying nuclear states of both parities. The TQOCH's dynamics are governed by collective parameters derived from multidimensionally constrained covariant density functional theo

Anna Bahrii

We prove well-posedness results for time-inhomogeneous stable-driven McKean-Vlasov stochastic differential equations with a convolution drift where the interaction kernel belongs to some Lebesgue-Besov space. The novelty of this work is that we manage to go below -1 in space regularity for such a kernel. This is achieved under additional smoothness condition

Lidong Li, Rui Huang, Lin Zhao

This paper presents a novel framework for stabilizing nonlinear systems represented in state-dependent form. We first reformulate the nonlinear dynamics as a state-dependent parameter-varying model and synthesize a stabilizing controller offline via tractable linear matrix inequalities (LMIs). The resulting controller guarantees local exponential stability,

Shan Xiong, Jiabao Chen, Ye Wang, Jialin Peng

Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biological and neuroscience research. Although unsupervised domain adaptation (UDA) methods can help mitigate domain shifts and reduce the high costs of annotating each domain, they typically have relatively low perform

Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin, Ehab Suleiman

Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task requiring expert intervention. In this work, we introduce RLABC (Reinforcement Learning for Accelerator Beamline Control), a Python-based library that reframes beamline optimizatio

Bin Yu, Xinming Wang, Shijie Lian, Haotian Li

Large language models (LLMs) have shown remarkable progress in complex reasoning tasks, largely enabled by test-time scaling (TTS) paradigms that allocate additional compute during inference. Among these, external TTS (particularly the Best-of-N selection paradigm) yields scalable performance improvements by selecting from multiple independently generated re

Yongxiang Hua, Haoyu Cao, Zhou Tao, Bocheng Li

Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models, offering substantial capacity while maintaining computational efficiency through dynamic, sparse activation of experts. However, existing routing mechanisms, typically based on similarity scoring, struggle to effectively capture the underlying input struc

Hongfei Fu, Dianming Hou, Zhonghua Qiao, Bingyin Zhang

In this paper, we investigate linear first- and second-order numerical schemes for the Allen--Cahn equation with a general (possibly degenerate) mobility. Compared with existing numerical methods, our schemes employ a novel dynamic stabilization approach that guarantees unconditional preservation of the maximum bound principle (MBP) and energy stability. A k

Jaekyun Park, Hye Won Chung

In the era of large-scale foundation models, fully fine-tuning pretrained networks for each downstream task is often prohibitively resource-intensive. Prompt tuning offers a lightweight alternative by introducing tunable prompts while keeping the backbone frozen. However, existing visual prompt tuning methods often fail to specialize the prompts or enrich th

Chien Thai, Mai Xuan Trang, Huong Ninh, Hoang Hiep Ly

Detecting rotated objects accurately and efficiently is a significant challenge in computer vision, particularly in applications such as aerial imagery, remote sensing, and autonomous driving. Although traditional object detection frameworks are effective for axis-aligned objects, they often underperform in scenarios involving rotated objects due to their li

Kunyu Peng, Di Wen, Jia Fu, Jiamin Wu

Referring Atomic Video Action Recognition (RAVAR) aims to recognize fine-grained, atomic-level actions of a specific person of interest conditioned on natural language descriptions. Distinct from conventional action recognition and detection tasks, RAVAR emphasizes precise language-guided action understanding, which is particularly critical for interactive h

Dimitris Stefanopoulos, Andreas Voskou

This report presents the winning solution for Task 2 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The goal of the challenge was to design and train a robust ANN-based model capable of achieving high accuracy in a binary classification task on both clean and adversarial data generated with t

Haoran Sun, Chen Cai, Huiping Zhuang, Kong Aik Lee

The rapid development of deepfake video technology has not only facilitated artistic creation but also made it easier to spread misinformation. Traditional deepfake video detection (DVD) methods face issues such as a lack of transparency in their principles and insufficient generalization capabilities to cope with evolving forgery techniques. This highlights

Yu Huang, Jiangtao Li, Yan Jiang, Ping Zhou

There exist extremely massive spiral galaxies in isolated environments, with stellar masses several times that of the Milky Way, yet their star formation rates (SFRs) are comparable to or even lower than that of the Milky Way. In this paper, we investigate the molecular gas properties of such galaxies to better understand the origin of their low SFRs. We pre

Dimitris Stefanopoulos, Andreas Voskou

This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while minimizing perturbations. Our approach employs a multi-round gra

Syed Rifat Raiyan, Md Farhan Ishmam, Abdullah Al Imran, Mohammad Ali Moni

Human communication heavily relies on laconism and inferential pragmatics, allowing listeners to successfully reconstruct rich meaning from sparse, telegraphic speech. In contrast, large language models (LLMs) owe much of their stellar performance to expansive input contexts, yet such verbosity inflates monetary costs, carbon footprint, and inference-time la

Aidyn Ubingazhibov, Rémi Pautrat, Iago Suárez, Shaohui Liu

Lines and points are complementary local features, whose combination has proven effective for applications such as SLAM and Structure-from-Motion. The backbone of these pipelines are the local feature matchers, establishing correspondences across images. Traditionally, point and line matching have been treated as independent tasks. Recently, GlueStick propos

Danielle Yaffe, Ferdinand Campe, Prachi Sharma, Dorothea Kolossa

Audio-visual speech enhancement (AVSE) has been found to be particularly useful at low signal-to-noise (SNR) ratios due to the immunity of the visual features to acoustic noise. However, a significant gap exists in AVSE methods tailored to enhance spatial audio under low-SNR conditions. The latter is of growing interest with augmented reality applications. T

Ehsan Roohi, Amirmehran Mahdavi

We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framework integrates three key components, a Fusion DeepONet operator learning architecture for capturing parameter dependencies, a physics-guided feature space that embeds a shock-aligned

Yingying Zhang, Dajun Song

Recently left Schur subcategories in a length abelian category were introduced by Enomoto, which unify torsion-free classes and wide subcategories. In this paper, we give a construction of left Schur subcategories in the recollements of length abelian categories. Moreover, we show that the construction restricts to wide subcategories and torsion-free classes

Riccardo Fantoni

We compare two kinds of affine localizations in physics: the localization in a short range polaron and the one in a Wick rotated Anderson stochastic model. The conditions on the interaction potential necessary to see the transnational symmetry breaking localization phase transition is identical in the two problems. We therefore suggest that they should belon

Tatsuya Shirai, Olivier Nourry, Yutaro Kashiwa, Kenji Fujiwara

Software vulnerabilities are constantly being reported and exploited in software products, causing significant impacts on society. In recent years, the main approach to vulnerability detection, fuzzing, has been integrated into the continuous integration process to run in short and frequent cycles. This continuous fuzzing allows for fast identification and r

Zahra Mobini, Ahmet Hasim Gokceoglu, Li Wang, Gunnar Peters

We exploit a general cluster-based network architecture for a fronthaul-limited user-centric cell-free massive multiple-input multiple-output (CF-mMIMO) system under different degrees of cooperation among the access points (APs) to achieve scalable implementation. In particular, we consider a CF-mMIMO system wherein the available APs are grouped into multipl

Nina Holden, Xin Sun

Originating in theoretical physics, Liouville quantum gravity (LQG) has been an important topic in probability theory and mathematical physics in the past two decades. In this proceeding, we review two aspects of this topic. The first is that LQG describes the random conformal geometry of the scaling limit of random planar maps. We highlight the convergence

Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang

This paper introduces a novel spatial scalar-on-function quantile regression model that extends classical scalar-on-function models to account for spatial dependence and heterogeneous conditional distributions. The proposed model incorporates spatial autocorrelation through a spatially lagged response and characterizes the entire conditional distribution of

Alok Panigrahi, Jayaprakash Katual, Satish Mulleti

Effective image deblurring typically relies on large and fully paired datasets of blurred and corresponding sharp images. However, obtaining such accurately aligned data in the real world poses a number of difficulties, limiting the effectiveness and generalizability of existing deblurring methods. To address this scarcity of data dependency, we present a no

Simran Soni, Neelima, Chaman Kumar, Goncalo dos Reis

We study McKean--Vlasov Stochastic Differential Equations (MV-SDEs) whose drift and diffusion coefficients are of superlinear growth in \textit{all} their variables thus also superlinear in the measure component (the meaning is specified in the body of the paper). We address the finite and infinite time horizon case. Our contribution is fourfold. (a) We esta

Alfonso Di Bartolo, Francesco Paolo Di Fatta, Gianmarco La Rosa

If one wishes to define a complete Leibniz algebra in such a way as to extend the notion of a complete Lie algebra, two distinct definitions can be found in the current literature. Since biderivations on complete Lie algebras have already been studied, in order to extend those results and considering that Leibniz algebras are, among others, a natural general

Asim Ilyas, Muhammad Faisal Khan, Rosita L. Sormani, Giacomo Tento

We consider a time-space fractional diffusion equation with a variable coefficient and investigate the inverse problem of reconstructing the source term, after regularizing the problem with the quasiboundary value method to mitigate the ill-posedness. The equation involves a Caputo fractional derivative in the space variable and a tempered fractional derivat

Dan Guo, Xibin Jin, Shuai Wang, Zhigang Wen

Edge robotics involves frequent exchanges of large-volume multi-modal data. Existing methods ignore the interdependency between robotic functionalities and communication conditions, leading to excessive communication overhead. This paper revolutionizes edge robotics systems through integrated perception, motion, and communication (IPMC). As such, robots can

Yu Zeng, Jinbao Li, Yong Yang

For a prime $p$ and an arbitrary finite group $G$, we show that if $p^{2}$ does not divide the size of each conjugacy class of \emph{$p$-regular} element (element of order not divisible by $p$) in $G$, then the largest power of $p$ dividing the index $|G:\mathbf{O}_{p}(G)|$ is at most $p^{2}$.

Dario Lucente, Giacomo Gradenigo, Luca Salasnich

One among the most intriguing results coming from the application of statistical mechanics to the study of brain is the understanding that it, as a dynamical system, is inherently out of equilibrium. In the realm of non-equilibrium statistical mechanics and stochastic processes the standard observable computed to discriminate whether a system is at equilibri

Baichen Yu, Jin Liu, Hansheng Wang

We develop here a semiparametric Gaussian mixture model (SGMM) for unsupervised learning with valuable spatial information taken into consideration. Specifically, we assume for each instance a random location. Then, conditional on this random location, we assume for the feature vector a standard Gaussian mixture model (GMM). The proposed SGMM allows the mixi

Adam Husted Kjelstrøm, Andreas Pavlogiannis, Jaco van de Pol

As quantum computing resources remain scarce and error rates high, minimizing the resource consumption of quantum circuits is essential for achieving practical quantum advantage. Here we consider the natural problem of, given a circuit $C$, computing a circuit $C'$ which behaves equivalently on a desired subspace, and that minimizes a quantum resource type,

Jiayi Guo, Haoxuan Li, Ye Tian, Peng Wu

While significant progress has been made in heterogeneous treatment effect (HTE) estimation, the evaluation of HTE estimators remains underdeveloped. In this article, we propose a robust evaluation framework based on relative error, which quantifies performance differences between two HTE estimators. We first derive the key theoretical conditions on the nuis

Jian Ma, Xinchen Lyu, Jun Jiang, Longhao Zou

Collaborative large language model (LLM) inference enables real-time, privacy-preserving AI services on resource-constrained edge devices by partitioning computational workloads between client devices and edge servers. However, this paradigm is severely hindered by communication bottlenecks caused by the transmission of high-dimensional intermediate activati

Elisabetta Rocchi

We introduce and study the Hesse pencil variety $H_8$, obtained as the Zariski closure in the Grassmannian $G(1,9)$ of the set of pencils generated by a smooth plane cubic and its Hessian. We prove that $H_8$ has dimension $8$ and can be realized as the intersection of $G(1,9)$ with ten hyperplanes corresponding to the Schur module $\mathbb{S}_{(5,1)}\mathbb

Rizhen Hu, Yutong He, Ran Yan, Mou Sun

As distributed optimization scales to meet the demands of Large Language Model (LLM) training, hardware failures become increasingly non-negligible. Existing fault-tolerant training methods often introduce significant computational or memory overhead, demanding additional resources. To address this challenge, we propose Memory- and Computation-efficient Faul

Emmanuel U. Ugwu, Zhang Xinming

Fire segmentation remains a critical challenge in computer vision due to flames' irregular boundaries, translucent edges, and highly variable intensities. While the Segment Anything Models (SAM and SAM2) have demonstrated impressive cross-domain generalization capabilities, their effectiveness in fire segmentation -- particularly under mobile deployment cons

Yuang Chen, Fengqian Guo, Chang Wu, Shuyi Liu

In the Industrial Internet of Things (IIoT), the frequent transmission of large amounts of data over wireless networks should meet the stringent timeliness requirements. Particularly, the freshness of packet status updates has a significant impact on the system performance. In this paper, we propose an age-of-information (AoI)-aware multi-base station (BS) r

Wenbin Li, Ken K. T. Hung, Shingyu Leung

We present a novel multilayer level-set method (MLSM) for eikonal-based first-arrival traveltime tomography. Unlike classical level-set approaches that rely solely on the zero-level set, the MLSM represents multiple phases through a sequence of $i_n$-level sets ($n = 0, 1, 2, \cdots$). Near each $i_n$-level set, the function is designed to behave like a loca

Vincent Guedj, Ahmed Zeriahi

Let $\Omega \Subset \C^n$ be a bounded strongly pseudoconvex domain. For any concave increasing weight $\chi : \R^- \longrightarrow \R^-$ such that $\chi(0) = 0$, we introduce and study finite energy classes $\mathcal E_\chi(\Omega)$ of plurisubharmonic functions, using the Orlicz space formalism. We investigate the range of the Monge-Amp\`ere operator on th

Minh-Khoi Nguyen-Nhat, Rachel S. Y. Teo, Laziz Abdullaev, Maurice Mok

Sparse Mixture of Experts (SMoE) has emerged as a promising solution to achieving unparalleled scalability in deep learning by decoupling model parameter count from computational cost. By activating only a small subset of parameters per sample, SMoE enables significant growth in model capacity while maintaining efficiency. However, SMoE struggles to adapt to

Changyue Shi, Minghao Chen, Yiping Mao, Chuxiao Yang

Bridging the gap between complex human instructions and precise 3D object grounding remains a significant challenge in vision and robotics. Existing 3D segmentation methods often struggle to interpret ambiguous, reasoning-based instructions, while 2D vision-language models that excel at such reasoning lack intrinsic 3D spatial understanding. In this paper, w

Didier Sornette

Uncertainty defines our age: it shapes climate, finance, technology, and society, yet remains profoundly misunderstood. We oscillate between the illusion of control and the paralysis of fatalism. This paper reframes uncertainty not as randomness but as ignorance: a product of poor models, institutional blindness, and cognitive bias. Drawing on insights from

Sen Zhan, Lingkang Jin, Haoyang Zhang, Nikolaos G. Paterakis

The secure operation of power distribution systems is challenged by the growing integration of distributed energy resources. Leveraging the flexibility of battery storage offers a cost-effective alternative to measures like generation curtailment, which results in energy losses. However, developing an effective operational model for battery storage is hinder

Achal Agrawal, Jeet Mukherjee

In this article we propose a novel method to perform unsupervised clustering of different forms of Institute names. We use only author and affiliation metadata to perform the clustering without any string or pattern matching. After analysing only 50000 articles from Crossref database, we see encouraging results which can be scaled up to provide even better r

Yujun Zheng, Xinya Chen, Xueqin Lu, Weiguo Sheng

Emotional stress often has a significant effect on the working performance of staff, but this effect is commonly neglected in existing staff scheduling methods. We study a call-center staff scheduling problem, which considers the evolution of work performance of staff under emotional stress. First, we present an emotional stress driven model that estimates t

P. Agnes, I. Ahmad, S. Albergo, I. Albuquerque

In the recent years, argon-based experiments looking for Dark Matter in the Universe have explored the non-standard scenario in which Dark Matter is made by low-mass Weakly Interacting Massive Particles, of mass in the range of 1-10 GeV instead of the canonical hundreds of GeV. Detecting such particles is challenging, as their expected signatures are nuclear

Tan-Phuc Nguyen, Thai-Hung Nguyen, Tien-Khai Nguyen, Cong-Duy-Nguyen Nguyen

In a recent paper~\cite{paper2}, we proposed the concept of optimal error bounds for an iterative process, which allows us to obtain the convergence result of the iterative sequence to the common fixed point of the nonexpansive mappings in Banach spaces. Moreover, we also achieve the comparison results between different iterative processes via optimal error

Bernd Finkbeiner, Julian Siber

Explainability is emerging as a key requirement for autonomous systems. While many works have focused on what constitutes a valid explanation, few have considered formalizing explainability as a system property. In this work, we approach this problem from the perspective of hyperproperties. We start with a combination of three prominent flavors of modal logi

Ning Sun, Peng Zhang, Pengfei Zhang

Understanding the emergence of complex correlations in strongly interacting systems remains a fundamental challenge in quantum many-body physics. One fruitful approach is to develop solvable toy models that encapsulate universal properties shared by realistic systems. In this work, we introduce the Brownian SYK-Hubbard model, which combines the all-to-all ra

Wenbiao Tao, Xinyuan Li, Yunshi Lan, Weining Qian

Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses. However, traditional RAG methods rely on fragment-level retrieval, limiting their ability to address query-focused summarization queries. GraphRAG introduces a graph-based paradigm for global knowledge reasoning, yet suffers fr

Volker Mehrmann, Manuel Schaller, Martin Stoll

This work considers the iterative solution of large-scale problems subject to non-symmetric matrices or operators arising in discretizations of (port-)Hamiltonian partial differential equations. We consider problems governed by an operator $\mathcal{A}=\mathcal{H}+\mathcal{S}$ with symmetric part $\mathcal{H}$ that is positive (semi-)definite and skew-symmet

Nick Bezhanishvili, Balder ten Cate, Rosalie Iemhoff

In this chapter, we present six different proofs of Craig interpolation for the modal logic K, each using a different set of techniques (model-theoretic, proof-theoretic, syntactic, automata-theoretic, using quasi-models, and algebraic). We compare the pros and cons of each proof technique.

Yiming Xu, Dongfang Xu, Shenghui Song, Dusit Niyato

The channel state information (CSI) of an eavesdropper is crucial for physical layer security (PLS) design, but it is difficult to obtain due to the passive and non-cooperative nature of the eavesdropper. To this end, integrated sensing and communication (ISAC) offers a novel solution by estimating the CSI of the eavesdropper based on sensing information. Ho

Yeh Keng Hao, Hsu Tzu Wei, Sun Min

With the increasing ubiquity of AR/VR devices, the deployment of deep learning models on edge devices has become a critical challenge. These devices require real-time inference, low power consumption, and minimal latency. Many framework designers face the conundrum of balancing efficiency and performance. We design a light framework that adopts an encoder-de

Xin Peng, Chong Wang

Recent advances in large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, raising expectations of revolutionary productivity gains. However, enterprise software development is largely driven by incremental evolution, where challenges extend far beyond routine coding and depend critically on tacit knowledge, includin

Jinqi Zhang, Lamei Zhang, Bin Zou

Synthetic Aperture Radar (SAR) image captioning enables scene-level semantic understanding and plays a crucial role in applications such as military intelligence and urban planning, but its development is limited by the scarcity of high-quality datasets. To address this, we present FSAR-Cap, a large-scale SAR captioning dataset with 14,480 images and 72,400

Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Salvatore Trani

We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense neural representations of MS-MARCO queries and passages are complemented and integrated with 253 hand-crafted lexical features extracted from the same corpus. Blending of the relevan

Renzhi He, Haowen Zhou, Yubei Chen, Yi Xue

Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical tomography techniques require hundreds of 2D images to reconstruct a 3D volume, hindering them from intravital imaging of biological samples undergoing rapid dynamics. This poses the

Serge Gratton, Philippe L. Toint

A very simple first-order algorithm is proposed for solving nonlinear optimization problems with deterministic nonlinear equality constraints. This algorithm adaptively selects steps in the plane tangent to the constraints or steps that reduce infeasibility, without using a merit function or filter. The tangent steps are based on the AdaGrad method for uncon

Yubin Luo, Li Yu, Tao Wu, Yuxiang Zhang

Digital twin (DT) is a core enabler of sixth generation (6G) mobile systems. As a prerequisite for DT, scatterer geometric reconstruction (SGR) in propagation environments is essential but typically requires extra sensors such as cameras and LiDAR. With integrated sensing and communication (ISAC) in 6G, we reinterpret the linear sampling method (LSM) from a

Doriana Armenise, Ginevra Battello, Andrea Brunello, Lorenza Driul

The fragmentation of obstetric information across electronic health record modules, device repositories, and laboratory systems, as it is common in hospitals, hinders both intrapartum care and reproducible research. In this work, we present a practical blueprint for transforming heterogeneous peripartum records into computable, queryable assets by designing

Fu-An Chao, Bi-Cheng Yan, Berlin Chen

In this paper, we explore the untapped potential of Whisper, a well-established automatic speech recognition (ASR) foundation model, in the context of L2 spoken language assessment (SLA). Unlike prior studies that extrinsically analyze transcriptions produced by Whisper, our approach goes a step further to probe its latent capabilities by extracting acoustic

Lihong Xu, Wenxiang Jiang

Traditional robust multi-objective optimization methods typically prioritize convergence while treating robustness as a secondary consideration. This approach can yield solutions that are not genuinely robust optimal under noise-affected scenarios. Furthermore, compared to population-based search methods, determining the robust optimal solution by evaluating

Runchu Donga, Peng Zhao, Guiqin Wang, Nan Qi

Real-time video analytics systems typically deploy lightweight models on edge devices to reduce latency. However, the distribution of data features may change over time due to various factors such as changing lighting and weather conditions, leading to decreased model accuracy. Recent frameworks try to address this issue by leveraging remote servers to conti

Naoyuki Kamiyama

The stable roommates problem is a non-bipartite version of the stable matching problem in a bipartite graph. In this paper, we consider the stable roommates problem with ties. In particular, we focus on strong stability, which is one of the main stability concepts in the stable roommates problem with ties. We propose a new polynomial-time algorithm for the p

Bishal Chhetri, B. V. Rathish Kumar

In this study, we present an interpretable deep learning framework for the early detection of breast cancer using quantitative features extracted from digitized fine needle aspirate (FNA) images of breast masses. Our deep neural network, using ReLU activations, the Adam optimizer, and a binary cross-entropy loss, delivers state-of-the-art classification perf

Riccardo Fantoni

In a recent trilogy we proposed a Statistical Theory of General Relativity spacetime. Here we apply our new theory to determine the (energy) ``density'' and (virial) ``temperature'' dependence of the structure of the spacetime quantum vacuum working on the simple case of a real massless scalar field in a local Lorentz frame.

Ze Tao, Jian Zhang, Haowei Li, Xianshuai Li

This paper proposes the Humanoid-inspired Structural Causal Model (HSCM), a novel causal framework inspired by human intelligence, designed to overcome the limitations of conventional domain generalization models. Unlike approaches that rely on statistics to capture data-label dependencies and learn distortion-invariant representations, HSCM replicates the h

David Peer, Sebastian Stabinger

Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent limitations in trustworthiness, including hallucinations, instability, and a lack of transparency. To address these challenges, we introduce a generic neuro-symbolic approach, which we call Autonomous Trustworthy Agents

Jian Zhang

Recent advancements in natural language processing, particularly with large language models (LLMs), are transforming how scientists engage with the literature. While the adoption of LLMs is increasing, concerns remain regarding potential information biases and computational costs. Rather than LLMs, I developed a framework to evaluate the feasibility of preci

Yaxin Pan, Chongze Wang, Shuyuan Liu, Fengzhu Ren

Two-dimensional(2D) multiferroic materials hold significant promise for advancing the miniaturization and integration of nanodevices. In this study, we demonstrate that 2D bilayer ScI2, which exhibits ferromagnetic(FM) ordering within each layer, enables the tuning of interlayer magnetic coupling, ferroelectricity, and valley polarization through interlayer

Jacob Bedrossian, Siming He, Sameer Iyer, Linfeng Li

In this paper, we develop a stability threshold theorem for the 2D incompressible Navier-Stokes equations on the channel, supplemented with the no-slip boundary condition. The initial datum is close to the Couette flow in the following sense: the shear component of the perturbation is small, but independent of the viscosity $\nu$. On the other hand, the $x$-

Tianhang Cheng, Albert J. Zhai, Evan Z. Chen, Rui Zhou

Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric

Han Wang, Chao Ning

Conformal Prediction (CP) is a powerful statistical machine learning tool to construct uncertainty sets with coverage guarantees, which has fueled its extensive adoption in generating prediction regions for decision-making tasks, e.g., Trajectory Optimization (TO) in uncertain environments. However, existing methods predominantly employ a sequential scheme,

Rishi Raj Sahoo, Surbhi Saswati Mohanty, Subhankar Mishra

Road potholes pose significant safety hazards and maintenance challenges, particularly on India's diverse and under-maintained road networks. This paper presents iWatchRoadv2, a fully automated end-to-end platform for real-time pothole detection, GPS-based geotagging, and dynamic road health visualization using OpenStreetMap (OSM). We curated a self-annotate

Nick Oh

Current approaches to enhancing LLM reasoning follows two isolated paradigms: Monitor-Generate methods like Plan-and-Solve (Wang et al., 2023) and SELF-DISCOVER (Zhou et al., 2024) excel at strategic planning but lack mechanisms to verify whether selected strategies succeed; while Generate-Verify approaches like Self-Verification (Weng et al., 2022) and SELF

Federico Ravenda, Seyed Ali Bahrainian, Andrea Raballo, Antonietta Mira

The rapid evolution of Large Language Models (LLMs) is transforming AI, opening new opportunities in sensitive and high-impact areas such as Mental Health (MH). Yet, despite these advancements, recent evidence reveals that smaller-scale models still struggle to deliver optimal performance in domain-specific applications. In this study, we present a cost-effi

Yue Zhang, Longnan Li, Junyan Dai, Xiaowen Zhang

Low emissivity (low-e) materials are crucial for conserving thermal energy in buildings, cold chain logistics and transportation by minimizing unwanted radiative heat loss or gain. However, their metallic nature intrinsically causes severe longwave attenuation, hindering their broad applications. Here, we introduce, for the first time, an all-dielectric long

Mohammad Javad Ahmadi, Iman Gandomi, Parisa Abdi, Seyed-Farzad Mohammadi

Computer-assisted surgery research requires large, deeply annotated video datasets that capture clinical and technical variability. Existing cataract surgery resources lack the diversity and annotation depth required to train generalizable deep-learning models. To address this gap, we present a dataset of 3,000 phacoemulsification cataract surgery videos acq

Pulin Li, Guocheng Wu, Li Yin, Yuxin Zheng

Social manufacturing leverages community collaboration and scattered resources to realize mass individualization in modern industry. However, this paradigm shift also introduces substantial challenges in quality control, particularly in defect detection. The main difficulties stem from three aspects. First, products often have highly customized configuration

William Liu

A time-dependent modeling framework for autogenous self-healing concrete that couples moisture diffusion with damage evolution was developed. Water transport follows Fick's second law with a damage-dependent diffusivity obtained by power-law interpolation between intact concrete and crack space. Healing reduces damage in proportion to the product of local mo

Nurali Akramov

In this work, we prove that the complement of the Brjuno set $\mathcal{B}$ has a zero capacity with respect to the kernel $k^1_\sigma(z,\xi)=\ln^2{|z-\xi|}\left|\ln{\ln{\left(e+\frac{1}{|z-\xi|}\right)}}\right|^\sigma$ for any$\sigma > 2$. Similarly, the complement of the Perez-Marco set $\mathcal{PM}$ has a zero capacity with respect to the kernel $k^2_\sig

Ali Shirali

From media platforms to chatbots, algorithms shape how people interact, learn, and discover information. Such interactions between users and an algorithm often unfold over multiple steps, during which strategic users can guide the algorithm to better align with their true interests by selectively engaging with content. However, users frequently exhibit incon

Shuai Li, Kejiang Chen, Jun Jiang, Jie Zhang

Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources, rendering them valuable digital assets. Therefore, it is essential to watermark LLMs to protect their copyright and trace unauthorized use or resale. Existing methods for watermarking LLMs primarily rely on training

Xinyi Li, Zhiqiang Guo, Qinglang Guo, Hao Jin

Large language models (LLMs) offer strong semantic reasoning capabilities for user modeling, but applying LLM-based simulation to an entire social network is computationally expensive and often unreliable for users with sparse behavioral histories. Meanwhile, conventional information diffusion models efficiently exploit historical propagation patterns and so

Ramesh Kumar Vaduganathan, Prasanta K. Panigrahi, Boris A. Malomed

Electromagnetically induced transparency (EIT) is well known as a quantum optical phenomenon that permits a normally opaque medium to become transparent due to the quantum interference between transition pathways. This work addresses multi-soliton dynamics in an EIT system modeled by the integrable Maxwell-Bloch (MB) equations for a three-level $\Lambda $-ty

Nilmadhab Das, Vishal Vaibhav, Yash Sunil Choudhary, V. Vijaya Saradhi

Argument Mining (AM) helps in automating the extraction of complex argumentative structures such as Argument Components (ACs) like Premise, Claim etc. and Argumentative Relations (ARs) like Support, Attack etc. in an argumentative text. Due to the inherent complexity of reasoning involved with this task, modelling dependencies between ACs and ARs is challeng

Zohreh Hashempour, Mehdi Nasri Nasrabadi, Nora Nassiri-Mofakham, Hamidreza Daniali

In the study of plasma, particularly in applications involving strong laser-plasma interactions, the propagation of a strong electromagnetic wave induces relativistic velocities in the electron flow. Given such conditions, the wave propagating through the plasma experiences modulational instability. In this paper, we investigate this instability using magnet

Angelo Giorgio Cavaliere, Riki Nagasawa, Shuta Yokoi, Tomoyuki Obuchi

We consider tensor factorizations based on sparse measurements of the components of relatively high rank tensors. The measurements are designed in a way that the underlying graph of interactions is a random graph. The setup will be useful in cases where a substantial amount of data is missing, as in completion of relatively high rank matrices for recommendat

Zhifei Gui, Ping Zhu, Dominique Franck Escande

This study applies the Hamiltonian method to analyze the nonlinear magnetic topology induced by Resonant Magnetic Perturbations (RMPs) in tokamaks. We investigate the system's chaotic behavior by comparing three methods: the renormalization method, Lyapunov exponents (LE), and weighted Birkhoff average (WBA). A strong consistency is found among these methods

Yuchen Xiao, Corwin Zigler, Peter H. Hennings, Alexandros Savvaidis

Evidence derived primarily from physical models has identified saltwater disposal as the dominant causal factor that contributes to induced seismicity. To complement physical models, statistical/machine learning (ML) models are designed to measure associations from observational data, either with parametric regression models or more flexible ML models. Howev