October 2025 arXiv papers — page 98

Jiamian Li, Bing-Zhao Li

Three-dimensional point clouds can be viewed as discrete samples of smooth manifolds, allowing spectral analysis using the Laplace-Beltrami operator (LBO). However, the traditional point cloud manifold harmonic transform (PMHT) is limited by its fixed basis functions and single spectral representation, which restricts its ability to capture complex geometric

Filip Bečanović, Jared Miller, Vincent Bonnet, Kosta Jovanović

Inverse optimization (Inverse optimal control) is the task of imputing a cost function such that given test points (trajectories) are (nearly) optimal with respect to the discovered cost. Prior methods in inverse optimization assume that the true cost is a convex combination of a set of convex basis functions and that this basis is consistent with the test p

Jiahao Huo, Mufhumudzi Muthivhi, Terence L. van Zyl, Fredrik Gustafsson

Current state-of-the-art Wildlife classification models are trained under the closed world setting. When exposed to unknown classes, they remain overconfident in their predictions. Open-set Recognition (OSR) aims to classify known classes while rejecting unknown samples. Several OSR methods have been proposed to model the closed-set distribution by observing

Amin Bahmanian, Vedran Krčadinac, Lucija Relić, Sho Suda

We define symmetric designs of dimension $n$ and propriety $d$, providing a unifying generalization of several classes of higher-dimensional symmetric designs previously studied. We focus on the case $n=d=3$, which leads to the following question: Can we fill the $v^3$ cells of a $v\times v\times v$ cube with $\{0,1\}$ in such a way that each layer parallel

Christina Gasper, Nisa Ulumuddin, Siyuan Zhang, Sang-Hyeok Lee

Intermetallics often exhibit complex crystal structures, which give rise to intricate defect structures that critically influence their mechanical and functional properties. Despite studies on individual defect types, a comprehensive understanding of the defect landscape in {\mu}-phases, a class of topologically close-packed phases, remains elusive. In this

Xintong Yang, Minglun Wei, Yu-Kun Lai, Ze Ji

Automating the manipulation of granular materials poses significant challenges due to complex contact dynamics, unpredictable material properties, and intricate system states. Existing approaches often fail to achieve efficiency and accuracy in such tasks. To fill the research gap, this article studies the small-scale and high-precision granular material dig

Biyi Wang, Karl Meerbergen, Raf Vandebril, Hengbin An

In \emph{Wang et al., A Shifted Laplace Rational Filter for Large-Scale Eigenvalue Problems}, the SLRF method was proposed to compute all eigenvalues of a symmetric definite generalized eigenvalue problem lying in an interval on the real positive axis. The current paper discusses a parallel implementation of this method, abbreviated as ParaSLRF. The parallel

Sebastian Schlor, Frank Allgöwer

Encrypted controllers using homomorphic encryption have proven to guarantee the privacy of measurement and control signals, as well as system and controller parameters, while regulating the system as intended. However, encrypting dynamic controllers has remained a challenge due to growing noise and overflow issues in the encoding. In this paper, we review re

Zhaoran Zhao, Xinli Yue, Jianhui Sun, Yuhao Xie

Image Quality Assessment (IQA) has progressed from scalar quality prediction to more interpretable, human-aligned evaluation paradigms. In this work, we address the emerging challenge of detailed and explainable IQA by proposing iDETEX-a unified multimodal large language model (MLLM) capable of simultaneously performing three key tasks: quality grounding, pe

Pierfrancesco Siena, Pasquale Claudio Africa, Michele Girfoglio, Gianluigi Rozza

Cardiovascular diseases are a leading cause of death in the world, driving the development of patient-specific and benchmark models for blood flow analysis. This chapter provides a theoretical overview of the main categories of Reduced Order Models (ROMs), focusing on both projection-based and data-driven approaches within a classical setup. We then present

Daria D. Tyurina, Sergey V. Stasenko, Konstantin V. Lushnikov, Maria V. Vedunova

This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. Based on th

Claude Godrèche, Jean-Marc Luck

The distribution of the first positive position reached by a random walker starting at the origin is central to the analysis of extremes and records in one-dimensional random walks. In this work, we present a detailed and self-contained analytical study of this distribution for symmetric finite-range lattice walks, whose steps are drawn from a distribution s

Pierre Gosselin, Aïleen Lotz

In a previous paper, we applied a field formalism to analyze capital allocation and accumulation within a microeconomic framework of investors and firms. The financial connections were modeled by a field of stakes, representing the links between agents. We showed that the resulting collective states were composed of interconnected groups of agents defined by

Filippus S. Roux

A generalization is provided for the notion of tags, as used in various formulations of physical scenarios. It leads to the definition of tagged vector spaces, based on a set of axioms for tags and their extractors. As an application, such a tagged vector space is used to provide, in the context of quantum optics, a formal mathematical description for the Di

Kun Yu, Jiabao Jin, Xiaoyao Zhong, Peng Cheng

Approximate Nearest Neighbor Search (ANNS) in high-dimensional space is an essential operator in many online services, such as information retrieval and recommendation. Indices constructed by the state-of-the-art ANNS algorithms must be stored in single machine's memory or disk for high recall rate and throughput, suffering from substantial storage cost, con

Fuming Lin WEilin Mou

In this paper, we consider high-dimensional Lp-quantile regression which only requires a low order moment of the error and is also a natural generalization of the above methods and Lp-regression as well. The loss function of Lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss funct

Idir Edjekouane, Alejandro González Garrido, Jorge Querol, Symeon Chatzinotas

Global Navigation Satellite Systems (GNSS) provide the backbone of Positioning, Navigation, and Timing (PNT) but remain vulnerable to interference. Low Earth Orbit (LEO) constellations within Fifth-Generation (5G) Non-Terrestrial Networks (NTN) can enhance resilience by jointly supporting communication and navigation. This paper presents the first quantitati

Ziming Wang, Chenyu Wang, He Gao, Hua Feng

The recent launches of the Einstein Probe (EP) and the Space Variable Objects Monitor (SVOM) mission have led to the detection of a growing number of long GRBs with significant, early soft X-ray flux during their gamma-ray emission, prompting the question of whether their multi-band prompt emission shares a common origin in region and mechanism. To address t

Wei Zhang, Zhanhao Hu, Xiao Li, Xiaopei Zhu

In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers have proposed various defense methods against adversarial patches, a typical form of physically-realizable attack. However, our experiments showed that simply enlarging the patch s

Pooja Bhat, Wafa Maftuhin, Michael Walter

Bond rupture under the action of external forces is induced by temperature fluctuations. We show that measured forces from single molecule force spectroscopy experiments can be predicted from two quantities describing the bond that are the barrier to break the bond in absence of force as well as the maximal force the bond can withstand. The former can be obt

Boris Latosh

We present the new version of FeynGrav, a package that provides a set of tools to work with Feynman rules for gravity models. The new version addresses two principal issues and includes changes that improve user experience. Firstly, we present a more sophisticated implementation of the BRST formalism for general relativity and quadratic gravity, which result

Yuejin Xu

In this paper, we mainly discuss the convergence behavior of diffuse domain method (DDM) for solving semilinear parabolic equations with Neumann boundary condition defined in general irregular domains. We use a phasefield function to approximate the irregular domain and when the interface thickness tends to zero, the phasefield function will converge to indi

Sangyoon Bae, Jiook Cha

We introduce CausalMamba, a scalable framework that addresses fundamental limitations in fMRI-based causal inference: the ill-posed nature of inferring neural causality from hemodynamically distorted BOLD signals and the computational intractability of existing methods like Dynamic Causal Modeling (DCM). Our approach decomposes this complex inverse problem i

Pei-Yao Liu

We prove an entanglement sum rule for $(d-1)$-dimensional quantum lattice models with finite abelian higher-form symmetries, obtained by minimally coupling a sector on $p$-simplices carrying a $p$-form $G$ symmetry to a sector on $(p+1)$-simplices carrying the dual $(d-p-2)$-form $\widehat G$ symmetry (with $\widehat G$ being the Pontryagin dual of $G$). The

Véronique Bernard, Sébastien Descotes-Genon, Marc Knecht, Bachir Moussallam

We reconsider the constraints on the form factors $W_+ (s)$ and $W_S (s)$, describing the radiative decay modes $K^+\to\pi^+ \ell^+\ell^-$ and $K_S\to\pi^0 \ell^+\ell^-$, associated with the general properties of analyticity and unitarity. Starting from the simple consideration of the asymptotic behaviours of the two combinations $2 W_+ (s) - W_S (s)$ and $W

Po-Chen Ko, Jiayuan Mao, Yu-Hsiang Fu, Hsien-Jeng Yeh

Video-based representations have gained prominence in planning and decision-making due to their ability to encode rich spatiotemporal dynamics and geometric relationships. These representations enable flexible and generalizable solutions for complex tasks such as object manipulation and navigation. However, existing video planning frameworks often struggle t

Lipeng Xie, Sen Huang, Zhuo Zhang, Anni Zou

Conventional reward modeling relies on gradient descent over neural weights, creating opaque, data-hungry "black boxes." We propose a paradigm shift from implicit to explicit reward parameterization, recasting optimization from continuous weight spaces to the discrete space of natural language rubrics. We introduce a training-free framework based on iterativ

Tal Barami, Nimrod Berman, Ilan Naiman, Amos H. Hason

Learning disentangled representations in sequential data is a key goal in deep learning, with broad applications in vision, audio, and time series. While real-world data involves multiple interacting semantic factors over time, prior work has mostly focused on simpler two-factor static and dynamic settings, primarily because such settings make data collectio

Maria Roginskaya

In this paper, we want to investigate dynamics of productivity in a society which is diverse when it comes to both the productivity and the perception of justice in distribution.

Paloma T. de Lima, Amir Nikabadi, Paweł Rzążewski

The \textit{longest path transversal number} of a connected graph $G$, denoted by $lpt(G)$, is the minimum size of a set of vertices of $G$ that intersects all longest paths in $G$. We present constant upper bounds for the longest path transversal number of \textit{hereditary classes of graphs}, that is, classes of graphs closed under taking induced subgraph

Tuowei Wang, Minxing Huang, Fengzu Li, Ligeng Chen

As the demand for human-like reasoning, multi-turn dialogues, and long-form responses grows, large language models (LLMs) are increasingly expected to support efficient and effective long-sequence decoding. However, due to limited DRAM capacity, long-seuqence LLM decoding on smartphones is constrained by the key-value cache (KVCache), whose memory footprint

Bo-Chian Chen, Manel Slokom

Existing sequential recommendation models, even advanced diffusion-based approaches, often struggle to capture the rich semantic intent underlying user behavior, especially for new users or long-tail items. This limitation stems from their reliance on ID-based embeddings, which lack semantic grounding. We introduce LLMDiRec, a new approach that addresses thi

Eduard Marin, Jinwoo Kim, Alessio Pavoni, Mauro Conti

Serverless computing has rapidly emerged as a prominent cloud paradigm, enabling developers to focus solely on application logic without the burden of managing servers or underlying infrastructure. Public serverless repositories have become key to accelerating the development of serverless applications. However, their growing popularity makes them attractive

Hans-Olav Tylli, Henrik Wirzenius

The closed subalgebra $\mathcal J$ of the Banach algebra $\mathcal L(X)$ of bounded linear operators on the Banach space $X$ is a non-trivial closed $\mathcal I$-subideal of $\mathcal L(X)$ if $\mathcal I$ is a closed ideal of $\mathcal L(X)$ and $\mathcal J$ is an ideal of $\mathcal I$, but $\mathcal J$ is not an ideal of $\mathcal L(X)$. We obtain a variet

Thorsten Fröhlich, Tim Schlippe

The evaluation of academic theses is a cornerstone of higher education, ensuring rigor and integrity. Traditional methods, though effective, are time-consuming and subject to evaluator variability. This paper presents RubiSCoT, an AI-supported framework designed to enhance thesis evaluation from proposal to final submission. Using advanced natural language p

Reo Eriguchi, Kazumasa Shinagawa

A card-based secure computation protocol is a method for $n$ parties to compute a function $f$ on their private inputs $(x_1,\ldots,x_n)$ using physical playing cards, in such a way that the suits of revealed cards leak no information beyond the value of $f(x_1,\ldots,x_n)$. A \textit{single-shuffle full-open} protocol is a minimal model of card-based secure

Jacek Gosciniak

Programmable integrated photonics has evolved into a potent platform for implementing diverse optical functions on a single chip through software-driven reconfiguration. At the core of these processors are the photonic waveguide meshes that enable flexible light routing and manipulation. However, recirculating hexagonal waveguide meshes, which currently cons

Sofia Garcia de Blas Garcia-Alcalde, Francesco Belardinelli

We present two novel symbolic algorithms for model checking the Alternating-time Temporal Logic ATL*, over both the infinite-trace and the finite-trace semantics. In particular, for infinite traces we design a novel symbolic reduction to parity games. We implement both methods in the ATL*AS model checker and evaluate it using synthetic benchmarks as well as

ZhaoYang Han, Qihan Lin, Hao Liang, Bowen Chen

We introduce \textbf{LongInsightBench}, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements, while integrating \textbf{visual, audio, and text} modalities. Our benchmark excels in three key areas: \textbf{a) Long-Duration, Information-Dense Videos

H. Bechir, Luc Chevalier, Mohend Chaouche, K. Boufala

The mechanical behaviour of isotropic and incompressible vulcanized natural rubbers (NR's) and that of quasi-incompressible carbon black filled vulcanized natural rubbers (NR 70) are considered both theoretically and experimentally. Based on the Seth strain measures in terms of the first invariant, an original form of the strain energy density function W is

Iris van der Giessen, Joost J. Joosten, Paul Mayaux, Vicent Navarro Arroyo

This paper investigates the contingency of logic within the framework of possible world semantics. Possible world semantics captures the meaning of necessitation, i.e., a statement is necessarily true if it holds in all possible worlds. Standard Kripkean semantics assumes that all possible worlds are governed by one single logic. We relax this assumption and

Panos Kalnis. Shuo Shang, Christian S. Jensen

Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broader audiences. Here, we propose MapMuse, a storytelling-based framework for interpreting spatio-temporal datasets, transforming them into compelling, narrative-driven experiences.

Mao Shi

In this paper we present an explicit counterexample of degree $n=7$, which shows that the conjecture proposed by Li et al. \cite{Li2013} regarding the first derivative bounds for rational B\'ezier curves is generally false. We further derive an explicit rational B\'ezier representation of the first derivative and propose a degree-elevation based computable u

Siran Dai, Qianqian Xu, Peisong Wen, Yang Liu

In this work, we observe a counterintuitive phenomenon in self-supervised learning (SSL): longer training may impair the performance of dense prediction tasks (e.g., semantic segmentation). We refer to this phenomenon as Self-supervised Dense Degradation (SDD) and demonstrate its consistent presence across sixteen state-of-the-art SSL methods with various lo

O. Goisot, H. vanLandeghem, R. Haettel, F. Robaut

Improvement of functional and structural fatigue endurance for applications of ferroelastic materials requires an optimization of their composition. A strategy for finding alloy compositions that minimize the transformation hysteresis is necessary. We propose an experimental high throughput methodology to explore the model \b{eta}-Cu-Zn-Al system. It is base

Maurizio Grasselli, Andrea Poiatti

We consider the classical initial and boundary value problem for the Cahn--Hilliard equation with non-degenerate mobility and singular (e.g., logarithmic) potential. We prove that any weak solution converges to a single equilibrium using only minimal assumptions, that is, the existence of a global weak solution which satisfies an energy inequality. This resu

Ambre Chabert, Yves Colin de Verdìère

Given a compact Riemannian surface $M$, with Laplace-Beltrami operator $\Delta$, for $\lambda > 0$, let $P_{\lambda,\lambda^{-\frac{1}{3}}}$ be the spectral projector on the bandwidth $[\lambda-\lambda^{-\frac{1}{3}}, \lambda + \lambda^{\frac{1}{3}}]$ associated to $\sqrt{-\Delta}$. We prove a polynomial improvement on the $L^2 \to L^{\infty}$ norm of $P_{\l

Sebastian Schlor, Andrea Iannelli, Junsoo Kim, Hyungbo Shim

In this paper, we present a novel method for solving a class of quadratically constrained quadratic optimization problems using only additions and multiplications. This approach enables solving constrained optimization problems on private data since the operations involved are compatible with the capabilities of homomorphic encryption schemes. To solve the c

Paul Johnson, Fei Qi

This paper constructs the cohomology theory for grading-restricted vertex superalgebras, generalizing Yi-Zhi Huang's cohomology theory of grading-restricted vertex algebras. To simplify the discussion, motivate the construction, and make it easier for the reader to understand the technical points, we also include the construction of the cohomology theory of

Shilpa Jangid, Hiroshi Okada

We propose a two-loop neutrino mass model where fermionic and bosonic dark matter (DM) candidates are simultaneously connected to the neutrinos. But the fermionic DM candidate is favored compared to the bosonic one due to generating the fermionic DM mass at one-loop level. In order to obtain our desired Lagrangian and Higgs potential, we introduce a $Z_3$ ga

Panayotis Smyrnelis, Marcel G. Clerc, Manuel Diaz-Zuniga, Michał Kowalczyk

In this paper we complete the study of global minimizers of a forced, non autonomous, one dimensional, phase transition model, initiated in [8]. Motivated by the recent findings in [9], revealing new configurations of topological structures in light, we consider a forcing term having two periods. We show that depending on the strength of the forcing, at most

Qihao Peng, Tierui Gong, Zihang Song, Qu Luo

Ground-satellite links for 6G networks face critical challenges, including severe path loss, tight size-weight-power limits, and congested spectrum, all of which significantly hinder the performance of traditional radio frequency (RF) front ends. This article introduces the Rydberg Atomic Quantum Receiver (RAQR) for onboard satellite systems, a millimeter-sc

Hajar Bakarou, Mohamed Sinane El Messoussi, Anaïs Ollagnier

Antisocial behavior (ASB) on social media -- including hate speech, harassment, and cyberbullying -- poses growing risks to platform safety and societal well-being. Prior research has focused largely on networks such as X and Reddit, while \textit{multi-party conversational settings} remain underexplored due to limited data. To address this gap, we use \text

Giovanni Placini, Jonas Stelzig, Leopold Zoller

All compact K\"ahler, or even $\partial\bar\partial$-manifolds, are rationally formal. Not all of them are strongly formal. Yet some of them are: For complete smooth complex toric varieties and homogeneous compact K\"ahler manifolds we show the stronger property that they are both rationally and strongly formal in a compatible way.

Amir Gharghabi, Mahdi Hakiminezhad, Maryam Shafaei, Shaghayegh Gharghabi

Effortless and ergonomically designed surgical lighting is critical for precision and safety during procedures. However, traditional systems often rely on manual adjustments, leading to surgeon fatigue, neck strain, and inconsistent illumination due to drift and shadowing. To address these challenges, we propose a novel surgical lighting system that leverage

A. C. Hughes, R. Srinivas, C. M. Löschnauer, H. M. Knaack

We introduce the 'smooth gate', an entangling method for trapped-ion qubits where residual spin-motion entanglement errors are adiabatically eliminated by ramping the gate detuning. We demonstrate electronically controlled two-qubit gates with an estimated error of $8.4(7)\times10^{-5}$ without ground-state cooling. We further show that the error remains $\l

Jiri Gavenda, Petr Svenda, Stanislav Bobon, Vladimir Sedlacek

A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity. We adapt the BlockSci

Tonmoy Malakar, Miss Nourin Nurain Amina, Zarin Tasnim Nijhum, Nazmus Shakib Lalin

In this study, we present a highly sensitive Surface Plasmon Resonance (SPR)-based biosensor integrated with a circular-lattice Photonic Crystal Fiber (PCF) for early-stage cancer detection. The proposed sensor leverages the synergy between SPR and PCF technologies to overcome the bulkiness and limited sensitivity of traditional SPR systems. A thin gold (Au)

Yandong Gu

We investigate singular value statistics for products of independent rectangular complex Ginibre matrices. When the rectangularity parameters of the matrices converge to a common limit in the asymptotic regime, the limiting spectral density is derived, and the local statistics in the bulk are shown to be governed by the universal sine kernel. This generalize

Yuchao Qin, Anjunyi Fan, Bonan Yan

Data centers handle vast volumes of data that require efficient lossless compression, yet emerging probabilistic models based methods are often computationally slow. To address this, we introduce RAS, the Range Asymmetric Numeral System Acceleration System, a hardware architecture that integrates the rANS algorithm into a lossless compression pipeline and el

Qinyi Tian, Laura E. Dalton

Non-destructive methods are essential for linking the microstructural geometry of porous materials to their mechanical behavior, as destructive testing is often infeasible due to limited material availability or irreproducible conditions. Micro-computed tomography (micro-CT) provides high resolution three dimensional reconstructions of porous microstructures

Mehdi Zekriyapanah Gashti, Mostafa Mohammadpour, Ghasem Farjamnia

Accurate segmentation and classification of white blood cells (WBCs) in microscopic images are essential for diagnosis and monitoring of many hematological disorders, yet remain challenging due to staining variability, complex backgrounds, and class imbalance. In this paper, we introduce a novel Saliency-Guided Cross-Layer Deep Feature Fusion framework (SG-C

George Ma, Anurag Koul, Qi Chen, Yawen Wu

Large Language Models (LLMs) excel at code-related tasks but often struggle in realistic software repositories, where project-specific APIs and cross-file dependencies are crucial. Retrieval-augmented methods mitigate this by injecting repository context at inference time. The low inference-time latency budget affects either retrieval quality or the added la

Xinkai Wang, Beibei Li, Zerui Shao, Ao Liu

Multimodal large language models (MLLMs) have become integral to a wide range of real-world applications by jointly reasoning over text and visual inputs. However, despite recent advances in safety alignment, MLLMs remain vulnerable to jailbreak attacks, where carefully crafted inputs can bypass safety mechanisms and elicit harmful responses. In this work, w

Yehor Tereshchenko, Mika Hämäläinen

This paper presents a comprehensive comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats. Traditional machine learning models with embeddings, large language models (LLMs) with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation (RAG) approach

Rishi Jha, Harold Triedman, Justin Wagle, Vitaly Shmatikov

Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system and exfiltrate sensitive information. Recently proposed defenses, such as LlamaFirewall, rely on alignment checks of inter-agent communications to ensure that all agent invocations are "related to" and "likely to

Tian-Yu Wang, Ren-Hui Chen, Yan Li, Ze-Hao Shen

Constructing a quantum memory node with the ability of long-distance atom-photon distribution is the essential task for future quantum networks, enabling distributed quantum computing, quantum cryptography and remote sensing. Here we report the demonstration of a quantum-network node with a simple cavity-free cold atomic ensemble. This node gives an initial

Katie Luo, Jingwei Ji, Tong He, Runsheng Xu

Current autonomous driving systems rely on specialized models for perceiving and predicting motion, which demonstrate reliable performance in standard conditions. However, generalizing cost-effectively to diverse real-world scenarios remains a significant challenge. To address this, we propose Plug-and-Forecast (PnF), a plug-and-play approach that augments e

Akul Sharma, Anand A. Joshi, Richard M. Leahy

Traumatic brain injury (TBI) disrupts thalamocortical connectivity, contributing to cognitive impairment and post-traumatic epilepsy (PTE). This study presents a novel tractography-based framework that leverages diffusion maps to capture microstructural and organizational changes in thalamic white matter pathways. By analyzing individual streamline character

Muhammad Asif, Asim Ihsan, Zhu Shoujin, Ali Ranjha

This study explores the synergy between rate-splitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to enable ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while improving spectral and energy efficiency. Specifically, we inve

Aranya Sarkar

In this article, we extend a well known result about real rank zero C* Algebras to higher real rank C* Algebras. The main technique used here is similar to the method in which we approximate continuous functions using projections. What we reach at the end, is similar to the fact that the self-adjoint elements of a real rank zero C* Algebra can be approximate

Lucas Schulze, Juliano Decico Negri, Victor Barasuol, Vivian Suzano Medeiros

Grey-box methods for system identification combine deep learning with physics-informed constraints, capturing complex dependencies while improving out-of-distribution generalization. Despite the growing importance of floating-base systems such as humanoids and quadrupeds, current grey-box models ignore their specific physical constraints. For instance, the i

Anthony Frion, David S Greenberg

Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing deterministic machine learning approach, we propose a variational inference-based extension in which the predicted state follo

Jingyu Xing, Chenwei Tang, Xinyu Liu, Deng Xiong

Reinforcement Learning (RL) has emerged as a powerful paradigm for advancing Large Language Models (LLMs), achieving remarkable performance in complex reasoning domains such as mathematics and code generation. However, current RL methods face a fundamental scalability bottleneck due to their heavy reliance on human-curated preference data or labeled datasets

Kangbo Ouyang, Qinqi Wu

A topological dynamical system $(X,T)$ is called CF-Nil($k$) if it is strictly ergodic and the maximal measurable and maximal topological $k$-step pro-nilfactors coincide as measure preserving systems. Through constructing specific ``CF-Nil'' models, we prove that for any ergodic system $(X,\mathcal{X},\mu,T)$, any nilsequence $\{\psi(m,n)\}_{m,n\in\mathbb{Z

Jiayu Bai, Zhanbo Feng, Zhijie Deng, Tianqi Hou

Consistency Models (CMs) have shown promise for efficient one-step generation. However, most existing CMs rely on manually designed discretization schemes, which can cause repeated adjustments for different noise schedules and datasets. To address this, we propose a unified framework for the automatic and adaptive discretization of CMs, formulating it as an

N. Aksaker, A. Demirci, N. Erzincan, A. Akyuz

In this study, we present the results of photoionization modeling for 124 planetary nebulae (PNe) in the Galactic bulge. Utilizing the {\scshape cloudy} code, we derived the effective temperatures (T$_{eff}$) of the central stars, with a peak distribution around $\sim$ 100,000 K, and luminosities clustering around $\sim$ 3,000 L$\odot$. The inner radii of th

Akihito Yoshii, Ryosuke Sonoda, Ramya Srinivasan

Existing deepfake detection methods often exhibit bias, lack transparency, and fail to capture temporal information, leading to biased decisions and unreliable results across different demographic groups. In this paper, we propose a fairness-aware deepfake detection framework that integrates temporal feature learning and demographic-aware data augmentation t

Avishek Lahiri, Yufang Hou, Debarshi Kumar Sanyal

Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive literature surveys. The existing approaches to automatic survey generation do not compare the structure of the generated surveys with those written by human experts. To address this

Chuhan Qi

Additive spanners are fundamental graph structures with wide applications in network design, graph sparsification, and distance approximation. In particular, a $4$-additive spanner is a subgraph that preserves all pairwise distances up to an additive error of $4$. In this paper, we present a new deterministic algorithm for constructing $4$-additive spanners

Fernando Salanova, Jesús Roche, Cristian Mahulea, Eduardo Montijano

The reliable execution of high-level missions in multi-robot systems with heterogeneous agents, requires robust methods for detecting spurious behaviors. In this paper, we address the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula, as incorrect task sequences, violations of spatial constraints, timing

Maarten Solleveld

This is a survey paper about representation theory and noncommutative geometry of reductive p-adic groups G. The main focus points are: 1. The structure of the Hecke algebra H(G), the Harish-Chandra-Schwartz algebra S(G) and the reduced C*-algebra $C_r^* (G)$. 2. The classification of irreducible G-representations in terms of supercuspidal representations. 3

Naoyuki Haba, Yasuhiro Shimizu, Toshifumi Yamada

We investigate the cosmological phase transition dynamics in a supersymmetric left-right symmetric model based on the gauge group $SU(3)_C \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$ that addresses the strong CP problem through extended parity symmetry and doublet-doublet splitting. We compute the finite temperature effective potential including one-loo

Yeongjun Kim, Sergej Flach, Alexei Andreanov

Flatbands (FB) with compact localized eigenstates (CLS) fall into three main categories, controlled by the algebraic properties of the CLS set: orthogonal, linearly independent, linearly dependent (singular). A CLS parametrization allows us to continuously tune a linearly independent FB into a limiting orthogonal or a linearly dependent (singular) one. We de

David Dereudre, Rafaël Digneaux

We investigate rigidity phenomena in one-dimensional point processes. We show that the existence of an $L^1$ transport map from a stationary lattice or the Lebesgue measure to a point process is sufficient to guarantee the properties of Number-Rigidity and Cyclic-Factor. We then apply this result to non-singular Riesz gases with parameter $s\in(-2,-1]$, defi

S. Bautista, W. Jung, C. A. Morales

We study expansive homeomorphisms of a compact metric space $X$ through the lens of the commutative $C^*$-algebra $C(X)$ of continuous complex-valued functions, viewed as observables of the system. We introduce the notion of expansive observables: elements of $C(X)$ whose level sets distinguish distinct orbits. We prove that the expansive observables form an

James P. Connolly, Ahmed Nejim, Alexandre Jaffré, J Alvarez

This work reports optical and electronic numerical modelling of a novel emerging structure which is the GaAs nanocrystal on Si tandem solar cell by epitaxial lateral overgrowth, a technique which allows defect free material growth. The techniqueconsists of creating nucleation sites in a silicon surface SiO2 layer and initiating growth of nanoscalescale seeds

Özkan Canay, {Ü}mit Kocabıcak

Understanding user behavior on the web is increasingly critical for optimizing user experience (UX). This study introduces Augmented Web Usage Mining (AWUM), a methodology designed to enhance web usage mining and improve UX by enriching the interaction data provided by CAWAL (Combined Application Log and Web Analytics), a framework for advanced web analytics

Mohd Ruhul Ameen, Akif Islam, Ayesha Siddiqua, Abu Saleh Musa Miah

News media can influence readers not only through the events they report but also through the emotional tone used to present them. This issue is especially important in digital news environments, where headlines often shape first impressions before readers open the full article. This study examines affective framing in Bengali digital journalism through corp

Chengxi Li, Yang Sun, Lei Chen, Yiwen Wang

This paper proposes smaRTLy: a new optimization technique for multiplexers in Register-Transfer Level (RTL) logic synthesis. Multiplexer trees are very common in RTL designs, and traditional tools like Yosys optimize them by traversing the tree and monitoring control port values. However, this method does not fully exploit the intrinsic logical relationships

Wei-Hsun Lee, Che-Yu Chang, Kuang-Yu Li

Driver identification has become an area of increasing interest in recent years, especially for data- driven applications, because biometric-based technologies may incur privacy issues. This study proposes a deep learning neural network architecture, an attention-based encoder (AttEnc), which uses an attention mechanism for driver identification and uses few

Franek Stark, Rohit Kumar, Shubham Vyas, Hannah Isermann

Planetary exploration missions require robots capable of navigating extreme and unknown environments. While wheeled rovers have dominated past missions, their mobility is limited to traversable surfaces. Legged robots, especially quadrupeds, can overcome these limitations by handling uneven, obstacle-rich, and deformable terrains. However, deploying such rob

Cătălin Paşcu Moca, Doru Sticlet, Balázs Dóra

We investigate non-stabilizerness, also known as ``magic,'' to understand criticality and exceptional points in non-Hermitian quantum many-body systems. Our focus is on parity-time ($\mathcal{PT}$) symmetric spin chains, specifically the non-Hermitian transverse-field Ising and XX models. We calculate stabilizer R\'enyi entropies in their ground states using

Zefan Cai, Haoyi Qiu, Haozhe Zhao, Ke Wan

Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferences. While these methods improve visual quality, they can unintentionally encode and amplify social biases. To systematically trace how such biases evolve throughout the alignment p

Haitian Yang, Wen-An Yong

This paper extends our recent results on multi-dimensional discrete-velocity models to the numerical level. By adopting an operator splitting scheme and introducing a suitable discrete Lyapunov function, we derive numerical control laws that ensure the corresponding numerical solutions decay exponentially in time. To handle the stiff source term, we also use

Wenyu Mao, Jiancan Wu, Guoqing Hu, Zhengyi Yang

Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with a multi-step denoising process. However, the multi-step process relies on discrete approximations, introducing discretization error that creates a trade-off between computati

I. P. Fernando, D. Keller

Building on the first-ever application of neural networks in TMD phenomenology: "Extraction of the Sivers function with deep neural networks", we now present a momentum space, physics-informed deep learning framework for the direct extraction of unpolarized transverse momentum dependent parton distributions (TMDs) from fixed target Drell-Yan data (E2

Veronica Danesi, Cristian Mendico, Xuan Tao, Kaizhi Wang

This paper explores the asymptotic properties of non-autonomous Lagrangian systems, assuming that the associated Tonelli Lagrangian converges to a time-periodic function. Specifically, given a continuous initial condition, we provide a suitable construction of a Lax-Oleinik semigroup such that it converges toward a periodic solution of the equation. Moreover

Stefania Ionescu, Robin Forsberg, Elsa Lichtenegger, Salima Jaoua

Throughout application domains, we now rely extensively on algorithmic systems to engage with ever-expanding datasets of information. Despite their benefits, these systems are often complex (comprising of many intricate tools, e.g., moderation, recommender systems, prediction models), of unknown structure (due to the lack of accompanying documentation), and

Shuodi Liu, Yingzhuo Liu, Zi Wang, Yusheng Wang

Large language models (LLMs) have demonstrated remarkable reasoning and planning capabilities, driving extensive research into task decomposition. Existing task decomposition methods focus primarily on memory, tool usage, and feedback mechanisms, achieving notable success in specific domains, but they often overlook the trade-off between performance and cost