October 2025 arXiv papers — page 133

Andrew W. Appel

We describe a machine-checked correctness proof of a C program that converts a coordinate-form (COO) sparse matrix to a compressed-sparse-row (CSR) matrix. The classic algorithm (sort the COO entries in lexicographic order by row,column; fill in the CSR arrays left to right) is concise but has rather intricate invariants. We illustrate a bottom-up methodolog

Chuhan Sun, Zipeng Wang

We study strong fractional maximal operator and fractional integral operator associated with Zygmund dilation defined on Heisenberg group. Characterizations are established for the L^p to L^q regularity of these two operators.

Spyridon Filippas, Lauri Oksanen, Miika Sarkkinen

We study the problem of recovering a time dependent matrix valued potential on a globally hyperbolic manifold from the knowledge of the source to solution map of a wave equation including a connection 1-form term. We exhibit sufficient conditions for solving this inverse problem under the assumption that the the manifold is stationary and that the connection

Chahat Ahuja, Partha Chowdhury, Subhashree Mohapatra

This work presents a novel approach to compute the eigenvalues of non-Hermitian matrices using an enhanced shifted QR algorithm. The existing QR algorithms fail to converge early in the case of non-hermitian matrices, and our approach shows significant improvement in convergence rate while maintaining accuracy for all test cases. In this work, though our pri

Jingkai Ying, Zhiyuan Qi, Yulong Feng, Zhijin Qin

Holographic video communication is considered a paradigm shift in visual communications, becoming increasingly popular for its ability to offer immersive experiences. This article provides an overview of holographic video communication and outlines the requirements of a holographic video communication system. Particularly, following a brief review of semanti

Kim Gfeller, Sabine Stoll, Chundra Cathcart, Paul Widmer

One of the most intriguing features of language is its constant change, with ongoing shifts in how meaning is expressed. Despite decades of research, the factors that determine how and why meanings evolve remain only partly understood. Colexification -- the phenomenon of expressing multiple distinct concepts using the same word form -- serves as a valuable w

Lucas Maystre, Alvaro Ortega Gonzalez, Charles Park, Rares Dolga

Embedding models trained separately on similar data often produce representations that encode stable information but are not directly interchangeable. This lack of interoperability raises challenges in several practical applications, such as model retraining, partial model upgrades, and multimodal search. Driven by these challenges, we study when two sets of

Chen Gong, Yan Zhuang, Zhenzhe Zheng, Yiliu Chen

Machine learning (ML) models are increasingly integrated into modern mobile apps to enable personalized and intelligent services. These models typically rely on rich input features derived from historical user behaviors to capture user intents. However, as ML-driven services become more prevalent, recording necessary user behavior data imposes substantial st

Muhammad Ishfaq Hussain, Ma Van Linh, Zubia Naz, Unse Fatima

Enhancing scene understanding in adverse visibility conditions remains a critical challenge for surveillance and autonomous navigation systems. Conventional imaging modalities, such as RGB and thermal infrared (MWIR / LWIR), when fused, often struggle to deliver comprehensive scene information, particularly under conditions of atmospheric interference or ina

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

Recently, IceCube reported neutrino emission from the Seyfert galaxy NGC 1068. Using 13.1 years of IceCube data, we present a follow-up search for neutrino sources in the northern sky. NGC 1068 remains the most significant neutrino source among 110 preselected gamma-ray emitters while also being spatially compatible with the most significant location in the

Elhabib Jaloum, Mohamed Amazioug

Quantum teleportation, a protocol that has received extensive and intensive attention in quantum information processing, allows a quantum state to be transferred from one particle to another. In this study, we analytically investigate fidelity ($F$), logarithmic negativity (LN), local quantum uncertainty (LQU) and local quantum Fisher information (LQFI) as a

Jude Haris, José Cano

Large Language Models (LLMs) have become increasingly prominent for daily tasks, from improving sound-totext translation to generating additional frames for the latest video games. With the help of LLM inference frameworks, such as llama.cpp, which support optimizations such as KV-caching and quantization, it is now easier than ever to deploy LLMs on edge de

Kei Itoh

This study presents an inter-universal mathematical-logical framework constructed upon the minimal axiom Cogito, ergo sum (CES), integrating the Intermediate Meta-Universe (IMU) and the Hierarchical State Grid (HSG). The CES defines existence as a reflexive correspondence --'to be' and 'to be sayable'--and positions any formal system, including ZFC or HoTT,

Shivani Ranjan, Anant Jain, Robin Badal, Amit Kumar

Background: Dementia, particularly Alzheimer's Disease (AD), is a progressive neurodegenerative disorder marked by cognitive decline. Early detection, especially at the Mild Cognitive Impairment (MCI) stage, is essential for timely intervention. Working Memory (WM) impairment is a key early indicator of neurodegeneration, affecting higher cognitive processes

Ben Hayes, Srivatsav Kunnawalkam Elayavalli, Gregory Patchell, Leonel Robert

We introduce and study a natural notion of selflessness for inclusions of C*-probability spaces, which in particular implies that all intermediate C*-algebras are selfless in the sense of Robert. We identify natural sources of selfless inclusions in the realms of Z-stable and free product C*-algebras. As an application of this, we prove selflessness for a ne

Levy Chaves, Eduardo Valle, Sandra Avila

Model merging provides a cost-effective and data-efficient combination of specialized deep neural networks through parameter integration. This technique leverages expert models across downstream tasks without requiring retraining. Most model merging approaches critically depend on scaling hyper-parameters $\lambda$, which weight each model's contribution glo

Yuxin Wang, Dennis Frauen, Jonas Schweisthal, Maresa Schröder

Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons. When dropout is informative (i.e., dependent on survival time), it introduces censoring bias, because of which treatment effect estimates are also biased. In this paper, we propose an assumption-lean framework to assess the robustness of con

Luka Baković, David Ohlin, Emma Tegling

We perform a validation analysis on the multipolar model of opinion dynamics. A general methodology for using the model on datasets of two correlated variables is proposed and tested using data on the relationship between COVID-19 vaccination rates and political participation in Sweden. The model is shown to successfully capture the opinion segregation demon

Agnese Lombardi, Alessandro Lenci

Language is fundamental to human cooperation, facilitating not only the exchange of information but also the coordination of actions through shared interpretations of situational contexts. This study explores whether the Generative Agent-Based Model (GABM) Concordia can effectively model Theory of Mind (ToM) within simulated real-world environments. Specific

Xinmiao Huang, Qisong He, Zhenglin Huang, Boxuan Wang

Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, augmented reality, and autonomous navigation. Unfortunately, existing benchmarks are inadequate in assessing spatial reasoning ability, especially the \emph{intrinsic-dynamic} spatial reasoning which is a fundamenta

Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu, Xiaoli Li

Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input data. It involves a cooperative game model where a generator generates the most human-intelligible parts of the input (i.e., rationales), followed by a predictor that makes predict

Benjamin Kempinski, Tal Kachman

Computing the Banzhaf value in network flow games is fundamental for quantifying agent influence in multi-agent systems, with applications ranging from cybersecurity to infrastructure planning. However, exact computation is intractable for systems with more than $\sim20$ agents due to exponential complexity $\mathcal{O}(2^m)$. While Monte Carlo sampling meth

Feng-Qi Cui, Yu-Tong Guo, Tianyue Zheng, Jinyang Huang

WiFi-based gesture recognition has emerged as a promising RF sensing paradigm for enabling non-contact and privacy-preserving human-computer interaction in AIoT environments. However, existing methods often suffer from limited generalization and semantic expressiveness due to the domain-sensitive nature of Channel State Information and the lack of high-level

Tien-En Chang, Argon Chen

A class of relative importance measures based on orthogonalization and reallocation, ORMs, has been found to effectively approximate the General Dominance index (GD). In particular, Johnson's Relative Weight (RW) has been deemed the most successful ORM in the literature. Nevertheless, the theoretical foundation of the ORMs remains unclear. To further underst

Stepan P. Sosnitskii

In the present paper, which is a development of an earlier study by the author \cite{Sosnitskii08}, we consider the stability of triangular libration points in the spatial circular restricted three-body problem and improve the result of author's work \cite{Sosnitskii08}. Unlike \cite{Sosnitskii08}, where the instability of libration points was established on

Buwei He, Yang Liu, Zhaowei Zhang, Zixia Jia

Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or rely on unrealistic pre-commitment assumptions. We introduce a type-induced commitment-communication mechanism that grounds Bayesian Persuasion (BP) in natural language dialogue without pre-commitment: the persuad

Abdelrahman Sayed Sayed, Pierre-Jean Meyer, Mohamed Ghazel

Neural ordinary differential equations (neural ODE) are powerful continuous-time machine learning models for depicting the behavior of complex dynamical systems, but their verification remains challenging due to limited reachability analysis tools adapted to them. We propose a novel interval-based reachability method that leverages continuous-time mixed mono

Ye Yuan, Mohammad Amin Shabani, Siqi Liu

Query-focused table summarization requires generating natural language summaries of tabular data conditioned on a user query, enabling users to access insights beyond fact retrieval. Existing approaches face key limitations: table-to-text models require costly fine-tuning and struggle with complex reasoning, prompt-based LLM methods suffer from token-limit a

Yani Feng, Michael K. Ng, Kejun Tang, Zhiwen Zhang

Discrete tensor train decomposition is widely employed to mitigate the curse of dimensionality in solving high-dimensional PDEs through traditional methods. However, the direct application of the tensor train method typically requires uniform grids of regular domains, which limits its application on non-uniform grids or irregular domains. To address the limi

Avra Banerjee, Arnab Bhowmik, Dwipesh Majumder

We investigate the collective excitations of Bose atoms in the condensate phase with finite-range interactions, modeled using the P\"oschl-Teller (PT) potential to explicitly account for the finite interaction range. Utilizing Bogoliubov theory, we derive the excitation spectrum and examine its dependence on interaction parameters. Our analysis reveals that

Patrick Bennett, Jade Nichols

A $t$-tone coloring of a graph $G$ assigns to each vertex a set of $t$ colors such that any pair of vertices $u, v$ with distance $d$ can share at most $d-1$ colors. In this note, we prove several new results on $t$-tone coloring. For example we prove a new result for trees of large maximum degree, as well as some results for the cartesian power of a graph.

Raphaël Ruimy

We give an alternative construction of Totaro's weight filtration on singular homology of the real points of a real algebraic variety. Our construction shows that this filtration comes from Bondarko's weight filtration on Voevodsky motives.

Zeynab Kaseb, Matthias Moller, Peter Palensky, Pedro P. Vergara

We present the first direct comparison between gate-based quantum computing (GQC) and adiabatic quantum computing (AQC) paradigms for solving the AC power flow (PF) equations. The PF problem is reformulated as a combinatorial optimization problem. For the GQC approach, the Quantum Approximate Optimization Algorithm (QAOA) is employed, while for the AQC appro

Na Lei, Mark A. Wolters, Wenqing He

We address the problem of survival regression modelling with multivariate responses and nonlinear covariate effects. Our model extends the proportional hazards model by introducing several weakly-parametric elements: the marginal baseline hazard functions are expressed as piecewise constants, association is modelled with copulas, and nonlinear covariate effe

Jiajun Zhang, Jianke Zhang, Zeyu Cui, Jiaxi Yang

Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured data remains largely unevaluated and underdeveloped. To address this gap, we introduce PlotCraft, a new benchmark featuring 1k challenging visualization tasks that cover a wide rang

Shashikant. A. Katre, Vikas S. Jadhav

Let l be an odd prime. For primes, p \equiv 1 (mod l), Gauss (l = 3) and Dickson (l = 5) considered the Diophantine systems in terms of which cyclotomic numbers of order 3 and 5 were obtained. The aim of this paper is to show how to obtain 1-error detecting [2, 1, 2] code and 1-error correcting [4, 2, 3] code in terms of the solutions of these diophantine sy

Tianyuan Yuan, Yicheng Liu, Chenhao Lu, Zhuoguang Chen

Vision-Language-Action (VLA) models have recently shown impressive generalization and language-guided manipulation capabilities. However, their performance degrades on tasks requiring precise spatial reasoning due to limited spatial reasoning inherited from Vision-Language Models (VLMs). Existing VLAs rely on extensive action-data pretraining to ground VLMs

Rafael Almada, Nuno Araújo, Pedro Patrício

Embryonic healing in epithelial tissues is distinct from adult wound healing, as it lacks inflammatory responses or immune cell recruitments, making it ideal to test models of wound healing driven primarily by epithelial dynamics. Many models have been developed to describe this process, ranging from simple mechanistic models to more elaborate multiscale sim

Volker Karle, Oriana K. Diessel, Vasil Rokaj, Ceren B. Dağ

Recent advances in chiral cavities that can couple coherently to two-dimensional materials have opened a powerful route to reshape electronic topology without an external drive. Here we establish the bulk-boundary correspondence for graphene embedded in a circularly polarized cavity. By combining exact diagonalization (ED) of zigzag ribbons, a semi-analytic

Junqing Huang, Haihui Wang, Michael Ruzhansky

In this paper, we propose a new variational framework for 3D surface denoising over triangulated meshes, which is inspired by the success of semi-sparse regularization in image processing. Differing from the uniformly sampled image data, mesh surfaces are typically represented by irregular, non-uniform structures, which thus complicate the direct application

Hrishikesh Jagtap, Moumanti Podder

In this work, we investigate Maker-Breaker directed triangle games, a directionally constrained variant of the classical Maker-Breaker triangle game. Our board of interest is a tournament, and the winning sets are all $3$-cycles present in the tournament. We begin by studying the Maker-Breaker directed triangle game played on a specially defined tournament c

Jiin Park, Misuk Kim

Recent attempts to integrate large language models (LLMs) into recommender systems have gained momentum, but most remain limited to simple text generation or static prompt-based inference, failing to capture the complexity of user preferences and real-world interactions. This study proposes the Multi-Aspect Driven LLM Agent MADRec, an autonomous LLM-based re

Fernando Castillo, Eduardo Brito, Sebastian Werner, Pille Pullonen-Raudvere

Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic found

Jacob Schaal

This paper develops a theory-driven automation exposure index based on Moravec's Paradox. Scoring 19,000 O*NET tasks on performance variance, tacit knowledge, data abundance, and algorithmic gaps reveals that management, STEM, and sciences occupations show the highest exposure. In contrast, maintenance, agriculture, and construction show the lowest. The posi

Yue Xing, Yingnan Deng, Heyao Liu, Ming Wang

This paper addresses the challenges of complex dependencies and diverse anomaly patterns in cloud service environments by proposing a dependency modeling and anomaly detection method that integrates contrastive learning. The method abstracts service interactions into a dependency graph, extracts temporal and structural features through embedding functions, a

Nikita Kachaev, Daniil Zelezetsky, Egor Cherepanov, Alexey K. Kovelev

Despite their effectiveness and popularity in offline or model-based reinforcement learning (RL), transformers remain underexplored in online model-free RL due to their sensitivity to training setups and model design decisions such as how to structure the policy and value networks, share components, or handle temporal information. In this paper, we show that

Weishi Wang, Hengchang Hu, Zhijie Zhang, Zhaochen Li

Document AI (DAI) has emerged as a vital application area, and is significantly transformed by the advent of large language models (LLMs). While earlier approaches relied on encoder-decoder architectures, decoder-only LLMs have revolutionized DAI, bringing remarkable advancements in understanding and generation. This survey provides a comprehensive overview

David Stewart, Gerardo Ortiz, Peter M. Kogge, Ricardo S. Decca

The U.S. Center for Quantum Technologies (CQT) is a multi-university consortium established under the National Science Foundation's (NSF) Industry-University Cooperative Research Centers (IUCRC) program. Led jointly by Purdue University, Indiana University (both Bloomington and Indianapolis campuses), and the University of Notre Dame, CQT integrates academic

MingZe Tang, Jubal Chandy Jacob

Recent Vision-Language Models (VLMs) enable zero-shot classification by aligning images and text in a shared space, a promising approach for data-scarce conditions. However, the influence of prompt design on recognizing visually similar categories, such as human postures, is not well understood. This study investigates how prompt specificity affects the zero

Xiang Lei, Qin Li, Min Zhang, Min Zhang

Large Language Models (LLMs) often exhibit factual inconsistencies and logical decay in extended, multi-turn dialogues, a challenge stemming from their reliance on static, pre-trained knowledge and an inability to reason adaptively over the dialogue history. Prevailing mitigation strategies, such as Retrieval-Augmented Generation (RAG) and agentic working me

Saeed Salehi

This work investigates transfer learning strategies to accelerate deep reinforcement learning (DRL) for multifidelity control of chaotic fluid flows. Progressive neural networks (PNNs), a modular architecture designed to preserve and reuse knowledge across tasks, are employed for the first time in the context of DRL-based flow control. In addition, a compreh

Angelos Athanasiadis, Nikolaos Tampouratzis, Ioannis Papaefstathiou

The growing demand for real-time processing in artificial intelligence applications, particularly those involving Convolutional Neural Networks (CNNs), has highlighted the need for efficient computational solutions. Conventional processors, very often, fall short in balancing performance, power consumption, and latency, especially in embedded systems and edg

Yisen Wang, Yichuan Mo, Hongjun Wang, Junyi Li

Despite the rapid progress of neural networks, they remain highly vulnerable to adversarial examples, for which adversarial training (AT) is currently the most effective defense. While AT has been extensively studied, its practical applications expose two major limitations: natural accuracy tends to degrade significantly compared with standard training, and

Qian Sun, Graham Hults, Susu Xu

Timely and reliable decision-making is vital for flood emergency response, yet it remains severely hindered by limited and imprecise situational awareness due to various budget and data accessibility constraints. Traditional flood management systems often rely on in-situ sensors to calibrate remote sensing-based large-scale flood depth forecasting models, an

Timothy Leese, Siobhan Patrick, Silvia Bergamini, Calum MacCormick

We present a novel implementation of lambda-enhanced gray molasses cooling in a non-standard beam geometry and with an inexpensive laser locking set-up. In contrast to the established use of resource-intensive phase locking methods, our laser system uses two independent lasers, frequency -locked to a spectral feature produced by an electromagnetically induce

Yuki Yada, Sho Akiyama, Ryo Watanabe, Yuta Ueno

On large-scale e-commerce platforms with tens of millions of active monthly users, recommending visually similar products is essential for enabling users to efficiently discover items that align with their preferences. This study presents the application of a vision-language model (VLM) -- which has demonstrated strong performance in image recognition and im

Shingo Ayabe, Hiroshi Kera, Kazuhiko Kawamoto

Offline reinforcement learning enables sample-efficient policy acquisition without risky online interaction, yet policies trained on static datasets remain brittle under action-space perturbations such as actuator faults. This study introduces an offline-to-online framework that trains policies on clean data and then performs adversarial fine-tuning, where p

Hamdan Al-Ali, Ali Reza Ghavamipour, Tommaso Caselli, Fatih Turkmen

Federated learning is a common method for privacy-preserving training of machine learning models. In this paper, we analyze the vulnerability of ASR models to attribute inference attacks in the federated setting. We test a non-parametric white-box attack method under a passive threat model on three ASR models: Wav2Vec2, HuBERT, and Whisper. The attack operat

Jie Gu, Tin Lun Lam, Chunxu Tian, Zhihao Xia

Modular Self-Reconfigurable Robot (MSRR) systems are a class of robots capable of forming higher-level robotic systems by altering the topological relationships between modules, offering enhanced adaptability and robustness in various environments. This paper presents a novel MSRR called MODUR, featuring dual-level reconfiguration capabilities designed to in

Alexandra Tudorache, Otilia Ducu

The search potential for sbottom pair production in an R-parity conserving scenario is explored in multi-lepton final states at LHC Run-3 and the HL-LHC. In this model, the sbottom decays via a chargino, $\tilde{b}_1 \to t \tilde{\chi}_1^\pm$, with a branching ratio (BR) of 100%. The chargino subsequently decays into a $W$ boson and the lightest neutralino,

Kazuhiro Sato

We introduce the target controllability score (TCS), a concept for evaluating node importance under actuator constraints and designated target objectives, formulated within a virtual system setting. The TCS consists of the target volumetric controllability score (VCS) and the target average energy controllability score (AECS), each defined as an optimal solu

Zehao Sha

In this note, we prove a 2-systolic inequality on compact positive scalar curvature K\"ahler surfaces admitting a nonconstant holomorphic map to a positive-genus compact Riemann surface. According to the classification of positive scalar curvature K\"ahler surfaces, any such surface must be a ruled surface fibred over a complex curve with positive genus.

Yang Cao, Sikun Yang, Kai He, Wenjun Ma

Measuring similarity between incomplete data is a fundamental challenge in web mining, recommendation systems, and user behavior analysis. Traditional approaches either discard incomplete data or perform imputation as a preprocessing step, leading to information loss and biased similarity estimates. This paper presents the proximity kernel, a new similarity

Karthik Avinash, Nikhil Pareek, Rishav Hada

The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust guardrailing systems that ensure safety, reliability, and compliance. Existing solutions often struggle with real-time oversight, multi-modal data handling, and explainability -- limitations that hinder their ado

Yuxiang Liu, Fanxu Meng, Zetong Li, Xutao Yu

In the massive multiple-input and multiple-output (Massive MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becoming classically intricate with the number of the transmitting antennas and the symbols increasing. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy intermediate-sc

Sipeng Yang, Jiayu Ji, Qingchuan Zhu, Zhiyao Yang

Quality assessment of videos is crucial for many computer graphics applications, including video games, virtual reality, and augmented reality, where visual performance has a significant impact on user experience. When test videos cannot be perfectly aligned with references or when references are unavailable, the significance of no-reference video quality as

Michael Anastos, Sahar Diskin, Lyuben Lichev, Maksim Zhukovskii

We consider bond percolation on the $d$-dimensional binary hypercube with $p=c/d$ for fixed $c>1$. We prove that the typical diameter of the giant component $L_1$ is of order $\Theta(d)$, and the typical mixing time of the lazy random walk on $L_1$ is of order $\Theta(d^2)$. This resolves long-standing open problems of Bollob\'as, Kohayakawa and {\L}uczak fr

Mathias Lerbech Jeppesen, Emilie Højbjerre-Frandsen

Covariate adjustment is a widely used technique in randomized clinical trials (RCTs) for improving the efficiency of treatment effect estimators. By adjusting for predictive baseline covariates, variance can be reduced, enhancing statistical precision and study power. Rosenblum and van der Laan [2010] use the framework of generalized linear models (GLMs) in

Guido Sterbini

This lecture provides an overview of the principles and methodologies involved in linear optics design. It aims to introduce key concepts such as the matrix formalism, the symplecticity, the quantities that are preserved in the single particle evolution, e.g., the Courant-Snyder invariant. It also covers the concept of beam emittance and matching conditions

Zhenyu Liu, Yunxin Li, Xuanyu Zhang, Qixun Teng

Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with music and speech often developed in isolation, hindering progress towards universal audio synthesis. This separation stems from inherent task conflicts and severe data imbalances, whi

Shota Takayama, Katsuhide Fujita

Multi-agent reinforcement learning focuses on training the behaviors of multiple learning agents that coexist in a shared environment. Recently, MARL models, such as the Multi-Agent Transformer (MAT) and ACtion dEpendent deep Q-learning (ACE), have significantly improved performance by leveraging sequential decision-making processes. Although these models ca

Qingyi Zhong, Junfeng Chen, Zhengyang Qiu, Jingyuan Li

Motivated by the discovery of superconductivity in bilayer La$_3$Ni$_2$O$_7$ at 80 K and the increased superconducting transition temperature, $T_\text{c}$, up to 92 K in single crystals of La$_2$SmNi$_2$O$_7$ under pressure, we systematically study the effect of Sm doping on the superconductivity and structure of La$_{3-x}$Sm$_x$Ni$_2$O$_7$ (0 $\leq$ x $\le

Katerina Korre, John Pavlopoulos

Proverbs are among the most fascinating language phenomena that transcend cultural and linguistic boundaries. Yet, much of the global landscape of proverbs remains underexplored, as many cultures preserve their traditional wisdom within their own communities due to the oral tradition of the phenomenon. Taking advantage of the current advances in Natural Lang

Serena Dipierro, Xavier Ros-Oton, Enrico Valdinoci, Marvin Weidner

We establish the optimal regularity of solutions to the Neumann problem for the fractional Laplacian, $(-\Delta)^s u=h$ in $\Omega$, with the external condition $\mathcal N^s u=0$ in $\Omega^c$. For this, a key point is to establish a 1D Liouville theorem for functions with growth, which we prove by using complex analysis and the Mellin transform. More preci

A. M. Riyas, D. Karinkuzhi, S. Van Eck, A. Choplin

Carbon-enhanced metal-poor (CEMP) stars are ancient stars enriched in carbon and heavy elements. Some of these stars exhibit enhanced s-process and/or r-process elements, hence are classified as CEMP-s, CEMP-rs, or CEMP-r. This classification is challenging due to the limited availability of heavy element abundances, particularly among r-process elements. He

Lorenzo Agosta

Quantifying wettability at the nanoscale remains challenging, as macroscopic contact-angle measurements fail to capture the molecular interactions that define hydrophilic and hydrophobic behavior. We derive an analytical relation linking the water contact angle to the lateral diffusion of interfacial molecules, establishing a quantitative connection between

Alexey D. Nekrasov, Thomas Dauser, Javier A. Garcia, Dominic J. Walton

The reflection of X-rays at the inner accretion disk around black holes imprints relativistically broadened features in the observed spectrum. Aside from the black hole properties and the ionization and density of the accretion disk, these features also depend on the location and geometry of the primary source of X-rays, often referred to as the corona. We p

Will Oxley, Rich Kerswell

Recent work has found that the well-known `lift-up' mechanism is not important for, and may even inhibit, the transient growth possible on streaky wall-bounded shear flows which is believed an important process in the near-wall cycle for turbulent flows. Moreover, artificially removing the wall-normal velocity has been found to unleash 3 orders of magnitude

Mario Angelelli, Morena Oliva, Serena Arima, Enrico Ciavolino

Artificial Intelligence (AI) and large language models (LLMs) are increasingly used in social and psychological research. Among potential applications, LLMs can be used to generate, customise, or adapt measurement instruments. This study presents a preliminary investigation of AI-generated questionnaires by comparing two ChatGPT-based adaptations of the Body

Fedor V. Fomin, Petr A. Golovach, Tanmay Inamdar, Saket Saurabh

The starting point of our work is a decade-old open question concerning the subexponential parameterized complexity of \textsc{2-Layer Crossing Minimization}. In this problem, the input is an $n$-vertex graph $G$ whose vertices are partitioned into two independent sets $V_1$ and $V_2$, and a non-negative integer $k$. The question is whether $G$ admits a 2-la

Xu Cai, Yang Wu, Qianli Chen, Haoran Wu

We present an ultra-efficient post-training method for shortcutting large-scale pre-trained flow matching diffusion models into efficient few-step samplers, enabled by novel velocity field self-distillation. While shortcutting in flow matching, originally introduced by shortcut models, offers flexible trajectory-skipping capabilities, it requires a specializ

Ao Zhou, Jianlei Yang, Tong Qiao, Yingjie Qi

The device-edge co-inference paradigm effectively bridges the gap between the high resource demands of Graph Neural Networks (GNNs) and limited device resources, making it a promising solution for advancing edge GNN applications. Existing research enhances GNN co-inference by leveraging offline model splitting and pipeline parallelism (PP), which enables mor

Yuan Feng, Haoyu Guo, JunLin Lv, S. Kevin Zhou

Large language models have revolutionized natural language processing, yet their deployment remains hampered by the substantial memory and runtime overhead of the transformer's Key-Value cache. To mitigate this, recent methods employ a scoring-aggregation framework to evict unimportant cache entries, based on the stability assumption-that a fixed subset of e

François Pacaud, Armin Nurkanović, Anton Pozharskiy, Alexis Montoison

We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangian (NCL), an augmented Lagrangian method in which the subproblems are solved using an interior-point method. NCL has two key advantages for large-scale SC-OPF. First, NCL handles d

Mohammad Sharifian, Abolfazl Bayat

Demonstration of quantum advantage for classical machine learning tasks remains a central goal for quantum technologies and artificial intelligence. Two major bottlenecks to this goal are the high dimensionality of practical datasets and limited performance of near-term quantum computers. Boson sampling is among the few models with experimentally verified qu

Hong-Kai Zheng, Piji Li

Vector Quantized Variational Autoencoders (VQ-VAEs) leverage self-supervised learning through reconstruction tasks to represent continuous vectors using the closest vectors in a codebook. However, issues such as codebook collapse persist in the VQ model. To address these issues, existing approaches employ implicit static codebooks or jointly optimize the ent

Bridget Smart, Max Ward, Matthew Roughan

Non-parametric entropy estimation on sequential data is a fundamental tool in signal processing, capturing information flow within or between processes to measure predictability, redundancy, or similarity. Methods based on longest common substrings (LCS) provide a non-parametric estimate of typical set size but are often inefficient, limiting use on real-wor

Ye Yuan, Mohammad Amin Shabani, Siqi Liu

Retrieval-Augmented Generation (RAG) systems rely on retrieving relevant evidence from a corpus to support downstream generation. The common practice of splitting a long document into multiple shorter passages enables finer-grained and targeted information retrieval. However, it also introduces challenges when a correct retrieval would require inference acro

Nicolas Menet, Aleksandar Terzić, Michael Hersche, Andreas Krause

Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We propose a scalable alternative based on Thompson sampling that eliminates the need for acquisition function maximization by directly parameterizing the probability that a candidate yie

Lina Alkarmi, Ziyuan Huang, Mingyan Liu

Algorithmic decision making is increasingly prevalent, but often vulnerable to strategic manipulation by agents seeking a favorable outcome. Prior research has shown that classifier abstention (allowing a classifier to decline making a decision due to insufficient confidence) can significantly increase classifier accuracy. This paper studies abstention withi

Divya Bhardwaj, Arnav Ramamoorthy, Poonam Goyal

Concealed weapon detection aims at detecting weapons hidden beneath a person's clothing or luggage. Various imaging modalities like Millimeter Wave, Microwave, Terahertz, Infrared, etc., are exploited for the concealed weapon detection task. These imaging modalities have their own limitations, such as poor resolution in microwave imaging, privacy concerns in

Tim Holzschuh

We study the Section Conjecture in \'etale homotopy theory for varieties over $\mathbb{R}$. We prove its pro-$2$ variant for equivariantly triangulable varieties. Examples include all smooth varieties as well as all (possibly singular) affine/projective varieties. Building on this, we derive the real Section Conjecture in the geometrically \'etale nilpotent

Erik Helmut, Niklas Funk, Tim Schneider, Cristiana de Farias

Contact-rich manipulation depends on applying the correct grasp forces throughout the manipulation task, especially when handling fragile or deformable objects. Most existing imitation learning approaches often treat visuotactile feedback only as an additional observation, leaving applied forces as an uncontrolled consequence of gripper commands. In this wor

Indira Sen, Marlene Lutz, Elisa Rogers, David Garcia

Many applications of Large Language Models (LLMs) require them to either simulate people or offer personalized functionality, making the demographic representativeness of LLMs crucial for equitable utility. At the same time, we know little about the extent to which these models actually reflect the demographic attributes and behaviors of certain groups or po

Héctor Jardón-Sánchez, László Márton Tóth

The aim of this paper is to investigate the spectral theory of unimodular random graphs and graphings representing them. We prove that Bernoulli graphings are relatively Ramanujan with respect to their skeleton Markov chain. That is, the part of their spectrum that comes from the random labels falls within the appropriate Alon-Boppana bound. This result comp

Baogang Song, Dongdong Zhao, Jianwen Xiang, Qiben Xu

Backdoor attacks pose a persistent security risk to deep neural networks (DNNs) due to their stealth and durability. While recent research has explored leveraging model unlearning mechanisms to enhance backdoor concealment, existing attack strategies still leave persistent traces that may be detected through static analysis. In this work, we introduce the fi

Zheng Wang, Jinjie Zhu, Xianbin Liu

Synchronization is a ubiquitous phenomenon in complex systems. The Kuramoto model serves as a paradigmatic framework for understanding how coupled oscillators achieve collective rhythm. Conventional approaches focus on pairwise interactions, but real-world systems frequently involve higher-order couplings among multiple elements. Previous studies have shown

Alexander Gräfe, Fabian Mager, Marco Zimmerling, Sebastian Trimpe

As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (TinyML), for example, due to privacy and latency concerns. However, CPS often comprise ultra-low-power microcontrollers, whose limited compute resources make training challenging. This

Peng Kuang, Yanli Wang, Xiaoyu Han, Yaowenqi Liu

Process reward models (PRMs) are a cornerstone of test-time scaling (TTS), designed to verify and select the best responses from large language models (LLMs). However, this promise is challenged by recent benchmarks where simple majority voting, which ignores PRM signals, occasionally outperforms standard PRM-based selection. This raises a critical question:

Ning-Jing Yang, Zhigao Huang, Jian-Min Zhang

Recent progress in spintronics within the paradigm of altermagnets (AMs) opens new avenues for next-generation electronic device design. Here, we establish a spin-corner locking mechanism that generates second-order topological states in two-dimensional (2D) altermagnetic systems, through effective model analysis. Remarkably, the breaking of Mxy symmetry und