November 2025 arXiv papers — page 141

Jia-Bin You, Jian Feng Kong, Jun Ye

We present a tensor network model (TNM) for forecasting nonlinear and chaotic dynamics, bridging quantum many-body methods with classical complex systems. The TNM leverages hierarchical tensor contractions to encode non-Markovian temporal correlations and multiscale structures, enabling compact and interpretable representations of chaotic flows. Using the Lo

Xuanchen Li, Chenrui Cui, Tianrui Wang, Meng Ge

Speech Large Language Models have achieved breakthroughs in multilingual speech-to-text translation. However, existing approaches often overlook semantic commonalities across source languages, leading to biased translation performance. In this work, we propose POTSA (Parallel Optimal Transport for Speech Alignment), a new framework based on cross-lingual par

Tobias Eisenreich, Nicholas Friedlaender, Stefan Wagner

Use case modeling employs user-centered scenarios to outline system requirements. These help to achieve consensus among relevant stakeholders. Because the manual creation of use case models is demanding and time-consuming, it is often skipped in practice. This study explores the potential of Large Language Models (LLMs) to assist in this tedious process. The

Sofia Brenner, Petr Gregor, Torsten Mütze, Francesco Verciani

An $n$-Venn diagram is a diagram in the plane consisting of $n$ simple closed curves that intersect only finitely many times such that each of the $2^n$ possible intersections is represented by a single connected region. An $n$-Venn diagram has at most $2^n-2$ crossings, and if this maximum number of crossings is attained, then only two curves intersect in e

Valery V. Ryzhikov

This paper studies homothetic and more general weighted averages for flows. Absolutely continuous convolutions of singular weights are considered, thereby strengthening Kozlov-Treshchev's result on nonuniform averages for ergodic flows. The concept of almost mixing, formulated in terms of homothetic weighted average convergences, is proposed. An example of a

Jiarui Liu, Weihao Xuan, Zhijing Jin, Mona Diab

Multimodal Large Language Models (MLLMs) often suffer from hallucinations, particularly errors in object existence, attributes, or relations, which undermine their reliability. We introduce TACO (Verified Atomic Confidence Estimation), a simple framework that mitigates hallucinations through self-verification and confidence calibration without relying on ext

José Miguel Mateos-Ramos, Frederik Zumegen, Henk Wymeersch, Christian Häger

Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel method to produce CC locations in true spatial coordinates with the aid of a digital twin (DT). Our main contribution i

Jason M. Pittman, Anton Phillips, Yesenia Medina-Santos, Brielle C. Stark

Analyzing spoken discourse is a valid means of quantifying language ability in persons with aphasia. There are many ways to quantify discourse, one common way being to evaluate the informativeness of the discourse. That is, given the total number of words produced, how many of those are context-relevant and accurate. This type of analysis is called Correct I

Mahya Ghorab, Ayush K. Ranga, Arnulf Materny, Veit Wagner

Integration of organic semiconductors into flexible electronics requires that their optoelectronic properties remain stable under mechanical deformation. Among these, the optical band gap governs exciton generation and limits photovoltaic voltage, making it a key parameter for strain-resilient design. In this work, we investigate band gap shifts in poly(3-he

Young Sul Cho

We study an explosive percolation model in which a link is randomly added and neighboring nodes sequentially rewire their links to suppress the growth of large clusters. In this manner, the rewiring nodes spread outward starting from the initial node closest to the added link. We show that a discontinuous transition emerges even when the total number of rewi

Xinyi Tong, Yiran Zhu, Jishang Chen, Chunru Zhan

Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise

Pedro N. Ferreira, Roman Lucrezi, Sangmin Lee, Lucy Nathwani

Quasicrystals (QCs) host long-range order without translational symmetry, a regime in which the very foundations of BCS theory are not straightforwardly applicable, yet experiments on QCs and their approximant crystals (ACs) point to conventional, $s$-wave, electron-phonon coupled superconductivity. Here we test the predictive power of the electron-phonon fr

Panya Trakoolgerntong, Tao Xiao, Masanari Kondo, Chaiyong Ragkhitwetsagul

Code review is a key practice in software engineering, where developers evaluate code changes to ensure quality and maintainability. Links to issues and external resources are often included in Pull Requests (PRs) to provide additional context, yet they are typically discarded in automated tasks such as PR summarization and code review comment generation. Th

Jiarui Liu, Kaustubh Dhole, Yingheng Wang, Haoyang Wen

Deductive reasoning is the process of deriving conclusions strictly from the given premises, without relying on external knowledge. We define honesty in this setting as a model's ability to respond only when the conclusion is logically entailed by the premises, and to abstain otherwise. However, current language models often fail to reason honestly, producin

Vukan Ninkovic, Dejan Vukobratovic

Error correcting codes play a central role in digital communication, ensuring that transmitted information can be accurately reconstructed despite channel impairments. Recently, autoencoder (AE) based approaches have gained attention for the end-to-end design of communication systems, offering a data driven alternative to conventional coding schemes. However

Eva Löcherbach, Dasha Loukianova, Elisa Marini

We consider a system of $N$ interacting particles, described by SDEs driven by Poisson random measures, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position. When this happens, all the other particles of the system receive a small random kick which is distributed according to a

Paul Strang, Zacharie Alès, Côme Bissuel, Olivier Juan

Mixed-Integer Linear Programming (MILP) lies at the core of many real-world combinatorial optimization (CO) problems, traditionally solved by branch-and-bound (B&B). A key driver influencing B&B solvers efficiency is the variable selection heuristic that guides branching decisions. Looking to move beyond static, hand-crafted heuristics, recent work has explo

Oishik Kar, Aswath Babu H

Motivated by the perspective of advanced time-series prediction and exploitation of Quantum Reservoir Computing (QRC), we explored the design and implementation of a Hybrid Photonic-Quantum Reservoir Computing (HPQRC) paradigm. It brings together the high-speed parallelism of photonic systems with the quantum reservoir's capacity of modeling complex, nonline

S. Das, V. K. S. Kashyap, B. Mohanty

The sensitivity of conventional direct dark matter searches for weakly interacting massive particles (WIMPs) is ultimately limited by coherent elastic neutrino-nucleus scattering (CEvNS), which produces nuclear recoils indistinguishable from WIMP signals and defines the so-called neutrino floor. While the effects of solar neutrinos, geoneutrinos, diffuse sup

Erik Hartman, Jonas Wallin, Johan Malmström, Jimmy Olsson

Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generative models have emerged as powerful tools for protein design, but steering them toward proteins with specified properties remains challenging. The Feynman-Kac (FK) framework provide

Zhichao Jiang, Peng Ding

Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomed

Niklas Bockius, Maximilian Braun, Kay Hofmann, Friederike Schmid

The generalized Langevin equation is used as a model for various coarse-grained physical processes, e.g., the time evolution of the velocity of a given larger particle in an implicitly represented solvent, when the relevant time scales of the dynamics of the larger particle and the solvent particles are not strictly separated. Since this equation involves an

Akseli Reunamo, Laura-Maria Peltonen, Hans Moen, Sampo Pyysalo

This paper reports on pretraining ModernBERT encoder models in six different sizes, ranging from 51M to 475M parameters, with a focus on limited multilingualism, emphasizing languages relevant to Finland. Our models are competitive with, or superior to, existing multilingual models. They outperform monolingual models on tasks that require a context longer th

Zeru Cheng, Yanjing Yang, He Zhang, Lanxin Yang

Software vulnerabilities are major risks to software systems. Recently, researchers have proposed many deep learning approaches to detect software vulnerabilities. However, their accuracy is limited in practice. One of the main causes is low-quality training data (i.e., source code). To this end, we propose a new approach: SPLVD (Self-Paced Learning for Soft

Lijun Zhang, Suyuan Liu, Siwei Wang, Shengju Yu

Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Con

Giulio Foggi Rota, Alessandro Chiarini, Marco Edoardo Rosti

Understanding how submerged vegetation modifies the water surface is crucial for modeling momentum exchange between shallow waters and the atmosphere. In particular, quantifying its impact on the equivalent aerodynamic roughness of the water surface is essential for improved boundary-layer parameterization in oceanic and atmospheric models. In this Letter, w

Tianle Pu, Jianing Li, Yingying Gao, Shixuan Liu

Mixed-Integer Linear Programming (MILP) is a cornerstone of combinatorial optimization, yet solving large-scale instances remains a significant computational challenge. Recently, Graph Neural Networks (GNNs) have shown promise in accelerating MILP solvers by predicting high-quality solutions. However, we identify that existing methods misalign with the intri

Liqin Tan, Pin Chen, Menghan Liu, Xiean Wang

We present MatUQ, a benchmark framework for evaluating graph neural networks (GNNs) on out-of-distribution (OOD) materials property prediction with uncertainty quantification (UQ). MatUQ comprises 1,375 OOD prediction tasks constructed from six materials datasets using five OFM-based and a newly proposed structure-aware splitting strategy, SOAP-LOCO, which c

David S. Pereira, Salvatore Capozziello, Francisco S. N Lobo, José Pedro Mimoso

Hybrid metric-Palatini gravity unifies the metric and Palatini formalisms while preserving a propagating scalar degree of freedom, offering a compelling route to modified gravity consistent with current observations. Motivated by this success, we consider an extended framework -- the hybrid metric-Palatini scalar-tensor (HMPST) theory -- in which an addition

Yuan Zhong, Yue Xiao, Yijia Li, Hao Chen

Recently, the pinching-antenna system (PASS) has emerged as a promising architecture owing to its ability to reconfigure large-scale path loss and signal phase by activating radiation points along a dielectric waveguide. However, existing studies mainly focus on line-shaped PASS architectures, whose limited spatial flexibility constrains their applicability

Michele Giusfredi, Stefano Iubini, Antonio Politi, Paolo Politi

The Discrete Non Linear Schr\"odinger (DNLS) model, due to the existence of two conserved quantities, displays an equilibrium transition between a homogeneous phase at positive absolute temperature and a localized phase at negative absolute temperature. Here, we provide a mean-field theory of DNLS through a suitable approximation of the grandcanonical partit

Yasheng Lyu

This paper investigates the existence of a global $C^{1,1}$ solution to the Dirichlet problem for the $k$-Hessian equation with a nonnegative right-hand side $f$, focusing on the required conditions for $f$. The conditions $f^{1/(k-1)}\in C^{1,1}(\overline{\Omega_{0}})$ and $f^{3/(2k-2)}\in C^{2,1}(\overline{\Omega_{0}})$, together with $f\geq0$ in a domain

Kunlin Wu, Abicumaran Uthamacumaran, Hector Zenil

Blood reference intervals (RIs) underpin diagnostic interpretation and therapeutic monitoring worldwide. However, many widely used RI systems originate from limited historical cohorts and have been propagated across health systems without harmonised derivation protocols, shared metadata, or cross-population validation. Consequently, the global RI landscape r

Itshak Lapidot, Yann Sepulcre, Tom Trigano

We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results illustrating the convergence of the proposed approach, and its relative performances is evaluated on synthesized 2-dimension

Petros Galanopoulos, Daniel Girela

In this article we address the question of characterizing the sequences of complex numbers $(\eta )=\{ \eta_n\}_{n=0}^\infty $ whose associated Rhaly operator $\mathcal R_{(\eta )}$ is bounded or compact on the Hardy spaces $H^p$ ($1\le p<\infty $), on the Bergman spaces $A^p_\alpha $, and on the Dirichlet spaces $\mathcal D^p_\alpha $ ($1\le p<\infty $, $\a

Bingquan Zhang, Xiaoxiao Liu, Yuchi Wang, Lei Zhou

Standardized patients (SPs) are indispensable for clinical skills training but remain expensive and difficult to scale. Although large language model (LLM)-based virtual standardized patients (VSPs) have been proposed as an alternative, their behavior remains unstable and lacks rigorous comparison with human standardized patients. We propose EasyMED, a multi

Dmytro Leontiev, Abicumaran Uthamacumaran, Riya Nagar, Hector Zenil

Ratios of common biomarkers and blood analytes are well established for early detection and predictive purposes. Early risk stratification in critical care is often limited by the delayed availability of complex severity scores. Complete blood count (CBC) parameters, available within hours of admission, may enable rapid prognostication. We conducted an exhau

Philipp Dingfelder, Christian Riess

Large language models are increasingly used for many applications. To prevent illicit use, it is desirable to be able to detect AI-generated text. Training and evaluation of such detectors critically depend on suitable benchmark datasets. Several groups took on the tedious work of collecting, curating, and publishing large and diverse datasets for this task.

S. Ceci, R. Omerović, H. Osmanović, M. Uroić

In hadron resonant scattering, there are four fundamental resonant parameters: real and imaginary part of the pole position, and the magnitude and the phase of the residue. Out of the four, the last one is the least understood. The search for the residue phase's physical meaning has focused on model-independent phases of the majority of the lowest-mass reson

Banhirup Sengupta, Peenal Gupta, Souvik Sengupta

Dilithium is one of the NIST approved lattice-based signature schemes. In this short note we describe the Bai-Galbraith signature scheme proposed in BG14, which differs to Dilithium, due to the fact that there is no public key compression. This lattice-based signature scheme is based on Learning with Errors (LWE).

P. Vermot, A. Ciurlo, D. Rouan, M. R. Morris

We used newly obtained observations from the Mid-Infrared Instrument (MIRI) equipped with the Medium Resolution Spectrometer (MRS) aboard the James Webb Space Telescope (JWST) to extract spectra covering the entire spectral range from 5 to 27~$\mu$m in the CND and in the CC. We used the photoionization code CLOUDY to generate synthetic spectra with the same

Phillip Sloan, Edwin Simpson, Majid Mirmehdi

Radiology is essential to modern healthcare, yet rising demand and staffing shortages continue to pose major challenges. Recent advances in artificial intelligence have the potential to support radiologists and help address these challenges. Given its widespread use and clinical importance, chest X-ray classification is well suited to augment radiologists' w

Francois Meyer, Jan Buys

Subword segmentation is typically applied in preprocessing and stays fixed during training. Alternatively, it can be learned during training to optimise the training objective. In this paper we study the learning dynamics of subword segmentation: if a language model can dynamically optimise tokenisation, how do its subwords evolve during pretraining and fine

Francesca Scarabel, Harry Coldwell, Tyler Cassidy

We study an infection-age structured epidemic model in which both the infectivity and the rate of loss of immunity depend on the time-since-infection. The model can be equivalently viewed as a nonlinear renewal equation for the incidence of infection or as a partial differential equation for the density of infected individuals. We explicitly consider gamma,

Yuhao Shen, Jiahe Qian, Shuping Zhang, Zhangtianyi Chen

Multimodal large language models (LLMs) are increasingly used to generate dermatology diagnostic narratives directly from images. However, reliable evaluation remains the primary bottleneck for responsible clinical deployment. We introduce a novel evaluation framework that combines DermBench, a meticulously curated benchmark, with DermEval, a robust automati

Simon König, Lukas Epple, Christian Becker

Coroutines are experiencing a renaissance as many modern programming languages support the use of cooperative multitasking for highly parallel or asynchronous applications. One of the greatest advantages of this is that concurrency and synchronization is manged entirely in the userspace, omitting heavy-weight system calls. However, we find that state-of-the-

Egor Yukhnevich, Anton Andreychuk

PIBT is a rule-based Multi-Agent Path Finding (MAPF) solver, widely used as a low-level planner or action sampler in many state-of-the-art approaches. Its primary advantage lies in its exceptional speed, enabling action selection for thousands of agents within milliseconds by considering only the immediate next timestep. However, this short-horizon design le

Xun Shao, Aoba Otani, Yuto Hirasuka, Runji Cai

This position paper envisions a next-generation elderly monitoring system that moves beyond fall detection toward the broader goal of Activities of Daily Living (ADL) recognition. Our ultimate aim is to design privacy-preserving, edge-deployed, and federated AI systems that can robustly detect and understand daily routines, supporting independence and dignit

Shenghua Feng, Jie An, Fanjiang Xu

Stochastic dynamical systems have emerged as fundamental models across numerous application domains, providing powerful mathematical representations for capturing uncertain system behavior. In this paper, we address the problem of runtime safety and reach-avoid probability prediction for discrete-time stochastic systems with online observations, i.e., estima

Kenji Kurosaki, Masahiko Arakawa

Impact cratering plays a crucial role in shaping the surfaces of small bodies, satellites, and planets, providing insights into their formation and the history of the Solar System. Small bodies are often covered with low-cohesion regolith. Using sand as a model of regolith, we constructed a numerical model for simulating impact on a sand target to investigat

Jyotirmoy De, Chiranjeeb Singha, Naresh Dadhich

The third law of black hole dynamics states that it is impossible, through any classical perturbation of a stationary configuration, to reduce the surface gravity of a black hole to zero. In this work, we examine the validity of this law for static, spherically symmetric charged black holes in the Lovelock theory of gravity. By studying infinitesimal variati

Jiaxin Yu, Guangyu Dai, Shuai Xing, Weiwei Zhang

Exciton condensation--an interaction-driven, macroscopically coherent paired-fermion state--offers the prospect for dissipationless energy transport in solids, akin to that in superconductivity. Although their light effective mass and strong Coulomb binding favour high transition temperatures, convincing demonstrations of pure-exciton condensation have hithe

Shuli Zeng, Sijia Zhang, Feng Wu, Shaojie Tang

Embedding deep neural networks (NNs) into mixed-integer programs (MIPs) is attractive for decision making with learned constraints, yet state-of-the-art monolithic linearisations blow up in size and quickly become intractable. In this paper, we introduce a novel dual-decomposition framework that relaxes the single coupling equality u=x with an augmented Lagr

Amal Sunny, Advay Gupta, Vishnu Sreekumar

Recent work has proposed using Large Language Models (LLMs) to quantify narrative flow through a measure called sequentiality, which combines topic and contextual terms. A recent critique argued that the original results were confounded by how topics were selected for the topic-based component, and noted that the metric had not been validated against ground-

Yanlin Wu, Xiaogang Yuan, Dezhi An

AI-generated video has advanced rapidly and poses serious challenges to content security and forensic analysis. Existing detectors rely mainly on pixel-level visual cues and generalize poorly to unseen generators. We propose DBINDS, a diffusion-model-inversion based detector that analyzes latent-space dynamics rather than pixels. We find that initial noise s

Zhaoyu Liu, Liu Cao, Lyutianyang Zhang, Dongyu Wei

The trade-off between energy and latency budgets is becoming significant due to the more stringent QoS requirements in 6G vehicular networks. However, comprehensively studying the trade-off between energy and latency budgets for 6G vehicular network with new Vehicle-to-Everything (V2X) features is still under-explored. This paper proposes a novel multi-hop,

Mounir Hajli

In this paper, we study the closed points of arithmetic schemes. We accomplish this by showing that the product of the cardinals of residue fields of closed points in an arithmetic scheme can be regularized. This regularization yields a new arithmetic invariant attached to the scheme. We compute it explicitly in several cases and find that it is always a tra

Daiki Shirafuji, Koji Tanaka, Tatsuhiko Saito

Recently, Large Language Models (LLMs) are gaining increased attention in the domain of Table Question Answering (TQA), particularly for extracting information from tables in documents. However, directly entering entire tables as long text into LLMs often leads to incorrect answers because most LLMs cannot inherently capture complex table structures. In this

Niclas Flehmig, Mary Ann Lundteigen, Shen Yin

The implementation of agentic AI systems has the potential of providing more helpful AI systems in a variety of applications. These systems work autonomously towards a defined goal with reduced external control. Despite their potential, one of their flaws is the insufficient reliability which makes them especially unsuitable for high-risk domains such as hea

Gerd Wachsmuth

We present a numerical method for the solution of Newton's problem of least resistance in the class of convex functions using a convex hull approach. We observe that the numerically computed solutions possess some symmetry. Further, their extremal points lie on several curves. By exploiting this conjectured structure, we are able to compute highly accurate s

Arvid Siqveland

In the preprint arXiv:2511.07900 we proved that there exists a localizing ring $A_M$ for $A$ an associative ring with unit, and $M=\oplus_{i=1}^rM_i$ a direct sum of $r\geq 1$ simple right $A$-modules. For a homomorphism of associative rings $A\rightarrow B$ we define the contraction of a simple $B$-module to $A.$ Then we define the set of aprime right $A$-m

Jian'an Zhang

We study the construction of SPX--VIX (multi\textendash product) option surfaces that are simultaneously free of static arbitrage and dynamically chain\textendash consistent across maturities. Our method unifies \emph{constructive} PCA--Smolyak approximation and a \emph{chain\textendash consistent} diffusion model with a tri\textendash marginal, martingale\t

Václav Kůla, Qipeng Kuang, Yuyi Wang, Yuanhong Wang

The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. Conditioning WFOMC on evidence -- fixing the truth values of a set of ground literals -- has been shown impossible in time polynomial in the domain size (unless $\mathsf{\#P \subseteq FP}$) even for fra

Lucas Chesnel

The aim of this lecture is to consider a concrete problem, namely the identification of situations of invisibility in waveguides, to present techniques and tools that may be useful in various fields of applied mathematics. To be more specific, we will be interested in the propagation of acoustic waves in guides which are unbounded in one direction. In genera

Xinren Zhang, Jiadong Yu, Zixin Zhong

Multi-Agent Systems (MAS) have emerged as a powerful paradigm for modeling complex interactions among autonomous entities in distributed environments. In Multi-Agent Reinforcement Learning (MARL), communication enables coordination but can lead to inefficient information exchange, since agents may generate redundant or non-essential messages. While prior wor

Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes

This article presents HOTFLoc++, an end-to-end hierarchical framework for LiDAR place recognition, re-ranking, and 6-DoF metric localisation in forests. Leveraging an octree-based transformer, our approach extracts features at multiple granularities to increase robustness to clutter, self-similarity, and viewpoint changes in challenging scenarios, including

Yuka Shiomatsu, Youka Kaku, Akira Matsumura, Tomohiro Fujita

Establishing quantum gravity theory remains one of the major challenges in modern physics, as the lack of experimental evidence makes it difficult to explore. In response to this challenge, proposals to test quantum entanglement induced by Newtonian gravity in table-top experiments have attracted significant attention as a potentially feasible approach far b

Marco de Cesare, Marcello Miranda, Achilleas P. Porfyriadis

We solve analytically the low-frequency s-wave dynamics of a massless scalar field propagating on a Schwarzschild-de Sitter black hole background. A rigorous application of the method of matched asymptotic expansions allows us to connect the scalar's evolution in the proximity of the black-hole horizon with that on cosmological scales. The scattering coeffic

Lei He, Diego García-Zamora, Yuming Zhu, Luis Martínez

Preference relations (PRs) are widely used to model expert judgments because they allow for eliciting the decision-makers' opinions from pairwise comparisons. Traditionally, PRs have been elicited using real numbers. However, in real-world decision-makers usually feel more comfortable using linguistic expressions closer to natural language. In this context,

Yohan Jung, Hyungi Lee, Wenlong Chen, Thomas Möllenhoff

Despite recent progress, continual learning still does not match the performance of batch training. To avoid catastrophic forgetting, we need to build compact memory of essential past knowledge, but no clear solution has yet emerged, even for shallow neural networks with just one or two layers. In this paper, we present a new method to build compact memory f

Shira Lifshitz, Ofir Lindenbaum, Gal Mishne, Ron Meir

Unsupervised feature selection (FS) is essential for high-dimensional learning tasks where labels are not available. It helps reduce noise, improve generalization, and enhance interpretability. However, most existing unsupervised FS methods evaluate features in isolation, even though informative signals often emerge from groups of related features. For examp

Wouter Jansen, Walter Daems, Jan Steckel

In-air acoustic imaging systems demand beamforming techniques that offer a high dynamic range and spatial resolution while also remaining robust. Conventional Delay-and-Sum (DAS) beamforming fails to meet these quality demands due to high sidelobes, a wide main lobe and the resulting low contrast, whereas advanced adaptive methods are typically precluded by

Sudip Kumar Kar, Valeriya Mykhaylova

A recently proposed Boltzmann local equilibrium Wigner function for massive spin-1/2 particles is generalized to the case of Fermi-Dirac statistics. The resulting formula ensures the correct normalization of the mean polarization vector and reproduces the generalized thermodynamic relations with spin that were obtained in earlier studies. Moreover, we show t

Jamil Khalouf-Rivera, Miguel Carvajal, Francisco Pérez-Bernal

One-dimensional quantum systems that undergo spontaneous symmetry-breaking, having a symmetric (non-degenerate) and a broken-symmetry (doubly-degenerate) phase, have been intensely studied in different branches of physics. In most cases, the spontaneously-broken symmetry is parity. However, it is possible to obtain similar phases in systems without parity sy

Xuyao Yu, Zijun Gong, Zhilu Lai

This paper presents a systematic analysis of inter-symbol and inter-carrier interference (ISCI) modeling in doubly dispersive channels for integrated sensing and communication (ISAC) systems. We propose a generalized OFDM (Weyl-Heisenberg) framework to evaluate four ISCI treatment approaches: (1) explicit estimation and compensation, (2) complete ignorance,

Igli Bajo, Frederik H. Bennhoff, Alessandro Ferrari

Recessions are periods in which the least productive firms in the economy exit, and as the economy recovers, they are replaced by new and more productive entrants. These cleansing effects improve the average firm productivity. At the same time, recessions induce a loss of varieties. In an economy with Homothetic Single Aggregator technology, we show that the

Linus Stuhlmann, Mauricio Fadel Argerich, Jonathan Fürst

Running LLMs locally has become increasingly common, but users face a complex design space across models, quantization levels, inference engines, and serving scenarios. Existing inference benchmarks are fragmented and focus on isolated goals, offering little guidance for practical deployments. We present Bench360, a framework for evaluating local LLM inferen

Eyup Yalcinkaya

We explore the deformation theory of instantons on locally conformal (LC) $Spin(7)$ manifolds. These structures, characterized by a non-parallel fundamental 4-form $\Phi$ satisfying $d\Phi = \theta \wedge \Phi$, represent a significant, yet geometrically constrained, class of non-integrable $G$-structures. We analyze the infinitesimal deformation complex for

Xin Zhao, Xiaojun Chen, Bingshan Liu, Zeyao Liu

Generative vision-language models like Stable Diffusion demonstrate remarkable capabilities in creative media synthesis, but they also pose substantial risks of producing unsafe, offensive, or culturally inappropriate content when prompted adversarially. Current defenses struggle to align outputs with human values without sacrificing generation quality or in

Matteo Bresciani, Mattia Dalla Brida, Leonardo Giusti, Michele Pepe

We present a detailed account of the theoretical progress and the computational strategy that led to the non-perturbative determination of the QCD Equation of State at temperatures up to the electroweak scale reported in [Phys. Rev. Lett. 134, 201904 (2025)]. The two key ingredients that make such a calculation feasible with controlled uncertainties are: (i)

Junfeng Xu, Nabadwip Sarkar, Sujoy Majumder

In this paper, using Nevanlinna's value distribution theory of meromorphic functions in several complex variables, we study for the existence of entire solutions $f$ in $\mathbb{C}^2$ of the following partial differential equation \[a_1\left(\frac{\partial f(z_1,z_2)}{\partial z_1}\right)^n+a_2f^n(z_1,z_2)=p_1e^{r(z_1,z_2)}+p_2e^{s(z_1,z_2)},\] where $n$ is

Thomas Lamby

Rademacher's Theorem can be interpreted as an almost-everywhere \emph{little-$o$ improvement principle}: if a function admits a uniform pointwise first-order Lipschitz control at every point, then this control improves to a vanishing one at almost every point. In the language of Calder\'on--Zygmund pointwise spaces, this means that \[ f \in T^\infty_1(x) \qu

Jiayin Zhu, Linlin Yang, Yicong Li, Angela Yao

Optimization-based text-to-3D methods distill guidance from 2D generative models via Score Distillation Sampling (SDS), but implicitly treat this guidance as static. This work shows that ignoring source dynamics yields inconsistent trajectories that suppress or merge semantic cues, leading to "semantic over-smoothing" artifacts. As such, we reformulate text-

Yuhao Wang, Xiaopeng Li, Cheng Gong, Ziru Liu

Reinforcement learning with verifiable rewards (RLVR) has been shown to enhance the reasoning capabilities of large language models (LLMs), enabling the development of large reasoning models (LRMs). However, LRMs such as DeepSeek-R1 and OpenAI o1 often generate verbose responses containing redundant or irrelevant reasoning step-a phenomenon known as overthin

Leyang Yang, Ziwei Wang, Xiaoxuan Tang, Sheng Zhou

With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation task

Yao Fu, Yihang Jin, Chunxia Zhang, Junmin Liu

Prompt learning has become a key method for adapting large language models to specific tasks with limited data. However, traditional gradient-based optimization methods for tuning prompts are computationally intensive, posing challenges for efficiency. We introduce ZOSA (Zero-Order Sharpness-Aware Minimization), a novel optimization framework that integrates

Ivan Gamov, Matthias Sauter, Samuel Huber, Quirin Buchinger

Tunable sources of entangled and single photons are essential for implementing entanglement-based quantum information protocols, as quantum teleportation and entanglement swapping depend on photon indistinguishability. Tunable devices are fabricated from indium arsenide (InAs) quantum dots (QDs) embedded in gallium arsenide (GaAs) nanomembranes placed on mon

Souji Shizuma

The prisoners and hats puzzle, or simply the hat puzzle, is a family of games in which a group of prisoners are each assigned a colored hat and are asked to guess the color of their own hat. Various versions of the puzzle arise depending on the number of prisoners, the number of possible hat colors, and the information available to them before and after the

M. A. Probst, W. Kollatschny, M. W. Ochmann, C. Sobrino Figaredo

WPVS 48 is a nearby narrow-line Seyfert 1 galaxy without previous analysis of the broad-line region (BLR) by means of optical spectroscopic reverberation mapping. By studying the continuum and emission line variability of WPVS 48, we aim to infer the BLR size as well as the mass of the central supermassive black hole (SMBH). We analyse data from a dedicated

Swati Priya, Twinkle Tripathy

This paper studies targeted opinion formation in multi-agent systems evolving over signed, time-varying directed graphs. The dynamics of each agent's state follow a Laplacian-based update rule driven by both cooperative and antagonistic interactions in the presence of exogenous factors. We formulate these exogenous factors as external control inputs and esta

Seham Nasr, Zhao Ren, David Johnson

Explainable AI (XAI) for Speech Emotion Recognition (SER) is critical for building transparent, trustworthy models. Current saliency-based methods, adapted from vision, highlight spectrogram regions but fail to show whether these regions correspond to meaningful acoustic markers of emotion, limiting faithfulness and interpretability. We propose a framework t

De Hu, Junsheng Hu, Cuicui Jiang

To achieve immersive spatial audio rendering on VR/AR devices, high-quality Head-Related Transfer Functions (HRTFs) are essential. In general, HRTFs are subject-dependent and position-dependent, and their measurement is time-consuming and tedious. To address this challenge, we propose the Graph Neural Field with Spatial-Correlation Augmentation (GraphNF-SCA)

Mu Zhou, Junbin Long, Yubiao Luo, Zhong Sun

Analog matrix computing (AMC) circuits based on resistive random-access memory (RRAM) have shown strong potential for accelerating matrix operations. However, as matrix size grows, interconnect resistance increasingly degrades computational accuracy and limits circuit scalability. Modeling and evaluating these effects are therefore critical for developing ef

Fei Song, Yi Li, Rui Wang, Jiahuan Zhou

Test-time prompt tuning for vision-language models has demonstrated impressive generalization capabilities under zero-shot settings. However, tuning the learnable prompts solely based on unlabeled test data may induce prompt optimization bias, ultimately leading to suboptimal performance on downstream tasks. In this work, we analyze the underlying causes of

Kangning Zhang, Wenxiang Jiao, Kounianhua Du, Yuan Lu

Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines where data generation and model training are executed as two separate, non-interactive processes. This approach fails to adaptively focus on a model's specific weaknesses and

Jiayue Yuan, Fangting Xie, Guangwen Ouyang, Changhai Ma

Multi-person global human mesh recovery (HMR) is crucial for understanding crowd dynamics and interactions. Traditional vision-based HMR methods sometimes face limitations in real-world scenarios due to mutual occlusions, insufficient lighting, and privacy concerns. Human-floor tactile interactions offer an occlusion-free and privacy-friendly alternative for

Jing Xiong, Liyang Fan, Hui Shen, Zunhai Su

Positional encoding is essential for large language models (LLMs) to represent sequence order, yet recent studies show that Rotary Position Embedding (RoPE) can induce massive activation. We investigate the source of these instabilities via a spectral analysis of RoPE, and show that its low-frequency components concentrate structured energy, producing low-ra

Théophile Chaumont-Frelet, Gregor Gantner

We consider linear reaction-diffusion equations posed on unbounded domains, and discretized by adaptive Lagrange finite elements. To obtain finite-dimensional spaces, it is necessary to introduce a truncation boundary, whereby only a bounded computational subdomain is meshed, leading to an approximation of the solution by zero in the remainder of the domain.

Junyi Liu, Stanley Kok

Gaussian process regression (GPR) is a popular nonparametric Bayesian method that provides predictive uncertainty estimates and is widely used in safety-critical applications. While prior research has introduced various uncertainty bounds, most existing approaches require access to specific input features, and rely on posterior mean and variance estimates or

Myeongsu Kim, Ikjun Yeom, Younghoon Kim

GPU clusters in multi-tenant settings often suffer from underutilization, making GPU-sharing technologies essential for efficient resource use. Among them, NVIDIA Multi-Instance GPU (MIG) has gained traction for providing hardware-level isolation that enables concurrent workloads without interference. However, MIG's hardware rigidity and the conventional one