November 2025 arXiv papers — page 177

Jinrui Huang, Xueqin Wang, Dong Liu, Jingguo Lan

This paper focuses on decentralized composite optimization over networks without a central coordinator. We propose a novel decentralized symmetric ADMM algorithm that incorporates multiple communication rounds within each iteration, derived from a new constraint formulation that enables information exchange beyond immediate neighbors. While increasing per-it

Kasra Asnaashari, Jeremy O. Richardson

We propose a method which combines the quantum-classical mapping approach to surface hopping (MASH) with the dissipative quantum dynamics of the Lindblad master equation. Like conventional surface-hopping methods, our approach is based on classical trajectories coupled to the dynamics of a quantum subsystem. However, instead of evolving the subsystem wavefun

Ritwik Bhaduri, Aabesh Bhattacharyya, Rina Foygel Barber, Lucas Janson

Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial data sets that are exchangeable with the real data under the null hypothesis (but not under the alternative), as this allows the analyst to conduct a valid test using any test statistic they desire. Such sampling is

Xu'an Dou, Delphine Salort, Didier Smets

A simple Markov process is considered involving a diffusion in one direction and a transport in a transverse direction. Quantitative mixing rate estimates are obtained with limited assumptions about the transport field, which might be highly irregular and/or highly degenerate, in particular quite far from satisfying an hypoellipticity type assumption.

S. Capozziello, E. Di Valentino, V. G. Gurzadyan

The papers included in this Focus Point collection are devoted to the studies on the cosmological tensions and challenges stimulated by the latest observational data. The first results of the LARES-2 laser ranging satellite on the high precision testing of the frame-dragging effect predicted by General Relativity are presented. The data on the S-stars monito

Hendrik Schrautzer, Moritz A. Goerzen, Bjarne Beyer, Soumyajyoti Haldar

Reliable control of skyrmion lifetime is essential for realizing spintronic devices, yet the role of higher-order exchange - which can lead to skyrmion stabilization - remains largely unexplored. Here we calculate lifetimes of isolated skyrmions and antiskyrmions at transition-metal interfaces based on an atomistic spin model that includes all fourth-order e

Andrii Hulianytskyi, Sergei Pereverzyev, Sergii Siryk, Nataliya Vasylyeva

In the paper, we propose an analytical and numerical approach to identify scalar parameters (coefficients, orders of fractional derivatives) in the multi-term fractional differential operator in time, $\mathbf{D}_t$. To this end, we analyze inverse problems with an additional nonlocal observation related to a linear subdiffusion equation $\mathbf{D}_{t}u-\ma

Huanyu Song, Zhengming Sheng, Linzheng Wang, Min Chen

Extremely high axial magnetic fields above the gigagauss (GG) level are supposed to exist in neutron stars, which may be a one of the critical parameters for their internal structures and be responsible for the X and gamma-ray emission from these stars. Here we show that such ultrahigh magnetic fields can be produced by multiple petawatt-class lasers interac

Hokyun Im, Euijin Jeong, Andrey Kolobov, Jianlong Fu

Vision-language-action models (VLAs) trained on large-scale robotic datasets have demonstrated strong performance on manipulation tasks, including bimanual tasks. However, because most public datasets focus on single-arm demonstrations, adapting VLAs for bimanual tasks typically requires substantial additional bimanual data and fine-tuning. To address this c

Rafael Gómez-Lurbe, Alexander Bernal, Armando Pérez, Bryan Zaldívar

We propose using variational quantum algorithms (VQAs) to simulate established quantum algorithms under realistic noise conditions, aiming to surpass the fidelity of theoretical circuits in noisy environments. Focusing on the Quantum Fourier Transform (QFT), we perform numerical simulations for two qubits under both coherent and incoherent noise. To enhance

Guadalupe Ahumada Acuña, Cecilia Bejarano, Rafael Ferraro

Born-Infeld electrogravity is defined through a Lagrangian that couples gravity and electromagnetism within a single determinantal structure. The field equations are derived in Palatini's formalism, where the metric, connection, and vector potential are varied independently in the action. As a result, the gravitational sector reduces to Einstein's eq

Nitin Saxena, Madhavan Venkatesh

We present a randomised algorithm to compute the local zeta function of a fixed smooth, projective surface over $\mathbb{Q}$, at any large prime $p$ of good reduction. The runtime of our algorithm is polynomial in $\log p$, resolving a conjecture of Couveignes and Edixhoven.

Jack Hong, Chenxiao Zhao, ChengLin Zhu, Weiheng Lu

Agentic multimodal models should not only comprehend text and images, but also actively invoke external tools, such as code execution environments and web search, and integrate these operations into reasoning. In this work, we introduce DeepEyesV2 and explore how to build an agentic multimodal model from the perspectives of data construction, training method

Guojiang Shao, Zuo Quan Xu, Qi Zhang

We investigate a portfolio selection problem involving multi competitive agents, each exhibiting mean-variance preferences. Unlike classical models, each agent's utility is determined by their relative wealth compared to the average wealth of all agents, introducing a competitive dynamic into the optimization framework. To address this game-theoretic problem

Ishan Kavathekar, Hemang Jain, Ameya Rathod, Ponnurangam Kumaraguru

Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities, leading to their widespread adoption across diverse tasks. As task complexity grows, multi-agent LLM systems are increasingly used to solve problems collaboratively. However, safety and security of these systems r

Haoyang Zhang, Shenbang Yang, Li Zhang, Benzhong Dai

The scaling laws reveal the underlying structural similarities shared by astrophysical systems across vastly different scales. In black hole accretion systems, the scaling relations between the characteristic damping timescales (CDTs) of light curves and black hole mass offer valuable insights into the underlying physical structure of accretion disks. Here,

Oriol Balló-Gimbernat, Marcos Arroyo-Sánchez, Paula García-Molina, Adan Garriga

Generative modeling is one of the most promising applications of quantum machine learning, yet training and deploying Quantum Generative Models (QGMs) on near-term hardware remains effectively intractable due to prohibitive gradient estimation and implementation costs. We propose a resource-efficient approach based on shallow Instantaneous Quantum Polynomial

Gabriel Serrão Seabra, Nikolaj T. Mücke, Vinicius Luiz Santos Silva, Alexandre A. Emerick

Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how machine learning methods can enhance data assimilation for GCS with a framework that integrates score-based diffusion models with machine learning-enhanced localization in channelize

Taihelong Zeng, Yun Lin, Yuhe Shi, Yan Li

The emergence of truck-drone collaborative systems in last-mile logistics has positioned the Traveling Salesman Problem with Drones (TSP-D) as a pivotal extension of classical routing optimization, where synchronized vehicle coordination promises substantial operational efficiency and reduced environmental impact, yet introduces NP-hard combinatorial complex

Humberto C. F. Lemos, Thiago Cordeiro, Adelcio C. Oliveira

The Euler-Bernoulli beam model has been studied classically and semi-classically. The semi-classical quantization is done in an analogous way to the quantization of the electromagnetic field, and we found an effect that is similar to the Casimir effect, which is the photonic Casimir effect. The Casimir force, by unit area, is proportional to the first mode e

Qi Sun, Dingju Zhou, Lina Zhang

The analysis of character appearance frequency is essential for understanding narrative structure, character prominence, and story progression in anime. In this work, we introduce OregairuChar, a benchmark dataset designed for appearance frequency analysis in the anime series My Teen Romantic Comedy SNAFU. The dataset comprises 1600 manually selected frames

Konstantinos Dareiotis, El Mehdi Haress, Khoa Lê

We study the long-time behaviour of solutions to a class of $d$-dimensional stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H \in (0,1)$. The drift consists of a dissipative Lipschitz term and a singular term of regularity $\gamma >1-1/(2H)$ in Besov-H\"older scales. We establish well-posedness and, through a Mark

Pedro H. M. Zanineli, Matheus Zaia Monteiro, Vinicius Francisco Wasques, Francielle Santo Pedro Simões

Predicting quantum wavefunction probability distributions is crucial for computational chemistry and materials science, yet machine learning (ML) models often face a trade-off between accuracy and interpretability. This study compares Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in modeling quantum probability distribu

Marko Shuntov, Olivier Ilbert, Claudia del P. Lagos, Sune Toft

We study the stellar mass function (SMF) of quiescent and star-forming galaxies and its dependence on morphology in 10 redshift bins at $0.2<z<5.5$ using the COSMOS2025 catalog built from $0.54 \, {\rm deg}^2$ JWST imaging from COSMOS-Web. Galaxies are selected by type using the $NUVrJ$ rest-frame color diagram and classified morphologically by bulge-to-tota

Liding Xu, Ye-Chao Liu, Sebastian Pokutta

The cone of positive-semidefinite (PSD) matrices is fundamental in convex optimization, and we extend this notion to tensors, defining PSD tensors, which correspond to separable quantum states. We study the convex optimization problem over the PSD tensor cone. While this convex cone admits a smooth reparameterization through tensor factorizations (analogous

Xinbo Wang, Shian Jia, Ziyang Huang, Jing Cao

Modern operating system schedulers employ a single, static policy, which struggles to deliver optimal performance across the diverse and dynamic workloads of contemporary systems. This "one-policy-fits-all" approach leads to significant compromises in fairness, throughput, and latency, particularly with the rise of heterogeneous hardware and varied applicati

Quentin Peres

We show that if $(X,g,J,\omega)$ is a K\"ahler manifold with an $SU(n+s)$-structure and a Hamiltonian holomorphic action of a compact torus $T^s$, then the usual symplectic quotient $Y$ inherits an $SU(n)$-structure provided the existence of special $1$-forms on $X$, called twist forms. We then give several applications of our results: on complex projective

Ankur Dey

In this article, we investigate the proposed duality between the island and the defect extremal surface (DES) prescriptions using the fine-grained entanglement entropy in Karch-Randall (KR) brane-world models with gravitating radiation baths. We consider the AdS$_3$ black string geometry and compute the entanglement entropy for radiation subsystems on an AdS

Na Zhang, Hong Chen, Qia Li, Junpeng Zhou

In this paper, we consider a squared $L_1/L_2$ regularized model for sparse signal recovery from noisy measurements. We first establish the existence of optimal solutions to the model under mild conditions. Next, we propose a proximal method for solving a general fractional optimization problem which has the squared $L_1/L_2$ regularized model as a special c

Leandro C. Souza, Laurent E. Dardenne, Renato Portugal

We propose a gate-based Quantum Genetic Algorithm (QGA) for real-valued global optimization. In this model, individuals are represented by quantum circuits whose measurement outcomes are decoded into real-valued vectors through binary discretization. Evolutionary operators act directly on circuit structures, allowing mutation and crossover to explore the spa

Tiziano Natali, Karin A. Olthof, Niels F. M. Kok, Koert F. D. Kuhlmann

Introduction: Accurate intraoperative delineation of colorectal liver metastases (CRLM) is crucial for achieving negative resection margins but remains challenging using intraoperative ultrasound (iUS) due to low contrast, noise, and operator dependency. Automated segmentation could enhance precision and efficiency in ultrasound-based navigation workflows. M

Hamed Rezazadeh, Mohammad Monfared, Meghdad Fazeli, Saeed Golestan

In recent years, virtual oscillator control, particularly the Andronov-Hopf oscillator (AHO), has received widespread attention for controlling grid-forming (GFM) inverters due to their superior dynamic response. However, traditional AHO systems feature droop coefficients that are dependent on the oscillator voltage amplitude, limiting their ability to maint

Jialin Hong, Diancong Jin, Xu Wang

This paper aims to investigate the asymptotic error distribution of several numerical methods for stochastic partial differential equations (SPDEs) with multiplicative noise. Firstly, we give the limit distribution of the normalized error process of the exponential Euler method in $\dot{H}^\eta$ for some $\eta>0$. A key finding is that the asymptotic error i

Mohamed Sanim Akremi, Rim Slama, Hedi Tabia

Online continuous motion recognition is a hot topic of research since it is more practical in real life application cases. Recently, Skeleton-based approaches have become increasingly popular, demonstrating the power of using such 3D temporal data. However, most of these works have focused on segment-based recognition and are not suitable for the online scen

Wang Zhenyu, Li Peiyuan, Shi Yongxiang, Wu Ruoyu

Full-waveform inversion (FWI) can produce high-resolution subsurface models, yet it remains inherently ill-posed, highly nonlinear, and computationally intensive. Although recent deep learning and numerical acceleration methods have improved speed and scalability, they often rely on single CNN architectures or single neural operators, which struggle to gener

Samuel Zamour

We establish vanishing results for the first cohomology group of nilpotent groups and Lie rings when the submodule of invariants is trivial. Our results are obtained within a model-theoretic setting, namely for structures that are definable in a finite-dimensional theory, which encompasses algebraic groups over algebraically closed fields, real semi-algebrai

Stefan Takacs

We propose and analyze a domain decomposition solver for the biharmonic problem. The problem is discretized in a conforming way using multi-patch Isogeometric Analysis. As first step, we discuss the setup of a sufficiently smooth discretization space. We focus on two dimensional computational domains that are parameterized with sufficiently smooth geometry f

R. Zöllner, F. Schuricht, T. Schmidt, W. Hofmann

The aim of this study is to give insights into the trajectory optimization w.r.t. energy consumption and recuperation for stacker cranes in a high-bay warehouse. Based on an analytical necessary optimality condition, a targeted numerical implementation is set up to perform systematic computations of optimal trajectories which are further categorized. Particu

Xincheng Yao, Yan Luo, Zefeng Qian, Chongyang Zhang

The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of anomaly detection (i.e., pretraining in natural images doesn't

Giovanna Souza Rodrigues Costa, Julio Cesar Martins, Odylio Denys Aguiar

This work investigates the temporal distribution of glitches detected by LIGO, focusing on the morphological classification provided by the Gravity Spy project. Starting from the hypothesis that these events follow a Poisson process, we developed a statistical methodology to evaluate the agreement between the empirical distribution of glitches and an ideal P

Stefan Buschenhenke, Spyridon Dendrinos, Isroil A. Ikromov, Detlef Müller

The classical cone multipliers are Fourier multiplier operators which localize to narrow $1/R$-neighborhoods of the truncated light cone in frequency space. By composing such convolution operators with suitable translation invariant Fourier integral operators (FIOs), we obtain what we call FIO-cone multipliers. We introduce and study classes of such FIO-cone

Martin Bicher, Maximilian Viehauser, Daniele Giannandrea, Hannah Kastinger

In recent years, dynamic agent-based population models, which model every inhabitant of a country as a statistically representative agent, have been gaining in popularity for decision support. This is mainly due to their high degree of flexibility with respect to their area of application. GEPOC ABM is one of these models. Developed in 2015, it is now a well

Jie Liu, Zuang Wang, Yongqiang Wang

Scalable decentralized optimization in large-scale systems hinges on efficient communication. A common way to reduce communication overhead is to perform multiple local updates between two communication rounds, as in federated learning. However, extending this strategy to fully decentralized settings poses fundamental challenges. Existing decentralized algor

María Olalla Olea-Romacho

We explore the observational consequences of resonant particle production during inflation, focusing on its impact on dark matter annihilation signals today. A transient burst of particle production generates localised features in the primordial power spectrum, enhancing the formation of compact small-scale dark matter structures known as prompt cusps. If da

Zilong Li, Jie Cao

Ancient people translated classical Chinese into Japanese using a system of annotations placed around characters. We abstract this process as sequence tagging tasks and fit them into modern language technologies. The research on this annotation and translation system faces a low resource problem. We alleviate this problem by introducing an LLM-based annotati

Peide Li, Liu Cao, Lyutianyang Zhang, Dongyu Wei

Radio Environment Map (REM) is transitioning from 5G homogeneous environments to B5G/6G heterogeneous landscapes. However, standard Federated Learning (FL), a natural fit for this distributed task, struggles with performance degradation in accuracy and communication efficiency under the non-independent and identically distributed (Non-IID) data conditions in

Juan C. Boschero, Ward van der Schoot, Niels M. P. Neumann

Quantum machine learning is one of the fields where quantum computers are expected to bring advantages over classical methods. However, the limited size of current computers restricts the exploitation of the full potential of quantum machine learning methods. Additionally, different computing paradigms, both quantum and classical, each have their own strengt

Rui Wu, Lizheng Wang, Yongjun Li

Judea Pearl's vision of Structural Causal Models (SCMs) as engines for counterfactual reasoning hinges on faithful abduction: the precise inference of latent exogenous noise. For decades, operationalizing this step for complex, non-linear mechanisms has remained a significant computational challenge. The advent of diffusion models, powerful universal functio

Dominika Popelová, Karel Veselský, Pavel Loiko, Abdelmjid Benayad

We report on the eye-safe laser performance of Tm3+-doped calcium fluoride crystals, modified with optically inactive "buffer" cations (Y3+, Lu3+, Gd3+, and La3+), under in-band diode-pumping at 1.68 um. In the free-running regime, the 1.5 at.% Tm, 4 at.% Y:CaF2 laser operates with a high slope efficiency of 47% with respect to absorbed pump power. By employ

Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Tobias Würth

Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators (GNSs) offer a promising alternative to traditional mesh-based physics simulators. Their speed and inherent differentiability make them particularly well suited for applications t

Henry D. J. Kerr, Peter Ashwin, Kyle C. A. Wedgwood

Travelling waves of neural firing activity are observed in brain tissue as a part of various sensory, motor and cognitive processes. They represent an object of major interest in the study of excitable networks, with analysis conducted in both neural field models and spiking neuronal networks. The latter class exposes the single-neuron dynamics directly, all

Saad Hamid, José Moran, Luca Mungo, Arnau Quera-Bofarull

Modelling how shocks propagate in supply chains is an increasingly important challenge in economics. Its relevance has been highlighted in recent years by events such as Covid-19 and the Russian invasion of Ukraine. Agent-based models (ABMs) are a promising approach for this problem. However, calibrating them is hard. We show empirically that it is possible

Pablo A. Ferrari, Stefano Olla

A Poisson line process is a random set of straight lines contained in the plane, as the image of the map $(x,v)\mapsto (x+vt)_{t\in\mathbb{R}}$, for each point $(x,v)$ of a Poisson process in the space-velocity plane. By associating a step with each line of the process, a random surface called multitime walk field is obtained. The diffusive rescaling of the

Mengqi Guo, Bo Xu, Yanyan Li, Gim Hee Lee

Novel view synthesis from monocular videos of dynamic scenes with unknown camera poses remains a fundamental challenge in computer vision and graphics. While recent advances in 3D representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown promising results for static scenes, they struggle with dynamic content and typica

Dhrumil Bhatt, Vidushi Kumar

The expansion of satellite-based quantum networks requires adaptive routing mechanisms that can sustain entanglement under dynamic orbital and atmospheric conditions. Conventional schemes, often tailored to static or idealised topologies, fail to capture the combined effects of orbital motion, fading, and trust variability in inter-satellite links. This work

Jean-Marco Alameddine, Felix Sattler, Maurice Stephan, Sarah Barnes

Muon scattering tomography is a well-established, non-invasive imaging technique using cosmic-ray muons. Simple algorithms, such as PoCA (Point of Closest Approach), are often utilized to reconstruct the volume of interest from the observed muon tracks. However, it is preferable to apply more advanced reconstruction algorithms to efficiently use the sparse m

João Henrique Andrade, Azahara DelaTorre, João Marcos do Ò, Jesse Ratzkin

The Delaunay metrics form a family of conformally flat, constant fractional Q-curvature metrics on a twice-punctured sphere. They are all (after a M\"obius transformation) rotationally symmetric and periodic, and admit several elegant variational descriptions. We prove that, when s is close to but less than 1, any complete, conformally flat constant Q-curvat

G. A. Lombardi, L. O. Kutelak, M. M. Piva, V. E. S. Frehse

Tunable electronic properties in magnetic materials lead to novel physical phenomena that have the potential to be exploited in the design of new spintronic devices. Here, we report the effect of uniaxial stress on the anomalous Hall effect (AHE) in the hexagonal frustrated antiferromagnetic Heusler compound Mn3Ge. Our x-ray diffraction results show that the

Pietro Caputo, Mario Morellini

We introduce and analyze a nonlinear exchange dynamics for Ising spin systems with arbitrary interactions. The evolution is governed by a quadratic Boltzmann-type equation that conserves the mean magnetization. Collisions are encoded through a spin-exchange kernel chosen so that the dynamics converge to the Ising model with the prescribed interaction and mea

Kousuke Nakano, Stefano Battaglia, Jürg Hutter

Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schr{\"o}dinger equation, suitable for modern many-core supercomputer architectures. Despite its potential, one of the major drawbacks that still hinders QMC applicatio

Jinghui Pi, Xingli Li, Yangqian Yan

Point-gap topology, characterized by spectral winding numbers, is crucial to non-Hermitian topological phases and dramatically alters real-time dynamics. In this paper, we study the evolution of quantum particles in dissipative systems with imaginary gap closing, using the saddle-point approximation method. For trivial point-gap systems, imaginary gap-closin

Jiang Lin, Xinyu Chen, Song Wu, Zhiqiu Zhang

Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining, limiting flexibility and generalization. Inversion-based approaches offer stronger alignment but incur high inference cost due to dual-path denoising. We present FreeControl, a t

Wenchong Chen, Xiao-Chuan Liu, Xu Yang

In this work, we study the color discrepancy of spanning trees in random graphs. We show that for the Erd\H{o}s-R\'enyi random graph $G(n,p)$ with $p$ above the connectivity threshold, the following holds with high probability: in every 2-edge-coloring of the graph, there exists a spanning tree with a linear number of leaves such that one color class contain

Chuchu Chen, Xinyu Chen, Jialin Hong

The law of the iterated logarithm (LIL) for the time-homogeneous Markov process with a unique invariant measure characterizes the almost sure maximum possible fluctuation of time averages around the ergodic limit. Whether a numerical approximation can preserve this asymptotic pathwise behavior remains an open problem. In this work, we give a positive answer

Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis

Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for large-scale or online applications. We propose a neural-operator framework for surrogate modeling of power system components, using Deep Operator Networks (DeepONets) to learn mapp

Varun Manjunath, Pranav Ramesh, Gopalakrishnan Srinivasan

NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to minimize energy-delay product on sparse edge workloads with competitive accuracy. The design extends integer-exact QCFS ANN-SNN conversion from layers to independent columns. It unifies INT8 storage with on-the-fly spike generation using an offline cost model t

Filipe C. Thewes, Yicheng Qiang, Oliver W. Paulin, David Zwicker

Phase separation in complex systems is a ubiquitous phenomenon. While simple theories predict coarsening until only macroscopically large phases remain, concrete models often exhibit patterns with finite length scales. To unify such models, we here propose a general field-theoretic model that combines phase separation with non-local interactions. Our analysi

Carlos Eduardo S. P. Corsino, Hermann Freire

We provide here a study of some competing ordering tendencies exhibited by the exactly solvable 2D Hatsugai-Kohmoto (HK) model on a square lattice. To this end, we investigate the interplay between superconductivity, charge-density wave (CDW) and pair-density wave (PDW) orders as a function of interaction, doping parameter, magnetic field, and uniaxial strai

Prakash Mudholkar, Chiranjeevi Vanarasa, Indranil Chakrabarty, Srinathan Kannan

The problem of revocation of quantum states after sharing is interesting and we ask: Is it possible for a dealer to revoke the state once shared, before the reconstruction process? Additional resources like bell states are used to help the dealer to get back the state. In a three-party scenario, we show an independent way to revoke, if, for any reason, the d

Muneer Ahmad

This study presents a comprehensive scientometric analysis of research productivity on Coronary Artery Disease (CAD) among the BRICS countries, Brazil, Russia, India, China, and South Africa, using data retrieved from the Web of Science database for the period 1990 to 2019. A total of 50,036 records were analyzed to assess publication growth trends, authorsh

André Peter Kelm, Max Braeschke, Emre Gülsoylu, Simone Frintrop

This paper presents Walk the Lines 2 (WtL2), a unique contour tracking algorithm specifically adapted for detailed segmentation of infrared (IR) ships and various objects in RGB.1 This extends the original Walk the Lines (WtL) [12], which focused solely on detailed ship segmentation in color. These innovative WtLs can replace the standard non-maximum suppres

Jorge Vázquez-Pérez, Daniel Expósito-Patiño, Marta Losada, Álvaro Carballido

The challenge of scaling quantum computers to gain computational power is expected to lead to architectures with multiple connected quantum processing units (QPUs), commonly referred to as Distributed Quantum Computing (DQC). In parallel, there is a growing momentum toward treating quantum computers as accelerators, integrating them into the heterogeneous ar

Li-Shuo Liu, Kai Shao, Hai-Dong Li, Xiangang Wan

Altermagnets hold great potential for spintronic applications, yet their intrinsic spin dynamics and associated transport properties remain largely unexplored. Here, we investigate spin-resolved quantum transport in a multi-terminal setup based on a $d$-wave altermagnet. It is found that the altermagnetic spin splitting in momentum space induces an interesti

Ryuji Hashimoto, Ryosuke Takata, Masahiro Suzuki, Yuki Tanaka

Agent-based models help explain stock price dynamics as emergent phenomena driven by interacting investors. In this modeling tradition, investor behavior has typically been captured by two distinct mechanisms -- learning and heterogeneous preferences -- which have been explored as separate paradigms in prior studies. However, the impact of their joint modeli

Huimin Hu, Michael Pradel

During software evolution, developers commonly face the problem of mapping a specific code region from one commit to another. For example, they may want to determine how the condition of an if-statement, a specific line in a configuration file, or the definition of a function changes. We call this the code mapping problem. Existing techniques, such as git di

Andrea Bedin, Joerg Widmer, Melanny Davila, Marco Canil

Localization is a key feature of future Sixth Generation (6G) net-works with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, renderin

Lars C. Bannow, Jan H. Thiede, Michał Ogryzek, Dmitry K. Efimov

We present a (1+1)-dimensional quantum model designed to describe nonsequential double ionization (NSDI) in homonuclear diatomic molecules exposed to strong linearly polarized laser fields. Extending the restricted-geometry framework previously developed for atomic systems by Eckhardt and Sacha, our approach captures key features of NSDI, including the chara

Benoît Zumer, Florent Daem, Alexandre Matzkin

We investigate wavepacket dynamics for a relativistic particle in a box evolving according to the relativistic Schr\"odinger (also known as the Salpeter) equation. We derive the solutions for an infinite well -- which contrary to the standard relativistic wave equations (such as the Klein-Gordon or Dirac equations) -- are well defined, and use these solution

Yichen Zhu, Feifei Feng

Robots operating in complex and uncertain environments face considerable challenges. Advanced robotic systems often rely on extensive datasets to learn manipulation tasks. In contrast, when humans are faced with unfamiliar tasks, such as assembling a chair, a common approach is to learn by watching video demonstrations. In this paper, we propose a novel meth

Guido Kings, Johannes Sprang

In this survey, we review the known results on the algebraicity of critical values of Hecke $L$-functions and explain the new developments in \cite{Kings-Sprang}.

Cansu Utku, Garrette Pauley Paran, Thomas-C. Jagau

Electronic resonances are metastable states with finite lifetimes, encountered in processes such as photodetachment, electron transmission, and Auger decay. Resonances appear in Hermitian quantum mechanics as increased density of states in the continuum rather than as discrete energy levels. To describe resonances accurately, including their coupling to the

Thomas Scarinzi, Davide Orsucci, Marco Ferrari, Luca Barletta

Quantum key distribution (QKD) is a cryptographic solution that leverages the properties of quantum mechanics to be resistant and secure even against an attacker with unlimited computational power. Satellite-based links are important in QKD because they can reach distances that the best fiber systems cannot. However, links between satellites in low Earth orb

Yunxin Li, Fan Liu, Haoqiu Xiong, Zhenkun Wang

Integrated Sensing and Communication (ISAC) has emerged as a promising solution in addressing the challenges of high-mobility scenarios in 5G NR Vehicle-to-Infrastructure (V2I) communications. This paper proposes a novel sensing-assisted handover framework that leverages ISAC capabilities to enable precise beamforming and proactive handover decisions. Two se

V. V. Ryazanov

A new approach to describing aerosol behavior is proposed. Boundary functionals of random process theory are applied to describe the behavior of aerosol concentrations during coagulation. It is shown that considering the first-passage time of a given aerosol concentration level corresponds to experimental results for the time dependence of aerosol concentrat

Zhibo Dong, Yong Huang, Shubao Sun, Wentao Cui

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using multiple network flows. To transcend this limitation, we propos

Yu Mao, Mohamed Saidi

The goal of this paper is to develop a group-theoretic algorithm, to reconstruct a number field (together with its maximal m-step solvable ex- tension for some positive integer m \geq 3) from the maximal m+9-step solv- able quotient of its absolute Galois group. If K is an imaginary quadratic field or Q, we establish a group-theoretic reconstruction algorith

Kinga Jasiewicz, Paweł Wójcik, Michał Nowak, Michał Zegrodnik

We study the interplay between altermagnetism and unconventional superconductivity for the case of two-dimensional square- and triangular-lattice systems. Our approach is based on an effective single particle Hamiltonian which mimics the alternating spin splitting characteristic for the $d$-$wave$ and $i$-$wave$ altermagnetic state. By supplementing the mode

Ayaki Sunaga, Timo Fleig

Diatomic molecules with an energetically low-lying $^3 \Delta_1$ state are attractive platforms to detect new physics beyond the Standard Model, such as parity- and time-reversal violating phenomena. One of the advantages of using a $^3 \Delta_1$ state is its tiny $\Lambda$-splitting due to the coupling between the electronic and rotational angular momenta,

Hayato Wakai, Shintaro Ishiwata, Atsuto Seko

Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global structure searches are performed for 11 bismuth-based binary systems, including Na-Bi, Ca-Bi, and Eu-Bi, under pressures ranging from 0 to 20 GPa, employing polynomial MLPs developed

Kamil Ciosek, Nicolò Felicioni, Juan Elenter, Ehsan Imani

We study whether otherwise-idle inference resources could reduce the scarce-GPU cost of training. Our analysis uses a simulated compute ledger in which fleet work is billed at a fraction of a scarce-GPU forward; all experiments run on a regular GPU. Our algorithm predicts gradients with a reduced-precision, inference-style reverse-mode program and combines m

Akash Das, Satya Ranjan Nayak, B. K. Singh

In this study, we employ a conventional deep neural network (NN) framework integrated with physics-based constraints to predict charged hadron multiplicity ($N_{\text{ch}}$) in heavy-ion collisions. The goal is to assess the performance of a purely data-driven deep neural network in comparison to a physics-informed neural network (PINN). To accomplish this,

Adrián Campazas-Vega, Claudia Álvarez-Aparicio, David Sobrín-Hidalgo, Laura Inyesto-Alonso

The deployment of autonomous systems has experienced remarkable growth in recent years, driven by their integration into sectors such as industry, medicine, logistics, and domestic environments. This expansion is accompanied by a series of security issues that entail significant risks due to the critical nature of autonomous systems, especially those operati

Cong-Thanh Do, Rama Doddipatla, Kate Knill

Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Distillation (KD) to transfer reasoning capability from a larger LLM to a smaller one. This paper examines the role of CoT in distilling the reasoning capability from larger LLMs to s

Gregory Verghese, Anthony Baptista, Chima Eke, Holly Rafique

The integration of artificial intelligence (AI) into pathology is advancing precision medicine by improving diagnosis, treatment planning, and patient outcomes. Digitised whole-slide images (WSIs) capture rich spatial and morphological information vital for understanding disease biology, yet their gigapixel scale and variability pose major challenges for sta

Johan Schubert, Patrik Hansen, Pontus Hörling, Ronnie Johansson

In this paper, we propose a methodology designed to support decision-making during the execution phase of military ground combat operations, with a focus on one's actions. This methodology generates and evaluates recommendations for various courses of action for a mechanized battalion, commencing with an initial set assessed by their anticipated outcomes. It

L. Amard, S. Mathis

The Tayler-Spruit dynamo (TSD) is able to generate a small-scale magnetic field in the differentially rotating stably stratified layers of stars and was recently observed in numerical simulations. In parallel, the propagation of internal gravity waves in stars can be modified in the presence of a magnetic field. Here we first want to estimate the interaction

Sourayan Banerjee, Amit Kuber

This paper is a sequel to a paper by the same authors, where they defined $K$-groups of model-theoretic structures, and computed $K_1$ of free modules over PIDs. In this paper, we compute $K_1$ of a right $M_q(R)$-module $M$, where $R$ is a division ring, $q\geq1$, and $|M_q(R)|\neq 2$. As a consequence, we obtain a (weak) Morita invariance $K_1(R_R)\cong K_

Ragini Gupta, Naman Raina, Bo Chen, Li Chen

Modern IoT deployments for environmental sensing produce high volume spatiotemporal data to support downstream tasks such as forecasting, typically powered by machine learning models. While existing filtering and strategic deployment techniques optimize collected data volume at the edge, they overlook how variations in sampling frequencies and spatial covera

Martin Siron, Inel Djafar, Ali Ramlaoui, Etienne du Fayette

The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability. Robust standardization and de-duplication methods are needed to address these issues and streamline materials research. We present LeMat-Bulk, a unified dataset combining Materials

Shiyuan Li, Baojiang Yan, Yihan Xie, Yixin Tong

Reconfigurable Intelligent Surface (RIS)-based direct modulation communication systems have garnered significant attention due to their low cost, low power consumption, and baseband-less characteristics. However, these systems face challenges such as the random time-varying coding state of the RIS and the difficulty in implementing beamforming in direct modu