October 2025 arXiv papers — page 87

Kelvin dos Santos Alves, Rogerio Teixeira Cavalcanti

The aim of the present paper is to present a careful and accessible discussion of the formal aspects of Boltzmann-Gibbs and Tsallis entropies. We begin with a brief overview of Boltzmann-Gibbs entropy, highlighting its main properties and the uniqueness theorems formulated by Shannon and Khinchin. Once these foundational results are established, we introduce

Chia-Hsuan Lu, Tony Tan, Michael Benedikt

Graph neural networks (GNNs) are the predominant architecture for learning over graphs. As with any machine learning model, an important issue is the detection of attacks, where an adversary can change the output with a small perturbation of the input. Techniques for solving the adversarial robustness problem - determining whether an attack exists - were ori

Antonio Lamanna

The rapid adoption of Low-Code Development Platforms (LCDPs) has created a critical need for systematic evaluation methodologies that enable organizations to make informed platform selection decisions. This paper presents a comprehensive evaluation framework based on five key criteria: Business Process Orchestration, UI/UX Customization, Integration and Inte

Ping Tang, Gerrit E. W. Bauer

Multiferroics are materials with coexisting electric and magnetic orders that are of central importance for fundamental research and technological applications. Unfortunately, intrinsic multiferroics that operate at room temperature remain rare due to an apparent incompatibility between magnetism and ferroelectricity. Here we predict that a pure ferroelectri

Joe Williams, Sebastiaan Krijt, Bertram Bitsch, Adrien Houge

The complex interplay between the growth, drift, and sublimation of ice-covered pebbles can strongly influence the volatile distribution and evolution of disc composition, and therefore impact the composition of forming planets. Classic pebble drift models treat volatile species individually as sublimating at their respective snowlines, although observations

Fouad Trad, Ali Chehab

Phishing websites remain a significant cybersecurity threat, necessitating accurate and cost-effective detection mechanisms. In this paper, we present CLASP, a novel system that effectively identifies phishing websites by leveraging multiple intelligent agents, built using large language models (LLMs), to analyze different aspects of a web resource. The syst

Jona Dirks, Nicole Schirrmacher, Sebastian Siebertz, Alexandre Vigny

A treedepth decomposition of an undirected graph $G$ is a rooted forest $F$ on the vertex set of $G$ such that every edge $uv\in E(G)$ is in ancestor-descendant relationship in $F$. Given a weight function $w\colon V(G)\rightarrow \mathbb{N}$, the weighted depth of a treedepth decomposition is the maximum weight of any path from the root to a leaf, where the

Yongmin Lee, Hye Won Chung

Multimodal dataset distillation aims to synthesize a small set of image-text pairs that enables efficient training of large-scale vision-language models. While dataset distillation has shown promise in unimodal tasks, extending it to multimodal contrastive learning presents key challenges: learning cross-modal alignment and managing the high computational co

Dennis Assenmacher, Paloma Piot, Katarina Laken, David Jurgens

Digital dehumanization, although a critical issue, remains largely overlooked within the field of computational linguistics and Natural Language Processing. The prevailing approach in current research concentrating primarily on a single aspect of dehumanization that identifies overtly negative statements as its core marker. This focus, while crucial for unde

Katerina Drakos, Eva Paraschou, Simay Toplu, Line Harder Clemmensen

Artificial intelligence has been introduced as a way to improve access to mental health support. However, most AI mental health chatbots rely on a limited range of disciplinary input, and fail to integrate expertise across the chatbot's lifecycle. This paper examines the cost-benefit trade-off of interdisciplinary collaboration in AI mental health chatbots.

P. Haerter, A. F. Bosio, E. D. Leonel, M. A. F. Sanjuán

We investigate the escape dynamics in an open circular billiard under the influence of a uniform gravitational field. The system properties are investigated as a function of the particle total energy and the size of two symmetrically placed holes in the boundary. Using a suite of quantitative tools including escape basins, basin entropy ($S_b$), mean escape

Md Mohiuddin, Mohammed Mehedi Hasan, Alamgir Kabir

The toxicity and stability issues of lead-based perovskites motivate the search for non-toxic, durable alternatives. This work examines lead-free $\mathrm{Mg_3ZBr_3}$ ($Z=\mathrm{As,Sb,Bi}$) halide perovskites as optoelectronic materials, with emphasis on $\mathrm{Mg_3AsBr_3}$ and $\mathrm{Mg_3SbBr_3}$. First-principles calculations establish cubic $Pm\bar{3

M. Muñoz-Echeverría, E. Pointecouteau, G. W. Pratt, J. -F. Macías-Pérez

In a self-similar paradigm of structure formation, the thermal pressure of the hot intra-cluster gas follows a universal distribution once the profile of each cluster is normalised based on the proper mass and redshift dependencies. The reconstruction of such a universal pressure profile requires an individual estimate of the mass of each cluster. In this co

Jinyu Hu, Chen Wu

Recently, V.Yu. Denisov proposed a new empirical formula incorporating the deformation of the daughter nucleus, which has significantly improved the description of $\alpha$-decay half-lives for even-even nuclei compared to formulas neglecting the deformation of the daughter nucleus. In this work, we generalize the deformation of the daughter nucleus proposed

Qiyu Yang, Shengbo Zhao

In this paper, we establish lower bounds for the maximum of derivatives of the Riemann zeta function on vertical homogeneous progressions. When the real part $\sigma$ lies within a suitable range, we show that the discrete case has a similar order of magnitude to the continuous case, using the resonance method.

Yusi Fan, Tian Wang, Zhiying Yan, Chang Liu

Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet informative features. This limitation becomes especially critical in high-dimensional datasets, where complex and interdependent feature rel

Erica Bertolini, Matteo Carrega, Nicola Maggiore, Daniel Sacco Shaikh

We construct a covariant and gauge-invariant theory describing massive fractons in three spacetime dimensions, based on a symmetric rank-2 tensor field. The model includes a Chern-Simons-like term that plays a dual role: it generates a topological mass for the tensor gauge field and simultaneously acts as a source of intrinsic fractonic matter. This dual mec

Zhenxing Zhang, Jiayan Teng, Zhuoyi Yang, Tiankun Cao

We present Kaleido, a subject-to-video~(S2V) generation framework, which aims to synthesize subject-consistent videos conditioned on multiple reference images of target subjects. Despite recent progress in S2V generation models, existing approaches remain inadequate at maintaining multi-subject consistency and at handling background disentanglement, often re

Maynard Koch, Florian Dolzmann, Thomas C. Schmidt, Matthias Wählisch

The DNS infrastructure is infamous for facilitating reflective amplification attacks. Various countermeasures such as server shielding, access control, rate limiting, and protocol restrictions have been implemented. Still, the threat remains throughout the deployment of DNS servers. In this paper, we report on and evaluate the often unnoticed threat that der

Gokturk Aytug Akarlar

Motivation: Standard genome-wide association studies in cancer genomics rely on statistical significance with multiple testing correction, but systematically fail in underpowered cohorts. In TCGA breast cancer (n=967, 133 deaths), low event rates (13.8%) create severe power limitations, producing false negatives for known drivers and false positives for larg

Bui Khanh Linh Do, Thanh H. Nguyen, Nghi Huynh Quang, Doanh Nguyen-Ngoc

As Electric Vehicle (EV) adoption accelerates in urban environments, optimizing charging infrastructure is vital for balancing user satisfaction, energy efficiency, and financial viability. This study advances beyond static models by proposing a digital twin framework that integrates agent-based decision support with embedded optimization to dynamically simu

Akimasa Hirata, Teruo Onishi, Naoki Shinohara, Valerio De Santis

Wireless power transfer (WPT) technologies are increasingly being applied in fields ranging from consumer electronics and electric vehicles to space-based energy systems and medical implants. While WPT offers contactless power delivery, it introduces electromagnetic field (EMF) emissions, necessitating careful assessment to address safety and public health c

Junhyeog Yun, Hyoun Jun Lee, Insu Jeon

Automating quantitative trading strategy development in dynamic markets is challenging, especially with increasing demand for personalized investment solutions. Existing methods often fail to explore the vast strategy space while preserving the diversity essential for robust performance across changing market conditions. We present QuantEvolve, an evolutiona

Behnam Rezaei Bezanjani, Seyyed Hamid Ghafouri, Reza Gholamrezaei

The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical

Yiding Feng, Mengfan Ma, Bo Peng, Zongqi Wan

In this work, we introduce and study contextual search in general principal-agent games, where a principal repeatedly interacts with agents by offering contracts based on contextual information and historical feedback, without knowing the agents' true costs or rewards. Our model generalizes classical contextual pricing by accommodating richer agent action sp

Dominic S. T. Keehan, Edward J. Anderson, Wolfram Wiesemann

We study data-driven decision problems where historical observations are generated by a time-evolving distribution whose consecutive shifts are bounded in Wasserstein distance. We address this nonstationarity using a distributionally robust optimization model with an ambiguity set that is a Wasserstein ball centered at a weighted empirical distribution, ther

Zhihong Lai, Luyang Zhao, Qian Shao

Despite prior advances in PINNs, significant challenges remain in localized solid mechanics problems because of the limitations of single network formulations in simultaneous resolution of smooth global responses and near-tip singularities, and inadequacy in discontinuity representation, leading to unstable training and limited accuracy. To address the chall

Zijie Xu, Minfeng Qi, Shiqing Wu, Lefeng Zhang

Multi-agent systems powered by large language models are advancing rapidly, yet the tension between mutual trust and security remains underexplored. We introduce and empirically validate the Trust-Vulnerability Paradox (TVP): increasing inter-agent trust to enhance coordination simultaneously expands risks of over-exposure and over-authorization. To investig

Yonghe Yu, Mujtaba Zahidy, Siyan Zhou, Caterina Viligar

Quantum repeaters are employed in quantum communication to overcome the long-distance transmission loss of quantum states. The quantum repeater is based on various key technologies, including quantum entanglement swapping, quantum memory, and entanglement purification. In particular, quantum purification can distil high-quality entanglement from the degraded

Tjaša Arčon, Marko Robnik-Šikonja, Polona Tratnik

Artificial intelligence approaches are being adapted to many research areas, including digital humanities. We built a methodology for large-scale analyses in folkloristics. Using machine learning and natural language processing, we automatically detected motifs in a large collection of Cinderella variants and analysed their similarities and differences with

Chunyang Li, Yilun Zheng, Xinting Huang, Tianqing Fang

The paradigm of LLM-as-a-judge is emerging as a scalable and efficient alternative to human evaluation, demonstrating strong performance on well-defined tasks. However, its reliability in open-ended tasks with dynamic environments and complex interactions remains unexplored. To bridge the gap, we introduce WebDevJudge, a systematic benchmark for assessing LL

Loc Phuc Truong Nguyen, Hung Thanh Do

As AI systems enter high-stakes domains, evaluation must extend beyond predictive accuracy to include explainability, fairness, robustness, and sustainability. We introduce RAISE (Responsible AI Scoring and Evaluation), a unified framework that quantifies model performance across these four dimensions and aggregates them into a single, holistic Responsibilit

Yue Wang, Lixian Zhang, Yimin Zhu, Yangguang Liu

The aim of this paper is to design a new type of grasping and perching unmanned aerial vehicle (UAV), called Flexbee, which features a soft vector-propulsion nozzle (SVPN). Compared to previous UAVs, Flexbee integrates flight, grasping, and perching functionalities into the four SVPNs. This integration offers advantages including decoupled position and attit

S. Tsiramua, H. Meladze, T. Davitashvili, J. M. Sanchez

In the present paper, the models of structural analysis and evaluation of efficiency indicators (reliability, fault tolerance, viability, and flexibility) of a multi core processor with variable structure, equipped with multi functional cores, are considered. Using logical probabilistic methods, the following has been developed: models for evaluating the rel

Jianjun Zhao

Abstraction is a fundamental principle in classical software engineering, which enables modularity, reusability, and scalability. However, quantum programs adhere to fundamentally different semantics, such as unitarity, entanglement, the no-cloning theorem, and the destructive nature of measurement, which introduce challenges to the safe use of classical abs

Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel, Tathagata Raha

Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and bias assessment often lack the necessary transparency, crea

Federico Barbero, Xiangming Gu, Christopher A. Choquette-Choo, Chawin Sitawarin

In this work, we show that it is possible to extract significant amounts of alignment training data from a post-trained model -- useful to steer the model to improve certain capabilities such as long-context reasoning, safety, instruction following, and maths. While the majority of related work on memorisation has focused on measuring success of training dat

Loc Phuc Truong Nguyen, Hung Thanh Do, Hung Truong Thanh Nguyen, Hung Cao

AI-assisted gait analysis holds promise for improving Parkinson's Disease (PD) care, but current clinical dashboards lack transparency and offer no meaningful way for clinicians to interrogate or contest AI decisions. To address this issue, we present Motion2Meaning, a clinician-centered framework that advances Contestable AI through a tightly integrated int

Abdullah Al-Khatib, Albert Gergus, Muneeb Ul Hassan, Abdelmajid Khelil

Very few available individual bandwidth reservation schemes provide efficient and cost-effective bandwidth reservation that is required for safety-critical and time-sensitive vehicular networked applications. These schemes allow vehicles to make reservation requests for the required resources. Accordingly, a Mobile Network Operator (MNO) can allocate and gua

Sanjay Kumar, Tim Brophy, Reenu Mohandas, Eoin Martino Grua

Robust perception in automated driving requires reliable performance under adverse conditions, where sensors may be affected by partial failures or environmental occlusions. Although existing autonomous driving datasets inherently contain sensor noise and environmental variability, very few enable controlled, parameterised, and reproducible degradations acro

Yuncheng Hua, Sion Weatherhead, Mehdi Jafari, Hao Xue

In this paper, we present SOCIA-Nabla, an end-to-end, agentic framework that treats simulator construction asinstance optimization over code within a textual computation graph. Specialized LLM-driven agents are embedded as graph nodes, and a workflow manager executes a loss-driven loop: code synthesis -> execution -> evaluation -> code repair. The optimizer

Enhan Li, Hongyang Du

Large Language Models (LLMs) increasingly rely on emerging protocols such as the Model Context Protocol (MCP) to invoke external tools and services. However, current tool routing mechanisms remain fragile because they only consider functional matching between users' queries and tools. In practice, user intent expressed through queries can be vague or undersp

J. François, L. Ravera

We present a manifestly diffeomorphism-invariant simple model of galaxy dynamics obtained by applying the Dressing Field Method (DFM) to a general-relativistic system comprising the metric and four scalar fields, phenomenologically representing the four-velocity of a cosmological fluid or dust field. The DFM, a systematic tool for extracting the gauge-invari

Ying Yao, Daniel J. Graham

Accurate annual average daily traffic (AADT) data are vital for transport planning and infrastructure management. However, automatic traffic detectors across national road networks often provide incomplete coverage, leading to underrepresentation of minor roads. While recent machine learning advances have improved AADT estimation at unmeasured locations, mos

Maia Tienstra, Gottfried Hastermann

We study non-linear Bayesian inverse problems arising from semilinear partial differential equations (PDEs) that can be transformed into linear Bayesian inverse problems. We are then able to extend the early stopping for Ensemble Kalman-Bucy Filter (EnKBF) to these types of linearisable nonlinear problems as a way to tune the prior distribution. Using the li

Zebin Yang, Sunjian Zheng, Tong Xie, Tianshi Xu

Object-goal navigation (ObjNav) tasks an agent with navigating to the location of a specific object in an unseen environment. Embodied agents equipped with large language models (LLMs) and online constructed navigation maps can perform ObjNav in a zero-shot manner. However, existing agents heavily rely on giant LLMs on the cloud, e.g., GPT-4, while directly

Will Chow

Large Language Models (LLMs), as the foundational architecture for next-generation interactive AI applications, not only power intelligent dialogue systems but also drive the evolution of embodied intelligence on edge devices, including humanoid robots, smart vehicles, and other scenarios. The applications running on these edge devices impose differentiated

Nicolas Fdida, Yves Mauriot, Lucien Vingert, Arnaud Ristori

There is a need for experimental data in conditions representative injection in rocket engines to validate or initiate droplet formation models used in numerical simulations. A new cryogenic vessel was built upon the MASCOTTE test bench to study the atomization of a single oxygen liquid jet, under non-reactive conditions, with simultaneous optical diagnostic

Elias Al Ghazal, Jad Mounayer, Beatriz Moya, Sebastian Rodriguez

Modeling and predicting the dynamics of complex multiscale systems remains a significant challenge due to their inherent nonlinearities and sensitivity to initial conditions, as well as limitations of traditional machine learning methods that fail to capture high frequency behaviours. To overcome these difficulties, we propose three approaches for multiscale

Alejandro Díaz-Caro, Octavio Malherbe, Rafael Romero

We present $\lambda_B$, a quantum-control $\lambda$-calculus that refines previous basis-sensitive systems by allowing abstractions to be expressed with respect to arbitrary -- possibly entangled -- bases. Each abstraction and let construct is annotated with a basis, and a new basis-dependent substitution governs the decomposition of value distributions. The

Giovanni De Muri, Mark Vero, Robin Staab, Martin Vechev

LLMs are often used by downstream users as teacher models for knowledge distillation, compressing their capabilities into memory-efficient models. However, as these teacher models may stem from untrusted parties, distillation can raise unexpected security risks. In this paper, we investigate the security implications of knowledge distillation from backdoored

Christian Schiffer, Zeynep Boztoprak, Jan-Oliver Kropp, Julia Thönnißen

Studying the cellular architecture of the human cerebral cortex is essential for understanding how the brain is organized from the micro to the macro level, and how it functions. However, investigating complex texture patterns in histological images using automatic methods that can be scaled across whole brains remains a challenge. Here we introduce CytoNet,

Soumyadip Das, Suman Kumar Roy, Rahul Rana, M Girish Chandra

Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in real-world applications, such as portfolio optimization, but pose significant computational challenges for large-scale instances. We propose a hybrid quantum-classical framework that leverages neutral atom quantum computing to address QUBO problems by mapping them to the Maximum Wei

Dušan Malić, Christian Fruhwirth-Reisinger, Alexander Prutsch, Wei Lin

This technical report outlines the top-ranking solution for RoboSense 2025: Track 3, achieving state-of-the-art performance on 3D object detection under various sensor placements. Our submission utilizes GBlobs, a local point cloud feature descriptor specifically designed to enhance model generalization across diverse LiDAR configurations. Current LiDAR-base

Aleksandr Azatov, Mohamed Mahdi Khalil, Motoo Suzuki

Composite axions offer a scenario where the axion emerges as a pion-like state, avoiding fine-tuning of elementary scalars and ameliorating the axion quality problem. Despite these advantages, their post-inflationary cosmology remains largely unexplored, with challenges including the domain wall problem and the presence of exotic relics. We propose two compo

Nicolas Fdida, Nathan Mallart-Martinez, Thomas Le Pichon, Axel Vincent-Randonnier

Near field liquid structures and penetration of a kerosene jet injected in a Mach 2 crossflow were studied experimentally in the LAPCAT-II Dual Mode Ramjet Combustor at Onera, using high spatial and temporal resolution imaging techniques. The experiments performed in this study provide measurements in high temperature conditions, with kerosene as liquid fuel

Max Schwarz, Sven Behnke

Immersive televisualization is important both for telepresence and teleoperation, but resolution and fidelity are often limited by communication bandwidth constraints. We propose a lightweight method for foveated compression of immersive televisualization video streams that can be easily integrated with common video codecs, reducing the required bandwidth if

Harry Mönig

Tip functionalization in AFM allows imaging organic nano-structures with sub-molecular resolution. Here, recent progress by using atomically defined copper-oxide tips is discussed. With their outstanding rigidity and elemental selectivity on metal-oxide surfaces, these probes constitute a powerful approach for the atomic-scale characterization of metal-oxide

Sarth Dubey, Subimal Ghosh, Udit Bhatia

Reliable hydrologic and flood forecasting requires models that remain stable when input data are delayed, missing, or inconsistent. However, most advances in rainfall-runoff prediction have been evaluated under ideal data conditions, emphasizing accuracy rather than operational resilience. Here, we develop an operationally ready emulator of the Global Flood

Bin Gu, Haitao Zhao, Jibo Wei

Robust speaker verification under noisy conditions remains an open challenge. Conventional deep learning methods learn a robust unified speaker representation space against diverse background noise and achieve significant improvement. In contrast, this paper presents a noise-conditioned mixture-ofexperts framework that decomposes the feature space into speci

Ranjini Bhattacharya, Souvik Roy

In this work, we present a novel investigation into the spin-dependent thermoelectric performance of an extended Su-Schrieffer-Heeger (SSH) model, showcasing for the first time how its intrinsic spin filtration mechanism can be strategically harnessed to function as an efficient spin thermoelectric generator. By introducing a Fibonacci-type aperiodic modulat

S. Q. Hou, C. Iliadis, M. Pignatari, J. B. Liu

Accurate $^{42}$Ti($p$,$\gamma$)$^{43}$V reaction rates are crucial for understanding the nucleosynthesis path of the rapid capture process (rp-process) that occurs in X-ray bursts. We aim to improve the thermonuclear rates of $^{42}$Ti($p$,$\gamma$)$^{43}$V based on more complete resonance information and accurate direct component, together with the recentl

Bin Gu, Lipeng Dai, Huipeng Du, Haitao Zhao

Learning robust speaker representations under noisy conditions presents significant challenges, which requires careful handling of both discriminative and noise-invariant properties. In this work, we proposed an anchor-based stage-wise learning strategy for robust speaker representation learning. Specifically, our approach begins by training a base model to

V. Yu. Kochkina, A. I. Kolbin, T. A. Fatkhullin, A. S. Vinokurov

We have analyzed phase-resolved photometric and spectroscopic observations of the eclipsing polar SDSS J002637.06+242915.6. The light curve has a M-shaped bright phase that was reproduced using a simple model of an accreting magnetic white dwarf. The hydrogen emission lines exhibit a narrow component formed on the irradiated hemispere of the donor. The Doppl

Hongru Song, Yu-an Liu, Ruqing Zhang, Jiafeng Guo

Product search is a crucial component of modern e-commerce platforms, with billions of user queries every day. In product search systems, first-stage retrieval should achieve high recall while ensuring efficient online deployment. Sparse retrieval is particularly attractive in this context due to its interpretability and storage efficiency. However, sparse r

Hanze Guo, Jing Yao, Xiao Zhou, Xiaoyuan Yi

As large language models (LLMs) become increasingly integrated into applications serving users across diverse cultures, communities and demographics, it is critical to align LLMs with pluralistic human values beyond average principles (e.g., HHH). In psychological and social value theories such as Schwartz's Value Theory, pluralistic values are represented b

Yushu Zhao, Yubin Qin, Yang Wang, Xiaolong Yang

Large language models achieve impressive performance across diverse tasks but exhibit high inference latency due to their large parameter sizes. While quantization reduces model size, it often leads to performance degradation compared to the full model. Speculative decoding remains lossless but typically incurs extra overheads. We propose SPEQ, an algorithm-

Alejandro Caballero, Thomas F. Allard, Paloma A. Huidobro

Studying the topology of spatiotemporal media poses a fundamental challenge: their remarkable properties stem from breaking spatial and temporal symmetries, yet this same breaking obscures their topological characterization. Here, we show that space-time symmetries persist in crystals with travelling-wave modulations whose velocities can be either lower (sub

Akihiko Monnai

The fluidity of the hot and dense QCD matter is a key characteristic of the medium created in high-energy heavy-ion collisions. We extend the framework of the relativistic hydrodynamic model to incorporate non-thermal momentum distributions that may emerge during the dynamical evolution of the collision system. Numerical simulations are performed to elucidat

Junwen Huang, Shishir Reddy Vutukur, Peter KT Yu, Nassir Navab

Typical template-based object pose pipelines estimate the pose by retrieving the closest matching template and aligning it with the observed image. However, failure to retrieve the correct template often leads to inaccurate pose predictions. To address this, we reformulate template-based object pose estimation as a ray alignment problem, where the viewing di

Christopher Ratigan, Kyle Heuton, Carissa Wang, Lenore Cowen

The ROC curve is widely used to assess binary classifiers. Yet for some applications, such as alert systems for monitoring hospitalized patients, conventional ROC analysis cannot meet two key deployment needs: enforcing a constraint on precision to avoid false alarm fatigue and imposing an upper bound on the number of predicted positives to represent the cap

Md Arafat Hossain, Jun Han, Muhammad Ashad Kabir, Steve Versteeg

Enterprise software systems are increasingly integrating with diverse services to meet expanding business demands. Testing these highly interconnected systems presents a challenge due to the need for access to the connected services. Service virtualization has emerged as a widely used technique to derive service models from recorded interactions, for service

Fang Nan, Hao Ma, Qinghua Guan, Josie Hughes

We present an online model-based reinforcement learning algorithm suitable for controlling complex robotic systems directly in the real world. Unlike prevailing sim-to-real pipelines that rely on extensive offline simulation and model-free policy optimization, our method builds a dynamics model from real-time interaction data and performs policy updates guid

Holger Bech Nielsen

Remarkably accurate fine structure constants are calculated from assumptions further developed from two earlier publications. We have put together a series of energy scales related to various physical phenomena such as the Planck scale, a scale, which we call ``fermion tip'' being a certain extrapolation related to the heaviest Fermions in the Standard Model

Sangyoon Bae, Mehdi Azabou, Blake Richards, Jiook Cha

Neural recordings exhibit a distinctive form of heterogeneity rooted in differences in cell types, intrinsic circuit dynamics, and stochastic stimulus-response variability that goes beyond ordinary dataset variability, mixing statistically regular neurons with highly stochastic, stimulus-contingent ones within the same dataset. This heterogeneity poses a cha

Sureyya Akin, Shruti T. Tiwari, Ram Bhattacharya, Sagar A. Raman

This search introduces the Multimodal Socialized Learning Framework (M-S2L), designed to foster emergent social intelligence in AI agents by integrating Multimodal Large Language Models (M-LLMs) with social learning mechanisms. The framework equips agents with multimodal perception (vision and text) and structured action capabilities, enabling physical manip

Thorsten Groh, Felix Affeld, Simon Stellmer

We present precision isotope shift spectroscopy of the $\mathrm{6s^{2}}\, {}^{1}\mathrm{S}_{0}{\rightarrow\,}\mathrm{6s\, 6p}\, {}^{3}\mathrm{P}_{1}$ intercombination line and the $\mathrm{6s\,6p}\, {}^{3}\mathrm{P}_{1}{\rightarrow\,}\mathrm{6s\,6d}\, {}^{3}\mathrm{D}_{J}$ ($J=1,2$) transitions in neutral mercury, performed on the five naturally abundant eve

Suman Kunwar

The rise of convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked

S. W. Duchesne, J. H. Cook, N. Hurley-Walker, A. J. M. Thomson

In this paper we present a wide-field radio survey at 300 MHz covering the sky from $-90^\circ \leq \delta_\text{J2000} \lesssim +40^\circ$ using the Murchison Widefield Array (MWA). This 300-MHz survey follows the Galactic and Extragalactic All-sky MWA (GLEAM) survey, and provides an additional comparatively high-frequency data point to existing multi-frequ

Alistair Plum, Anne-Marie Lutgen, Christoph Purschke, Achim Rettinger

Large language models have become the latest trend in natural language processing, heavily featuring in the digital tools we use every day. However, their replies often reflect a narrow cultural viewpoint that overlooks the diversity of global users. This missing capability could be referred to as cultural reasoning, which we define here as the capacity of a

Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami, Muhammad Waseem

In this study, we present a solution for the modernization of legacy applications, an area of code generation where LLM-based multi-agent systems are proving essential for complex multi-phased tasks. Legacy applications often contain deprecated components that create compatibility, security, and reliability risks, but high resource costs make companies hesit

Osama Al Haddad, Muhammad Ikram, Ejaz Ahmed, Young Lee

Security analysts face increasing pressure to triage large and complex vulnerability backlogs. Large Language Models (LLMs) offer a potential aid by automating parts of the interpretation process. We evaluate four models (ChatGPT, Claude, Gemini, and DeepSeek) across twelve prompting techniques to interpret semi-structured and unstructured vulnerability info

Huanqing Chen, Zhi Li, Menglai Lei, Muhammet Genc

Resonant cavity LEDs (RCLEDs) exhibit excellent temporal and spatial coherence with narrow spectral linewidth and small divergence angle, which is of great importance for micro-displays. In this paper, we demonstrate a novel method to create GaN-based RCLEDs by using photo-electrochemical etching and micro-transfer printing (MTP) technology. Through systemat

Matthias Johann Steiner

Let $\mathbb{F}_q$ be a finite field, and let $F \in \mathbb{F}_q [X]$ be a polynomial with $d = \text{deg} \left( F \right)$ such that $\gcd \left( d, q \right) = 1$. In this paper we prove that the $c$-Boomerang uniformity, $c \neq 0$, of $F$ is bounded by - $d^2$ if $c^2 \neq 1$, - $d \cdot (d - 1)$ if $c = -1$, - $d \cdot (d - 2)$ if $c = 1$. For all cas

Y. H. Chen, C. M. Duan, H. Shu

IU Leo was first identified as a cataclysmic variable star in 2006. Based on an image data and a distance value, we derived that the circumbinary envelope of IU Leo was $\sim$3,745\,AU on the optical band. According the multi-band photometric data, we calculated a $T_{eff}$ of a few hundred Kelvin for the circumbinary envelope of IU Leo. We reviewed the phys

N. Shavlakadze, N. Odishelidze, B. Pachulia, F. Criado-Aldeanueva

The dynamical boundary value problem for viscoelastic half-space with cut in the form of a strip is considered. The problem is reduced to the singular integral equation of first kind. Using the method of orthogonal polynomials, the integral equation is reduced to an infinite system of linear algebraic equations. The quasi-completely regularity of the obtaine

Adrian Fischer

We revisit the problem of parameter estimation for discrete probability distributions with values in $\mathbb{Z}^d$. To this end, we adapt a technique called Stein's Method of Moments to discrete distributions which often gives closed-form estimators when standard methods such as maximum likelihood estimation (MLE) require numerical optimization. These new e

Wei-Chia Chang, Yan-Ann Chen

Vehicle make and model recognition (VMMR) is an important task in intelligent transportation systems, but existing approaches struggle to adapt to newly released models. Contrastive Language-Image Pretraining (CLIP) provides strong visual-text alignment, yet its fixed pretrained weights limit performance without costly image-specific finetuning. We propose a

Tung-Anh Nguyen, Van-Phuc Bui, Shashi Raj Pandey, Kim Hue Ta

This paper introduces FedSVD, a novel unsupervised federated learning framework for real-time anomaly detection in IoT networks. By leveraging Singular Value Decomposition (SVD) and optimization on the Grassmann manifolds, FedSVD enables accurate detection of both known and unknown intrusions without relying on labeled data or centralized data sharing. Tailo

Shadi Zeinali, Zarghaam Heidar Rizvi, Frank Wuttke

This study experimentally investigates the influence of CaCO3 polymorphs on the thermal conductivity (TC) of biocement and biocemented sand. Calcite-rich biocements consistently showed higher TC than vaterite-dominated ones, regardless of density or saturation. Vaterite-rich biocement, resulting from rapid precipitation and elevated organic content, yields i

Hyewon Lee, Junghyun Oh, Minkyung Song, Soyoung Park

This study presents the multilingual e-commerce search system developed by the DILAB team, which achieved 5th place on the final leaderboard with a competitive overall score of 0.8819, demonstrating stable and high-performing results across evaluation metrics. To address challenges in multilingual query-item understanding, we designed a multi-stage pipeline

M. A. Kurkov

We study the noncommutative $U(1)$ gauge theory on the $\kappa$-Minkowski space-time at the semiclassical approximation. We construct exact solutions of the deformed Maxwell equations in vacuum, describing localized signals propagating in a given direction. The propagation velocity appears to be arbitrary. We figure out that the wave packets with different v

E. Caffau, M. Steffen, P. Molaro, P. Bonifacio

The vast majority of the most iron-poor stars in the Galaxy exhibit a strong carbon enhancement, with C/H ratios only about two orders of magnitude below solar. This unusual chemical composition likely reflects the properties of the gas cloud from which these stars formed, having been enriched by one, or at most a few, supernovae. A remarkable member of this

Amirhossein Akbar Tabatabai, Vitor Greati, Revantha Ramanayake

Inspired by Leivant's work on absolute predicativism, Bellantoni and Cook in 1992 introduced a structurally restricted form of recursion called predicative recursion. Using this recursion scheme on the inductive structures of natural numbers and binary strings, they provide a structural and machine-independent characterization of the classes of linear-space

Anamitra Ghorui, Uday P. Khedker

Analysis of entire programs as a single unit, or whole-program analysis, involves propagation of large amounts of information through the control flow of the program. This is especially true for pointer analysis, where, unless significant compromises are made in the precision of the analysis, there is a combinatorial blowup of information. One of the key pro

M. A. Burlak, K. N. Grankin, A. V. Dodin, N. V. Emelyanov

Based on the analysis of the long-term light curve of the young binary DF Tau spanning approximately 125 years, we infer that its brightness variations are associated with changes in the accretion rate from the circumstellar protoplanetary disk onto the primary. We have also substantially improved the orbital parameters of DF Tau, which enables us to align i

Lies Beers, Raffaella Mulas

We introduce and study Fair and Tolerant colorings (FAT colorings), where each vertex tolerates a given fraction of same-colored neighbors while fairness is preserved across the other coloring classes. Moreover, we define the FAT chromatic number $\chi^{\mathrm{FAT}}(G)$ as the largest integer $k$ for which $G$ admits a FAT $k$-coloring. We establish general

Kangzhong Wang, Zitong Shen, Youqian Zhang, Michael MK Cheung

Phone scams remain a pervasive threat to both personal safety and financial security worldwide. Recent advances in large language models (LLMs) have demonstrated strong potential in detecting fraudulent behavior by analyzing transcribed phone conversations. However, these capabilities introduce notable privacy risks, as such conversations frequently contain

Sheng-He Zhu, Fu-Lai Wang, Xiang Liu

In this work, we conduct a systematic investigation of the electromagnetic properties, specifically the magnetic moments and the M1 radiative decay behavior, of the predicted $\Xi_c^{(',*)}D^{(*)}_s$-type double-charm hidden-strangeness molecular pentaquarks. The study is carried out within the framework of the constituent quark model to evaluate these elect

Chanyoung Chung, Kyeongryul Lee, Sunbin Park, Joyce Jiyoung Whang

Recommender systems have long been built upon the modeling of interactions between users and items, while recent studies have sought to broaden this paradigm by generalizing to new users and items, incorporating diverse information sources, and transferring knowledge across domains. Nevertheless, these efforts have largely focused on individual aspects, hind