November 2025 arXiv papers — page 130

Andrea Schimmenti, Valentina Pasqual, Fabio Vitali, Marieke van Erp

Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse into structured Knowledge Graphs (KGs). This paper introduces ATR4CH (Adaptive Text-to-RDF for Cultural Heritage), a systematic five-step methodology for Large Language Model-based Knowledge Extraction from Cultu

Desmond J. Higham, Francesco Hrobat, Francesco Tudisco

Generalized friendship paradoxes occur when, on average, our friends have more of some attribute than us. These paradoxes are relevant to many aspects of human interaction, notably in social science and epidemiology. Here, we derive new theoretical results concerning the inevitability of a paradox arising, using a linear algebra perspective. Following the se

Sergey Pilipenko, Gustavo Yepes, Stefan Gottlöber, Steffen Knollmann

Ginnungagap is a fully parallel (MPI+OpenMP) code designed to generate cosmological initial conditions for simulations involving very large numbers of particles. It operates in several modes, including the creation of initial conditions with either uniform or spatially varying resolution (for "zoom-in" simulations). The initial conditions can be fully random

Mengzhu Wang, Changyuan Deng, Shanshan Wang, Nan Yin

Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent methods like CLIP-based semantic augmentation have shown promise, they often overlook the underlying structure of feature distributions and frequency-domain characteristics that are cri

Matis Marcadier, Nicolas Forget, Yoann Pertot, Aurelie Jullien

The spectral coherence properties of supercontinuum generation in polarization-maintaining all-normal dispersion fibers are investigated. Stochastic phase noise induced by energy fluctuations, along with spectrally-resolved intensity-to-phase transfer coefficients, are quantitatively analyzed, confirming the high coherence of the generated supercontinuum. Ou

J. J. Chebly, C. K. Louis, A. Strugarek, J. D. Alvarado-Gómez

Radio emission from star planet interactions (SPI) beyond our solar system has yet to be firmly detected, primarily due to challenges such as weak signals, directional beaming effects, and low frequency emissions that are blocked by the ionosphere of Earth. Addressing these obstacles calls for strategic target selection. This proof of concept study aims to i

Shuhei Shibata

This paper investigates functional limit theorems for the Elephant Random Walk (ERW) on general periodic structures, extending the Bertenghi's results on $\mathbb{Z}^d$. Our results reveal new structure-dependent quantities that do not appear in the classical setting $\mathbb{Z}^d$, highlighting how the underlying structure affects the asymptotic behavior of

Milagros Delgado, Wladimir E. Banda-Barragán

Creating software dedicated to simulation is essential for teaching and research in Science, Technology, Engineering, and Mathematics (STEM). Physics lecturing can be more effective when digital twins are used to accompany theory classes. Research in physics has greatly benefited from the advent of modern, high-level programming languages, which facilitate t

Andreas Höfinger, Andrey A. Voronov, David Schmoll, Sabri Koraltan

The excitation and detection of propagating spin waves with lithographed nanoantennas underpin both classical magnonic circuits and emerging quantum technologies. Here, we establish a framework for all-electrical propagating spin-wave spectroscopy (AEPSWS) that links realistic electromagnetic drive fields to micromagnetic dynamics. Using finite-element (FE)

Zhong-Yu Wang, Zhe Liu, Xiang Liu

Based on the world's largest $\psi(3686)$ data sample, the BESIII Collaboration recently reported a substructure near the $\pi^+\pi^-$ mass threshold in the decay $\psi(3686) \to J/\psi \pi^+\pi^-$, challenging the established understanding of the dipion invariant mass spectrum. We propose that this substructure arises directly from dipion correlations. Usin

Christopher Mantzaris, Ajda Fošner

Europe's largest bank by assets as of 2025 started out in the 1860s in one of Europe's colonies: The Hongkong and Shanghai Banking Co (HSBC). Multiple wars forced Qing China and later the young Republic of China into a series of unequal treaties, one of which was the forced legalisation of the opium trade from parts of the British Empire into China, another

Zicheng Hu, Yuchen Wang, Cheng Chen

Decentralized cooperative multi-agent multi-armed bandits (DeCMA2B) considers how multiple agents collaborate in a decentralized multi-armed bandit setting. Though this problem has been extensively studied in previous work, most existing methods remain susceptible to various adversarial attacks. In this paper, we first study DeCMA2B with adversarial corrupti

Alistair O'Brien, Didier Rémy, Gabriel Scherer

The Damas-Hindley-Milner (ML) type system owes its success to principality, the property that every well-typed expression has a unique most general type. This makes inference predictable and efficient. Unfortunately, many extensions of ML (GADTs, higher-rank polymorphism, and static overloading) endanger princpality by introducing _fragile_ constructs that r

Christos Charalambous

This study introduces an agent-based model to study how regret, uncertainty, and social norms interact to shape vaccination behavior during epidemics. The model integrates three behavioral mechanisms, anticipated regret, evolving norms, and uncertainty-dependent trust, within a unified learning framework. Grounded in psychology and behavioral economics, it c

Yunduo Zhou, Bo Dong, Chang Li, Yuanchen Wang

Homeostatic mechanisms play a crucial role in maintaining optimal functionality within the neural circuits of the brain. By regulating physiological and biochemical processes, these mechanisms ensure the stability of an organism's internal environment, enabling it to better adapt to external changes. Among these mechanisms, the Bienenstock, Cooper, and Munro

Marien Renaud, Eliot Guez, Arthur Leclaire, Nicolas Papadakis

One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architecture

Tomáš Čížek, Martin Balko, Martin Schmid

Proof-Number Search is a best-first search algorithm with many successful applications, especially in game solving. As large-scale computing clusters become increasingly accessible, parallelization is a natural way to accelerate computation. However, existing parallel versions of Proof-Number Search are known to scale poorly on many CPU cores. Using two para

Guduru Manoj, Neel Prabhanjan Rachamalla, Ashish Kulkarni, Gautam Rajeev

In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particularly beneficial in low-resource language settings where the benefits of recent LLMs have been unevenly distributed across languages. In this work, we present a systematic study on t

D. Evan Piephoff, Jianshu Cao

We examine a biomolecular machine involving a driven, observable process coupled to a hidden process in a kinetically cooperative manner. A stochastic thermodynamics framework is employed to analyze a fluctuation theorem for the first-passage time of the observable process under nonequilibrium steady-state conditions. Based on a generic kinetic model, we dem

Sophia Loizidou, Christophe Ley, Shogo Kato, Kanti V. Mardia

In this paper, we propose a new flexible family of distributions for data that consist of three angles, two angles and one linear component, or one angle and two linear components. To achieve this, we equip the recently proposed trivariate wrapped Cauchy copula with non-uniform marginals and develop a parameter estimation procedure. We compare our model to i

Oscar Damanik, Giacomo Bastianel, Dirk Van Hertem, Hakan Ergun

This paper presents a security-constrained optimal power flow (SCOPF) model for HVDC grids that optimizes the asymmetrical operation of bipolar converter stations, i.e., different current injections of the positive and negative converter poles, to minimize operational costs under post-contingency conditions caused by single converter pole outages. The optimi

Wenti Yin, Huaxin Zhang, Xiang Wang, Yuqing Lu

Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to detect the most salient response segments, while neglecting

Qingao Yi, Jiaang Duan, Hanwen Hu, Qin Hua

Training large language models (LLMs) poses significant challenges regarding computational resources and memory capacity. Although distributed training techniques help mitigate these issues, they still suffer from considerable communication overhead. Existing approaches primarily rely on static gradient compression to enhance communication efficiency; howeve

Tobias Heibges, Claire Guépin, Diksha Garg, Luke Kupari

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) mission launched from Wanaka New Zealand on May 13, 2023. The mission ended after 36 h due to a balloon leak that resulted in the payload being lost in the Pacific Ocean. Over the course of the mission, the onboard Cherenkov Telescope (CT) was pointed just below the Earth's limb

Byungchang So

The magnitude of metric spaces does not appear to possess a simple, convenient continuity property, and previous studies have presented affirmative results under additional constraints or weaker notions, as well as counterexamples. In this vein, we discuss the continuity of magnitude of finite positive definite metric spaces with respect to the Gromov-Hausdo

Nada F. Alshehri, Istvan Ballai, Viktor Fedun, Gary Verth

Our study investigates the properties of Alfv\'en waves in partially ionised solar plasmas in the presence of steady, field-aligned, flows of charged and neutral particles. Our work aims to understand how such flows modify wave propagation and damping in environments where ion-neutral collisions are significant. We employ a two-fluid model that treats ions a

Sunil Chandra, Pankaj Kushwaha, Pranjupriya Goswami, Michael Zacharias

We present an extensive analysis of the multi-wavelength data of the low-synchrotron-peaked BL Lac object CGRaBS J0211+1051, which has been gathered over more than ten years with many observatories. Two major gamma-ray flares have been observed during the Fermi era: one in January 2011 and other in June 2019. During these events, CGRaBS J0211+1051 was also b

Carlos Gustavo Moreira, Harold Erazo, Nicolas Angelini

We study the generalized Hausdorff dimension of some natural subsets of $k^{-1}(3)$, where $k^{-1}(3)$ consists of the real numbers $x$ for which $\left| x-\frac{p}{q} \right|<\frac{1}{(3+\varepsilon)q^2}$ has infinitely many rational solutions $\frac{p}{q}$ for any $\varepsilon<0$ but only finitely many for any $\varepsilon>0$. It is well known that $k^{-1}

Shuangyu Lyu, Chuan Luo, Ruizhi Shi, Wei Wu

This work focuses on effectively generating diverse solutions for satisfiability modulo theories (SMT) formulas, targeting the theories of bit-vectors, arrays, and uninterpreted functions, which is a critical task in software and hardware testing. Generating diverse SMT solutions helps uncover faults and detect safety violations during the verification and t

Yan Zhuang, Minhao Liu, Yanru Zhang, Jiawen Deng

Multimodal Sentiment Analysis (MSA) aims to infer human sentiment by integrating information from multiple modalities such as text, audio, and video. In real-world scenarios, however, the presence of missing modalities and noisy signals significantly hinders the robustness and accuracy of existing models. While prior works have made progress on these issues,

Christopher Mantzaris, Ajda Fošner

This is the first scientific article since 2010 counting the words which are effective and permanent federal law in the United States (US) Code. The latest version of the US Code --published in 2025-- is the largest since 1991, encompassing over 24.4 million words. The low since 1991 was 1993 at roughly 15 million words. The word count grew in 30 out of 33 y

Matteo Taffetani, Matteo Pezzulla

Large deformations play a central role in the shape transformations of slender active and biological structures. A classical example is the eversion of the Volvox embryo, which demonstrates the need for shell theories that can describe large strains, rotations, and the presence of incompatible stimuli. In this work, a reduced two-dimensional morphoelastic en

Dávid Kószó, Tamás Aladics, Rudolf Ferenc, Péter Hegedűs

Static Code Analysis (SCA) tools, while invaluable for identifying potential coding problems, functional bugs, or vulnerabilities, often generate an overwhelming number of warnings, many of which are non-actionable. This overload of alerts leads to ``alert fatigue'', a phenomenon where developers become desensitized to warnings, potentially overlooking criti

K. Setoodehnia, J. H. Kelley

In this document, experimental nuclear structure data are evaluated for 16Be. The details of each reaction populating 16Be levels are compiled and evaluated. The combined results provide a set of adopted values that include level energies, spins and parities, level widths, decay types and other nuclear properties.

K. Setoodehnia, J. H. Kelley

In this document, experimental nuclear structure data are evaluated for 15Be. The details of each reaction populating 15Be levels are compiled and evaluated. The combined results provide a set of adopted values that include level energies, spins and parities, level widths, decay types and other nuclear properties.

Fuyuan Cao, Jiaxuan Zhang, Xiaoli Li

Estimating Individual Treatment Effects (ITE) from observational data is challenging due to confounding bias. Most studies tackle this bias by balancing distributions globally, but ignore individual heterogeneity and fail to capture the local structure that represents the natural clustering among individuals, which ultimately compromises ITE estimation. Whil

Sajal Mukherjee, Pritam Chandra Pramanik

Combinatorial analogues of classical Borsuk-Ulam-type theorems (e.g., Tucker's lemma, $\mathbb{Z}_p$-Tucker's lemma, etc.) have numerous important applications in combinatorics. In this paper, we formulate a combinatorial degree version of a generalized $\mathbb{Z}_p$-Tucker's lemma. Our proof is purely combinatorial in the sense that it does not involve hom

Surangana Sengupta, Björn Kubala, Joachim Ankerhold, Ciprian Padurariu

Resolved-sideband cooling is a standard technique in cavity optomechanics enabling quantum control of mechanical motion, but its performance is ultimately limited by quantum backaction heating. This fundamental effect imposes a limit on the minimum achievable mechanical phonon number, establishing a finite-temperature floor regardless of the applied cooling

Arpit Kottur, Jui Mahajan, Raka Dabhade

We propose a dynamical dark energy model based on a canonical scalar field with a hybrid potential of the form $V(\phi) = V_{0}e^{-\lambda\phi} + V_{1}\phi^{n}$. We constrain the model's 11-dimensional parameter space using a comprehensive combination of cosmological data, including the Planck 2018 Cosmic Microwave Background (CMB) power spectra, Baryon Acou

Yu Deng, Baozhu Zhao, Junyan Su, Xiaohan Zhang

Three-dimensional reconstruction in scenes with extreme depth variations remains challenging due to inconsistent supervisory signals between near-field and far-field regions. Existing methods fail to simultaneously address inaccurate depth estimation in distant areas and structural degradation in close-range regions. This paper proposes a novel computational

Annabel Kropf, Ivo Schulthess

This document serves as a conceptual and practical introduction to Strong-Field Quantum Electrodynamics (SFQED), written from the standpoint of experimental physicists. Rather than providing a comprehensive theoretical review, the document focuses on the core ideas, terminology, and challenges in SFQED that are most relevant to experimental design and interp

Hui Wang, Xukun Feng, Jin Cao, Huiying Liu

Despite recent advances in orbitronics, generating out-of-plane orbital torques essential for field-free deterministic switching of perpendicular magnetization remains a key challenge. Here, we propose a strategy to produce such unconventional torques across broad classes of materials, by leveraging the nonlinear orbital Hall effect. We demonstrate that this

Alessandro Matteo Rossi, Monica Rainer, Francesco Borsa, Stefano Facchini

Context. True spin-orbit obliquities {\Psi} offer valuable insights into the evolutionary history of exoplanetary systems. Previous studies have suggested that exoplanets tend to occupy either aligned or perpendicular orbits. However, recent research has indicated potential biases caused by the low sample, questioning whether this dichotomy would persist wit

ALICE Collaboration

Particle identification (PID) is a fundamental aspect of the ALICE detector system, central to its heavy-ion and proton-proton physics programs. Among the different PID strategies, ALICE uses the Time-Of-Flight (TOF) detector to identify particles at intermediate momenta ($0.5 < p_{\rm T} < 4$ GeV/$c$). The ALICE TOF detector performed successfully during th

Honghao Wang, Qingqing Wu, Yifan Jiang, Ziyuan Zheng

Low-altitude unmanned aerial vehicle (UAV) networks are integral to future 6G integrated sensing and communication (ISAC) systems. However, their deployment is hindered by challenges stemming from high mobility of UAVs, complex propagation environments, and the inherent trade-offs between coexisting sensing and communication functions. This article proposes

Patrick Feifel, Benedikt Franke, Frank Bonarens, Frank Köster

Reliable pedestrian detection represents a crucial step towards automated driving systems. However, the current performance benchmarks exhibit weaknesses. The currently applied metrics for various subsets of a validation dataset prohibit a realistic performance evaluation of a DNN for pedestrian detection. As image segmentation supplies fine-grained informat

Juntang Yang, Mohamed Khalil Ben-Larbi

This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact representation of the attitude constraint zone. The reward funct

Ibrahima Bah, Emanuele Berti, Bogdan Ganchev, David Pereñiguez

We study the bound states of a massive scalar field around a topological star, and show that these are strictly normal modes. This yields a genuine gravitational atom, sharply distinguishing horizonless objects from black holes. We show that the modes are controlled by the field's Compton wavelength compared to the size of the star. When the Compton waveleng

He Jiang, Yi Guo, Shikai Guo, Huijiang Liu

Timing optimization during global placement is critical for achieving optimal circuit performance and remains a key challenge in modern Field Programmable Gate Array (FPGA) design. As FPGA designs scale and heterogeneous resources increase, dense interconnects introduce significant resistive and capacitive effects, making timing closure increasingly difficul

Shuaitong Liu, Renjue Li, Lijia Yu, Lijun Zhang

Recent advances in Chain-of-Thought (CoT) prompting have substantially improved the reasoning capabilities of large language models (LLMs), but have also introduced their computational efficiency as a new attack surface. In this paper, we propose BadThink, the first backdoor attack designed to deliberately induce "overthinking" behavior in CoT-enabled LLMs w

Christopher Mantzaris, Ajda Fosner

Tax work is costly for society: Administrative tax labour is typically to a high degree shuffled off the government and onto every taxpayer by law. The higher the burden of any tax system, the costlier for society, as taxpayers are unable to engage in proper wealth creation when being kept busy with administrative tax work. This research finds evidence for a

ALICE Collaboration

(Multi-)strange particle production rates and transverse momentum distributions are measured at midrapidity ($|y| < 0.5$) as a function of the charged-particle multiplicity density by the ALICE experiment at the LHC, using proton-proton collisions at a center-of-mass energy of ${\bf \sqrt{s}~=~5.02}$~TeV. This study extends similar studies performed at ${\bf

A. L. Zibinskiy, D. S. Smirnov

The measurements of Hanle and polarization recovery effects for localized charge carriers are the basic tools for determining parameters of the spin dynamics, such as strength of the hyperfine interaction, for example, in quantum dots. We describe the dependence of the spin polarization of localized electrons on transverse and longitudinal magnetic fields ta

Changyuan Tian, Zhicong Lu, Shuang Qian, Nayu Liu

To improve Multi-step Mathematical Reasoning (MsMR) of Large Language Models (LLMs), it is crucial to obtain scalable supervision from the corpus by automatically critiquing mistakes in the reasoning process of MsMR and rendering a final verdict of the problem-solution. Most existing methods rely on crafting high-quality supervised fine-tuning demonstrations

Karim Nasreddine, Christo Kurisummoottil Thomas, Walid Saad

Traditional joint source-channel coding employs static learned semantic representations that cannot dynamically adapt to evolving source distributions. Shared semantic memories between transmitter and receiver can potentially enable bandwidth savings by reusing previously transmitted concepts as context to reconstruct data, but require effective mechanisms t

Dongwan Kim, Viresh Ranjan, Takashi Nagata, Arnab Dhua

Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherently struggles to effectively integrate visual features due to the inherent mismatch between text and vision modalities. We tackle this issue from a novel perspective in which the LLM not only serves as a language model but also a powerful vision encoder. To

Sumin Lee, Sungwon Park, Jeasurk Yang, Jihee Kim

Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-re

Laurent Loosveldt, Yassine Nachit, Ivan Nourdin, Ciprian Tudor

We investigate the smoothness of the densities of the finite-dimensional distributions of the Rosenblatt process. Within the Malliavin calculus framework, we prove that Rosenblatt random vectors are nondegenerate in the Malliavin sense. As a consequence, their densities belong to the Schwartz space of rapidly decreasing smooth functions. The proof relies on

Li-Yuan Zhang, Ze-Hua Zhang, Che Ming Ko, Yu-Gang Ma

High-energy nuclear collisions provide a unique environment for synthesizing both nuclei and antinuclei (such as $\bar{d}$ and $^4\overline{\text{He}}$) at temperatures ($k_BT\sim100$ MeV) much higher than their binding energies per nucleon of a few MeV. The underlying production mechanism, whether through statistical hadronization, nucleon coalescence, or d

Paolo Astrino

Organizations handling sensitive documents face a critical dilemma: adopt cloud-based AI systems that offer powerful question-answering capabilities but compromise data privacy, or maintain local processing that ensures security but delivers poor accuracy. We present a question-answering system that resolves this trade-off by combining semantic understanding

Florian Ebmeier, Nicole Ludwig, Jannik Thuemmel, Georg Martius

Solar thermal systems (STS) present a promising avenue for low-carbon heat generation, with a well-running system providing heat at minimal cost and carbon emissions. However, STS can exhibit faults due to improper installation, maintenance, or operation, often resulting in a substantial reduction in efficiency or even damage to the system. As monitoring at

Francesco Mazza, Marco Trinari, Chiara Sepali, Chiara Cappelli

The multiscale model combining the multiconfigurational self-consistent field (MCSCF) method with the fully atomistic polarizable Fluctuating Charges (FQ) force field (J. Chem. Theory Comput. 2024, 20, 9954-9967) is here extended to the calculation of analytical nuclear gradients. The gradients are derived from first principles, implemented in the OpenMolcas

Sebastian Knauer, Roman Verba, Rostyslav O. Serha, Denys Slobodianiuk

Materials are commonly distinguished by their magnetic response into diamagnetic, paramagnetic, and magnetically ordered (ferro-, ferri-, and antiferromagnetic) phases. Diamagnets and paramagnets lack spontaneous long-range order, whereas ordered magnets develop such order below their Curie or N\'eel temperature and support single spin-wave excitations (magn

Athanasios Christou Micheas

We present methods that provide all zeroes and extrema of a function that do not require differentiation. Using point process theory, we are able to describe the locations of zeroes or maxima, their number, as well as their distribution over a given window of observation. The algorithms in order to accomplish the theoretical development are also provided, an

Marien-Lorenzo Hanot, Kaibo Hu

We derive a reformulation of the linearized Arnowitt-Deser-Misner (ADM) equations as a Hodge-Dirac wave system with the divdiv complex, addressing challenges in numerical relativity such as gauge fixing, constraint propagation, and tensor symmetries. The differential and algebraic structures of the divdiv complex ensure the well-posedness of the formulation

Aswin Arun, Christo Kurisummoottil Thomas, Rimalpudi Sarvendranath, Walid Saad

Despite the advantages of multi-agent reinforcement learning (MARL) for wireless use case such as medium access control (MAC), their real-world deployment in Internet of Things (IoT) is hindered by their sample inefficiency. To alleviate this challenge, one can leverage model-based reinforcement learning (MBRL) solutions, however, conventional MBRL approache

Unnikrishnan Radhakrishnan

Small and medium-sized enterprises (SMEs) still depend heavily on tacit, experience-based know-how that rarely makes its way into formal documentation. This paper introduces a large-language-model (LLM)-driven conversational assistant that captures such knowledge on the shop floor and converts it incrementally and interactively into standards-compliant Busin

Hau-Wen Huang

This paper establishes a comprehensive algebraic framework linking the Lie algebra $\mathfrak{so}_{3}$ to the Askey--Wilson algebras. First, we provide a manifestly symmetric reformulation of the algebra homomorphism from the universal Racah algebra $\Re$ to $U(\mathfrak{sl}_2)$ by exploiting a Lie algebra isomorphism between $\mathfrak{sl}_{2}$ and $\mathfr

Sreyan Ghosh, Arushi Goel, Lasha Koroshinadze, Sang-gil Lee

We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research has progressed rapidly, music remains challenging due to its dynamic, layered, and information-dense nature. Progress has been further limited by the difficulty of scaling open au

Peter Athron, Satyabrata Datta, Zhao-Yang Zhang

We explore the cosmological dynamics of a supercooled first-order phase transition in the classically conformal $U(1)_{B-L}$ extension of the Standard Model, where radiative symmetry breaking simultaneously generates the right-handed neutrino (RHN) masses, and a strong stochastic gravitational-wave (GW) background. The slow decay of the scalar field into RHN

Yuping Yan, Yuhan Xie, Yuanshuai Li, Yingchao Yu

Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible output of unsafe contents, ranging from toxic language and biased imagery to privacy violations and harmful misinformation. Current safety benchmarks remain highly limited in both m

Christoph Bosch, John K. L. Wong, Martin Paulikat, Myroslav Zapukhlyak

Rapid progress in computational pathology is increasingly driven by vision foundation models pretrained on vast histopathology datasets. While recent efforts have prioritized training on an ever-larger amount of patches, we take an alternative approach focused on data diversity. Our foundation model, Athena, was initialized from a pretrained model and traine

Dušan Popov

In the paper we developed a procedure for constructing generalized coherent states with shifted argument, as a result of the action of the generalized displacement operator. This was based on the action of a pair of nonlinear ladder operators, which generate nonlinear coherent states. To examine the properties of coherent states with shifted argument, the ru

Belona Sonna, Alban Grastien, Claire Benn

Privacy leakage in AI-based decision processes poses significant risks, particularly when sensitive information can be inferred. We propose a formal framework to audit privacy leakage using abductive explanations, which identifies minimal sufficient evidence justifying model decisions and determines whether sensitive information disclosed. Our framework form

Haowen Jiang, Xinyu Huang, You Lu, Dingji Wang

Recent advancements in end-to-end autonomous driving systems (ADSs) underscore their potential for perception and planning capabilities. However, challenges remain. Complex driving scenarios contain rich semantic information, yet ambiguous or noisy semantics can compromise decision reliability, while interference between multiple driving tasks may hinder opt

Won Ik Cho, Woonghee Han, Kyung Seo Ki, Young Min Kim

The rise of agentic systems that combine orchestration, tool use, and conversational capabilities, has been more visible by the recent advent of large language models (LLMs). While open-domain frameworks exist, applying them in private domains remains difficult due to heterogeneous tool formats, domain-specific jargon, restricted accessibility of APIs, and c

Adrien Lafage, Olivier Laurent, Firas Gabetni, Gianni Franchi

Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle to accurately quantify the uncertainty of their predictions, limiting their broader adoption in critical real-world applications. Uncertainty Quantification (UQ) for Deep Learning

Jing He, Han Zhang, Yuanhui Xiao, Wei Guo

Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative

Dogan Akpinar

We compute the classical one-loop gravitational Compton amplitude describing the scattering of a graviton off a massive spinning compact object at the second post-Minkowskian order, including terms through the quartic order in spin. Our analysis includes spin-induced finite-size effects up to the hexadecapolar order, and extends recent results obtained for m

Yanbei Jiang, Chao Lei, Yihao Ding, Krista Ehinger

Despite significant progress, Vision-Language Models (VLMs) still struggle with complex visual reasoning, where multi-step dependencies cause early errors to cascade through the reasoning chain. Existing post-training paradigms are limited: Supervised Fine-Tuning (SFT) relies on costly step-level annotations, while Reinforcement Learning with Verifiable Rewa

Lukas Worch, Kavin Arunasalam, Neil Mulcahy, Syeda Ramin Jannat

Understanding degradation processes in lithium ion batteries is essential for improving long term performance and advancing sustainable energy technologies. Tin selenide (SnSe) has emerged as a promising anode material due to the high theoretical capacity of tin. Unlike conventional intercalation based electrodes, SnSe undergoes conversion and alloying react

Martin Braas, Lukas Esterle

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, yet their applicability to dialogue systems in computer games remains limited. This limitation arises from their substantial hardware requirements, latency constraints, and the necessity to maintain clearly defined knowledge boundaries within a game setting.

Mai Zhang, Yu Wang, Chang-Ling Zou, Lei Ying

Harnessing a beam of slow free electrons in artificial photonic structures offers a powerful, tunable platform for generating nonclassical light without the need for heavy physical equipment. Here we present a theory of nonclassical lasing, demonstrating how incoherent electrons in photonic crystal cavities can coherently emit photons through collective dyna

Dieter Vandesande, Jordi Coll, Bart Bogaerts

Over the past few decades, combinatorial solvers have seen remarkable performance improvements, enabling their practical use in real-world applications. In some of these applications, ensuring the correctness of the solver's output is critical. However, the complexity of modern solvers makes them susceptible to bugs in their source code. In the domain of sat

Shahaf S. Shperberg, Natalie Morad, Lior Siag, Ariel Felner

Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art o

Xin Sun, Daniel Ståhl, Kristian Sandahl, Christoph Kessler

In recent years, large language models have been widely integrated into software engineering workflows, supporting tasks like code generation. While prior evaluations focus on functional correctness, there is still a limited understanding of the non-functional quality characteristics of generated code. Guided by the ISO/IEC 25010 quality model, this study ad

R. F. Shamoyan, M. G. Bashmakova

The intention of this survey to collect in one paper many recent results and advances related with Bergman type projection acting in various spaces of analytic functions in several complex variables in the unit ball, tubular domains over symmetric cones and bounded strongly pseudoconvex domains between function spaces of different dimensions. Various new int

Philipp Baumeister, Francesca Miozzi, Claire Marie Guimond, Marie-Luise Steinmeyer

Most our knowledge about rocky exoplanets is based on their measure of mass and radius. These two parameters are routinely measured and are used to categorise different populations of observed exoplanets. They are also tightly linked to the planet's properties, in particular those of the interior. As such they offer the unique opportunity to interpret the ob

Zhe Xu, Zhicai Wang, Junkang Wu, Jinda Lu

Large Vision-Language Models (LVLMs) often suffer from object hallucination, making erroneous judgments about the presence of objects in images. We propose this primar- ily stems from spurious correlations arising when models strongly associate highly co-occurring objects during train- ing, leading to hallucinated objects influenced by visual con- text. Curr

Rajab Aghamov, Christel Baier, Joel Ouaknine, Jakob Piribauer

Dynamic Bayesian networks (DBNs) are compact graphical representations used to model probabilistic systems where interdependent random variables and their distributions evolve over time. In this paper, we study the verification of the evolution of conditional-independence (CI) propositions against temporal logic specifications. To this end, we consider two s

Tomasz Truderung

A simple and practical method for achieving everlasting privacy in e-voting systems, without relying on advanced cryptographic techniques, is to use anonymous voter credentials. The simplicity of this approach may, however, create some challenges, when combined with other security features, such as cast-as-intended verifiability with second device and second

Gal Hadar, Forest Agostinelli, Shahaf S. Shperberg

Many sequential decision-making problems can be formulated as shortest-path problems, where the objective is to reach a goal state from a given starting state. Heuristic search is a standard approach for solving such problems, relying on a heuristic function to estimate the cost to the goal from any given state. Recent approaches leverage reinforcement learn

Mohsen Khodadi, Gaetano Lambiase, Javad. T. Firouzjaee

Based on a comprehensive analysis of recent observational data-a combination of DESI DR1, Planck CMB, and Pantheon+ SN Ia-this study critically evaluates the two dark energy (DE) proposals within Harada's Conformal Killing Gravity (CKG) model. The model in question predicts either a dominant phantom-type effective DE component with EoS $\omega = -5/3$ or a h

He Zhang, Wenqian Cui, Haoning Xu, Xiaohui Li

Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex models. However, existing benchmarks primarily focus on evaluating single-round interactions, neglecting the complexities of multi-round communication. Evaluating FD-SLMs in multi-

Lucas Rudelt, Fabian Mikulasch, Viola Priesemann, André Ferreira Castro

Animals use past experiences to adapt future behavior. To enable this rapid learning, vertebrates and invertebrates have evolved analogous neural structures like the vertebrate cerebellum or insect mushroom body. A defining feature of these circuits is a large expansion layer, which re-codes sensory inputs to improve pattern separation, a prerequisite to lea

Han-Pu Liang, Chuan-Nan Li, Xin-Ru Tang, Xun Xu

Alloying compound AX with another compound BX is widely used to tune material properties. For disordered alloys, due to the lack of periodicity, it has been challenging to calculate and study their material properties. Special quasi-random structure (SQS) method has been developed and widely used to treat this issue by matching averaged atomic correlation fu

Leszek Sliwko, Vladimir Getov

This paper presents a novel approach to categorization of modern workload schedulers. We provide descriptions of three classes of schedulers: Operating Systems Process Schedulers, Cluster Systems Jobs Schedulers and Big Data Schedulers. We describe their evolution from early adoptions to modern implementations, considering both the use and features of algori

Jinxiang Zhou, Liming Yang, Yaping Wang, Jie Wu

In this study, an enthalpy-based lattice Boltzmann flux solver is developed to simulate the freezing dynamics of a ferrofluid droplet under a uniform magnetic field. The accuracy and robustness of the solver are first validated through three benchmark tests: conductive freezing, static droplet freezing, and ferrofluid droplet deformation. The solver is then

Haijin Ding

We investigate photon transport and blockade based on the architecture where a waveguide is coupled to a microring resonator at two distinct points. This two-point coupling configuration between the waveguide and resonator gives rise to non-Markovian dynamics, which is induced by the photon transmission delay in the waveguide between the two coupled points.

Qian Jin, Yumeng Liu, Yuqi Jiang, Qi Sun

Reliable, generalizable data foundations are critical for enabling large-scale models in computational lithography. However, essential tasks-mask generation, rule violation detection, and layout optimization-are often handled in isolation, hindered by scarce datasets and limited modeling approaches. To address these challenges, we introduce Unitho, a unified