November 2025 arXiv papers — page 151

Xianshuai Shi, Jianfeng Zhu, Leibo Liu

This paper presents a gate-level Boolean evolutionary geometric attention neural network that models images as Boolean fields governed by logic gates. Each pixel is a Boolean variable (0 or 1) embedded on a two-dimensional geometric manifold (for example, a discrete toroidal lattice), which defines adjacency and information propagation among pixels. The netw

Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis, Schahram Dustdar

Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL does not natively consider infrastructure efficiency, a critical concern for systems operating in resource-constrained environments. Several Reinforcement Learning (RL) based solutions offer improved client sele

A. Chmeruk, D. Jones, R. Balducci, J. Ebad-Allah

The ability to control the magnetic state provides a powerful means to tune the underlying band topology, enabling transitions between distinct electronic phases and the emergence of novel quantum phenomena. In this work, we address the evolution of ferromagnetic state upon applying external pressures up to 10.8~GPa using a combined experimental and theoreti

Luoping Cui, Hanqing Liu, Mingjie Liu, Endian Lin

Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (<= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the firs

Kushal Tatariya, Wessel Poelman, Miryam de Lhoneux

Language model architectures are predominantly first created for English and subsequently applied to other languages. It is an open question whether this architectural bias leads to degraded performance for languages that are structurally different from English. We examine one specific architectural choice: positional encodings, through the lens of the trade

Qiuli Li, Fuliang Lu, Heping Zhang

A graph of order $n$ is said to be \emph{$k$-factor-critical} ($0\leq k <n$) if the removal of any $k$ vertices results in a graph with a perfect matching. A $k$-factor-critical graph $G$ is \emph{minimal} if $G-e$ is not $k$-factor-critical for any edge $e$ in $G$. Favaron and Shi posed the conjecture that every minimal $k$-factor-critical graph is of minim

Returaj Burnwal, Nirav Pravinbhai Bhatt, Balaraman Ravindran

In this work, we study the problem of offline safe imitation learning (IL). In many real-world settings, online interactions can be risky, and accurately specifying the reward and the safety cost information at each timestep can be difficult. However, it is often feasible to collect trajectories reflecting undesirable or risky behavior, implicitly conveying

Zhuoheng Ran, Chong Wu, Renjie Xu, Maolin Che

The success of neural networks such as convolutional neural networks (CNNs) has been largely attributed to their effective and widespread deployment on customised computing platforms, including field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). In the current era, Transformer-based architectures underpin the majority

Mustafa Alfarhan, Fuqiang Chen, George Turkiyyah, David Keyes

Full Waveform Inversion (FWI) is a powerful technique for estimating high-resolution subsurface velocity models by minimizing the discrepancy between modeled and observed seismic data. However, the oscillatory nature of seismic waveforms makes point-wise discrepancy measures highly prone to cycle-skipping, especially when the initial velocity model is inadeq

Lixu Sun, Nurmemet Yolwas, Wushour Silamu

Scene Text Recognition (STR) remains challenging due to real-world complexities, where decoupled visual-linguistic optimization in existing frameworks amplifies error propagation through cross-modal misalignment. Visual encoders exhibit attention bias toward background distractors, while decoders suffer from spatial misalignment when parsing geometrically de

Maryam Zolnoori, Hossein Azadmaleki, Yasaman Haghbin, Ali Zolnour

Alzheimer's disease and related dementias (ADRD) affect one in five adults over 60, yet more than half of individuals with cognitive decline remain undiagnosed. Speech-based assessments show promise for early detection, as phonetic motor planning deficits alter acoustic features (e.g., pitch, tone), while memory and language impairments lead to syntactic and

Jingcheng Qi, Giuliana Materzanini, Gian-Marco Rignanese, Maria Clelia Righi

Friction accounts for up to 30% of global energy consumption, underscoring the urgent need for superlubricity in advanced materials. Two-dimensional (2D) electrides are layered materials with cationic layers separated by 2D confined electrons that act as anions. This study reveals the unique frictional properties of these compounds and the underlying mechani

Mehmet Batuhan Duman, Alejandro Carnero, Cristian Martín, Daniel Garrido

Foam formation in Wastewater Treatment Plants (WTPs) is a major challenge that can reduce treatment efficiency and increase costs. The ability to automatically examine changes in real-time with respect to the percentage of foam can be of great benefit to the plant. However, large amounts of labeled data are required to train standard Machine Learning (ML) mo

Giovanni Romano, Raffaele Barretta

Shortly after the middle of the past century, a comprehensive presentation of Continuum Mechanics was written under supervision of Clifford Ambrose Truesdell III in two volumes of Siegfried Fluegge's Handbuch der Physik, a first in 1960 with Richard Toupin on The Classical Field Theories (the monster), including an Appendix on Tensor Analysis by Jerald LaVer

Dmitrii Tarasov, Elizaveta Goncharova, Kuznetsov Andrey

This work investigates context compression for Large Language Models (LLMs) using learned compression tokens to reduce the memory and computational demands of processing long sequences. We demonstrate that pre-trained LLMs can be fine-tuned to compress their context by factors of 2x to 8x without significant performance degradation, as evaluated on both shor

Weiye Li, Wenyi Tang

Source Code Model learn the proper embeddings from source codes, demonstrating significant success in various software engineering or security tasks. The recent explosive development of LLM extends the family of SCMs,bringing LLMs for code that revolutionize development workflows. Investigating different kinds of SCM vulnerability is the cornerstone for the

Raquel Montero, Natalia Moskvina, Paolo Morosi, Tamara Serrano

Quantification has been proven to be a particularly difficult linguistic phenomenon for (Multimodal) Large Language Models (MLLMs). However, given that quantification interfaces with the logic, pragmatic, and numerical domains, the exact reasons for the poor performance are still unclear. This paper looks at three key features of human quantification shared

Askin Altinoklu, Leila Musavian

This paper presents an optimal power splitting and beamforming design for co-located simultaneous wireless information and power transfer (SWIPT) users in Dynamic Metasurface Antenna (DMA)-aided multiuser multiple-input single-output (MISO) systems. The objective is to minimize transmit power while meeting users signal-to-interference-plus-noise ratio (SINR)

Alin Morariu, Jess Bridgen, Chris Jewell

gemlib is a Python library for defining, simulating, and calibrating Markov state-transition models. Stochastic models are often computationally intensive, making them impractical to use in pandemic response efforts despite their favourable interpretations compared to their deterministic counterparts. gemlib decomposes state-transition models into three key

Furio Surfaro, Peixuan Liang, Hadra Banks, Fajun Zhang

The ion-activated patchy particle model is an important theoretical framework to investigate the phase behaviour of globular proteins in the presence of multivalent ions. In this work, we study and highlight the influence of patch heterogeneity on the extension, appearance and disappearance of the liquid-liquid coexistence region of the phase diagram. We dem

V. A. Yerokhin, B. Sikora, Z. Harman, C. H. Keitel

The two-loop electron self-energy correction is one of the most problematic QED effects and, for a long time, was the dominant source of uncertainty in the theoretical prediction of the bound-electron $g$ factor in hydrogen-like ions. A major breakthrough was recently achieved in [B. Sikora et al. Phys. Rev. Lett. 134, 123001 (2025)], where this effect was c

Nihal Z. Miaji, Adi Singhania, Matthias E. Goh, Callista Le

Cartograms depict geographic regions with areas proportional to quantitative data. However, when created using density-equalizing map projections, cartograms may exhibit invalid topologies if boundary polygons are drawn using only a finite set of vertices connected by straight lines. Here we introduce a method for topology-preserving line densification that

Jorge Paz-Ruza, João Gama, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas

Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green AI still lack standardized, model-agnostic evaluation protocols. Recently, sustainability auditing pipelines for ML and usual practices by researchers show three main pitfalls: 1)

Arnab Maity, Manasa, Pavan Kumar C, Raghavendra Ramachandra

Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features fro

Tengfei Bai, Pengfei Guo, Jingshi Xu

We introduce the mixed Bourgain-Morrey spaces and obtain their preduals. The boundedness of Hardy-Littlewood maximal operator, iterated maximal operator, fractional integral operator, singular integral operator on these spaces is proved. The Littlewood-Paley theory for mixed Bourgain-Morrey spaces and their preduals are established. As applications, we consi

Georg Rottenwalter, Marcel Tilly, Christian Bielenberg, Katharina Obermeier

Machine learning has significant potential for optimizing various industrial processes. However, data acquisition remains a major challenge as it is both time-consuming and costly. Synthetic data offers a promising solution to augment insufficient data sets and improve the robustness of machine learning models. In this paper, we investigate the feasibility o

Alexander D. Kolesnik

We present a diffusion model of surface soil pollution from a stationary source based on the symmetric stochastic motion at finite speed in the plane $\Bbb R^2$, also called the planar Markov random flight, whose lifetime is a random variable with given distribution. We consider a heavy-particle model, in which the lifetime is supposed to be an exponentially

Taro Mashimo, Masashi Abe, Athanasios Laliotis, Satoshi Tojo

Cold atomic ensembles offer precise tools for probing near-field interactions, yet experimental data linking atom dynamics to surface-induced forces remains limited. This study investigated the interaction between atoms and a dielectric surface using an atomic fountain measurement technique, in which cold rubidium atoms were released from a moving optical di

Himashi Peiris, Sizhe Wang, Gary Egan, Mehrtash Harandi

Prompt-driven vision foundation models, such as the Segment Anything Model, have recently demonstrated remarkable adaptability in computer vision. However, their direct application to medical imaging remains challenging due to heterogeneous tissue structures, imaging artefacts, and low-contrast boundaries, particularly in tumours and cancer primaries leading

Manasa, Sushrut Patwardhan, Narayan Vetrekar, Pavan Kumar

Face recognition systems are increasingly deployed across a wide range of applications, including smartphone authentication, access control, and border security. However, these systems remain vulnerable to presentation attacks (PAs), which can significantly compromise their reliability. In this work, we introduce a new dataset focused on a novel and realisti

Tian Qiu, Ruidong Li, Cunhua Pan, Taihaon Zhang

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coup

Pierre Del Moral, Mathieu Gerber

This paper develops a novel operator theoretic framework to study the contraction properties of Markov semigroups with respect to a general class of Kantorovich semi-distances, which notably includes Wasserstein distances. The rather simple contraction cost framework developed in this article, which combines standard Lyapunov techniques with local contractio

Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert

This paper examines how science fiction destabilises ontological categories by measuring conceptual permeability across the terms human, animal, and machine using masked language modelling (MLM). Drawing on corpora of science fiction (Gollancz SF Masterworks) and general fiction (NovelTM), we operationalise Darko Suvin's theory of estrangement as computation

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable

Chao Zhou, Changsheng You, Cong Zhou, Hai Lin

In this paper, we propose to employ a modular-based movable extremely large-scale array (XL-array) at Alice for enhancing covert communication performance. Compared with existing work that mostly considered either far-field or near-field covert communications, we consider in this paper a more general and practical mixed-field scenario, where multiple Bobs ar

Antoine Thibault Vié, Leonid Fridman, Roberto Galeazzi, Dimitrios Papageorgiou

This article presents an adaptive Super-Twisting Sliding Mode Control framework for uncertain first-order systems, with rate-bounded perturbations, where the bound is constant but unknown. Positive definite barrier functions, when used in self-tuning super-twisting controllers may introduce some conservatism in relation to initial estimations of the perturba

Mislav Has, Tao Xiong, Fehmi Ben Abdesslem, Mario Kušek

The increasing heterogeneity of hardware and software in the Internet of Things (IoT) poses a major challenge for the portability, maintainability and deployment of software on devices with limited resources. WebAssembly (WASM), originally designed for the web, is increasingly recognized as a portable, secure and efficient runtime environment that can overco

Mamoon Safadi, Nir Kuchuk, Ohad Lib, Yaron Bromberg

Scattering of non-classical light is enabling new ways to study and control photon transport. However, advances in this field often rely on simplifying assumptions regarding the quantum light's generation and its source. In this work, we relax some of these assumptions and probe the behavior of entangled photon pairs passing through a disordered layer after

Zhixiong Zhao, Fangxin Liu, Junjie Wang, Chenyang Guan

The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deployment on end-user devices. In this paper, we revisit the challenge of extreme LLM compression -- targeting ultra-low-bit quantization for both activations and weights -- from a Fourier frequency domain perspective. We p

Shohei Okawa, Yuji Omura

We investigate the potential of leptonic meson decays $M \to \ell \bar\nu_\ell$, where $M$ is a pseudo-scalar meson, as a probe of neutrino portal dark matter. The model of our focus features a neutral fermion $\psi$ and scalar $\phi$, which are coupled predominantly to neutrinos in the form ${\cal L} \supset \lambda\,\overline\nu_L\,\phi\,\psi_R$. This inte

Tony Lindeberg

This paper gives an in-depth theoretical analysis of the direction and speed selectivity properties of idealized models of the spatio-temporal receptive fields of simple cells and complex cells, based on the generalized Gaussian derivative model for visual receptive fields. According to this theory, the receptive fields are modelled as velocity-adapted affin

Przemysław Koprowski

We study the class of polynomials that map a local field (i.e., the completion of a number field at a non-Archimedean place) into the subset of its $p$-th powers, where $p$ is the residue characteristic of the field in question. We present a characterization of such polynomials and show that this class is always much broader than the class of $p$-th powers o

Marija Bliznac Trebješanin, Pavao Radić

We study the extensibility of $D(4)$-pairs $\{a,b\}$, where $b = ka$ and $k \in \{7,8,10,11,12,13\}$. Firstly, we show that it can be extended to a $D(4)$-triple with an element c, which is a member of a family of positive integers depending on a. Then, we prove that such a triple has a unique extension to a $D(4)$-quadruple.

Sabrina Patania, Luca Annese, Anita Pellegrini, Silvia Serino

Recent advances in Large Language Models (LLMs) and multimodal foundation models have significantly broadened their application in robotics and collaborative systems. However, effective multi-agent interaction necessitates robust perspective-taking capabilities, enabling models to interpret both physical and epistemic viewpoints. Current training paradigms o

Dheeraj Narasimha, Nicolas Gast

We consider a general infinite horizon Heterogeneous Restless multi-armed Bandit (RMAB). Heterogeneity is a fundamental problem for many real-world systems largely because it resists many concentration arguments. In this paper, we assume that each of the $N$ arms can have different model parameters. Model predictive control is a well-known control strategy t

Tom R. Rieckmann, Stefan Scheel, A. Douglas K. Plato

Current experimental quantum computing devices are limited by noise, mainly originating from entangling gates. If an efficient gate sequence for an operation is unknown, one often employs layered parameterized quantum circuits, especially hardware-efficient ans\"atze, with fixed entangling layer structures. We demonstrate a reinforcement learning algorithm t

Di Ben, Bing-Song Zou

This study estimates the contributions of hidden-strangeness hadronic molecular states ($N(2080)3/2^-$, $N(2270)3/2^-$) to $\phi$ production and hidden-charm $P_c$ states to $J/\psi$ production in $pp$ collisions. The calculated cross-sections reach $\sim 10~\mu b$ for $pp\to pp\phi$ and $\sim 0.1~nb$ for $pp\to pp J/\psi$ at energies $\sim 2$~GeV above thre

Kaicheng Zhang, David N. Reynolds, Piero Deidda, Francesco Tudisco

Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. In this work, we introduce the Complex-Valued Stuart-Landau Graph Neural Network (SLGNN), a novel architecture grounded in Stuart-Landau oscillator dynamics. Stuart-Landau oscillato

Julian Irigoyen, Arthur Söhler, Andreas Søeborg Kirkedal

We challenge the conventional view of neural network pruning as solely a compression technique, demonstrating that one-shot magnitude pruning serves as a powerful implicit regularizer for ASR. Using Whisper-small, we combine gradient- and Fisher-based sensitivity diagnostics with targeted, component-wise pruning. This reveals architectural asymmetries: decod

Robert Ganian, Marlene Gründel, Simon Wietheger

Pearl's Causal Hierarchy (PCH) is a central framework for reasoning about probabilistic, interventional, and counterfactual statements, yet the satisfiability problem for PCH formulas is computationally intractable in almost all classical settings. We revisit this challenge through the lens of parameterized complexity and identify the first gateways to tract

Wassim Kabbani, Kiran Raja, Raghavendra Ramachandra, Christoph Busch

Face morphing attacks threaten the integrity of biometric identity systems by enabling multiple individuals to share a single identity. To develop and evaluate effective morphing attack detection (MAD) systems, we need access to high-quality, realistic morphed images that reflect the challenges posed in real-world scenarios. However, existing morph generatio

Giacomo Cordoni, Sajay Sunny Mathew, Christoph Federrath

The angular momentum evolution of stars is crucial for understanding the formation and evolution of stars and star clusters. Using high-resolution magnetohydrodynamical (MHD) simulations of star formation in clouds with different physical properties, we study the initial distribution of stellar rotation periods in young clusters. We compare these results wit

Linda M. Haines

This paper is about the use of the Wallenius noncentral hypergeometric distribution for analysing contingency tables with two or more groups and two categories and with row margins and sample size, that is both margins, fixed. The parameters of the distribution are taken to be weights which are positive and sum to one and are thus defined on a regular simple

Aditi Singhania, Krutik Malani, Riddhi Dhawan, Arushi Jain

Evaluating identity preservation in generative models remains a critical yet unresolved challenge. Existing metrics rely on global embeddings or coarse VLM prompting, failing to capture fine-grained identity changes and providing limited diagnostic insight. We introduce Beyond the Pixels, a hierarchical evaluation framework that decomposes identity assessmen

Muthukumar Pandaram, Jakob Hollenstein, David Drexel, Samuele Tosatto

The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs of such dynamics models are sparsely connected, with each of the future state variables depending only on a small subset of the current state variables, and that learning may there

Abdullah Muhammad Moosa, Nusrat Sultana, Mahdi Muhammad Moosa, Md. Miraiz Hossain

This research presents a comprehensive investigation into Bangla authorship attribution, introducing a new balanced benchmark corpus BARD10 (Bangla Authorship Recognition Dataset of 10 authors) and systematically analyzing the impact of stop-word removal across classical and deep learning models to uncover the stylistic significance of Bangla stop-words. BAR

Yuequn Li, Fei Guo

This paper investigates a weakly coupled system of semilinear Euler-Poisson-Darboux-Tricomi equations (EPDTS) with power-type nonlinear terms. More precisely, in the case where the damping terms dominate over the mass terms, the critical curve in the $p-q$ plane that delineates the threshold between global existence and blow-up for the EPDTS is given by \beg

William Hu, Drew Wadsworth, Sean Siddens, Stanley Winata

AMD GPUs offer state-of-the-art compute and memory bandwidth; however, peak performance AMD kernels are written in raw assembly. To address the difficulty of mapping AI algorithms to hardware, recent work proposes C++ embedded and PyTorch-inspired domain-specific languages like ThunderKittens (TK) to simplify high performance AI kernel development on NVIDIA

Stella C. Dong

This paper develops a prudential framework for assessing the reliability of large language models (LLMs) in reinsurance. A five-pillar architecture--governance, data lineage, assurance, resilience, and regulatory alignment--translates supervisory expectations from Solvency II, SR 11-7, and guidance from EIOPA (2025), NAIC (2023), and IAIS (2024) into measura

Ziyu Fan, Zhijian Huang, Yahan Li, Xiaowen Hu

Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property

Jiacheng Wu, Ruiqi Zhang, Jie Chen

Reconstructing human avatars using generative priors is essential for achieving versatile and realistic avatar models. Traditional approaches often rely on volumetric representations guided by generative models, but these methods require extensive volumetric rendering queries, leading to slow training. Alternatively, surface-based representations offer faste

Linus Heck, Filip Macák, Milan Češka, Sebastian Junges

The ability to compute reward-optimal policies for given and known finite Markov decision processes (MDPs) underpins a variety of applications across planning, controller synthesis, and verification. However, we often want policies (1) to be robust, i.e., they perform well on perturbations of the MDP and (2) to satisfy additional structural constraints regar

Jinbo Li, Peng Liu, Long Chen, Witold Pedrycz

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalabilit

Yoshihito Kuno, Ikuo Ichinose

Some of lattice-gauge-theory models, in particular gauge-Higgs model (GHM), can be regarded and work as a subsystem code. This work studies the effect of local-gauge-symmetric decoherence on the GHM from the perspective of the subsystem code. We clarify the global phase diagram of the subsystem code. In particular, the decoherence induces an unconventional c

Cameron Braunstein, Mariya Toneva, Eddy Ilg

Latent diffusion models such as Stable Diffusion achieve state-of-the-art results on text-to-image generation tasks. However, the extent to which these models have a semantic understanding of the images they generate is not well understood. In this work, we investigate whether the internal representations used by these models during text-to-image generation

Clément Erignoux, Assaf Shapira, Marielle Simon

We consider, in any dimension, the constrained lattice gas introduced by Rossi et al., which is an exclusion process on a d-dimensional lattice following the additional constraint that only particles with at least one occupied neighbour can jump. In dimension d > 2, this model features self-organized criticality at some critical density of particles. Numeric

Nadav Merlis, Kyoungseok Jang, Nicolò Cesa-Bianchi

We study an online linear regression setting in which the observed feature vectors are corrupted by noise and the learner can pay to reduce the noise level. In practice, this may happen for several reasons: for example, because features can be measured more accurately using more expensive equipment, or because data providers can be incentivized to release le

Jinbo Li, Hesam Izakian, Witold Pedrycz, Iqbal Jamal

Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal and variable relatio

Ying Wang, Zhaodong Sun, Xu Cheng, Zuxian He

Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labeled signals for supervised training. To

Yuichi Goto, Hiroko Kato Solvang, Masanobu Taniguchi, Tone Falkenhaug

Assessing marine ecosystems is important for understanding the impacts of climate change and human activity, as well as for maintaining healthy oceans and ecosystems. In marine science, it is common for biologists and geologists to identify regional differences based on expert knowledge, frequently through data visualization. However, time series data collec

Loris Di Cairano, Matteo Gori, Reza Karimpour, Alexandre Tkatchenko

Interactions between objects can be classified as fundamental or emergent. Fundamental interactions are either extremely short-range or decay inversely with the separation distance, such as the Coulomb potential between charges or the gravitational attraction between masses. In contrast, emergent quantum van der Waals (vdW) and Casimir interactions decay con

P. A. Arrutia Sota, E. C. Cortes Garcia, V. A. Sansipersico

Slow resonant extraction from synchrotrons via radio-frequency knock-out is a well-established technique to deliver charged particle beams for various applications. In this contribution, we present explicit analytical expressions for calculating the number of particles slowly extracted over time, commonly referred to as spills. The proposed formulation enabl

José Martín-Roca, Daniel Escobar Ortiz, Chantal Valeriani, Horacio Serna

Collective motion is ubiquitous in active systems at all length and time scales. The mechanisms behind such collective motion usually are alignment interactions between active particles, effective alignment after collisions between agents or symmetry-breaking fluctuations induced by passive species in active suspensions. In this article, we introduce a new t

Cheng Yuan, Jiawei Shao, Xuelong Li

Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The widespread adoption of test-time scaling further intensifies the tension between model capability and resource consumption. However, a rigorous metric that accurately reflects an LLM'

Peisen Zhao, Ole Behrens, Maja Benning, Peter Fierlinger

We report on a laboratory search for ultralight dark photon dark matter using a single-site, two-sensor scalar magnetometer array. The experiment employs two scalar optically pumped magnetometers (OPMs) operated in a differential configuration to suppress common-mode noise and enhance sensitivity to spatially coherent dark photon fields. We analyze 10.5 hour

Bitap Raj Thakuria, Trishna Kalita, Manash Jyoti Sarmah, Himangshu Prabal Goswami

We introduce a cavity-coupled finite quantum system which can act as a quantum battery by harnessing noise induced coherences. We apply the methodology of full counting statistics to capture higher-order fluctuations of quanta exchange in the storage station. Together with the thermodynamic parameters, the fluctuations constitute a training platform for unsu

Mads Carlsen, Xiaohan Zeng, Haixing Fang, Moritz Frewein

In this study we present spatially resolved texture and orientation maps from a cube-shaped sample of Ti-6Al-4V alloy, reconstructed by means of texture tomography (TT). Unlike grain resolved 3DXRD techniques which require "spotty" diffraction patterns, TT can reconstruct local (voxelized) orientation distribution function (ODFs) from continuous diffraction

Aditi Singhania, Arushi Jain, Krutik Malani, Riddhi Dhawan

Subject-driven image generation aims to synthesize novel depictions of a specific subject across diverse contexts while preserving its core identity features. Achieving both strong identity consistency and high prompt diversity presents a fundamental trade-off. We propose a LoRA fine-tuned diffusion model employing a latent concatenation strategy, which join

Junxiao Han, Zheng Yu, Lingfeng Bao, Jiakun Liu

The widespread adoption of open-source software (OSS) has accelerated software innovation but also increased security risks due to the rapid propagation of vulnerabilities and silent patch releases. In recent years, large language models (LLMs) and LLM-based agents have demonstrated remarkable capabilities in various software engineering (SE) tasks, enabling

Stefan Albert Horstmann, Sandy Hong, Maziar Niazian, Cristiana Santos

Since the introduction of the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), software developers increasingly have to make privacy-related decisions during system design and implementation. However, past research showed that they often lack legal expertise and struggle with privacy-compliant development. To

Steven De Keninck, Martin Roelfs, Leo Dorst, David Eelbode

We revisit the geometric foundations of mesh representation through the lens of Plane-based Geometric Algebra (PGA), questioning its efficiency and expressiveness for discrete geometry. We find how $k$-simplices (vertices, edges, faces, ...) and $k$-complexes (point clouds, line complexes, meshes, ...) can be written compactly as joins of vertices and their

Michal Stratený, Georgios Lukes-Gerakopoulos, Ondřej Zelenka

The forthcoming space-based gravitational-wave observatory Laser Interferometer Space Antenna (LISA) should enable the detection of Extreme Mass Ratio Inspirals (EMRIs), in which a stellar-mass compact object gradually inspirals into a supermassive black hole while emitting gravitational waves. Modeling the waveforms of such systems is a challenging task, re

Nived Johny, Jonas Junker, Bernd Schulte, Dennis Wilken

We report the realization of an all-optical, tabletop effective-negative-mass oscillator (ENMO) scheme capable of canceling quantum noise when cascaded with an opto-mechanical sensor susceptible to (quantum) radiation pressure noise. Our coherent quantum noise cancellation (CQNC) scheme offers a broadband cancellation capability with a tunable, wavelength-fl

Ziyuan Gao

Medical image segmentation requires large annotated datasets, creating a significant bottleneck for clinical applications. While few-shot segmentation methods can learn from minimal examples, existing approaches demonstrate suboptimal performance in precise boundary delineation for medical images, particularly when anatomically similar regions appear without

Zhishen Sun, Guang Dai, Haishan Ye

LLMs demonstrate performance comparable to human abilities in complex tasks such as mathematical reasoning, but their robustness in mathematical reasoning under minor input perturbations still lacks systematic investigation. Existing methods generally suffer from limited scalability, weak semantic preservation, and high costs. Therefore, we propose MSCR, an

Yunqi Shi, Xi Lin, Zhiang Wang, Siyuan Xu

This work introduces the Re$^{\text{2}}$MaP method, which generates expert-quality macro placements through recursively prototyping and packing tree-based relocating. We first perform multi-level macro grouping and PPA-aware cell clustering to produce a unified connection matrix that captures both wirelength and dataflow among macros and clusters. Next, we u

Suparna Sarkar, Soumya Satpathi, Swapan K. Pati

We investigate localization transition in an open quasiperiodic ladder where the quasiperiodicity is described by the Aubry-Andr\'e-Harper model. While previous studies have shown that higher-order hopping or constrained quasiperiodic potentials can induce a mixed-phase zone in one dimension, we demonstrate that the dissipation can induce mixed phase zone in

Po-Chung Hsieh, Chin-Po Chen, Jeng-Lin Li, Ming-Ching Chang

Recent LLMs have demonstrated sophisticated problem-solving capabilities on various benchmarks through advanced reasoning algorithms. However, the key research question of identifying reasoning steps that balance complexity and computational efficiency remains unsolved. Recent research has increasingly drawn upon psychological theories to explore strategies

Jing Li, Shan He, Yueqing Li, Weiwei Wang

In this work, a microscopic effective nucleon-nucleon interaction based on the Dirac-Brueckner-Hartree-Fock $G$ matrix starting from a bare nucleon-nucleon interaction is used to explore the $\alpha$-decay half-lives of the nuclei near $N=Z$ line. Specifically, the $\alpha$-nucleus potential is constructed by doubly folding the effective nucleon-nucleon inte

Olaf Beyersdorff, Ilario Bonacina, Kaspar Kasche, Meena Mahajan

We introduce new semi-algebraic proof systems for Quantified Boolean Formulas (QBF) analogous to the propositional systems Nullstellensatz, Sherali-Adams and Sum-of-Squares. We transfer to this setting techniques both from the QBF literature (strategy extraction) and from propositional proof complexity (size-degree relations and pseudo-expectation). We obtai

Weixu Wang, Xiaobo Zhou, Xin Qiao, Lei Wang

Long-term Time Series Forecasting is crucial across numerous critical domains, yet its accuracy remains fundamentally constrained by the receptive field bottleneck in existing models. Mainstream Transformer- and Multi-layer Perceptron (MLP)-based methods mainly rely on finite look-back windows, limiting their ability to model long-term dependencies and hurti

Jer Pelhan, Alan Lukezic, Matej Kristan

Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple sizes is to merge backbone features of different resolutions. Furthermore, to enable small object detection in densely populated regions, the input image is commonly upsampled and t

Marcel M. Popescu, Julia de León, George Pantelimon Prodan, Michael Küppers

The HyperScout-H (HS-H) instrument is one of the payloads aboard ESA's Hera spacecraft. Hera is a planetary defence mission that aims to provide a detailed characterization of the near-Earth binary asteroid (65803) Didymos-Dimorphos after the NASA/DART mission impact. HS-H is a versatile dual-use payload, functioning as a hyperspectral imager that captures b

Aya Elgebaly, Nikolaos Delopoulos, Juliane Hörner-Rieber, Carolin Rippke

Automated medical image segmentation suffers from high inter-observer variability, particularly in tasks such as lung nodule delineation, where experts often disagree. Existing approaches either collapse this variability into a consensus mask or rely on separate model branches for each annotator. We introduce ProSona, a two-stage framework that learns a cont

Jorge Becerra

We introduce a class of decorated abstract graphs, that we call XC-tangles, that provides a very convenient framework to study quantum invariants of tangles and virtual tangles. These can be viewed as a far-reaching generalisation of rotational tangle diagrams for (virtual) upwards tangles, and constitute the topological analogue of XC-algebras, the minimum

Hiromu Matsumoto, Tsukasa Maruhashi, Yosuke Kayanuma, Yadong Han

Zone-folded acoustic phonons (6 THz) in 4H silicon carbide (SiC) have been coherently excited using a femtosecond near-infrared pulse and measured through transient reflectivity with a pump and probe protocol. Their amplitude is coherently controlled with 300-attoseconds precision and the results show interference fringe patterns due to electronic and phonon

Xueliang Zhao, Wei Wu, Jian Guan, Qintong Li

In modern sequential decision-making systems, the construction of an optimal candidate action space is critical to efficient inference. However, existing approaches either rely on manually defined action spaces that lack scalability or utilize unstructured spaces that render exhaustive search computationally prohibitive. In this paper, we propose a novel fra

Houzhang Fang, Shukai Guo, Qiuhuan Chen, Yi Chang

Moving infrared small target detection (IRSTD) plays a critical role in practical applications, such as surveillance of unmanned aerial vehicles (UAVs) and UAV-based search system. Moving IRSTD still remains highly challenging due to weak target features and complex background interference. Accurate spatio-temporal feature modeling is crucial for moving targ

JV Roig

Enterprise adoption of agentic AI systems requires reliable evaluation methods that reflect real-world deployment scenarios. Traditional LLM benchmarks suffer from training data contamination and fail to measure agentic capabilities such as multi-step tool use and decision-making under uncertainty. We present the Kamiwaza Agentic Merit Index (KAMI) v0.1, an

Vittorio Franzese, Matteo El Hariry

This paper presents a method for determining spacecraft angular rates using event-based camera sensing. This is achieved by analyzing the temporal distribution of brightness events triggered by the apparent motion of stars. The location and polarity of the events are used to infer the apparent motion field of the stars, which is, in turn, employed to estimat