October 2025 arXiv papers — page 193

Gianmarco Perantoni, Giulio Weikmann, Lorenzo Bruzzone

The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs) integrated

Ruifang Liu, Ao Fan, Jinlong Shu

The {\it toughness} $\tau(G)=\mathrm{min}\{\frac{|S|}{c(G-S)}: S~\mbox{is a vertex cut in}~G\}$ for $G\ncong K_n,$ which was initially proposed by Chv\'{a}tal in 1973. A graph $G$ is called {\it $t$-tough} if $\tau(G)\geq t.$ Let $\lambda_i(G)$ be the $i$-th largest eigenvalue of the adjacency matrix of a graph $G$. In 1996, Brouwer conjectured that $\tau(G)

Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho

This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile

Keyi Ding, Carrie Filion, Rosemary F. G. Wyse, Evan N. Kirby

We present our photometric method, which combines Subaru/HSC $NB515$, g, and i band filters to distinguish giant stars in Local Group galaxies from Milky Way dwarf contamination. The $NB515$ filter is a narrow-band filter that covers the MgI+MgH features at $5150$ \r{A}, and is sensitive to stellar surface gravity. Using synthetic photometry derived from lar

Joanna Ćwiąkała, Waldemar Gajda, Michał Ćwiąkała, Ernest Górka

This study investigates the significance of emotional intelligence (EI) as a fundamental component of effective leadership and its impact on building cohesive, motivated, and high-performing teams. Drawing on data from a survey of 100 professionals, the research examines how EI competencies including self-awareness, self-regulation, empathy, and social skill

L. Lange, T. Bertrand, V. Belissa, S. Capry

Context. The equatorial region of Cthulhu as revealed by New Horizons appears to be generally dark and largely devoid of volatiles because its surface albedo is low. Localized bright patches, however, which are interpreted as CH4 frost, are observed on crater rims and slopes. Aims. Previous studies suggested that these frosts might result from the peculiar i

Yidan Wang, Jing Han, Pei Wang, Di Li

Active repeating Fast Radio Bursts (FRBs), with their large number of bursts, burst energy distribution, and their potential energy evolution, offer critical insights into the FRBs emission mechanisms. Traditional pipelines search for bursts through conducting dedispersion trials and looking for signals above certain fluence thresholds, both of which could r

Jihang Zhu, Chunli Huang

We study the magnetic-field response of interacting electron systems within mean-field theory using perturbation theory. We show that the linear response of the mean-field density-matrix to a weak magnetic field is purely geometric: it depends only on wavefunction derivatives, the Berry connections linking the occupied and unoccupied subspaces, and does not

Neel Prabhanjan Rachamalla, Aravind Konakalla, Gautam Rajeev, Ashish Kulkarni

The effectiveness of Large Language Models (LLMs) depends heavily on the availability of high-quality post-training data, particularly instruction-tuning and preference-based examples. Existing open-source datasets, however, often lack multilingual coverage, cultural grounding, and suffer from task diversity gaps that are especially pronounced for Indian lan

Markus Reuter, Tobias Lingenberg, Rūta Liepiņa, Francesca Lagioia

Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval s

Martin Wilhelm, Franz Freitag, Max Tzschoppe, Thilo Pionteck

Heterogeneous computing systems, which combine general-purpose processors with specialized accelerators, are increasingly important for optimizing the performance of modern applications. A central challenge is to decide which parts of an application should be executed on which accelerator or, more generally, how to map the tasks of an application to availabl

Mrityunjay Kumar

Many users interact with AI tools like ChatGPT using a mental model that treats the system as human-like, which we call Model H. According to goal-setting theory, increased specificity in goals should reduce performance variance. If Model H holds, then prompting a chatbot with more detailed instructions should lead to more consistent evaluation behavior. Thi

H. Roch, G. Pihan, A. Monnai, S. Ryu

We employ the SMASH transport model to provide event-by-event initial conditions for the energy-momentum tensor and conserved charge currents in hydrodynamic simulations of relativistic heavy-ion collisions. We study the fluctuations and dynamical evolution of three conserved charge currents (net baryon, net electric charges, and net strangeness) with a 4D l

Artur Horal, Daniel Pina, Henrique Paz, Iago Paulo

This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models (LLMs) in AI-assisted software development. Our work is driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbr

Viktor Mirjanić, Daattavya Aggarwal, Challenger Mishra

We study a generalisation of the quality of an ABC triple that we call the weighted average multiplicity (WAM), in which the logarithmic heights of prime factors are raised to a complex exponent s. The WAM is connected to the standard ABC conjecture at s=1. We show that for real part of s less than 1, WAM is unbounded over ABC triples both for integers and p

Davide Oberto, Maria Strazzullo, Stefano Berrone

In this contribution, we focus on the Reynolds-averaged Navier-Stokes (RANS) models and their exploitation to build reliable reduced-order models to further accelerate predictions for real-time applications and many-query scenarios. Specifically, we investigate how machine learning can be employed to enhance the predictive capabilities of the model, both at

Federico Ambrosino, Jörg Teschner

The analytic Langlands correspondence proposed by Etingof, Frenkel and Kazhdan describes the solution to the spectral problems naturally arising in the quantisation of the Hitchin integrable systems in terms of real opers, certain second order differential operators on a Riemann surface having real monodromy. We prove this correspondence in the cases associa

Pengyue Yang, Haolin Jin, Qingwen Zeng, Jiawen Wen

The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models. While this rapid growth accelerates innovation, it has simultaneously created significant challenges in model discovery and adoption. Users struggle to navigate this landscape due to inconsistent, incomplete, and imbalanced documentatio

Girolamo Macaluso, Lorenzo Mandelli, Mirko Bicchierai, Stefano Berretti

Diffusion models have recently advanced human motion generation, producing realistic and diverse animations from textual prompts. However, adapting these models to unseen actions or styles typically requires additional motion capture data and full retraining, which is costly and difficult to scale. We propose a post-training framework based on Reinforcement

Jingbiao Mei, Mingsheng Sun, Jinghong Chen, Pengda Qin

Hateful memes have emerged as a particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior approaches rely on direct detection, producing only binary predictions. Such models fail to provide the context and explanations that real-world moderation requires. Recent Explain-then-Detect approaches, using

Rohith Mahadevan

Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of se

Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho

This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientation from observed portfolio allocations. Using controlled synthetic data, we assess the estimator's statistical properties such as consistency, coverage, and dynamic regret. The mode

Wallace Jaffray, Sven Stengel, Alexandra Boltasseva, Vladimir M. Shalaev

Controlling the polarization state of light with sub-picosecond speed and subwavelength precision remains a key challenge for next-generation nanophotonic devices. Conventional methods such as birefringent crystals, liquid crystals, or electro-optic Pockels cells are limited in speed, compactness, and energy efficiency. While structured materials and two-dim

Masanari Kondo, Mahmoud Alfadel, Shane McIntosh, Yasutaka Kamei

Design-level decisions in open-source software (OSS) projects are often made through structured mechanisms such as proposals, which require substantial community discussion and review. Despite their importance, the proposal process is resource-intensive and often leads to contributor frustration, especially when proposals are declined without clear feedback.

Sheng Chen, Jing Liu

We establish the asymptotic formula for the number of integral points in non-compact symmetric homogeneous spaces of semi-simple simply connected algebraic groups over global function fields, given by the sum of the products of local densities twisted by suitable Brauer elements.

Andreas Wichert

We present a novel quantum storage algorithm for k binary vectors of dimension m into a superposition of a m qubit quantum state based on a permutation technique. We compare this algorithm to the storage algorithm proposed by Ventura and Martinez. The permutation technique is simpler and can lead to an additional reduction through the reduce algorithm. To re

Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli

Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that intermittently replaces finetuned weights with their pretrained reference, through the lens of a single-run, weight-sharing implicit ensemble. This perspective reveals three key l

Dmitriy F. Kuznetsov

The article is devoted to the systematic derivation of new representations of the Hu-Meyer formulas. The formula expressing a multiple Wiener stochastic integral through the sum of multiple Stratonovich stochastic integrals and the formula expressing a multiple Stratonovich stochastic integral through the sum of multiple Wiener stochastic integrals are deriv

Xinyi Gao, Jingxi Zhang, Lijian Chen, Tong Chen

Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the knowledge within RDBs for predictive tasks, recent advances leverage graph representation learning to capture complex inter-table relations as multi-hop dependencies. Despite achievin

J. M. Daniels-Holgate

When mean curvature flow evolves non-uniquely, the flow is said to fatten. The work of Ilmanen shows that any weak MCF is supported inside the fattening, and work of Hershkovits--White identified canonical weak flows supported on the boundary of the fattening, known as the outermost flows. It is natural to ask, when the flow fattens, are there weak mean curv

Jose L. Gomez, Octavio M. Guilera, Marcelo M. Miller Bertolami, Elisa Castro-Martínez

Context. Recent studies highlight the importance of stellar mass sampling when determining disc lifetimes through observed disc fractions. Low-mass stars host discs with average lifetimes exceeding 5~Myr, and accretion rates show a strong correlation with stellar mass ($\dot{M} \propto M_\star^2$). Aims. We aim to identify the optimal parameters of a protopl

Alexey V. Yulin, Dmitry A. Zezyulin

We investigate the transverse instability of two-component solitons forming in a planar waveguide operating in the regime of strong light-matter coupling. The instability emerges as a result of the coupling between transverse diffraction of the photonic component and nonlinearity of the material excitations. Solutions of three different forms are addressed w

Christina Wang, Cristián Peña, Adolf Bornheim, Shuoxing Wu

We present a detailed study of an 8-channel $1\times1$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV hadron beam and 120 GeV muon beam at the CERN Super Proton Synchrotron H6 beamline. Following up on our first detailed characterization of the efficiency and response of an SMSPD fabricated on a 3 nm WSi film,

Ustin M. Yanikov, Vasily A. Kulikov, Andrey M. Shirokov

We apply the version of the Efros method utilizing oscillator expansion of wave functions to the Coulomb scattering problem using our recent developments of the HORSE formalism. The approach yields accurate phase shifts and cross sections with significantly reduced computational cost compared to the full HORSE method, while maintaining agreement with exact s

Muris Sladić, Veronica Valeros, Carlos Catania, Sebastian Garcia

There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service, mainly SSH shells. These systems - but also the deception technologies not based on LLMs - lack an extensive evaluation that includes human attackers. Generative AI has recently become a valuable asset for cybersecur

Zhantao Yang, Huangji Wang, Ruili Feng, Han Zhang

Generating captions for long and complex videos is both critical and challenging, with significant implications for the growing fields of text-to-video generation and multi-modal understanding. One key challenge in long video captioning is accurately recognizing the same individuals who appear in different frames, which we refer to as the ID-Matching problem

Jort Vincenti, Metod Jazbec, Guoxuan Xia

Visual Autoregressive Models (VAR) offer efficient and high-quality image generation but suffer from computational redundancy due to repeated Transformer calls at increasing resolutions. We introduce a dynamic Mixture-of-Experts router integrated into VAR. The new architecture allows to trade compute for quality through scale-aware thresholding. This thresho

Yanshi Sun, Zhiguo Ding, George K. Karagiannidis

Recently, the study on pinching-antenna technique has attracted significant attention. However, most relevant literature focuses on a single-cell scenario, where the effect from the interfering pinching-antennas on waveguides connected to spatially distributed base stations (BSs) was ignored. To fulfill this knowledge gap, this letter aims to provide an anal

Zhiyue Zuo, Masoud Ghalaii, Stefano Pirandola

Future global quantum communication networks, or quantum Internet, will realize high-rate secure communication and entanglement distribution for large-scale users over long distances. Continuous variable (CV) quantum key distribution (QKD) provides a powerful setting for secure quantum communications, thanks to the use of room-temperature off-the-shelf optic

Shoumeng Qiu, Xinrun Li, Yang Long, Xiangyang Xue

The online construction of vectorized high-definition (HD) maps is a cornerstone of modern autonomous driving systems. State-of-the-art approaches, particularly those based on the DETR framework, formulate this as an instance detection problem. However, their reliance on independent, learnable object queries results in a predominantly local query perspective

L. Mashonkina, A. Smogorzhevskii

Palladium is one of poorly observed neutron-capture elements. Abundance determinations for stellar samples covering a broad metallicity range are needed for better understanding the mechanisms of Pd synthesis during the Galaxy evolution. We aim to obtain accurate abundances of Pd for the Sun and the sample of metal-poor stars based on the non-local thermodyn

Huanning Dong, Fan Li, Ping Kuang, Jianwen Min

Recent advancements in Text-to-3D modeling have shown significant potential for the creation of 3D content. However, due to the complex geometric shapes of objects in the natural world, generating 3D content remains a challenging task. Current methods either leverage 2D diffusion priors to recover 3D geometry, or train the model directly based on specific 3D

Ali Hamedani, Andrea E. Sand

Silicon carbide (SiC) has long been a subject of study for its application in harsh environments. Existing empirical interatomic potentials for 3C-SiC show significant discrepancies in predicting the properties that are crucial in describing the evolution of defects generated in collision cascades. We present a Gaussian approximation potential model for 3C-S

Bryan R. Christ, Penelope Molitz, Beau LeBlond, Zachary Gottesman

Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students' interests and ability levels can enhance learning. However, teachers struggle to find time to customize MWPs for students given large class sizes and increasing burnout. We propose that LLMs can support math education by generating MWPs customized to student inte

Aaron Kettner

We introduce twisted topological correspondences, which generalize both Katsura's topological correspondences as well as the twisted topological graphs introduced by Li. We show that, up to isomorphism, they are in bijection with certain principal bundles. This makes it possible to study topological correspondences using the machinery of principal and fiber

Riccardo Ferrazzoli, Enrico Costa, Sergio Fabiani, Philip Kaaret

We present a comprehensive characterization of the on-orbit imaging performance of the three telescopes on board the Imaging X-ray Polarimetry Explorer (IXPE). Each telescope comprises a Wolter-I mirror module assembly and a Gas Pixel Detector focal-plane detector unit (DU). We analyze data from point-like X-ray sources and fit a composite point spread funct

P. Giommi, M. Doro, M. Gouvêa, L. Fronte

We present a systematic reassessment of 5,062 high-Galactic latitude gamma-ray sources from the Fermi-LAT 4FGL-DR4 catalog using Firmamento, a web-based platform for multi-frequency source discovery and analysis. Our goal is to provide an independent evaluation of LAT gamma-ray source associations through alternative spectral and spatial methods that combine

Vaibhav Srivastav, Steven Zheng, Eric Bezzam, Eustache Le Bihan

We present the Open ASR Leaderboard, a reproducible benchmarking platform with community contributions from academia and industry. It compares 86 open-source and proprietary systems across 12 datasets, with English short- and long-form and multilingual short-form tracks. We standardize word error rate (WER) and inverse real-time factor (RTFx) evaluation for

Frank Vallentin

The aim of this paper is to highlight recent progress in using conic optimization methods to study geometric packing problems. We will look at four geometric packing problems of different kinds: two on the unit sphere -- the kissing number problem and measurable $\pi/2$-avoiding sets -- and two in Euclidean space -- the sphere packing problem and measurable

Markus Reineke

We prove that generating subspaces of matrix rings over finite fields are counted by polynomials. We use this result to define and study two-variable versions of polynomials counting isomorphism classes of absolutely irreducible representations of free algebras.

Seongyeon Kim, Ihyeok Seo

We establish Morawetz-type estimates for solutions to the elastic wave equation with singular weights of the form $|x|^{-\alpha}$ or $|(x,t)|^{-\alpha}$. In particular, we show that space-time weights $|(x,t)|^{-\alpha}$ admit stronger singularities and require weaker regularity assumptions on the initial data compared to purely spatial weights $|x|^{-\alpha

Baraq Lipshitz, Alessio Melone, Charalampos Maraziaris, Muhammed Bilal

Sparse Ternary General Matrix-Matrix Multiplication (GEMM) remains under-optimized in existing libraries for Apple Silicon CPUs. We present a Sparse Ternary GEMM kernel optimized specifically for Apple's M-series processors. We propose a set of architecture-aware optimizations, including a novel blocked and interleaved sparse data format to improve memory lo

Katsushi Ito, Hongfei Shu, Jingjing Yang

We study the deformed supersymmetric quantum mechanics with a polynomial superpotential with $\hbar$ correction. In the minimal chamber, where all turning points are real and distinct, it was shown that the exact Wentzel--Kramers--Brillouin periods obey the ${\mathbb Z}_4$-extended thermodynamic Bethe ansatz (TBA) equations of the undeformed potential. By ch

Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan, Eric Granger

Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial computational costs due to the need to train and store multiple models. Dropout offers a lightweight alternative by simulating ensembles through random neuron deactivation; however, whe

Zheng-An Chen, Tao Luo

Although transformer-based models have shown exceptional empirical performance, the fundamental principles governing their training dynamics are inadequately characterized beyond configuration-specific studies. Inspired by empirical evidence showing improved reasoning capabilities under small initialization scales in language models, we employ the gradient f

Minju Gwak, Guijin Son, Jaehyung Kim

The Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information. In this work, we revisit this principle in the context of Large Language Model (LLM) reasoning, asking whether step-level uniformity reflects reasoning quality. To this end, we introduce a novel framework to quantify

Bing Li, Wuqi Wang, Yanan Zhang, Jingzheng Li

LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is essential for thoroughly testing these detection systems and exposing their vulnerabilities before real-world deployment. However, existing adversarial attacks that add optimized pert

Philip Huff, Nishka Gandu, Pavel Novák

We examine the state of publicly available information about known exploitable vulnerabilities applicable to operational technology (OT) environments. Specifically, we analyze the Known Exploitable Vulnerabilities Catalog (KEVC) maintained by the US Department of Homeland Security Cybersecurity and Infrastructure Security Agency (CISA) to assess whether curr

Riccardo Giordana Pozzi

We study universal features of defect correlation functions in supersymmetric defect CFTs, focusing on four-point functions of the displacement supermultiplet. By perturbing the leading-order correlators at strong coupling, we derive constraints that identify the operators exchanged at next-to-leading order. From this, we determine the conditions under which

Junghwan Lim, Sungmin Lee, Dongseok Kim, Wai Ting Cheung

The self-attention mechanism, while foundational to modern Transformer architectures, suffers from a critical inefficiency: it frequently allocates substantial attention to redundant or noisy context. Differential Attention addressed this by using subtractive attention maps for signal and noise, but its required balanced head allocation imposes rigid constra

Pedro C. S. Costa, Yuval R. Sanders, Pedro Paulo Balbi, Gavin K. Brennen

We investigate the density classification task (DCT) -- determining the majority bit in a one-dimensional binary lattice -- within a quantum cellular automaton (CA) framework. While there is no one-dimensional two-state, radius $r \geq 1$, deterministic CA with periodic boundary conditions that solves the DCT perfectly, we explore whether a unitary quantum m

Ruifeng Gao, Hao Zhang, Jue Wang, Ye Li

In maritime wireless networks, the evaporation duct effect has been known as a preferable condition for long-range transmissions. However, how to effectively utilize the duct effect for efficient communication design is still open for investigation. In this paper, we consider a typical scenario of ship-to-shore data transmission, where a ship collects data f

Flank D. M. Bezerra, Luís M. Salge

In this paper, we study a class of higher-order semilinear evolution equations inspired by the Moore-Gibson-Thompson model introduced by Dell'Oro, Liverani and Pata (2023), involving strongly elliptic operators of order ($2m$) with homogeneous boundary conditions. The associated unbounded linear operator is a sectorial operator with zero belonging to the res

Andrii Sokolov, Conor Power, Elena Blokhina

Scalable quantum information processing in spin-based architectures necessitates the a bility to reliably shuttle quantum states across extended device regions with minimal decoherence. In this work, we present a physics-informed algorithm for optimizing electrostatic bias equences that enable conveyor-mode electron transport in silicon-based quantum dot dev

Luc Ramsès Talla Waffo

We investigate the values of the Riemann zeta function at odd integers and the Dirichlet beta function at even integers, by collecting several distinct analytic frameworks converging to these values, thus providing a unifying perspective. Beyond analytic interest, these formulas motivate linear independence conjectures which, if established, would imply the

Shiva Meucci

Fresnel derived the drag coefficient of moving transparent matter from a mechanical argument in 1818, and a century of interferometry confirmed it exactly as he gave it. We show that the mechanical underpinning he sought was workable in principle and wrong in one word: density, where the mechanics says compliance. The complete first-order coefficient follows

Mirek Giersz, Abbas Askar, Arkadiusz Hypki, Jongsuk Hong

The formation of stars with light-element abundance variations in globular clusters and the subsequent dynamical evolution of these multiple populations remains an open question. One of the most widely discussed is the AGB scenario, in which chemically processed material from the envelopes of AGB stars mixes with re-accreted primordial gas flowing into the c

Ricardo Campos, Bruno Vallette

We apply the effective integration theory of Lie-graph algebras, developed recently by the authors, to the deformation and homotopy theories of types of bialgebras, that is structures controlled by a properad, like associative bialgebras, (involutive) Lie bialgebras, Frobenius bialgebras, double Poisson bialgebras, pre-Calabi--Yau algebras, quantum Airy stru

Or Feldman, Krishna Sri Ipsit Mantri, Moshe Eliasof, Chaim Baskin

Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adapting state-of-the-art dynamic link property prediction models to node affinity prediction. However, simple heuristics, such as Persistent Forec

S. Rukdee, M. Güdel, I. Vilović, K. Poppenhäger

K2-18b lies near the radius valley that separates super-Earths and sub-Neptunes, marking a key transitional regime in planetary and atmospheric composition. The system offers a valuable opportunity to study how M-dwarf high-energy stellar radiation influences atmospheric stability and the potential for sustaining volatile species, especially important in the

Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen, Hai L. Vu

The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Quantum Fusion Layer (QFL), replaces classical fusion schemes with a hybrid quantum-classical procedure that uses parameterized quantum circuits to learn entangled feature interacti

He Huang, Zilong Liu, Zeping Sui, Wei Huang

This paper introduces a novel cooperative vehicular communication algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bou

Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Björnson

This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data.

Jianhan Zhang, Jitao Wang, Chengchun Shi, John D. Piette

Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm, while optimized to maximize overall population benefits, may disadvantage certa

Ricardo Campos, Bruno Vallette

We develop the Lie theory of Lie-admissible algebras whose product is enriched with higher operations modeled on directed graphs with a view to apply it to the deformation theories controlled by this kind of Lie algebras. We produce effective formulas for their exponential map, their gauge group structure and the action on Maurer--Cartan elements. The main m

Uilton Cesar Peres Junior, Carla Silva Oliveira, André Ebling Brondan

Let $G$ be a connected graph of order $n$, and $A(G)$ and $D(G)$ its adjacency and degree diagonal matrices, respectively. For a parameter $\alpha \in [0,1]$, Nikiforov~(2017) introduced the convex combination $A_{\alpha}(G) = \alpha D(G) + (1 - \alpha)A(G)$. In this paper, we investigate the spectral distribution of $A_\alpha(G)$-eigenvalues, over subinterv

Gregoire Marc

We define a generalization of (coloured) operads based on double lax functors and we construct a model structure on the associated category of generalized simplicial (coloured) operads. In particular, we obtain a model structure on the category of simplicial (coloured) O-operads of Nardin and Shah.

Fiorenzo Stoppa, Turan Bulmus, Steven Bloemen, Stephen J. Smartt

Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders i

Krzysztof Dębicki, Enkelejd Hashorva, Zbigniew Michna

For a centered, homogeneous R^d-valued Gaussian random field X(t), t in R^k, with covariance matrix function R(s,t) = E[X(s) X(t)^T], we investigate the exact asymptotics of kappa_u(x) = P( theta(u) * integral over [0,T]^k of 1{X(t) > u b} dt > x ), where b = (b1, ..., bd)^T, as u -> infinity, with x >= 0 and T > 0, and theta(u) is a scaling function related

Hichem Sahbi

Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user

Armando Bellante, Stefano Vanerio, Stefano Zanero

We study quantum sparse recovery in non-orthogonal, overcomplete dictionaries: given coherent quantum access to a state and a dictionary of vectors, the goal is to reconstruct the state up to $\ell_2$ error using as few vectors as possible. We first show that the general recovery problem is NP-hard, ruling out efficient exact algorithms in full generality. T

Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis, Nikolaos Polatidis

As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendatio

Giulio Malavolta

Foundational results in theoretical computer science have established that everything provable, is provable in zero knowledge. However, this assertion fundamentally assumes a classical interpretation of computation and many interesting physical statements that one can hope to prove are not characterized. In this work, we consider decision problems, where the

Louisa F. Bröring, Jesse Pajwani, Anna M. Viergever

The $\mathbb{A}^1$-Euler characteristic is a refinement in algebraic geometry of the classical topological Euler characteristic, which can be constructed using motivic homotopy theory. This invariant is a quadratic form rather than an integer, which carries a lot of information, but is difficult to compute in practice. In this survey, we discuss a conjectura

Susmita Das, Mala Das, Vimal Kumar, Suraj Ali

The Indian Dark matter search Experiment (InDEx) has been initiated at Jaduguda Underground Science Laboratory (JUSL) to explore the low mass region of dark matter. The detectors used by InDEx are superheated droplet detectors with active liquid C2H2F4. The run1 of InDEx was with 2.47 kg-days of exposure at a threshold of 5.87 keV. In the present work, the r

V. Sguera, L. Sidoli

We present new broad-band X-ray results aimed at the identification and characterization of four poorly studied hard X-ray transients discovered by INTEGRAL: IGR J16426+6536, IGR J09446-2636, IGR J21268+6203, and IGR J02447+7046. The key properties and X-ray behavior of these sources have remained largely unknown until now. We investigated the temporal, spec

Adrián Pérez-Herrero, Paulo Félix, Jesús Presedo, Carl Henrik Ek

We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the parameters of a Switching Linear Dynamical System and a Gaussian process prior to model the st

Sakineh Mizani, Martin Oettel, Péter Gurin, Szabolcs Varga

We report the discovery of a mixed orientational structure in the quasi-one-dimensional fluid of hard non-spherical bodies with the exact calculation of the thermodynamic and structural quantities using the transfer operator method. The mixed arrangement, which is spatially uniform, but orientionally ordered, cannot be identified with conventional mesophases

Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin

Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from interacting during the user's turn and can lead to high response latency while it waits to think. Consequently, thinking after receiving the full input is not suitable for speech-to

Fangzhou Zhao, Yao Sun, Jianglin Lan, Muhammad Ali Imran

The primary challenge in autonomous lunar landing missions lies in the unreliable local control system, which has limited capacity to handle high-dynamic conditions, severely affecting landing precision and safety. Recent advancements in lunar satellite communication make it possible to establish a wireless link between lunar orbit satellites and the lunar l

Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang

Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current

Xinle Chang, Yang Yang, Yueran Li, Zhengcen Li

Functional variability in both gray matter (GM) and white matter (WM) is closely associated with human brain cognitive and developmental processes, and is commonly assessed using functional connectivity (FC). However, as a correlation-based approach, FC captures the co-fluctuation between brain regions rather than the intensity of neural activity in each reg

Ke Guo, Haochen Liu, Xiaojun Wu, Chen Lv

Realistic traffic simulation is critical for the development of autonomous driving systems and urban mobility planning, yet existing imitation learning approaches often fail to model realistic traffic behaviors. Behavior cloning suffers from covariate shift, while Generative Adversarial Imitation Learning (GAIL) is notoriously unstable in multi-agent setting

Alexandros Vassiliades, Nikolaos Polatidis, Stamatios Samaras, Sotiris Diplaris

This study explores the explainability capabilities of large language models (LLMs), when employed to autonomously generate machine learning (ML) solutions. We examine two classification tasks: (i) a binary classification problem focused on predicting driver alertness states, and (ii) a multilabel classification problem based on the yeast dataset. Three stat

Hacane Hechehouche, Andre Antakli, Matthias Klusch

There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledg

Iago Xabier Vázquez, Javier Sedano, Muhammad Afzal, Ángel Miguel García-Vico

Anomaly detection is a key task across domains such as industry, healthcare, and cybersecurity. Many real-world anomaly detection problems involve analyzing multiple features over time, making time series analysis a natural approach for such problems. While deep learning models have achieved strong performance in this field, their trend to exhibit high energ

Haocan Sun, Di Wu, Weizi Liu, Guoming Yu

Concerns over the potential over-pathologization of generative AI (GenAI) use and the lack of conceptual clarity surrounding GenAI addiction call for empirical tools and theoretical refinement. This study developed and validated the PUGenAIS-9 (Problematic Use of Generative Artificial Intelligence Scale-9 items) and examined whether PUGenAIS reflects addicti

Shaojie Zhang, Ke Chen

Constrained clustering integrates domain knowledge through pairwise constraints. However, existing deep constrained clustering (DCC) methods are either limited by anchors inherent in end-to-end modeling or struggle with learning discriminative Euclidean embedding, restricting their scalability and real-world applicability. To avoid their respective pitfalls,

Iulian Cîmpean, Ionel Popescu, Arghir Zarnescu

In this paper we derive quantitative boundary H\"older estimates, with explicit constants, for the inhomogeneous Poisson problem in a bounded open set $D\subset \mathbb{R}^d$. Our approach has two main steps: firstly, we consider an arbitrary $D$ as above and prove that the boundary $\alpha$-H\"older regularity of the solution the Poisson equation is control

Mariana P. Júlio, Justin I. Read, Marcel S. Pawlowski, Pengfei Li

A tight correlation between the baryonic and observed acceleration of galaxies has been reported over a wide range of mass ($10^8 < M_{\rm bar}/{\rm M}_\odot < 10^{11}$) - the Radial Acceleration Relation (RAR). This has been interpreted as evidence that dark matter is actually a manifestation of some modified weak-field gravity theory. In this paper, we stu