May 2025 arXiv papers — page 99

Nudrat Habib, Tosin Adewumi, Marcus Liwicki, Elisa Barney

Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a systematic literature review on two key sub-tasks of authorship analysis; Author Attribution and Author Verification. The review explores SOTA methodologies, ranging from traditiona

Yuhao Wang, Wenjie Qu, Shengfang Zhai, Yanze Jiang

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by incorporating external knowledge bases, but this may expose them to extraction attacks, leading to potential copyright and privacy risks. However, existing extraction methods typically rely on malicious inputs such as prompt injection or jailbreaking, making them easily dete

Changchun Yang, Weiqian Dai, Yilan Zhang, Siyuan Chen

Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aberrations. However, developing an AI model are hindered by the overwhelming complexity and diversity of chromosomal abnormalities, requiring extensive annotation efforts, while automated methods remain task-specifi

Santabrata Das, Md Riajul Haque, Jitumani Kalita, Rajesh Karmakar

We demonstrate that general relativistic corrections to the accretion of relativistic matter onto primordial black holes (PBHs) can significantly enhance their mass growth during the early Universe. Contrary to previous Newtonian treatments, our analysis reveals that PBH masses can increase by an order of magnitude before evaporation, leading to substantial

Yueheng Li, Guangming Xie, Zongqing Lu

Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem. To address this, we introduce Guided Policy Optimization (GPO), a frame

Leon Chlon, Maggie Chlon, MarcAntonio M. Awada

Real-world multimodal systems routinely face missing-input scenarios, and in reality, robots lose audio in a factory or a clinical record omits lab tests at inference time. Standard fusion layers either preserve robustness or calibration but never both. We introduce Adaptive Entropy-Gated Contrastive Fusion (AECF), a single light-weight layer that (i) adapts

Arnab Char, Paras Vinubhai Maniya, Dinabandhu Pradhan

The game of cops and robber is a two-player turn-based game played on a graph where the cops try to capture the robber. The cop number of a graph $G$, denoted by $c(G)$ is the minimum number of cops required to capture the robber. For a given class of graphs ${\cal F}$, let $c({\cal F}):=\sup\{c(F)|F\in {\cal F}\}$, and let Forb$({\cal F})$ denote the class

Xiaokui Yang, Kaijie Zhang

Let $M$ be a compact complex manifold of dimension $n\geq 2$. We prove that for any Hermitian metric $\omega$ on $M$, there exists a unique smooth function $f$ (up to additive constants) such that the conformal metric $\omega_g =e^f \omega$ solves the fourth-order nonlinear PDE $$\square_g^*(s_g|s_g|^{n-2})=0,$$ where $s_g$ is the Chern scalar curvature of $

Nikolaos Chaidos, Angeliki Dimitriou, Maria Lymperaiou, Giorgos Stamou

Despite the dominance of convolutional and transformer-based architectures in image-to-image retrieval, these models are prone to biases arising from low-level visual features, such as color. Recognizing the lack of semantic understanding as a key limitation, we propose a novel scene graph-based retrieval framework that emphasizes semantic content over super

Uranik Berisha, Jens Mehnert, Alexandru Paul Condurache

Vision Transformers have emerged as the state-of-the-art models in various Computer Vision tasks, but their high computational and resource demands pose significant challenges. While Mixture-of-Experts (MoE) can make these models more efficient, they often require costly retraining or even training from scratch. Recent developments aim to reduce these comput

Pei Yuan, Shengyu Zhang

The $n$-qubit $k$-weight Dicke states $|D^n_k\rangle$, defined as the uniform superposition of all computational basis states with exactly $k$ qubits in state $|1\rangle$, form a basis of the symmetric subspace and represent an important class of entangled quantum states with broad applications in quantum computing. We propose deterministic quantum circuits

Ryota Soga, Shintaro Shiba, Quan Kong, Norimasa Kobori

In the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD), sensors that serve as the ``eyes'' for sensing the vehicle's surrounding environment are essential. Traditionally, image sensors and LiDAR have played this role. However, a new type of vision sensor, event cameras, has recently attracted attention. Event cameras respond t

Valentina Mantovani Sarti

The interaction between baryons and antibaryons remains a fundamental topic in hadronic physics, particularly due to its potential to reveal exotic bound states such as baryonia. While the proton-antiproton system has been extensively studied, mainly via scattering experiments, interactions involving antihyperons, such as proton-antilambda and lambda-antilam

Bahar Radmehr, Ekaterina Shved, Fatma Betül Güreş, Adish Singla

Clickstream data from digital learning environments offer valuable insights into students' learning behaviors, but are challenging to interpret due to their high dimensionality and granularity. Prior approaches have relied mainly on handcrafted features, expert labeling, clustering, or supervised models, therefore often lacking generalizability and scalabili

Stephen Asiedu, David Watson

Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, differentiation of direct from indirect relationships, and hidden confounders. We introduce GENESIS (GEne Network inference from Expression SIgnals and SNPs), a constraint-based algorithm t

Alessandro Gianola, Marco Montali, Sarah Winkler

Real-world processes often involve interdependent objects that also carry data values, such as integers, reals, or strings. However, existing process formalisms fall short to combine key modeling features, such as tracking object identities, supporting complex datatypes, handling dependencies among them, and object-aware synchronization. Object-centric Petri

Naiqi Li, Yuqiu Xie, Peiyuan Liu, Tao Dai

Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under constraints is NP-hard in general. To overcome this difficulty, various relaxations of the rank function were studied. However, optimization of these relaxed LRRs typically depend

Zirui Song, Qian Jiang, Mingxuan Cui, Mingzhe Li

The rise of Large Audio Language Models (LAMs) brings both potential and risks, as their audio outputs may contain harmful or unethical content. However, current research lacks a systematic, quantitative evaluation of LAM safety especially against jailbreak attacks, which are challenging due to the temporal and semantic nature of speech. To bridge this gap,

Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang, Junxiao Yang

Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do not necessarily translate to improved safety performance-and in some cases, may even degrade it. This raises an important research question: how should we enhance the safety of LRMs

Mengting Li, Hui Chen, Sigurd Sandor Petersen, Huiping Huang

High-accuracy localization is a key enabler for integrated sensing and communication (ISAC), playing an essential role in various applications such as autonomous driving. Antenna arrays and reconfigurable intelligent surface (RIS) are incorporated into these systems to achieve high angular resolution, assisting in the localization process. However, array and

Junchuan Zhao, Xintong Wang, Ye Wang

Recent advances in discrete audio codecs have significantly improved speech representation modeling, while codec language models have enabled in-context learning for zero-shot speech synthesis. Inspired by this, we propose a voice conversion (VC) model within the VALLE-X framework, leveraging its strong in-context learning capabilities for speaker adaptation

Hichem Boussaid, Lucrezia Tosato, Flora Weissgerber, Camille Kurtz

The extraction of visual features is an essential step in Visual Question Answering (VQA). Building a good visual representation of the analyzed scene is indeed one of the essential keys for the system to be able to correctly understand the latter in order to answer complex questions. In many fields such as remote sensing, the visual feature extraction step

Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu

Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of "Internal Self-Recovery

Daniel Coumbe, Aria Rahmaty

Asymptotically Weyl-invariant gravity (AWIG) is further developed within the Palatini formalism as a power-counting renormalizable alternative to general relativity (GR). An expression for the dimensionless exponent n(R) is derived based on dynamical dimensional reduction. We show that this version of AWIG naturally resolves several theoretical issues normal

Huilin Zhu, Senyao Li, Jingling Yuan, Zhengwei Yang

Expanding pre-trained zero-shot counting models to handle unseen categories requires more than simply adding new prompts, as this approach does not achieve the necessary alignment between text and visual features for accurate counting. We introduce RichCount, the first framework to address these limitations, employing a two-stage training strategy that enhan

Zuzana Konopkova, Eric Edmund, Orianna B Ball, Agnes Dewaele

The crystallographic structure of iron under extreme conditions is a key benchmark for cutting-edge experimental and numerical methods. Moreover, it plays a crucial role in understanding planetary cores, as it significantly influences the interpretation of observational data and, consequently, insights into their internal structure and dynamics. However, eve

Ueli Töpfer, Maximilian R. Bailey, Sanjay Schreiber, Federico Paratore

Recent advancements in active colloidal systems aim to mimic key characteristics of biological microswimmers, particularly their adaptive motility in response to environmental changes. While many approaches rely on externally imposing a propulsive force, achieving true autonomous and self-regulating adaptation to the environment remains limited. In this stud

Jicheng Ma, Yunyan Yang

Community detection is an important problem in graph neural networks. Recently, algorithms based on Ricci curvature flows have gained significant attention. It was suggested by Ollivier (2009), and applied to community detection by Ni et al (2019) and Lai et al (2022). Its mathematical theory was due to Bai et al (2024) and Li-M\"unch (2025). In particular,

Alfonso de Gregorio

Open-weight general-purpose AI (GPAI) models offer significant benefits but also introduce substantial cybersecurity risks, as demonstrated by the offensive capabilities of models like DeepSeek-R1 in evaluations such as MITRE's OCCULT. These publicly available models empower a wider range of actors to automate and scale cyberattacks, challenging traditional

Hervé Déjean, Stéphane Clinchant

Reranking, the process of refining the output of a first-stage retriever, is often considered computationally expensive, especially with Large Language Models. Borrowing from recent advances in document compression for RAG, we reduce the input size by compressing documents into fixed-size embedding representations. We then teach a reranker to use compressed

Changhong Li, Shashwat Khandelwal, Shreejith Shanker

Complex electronic control unit (ECU) architectures, software models and in-vehicle networks are consistently improving safety and comfort functions in modern vehicles. However, the extended functionality and increased connectivity introduce new security risks and vulnerabilities that can be exploited on legacy automotive networks such as the controller area

Yiming Huang, Biquan Bie, Zuqiu Na, Weilin Ruan

The rise of Large Language Models (LLMs) like ChatGPT has advanced natural language processing, yet concerns about cognitive biases are growing. In this paper, we investigate the anchoring effect, a cognitive bias where the mind relies heavily on the first information as anchors to make affected judgments. We explore whether LLMs are affected by anchoring, t

Duncan Bart, Bruno Endres Forlin, Ana-Lucia Varbanescu, Marco Ottavi

Integer quantization has emerged as a critical technique to facilitate deployment on resource-constrained devices. Although they do reduce the complexity of the learning models, their inference performance is often prone to quantization-induced errors. To this end, we introduce InTreeger: an end-to-end framework that takes a training dataset as input, and ou

Nicholas Thomson, Laura Moschini, Alexis Diaz-Torres

Low-energy fusion of heavy ions is a fascinating coupling-assisted quantum tunnelling problem, whose understanding is crucial for advancing the synthesis of new elements and isotopes. Quantum dynamical coupled-channels calculations of laser-assisted $^{16}$O + $^{238}$U fusion are presented for both a central collision and the total fusion cross-sections, su

DongGeon Lee, Joonwon Jang, Jihae Jeong, Hwanjo Yu

Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted with meme images that ordinary users share? To investigate this question, we introduce MemeSafetyBench, a 50,430-instance benchmark pairing real meme images with both harmful and

Umair Shahzad

The electric power system is one of the largest and most intricate infrastructures. Therefore, it is critical to assess and maintain its security. A power system security assessment is indispensable for identifying post-contingency issues, taking corrective measures, and protecting the system from blackouts. This paper examined the impact of wind generation

Andrea Albano, Paola Stefanelli

We introduce a novel algebraic structure called di-skew brace by which we show that generalized digroups systematically yield bijective, non-degenerate solutions to the set-theoretic Yang-Baxter equation. We study the structural properties of these solutions with a particular focus on their left derived shelves, which belong to the class of conjugation racks

Yiming Huang, Junyan Zhang, Zihao Wang, Biquan Bie

Large Language Models (LLMs) have become powerful, but hallucinations remain a vital obstacle to their trustworthy use. Previous works improved the capability of hallucination detection by measuring uncertainty. But they can not explain the provenance behind why hallucinations occur, particularly in identifying which part of the inputs tends to trigger hallu

Hendrik Junkawitsch, Guoxing Sun, Heming Zhu, Christian Theobalt

With recent advancements in neural rendering and motion capture algorithms, remarkable progress has been made in photorealistic human avatar modeling, unlocking immense potential for applications in virtual reality, augmented reality, remote communication, and industries such as gaming, film, and medicine. However, existing methods fail to provide complete,

Pablo Dorta-González, Emilio Gómez-Déniz

This work aims to study a count response random variable, the number of citations of a research paper, affected by some explanatory variables through a suitable regression model. Due to the fact that the count variable exhibits substantial variation since the sample variance is larger than the sample mean, the classical Poisson regression model seems not to

Aijuan Song, Guohua Wu

Combinatorial optimization problems (COPs) with discrete variables and finite search space are critical across numerous fields, and solving them in metaheuristic algorithms is popular. However, addressing a specific COP typically requires developing a tailored and handcrafted algorithm. Even minor adjustments, such as constraint changes, may necessitate algo

Anas Ali, Mubashar Husain, Peter Hans

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for external attackers, fall short in identifying these subtle and con

Gennaro Infante, Giuseppe Antonio Veltri

We discuss, via a version of the Birkhoff-Kellogg theorem, the existence of positive and negative eigenvalues of Hammerstein integral equations with sign-changing nonlinearities and functional terms. The corresponding eigenfunctions have a given norm that, in turn, provides a location for the eigenvalues. As an application, we study the solvability of parame

Seitaro Yoneda, Akira Furui

In electromyogram (EMG)-based motion recognition, a subject-specific classifier is typically trained with sufficient labeled data. However, this process demands extensive data collection over extended periods, burdening the subject. To address this, utilizing information from pre-training on multiple subjects for the training of the target subject could be b

Zijian Lin, Yang Zhang, Yougen Yuan, Yuming Yan

Modern autoregressive speech synthesis models leveraging language models have demonstrated remarkable performance. However, the sequential nature of next token prediction in these models leads to significant latency, hindering their deployment in scenarios where inference speed is critical. In this work, we propose Speech Speculative Decoding (SSD), a novel

David Gimeno-Gómez, Rubén Solera-Ureña, Anna Pompili, Carlos-D. Martínez-Hinarejos

The automatic identification of medication states of Parkinson's disease (PD) patients can assist clinicians in monitoring and scheduling personalized treatments, as well as studying the effects of medication in alleviating the motor symptoms that characterize the disease. This paper explores speech as a non-invasive and accessible biomarker for identifying

Mohammad Belal, Nguyen Luong, Talayeh Aledavood, Juhi Kulshrestha

In today's digital era, internet plays a pervasive role in our lives, influencing everyday activities such as communication, work, and leisure. This online engagement intertwines with offline experiences, shaping individuals' overall well-being. Despite its significance, existing research often falls short in capturing the relationship between internet use a

Anas Ali, Mubashar Husain, Peter Hans

Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure. In this work, w

B. C. Backes, J. Dobaczewski, D. Muir, W. Nazarewicz

The dependence of the $E2$ matrix elements on isospin projection $T_z$ is linked to the conservation of the isospin symmetry. To study this conjecture, we calculated the ${B(E2: 2^+ \rightarrow 0^+)}$ rates for the even-even $T=1$ mirror nuclei with $42$ $\leq$ $A$ $\leq$ $98$ within nuclear density functional theory, employing the generalized Bohr Hamiltoni

Umair Shahzad

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Current deterministic industry practices of transient stabil

Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami

Mapping and understanding complex 3D environments is fundamental to how autonomous systems perceive and interact with the physical world, requiring both precise geometric reconstruction and rich semantic comprehension. While existing 3D semantic mapping systems excel at reconstructing and identifying predefined object instances, they lack the flexibility to

Peng Wang, Ruihan Tao, Qiguang Chen, Mengkang Hu

Recently, large language model (LLM)-based agents have achieved significant success in interactive environments, attracting significant academic and industrial attention. Despite these advancements, current research predominantly focuses on English scenarios. In reality, there are over 7,000 languages worldwide, all of which demand access to comparable agent

Mounssif Krouka, Chaouki Ben Issaid, Mehdi Bennis

Federated learning (FL) faces critical challenges, particularly in heterogeneous environments where non-independent and identically distributed data across clients can lead to unfair and inefficient model performance. In this work, we introduce \textit{DRDM}, a novel algorithm that addresses these issues by combining a distributionally robust optimization (D

Minghao Shao, Haoran Xi, Nanda Rani, Meet Udeshi

Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated cybersecurity capabilities on Capture-The-Flag (CTF) competitions, they have two key limitations: accessing latest cybersecurity expertise beyond training data, and integrating new

Christian Kanzow, Tanja Neder

A proximal safeguarded augmented Lagrangian method for minimizing the difference of convex (DC) functions over a nonempty, closed and convex set with additional linear equality as well as convex inequality constraints is presented. Thereby, all functions involved may be nonsmooth. Iterates (of the primal variable) are obtained by solving convex optimization

Yicheng Gu, Chaoren Wang, Zhizheng Wu, Lauri Juvela

Pitch manipulation is the process of producers adjusting the pitch of an audio segment to a specific key and intonation, which is essential in music production. Neural-network-based pitch-manipulation systems have been popular in recent years due to their superior synthesis quality compared to classical DSP methods. However, their performance is still limite

Dasol Choi, Seunghyun Lee, Youngsook Song

Vision-Language Models (VLMs) have shown capabilities in interpreting visual content, but their reliability in safety-critical scenarios remains insufficiently explored. We introduce VERI, a diagnostic benchmark comprising 200 synthetic images (100 contrastive pairs) and an additional 50 real-world images (25 pairs) for validation. Each emergency scene is pa

Stefan Pasch

As large language models (LLMs) are increasingly deployed in high-stakes settings, their ability to refuse ethically sensitive prompts-such as those involving hate speech or illegal activities-has become central to content moderation and responsible AI practices. While refusal responses can be viewed as evidence of ethical alignment and safety-conscious beha

Lu Li, Cunhang Fan, Hongyu Zhang, Jingjing Zhang

Auditory attention detection (AAD) aims to detect the target speaker in a multi-talker environment from brain signals, such as electroencephalography (EEG), which has made great progress. However, most AAD methods solely utilize attention mechanisms sequentially and overlook valuable multi-scale contextual information within EEG signals, limiting their abili

Ingeol Baek, Hwan Chang, Sunghyun Ryu, Hwanhee Lee

Despite significant advancements in Large Vision Language Models (LVLMs), a gap remains, particularly regarding their interpretability and how they locate and interpret textual information within images. In this paper, we explore various LVLMs to identify the specific heads responsible for recognizing text from images, which we term the Optical Character Rec

Zeqian Chen

The introduction of the transformer architecture in 2017 marked the most striking advancement in natural language processing. The transformer is a model architecture relying entirely on an attention mechanism to draw global dependencies between input and output. However, we believe there is a gap in our theoretical understanding of what the transformer is, a

C. H. Kim, K. Y. Chae, M. S. Smith

Given the importance of nuclear mass predictions, numerous models have been developed to extrapolate the measured data into unknown regions. While neural networks -- the core of modern artificial intelligence -- have been recently suggested as powerful methods, showcasing high predictive power in the measured region, their ability to extrapolate remains ques

Guillermo Gamboa Quintero, Ida Kantor

A $3$-partition of an $n$-element set $V$ is a triple of pairwise disjoint nonempty subsets $X,Y,Z$ such that $V=X\cup Y\cup Z$. We determine the minimum size $\varphi_3(n)$ of a set $\mathcal{E}$ of triples such that for every 3-partition $X,Y,Z$ of the set $\{1,\dots,n\}$, there is some $\{x,y,z\}\in \mathcal{E}$ with $x\in X$, $y\in Y$, and $z\in Z$. In p

Jan Lorenzen, Neetesh Singh, Kai Wang, Sonia M. Garcia-Blanco

High-power amplifiers are of great importance in many optical systems deployed in optical sensing, ranging, medical surgery, material processing and more. Likewise, high-gain, low-noise amplifiers with low polarization dependence are critical components of long-range optical communication systems. Integrated photonic solutions show great potential in challen

Ligong Bian, Yuefeng Di, Yongtao Jia, Yang Li

In this Letter, we numerically present the possibility of the first-order phase transition occurring through the thermal fluctuation in the early universe. We find that when the temperature is slightly higher than the mass scale of the background field, the bubble-like field configurations appear proceeded by oscillons, which expand and collide to finish the

Anatole Dahan

We introduce an extension of fixed-point logic ($\mathsf{FP}$) with a group-order operator ($\mathsf{ord}$), that computes the size of a group generated by a definable set of permutations. This operation is a generalization of the rank operator ($\mathsf{rk}$). We show that $\mathsf{FP} + \mathsf{ord}$ constitutes a new candidate logic for the class of polyn

Hans-Peter Schröcker, Zbyněk Šír

We present a method for constructing all bounded rational motions that frame a space curve $\mathbf{r}(t)$. This means that the motion guides an orthogonal frame along the curve such that one frame axis is in direction of the curve tangent. Existence of (bounded) framing motions is equivalent to $\mathbf{r}(t)$ being a (bounded) rational Pythagorean Hodograp

Angelique Mangubat, Shane Gilroy

Road safety is a critical challenge, particularly for cyclists, who are among the most vulnerable road users. This study aims to enhance road safety by proposing a novel benchmark for bicycle occlusion level classification using advanced computer vision techniques. Utilizing a parts-based detection model, images are annotated and processed through a custom i

Nikesh Lilani, Dilpreet Sandhu, Subhash Mahapatra

In this work, we study quantum chaos in a variety of holographic QCD models at finite temperature and chemical potentials. This includes the 1 R-Charge black hole (1RCBH) model, the 2 R-Charge black hole (2RCBH) model, a potential reconstruction-based analytic bottom-up model, and a numerical bottom-up model. All these models are different avatars of the Ein

Weiming Zhang, Qingyao Li, Xinyi Dai, Jizheng Chen

Debugging is a critical aspect of LLM's coding ability. Early debugging efforts primarily focused on code-level analysis, which often falls short when addressing complex programming errors that require a deeper understanding of algorithmic logic. Recent advancements in large language models (LLMs) have shifted attention toward leveraging natural language rea

Ahmed Farooq

In this work we propose an energy functional along the lines of Modern Hopfield Networks (MNH), the stationary points of which correspond to the attention due to Vaswani et al. [12], thus unifying both frameworks. The minima of this landscape form "context wells" - stable configurations that encapsulate the contextual relationships among tokens. A compelling

Xabier de Zuazo, Eva Navas, Ibon Saratxaga, Mathieu Bourguignon

Understanding the neural mechanisms underlying speech production is essential for both advancing cognitive neuroscience theory and developing practical communication technologies. In this study, we investigated magnetoencephalography signals to decode phones from brain activity during speech production and perception (passive listening and voice playback) ta

Ryo Kishino, Yusuke Takase, Momose Oyama, Hiroaki Yamagiwa

Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterogeneous settings. We extend this framework to training checkpoints and intermediate layers, and establish a consistent scale for KL divergence across pretraining, model size, random seeds, quantization, fine-tunin

Tjasa Vrhovnik

Given an open Riemann surface $M$, we prove that every nonflat conformal minimal immersion $M\to\mathbb{R}^n$ ($n\geq 3$) is homotopic through nonflat conformal minimal immersions $M\to\mathbb{R}^n$ to a proper one. If $n\geq 5$, it may be chosen in addition injective, hence a proper conformal minimal embedding. Prescribing its flux, as a consequence, every

Marc Allain, Selin Aslan, Wim Coene, Sjoerd Dirksen

Phase retrieval is an inverse problem that, on one hand, is crucial in many applications across imaging and physics, and, on the other hand, leads to deep research questions in theoretical signal processing and applied harmonic analysis. This survey paper is an outcome of the recent workshop Phase Retrieval in Mathematics and Applications (PRiMA) (held on Au

Kaitlynn Snyder, Andrew D. Hollingsworth, Fook Chiong Cheong, Rushna Quddus

Holographic molecular binding assays detect macromolecules binding to colloidal probe beads by monitoring nanometer-scale changes in the beads' diameters with holographic microscopy. Measured changes are interpreted with Maxwell Garnett effective-medium theory to infer the surface coverage of analyte molecules and therefore to measure the analyte concentrati

Gabriele Barbagallo, José Luis V. Cerdeira, Carmen Gómez-Fayrén, Tomás Ortín

It has recently been shown that one can derive consistent thermodynamical expressions in the Lorentzian Taub--NUT spacetime keeping the Misner-string singularities and taking into account their contributions in the Komar integrals. We show how the same results are obtained when the Mister-string singularities are removed by using Misner's procedure because,

Yifei Liu, Yu Cui, Haibin Zhang

While tool learning significantly enhances the capabilities of large language models (LLMs), it also introduces substantial security risks. Prior research has revealed various vulnerabilities in traditional LLMs during tool learning. However, the safety of newly emerging reasoning LLMs (RLLMs), such as DeepSeek-R1, in the context of tool learning remains und

Enric Junqué de Fortuny

Despite the wide-scale usage and development of emotion classification datasets in NLP, the field lacks a standardized, large-scale resource that follows a psychologically grounded taxonomy. Existing datasets either use inconsistent emotion categories, suffer from limited sample size, or focus on specific domains. The Super Emotion Dataset addresses this gap

Xiang Liu, Hong Chen, Xuming Hu, Xiaowen Chu

Large Language Models (LLMs) are increasingly deployed in multi-turn conversational applications, where the management of the Key-Value (KV) Cache presents a significant bottleneck. The linear growth of the KV Cache with dialogue history imposes substantial computational costs, and existing eviction strategies often degrade performance by repeatedly compress

Zin Arai, Yi-Chiuan Chen

For the family of H\'{e}non maps $(x,y)\mapsto (\sqrt{a}(1-x^2)-b y,x)$ of $\mathbb{R}^2$, the so-called anti-integrable (AI) limit concerns the limit $a\to\infty$ with fixed Jacobian $b$. At the AI limit, the dynamics reduces to a subshift of finite type. There is a one-to-one correspondence between sequences allowed by the subshift and the AI orbits. The t

Jacob E. Kooi, Zhao Yang, Vincent François-Lavet

Neural network architectures have a large impact in machine learning. In reinforcement learning, network architectures have remained notably simple, as changes often lead to small gains in performance. This work introduces a novel encoder architecture for pixel-based model-free reinforcement learning. The Hadamax (\textbf{Hada}mard \textbf{max}-pooling) enco

Faruk Alpay

Alpay Algebra is introduced as a universal, category-theoretic framework that unifies classical algebraic structures with modern needs in symbolic recursion and explainable AI. Starting from a minimal list of axioms, we model each algebra as an object in a small cartesian closed category $\mathcal{A}$ and define a transfinite evolution functor $\phi\colon\ma

Radu Andrei, Mingyao Guo, Mustafa Ali, Hoon Kim

We study the behavior of the 2D repulsive Hubbard model on a square lattice at half filling, under strong driving with ac electric fields, by employing a time-dependent Gaussian variational approach. Within the same theoretical framework, we analytically obtain the conventional Keldysh crossover between multiphoton and tunneling photoexcitation mechanisms, a

Kaito Ariu, Po-An Wang, Alexandre Proutiere, Kenshi Abe

We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model with static sampling. The goal is to decide whether the value of a given policy exceeds a specified threshold while minimizing the number of samples. We first derive an instance-dependent lower bound that any reasonable

Maximilian Kloppe, Stefan J. Maurer, Tobias Abele, Kerstin Göpfrich

Real-time deformability cytometry (RT-DC) enables high-throughput, contact-free mechanical characterization of soft microscopic objects. Here we apply this technique to giant unilamellar vesicles (GUVs). To interpret vesicle deformation in RT-DC, we present a simulation-based model taking into account the area expansion modulus as the dominant mechanical par

Yuanlin Chu, Bo Wang, Xiang Liu, Hong Chen

Large language models (LLMs) have achieved impressive results on multi-step mathematical reasoning, yet at the cost of high computational overhead. This challenge is particularly acute for test-time scaling methods such as parallel decoding, which increase answer diversity but scale poorly in efficiency. To address this efficiency-accuracy trade-off, we prop

Zhang XueGuang

In this manuscript, considering evolution of fallback accreting debris in a central Tidal Disruption Event (TDE), the outer boundary increased with time of the disk-like broad emission line regions (BLRs) lying into central accretion disk will lead expected broad emission lines changed from double-peaked to single-peaked. Considering common elliptical orbita

Andrey Urusov, Rostislav Berezovskiy, Anatoly Krestenko, Andrei Kornilov

Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical

Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu

The misuse of large language models (LLMs), such as academic plagiarism, has driven the development of detectors to identify LLM-generated texts. To bypass these detectors, paraphrase attacks have emerged to purposely rewrite these texts to evade detection. Despite the success, existing methods require substantial data and computational budgets to train a sp

Hon Ming Yam, Zhongliang Guo, Chun Pong Lau

The proliferation of diffusion-based deepfake technologies poses significant risks for unauthorized and unethical facial image manipulation. While traditional countermeasures have primarily focused on passive detection methods, this paper introduces a novel proactive defense strategy through adversarial attacks that preemptively protect facial images from be

Jacob S. Lund, Jeff Murugan, Jonathan P. Shock

We investigate the impact of internal spin on chaos in billiard systems. Extending the standard point-particle billiard by coupling translational and rotational degrees of freedom through a dimensionless spin parameter $\alpha = I/(mr^2) \in [0,1]$, we find that spin reduces chaos monotonically but does not eliminate it. In the Bunimovich stadium and Sinai b

Bernardin Ligan, Khalide Jbilou, Fahd Kalloubi, Ahmed Ratnani

Foundation models have achieved great success across diverse domains, including remote sensing (RS), thanks to their versatility and strong generalization abilities. However, most RS foundation models are designed for multispectral data, while hyperspectral imagery (HSI) - with its hundreds of spectral bands - remains less explored. Fine-tuning such models f

Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu

The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic features for various speech signals, called cross-modal (CM), and 2) learning alignment of difference languages in long sequences, called cross-lingual (CL). We propose the unit lang

Ria Shekhawat, Hailin Li, Raghavendra Ramachandra, Sushma Venkatesh

Leveraging the power of multimodal large language models (LLMs) offers a promising approach to enhancing the accuracy and interpretability of morphing attack detection (MAD), especially in real-world biometric applications. This work introduces the use of LLMs for differential morphing attack detection (D-MAD). To the best of our knowledge, this is the first

Md. Arquam, Suchi Kumari, Utkarsh Tiwari, Mohammad Al-saffar

The spread of infectious diseases is often influenced by human mobility across different geographical regions. Although numerous studies have investigated how diseases like SARS and COVID-19 spread from China to various global locations, there remains a gap in understanding how the movement of individuals contributes to disease transmission on a more persona

Antonio J. Durán

We study the number of real zeros of finite combinations of $K+1$ consecutive normalized Hermite polynomials of the form $$ q_n(x)=\sum_{j=0}^K\gamma_j\tilde H_{n-j}(x),\quad n\ge K, $$ where $\gamma_j$, $j=0,\dots ,K$, are real numbers with $\gamma_0=1$, $\gamma_K\not =0$. We consider two different normalizations of Hermite polynomials: the standard one (i.

Anqiao Ouyang, Hongyi Ke, Qi Wang

We present the Fourier-Invertible Neural Encoder (FINE), a compact and interpretable architecture for dimension reduction in translation-equivariant datasets. FINE integrates reversible filters and monotonic activation functions with a Fourier truncation bottleneck, achieving information-preserving compression that respects translational symmetry. This desig

Ninh Tran, Dennis Leung

Replicability is a lynchpin for credible discoveries. The partial conjunction (PC) p-value, which combines individual base p-values from multiple similar studies, can gauge whether a feature of interest exhibits replicated signals across studies. However, when a large set of features are examined as in high-throughput experiments, testing for their replicate

Jeremy Singer, Steve Draper

Esoteric programming languages are challenging to learn, but their unusual features and constraints may serve to improve programming ability. From languages designed to be intentionally obtuse (e.g. INTERCAL) to others targeting artistic expression (e.g. Piet) or exploring the nature of computation (e.g. Fractan), there is rich variety in the realm of esoter