March 2026 arXiv papers — page 65

Sebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi, Anirbit Mukherjee

This work establishes rigorous first-of-its-kind upper bounds on the generalization error for the method of approximating solutions to the (d+1)-dimensional incompressible Navier-Stokes equations by training depth-2 neural networks trained via the unsupervised Physics-Informed Neural Network (PINN) framework. This is achieved by bounding the Rademacher compl

Gauvain Devillez, Sven D'hondt, Jan Goedgebeur

The House of Graphs is an online database of graphs which can be accessed at https://houseofgraphs.org/. It serves as a central repository for complete lists of graphs for various graph classes. However, its main feature is a searchable database of so-called "interesting" graphs. The development of the original House of Graphs started in 2010 and it was comp

Tommaso Rossi, Alec Jacopo Almo Schiavoni Piazza, Alessandro Socionovo

We study the regularity and branching of strictly abnormal minimizing geodesics in sub-Riemannian geometry. We construct examples of real-analytic sub-Riemannian manifolds admitting minimizing geodesics that lose regularity at an interior point of their domain and exhibit branching, thereby resolving longstanding open questions. Moreover, using a lifting pro

Basit Alawode, Arif Mahmood, Muaz Khalifa Al-Radi, Shahad Albastaki

Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing Computational Pathology (CPath) Multimodal Large Language Models (MLLMs) typically compress an entire WSI into a single embedding, which hinders fine-grained grounding and ignores

Francesca Matteucci

The time-delay model is the way we interpret the diagram [X/Fe] vs. [Fe/H], where X is the abundance of a generic element from carbon to uranium. This interpretation is based on the lifetimes of stars of different masses producing different elements. The abundance of Fe ([Fe/H]) traces the "stellar metallicity" and is due to supernovae Type Ia, which are bel

Tim Dartois, Signe Seidelin, Aurélien Drezet

We develop an explicit configuration-space formulation of EPR-Bell experiments within the framework of de Broglie-Bohm theory, in which joint measurement outcomes arise from a deterministic mapping from initial particle configurations to outcome pairs. This construction induces a partition of the hidden-variable configuration space into domains associated wi

Yechao Zhang, Shiqian Zhao, Jie Zhang, Gelei Deng

We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory and subsequently influence user-facing behavior without the user's awareness. This vulnerability arises from an architectural design shared across the Claw ecosystem:

Tien Rahayu Tulili, Ayushi Rastogi, Andrea Capiluppi

Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine

Bijan Saha

In the context of a Bianchi type-VI space-time characterized by Lyras geometry, we investigate the influence of a nonlinear spinor field on the evolution of the Universe. Our previous research has examined the nonlinear spinor field within Bianchi diagonal models, revealing that the spinor field exhibits non-trivial non-diagonal components of the energy-mome

Jun-Yong Yan, Fang-Yuan Li, Zhou Zhou, Yue-Yao Mu

Structured quantum light is crucial for high-dimensional quantum information processing, yet its direct generation from quantum emitters remains challenging due to their intrinsic locality and omnidirectional radiation. Metasurfaces have been adopted for quantum-light wavefront shaping, typically in cascaded or stacked configurations that suffer from low eff

Shangshuai Li, Ken-ichi Maruno, Da-jun Zhang

We show a unified approach to the Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy and the unreduced derivative nonlinear Schr\"odinger (DNLS) hierarchies (including the Kaup-Newell, Chen-Lee-Liu, Gerdjikov-Ivanov and a generalized DNLS), together with their multi-component extensions, in the framework of the anti-self-dual Yang-Mills (ASDYM) reduction. By restri

Quentin Cohen-Solal, Tristan Cazenave

Recent advances in game AI, such as AlphaZero and Ath\'enan, have achieved superhuman performance across a wide range of board games. While highly powerful, these agents are ill-suited for human-AI interaction, as they consistently overwhelm human players, offering little enjoyment and limited educational value. This paper addresses the problem of balanced p

Lennart Dabelow, Christian Eidecker-Dunkel, Peter Reimann

Eigenstate thermalization refers to the property that an energy eigenstate of a many-body system is indistinguishable from a thermal equilibrium ensemble at the same energy as far as expectation values of local observables are concerned. In systems with degeneracies, the choice of an energy eigenbasis is not unique and the fraction of basis states exhibiting

Saurabh Kataria, Xiao Hu

Audio-Language Models (ALMs) are making strides in understanding speech and non-speech audio. However, domain-specialist Foundation Models (FMs) remain the best for closed-ended speech processing tasks such as Speech Emotion Recognition (SER). Using ALMs for Zero-shot SER is a popular choice, but their potential to work with specialists to achieve state-of-t

Adam Parusiński, Armin Rainer

The ordered eigenvalues define a Lipschitz map on the real vector space of Hermitian $d \times d$ matrices. We prove that this map acts continuously, but not uniformly continuously, by superposition on the Sobolev spaces $W^{1,q}$, for all $1 \le q < \infty$, on bounded open domains. For $q=\infty$, the action is still well-defined and bounded but not contin

Amirmohammad Farzaneh, Osvaldo Simeone

Formal model evaluation methods typically certify that a model satisfies a prescribed target key performance indicator (KPI) level. However, in many applications, the relevant target KPI level may not be known a priori, and the user may instead wish to compare candidate models by analyzing the full trade-offs between performance and reliability achievable at

Jun-Peng Zhu, Qizhi Wang, Yulong Zhai, Yishen Sun

Flaky failure triage is crucial for keeping distributed database continuous integration (CI) efficient and reliable. After a failure is observed, operators must quickly decide whether to auto-rerun the job as likely flaky or escalate it as likely persistent, often under CPU-only millisecond budgets. Existing approaches remain difficult to deploy in this sett

Chinmoy Bhattacharjee, Rowan O'Clarey

We consider square-integrable functionals of Poisson point processes for which the variance upper bound provided by the classical Poincar\'{e} inequality is suboptimal, a phenomenon known as superconcentration. In this paper, we establish a rigorous mathematical equivalence between superconcentration and the chaotic behaviour of the functional, and certain a

Morten Nissov, Mohit Singh, Kostas Alexis

Visual-Inertial Odometry (VIO) is a staple for reliable state estimation on constrained and lightweight platforms due to its versatility and demonstrated performance. However, pertinent challenges regarding robust operation in dark, low-texture, obscured environments complicate the use of such methods. Alternatively, Frequency Modulated Continuous Wave (FMCW

Norikazu Yamada

Spectral functions play a central role in the characterization of a wide range of physical systems, including strongly interacting quantum field theories and many-body systems. Their non-perturbative determination from Euclidean correlation functions constitutes a well-known ill-posed inverse problem and has motivated the development of numerous reconstructi

Amith Nagarajan, Thomas Altman

A tremendous number of critical database systems lack adequate documentation. Declared primary keys are absent, foreign key constraints have been dropped for performance, column names are cryptic abbreviations, and no entity-relationship diagrams exist. We present DBAutoDoc, a system that automates the discovery and documentation of undocumented relational d

Wenfeng Wang, Xiaofeng Hou, Peng Tang, Hengyi Zhou

Retrieval-Augmented Generation (RAG) systems enhance the performance of large language models (LLMs) by incorporating supplementary retrieved documents, enabling more accurate and context-aware responses. However, integrating these external documents often results in very long input sequences, which significantly increases computation costs during the prefil

Zikang Huang, Meng Ge, Tianrui Wang, Xuanchen Li

Self-supervised learning (SSL) has advanced speech processing. However, existing speech SSL methods typically assume a single sampling rate and struggle with mixed-rate data due to temporal resolution mismatch. To address this limitation, we propose MSRHuBERT, a multi-sampling-rate adaptive pre-training method. Building on HuBERT, we replace its single-rate

Julian Oestreich, Maximilian Bley, Frank Binder, Lydia Müller

Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open whether these gains transfer to specialized tasks. We study supervised RAG fine-tuning (RAG-SFT) for requirements document generation in the electronics engineering domain, adapting two 7B mod

Geng-Hua Li, Hai-Yi Zhao, Xiangkai Sun

For a linear equality constrained convex optimization problem involving two objective functions with a ``nonsmooth" + ``nonsmooth" composite structure, we study two algorithms derived from a mixed-order dynamical system which incorporates time scales and a Tikhonov regularization term. We observe that different types of multipliers lead to distinct algorithm

Antonio J. Martínez Aparicio, Clara Torres-Latorre

We study sharp conditions for the existence and nonexistence of infinitely many nonnegative solutions to the problem $-\Delta_p u = \lambda f(u)$ in a bounded domain with Dirichlet boundary conditions, where $f$ is a continuous function with a sequence of positive zeros converging to zero or diverging to infinity. Under a growth condition on the primitive $F

Paul Leonard Wolff, Hugo Buurmeijer, Luis Pabon, John Irvin Alora

Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading method for reducing high-dimensional nonlinear dynamical systems to low-dimensional invariant manifolds. Our proposed control-augmented SSMs (caSSMs) extend this methodology by exp

Kehao Lin, Yufeng Wu, Siyu Yang

Let $C$ be the middle-third Cantor set. We show that \[\left\{\frac{1}{n!}: n\in\mathbb{N}\right\}\cap C=\left\{1, \frac{1}{5!}\right\}.\] This answers a question recently posed by Jiang [J. Lond. Math. Soc., 113(1):e70408, 2026]. Our approach extends to general missing-digit sets, showing that, in any such set, there are only finitely many elements of the f

Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Maria Perez-Ortiz

The accelerating pace of climate change introduces profound non-stationarities that challenge the ability of Machine Learning based climate emulators to generalize beyond their training distributions. While these emulators offer computationally efficient alternatives to traditional Earth System Models, their reliability remains a potential bottleneck under "

Ilayda Celenk, Willem van Jaarsveld, Ivo J. B. F. Adan, Alp Akcay

We study a variant of the online bin packing problem that arises in filament-based 3D printing systems operating in make-to-order settings, where only a limited number of filament reels of finite capacity can be handled at once. Components are assigned to reels upon arrival and insufficient reels are discarded to be replaced with new ones, resulting in mater

Chenghao Huang, Jiarong Fan, Weiqing Wang, Hao Wang

As power systems advance toward net-zero targets, behind-the-meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local

António Cardoso, Pedro Sousa, Tania Pereira, Hélder P. Oliveira

Currently, a central challenge and bottleneck in the deployment and validation of computer-aided diagnosis (CAD) models within the field of medical imaging is data scarcity. For lung cancer, one of the most prevalent types worldwide, limited datasets can delay diagnosis and have an impact on patient outcome. Generative AI offers a promising solution for this

Huan-Can Liang, Yi Liao, Xiao-Dong Ma, Mu-Yuan Song

The origin of neutrino mass and the nature of dark matter (DM) remain unresolved puzzles in particle physics, and an appealing possibility is to address both in a unified picture. This paper explores a light fermionic DM candidate within the scotogenic inverse seesaw model, which can simultaneously provide a mechanism for neutrino mass generation. By incorpo

Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau

Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural ne

Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters inclu

Mehmet Kaan Erol

The rapid compression of large vision-language models (VLMs) for edge deployment raises an underexplored question: do compact models fail differently, not merely more often? This study compares a 7-billion-parameter quantised VLM (Qwen2.5-VL-7B, 4-bit NF4) against a 500-million-parameter FP16 model (SmolVLM2-500M) across 4,000 samples from VQAv2 and COCO Cap

Marios Impraimakis, Daniel Vazquez, Feiyu Zhou

The interpretable object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach refers to a key limitation in computer vision for autonomous vehicles perception, and beyond. These systems offer limited transparency regarding the reliability of their confidence scores in visually degraded or ambiguous scenes. To

Jiayu Ran

We study zero-uncertainty states with quantum memory from an operator-algebraic perspective, which naturally accommodates degenerate projective-valued measurements. In the equal-dimension setting, we prove a rigidity theorem for purity and maximal entanglement. We then analyze two mechanisms by which this rigidity can fail: one arising from proper observable

Marta Latorre, Sergio Segura de León

This paper is devoted to the study of the Dirichlet problem for the parabolic equation driven by the $1$--Laplacian operator under minimal integrability assumptions. Specifically, we consider \begin{equation*} u'-\Div(Du/|D u|)=f\qquad\text{ in } (0,+\infty)\times\Omega\,, \end{equation*} where $\Omega\subset\R^N$ is a bounded open set with Lipschitz boundar

Guoyang Zhao, Weiqing Qi, Kai Zhang, Chenguang Zhang

Traffic Sign Recognition (TSR) is a core perception capability for autonomous driving, where robustness to cross-region variation, long-tailed categories, and semantic ambiguity is essential for reliable real-world deployment. Despite steady progress in recognition accuracy, existing traffic sign datasets and benchmarks offer limited diagnostic insight into

Alessio Bottini, Emanuele Macrì, Paolo Stellari

We review recent developments in the theory of compact hyper-K\"ahler varieties, from the viewpoint of Lagrangian fibrations, moduli spaces of stable sheaves, and derived categories. These notes originated from the lecture by the second named author at the 2025 Summer Institute in Algebraic Geometry, Colorado State University, Fort Collins (USA), July 14 - A

Jianwen Cao, Jiaxu Xing, Nico Messikommer, Davide Scaramuzza

Event cameras provide robust visual signals under fast motion and challenging illumination conditions thanks to their microsecond latency and high dynamic range. However, their unique sensing characteristics and limited labeled data make it challenging to train event-based visual foundation models (VFMs), which are crucial for learning visual features transf

Buddhi Kothalawala, Henning Koehler, Muhammad Farhan

The Maximum Common Subgraph (MCS) problem plays a key role in many applications, including cheminformatics, bioinformatics, and pattern recognition, where it is used to identify the largest shared substructure between two graphs. Although symmetry exploitation is a powerful means of reducing search space in combinatorial optimization, its potential in MCS al

ByeongCheol Lee, Hyun Seok Seong, Sangeek Hyun, Gilhan Park

A sliding-window inference strategy is commonly adopted in recent training-free open-vocabulary semantic segmentation methods to overcome limitation of the CLIP in processing high-resolution images. However, this approach introduces a new challenge: each window is processed independently, leading to semantic discrepancy across windows. To address this issue,

Tom Bidewell, Artemis Deligianni, Tuğrulcan Elmas, Clare Llewellyn

The influence of gender on online political communication remains contested, with existing scholarship providing mixed evidence as to whether gender shapes political messaging in digital environments. However, this debate has largely centred on mainstream platforms such as X (formerly Twitter), leaving the dynamics of alt-tech social media underexamined. Thi

Ilia Mosaki, A. V. Turlapov

Bose-Einstein condensates initially arranged in a long chain freely expand and interfere. If the initial phases of the condensates are identical, the initial density distribution is restored periodically during the expansion, giving rise to the Talbot effect. Even a slight disorder in the initial phases leads to a transformation of the interference pattern.

Ashu Kushwaha, Teruaki Suyama

Primordial black holes (PBHs) can form in the early Universe, for instance during radiation domination, from the collapse of large-amplitude density perturbations shortly after horizon re-entry. This mechanism establishes an approximate one-to-one correspondence between the PBH mass and the scale of the peak in the primordial curvature perturbations. Consequ

Moslem Rashidi, Luke B. Connelly, Gianluca Fiorentini

We study how a first heart-failure hospitalization, an adverse health shock, changes patients' care, and whether a nurse-led chronic-care program sustains those post-shock investments. Using linked population-wide administrative records from Italy's Romagna Local Health Authority (2017-2023), we anchor event time at each patient's first CHF admission and exp

Chanyoung Gwak, Yoonwoo Jeong, Byungwoo Jeon, Hyunseok Lee

Precise spatial understanding from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs), as their visual representations are predominantly semantic and lack explicit geometric grounding. While existing approaches augment visual tokens with geometric cues from visual geometry models, their MLLM is still required to im

Issaku Kanamori, Yasumichi Aoki, Hidenori Fukaya, Jishnu Goswami

The topological susceptibility is one of the quantities that has a large discretization error, and the error can be sensitive to the choice of fermion action. We report on our results from physical point simulations with 2+1 flavor M\"obius domain wall fermion at finite temperature. We also present the chiral condensate and disconnected susceptibility. The t

Samar Heydari, Jawher Said, Galip Ümit Yolcu, Evgenii Kortukov

Deep learning models for flood and wildfire segmentation and object detection enable precise, real-time disaster localization when deployed on embedded drone platforms. However, in natural disaster management, the lack of transparency in their decision-making process hinders human trust required for emergency response. To address this, we present an explaina

Kensuke Arakawa

We prove that two models of (monochromatic) enriched $\infty$-operads, due to Brantner and Chu--Haugseng, are equivalent. We show this as a consequence of the equivalence of two models of monoidal $\infty$-categories of symmetric sequences and the composition product, due to Brantner and Haugseng. As a consequence, constructions and results formulated in eit

Christophe Parisel

In a companion paper, we prove that the Burau-Lyapunov exponent LE discriminates focused from dispersed privilege escalation ratchets in cloud IAM graphs, and that no abelian statistic can replicate this discrimination. To strengthen this claim beyond its synthetic validation corpus, we apply the identical pipeline, with zero parameter retuning, to solar cor

Kensuke Arakawa

We study the relation of two frameworks for multiplicative homotopy theories: Presentably symmetric monoidal $\infty$-categories and combinatorial symmetric monoidal model categories. Our main theorem establishes an equivalence of their homotopy theories. As consequences, we solve Pavlov's conjecture and obtain a solution to a special case of Hovey's 10th pr

Maja Gwozdz

Hochman asked whether there exists a cellular automaton $F$ such that every cellular automaton is a factor of $F$ in the dynamical sense. In particular, we do not require the factor map to commute with the spatial shifts. We show that no such cellular automaton exists. More generally, if $F$ weakly factors onto the radius-zero $q$-clock automaton $C_q^{(k)}$

Anders Rolighed Larsen, Sneha Das, Nicole Nadine Lønfeldt, Paula Petcu

Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance emotional awareness in AI systems, and detect symptoms of mental health disorders such as anxiety and depression. However, the uncertainty in such systems remains high, and the ap

Davide Scassola, Dylan Ponsford, Adrián Javaloy, Sebastiano Saccani

Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion-based models are the current state-of-the-art (SotA) model class, achieving almost perfect performance on commonly used benchmarks. In this paper, we question the perception of progress for tabular data gen

Haozhen Cheng, Hüseyin K. Çakmak, Veit Hagenmeyer

Accurate building energy models are crucial for analyzing sector-coupled energy systems, where buildings interact with electrified heating, energy storage, and advanced control across various scenarios. High-fidelity (HiFi) white-box models that resolve hydronic distribution and emitter dynamics can capture short-term transients, yet their numerical stiffnes

Xunzhuo Liu, Bowei He, Xue Liu, Andy Luo

Production AI agents frequently receive user-specific queries that are highly repetitive, with up to 47\% being semantically similar to prior interactions, yet each query is typically processed with the same computational cost. We argue that this redundancy can be exploited through conversational memory, transforming repetition from a cost burden into an eff

Moritz Gstür, Gustav Keppler, Mohammed Ramadan, Ghada Elbez

Critical energy infrastructures increasingly rely on information and communication technology for monitoring and control, which leads to new challenges with regard to cybersecurity. Recent advancements in this domain, including attribute-based access control (ABAC), have not been sufficiently addressed by established standards such as IEC 61850 and IEC 62351

Grazia Di Bello, Fabrizio Pavan, Vittorio Cataudella, Donato Farina

We investigate the relation between non-Hermitian Hamiltonian and Lindblad dynamics in nonequilibrium open quantum systems. Non-Hermitian models can extend phase diagrams and enable sensing advantages, but such effects often rely on postselection, raising questions about their relevance for unconditional dynamics. Using a minimal two-qubit setup mediating a

Aniket Roy, Maitreya Suin, Rama Chellappa

Recent advances in text-to-image diffusion models have substantially improved the quality of image customization, enabling the synthesis of highly realistic images. Despite this progress, achieving fast and efficient personalization remains a key challenge, particularly for real-world applications. Existing approaches primarily accelerate customization for h

Isabella Mastroianni, Marco Guerra, Ulderico Fugacci, Emanuela De Negri

We compare several classes of biparameter persistence modules: $\gamma$-products of monoparameter modules, hook-decomposable modules, modules admitting a Smith-type structure theorem, and modules of projective dimension at most 1. We determine all logical implications among these classes, providing explicit counterexamples showing that the converses fail whe

Yutao Luo, Haotian Zhu, Shuchao Pang, Zhigang Lu

The rapid adoption of mobile graphical user interface (GUI) agents, which autonomously control applications and operating systems (OS), exposes new system-level attack surfaces. Existing backdoors against web GUI agents and general GenAI models rely on environmental injection or deceptive pop-ups to mislead the agent operation. However, these techniques do n

Amir Weiss

We derive fundamental accuracy limits for distributed localization when a fusion center has access only to independently rate-distortion (RD)-optimally compressed versions of multi-sensor observations, under a line-of-sight propagation model with a Gaussian wideband waveform. Using the Gaussian RD test-channel model together with a Whittle spectral Fisher-in

Tzu-Ti Wei, Yu-Han Tseng, Jun-Yi Lin, Yu-Chee Tseng

Steganography conceals secret information within innocuous carriers while preserving visual fidelity and enabling reliable recovery. Recent unified networks operate normally under untriggered conditions but switch to hidden steganographic tasks when triggered. PUSNet follows this paradigm by performing image purification during normal operation and steganogr

Fabien Bernier, Salah Ghamizi, Pantelis Dogoulis, Maxime Cordy

Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and optimization problems with constraints remains scarcely explored. In this paper, we investigate whether LLMs can reason and optimize under the physical and operational constraints of Optimal Power Flow (OPF) prob

Antonio D. Villegas-Yeguas, Guillermo R-García, Tzipi Kahana, Jorge Pinares Toledo

The comparison of dental records is a standardized technique in forensic dentistry used to speed up the identification of individuals in multiple-comparison scenarios. Specifically, the odontogram comparison is a procedure to compute criteria that will be used to perform a ranking. State-of-the-art automatic methods either make use of simple techniques, with

Gian-Marco Schnüriger, Martin Kroner, Emre Togan, Patrick Knüppel

Cavity exciton--polaritons are quasiparticles that form when quantum well excitons hybridize with a cavity mode. Here, we carry out photon correlation measurements under continuous wave resonant laser excitation to demonstrate quantum correlations between cavity--polaritons. Our experiments reveal an unexpectedly strong dependence of polariton interactions o

Pierre Dehornoy

For $\mathcal{O}$ a hyperbolic orientable 2-orbifold of genus $g$ with at most $2g+6$ conic points, we prove that the geodesic flow on the unitary tangent bundle$\mathrm{T}^1\mathcal{O}$ admits a Birkhoff section whose genus is one. Together with a result of Minakawa, this implies that this flow is almost equivalent to the suspension flow of the $(\begin{sma

Xuanyu Zhang, Weiqi Li, Qunliang Xing, Jingfen Xie

Video restoration in real-world scenarios is challenged by heterogeneous degradations, where static architectures and fixed inference pipelines often fail to generalize. Recent agent-based approaches offer dynamic decision making, yet existing video restoration agents remain limited by insufficient quality perception and inefficient search strategies. We pro

Koléhè Coulibaly-Pasquier

In this paper we show that the cutoff in separation profile for Brownian motion on flat torus T n\,; on spheres S n\,; on real, complex and quaternionic projective space resp. P n pRq, P n pCq and P n pHq, is the tail distribution of some explicit Gumbel distribution. The proof is based on intertwining, dual process together with a representation formula of

Nanako Shimaoka, Naoyuki Kamiyama, Shinji Hotta, Sayuri Kohmura

In this paper, we consider a structure-aware optimization problem for decision diagrams used for health guidance. In particular, we focus on decision diagrams that decide to whom public sectors suggest consulting a medical worker. Furthermore, these diagrams decide which notification method should be used for each target person. In this paper, we formulate t

Pierre L. Guillon, Robin Angles, Yanick Sarazin, Özgür D. Gürcan

Reduced models based on an anisotropic truncation of the Fourier space, retaining only a few poloidal wave-numbers while keeping the full radial resolution, are developed and first applied to the Hasegawa-Wakatani system. The impact of the truncation is studied first by considering the fixed-gradient formulation, and by comparing to direct numerical simulati

Somayeh Mehrabankar, Farkhondeh Abbasnezhad, Davood Afshar, Aurelian Isar

We investigate the dynamical evolution of quantum discord, entanglement and purity in an open quantum system of two coupled asymmetric harmonic oscillators interacting with a thermal environment. Using the Kossakowski-Lindblad master equation we analyze the time evolution starting with a squeezed vacuum state. In contrast to our previous study on entanglemen

S. Gerke, Q. Guo, G. Gutin, Y. Hao

In a digraph $D=(V,A)$, an oriented path is a sequence $P=x_1x_2\dots x_p$ of distinct vertices such that either $x_ix_{i+1}\in A$ or $x_{i+1}x_{i}\in A$ or both for every $i\in [p-1]$. If $x_ix_{i+1}\in A$ in $P$, then $x_ix_{i+1}$ is a forward arc of $P$; otherwise, $x_{i+1}x_{i}$ is a backward arc. The independence number $\alpha(D)$ is the maximum intege

Shida Jiang, Jaewoong Lee, Shengyu Tao, Scott Moura

Nonlinear extensions of the Kalman filter (KF), such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are indispensable for state estimation in complex dynamical systems, yet the conditions for a nonlinear KF to provide robust and accurate estimations remain poorly understood. This work proposes a theoretical framework that identifi

Binbin Xu

Equal-contribution authorship, in which two or more authors are designated as having contributed equally, is increasingly common in scientific publishing. Using approximately 480,000 tagged records from PubMed and PMC (2010-2024), we examine temporal trends, journal-level patterns, geographic distributions, and byline positions of equal-contributing authors.

Jintao Cheng, Weibin Li, Haozhe Wang, Gang Wang

Efficient visual representation is a central image-processing challenge in embodied manipulation, where policies repeatedly process dense visual-token sequences during closed-loop control. Existing methods rank or prune tokens using semantic relevance, VLM attention, cross-frame redundancy, or motion in the action space. These signals may discard task-releva

Ghislain Geniaux, César Martinez, Samuel Soubeyrand

This paper proposes tds mgtwr, a multiscale geographically and temporally weighted regression (MGTWR) model with covariate-specific spatial and temporal scales. The approach combines a separable spatio-temporal kernel with a Top-Down Scale (TDS) calibration scheme, where spatial and temporal bandwidths are selected for each covariate through a coordinate-wis

Adrián Detavernier, Jasper De Bock

We consider two approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We explain the conceptual differences between the two approaches, compare both approaches on a number of benchmark datasets and show that RQ is capable of outperforming UQ, both in a stand

Najeeb Jebreel, David Sánchez, Josep Domingo-Ferrer

Membership inference attacks (MIAs) aim to determine whether a data sample was included in a machine learning (ML) model's training set and have become the de facto standard for measuring privacy leakages in ML. We propose an evaluation framework that defines the conditions under which MIAs constitute a genuine privacy threat, and review representative MIAs

Yan Zhao, Li-Juan Li, Zheng-Peng Xu, Liu Ye

Quantum steering, as a manifestation of nonlocal quantum correlations, plays a crucial role in enabling various quantum information processing tasks. However, practical implementations are often hindered by significant challenges arising from imperfect or untrusted measurement devices. This study investigates the impact of measurement inaccuracies on quantum

Nils A. Herrmann, Tobias Eder, Jingyi He, Georg Groh

Current multimodal toxicity benchmarks typically use a single binary hatefulness label. This coarse approach conflates two fundamentally different characteristics of expression: tone and content. Drawing on communication science theory, we introduce a fine-grained annotation scheme that distinguishes two separable dimensions: incivility (rude or dismissive t

Benjamin Gutteridge, Michael Bronstein, Xiaowen Dong

Graph foundation models (GFMs) have recently attracted interest due to the promise of graph neural network (GNN) architectures that generalize zero-shot across graphs of arbitrary scales, feature dimensions, and domains. While existing work has demonstrated this ability empirically across diverse real-world benchmarks, these tasks share a crucial hidden limi

Yoon Huh, Jeongho Kang, Wan Choi

Diffusion models (DMs) have achieved remarkable success across various domains owing to their strong generative and denoising capabilities. Meanwhile, semantic communication based on neural joint source-channel coding (JSCC) has emerged as a promising paradigm for robust and efficient image transmission. However, severe channel noise can still distort the tr

Yue Xiao, Ling Jiang, Sen Nie, Ding Li

Provenance-based Intrusion Detection Systems (PIDSes) have been widely used to detect Advanced Persistent Threats (APTs). Although many studies achieve high performance in the evaluations of their original papers, their performance in industrial scenarios remains unclear. To fill this gap, we conduct the first systematic evaluation and analysis of PIDSes in

Dan-Fang Zhang, Jing-Ting Li, Wen-Zhang Wang, Wei-Hao Xu

The Weak Equivalence Principle (WEP) is a central pillar of general relativity. Its precise test with quantum systems in space offers a unique window onto new physics. Here we report the first in-orbit quantum test of the WEP. A dual-species (85Rb/87Rb) atom interferometer is realized aboard the China Space Station. Methods of platform motion suppression, fl

Marco Damele

Rump proved in \cite[Theorem~1]{Rump2018ClassificationOC} that if a finite skew brace has cyclic additive group, then its multiplicative group is solvable and almost Sylow cyclic. In this paper we show that this rigidity persists when the additive group is a \(Z\)-group. More precisely, we prove that if \(B\) is a finite skew brace whose additive group is a

Mathieu Carriere, Yuichi Ike, Théo Lacombe, Naoki Nishikawa

Computational topology provides a tool, persistent homology, to extract quantitative descriptors from structured objects (images, graphs, point clouds, etc). These descriptors can then be involved in optimization problems, typically as a way to incorporate topological priors or to regularize machine learning models. This is usually achieved by minimizing ade

Yuhui Wang, Zhixiong Yang, Ming Zhang, Shihan Dou

In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by large language models, which can assist in tracking and analyzing malfunctions, we propose a novel textual representation of fault trees. Building on it, we construct a benchmark for

Jawid Ahmad Baktash, Mosa Ebrahimi, Mohammad Zarif Joya, Mursal Dawodi

Dari, the primary language of Afghanistan, is spoken by tens of millions of people yet remains largely absent from the misinformation detection literature. We address this gap with DariMis, the first manually annotated dataset of 9,224 Dari-language YouTube videos, labeled across two dimensions: Information Type (Misinformation, Partly True, True) and Harm L

Stefania Stan, Marzio Lunghi, Vito Vargetto, Claudio Ricci

Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: g

Hui-Yu Xing

Using the Drell-Levy-Yan relation, the pion and kaon elementary fragmentation functions (EFFs) are obtained from their hadron-scale parton distribution functions (DFs). These EFFs serve as driving terms in the hadron cascade equations, whose solution yields the complete array of hadron-scale fragmentation functions (FFs) for pion and kaon production in high

G. Canocchi, E. X. Wang, A. M. Amarsi, K. Lind

Neutral sodium is an important tracer of the Galactic chemical evolution, a powerful diagnostic of different stellar populations, and the subject of detailed studies of exoplanet atmospheres via transmission spectroscopy. This work aims to study and quantify the errors in stellar analyses of Na I lines caused by the use of one-dimensional (1D) hydrostatic mo

Mario Castro, José A. Cuesta

The central limit theorem provides the theoretical foundation for the universality of the normal distribution: under broad conditions, the asymptotic distribution of a sum of independent random variables approaches a Gaussian. Yet, physical systems described by positive random variable -- from earthquakes to microbial growth to epidemic spreading -- consiste

Peter J. Forrester, Anas A. Rahman, Bo-Jian Shen

Recent work of Bornemann has uncovered hitherto hidden integrable structures relating to the asymptotic expansion of quantities at the soft edge of Gaussian and Laguerre random matrix ensembles. These quantities are spacing distributions and the eigenvalue density, and the findings cover the cases of the three symmetry classes orthogonal, unitary and symplec

G. Angloher, M. R. Bharadwaj, A. Böhmer, M. Cababie

Direct detection of nuclear recoils due to sub-GeV dark matter is challenging because of the small kinetic energy of the light dark matter particles. Although limits down to a few hundred MeV have been reached using specially designed low threshold detectors, further improvements are now constrained more by background event rates than by energy thresholds. H

Alexander Breev, Maxim Dvornikov

We study the evolution of neutrinos electroweakly interacting with a rotating matter. The description of neutrinos is based on the Dirac equation in the corotating noninertial frame where matter is at rest. We find solution of this Dirac equation, where the matter angular velocity is accounted for exactly, for massless neutrinos. In case of massive particles

Weijun Li, Arnaud Grivet Sébert, Qiongkai Xu, Annabelle McIver

The growing use of large language models has increased interest in sharing textual data in a privacy-preserving manner. One prominent line of work addresses this challenge through text rewriting under Local Differential Privacy (LDP), where input texts are locally obfuscated before release with formal privacy guarantees. These guarantees are typically expres