October 2025 arXiv papers — page 68

Alexandru Oarga, Yilun Du

Generalization is a key challenge in machine learning, specifically in reasoning tasks, where models are expected to solve problems more complex than those encountered during training. Existing approaches typically train reasoning models in an end-to-end fashion, directly mapping input instances to solutions. While this allows models to learn useful heuristi

L. Elisa Celis, Lingxiao Huang, Milind Sohoni, Nisheeth K. Vishnoi

Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort. Yet persistent disparities across race, gender, and class challenge this ideal. Some attribute these gaps to structural inequality; others to individual choice. We develop a game-theoretic model in which candidates from different socioeconomic groups differ in their

Mark He Huang, Lin Geng Foo, Christian Theobalt, Ying Sun

Free-moving object reconstruction from monocular video remains challenging, particularly without reliable pose or depth cues and under arbitrary object motion. We introduce OnlineSplatter, a novel online feed-forward framework generating high-quality, object-centric 3D Gaussians directly from RGB frames without requiring camera pose, depth priors, or bundle

Changan Liu, Zixuan Xie, Ahad N. Zehmakan, Zhongzhi Zhang

Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes. Despite its ability to capture rich graph structural information and its wide range of applications, computing this measure for large networks remains impractical due to

Anisha Dutta

Accurate assessment of urban canopy coverage is crucial for informed urban planning, effective environmental monitoring, and mitigating the impacts of climate change. Traditional practices often face limitations due to inadequate technical requirements, difficulties in scaling and data processing, and the lack of specialized expertise. This study presents an

Heejin Do, Jaehui Hwang, Dongyoon Han, Seong Joon Oh

Evaluating large language models (LLMs) on final-answer correctness is the dominant paradigm. This approach, however, provides a coarse signal for model improvement and overlooks the quality of the underlying reasoning process. We argue that a more granular evaluation of reasoning offers a more effective path to building robust models. We decompose reasoning

Zitong Lan, Yiduo Hao, Mingmin Zhao

Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions. In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in visi

Alaa Ahmed, Maha N. Haji

Offshore wind and wave energy offer high energy density and availability. While offshore wind has matured significantly, wave energy remains costly and under development. Integrating both technologies into a hybrid system can enhance power generation, stabilize output, and reduce costs. This study explores the benefits of combining an offshore floating wind

Dildar Ali, Suman Banerjee, Yamuna Prasad

The billboard advertisement has emerged as an effective out-of-home advertisement technique where the objective is to choose a limited number of slots to play some advertisement content (e.g., animation, video, etc.) with the hope that the content will be visible to a large number of travelers, and this will be helpful to earn more revenue. In this paper, we

Federico Cianci

In this paper we introduce a model of dynamic crack growth in viscoelastic material, where the damping term depends on the history of the deformation. The model is based on a dynamic energy dissipation balance and on a maximal dissipation condition. Our main result is an existence theorem in dimension two under some a priori regularity constraints on the cra

Isabella Alvarenga, Aurelia Deshayes

We study the behaviour of the rightmost occupied site in two models: the Spont process and the contact process with inherited sterility, in dimension 1. Both can be viewed as contact processes evolving in dynamic random environments, where the environment may itself depend on the state of the process. In the Spont process, blocking particles appear spontaneo

Julie Kienzle, Serge Bisaillon, Teodor Gabriel Crainic, Emma Frejinger

Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses tactical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a

Ziyu Ye, Chen Ju, Chaofan Ma, Xiaoyun Zhang

Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaptation attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a nove

Archana Dixit, Manish Yadav, Anirudh Pradhan, M. S. Barak

In this work, we investigate a cosmological scenario with a time-dependent cosmological constant $\Lambda$(t) within the spatially flat Friedmann-Lema\^itre-Robertson-Walker (FLRW) framework. Here we study a power-law $\Lambda(t)$CDM model characterized by a dynamic cosmological constant expressed as a function of the Hubble parameter and its derivative $\La

Yunyi Shen, Alexander Gagliano

Self-supervised learning has become a central strategy for representation learning, but the majority of architectures used for encoding data have only been validated on regularly-sampled inputs such as images, audios. and videos. In many scientific domains, data instead arrive as long, irregular, and multimodal sequences. To extract semantic information from

Dietrich Bödeker, Philipp Klose

The electroweak sphaleron rate in the high temperature phase of the Standard Model is inversely proportional to the weak-isospin conductivity. So far, only electroweak interactions were included in its computation. Here we take into account quark scattering through strong interactions at leading-log order. These reduce the quark contribution to the conductiv

Al Jay Lan J. Alamin, Melquezedec James T. Cruz, Bryan S. Hernandez, Eduardo R. Mendoza

Understanding the similarities and differences of the long term impact of different carbon dioxide removal (CDR) techniques is essential in determining the most effective and sustainable strategies to mitigate climate change. In particular, direct ocean capture (DOC) has emerged as a promising approach. In contrast to direct air capture (DAC) which separates

Aleksander Latyshev, Ines Safi

We consider Fractional Quantum Hall (FQH) edges with a spatially local Quantum Point Contact (QPC). Within the Unified Nonequilibrium Perturbative (UNEP) framework, without assumptions on the underlying Hamiltonian $H_{0}$ for the edges, we search for the associated backscattering DC current and noise compatible with the anyonic time exchange (ATE) constrain

Ali Rajaei, Peter Palensky, Jochen L. Cremer

Network topology optimization (NTO) via busbar splitting can mitigate transmission grid congestion and reduce redispatch costs. However, solving this mixed-integer nonlinear problem for large-scale systems in near-real-time is currently intractable with existing solvers. Machine learning (ML) approaches have emerged as a promising alternative, but they have

Simon Schindler, Christoph Binder, Lukas Lürzer, Stefan Huber

Machine Learning Operations (MLOps) practices are increas- ingly adopted in industrial settings, yet their integration with Opera- tional Technology (OT) systems presents significant challenges. This pa- per analyzes the fundamental obstacles in combining MLOps with OT en- vironments and proposes a systematic approach to embed MLOps prac- tices into establis

C. Taricco, I. Bizzarri, C. Dionese, S. Mancuso

Solar activity variations strongly impact the modulation of the flux of low-energy Galactic Cosmic Rays (GCRs) reaching the Earth. The secondary particles, which originate from the interaction of GCRs with the atmosphere, can be revealed by an array of ground detectors. We show that the low-threshold rate (scaler) time series recorded over 16 years of operat

Marc Dufay, Roger Wattenhofer

In the online Min-cost Perfect Matching with Delays (MPMD) problem, $m$ requests in a metric space are submitted at different times by an adversary. The goal is to match all requests while (i) minimizing the sum of the distances between matched pairs as well as (ii) how long each request remained unmatched after it appeared. While there exist almost optimal

Moslem Zarei, Mehdi Abdi, Nicola Bartolo, Sabino Matarrese

We employ the quantum Boltzmann equation to analyze the gravitationally generated entanglement between two remote qubits by considering two explicit microscopic models. A graviton propagator is employed as the mediator of the interactions, while the qubits are considered in a spatial superposition state. Such a setup, in the case of any entanglement generati

Muhammad Atif Butt, Alexandra Gomez-Villa, Tao Wu, Javier Vazquez-Corral

Recent years have seen impressive advances in text-to-image generation, with image generative or unified models producing high-quality images from text. Yet these models still struggle with fine-grained color controllability, often failing to accurately match colors specified in text prompts. While existing benchmarks evaluate compositional reasoning and pro

Damianos Iosifidis, Sotirios Karamitsos

We study inflation in the framework of extended metric-affine F(R) gravity, where all even-parity quadratic invariants of torsion and non-metricity are included in the Lagrangian alongside the F(R) term. The extended theory admits a scalar-tensor description with a non-canonical kinetic term featuring poles. As a result, the inflationary dynamics and predict

Federico Cianci, Gianni Dal Maso

We study some hyperbolic partial integro-differential systems in domains with time dependent cracks. In particular, we give conditions on the cracks which imply the uniqueness of the solution with prescribed initial-boundary conditions, and its continuous dependence on the cracks.

Maxime André, Marco Raglianti, Souhaila Serbout, Anthony Cleve

Microservices architectures are an integral part of modern software development. Their adoption brings significant changes to database management. Instead of relying on a single database, a microservices architecture is typically composed of multiple, smaller, heterogeneous, and distributed DBs. In these data-intensive systems, the variety and combination of

Alice C. Quillen, Abobakar Sediq Miakhel

We explore using an ergodic Floquet quantum system on a torus to generate pseudo-random unitary operators. We choose a regime of the perturbed Harper model with strong perturbations and perturbation frequency exceeding the libration frequency to ensure that the system has an ergodic region that covers phase space and lacks resonant substructure. We generate

Miroslav Bulíček, Josef Málek, Endre Süli

The paper is concerned with the mathematical analysis of a class of thermodynamically consistent kinetic models for nonisothermal flows of dilute polymeric fluids, based on the identification of energy storage mechanisms and entropy production mechanisms in the fluid under consideration. The model involves a system of nonlinear partial differential equations

Jiahao Meng, Xiangtai Li, Haochen Wang, Yue Tan

Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence appears. Recent models such as OpenAI-o3 have sparked wide interest in evidence-centered reasoning for images, yet extending this ability to videos is more challenging due to the need for joint temporal tracking and spatial localization across dy

Ding Zou, Feifan Wang, Mengyu Ge, Siyuan Fan

The realization of Artificial General Intelligence (AGI) necessitates Embodied AI agents capable of robust spatial perception, effective task planning, and adaptive execution in physical environments. However, current large language models (LLMs) and multimodal LLMs (MLLMs) for embodied tasks suffer from key limitations, including a significant gap between m

Koyena Bose, Steven H. Simon, Ajit C. Balram

Many strongly correlated states, such as those arising in the fractional quantum Hall effect and spin liquids, are described by wave functions obtained by dividing particles into multiple clusters, constructing a readily evaluable wave function in each cluster, and (anti)symmetrizing across these clusters. We introduce a method to compute quantities such as

A. I. Lomakin, E. M. Baeva, N. A. Titova, A. V. Semenov

We investigate electron transport and phase-breaking processes in thin titanium nitride (TiN) films of epitaxial quality. Previous studies show that a minute surface magnetic disorder significantly reduces the critical temperature ($T_\mathrm{c}$) and broadens the superconducting transition as the film thickness and device size decrease. We measure electron

Alexandre Benoit

Machine-learning force fields can deliver accurate molecular dynamics (MD) at high computational cost. For SO(3)-equivariant models such as MACE, there is little systematic evidence on whether reduced-precision arithmetic and GPU-optimized kernels can cut this cost without harming physical fidelity. This thesis aims to make MACE cheaper and faster while pres

Ricardo Oliveira, Nicolau Sobrosa, Pedro Ribeiro, Bruno Amorim

Quasiperiodic moir\'e materials provide a new platform for realizing critical electronic states, yet a direct and experimentally practical method to characterize this criticality has been lacking. We show that a multifractal analysis of the local density of states (LDOS), accessible via scanning tunneling microscopy, offers an unambiguous signature of critic

J. A. Sellwood, Victor P. Debattista, R. G. Carlberg

The well-known bar instability of rotationally-supported disk galaxy models has been studied extensively since its first discovery over half a century ago. We were therefore very surprised to find cases of disks embedded in rigid halos, which on the basis of widely-cited criteria should be unstable, that appeared to be robustly stable. Here we show that the

Amartya Chakrabortty, Georges Griso, Julia Orlik

This paper investigates the homogenization, dimension reduction, and linearization of a composite plate subjected to external loading within the framework of non-linear elasticity problem. The total elastic energy of the problem is of order $\sim h^2\varepsilon^{2a+3}$, where $a\geq1$. The paper is divided into two parts: The first part presents the simultan

Enyu Shi, Jiayi Zhang, Zhilong Liu, Ziheng Liu

The sixth-generation (6G) wireless networks are expected to deliver ubiquitous connectivity, resilient coverage, and intelligence-driven services in highly dynamic environments. To achieve these goals, distributed wireless architectures such as cell-free massive multiple-input multiple-output (MIMO) have attracted significant attention due to their scalabili

Stefano Longhi

Controlling atom-photon interactions in engineered environments is central to quantum optics and emerging quantum technologies. Non-Hermitian (NH) photonic baths, where dissipation fundamentally reshapes spectral and dynamical properties, provide versatile platforms for such control. Here we investigate the relaxation dynamics of a single two-level quantum e

Feilong Zhang, Jianxin Dai, Zhaohui Yang, Kai-Kit Wong

Fluid antenna is a promising wireless communication technology that enhances communication rate by changing the antenna positions. This article proposes a new communication system that combines multiple-input single-output (MISO) fluid antennas with traditional fixed-position antennas, utilizing antenna position optimization to improve energy harvesting effi

Susan Ariel Aaronson, Michael Moreno

The worlds people have strong opinions about artificial intelligence (AI), and they want policymakers to listen. Governments are inviting public comment on AI, but as they translate input into policy, much of what citizens say is lost. Policymakers are missing a critical opportunity to build trust in AI and its governance. This paper compares three countries

Zhouwei Zhai, Mengxiang Chen, Haoyun Xia, Jin Li

The retrieval-ranking paradigm has long dominated e-commerce search, but its reliance on query-item matching fundamentally misaligns with multi-stage cognitive decision processes of platform users. This misalignment introduces critical limitations: semantic gaps in complex queries, high decision costs due to cross-platform information foraging, and the absen

Wei Shao, Yuhao Wang, Rongguang He, Muhammad Ejaz Ahmed

Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging predefined signatures and static heuristics to identify and block malicious traffic. However, the emergence of AI-driven techniques presents new challenges to SDN security, potentially compromising the efficacy of exist

Sérgio Carrôlo, Dmitry Chicherin, Johannes Henn, Qinglin Yang

We present a symbol-level bootstrap construction of the planar, two-loop six-gluon scattering amplitude for the --++++ helicity configuration in QCD, focusing on the maximal weight pieces-the "most complicated terms" in the sense of Lipatov et al. Building on recent advances in the understanding of the relevant function space, we incorporate as a crucial new

Tingting Huang, Jundong Chen, Huanqiang Zeng, Guofa Cai

In traditional chaos shift keying (CSK) communication systems, implementing chaotic synchronization techniques is costly but practically unattainable in a noisy environment. This paper proposes a combined chaotic sequences-based $M$-ary CSK (CCS-$M$-CSK) system that eliminates the need for chaotic synchronization. At the transmitter, the chaotic sequence is

Harald Monsuur

We present a pollution-free first order system least squares (FOSLS) formulation for the Helmholtz equation, solved iteratively using a block preconditioner. This preconditioner consists of two components: one for the Schur complement, which corresponds to a preconditioner on $L_2(\Omega)$, and another defined on the test space, which we ensure remains Hermi

Mirzabek Alloqulov, Ahmadjon Abdujabbarov, Bobomurat Ahmedov, Chengxun Yuan

The gravitational weak lensing of neutrinos in the presence of a Schwarzschild black hole surrounded by a Dehnen-type dark matter halo is investigated. The event horizon structure is explored, and the existence of the BH is studied in two different slices of the parameter space. Additionally, we derive analytic expressions for the oscillation phase and trans

Lingyu Guo, Victor Zhenyu Guo, Mengyao Jing

Exponential sums with monomials are highly related to many interesting problems in number theory and well studied by many literatures. In this paper, we consider the exponential sums with polynomials and prove a new upper bound. As an application, we study the Piatetski-Shapiro sequence of the form $(\lfloor n^c \rfloor)$ where $c > 1$ is not an integer. We

A. Galan, S. Schuldt, G. B. Caminha, S. H. Suyu

Strong gravitational lensing by massive galaxy clusters offers rare opportunities to observe multiple images of distant ($z \gtrsim 2$) Type Ia supernovae (SNe) and to resolve the properties of their host galaxies. A recent outstanding example is the Type Ia SN H0pe ($z = 1.78$), which the James Webb Space Telescope (JWST) discovered in NIRCam images, when t

Shuning Zhang

Unmanned aerial vehicles (UAVs) operating in dynamic wind fields must generate safe and energy-efficient trajectories under physical and environmental constraints. Traditional planners, such as A* and kinodynamic RRT*, often yield suboptimal or non-smooth paths due to discretization and sampling limitations. This paper presents a physics-informed neural netw

Rui Zhou, Xintong Shi, Hai Lin, Yan Ren

Topological photonics offers transformative potential for robust integrated waveguide devices due to their backscattering-immune properties. However, their integration faces two fundamental challenges: mode symmetry mismatch with conventional waveguides and prohibitive dimensions. We successfully overcome these two critical challenges by introducing a novel

Tingting Huang, Jundong Chen, Huanqiang Zeng, Guofa Cai

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest due to their benefits in physical layer security. However, most existing MU chaotic communication systems, particularly those based on non-coherent detection, suffer from low spectral e

Parvathy Menon, Sivarani T, Sriram S, Manjunath Bestha

Transmission spectroscopy has proven to be an effective technique for characterizing exoplanet atmospheres. However, transmission spectroscopy requires planetary transits, which occur for only a small fraction of planetary systems due to geometric alignment constraints; hence, characterizing exoplanets through their reflected spectrum of host stars will be h

Xiaohan Sun, Carol O'Sullivan

In this paper, we investigate how users perceive the visual quality of crowd character representations at different levels of detail (LoD) and viewing distances. Each representation, including geometric meshes, image-based impostors, Neural Radiance Fields (NeRFs), and 3D Gaussians, exhibits distinct trade-offs between visual fidelity and computational perfo

Ekaterina Konopleva, Gleb Fedorov, Oleg Astafiev

In recent years, quantum simulators of topological models have been extensively studied across a variety of platforms and regimes. A new promising research direction makes use of meta-atoms with multiple intrinsic degrees of freedom, which to date have been predominantly studied in the classical regime. Here, we propose a superconducting quantum simulator to

Alexandre Benoit, Catherine Aitken, Yu He

Graph rewiring has emerged as a key technique to alleviate over-squashing in Graph Neural Networks (GNNs) and Graph Transformers by modifying the graph topology to improve information flow. While effective, rewiring inherently alters the graph's structure, raising the risk of distorting important topology-dependent signals. Yet, despite the growing use of re

Swati Gupta, Jai Moondra, Mohit Singh

Many multiobjective real-world problems, such as facility location and bus routing, become more complex when optimizing the priorities of multiple stakeholders. These are often modeled using infinite classes of objectives, e.g., $L_p$ norms over group distances induced by feasible solutions in a fixed domain. Traditionally, the literature has considered expl

Haojie Zhang, Junhuai Xu, Pengcheng Li, Zhi Qin

The Richardson-Lucy algorithm is applied to reconstruct the three-dimensional source function of identical pions from their two-particle correlation functions. The algorithm's performance is first evaluated through simulations with Gaussian-type initial source functions. Its imaging quality and robustness are further demonstrated with experimental data from

Logan de Monchaux-Irons, T-Y Dora Tang, Christoph A. Weber, Thomas C. T. Michaels

Living systems must maintain robust biochemical function despite fluctuations that span a wide range of timescales. Biomolecular condensates formed by liquid-liquid phase separation (LLPS) have been shown to buffer concentration fluctuations, but the principles governing their dynamic regulation remain unclear. We address this by probing the response of LLPS

Carlos Escudero, Helder Rojas

Diffusion theory establishes a fundamental connection between stochastic differential equations and partial differential equations. The solution of a partial differential equation known as the Fokker-Planck equation describes the probability density of the stochastic process that solves a corresponding stochastic differential equation. The kinetic interpreta

Jacob Ayers, Richard Hahnloser, Julia Ulrich, Lothar Sebastian Krapp

Conditional differential entropy provides an intuitive measure for relatively ranking time-series complexity by quantifying uncertainty in future observations given past context. However, its direct computation for high-dimensional processes from unknown distributions is often intractable. This paper builds on the information theoretic prediction error bound

Fuchen Li, Yansong Du, Wenbo Cheng, Xiaoxia Zhou

Consumer-grade camera systems often struggle to maintain stable image quality under complex illumination conditions such as low light, high dynamic range, and backlighting, as well as spatial color temperature variation. These issues lead to underexposure, color casts, and tonal inconsistency, which degrade the performance of downstream vision tasks. To addr

Marziyeh Bamdad, Hans-Peter Hutter, Alireza Darvishy

Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting remains an open challenge. Such conditions are common in applications such as assistive navigation for the visually impaired. These challenges undermine localization accuracy and tracking stability, reducing navig

Jinchang Luo, Mingquan Cheng, Fan Wan, Ni Li

Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA) remains limited by two fundamental limitations: (i) global planning absence to structure multi-step reasoning, and (ii) unfaithful execution, which hinders effective query formulat

Nikolaus Huber, Susanne Graf, Philipp Rümmer, Wang Yi

This paper introduces a compilation scheme for programs written in the Mimosa programming language, which builds upon the MIMOS model of computation. Mimosa describes embedded systems software as a collection of time-triggered processes which communicate through FIFO queues. We formally describe an adaptation of the Lustre compilation scheme to the semantics

Eva Lu, Dongdong Wang

Africa holds the world's highest solar irradiance yet has <2% of global photovoltaic (PV) capacity, leaving 600 million people without electricity access. However, climate change impacts on its 10 TW potential remain understudied. Using four decades of ERA5 reanalysis data (1980-2020) at 0.25 degree resolution, we quantify the contributions of key climate fa

Yoichi Takeda

With an aim of clarifying the extent and parameter-dependence of compositional anomaly of barium in A-type stars, Ba abundances were spectroscopically determined based on BaII 6141/6496 lines for 89 (23 A-type and 66 F--G-type) main-sequence stars belonging to the members of Hyades cluster by taking into account the non-LTE effect and the hyper-fine-structur

Nikita Kramarev, Andrey Yudin

The process of long-term stable mass transfer (or stripping) in a close neutron star binary system is possible at a sufficiently large initial asymmetry of the component masses. At the final stage of the evolution of such systems, the low-mass neutron star fills its Roche lobe, whereupon its mass is gradually transferred to the more massive component. At a c

Jackson Loth, Pedro Sarmento, Mark Sandler, Mathieu Barthet

Music generation in the audio domain using artificial intelligence (AI) has witnessed steady progress in recent years. However for some instruments, particularly the guitar, controllable instrument synthesis remains limited in expressivity. We introduce GuitarFlow, a model designed specifically for electric guitar synthesis. The generative process is guided

Diego Cifelli, Adolfo Anta

The increasing share of converter based resources in power systems calls for scalable methods to analyse stability without relying on exhaustive system wide simulations. Decentralized small gain and small-phase criteria have recently been proposed for this purpose, but their applicability to grid forming converters is severely limited by the sectoriality ass

Sangmitra Madhusudan, Kaige Chen, Ali Emami

When language models correctly parse "The cat that the dog chased meowed," are they analyzing syntax or simply familiar with dogs chasing cats? Despite extensive benchmarking, we lack methods to distinguish structural understanding from semantic pattern matching. We introduce CenterBench, a dataset of 9,720 comprehension questions on center-embedded sentence

Jacopo Di Ventura, Jan Felix Kleuker, Aske Plaat, Thomas Moerland

Zero-shot reinforcement learning (RL) has emerged as a setting for developing general agents, capable of solving downstream tasks without additional training or planning at test-time. While conventional RL optimizes policies for fixed rewards, zero-shot RL requires learning representations that enable immediate adaptation to arbitrary reward functions. As th

Weiwei Zhuang, Weiqi Yang, Jiahua Chen

We establish the validity of bootstrap methods for empirical likelihood (EL) inference under the density ratio model (DRM). In particular, we prove that the bootstrap maximum EL estimators share the same limiting distribution as their population counterparts, both at the parameter level and for distribution functionals. Our results extend existing pointwise

Abdulmomen Ghalkha, Zhuojun Tian, Chaouki Ben Issaid, Mehdi Bennis

Conventional multimodal alignment methods assume mutual redundancy across all modalities, an assumption that fails in real-world distributed scenarios. We propose SheafAlign, a sheaf-theoretic framework for decentralized multimodal alignment that replaces single-space alignment with multiple comparison spaces. This approach models pairwise modality relations

Guillermo Carbajal, Andrés Almansa, Pablo Musé

Motion blur caused by camera shake, particularly under large or rotational movements, remains a major challenge in image restoration. We propose a deep learning framework that jointly estimates the latent sharp image and the underlying camera motion trajectory from a single blurry image. Our method leverages the Projective Motion Blur Model (PMBM), implement

Alphonse P. Magnus

It is shown how to define difference operators and equations on particular lattices $\{x_n\}$, $2n\in\mathbb{Z}$, such that the divided difference operator $(\mathcal{D}f)(x_{n+1/2})= (f(x_{n+1})-f(x_n))/(x_{n+1}-x_n)$ has the property that $\mathcal{D}f$ is a rational function of degree $2d$ when $f$ is a rational function of degree $d$. It is then shown th

M. Frelek-Kozak, K. Mulewska, M. Wilczopolska, D. Kalita

Nickel based superalloys display outstanding properties such as excellent creep strength, remarkable fracture toughness parameters, and corrosion resistance. For this reason, Ni based materials are considered as materials dedicated to the IV generation of nuclear reactors. Although these materials seem promising candidates, their radiation resistance and imp

Domingo García-Senz, Rubén M. Cabezón

Artificial viscosity is commonly employed in smoothed particle hydrodynamics (SPH) to model dissipation in hydrodynamic simulations. However, its practical implementation today relies, in many cases, on complex numerical switches to restrict its application to regions where dissipation is physically warranted, such as shocks. These switches, while essential,

Wadim Gerner

In [J. Cantarella, D. DeTurck, H. Gluck and M. Teytel, J. Math. Phys. 41:5615 (2000)] the helicity isoperimetric problem which asks to find a smooth domain of fixed volume which maximises Biot-Savart helicity among all other smooth domains of fixed volume was initiated. It was shown that if an optimal domain exists, all of its boundary components must be tor

Patrycja Balik, Szymon Jędras, Piotr Polesiuk

Type-and-effect systems help the programmer to organize data and computational effects in a program. While for traditional type systems expressive variants with sophisticated inference algorithms have been developed and widely used in programming languages, type-and-effect systems did not yet gain widespread adoption. One reason for this is that type-and-eff

Lixiong Qin, Yang Zhang, Mei Wang, Jiani Hu

The advancement of Multimodal Large Language Models (MLLMs) has bridged the gap between vision and language tasks, enabling the implementation of Explainable DeepFake Analysis (XDFA). However, current methods suffer from a lack of fine-grained awareness: the description of artifacts in data annotation is unreliable and coarse-grained, and the models fail to

Vanessa Brzić, Satoshi Yoshida, Mio Murao, Marco Túlio Quintino

In higher-order quantum computing (HOQC), one typically considers the universal transformation of unknown quantum operations, treated as blackboxes. It is also implicitly assumed that the resulting operation must act on arbitrary, and thus unknown, input states. In this work, we explore a variant of this framework in which the operation remains unknown, but

Constantine Frost, Chad Council, Margaret McGuinness, Nathaniel Hanson

Despite well-reported instances of robots being used in disaster response, there is scant published data on the internal composition of the void spaces within structural collapse incidents. Data collected during these incidents is mired in legal constraints, as ownership is often tied to the responding agencies, with little hope of public release for researc

Mariia Gumberidze, Vladyslav C. Usenko

We theoretically analyze the feasibility of entanglement-based quantum key distribution (QKD) protocols considering widely used spontaneous parametric down-conversion (SPDC) and novel quantum dot (QD) sources. We account for multiphoton emission in SPDC sources and fine-structure splitting (FSS) in QD. In addition, we incorporate imperfect detection, includi

Daria A. Dolinina, Dmitry V. Turaev, Andrei G. Vladimirov

Soliton dynamics in coupled Kerr microcavities is an important aspect of frequency comb technologies, with applications in optical communication and precision metrology. We investigate a minimal system consisting of two nearly identical coupled Kerr microresonators, each operating in the soliton regime and driven by a separate coherent beam, and analyze the

Zhenhao Cai, Jian Ding

The question of understanding the scaling limit of metric graph critical loop soup clusters and its relation to loop soups in the continuum appears to be one of the subtle cases that reveal interesting new scenarios about scaling limits, with a mixture of macroscopic and microscopic randomness. In the present paper, we show that in three dimensions, scaling

Felix Sefzig

We give an explicit construction of the p-adic de Rham comparison isomorphism for 1-motives. In particular, we prove that our construction recovers the classical de Rham comparison isomorphism and is functorial with respect to morphisms of rigid 1-motives. In the second part, we construct a new ring of motivic p-adic periods using the formalism of rigid anal

Yaobin Hua, Zhenglong Ban, Tian-You Ren, Jia-Jun Yin

Within a framework requiring a well-defined event horizon and matter obeying the weak energy condition, we employ gravitational decoupling method to construct non-singular hairy black holes: spherically or axially symmetric. These solutions arise from a deformation of the Minkowski vacuum, where the maximum deformation can yield the Schwarzschild metric for

Jakub Kmec, Petr Boril, Fraser Bradfield, Karel Cerny

Ultra-high-energy cosmic rays (UHECRs) remain one of the greatest mysteries in astroparticle physics. The Fluorescence detector Array of Single-pixel Telescopes (FAST) is a next-generation cosmic ray experiment which utilizes ground-based fluorescence telescopes designed to detect these extremely rare particles at energies exceeding 30 EeV. FAST offers a cos

Lorenzo Grimaldi, Pablo Villegas, Alessandro Vezzani, Raffaella Burioni

&#34;Every object that biology studies is a system of systems.&#34; (François Jacob, 1974). Most networks feature intricate architectures originating from tinkering, a repetitive use of existing components where structures are not invented but reshaped. Linking the properties of primitive components to the emergent behavior of composite networks remains a ke

Xiaohan Lan, Fanfan Liu, Haibo Qiu, Siqi Yang

Inspired by recent advancements in LLM reasoning, the field of multimodal reasoning has seen remarkable progress, achieving significant performance gains on intricate tasks such as mathematical problem-solving. Despite this progress, current multimodal large reasoning models exhibit two key limitations. They tend to employ computationally expensive reasoning

Mohamed Seif, Malcolm Egan, Andrea J. Goldsmith, H. Vincent Poor

AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for vision and perception tasks such as in autonomous driving and environmental monitoring. AI systems rely both on efficient model learning and inference. In the inference phase, features extracted from sensing data ar

Zhenhao Cai, Jian Ding

We prove that for the Gaussian free field (GFF) on the metric graph of $\mathbb{Z}^d$ (for all $d\ge 3$ except the critical dimension $d_c=6$), with uniformly positive probability there exist two distinct sign clusters of diameter at least $cN$ within a box of size $N$ such that their graph distance is less than $N^{-[(d-2)\vee (2d-8)]}$. This phenomenon con

Xu Hu, Bin Lin, Xiao Lu, Ping Wang

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractabl

Lea Salome Brugger, Xavier Denis, Peter Müller

Deductive verification is an effective method to ensure that a given system exposes the intended behavior. In spite of its proven usefulness and feasibility in selected projects, deductive verification is still not a mainstream technique. To pave the way to widespread use, we present a study investigating the factors enabling successful applications of deduc

Zhiyu Lin, Jingwen Yang, Jiale Zhao, Meng Liu

Recent speech-to-speech (S2S) models generate intelligible speech but still lack natural expressiveness, largely due to the absence of a reliable evaluation metric. Existing approaches, such as subjective MOS ratings, low-level acoustic features, and emotion recognition are costly, limited, or incomplete. To address this, we present DeEAR (Decoding the Expre

Yixiong Yang, Tao Wu, Senmao Li, Shiqi Yang

Recent progress in accelerating text-to-image diffusion models enables high-fidelity synthesis within a single denoising step. However, customizing the fast one-step models remains challenging, as existing methods consistently fail to produce acceptable results, underscoring the need for new methodologies to personalize one-step models. Therefore, we propose

Cheng-Chiang Tsai

Let $G$ be a $p$-adic reductive group with $p$ ``very large.'' For any irreducible admissible representation $\pi$ of $G$ over an algebraically closed field $C$ of characteristic $\not=p$, we define a ``local character expansion'' of $\pi$ with coefficients $c_{\mathcal{O}}(\pi)\in\mathbb{Q}$, that does not use the character of $\pi$ directly but instead use

Cheng-Chiang Tsai

We define the ``lifted character'' of mod-$\ell$ representations of $p$-adic reductive groups where $\ell\not=p$, on compact elements with pro-orders not divisible by $\ell$. We generalize the local character expansion results of Howe, Harish-Chandra and DeBacker to such lifted characters. We show that the result of Moeglin-Waldspurger and Varma on degenerat

Zsófia Juhász, Máté Bartalos

A scarcely known generalization of Goldbach's conjecture introduced by Hardy and Littlewood states that for every pair of (relatively prime) positive integers m1 and m2, every sufficiently large integer n satisfying certain simple congruence criteria can be $(m_1,m_2)$-partitioned as $n = m_1p+m_2q$ for some primes $p$ and $q$. While the size of the minimal