November 2025 arXiv papers — page 132

Arani Maiti, Sauvik Roy, Abhi Mondal, Ayan Banerjee

Enhanced beam shifts mediated by surface plasmon resonance (SPR) at metal-dielectric interfaces have been widely investigated. However, research on the associated Imbert-Fedorov or spin Hall shifts, driven by the spin-orbit interaction of structured light in structured interfaces, has been comparatively scarce and limited. We explore the reflection character

Zhanhong Fang, Debing Wang, Jinbiao Chen, Jiahai Wang

Neural solvers have demonstrated remarkable success in combinatorial optimization, often surpassing traditional heuristics in speed, solution quality, and generalization. However, their efficacy deteriorates significantly when confronted with complex constraints that cannot be effectively managed through simple masking mechanisms. To address this limitation,

Emil R. Ingelsten, Madox C. McGrae-Menge, E. Paulo Alves, Istvan Pusztai

The dual aims of accuracy and computational efficiency in computational plasma physics lend themselves well to the use of fluid models. The first of these goals, however, is only satisfied for such models insofar as the utilized closure can capture the neglected kinetic physics -- something which has proven challenging for multi-scale collisionless processes

Jaime Sebastian Burbano, Arnova Abdullah, Eldiyar Zhantileuov, Mohan Liyanage

Latency-sensitive embedded applications increasingly rely on edge computing, yet dynamic network congestion in multi-server architectures challenges proper edge server selection. This paper proposes a lightweight server-selection method for edge applications that fuses latency prediction with adaptive reliability and hysteresis-based handover. Using passive

Bao-Xi Sun, Qin-Qin Cao, Ying-Tai Sun

The one-pion exchange interaction between the kaon and the vector antikaon is investigated by solving the Schr\"odinger equation in the S-wave approximation. In addition to the particle $f_1(1285)$, another bound state of $K \bar{K}^*$ is obtained, which is approximately 9 MeV below the threshold of $K \bar{K}^*$ and labeled $f_1(1378)$ for convenience in th

Olaf Parczyk, Silas Rathke, Tibor Szabó

We study a problem of Santos about the largest possible diameter of a $d$-dimensional (abstract) simplicial complex on $n$ vertices. For dimension 2, we determine the exact value of the maximum for every $n$ using an explicit construction. We also come across a tantalizing open problem about the packing of squares of Hamilton cycles in the complete graph and

Miguel Casasnovas, Francesc Wilhelmi, Richard Combes, Maksymilian Wojnar

Due to its static protocol design, IEEE 802.11 (aka Wi-Fi) channel access lacks adaptability to address dynamic network conditions, resulting in inefficient spectrum utilization, unnecessary contention, and packet collisions. This paper investigates reinforcement learning (RL) solutions to optimize Wi-Fi's medium access control (MAC). In particular, a multi-

Zhicheng Cai, Hao Zhu, Linsen Chen, Qiu Shen

Implicit neural representation (INR) models signals as continuous functions using neural networks, offering efficient and differentiable optimization for inverse problems across diverse disciplines. However, the representational capacity of INR defined by the range of functions the neural network can characterize, is inherently limited by the low-dimensional

Jun Masaki, Ariaki Higashi, Naoko Shinagawa, Kazuhiko Hirata

The functional independence measure (FIM) is widely used to evaluate patients' physical independence in activities of daily living. However, traditional FIM assessment imposes a significant burden on both patients and healthcare professionals. To address this challenge, we propose an automated FIM score estimation method that utilizes simple exercises differ

Rosa M. Fernández-Alcalá, José D. Jiménez-López, Jesús Navarro-Moreno, Juan C. Ruiz-Molina

The challenge of distributed fusion estimation is investigated for a class of four-dimensional (4D) commutative hypercomplex signals that are $\mathbb{T}_k$-proper factorizable, within the framework of multiple-sensor networks with different fading measurement rates. The fading effects affecting each sensor's measurements are modeled as a stochastic variable

Jun Zhang, Yi Li, Yue Liu, Changping Wang

As an intelligent infrastructure connecting users with commercial content, advertising recommendation systems play a central role in information flow and value creation within the digital economy. However, existing multi-stage advertising recommendation systems suffer from objective misalignment and error propagation, making it difficult to achieve global op

Jonas Azzam, Mihalis Mourgoglou, Michele Villa

Given a corkscrew domain with uniformly rectifiable boundary, we construct a surjective trace map onto the $L^p$ Hajlasz-Sobolev space on the boundary from the space of functions on the domain with $L^p$ norm involving the non-tangential maximal function of the gradient and the conical square function of the Hessian. This fundametally uses the Dorronsoro the

Mayank Vatsa, Aparna Bharati, Richa Singh

The architectural blueprint of today's leading text-to-image models contains a fundamental flaw: an inability to handle logical composition. This survey investigates this breakdown across three core primitives-negation, counting, and spatial relations. Our analysis reveals a dramatic performance collapse: models that are accurate on single primitives fail pr

Kwing Hei Li, Alejandro Aguirre, Joseph Tassarotti, Lars Birkedal

We present Foxtrot, the first higher-order separation logic for proving contextual refinement of higher-order concurrent probabilistic programs with higher-order local state. From a high level, Foxtrot inherits various concurrency reasoning principles from standard concurrent separation logic, e.g. invariants and ghost resources, and supports advanced probab

Mingda Jia, Weiliang Meng, Zenghuang Fu, Yiheng Li

Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance. However, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture th

Maoran Wang, Xingju Cai, Yongxin Chen

This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problem

Qinfeng Li, Miao Pan, Jintao Chen, Fu Teng

Model merging has emerged as an efficient technique for expanding large language models (LLMs) by integrating specialized expert models. However, it also introduces a new threat: model merging stealing, where free-riders exploit models through unauthorized model merging. Unfortunately, existing defense mechanisms fail to provide effective protection. Specifi

Daniele Barducci

We study the prospects of the proposed $\mu$TRISTAN experiment, running in the energy asymmetric $\mu^+ e^-$ mode, in probing long lived particles (LLPs) arising from the decay of the Standard Model Higgs boson. We focus on the proposed runs with $\{E_{\mu^+}, E_{e^-}\} = \{1\,{\rm TeV},\,30\,{\rm GeV}\}$ and $\{E_{\mu^+}, E_{e^-}\} = \{3\,{\rm TeV},\,50\,{\

Jieting Wang, Xiaolei Shang, Feijiang Li, Furong Peng

Time series forecasting relies on predicting future values from historical data, yet most state-of-the-art approaches-including transformer and multilayer perceptron-based models-optimize using Mean Squared Error (MSE), which has two fundamental weaknesses: its point-wise error computation fails to capture temporal relationships, and it does not account for

Mouhammed Achhab, Pierre Jehel, Fabrice Gatuingt

Reinforced concrete rail bridges are essential components of railway infrastructure, where reliability, durability, and adaptability are key design priorities. However, the design process is often complicated by uncertainties stemming from unforeseen construction constraints, such as the need to reposition piers or alter geometric characteristics. These desi

Qinfeng Li, Miao Pan, Ke Xiong, Ge Su

Retrieval-Augmented Generation (RAG) systems deployed over proprietary knowledge bases face growing threats from reconstruction attacks that aggregate model responses to replicate knowledge bases. Such attacks exploit both intra-class and inter-class paths, progressively extracting fine-grained knowledge within topics and diffusing it across semantically rel

Thomas Kluge, Arthur Hirsch-Passicos, Jannis Schulz, Mungo Frost

Understanding how laser pulses compress solids into high-energy-density states requires diagnostics that simultaneously resolve macroscopic geometry and nanometer-scale structure. Here we present a combined X-ray imaging (XRM) and small-angle X-ray scattering (SAXS) approach that bridges this diagnostic gap. Using the Matter in Extreme Conditions end station

Yasuhiro Homma, Manon Bas Dit Nugues, Arnaud Dubory, Charles-Henri Flouzat-Lachaniette

Summers osteotomy is a technique used to increase bone height and to improve bone density in dental implant surgery. The two main risks of this surgery, which is done by impacting an osteotome in bone tissue, are i) to perforate the sinus membrane and ii) the occurrence of benign paroxysmal vertigo, which are both related to excessive impacts during the oste

Álvaro Tejero, Martín de la Rosa

In this work, we present a geometrical formulation of quantum thermodynamics based on contact geometry and principal fiber bundles. The quantum thermodynamic state space is modeled as a contact manifold, with equilibrium Gibbs states forming a Legendrian submanifold that encodes the fundamental thermodynamic relations. A principal fiber bundle over the manif

Qizhe Li, Haolong Chen, Jiansheng Li, Shuqi Chai

Diagnosing the root causes of Quality of Experience (QoE) degradations in operational mobile networks is challenging due to complex cross-layer interactions among kernel performance indicators (KPIs) and the scarcity of reliable expert annotations. Although rule-based heuristics can generate labels at scale, they are noisy and coarse-grained, limiting the ac

Arthur Bouffandeau, Sabine Bensamoun, Robert Schleip, Giuseppe Rosi

Background: Palpation is the most widely used approach to empirically assess the mechanical properties of superficial tissues. While elastography is used for volume measurements, it remains difficult to assess skin properties with non-invasive methods. This study aimed to compare the performances of an impact-based analysis method (IBAM) consisting in studyi

Andrea Loi, Roberto Mossa, Fabio Zuddas

We extend the polydisk theorem of [21], originally established for classical Cartan-Hartogs domains, to Hartogs domains over arbitrary (possibly reducible and exceptional) bounded symmetric domains. We further establish a dual counterpart of this result. As an application, we show that the dual of a Hartogs domain over a bounded symmetric domain admits no to

Bas-Dit-Nugues Manon, Teddy Ketani, Claire Bastard, Giuseppe Rosi

High tibial osteotomy is a common procedure for knee osteoarthritis during which the surgeon partially opens the tibia and must stop impacting when cortical bone is reached by the osteotome. Surgeons rely on their proprioception and fluoroscopy to conduct the surgery. Our group has developed an instrumented hammer to assess the mechanical properties of the m

Philipp Seeberger, Steffen Freisinger, Tobias Bocklet, Korbinian Riedhammer

Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insights requires real-time processing of vast amounts of data. A major challenge in existing systems is their exposure to event-related biases, which negatively affects their ability to

Kodai Kaneyasu, Till Dieminger, Matthew Franks, Davide Sgalaberna

High-resolution 3D tracking with sub-nanosecond timing is required for the detection of elementary particles, such as neutrinos. Conventional detectors, which utilize analog silicon photomultipliers, face challenges in balancing spatial resolution and scalability. To address this issue, a CMOS single-photon avalanche diode (SPAD)-based high-resolution partic

Borui Cai, Yao Zhao

We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM). Unlike existing foundation models (FMs), which specialize in pattern learning within specific domains such as language, vision, or time series, IFM aims to acquire the underlying mechanisms of intelligence by learning directly fr

Zoltan Nagy, Irinel-Constantin Morarescu, Lucian Busoniu

This paper studies a class of consensus dynamics where the interactions between agents are affected by a time-varying unknown scaling factor. This situation is encountered in the control of robotic fleets over a wireless network or in opinion dynamics where the confidence given to the peers varies in time. Firstly, we establish conditions under which practic

Susumu Katayama

Function approximation using Haar basis systems offers an efficient implementation when compressed via Patricia trees while retaining the flexibility of wavelets for both global and local fitting. However, like B-spline-based approximations, achieving high accuracy in high dimensions remains challenging. This paper proposes KAN/H, a variant of the Kolmogorov

Hossein Kavianirad, Satoshi Endo, Davide Astarita, Lorenzo Amato

Hybrid assistive systems that integrate functional electrical stimulation (FES) and robotic exoskeletons offer a promising approach for neurorehabilitation. However, control of these systems remains challenging due to actuator redundancy and heterogeneous assistive device constraints. This paper introduces a novel cooperative control architecture based on dy

Batoul Dhaini, Joël Daouk, Hervé Schohn, Philippe Arnoux

In order to find a good candidate for F{\"o}rster Resonance Energy Transfer (FRET)-mediated X-ray-induced photodynamic therapy (X-PDT) for the treatment of cancer, lanthanide (Ln)-based AGuIX nanoparticles (NPs) conjugated with Rose Bengal (RB) as a photosensitizer (PS) were synthesized. X-PDT overcomes the problem of the poor penetration of visible light in

Wei Tian, YuhaoZhou

This paper introduces ChineseErrorCorrector3-4B, a unified model for Chinese spelling and grammatical error correction based on Qwen3-4B. The model demonstrates outstanding performance in general text correction tasks and achieves state-of-the-art results in both spelling correction (CSC) and grammatical correction (CGC). On several authoritative benchmark d

Thomas Morand

We study the Lyapunov exponents of models that are close to skew product systems over a C__ uniformly expanding transformation of the circle. For a continuous fibre map $\phi$, analytic, increasing, and convex in the fibre variable, we consider the smallest invariant function q satisfying q(x) = $\phi$(x, q(T x)). We provide rigorous numerical bounds on two

Michel Benaïm, Jérémy Colombo, Edouard Strickler

We consider the classical two-dimensional Rosenzweig-MacArthur prey-predator model with a degenerate noise, whereby only the prey variable is subject to small environmental fluctuations. This model has already been introduced in arXiv:1806.08450 and partially investigated by exhibiting conditions ensuring persistence. In this paper, we extend the results to

Wenyu Wang, Zhetao Hu, Yiquan Zhou, Jiacheng Xu

In voice conversion (VC), it is crucial to preserve complete semantic information while accurately modeling the target speaker's timbre and prosody. This paper proposes FabasedVC to achieve VC with enhanced similarity in timbre, prosody, and duration to the target speaker, as well as improved content integrity. It is an end-to-end VITS-based VC system that i

Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius, Edward Johns

Humans are remarkably efficient at learning tasks from demonstrations, but today's imitation learning methods for robot manipulation often require hundreds or thousands of demonstrations per task. We investigate two fundamental priors for improving learning efficiency: decomposing manipulation trajectories into sequential alignment and interaction phases, an

Alex Billi, Lorenzo Monaco, Francesco R. Ferraro, Alessio Mucciarelli

In this work we study the rotational velocities of a sample of blue straggler stars (BSSs) and reference stars belonging to the Galactic globular cluster NGC 1851, using high-resolution spectra acquired with FLAMES-GIRAFFE at the ESO/VLT. After field decontamination based on radial velocities and proper motions, the final sample of member stars is composed o

Yanchen Deng, Chendong Zhao, Yixuan Li, Bijun Tang

The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast alloys. MATAI integrates a curated alloy database, deep neural network-based pr

Jueun Ko, Hyewon Park, Hyesong Choi, Dongbo Min

Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input-invariant adaptation strate

Xian-Hui Ge

We develop a conceptual parallel between the black hole information problem and Zeno's paradox, highlighting the role of limiting procedures that turn formally infinite constructions into finite physical observables. Building on the replica--wormhole paradigm, we move beyond unitarity restoration to formulate a quantitative notion of irreversibility in Hawki

Aqsa Mushtaq, Chaimae Banouni, Mahboob Ul Haq, S. M. Zangi

We investigate the dynamics of key quantum correlations - Negativity (NG), Quantum Discord (QD), and Quantum-Memory-Assisted Entropic Uncertainty (QM-EUR) - in a bipartite two-qubit system under the influence of external pulses and various decoherence channels, including amplitude damping (gamma_amp), pure dephasing (gamma_deph), and pulse-induced dephasing

Sherjeel Shabih, Lukas Pielsticker, Florian Dobener, Andrea Albino

Scientific data across physics, materials science, and materials engineering often lacks adherence to FAIR principles (Barker et al., 2022; Jacobsen et al., 2020; M. D. Wilkinson et al., 2016; S. R. Wilkinson et al., 2025) due to incompatible instrument-specific formats and diverse standardization practices. pynxtools is a Python software development framewo

Lionel Roques

We study a Fisher-KPP equation with spatially periodic diffusion and reaction terms. We identify a class of periodic media for which the equation admits an explicit, closed-form solution. Through a nonlinear change of variables, the problem is reduced to the homogeneous Fisher-KPP equation, allowing us to construct an exact pulsating traveling front that con

Fabian Mies, Benedikt Wilkens

The fractional Brownian motion (fBm) is parameterized by the Hurst exponent $H\in(0,1)$, which determines the dependence structure and regularity of sample paths. Empirical findings suggest that the Hurst exponent may be non-constant in time, giving rise to the so-called multifractional Brownian motion (mBm). The It\^o-mBm is an alternative to the classical

Marianne Korner, Christos N. Likos

We report on a profession-oriented course we offered at the University of Vienna, aimed at physics education teacher students. The course on Theoretical Classical Mechanics has been conceived and designed from its outset with the explicit goal of bridging the gap between the abstract, mathematical notions employed in Theoretical Physics with the concrete fut

Xiaofeng Cai, Yibing Chen, Kunkai Fu, Liujun Pan

We develop a third-order conservative semi-Lagrangian discontinuous Galerkin (SLDG) scheme for solving linear transport equations on curvilinear unstructured triangular meshes, tailored for complex geometries. To ensure third-order spatial accuracy while strictly preserving mass, we develop a high-order conservative intersection-based remapping algorithm for

D Han-Kwan, É Miot, A Moussa, I Moyano

We study the problem of uniqueness of Leray solutions to the three-dimensional Vlasov-Navier-Stokes system. We establish uniqueness whenever the fluid velocity field belongs to the Cannone-Meyer-Planchon class, which allows to go beyond the Osgood uniqueness class. A stability estimate in this setting is also provided.

Giorgio Morales, Frederic Jurie, Jalal Fadili

While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of these models is the uncertainty quantification surrounding their distribution approximation capabilities. Current evaluation methodologies focus predominantly on measuring the closen

Zihan Wang, Guansong Pang, Wenjun Miao, Jin Zheng

Recent advances in Large Visual Language Models (LVLMs) have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraining and instruction tuning. However, the security vulnerabilities of LVLMs have become increasingly concerning, particularly their susceptibility to backdoor attacks. Existing backd

Naomie Messudom, Antonella Cavanna, Ali Madouri, Carlos Macias

Re-using the substrate is identified as a method for reducing the cost of high efficiency III-V solar cells. The approach investigated here consists in inserting a graphene layer onto a (001)GaAs substrate prior to the epitaxial growth of GaAs. To obtain a monocrystalline GaAs grown layer, the graphene layer is patterned, followed by a two-step epitaxial gro

A. A. Molavi Choobini, M. Shahmansouri

The detailed theoretical and numerical investigation of hybrid laser plasma RF accelerators, elucidating the mechanisms governing transverse beam dynamics, betatron polarization, and radiation reaction in ultra-relativistic electron bunches is presented. This framework combines analytical models of spatiotemporal plasma wakefield modulation, phase-dependent

Guoqiang Xiong, Haiyan Guan

Let $\mathcal{D}=(\mathcal{P},\mathcal{B})$ be a non-trivial block-transitive $t$-$(k^2,k,\lambda)$ design with $G\leq \Aut(\mathcal{D})$ and $X\unlhd G\leq \Aut(X)$, where $X=PSL(n,q)(n\geq3).$ We prove that $t=2$ and the parameters $(n,q,v,k)$ is $(3,3,144,12),(4,7,400,20)$ or $(5,3,121,11).$ Moreover, $\mathcal{D}$ is a $2$-$(144,12,\lambda)$ design with

Yiming Tang, Abhijeet Sinha, Dianbo Liu

Although recent generative models are remarkably capable of producing instruction-following and realistic outputs, they remain prone to notable physical plausibility failures. Though critical in applications, these physical plausibility errors often escape detection by existing evaluation methods. Furthermore, no framework exists for automatically identifyin

Satu Johansson, Taneli Riihonen

In this paper, military use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre-trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others. First, we interrogate a GPT-

Qilang Ye, Yu Zhou, Lian He, Jie Zhang

Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs understand skeleton? 2) How can LLMs distinguish among actions? To

Haroun Elleuch, Youssef Saidi, Salima Mdhaffar, Yannick Estève

This paper describes Elyadata \& LIA's joint submission to the NADI multi-dialectal Arabic Speech Processing 2025. We participated in the Spoken Arabic Dialect Identification (ADI) and multi-dialectal Arabic ASR subtasks. Our submission ranked first for the ADI subtask and second for the multi-dialectal Arabic ASR subtask among all participants. Our ADI syst

Abu Sufian, Cosimo Distante, Marco Leo, Hanan Salam

Text-to-image (T2I) generative models are largely used in AI-powered real-world applications and value creation. However, their strategic deployment raises critical concerns for responsible AI management, particularly regarding the reproduction and amplification of race- and gender-related stereotypes that can undermine organizational ethics. In this work, w

Leonardo Pesce, Jiawen Wei, Gianmarco Mengaldo

Post-hoc explainability methods are a subset of Machine Learning (ML) that aim to provide a reason for why a model behaves in a certain way. In this paper, we show a new black-box model-agnostic adversarial attack for post-hoc explainable Artificial Intelligence (XAI), particularly in the image domain. The goal of the attack is to modify the original explana

Haidong Huang, Haiyue Zhu. Jiayu Song, Xixin Zhao, Yaohua Zhou

Offline-to-online reinforcement learning (O2O-RL) has emerged as a promising paradigm for safe and efficient robotic policy deployment but suffers from two fundamental challenges: limited coverage of multimodal behaviors and distributional shifts during online adaptation. We propose UEPO, a unified generative framework inspired by large language model pretra

Nicklas Ramberg, Daniel Schmitt

We investigate the emergence of a resonant behavior in axion-trapped misalignment models featuring finite-temperature potential barriers. As the temperature decreases and the field is released from its trapped configuration, inhomogeneities are exponentially amplified through an instability in their equation of motion, leading to the fragmentation of the axi

Kan Yan, Li Cheng, Yizhi Hu, Junjie Gao

Half metals, which are amenable to perfect spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to giant tunneling magnetoresistance. As

Jari Desmet

In this paper, we determine the connected component of the automorphism group scheme of Matsuo algebras over fields of characteristic not $3$.

Kateryna Hlyniana

We study a local thinning $T_r$ that retains a point with probability $p(n_r)$, where $n_r$ counts neighbors within radius $r$. For Poisson input with spatially varying intensity, we obtain an exact intensity via a Poisson--mixture formula and a small-radius expansion. For homogeneous input we give a closed-form pair correlation based on the three-region ove

Zhiyuan Wang, Chenglang Yang, Jian Zhou

Aganagic, Dijkgraaf, Klemm, Mari\~{n}o and Vafa \cite{adkmv} predicted that the open string partition function on a smooth toric Calabi--Yau threefold should be a tau-function of multi-component KP hierarchy after considering the contributions from nonzero fermion number fluxes through loops in the toric diagram. In this paper, we prove their prediction in t

Yuxiang Duan, Ao Li, Yingqin Li, Luyu Li

Multimodal large language models (MLLMs) have shown remarkable capabilities in a wide range of vision-language tasks. However, the large number of visual tokens introduces significant computational overhead. To address this issue, visual token pruning has emerged as a key technique for enhancing the efficiency of MLLMs. In cognitive science, humans tend to f

Yasuyuki Kawahigashi

Researchers in condensed matter physics recently study two-dimensional topological order in terms of tensor networks involving certain 3- and 4-tensors. Their 3-tensors satisfying the "zipper condition" play an important role there and such 3-tensors can be made into certain 2-tensors by combining two wires into one. We identify their 4-tensors with bi-unita

Yizheng Wang, Timon Rabczuk, Yinghua Liu

Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; however, they typically require predefined functional assumptions, which poses significant challenges when dealing with unknown functional st

Yi Liu, Yuan Wang, Ying Gao, Tonia Poteat

Violations of the positivity assumption can render conventional causal estimands unidentifiable, including the average treatment effect (ATE), the average treatment effect on the treated (ATT), and the average treatment effect on the controls (ATC). Shifting the inferential focus to their alternative counterparts -- the weighted ATE (WATE), the weighted ATT

Xiangyue Zhang, Jianfang Li, Jianqiang Ren, Jiaxu Zhang

Reliable long-horizon co-speech gesture generation requires precise motion representation and consistent structural priors across all joints. Existing generative methods typically operate on local joint rotations, which are defined hierarchically based on the skeleton structure. This leads to cumulative errors during generation, manifesting as unstable and i

Xanh Ho, Yun-Ang Wu, Sunisth Kumar, Florian Boudin

With the growing number of submitted scientific papers, there is an increasing demand for systems that can assist reviewers in evaluating research claims. Experimental results are a core component of scientific work, often presented in varying formats such as tables or charts. Understanding how robust current multimodal large language models (multimodal LLMs

Gwangyeon Ahn, Jiwan Seo, Joonhyuk Kang

We propose Vision-Language Feature-based Multimodal Semantic Communication (VLF-MSC), a unified system that transmits a single compact vision-language representation to support both image and text generation at the receiver. Unlike existing semantic communication techniques that process each modality separately, VLF-MSC employs a pre-trained vision-language

Yuhao Ren, Yiting Liu, Yanfei Zhou, Zhiyu Zheng

Global placement is a critical step with high computational complexity in VLSI physical design. Modern analytical placers formulate the placement problem as a nonlinear optimization, where initialization strongly affects both convergence behavior and final placement quality. However, existing initialization methods exhibit a trade-off: area-aware initializer

Shuxin Zhuang, Linjian Meng, Shuxin Li, Minming Li

Urban Network Security Games (UNSGs), which model the strategic allocation of limited security resources on city road networks, are critical for urban safety. However, finding a Nash Equilibrium (NE) in large-scale UNSGs is challenging due to their massive and combinatorial action spaces. One common approach to addressing these games is the Policy-Space Resp

Zhiwei Yun, Xinwen Zhu

For a quasi-split tamely connected reductive group G over a p-adic field, we prove that its (monodromic) affine Hecke category is canonically equivalent to its equal characteristic counterpart as monoidal categories.

Yonatan Sverdlov, Eitan Rosen, Nadav Dym

Many neural networks for point clouds are, by design, invariant to the symmetries of this datatype: permutations and rigid motions. The purpose of this paper is to examine whether such networks preserve natural symmetry aware distances on the point cloud spaces, through the notion of bi-Lipschitz equivalence. This inquiry is motivated by recent work in the E

Haroun Elleuch, Salima Mdhaffar, Yannick Estève, Fethi Bougares

We present ADI-20, an extension of the previously published ADI-17 Arabic Dialect Identification (ADI) dataset. ADI-20 covers all Arabic-speaking countries' dialects. It comprises 3,556 hours from 19 Arabic dialects in addition to Modern Standard Arabic (MSA). We used this dataset to train and evaluate various state-of-the-art ADI systems. We explored fine-t

Zhangcheng Feng, Defeng Sun, Yancheng Yuan, Guojun Zhang

This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smooth objectives, which achieves a non-ergodic $O(1/k)$ iteration complexity regarding Karush--Kuhn--Tucker residual. By leveraging the symmetric Gauss--Seidel decomposition, the dHPR

Muzhou Yang, Wuzhou Quan, Mingqiang Wei

Confidence alone is often misleading in hyperspectral image classification, as models tend to mistake high predictive scores for correctness while lacking awareness of uncertainty. This leads to confirmation bias, especially under sparse annotations or class imbalance, where models overfit confident errors and fail to generalize. We propose CABIN (Cognitive-

Yuxuan Zhou, Yubin Wang, Bin Wang, Chen Ning

Large language models (LLMs) have shown great promise in the medical domain, achieving strong performance on several benchmarks. However, they continue to underperform in real-world medical scenarios, which often demand stronger context-awareness, i.e., the ability to recognize missing or critical details (e.g., user identity, medical history, risk factors)

Buket Özkaya

Semenov and Trifonov [22] developed a spectral theory for quasi-cyclic codes and formulated a BCH-like minimum distance bound. Their approach was generalized by Zeh and Ling [24], by using the HT bound. The first spectral bound for quasi-twisted codes appeared in [7], which generalizes Semenov-Trifonov and Zeh-Ling bounds, but its overall performance was obs

Bodong Du, Honglong Yang, Xiaomeng Li

Vision-language models have shown promising results in radiology report generation. However, most existing methods generate reports as flat text and do not explicitly model the semantic dependency between the Findings and Impression sections, which can lead to inconsistencies between clinical observations and diagnostic conclusions. In this paper, we propose

Lennart Justin Schulze, Vito Zago, Giuseppe Bilotta, Robert Anthony Dalrymple

Basic Smoothed Particle Hydrodynamics (SPH) models exhibit excessive, numerical dissipation in the simulation of water wave propagation. This can be remedied using higher-order approaches such as kernel gradient correction, which introduce additional computational effort. The present work demonstrates, that the higher-order scheme is only required in a limit

Yiwen Wang, Vivek Shah, Marcos Antonio Vaz Salles, Claudia Bauzer Medeiros

Novel reactive moving object applications require solutions to support object reactive behaviors as a way to query and update dynamic data. While moving object scenarios have long been researched in the context of spatio-temporal data management, reactive behavior is usually left to complex end-user implementations. However, it is not just a matter of hardwi

Huimin Ren, Yan Liang, Baiqiao Su, Chaobo Sun

The ability of Large Language Models (LLMs) to precisely follow complex and fine-grained lexical instructions is a cornerstone of their utility and controllability. However, evaluating this capability remains a significant challenge. Current methods either rely on subjective and costly human evaluation or on automated LLM-as-a-judge systems, which suffer fro

Muhammad Kashif, Shaf Khalid, Alberto Marchisio, Nouhaila Innan

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale quantum (NISQ) era. While quantum circuits are not naturally measured in floating point operations (FLOPs), most HQNNs (in NISQ era) are still trained on classical simulators where

Xiang Guo, Xiaojun Zhang, Yong Li, Zhihai Wang

We investigate enantiodetection for both a single cyclic three-level chiral molecule and finite ensembles of such molecules by monitoring the steady-state intracavity photon number in a cavity-QED platform. Our scheme exploits the intrinsic global $\pi$-phase difference between opposite enantiomers to engineer destructive and/or constructive interference pat

Luming Yang, Haoxian Liu, Siqing Li, Alper Yilmaz

Fine-grained action evaluation in medical vision faces unique challenges due to the unavailability of comprehensive datasets, stringent precision requirements, and insufficient spatiotemporal dynamic modeling of very rapid actions. To support development and evaluation, we introduce CPREval-6k, a multi-view, multi-label medical action benchmark containing 6,

Qilang Ye, Wei Zeng, Meng Liu, Jie Zhang

Can Multimodal Large Language Models (MLLMs) discern confused objects that are visually present but audio-absent? To study this, we introduce a new benchmark, AV-ConfuseBench, which simulates an ``Audio-Visual Confusion'' scene by modifying the corresponding sound of an object in the video, e.g., mute the sounding object and ask MLLMs Is there a/an muted-obj

Shiqi Chen, Xuesong Chen

An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem i

Makoto Kawashima

In this article, we construct new Pad\'{e} approximations for the \emph{product} of binomial functions and powers of logarithmic functions. While several explicit Pad\'{e} approximants are known for powers of exponential functions, binomial functions, and logarithmic functions individually, an explicit Pad\'{e} construction for the product of these functions

Zijing Liu, Bin Feng, He Cao, Yu Li

Protein structure tokenization converts 3D structures into discrete or vectorized representations, enabling the integration of structural and sequence data. Despite many recent works on structure tokenization, the properties of the underlying discrete representations are not well understood. In this work, we first demonstrate that the successful utilization

Yun Wang, Lingyun Yang, Senhao Yu, Yixiao Wang

Mixture-of-Experts (MoE) architectures scale language models by activating only a subset of specialized expert networks for each input token, thereby reducing the number of floating-point operations. However, the growing size of modern MoE models causes their full parameter sets to exceed GPU memory capacity; for example, Mixtral-8x7B has 45 billion paramete

Z. Rajabi Najjar, K. Azizi

We investigate the pseudoscalar ($\eta_t$) and vector ($\psi_t$) toponium states, as well as the triply-top baryon ($\Omega_{ttt}$), using the QCD sum-rule method. This study was motivated by the recent observation of a pseudoscalar enhancement near the $t\bar{t}$ threshold, reported by the CMS and ATLAS collaborations with a statistical significance exceedi

Yiwei Liao, Shurui Tu, Yujie Zhou, Dongzi Jin

Semantic communication is a novel communication paradigm that focuses on the transportation and delivery of the \emph{meaning} of messages. Recent results have verified that a graphical structure provides the most expressive and structurally faithful formalism for representing the relational semantics in most information sources. However, most existing works

Zhenhe Li, Can Lin, Ling Zheng, Wen-Da Wei

Multi-turn instruction following is essential for building intelligent conversational systems that can consistently adhere to instructions across dialogue turns. However, existing approaches to enhancing multi-turn instruction following primarily rely on collecting or generating large-scale multi-turn dialogue datasets to fine-tune large language models (LLM

Go Tsuruoka, Takami Sato, Qi Alfred Chen, Kazuki Nomoto

Traffic sign recognition plays a critical role in ensuring safe and efficient transportation of autonomous vehicles but remain vulnerable to adversarial attacks using stickers or laser projections. While existing attack vectors demonstrate security concerns, they suffer from visual detectability or implementation constraints, suggesting unexplored vulnerabil