November 2025 arXiv papers — page 143

Parsa Hedayatnia, Tina Tavakkoli, Hadi Amini, Mohammad Allahbakhsh

Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulne

Shuanglin Huang, Xuefeng Feng, Yun-Kau Lau

Motivated by a geometric understanding of the angular velocity of a Kerr black hole in terms of a quasi-conformal map that describes a 2d Beltrami fluid flow, a new way to construct initial data sets for binary rotating black holes by prescribing the angular velocities of the two black holes at their horizons is discussed. A set of elliptic equations with pr

Mingjie Zhao, Zhanpei Huang, Yang Lu, Mengke Li

Categorical attributes with qualitative values are ubiquitous in cluster analysis of real datasets. Unlike the Euclidean distance of numerical attributes, the categorical attributes lack well-defined relationships of their possible values (also called categories interchangeably), which hampers the exploration of compact categorical data clusters. Although mo

Shengbo Wang, Hong Sun, Ke Li

Interactive preference elicitation (IPE) aims to substantially reduce human effort while acquiring human preferences in wide personalization systems. Dueling bandit (DB) algorithms enable optimal decision-making in IPE building on pairwise comparisons. However, they remain inefficient when human feedback is sparse. Existing methods address sparsity by heavil

Jeroen S. W. Lamb, Martin Rasmussen, Kalle G. Timperi

We give an example of a planar set $E\subset \mathbb{R}^2$ for which the boundary $\partial E_\varepsilon$ of its $\varepsilon$-neighbourhood $E_\varepsilon = \{x \in \mathbb{R}^2 \, : \, \textrm{dist}(x, E) \leq \varepsilon \}$ is nowhere $C^1$-smooth, in the sense that the set of singularities on the boundary is countably dense (where we note that the latt

Penghui Niu, Taotao Cai, Suqi Zhang, Junhua Gua

Ground-based remote sensing cloud image sequence extrapolation is a key research area in the development of photovoltaic power systems. However, existing approaches exhibit several limitations:(1)they primarily rely on static kernels to augment feature information, lacking adaptive mechanisms to extract features at varying resolutions dynamically;(2)temporal

Yousef Emami, Radha Reddy, Azadeh Pourkabirian, Miguel Gutierrez Gaitan

Autonomous Vehicles (AVs) are poised to revolutionize emergency services by enabling faster, safer, and more efficient responses. This transformation is driven by advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL), which allows AVs to navigate complex environments and make critical decisions in real time. However, conventional

Farjana Yesmin

Healthcare federated learning requires strong privacy guarantees while maintaining computational efficiency across resource-constrained medical institutions. This paper presents MedHE, a novel framework combining adaptive gradient sparsification with CKKS homomorphic encryption to enable privacy-preserving collaborative learning on sensitive medical data. Ou

Liangwei Yang, Jing Ma, Jianguo Zhang, Zhiwei Liu

Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation. However, the representation spaces of modern PLMs are highly non--linear and geometrically structured, where textual embeddings reside on curved semantic man

Jiale Ye, Jiaheng Wang, Yu-xi Liu

Accurate electromagnetic (EM) feature extraction, including element characterization, eigenmodes, and field distributions, is essential for superconducting quantum circuit design. To streamline this process, we present a workflow built around Palace, an open-source, high-performance finite element method solver tailored for quantum applications. Starting fro

Weihao Liu, Mikhail Dobynde, Jingnan Guo, Jordanka Semkova

Understanding the long-term variation of the galactic cosmic ray (GCR) radiation environment is critical for assessing radiation risks in space exploration missions. In this study, we systematically model the linear energy transfer (LET) spectra of GCRs and the corresponding radiation quality factor, <Q>, in deep space and shielding environments. The Badhwar

Anushka Sanjay Shelke, Aditya Sneh, Arya Adyasha, Haroon R. Lone

Fairness in AI-driven stress detection is critical for equitable mental healthcare, yet existing models frequently exhibit gender bias, particularly in data-scarce scenarios. To address this, we propose FairM2S, a fairness-aware meta-learning framework for stress detection leveraging audio-visual data. FairM2S integrates Equalized Odds constraints during bot

Oussama Jebbar, Ferhat Khendek, Maria Toeroe

Live testing is performed in the production environment ideally without causing unacceptable disturbance to the production traffic. Thus, test activities have to be orchestrated properly to avoid interferences with the production traffic. A test plan is the road map that specifies how the test activities need to be orchestrated. Developing a test plan includ

Rolf Bader, Niko Plath, Patrick Kontopidis

The sound perception of wood aging is investigated on a Dulcken harpsichord of 1755 from the Museum of Applied Arts in Hamburg, Germany using a Finite-Difference Time Domain (FDTD) model of the harpsichords soundboard. The soundboard thickness was measured on the instrument at 497 positions during strings being deattached and used in the model. Impulse respo

Zhenyuan Huang, Hui Zhang, Wenzhong Tang, Haijun Yang

Amid growing demands for data privacy and advances in computational infrastructure, federated learning (FL) has emerged as a prominent distributed learning paradigm. Nevertheless, differences in data distribution (such as covariate and semantic shifts) severely affect its reliability in real-world deployments. To address this issue, we propose FedSDWC, a cau

Kai Du

We develop a fully constructive, conservative, and collision-level realization of Kac's program for the spatially homogeneous Landau equation across the full interaction range, including the Coulomb case. Our model is the microcanonical binary-collision (MBC) process: a reversible pure-jump $N$-particle Markov process that is Landau-native, realizing the gra

Ekta Tiwari

Let $G$ denote the unramified quasi-split unitary group $\mathbb{U}(1,1)(F)$ over a $p$-adic field $F$ with residual characteristic $p \neq 2$. In this article, we determine the branching rules for all irreducible supercuspidal representations of $G$, that is, we explicitly describe their decomposition upon restriction to a fixed maximal compact subgroup $\m

Dingji Wang, You Lu, Bihuan Chen, Shuo Hao

End-to-end autonomous driving systems (ADSs), with their strong capabilities in environmental perception and generalizable driving decisions, are attracting growing attention from both academia and industry. However, once deployed on public roads, ADSs are inevitably exposed to diverse driving hazards that may compromise safety and degrade system performance

Maryam Shiravand, Saeed Fakhry, Javad T. Firouzjaee, Ali Tizfahm

Cosmological inflation remains a key paradigm for explaining the earliest stages of the Universe, yet the theoretical limitations of General Relativity (GR) motivate the development of alternative formulations capable of addressing both early and late cosmic acceleration. In this work, we investigate cosmological inflation within the $f(Q,\mathcal{L}_{m})$ g

Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz Shahrzad

LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Han

Leonie Böhlke, Tim Ziemer, Rolf Bader

Subjective room acoustics impressions play an important role for the performance and reception of music in concert venues and auralizations. Therefore, room acoustics since the 20th century dealt with the relationship between objective, acoustic parameters and subjective impressions of room acoustics. One common approach is to correlate acoustic measures wit

Jinkun You, Jiaxue Li, Jie Zhang, Yicong Zhou

Existing unsupervised image alignment methods exhibit limited accuracy and high computational complexity. To address these challenges, we propose a dense cross-scale image alignment model. It takes into account the correlations between cross-scale features to decrease the alignment difficulty. Our model supports flexible trade-offs between accuracy and effic

Zinovy Malkin

In this paper, progress in improving the coordinates uncertainty of extragalactic radio sources derived from astrometric and geodetic VLBI observations is investigated. For this purpose, 30 catalogs of radio source positions computed in 1997-2025 were analyzed. Over these years, the median source position uncertainty represented by the semi-major axis of the

Tianao Xiang, Mingjian Zhi, Yuanguo Bi, Lin Cai

Large Language Models (LLMs) have impressive data fusion and reasoning capabilities for autonomous driving (AD). However, training LLMs for AD faces significant challenges including high computation transmission costs, and privacy concerns associated with sensitive driving data. Federated Learning (FL) is promising for enabling autonomous vehicles (AVs) to c

Simon Kuang, Xinfan Lin

Instrumental variables (eliminate the bias that afflicts least-squares identification of dynamical systems through noisy data, yet traditionally relies on external instruments that are seldom available for nonlinear time series data. We propose an IV estimator that synthesizes instruments from the data. We establish finite-sample $L^{p}$ consistency for all

Ye Jin Choi, Sebastian Kurtek, Simeng Zhu, Karthik Bharath

Intra-tumor heterogeneity driving disease progression is characterized by distinct growth and spatial proliferation patterns of cells and their nuclei within tumor and non-tumor tissues. A widely accepted hypothesis is that these spatial patterns are correlated with morphology of the cells and their nuclei. Nevertheless, tools to quantify the correlation, wi

Hayate Toba, Atsushi Yano, Takuya Azumi

Estimating the probabilistic Worst-Case Execution Time (pWCET) is essential for ensuring the timing correctness of real-time applications, such as in robot IoT systems and autonomous driving systems. While methods based on Extreme Value Theory (EVT) can provide tight bounds, they suffer from model uncertainty due to the need to decide where the upper tail of

Penghui Niu, Jiashuai She, Taotao Cai, Yajuan Zhang

Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting. Current deep learning approaches primarily focus on encoder-decoder architectural refinements. However, existing methodologies exhibit several limitations:(1)they rely on dilated convolutions for multi-scale context extraction, lacking the partial feature

Geonwoo Bang, Dongho Kim, Moohong Min

Evaluating complex texts across domains requires converting user defined criteria into quantitative, explainable indicators, which is a persistent challenge in search and recommendation systems. Single prompt LLM evaluations suffer from complexity and latency issues, while criterion specific decomposition approaches rely on naive averaging or opaque black-bo

Weicheng Gao

Radar-based human activity recognition (HAR) still lacks a comprehensive simulation method. Existing software is developed based on models or motion-captured data, resulting in limited flexibility. To address this issue, a simulator that directly generates Doppler spectra from recorded video footage (RadHARSimulator V2) is presented in this paper. Both compu

Aloïs Duguet, Tobias Harks, Martin Schmidt, Julian Schwarz

We consider combinatorial multi-item markets and propose the notion of a $\Delta$-regret Walras equilibrium, which is an allocation of items to players and a set of item prices that achieve the following goals: prices clear the market, the allocation is capacity-feasible, and the players' strategies lead to a total regret of $\Delta$. The regret is defined a

Mingyang Yu, Haorui Yang, Kangning An, Xinjian Wei

With the widespread adoption of unmanned aerial vehicles (UAV), effective path planning has become increasingly important. Although traditional search methods have been extensively applied, metaheuristic algorithms have gained popularity due to their efficiency and problem-specific heuristics. However, challenges such as premature convergence and lack of sol

Abraham Francisco Arellano Tavara, Umesh Kumar, Jathurshan Pradeepkumar, Jimeng Sun

Variants of Uncertain Significance (VUS) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is incomplete. Progress is further limited by inconsistent annotations across sources and the absence of a prostate-specific benchmark for fair comparison. We introduce Prostate-VarBenc

Wei-Yang Liu, Edward Shuryak, Ismail Zahed

In the Operator Product Expansion (OPE) of hard scattering amplitudes, the twist-3 operators describe local colored Lorentz forces acting on a quark, thereby providing a measure of the strength of the gluon fields. Its value is directly accessible from the nucleon twist-3 polarized $g_2$-parton distribution function. In the semiclassical (instanton-based) QC

Liu Yu, Zhonghao Chen, Ping Kuang, Zhikun Feng

Object hallucination remains a critical challenge in Large Vision-Language Models (LVLMs), where models generate content inconsistent with visual inputs. Existing language-decoder based mitigation approaches often regulate visual or textual attention independently, overlooking their interaction as two key causal factors. To address this, we propose Owl (Bi-m

Jacob Desmond, Ryan Wartmann, Chng Wei Lau, Steven Thomas

Healthcare data are generated in many different formats, which makes it difficult to integrate and reuse across institutions and studies. Standardisation is required to enable consistent large-scale analysis. The OMOP-CDM, developed by the OHDSI community, provides one widely adopted standard. Our framework achieves schema-agnostic transformation by extendin

Simon Kuang, Xinfan Lin

The Kalman filter and Rauch-Tung-Striebel (RTS) smoother are optimal for state estimation in linear dynamic systems. With nonlinear systems, the challenge consists in how to propagate uncertainty through the state transitions and output function. For the case of a neural network model, we enable accurate uncertainty propagation using a recent state-of-the-ar

Maarten Derickx, Filip Najman

We determine all integers $N$ for which the modular curve $X_0(N)$ admits a sporadic CM point (of any degree), as well as all $N$ for which $X_0(N)$ admits a sporadic point, whether CM or non-CM. In a sense, our results generalize the classification of isogenies of elliptic curves over $\Q$ due to Mazur and Kenku: their work determines the $X_0(N)$ with degr

Zijian Chen, Wenjun Zhang, Guangtao Zhai

The potential data contamination issue in contemporary large language models (LLMs) benchmarks presents a fundamental challenge to establishing trustworthy evaluation frameworks. Meanwhile, they predominantly assume benign, resource-rich settings, leaving the behavior of LLMs under pressure unexplored. In this paper, we introduce \textsc{Squid Game}, a dynam

Udaya Bhasker Cheerala, Varun Teja Chirukuri, Venkata Akhil Kumar Gummadi, Jintu Moni Bhuyan

Wildfires pose a significant global threat to ecosystems worldwide, with California experiencing recurring fires due to various factors, including climate, topographical features, vegetation patterns, and human activities. This study aims to develop a comprehensive wildfire risk map for California by applying the random forest (RF) algorithm, augmented with

Xue Jiang, Yuanhe Li, Zhe Li

As a generalization of Hermite interpolation problem, Birkhoff interpolation is an important subject in numerical approximation. This paper generalizes the existing Generalized Recursive Polynomial Interpolation Algorithm (GRPIA) that is used to compute the Hermite interpolation polynomial. Based on the theory of the Schur complement and the Sylvester identi

Ziyi Song, Chen Xia, Chenbing Wang, Haibao Yu

Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While recent multi-agent approaches introduce cooperation, they often focus merely on perception-level tasks, overlooking the alignment with downstream planning and control, or fall short

Francesco Alemanno

Fixed-point solvers are ubiquitous in nonlinear PDEs, yet their progress collapses whenever the Jacobian at the solution carries an eigenvalue arbitrarily close to one. We ask whether such stagnation can be removed without storing long histories or solving dense least squares. Under two assumptions -- (A1) the linearised error $e_n$ is dominated by a multipl

Soumya Chakrabarti, Naresh Dadhich, Chiranjeeb Singha

We prove that there can not be a smooth matching of the Generalized Vaidya metric with an exterior Schwarzschild/Vaidya patch across a finite boundary hypersurface unless the mass function is a function of the null coordinate alone. By explicitly deriving the extrinsic curvature components, we show that for $\partial m / \partial r \neq 0$ one has a disconti

Jinlin Fan, Feilong Wang, Ruolin Chai Zhibin Zhao, Qiongtao Xie

We investigate a two-parametric family of one-dimensional non-Hermitian complex potentials with parity-time ($\mathcal{PT}$) symmetry. We find that there exist two distinct types of phase transitions, from an unbroken phase (characterized by a real spectrum) to a broken phase (where the spectrum becomes complex). The first type involves the emergence of a pa

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme J. Ackland

The discovery of superconductivity in MgB$_2$ (\(T_c = 39\) K) \cite{nagamatsu2001superconductivity} established metal diborides (MB$_2$) as a promising class of conventional superconductors. Recent advances in fabrication techniques have enabled the synthesis of 2D MgB$_2$ with a \(T_c\) of 36 K \cite{cheng2018fabrication}, reigniting interest in layered me

Anshu Arora, Kaluguri Yashaswini, Satish Mulleti

Integrate-and-fire time encoding machines (IF-TEMs) provide an efficient framework for asynchronous sampling of bandlimited signals through discrete firing times. However, conventional IF-TEMs often exhibit excessive oversampling, leading to inefficient encoding for signals with smoothly distributed information. This letter introduces a linear-bias IF-TEM (L

Ameneh Zarei, Mahmood Ahmadi, Farhad Mardukhi

The Internet of Things (IoT) is transforming industries by connecting billions of devices to collect, process, and share data. However, the massive data volumes and real-time demands of IoT applications strain traditional cloud computing architectures. This paper explores the complementary roles of cloud, fog, and edge computing in enhancing IoT performance,

Alvin Chauhan

Although Large Language Models (LLMs) show exceptional fluency, efforts persist to extract stronger reasoning capabilities from them. Drawing on search-based interpretations of LLM computation, this paper advances a systematic framework for understanding LLM reasoning and optimization. Namely, that enhancing reasoning is best achieved by structuring a multi-

Qinyang Song, Ming Dong, Lei Zhao

In hypersonic boundary-layer flows over blunt bodies, laminar-turbulent transition exhibits two distinct regimes: for small nose radii, increased bluntness delays transition; beyond a critical radius, further increasing bluntness reverses this trend. The latter regime corresponds to a bypass transition route, whose onset remains challenging to predict. The p

Zhouxing Tan, Ruochong Xiong, Yulong Wan, Jinlong Ma

Emotional support is a core capability in human-AI interaction, with applications including psychological counseling, role play, and companionship. However, existing evaluations of large language models (LLMs) often rely on short, static dialogues and fail to capture the dynamic and long-term nature of emotional support. To overcome this limitation, we shift

Yuka Haruki, Shigeru Ishikura, Kazuya Demachi, Teruaki Hayashi

As organizations continue to access diverse datasets, the demand for effective data integration has increased. Key tasks in this process, such as schema matching and entity resolution, are essential but often require significant effort. Although previous studies have aimed to automate these tasks, the influence of dataset characteristics on the matching effe

Jarin Tasnim, Debasish Chakroborti, Chanchal K. Roy, Kevin A. Schneider

Maintaining software is an ongoing process that stretches beyond the initial release. Stable software versions continuously evolve to fix bugs, add improvements, address security issues, and ensure compatibility. This ongoing support involves Backporting, which means taking a fix or update from a newer version and applying it to an older version of the same

Antonio Patrón Castro

This thesis is devoted to the study of physical systems embedded within the field of non-equilibrium statistical mechanics. Specifically, the state of the systems of interest constitutes a stochastic process that can be externally driven by a set of controllable parameters. On the one hand, for systems in contact with a thermal bath, we have studied the emer

Zilinghan Li, Aditya Sinha, Yijiang Li, Kyle Chard

Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data privacy, ownership, and compliance constraints are critical. However, building user-friendly enterprise-level FL frameworks that are both scalable and privacy-preserving remains chal

Jiazhou Zhou, Qing Jiang, Kanghao Chen, Lutao Jiang

Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive reliance on positive indicators based on given prompts like text descriptions or visual exemplars. This positive-only paradigm experiences consistent vulnerability to visually similar bu

Xiran Chen, Quan Zou, Qinyu Cai, Xiaofeng Chen

Accurate cell type annotation across datasets is a key challenge in single-cell analysis. snRNA-seq enables profiling of frozen or difficult-to-dissociate tissues, complementing scRNA-seq by capturing fragile or rare cell types. However, cross-annotation between these two datasets remains largely unexplored, as existing methods treat them independently. We i

Isshin Arai, Tomoaki Itano

Reconstructing the causal structure of physical systems from observational data constitutes a fundamental inverse problem. Here we show that the reconstruction dimension---defined as an upper bound on the number of recoverable components---is determined by the group-representation structure of the observation spaces and reconstruction maps. This formulation

Daijiro Kabata, Mari Ito, Tokito Koga, Kazuma Yunoki

Background: Accurate prediction of surgical case duration underpins operating room (OR) scheduling, yet existing models often depend on site- or surgeon-specific inputs and rarely undergo external validation, limiting generalisability. Methods: We undertook a retrospective multicentre study using routinely collected perioperative data from two general hospit

Ji Shi, Nicolas Charon, Andreas Mang, Demetrio Labate

We introduce a novel, efficient framework for clustering data on high-dimensional, non-Euclidean manifolds that overcomes the computational challenges associated with standard intrinsic methods. The key innovation is the use of the $p$-Fr\'{e}chet map $F^p : \mathcal{M} \to \mathbb{R}^\ell$ -- defined on a generic metric space $\mathcal{M}$ -- which embeds t

Dibakar Roy Sarkar, Ján Drgoňa, Somdatta Goswami

We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates time-integrated deep operator networks (TI-DeepONet) as differentiable PDE surrogate models within differentiable predictive control (DPC), a self-supervised learning framework for constrained neural control policies. The TI-DeepONet architecture lea

Puheng Li, Tijana Zrnic, Emmanuel Candès

Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by prioritizing the collection of labels where the model is most uncertain. The drawback, however

Xiaolan Huang, Shibo Shu, Miao Li, Sunil R. Golwala

The Next-generation Extended Wavelength Multi-band Sub/millimeter Inductance Camera (NEW-MUSIC), located on the Leighton Chajnantor Telescope (LCT), will be the first six-band trans-millimeter wave polarimeter. This paper proposes a broadband, hierarchical phased-array antenna with integrated band-defining filters necessary to realize NEW-MUSIC. It covers a

Yukari Ishiakwa, Ryo Hattori, Yongzhao Yao, Daiki Katsube

We demonstrate a nondestructive, high-throughput method for observing dislocations in GaN (0001) using phase-contrast microscopy (PCM). The PCM images (359x300 $\mu$m$^2$) analyzed in this study were acquired with an exposure time of 3 ms per image. The one-to-one correspondence between threading dislocation (TD) contrasts in PCM images and the corresponding

Xinyu Wang, Wenjun Yao, Fanghui Song, Zhichang Guo

Image segmentation is a core task in image processing, yet many methods degrade when images are heavily corrupted by noise and exhibit intensity inhomogeneity. Within the iterative-convolution thresholding method (ICTM) framework, we propose a variational segmentation model that integrates denoising terms. Specifically, the denoising component consists of an

Yifei Sun, Yuzhi He, Junhao Jia, Jinhong Wang

Microaneurysms (MAs), the earliest pathognomonic signs of Diabetic Retinopathy (DR), present as sub-60 $\mu m$ lesions in fundus images with highly variable photometric and morphological characteristics, rendering manual screening not only labor-intensive but inherently error-prone. While diffusion-based anomaly detection has emerged as a promising approach

Michael Nercessian, Wenxin Zhang, Alexander Schubert, Daphne Yang

Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hinder timely validation as new models emerge rapidly. Here, we propose BRIDGE, a data-reuse RCT design for AI-based risk models. AI risk models support a broad range of interventions,

Yunfei Yang, Xiaojun Chen, Yuexin Xuan, Zhendong Zhao

Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific input-output pairs. However, existing watermarks are easily removed when facing model stealing attacks, and make it difficult for model owners to effectively verify the copyright of stolen models. In this paper, we analyze

Changping Sun

In this letter, first, we prove that the orthonormal basis of rational Littlewood-Paley wavelet with rational dilation factor M=p/q first proposed by Auscher does not hold for all rational numbers. It does not hold if q is not equal to 1. In other words, it is not an orthonormal basis if the rational dilation factor M is not an integer. Then, to make up for

Shengmin Piao, Sanghyun Park

Recent advances in large reasoning models have been driven by reinforcement learning and test-time scaling, accompanied by growing interest in latent rather than purely textual reasoning. However, existing latent reasoning methods lack mechanisms to ensure stable reasoning dynamics in latent space and a systematic way to interleave implicit and explicit reas

Preesha Gehlot, Anna Rapberger, Fabrizio Russo, Francesca Toni

Assumption-Based Argumentation (ABA) is a powerful structured argumentation formalism, but exact computation of extensions under stable semantics is intractable for large frameworks. We present the first Graph Neural Network (GNN) approach to approximate credulous acceptance in ABA. To leverage GNNs, we model ABA frameworks via a dependency graph representat

Yukari Ishiakwa, Daiki Katsube, Yongzhao Yao, Koji Sato

This study presents a nondestructive, high-resolution method for three-dimensional imaging of threading dislocations in beta-$Ga_{2}O_{3}$ (010) using phase-contrast microscopy (PCM). A one-to-one correspondence between dislocation contrasts in PCM images and synchrotron X-ray topography (SR-XRT) images confirms the detection capability of PCM. Compared to S

Haotian Yin, Aleksander Plocharski, Michal Jan Wlodarczyk, Przemyslaw Musialski

We introduce a finite-difference framework for curvature regularization in neural signed distance field (SDF) learning. Existing approaches enforce curvature priors using full Hessian information obtained via second-order automatic differentiation, which is accurate but computationally expensive. Others reduced this overhead by avoiding explicit Hessian asse

Feridun Tasdan, Rukiye Dagalp

This article proposes an improved version of the Spearman rank correlation based on using Wilcoxon rank score function. A smoothed empirical cumulative distribution function (ecdf)computes the smoothed ranks and replaces the regular ranks in the Wilcoxon rank score function. The smoothed Wilcoxon rank scores are then used for estimation of the Spearman's cor

Jingtian Ma, Jingyuan Wang, Wayne Xin Zhao, Guoping Liu

Nowadays, navigation and ride-sharing apps have collected numerous images with spatio-temporal data. A core technology for analyzing such images, associated with spatiotemporal information, is Traffic Scene Understanding (TSU), which aims to provide a comprehensive description of the traffic scene. Unlike traditional spatio-temporal data analysis tasks, the

Zihua Wang, Jiarui Wang, Haiyang Xu, Ming Yan

In-context learning (ICL) has emerged as a powerful paradigm for Large Visual Language Models (LVLMs), enabling them to leverage a few examples directly from input contexts. However, the effectiveness of this approach is heavily reliant on the selection of demonstrations, a process that is NP-hard. Traditional strategies, including random, similarity-based s

Anna Ishizaki, Takuma Fukui, Hiroaki Nishi

This study aims to realize a mechanism for packet processing in the edge domain while maintaining network transparency, in order to accommodate diverse service requirements in smart communities. Since conventional flow control, which operates on a per-packet basis, lacks flexibility, we propose a content-based fine-grained flow management method that enables

Sheikh Mohd Ta-Seen Afrid, He Lin Zhao, Arend M. van der Zande, Shaloo Rakheja

Strain fundamentally alters carrier transport in semiconductors by modifying their band structure and scattering pathways. In transition-metal dichalcogenides (TMDs), an emerging class of 2D semiconductors, we show that mobility modulation under biaxial strain is dictated by changes in inter-valley scattering rather than effective mass renormalization as in

Banghao Zhou, Lixiang Guo, Yi-Chun Tsai, Albert van der Kogel

Objective: While FLASH radiotherapy is recognized for short-term normal tissue sparing, its durability in late-responding organs remains uncertain, limiting clinical adoption. With its clinical importance and steep dose-response, the spinal cord is an ideal model for evaluating FLASH effect on late toxicity. This work introduces a robust image-guided researc

Junkang Huang, Z. D. Wang, Tao Zhou

We employed the self-consistent method on a two-orbital type-II time-reversal-symmetric Weyl semimetal, revealing a hybrid pairing of singlet $s$-wave and triplet $p$-wave. We present a detailed analysis of the normal-state electronic structure and the self-consistent results. Our findings indicate that the selection of hybrid pairings is governed by distinc

Sicheng Yang, Yukai Huang, Weitong Cai, Shitong Sun

The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs) struggle to resolve these multimodal ambiguous inputs, ofte

Mingfu Shao, Hui Wang, Yuyang Li, Jiaben Lin

Solar flares, the most powerful explosive phenomena in the solar system, may pose significant hazards to spaceborne satellites and ground-based infrastructure. Despite decades of intensive research, reliable flare prediction remains a challenging task. Large Language Models, as a milestone in artificial intelligence, exhibit exceptional general knowledge and

Robert Angarone, Jang Soo Kim, Jaeseong Oh, Daniel Soskin

We study positivity properties of Hadamard products of Jacobi-Trudi matrices. Mal\'{o} proved that the Hadamard (entrywise) product of two totally positive upper-triangular Toeplitz matrices whose Toeplitz sequences are the coefficient sequences of real-rooted polynomials with nonpositive zeros is again totally positive. Sokal conjectured that this result ca

Maryam Dialameh, Hossein Rajabzadeh, Weiwei Zhang, Walid Ahmed

We present Bayesian Mixture of Experts (Bayesian-MoE), a post-hoc uncertainty estimation framework for fine-tuned large language models (LLMs) based on Mixture-of-Experts architectures. Our method applies a structured Laplace approximation to the second linear layer of each expert, enabling calibrated uncertainty estimation without modifying the original tra

RuiQiang Zhang, Zehua Ma, Guanjie Wang, Chang Liu

With the deepening trend of paperless workflows, signatures as a means of identity authentication are gradually shifting from traditional ink-on-paper to electronic formats.Despite the availability of dynamic pressure-sensitive and PKI-based digital signatures, static scanned signatures remain prevalent in practice due to their convenience. However, these st

Stephen R. Taylor

Arrays of precisely-timed millisecond pulsars are used to search for gravitational waves with periods of months to decades. Gravitational waves affect the path of radio pulses propagating from a pulsar to Earth, causing the arrival times of those pulses to deviate from expectations based on the physical characteristics of the pulsar system. By correlating th

Maria-Romina Ivan, Nandi Wang

Given a finite poset $\mathcal P$, how small can a family $\mathcal F$ of subsets of $[n]$ be such that $\mathcal F$ does not contain an induced copy of $\mathcal P$, but $\mathcal F\cup\{X\}$ contains such a copy for all $X\in\mathcal P([n])\setminus\mathcal F$? This is known as the induced saturation number of $\mathcal P$, denoted by $\text{sat}^*(n,\math

Daoming Zhu, Xinghong Cheng, Yanbo Shen, Chunsong Lu

A high spatiotemporal resolution and accurate middle-to-long-term prediction data is essential to support China's dual-carbon targets under global warming scenarios. In this study, we simulated hourly solar radiation at a 10 km* 10 km resolution in January, April, July, and October at five-year intervals from 2015 to 2060 across China using the WRF-Chem mode

Moustapha Diallo, Brian McDonald

The VC-dimension, introduced by Vapnik and Chervonenkis in 1968 in the context of learning theory, has in recent years provided a rich source of problems in combinatorial geometry. Given $E\subseteq \mathbb{F}_q^d$ or $E\subseteq \mathbb{R}^d$, finding lower bounds on the VC-dimension of hypothesis classes defined by geometric objects such as spheres and hyp

Eugene Park, Jihoon Keum, Ji-Hwan Baek, Hyuncheol Kim

Probing the dynamical magnetic properties of two-dimensional (2D) materials requires sensitive techniques capable of detecting small magnetic fields from nanoscale samples. We demonstrate quantitative AC and DC magnetometry of a ferromagnetic Fe3-xGeTe2 nanoflakes using ultrasensitive graphene Hall sensors. These devices achieve record-low magnetic field det

Hiroaki Kikuchi, Kenta Kumagai

In this paper, we investigate semilinear elliptic equations with general exponential-type nonlinearities in two dimensions. For such nonlinearities, we establish two main results. The first is the construction of a singular solution. Recently, Fujishima, Ioku, Ruf, and Terraneo [10] proved the existence of singular solutions under certain assumptions for non

Luz Ángela García, Eduard Piratova-Moreno, Felipe González-Alarcón, Jhonier Rangel

This theoretical work investigates different models to predict the redshift of Fast Radio Bursts (FRBs) from their observed dispersion measure (DM) and other reported properties. We performed an extensive revision of the FRBs with confirmed galaxy hosts in the literature and compiled an updated catalog. With this sample of FRBs, composed of 117 unique transi

Adriana Gavidia, Sunil Golwala, Andrew D. Beyer, Daniel Cunnane

We report on the optical characterization of the AlMn kinetic inductance detectors (KIDs) in development for use in the Next-generation Extended Wavelength-MUltiband Sub/millimeter Inductance Camera (NEW-MUSIC) on the Leighton Chajnantor Telescope (LCT). NEW-MUSIC will cover 80-420 GHz, split into six spectral bands, with polarimetry. This broad spectral cov

Md. Tanzeem Rahat, Md. Manzurul Hasan

Given two sequences $A[1..n]$ and $B[1..m]$ over a totally ordered alphabet, the \emph{Longest Common Bitonic Subsequence} (LCBS) problem asks for a longest common subsequence that is strictly increasing up to a single peak element and strictly decreasing thereafter (allowing either phase to be empty). The only explicitly documented approach evaluates a quad

Thu Nguyen, Lam Si Tung Ho

Time series prediction is challenging due to our limited understanding of the underlying dynamics. Conventional models such as ARIMA and Holt's linear trend model experience difficulty in identifying nonlinear patterns in time series. In contrast, machine learning models excel at learning complex patterns and handling high-dimensional data; however, they are

Jens Malmquist

Let $X$ be an isotropic unimodal L\'{e}vy jump process on $\mathbb{R}^d$. We develop probabilistic methods which in many cases allow us to determine whether $X$ satisfies the elliptic Harnack inequality (EHI), by looking only at the jump kernel of $X$, and its truncated second moments. Both our positive results and our negative results can be applied to subo

Qinyi Zhang, Duanyu Feng, Ronghui Han, Yangshuai Wang

Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized Mic

Junhui Liao

The ALETHEIA project aims to search for low-mass dark matter using liquid helium (LHe)-filled time projection chambers (TPCs). While liquid argon and liquid xenon TPCs have been extensively employed in the field of direct dark matter detection, successful development of LHe TPCs has not yet been achieved. Launched in 2020, our project has made significant pr

Phil Attard

The modern theory of Bose-Einstein condensation, superfluidity, and superconductivity is reviewed. The thermodynamic principle for superfluid flow and the equation of motion for condensed bosons are given. Computer simulations of Lennard-Jones $^4$He give the $\lambda$-transition and the superfluid viscosity. The statistical mechanical theory of high-tempera

Shibo Diao

We need to collect data in any science and reliability is a fundamental problem for measurement in all of science. Reliability means calculation the variance ratio. Reliability was defined as the fraction of an observed score variance that was not error. here are a lot of methods to estimated reliability. All of these indicators of dependability and stabilit