November 2025 arXiv papers — page 85

Shan Shan

Achieving Sustainable Development Goal 7 (Affordable and Clean Energy) requires not only technological innovation but also a deeper understanding of the socioeconomic factors influencing energy access and carbon emissions. While these factors are gaining attention, critical questions remain, particularly regarding how to quantify their impacts on energy syst

Wen Shang, Yuan Liao, Vasilis Friderikos, Halim Yanikomeroglu

Despite the significant attention that aerial base stations (ABSs) have received recently, their practical implementation is severely weakened by their limited endurance due to the battery constraints of drones. To overcome this fundamental limitation and barrier for wider adoption, we propose the concept of robotic aerial base stations (RABSs) that are equi

Yi Peng, Hans-Martin Heyn, Jennifer Horkoff

In software engineering processes for machine learning (ML)-enabled systems, integrating and verifying ML components is a major challenge. A prerequisite is the specification of ML component requirements, including models and data, an area where traditional requirements engineering (RE) processes face new obstacles. An underexplored source of RE-relevant inf

Sufiaan Ahmed, Tyrese Lindsay, James W. Roberts

In soccer penalty kicks, goalkeepers that orient their arms upward compared to downward can be misperceived as being taller - effectively recreating the Muller-Lyer illusion. The present study elaborates on previous research surrounding a potential illusion-induced bias in penalty kicks. Participants were exposed to goalkeeper configurations within a virtual

Ronika Sarkar, Arka Bandyopadhyay, Awadhesh Narayan, Diptiman Sen

We introduce a mechanism that produces a Hall-like transverse response in time-reversal-invariant materials, driven entirely by geometric effects. Specifically, we demonstrate that a tilted potential interface causes electron wave packets to undergo a refractionlike deflection upon transmission through the barrier, leading to a finite transverse current and

Zijie Lin, Kangbo Ouyang

We construct a minimal subshift \((X^{*},\sigma)\) that serves as an open proximal extension of its maximal equicontinuous factor. We establish that every point in this subshift is multiply recurrent minimal. This work solves an open problem raised by Huang, Shao and Ye regarding the existence of minimal PI-systems such that each point is multiply minimal.

Rajibul Haque, Ujjal Karmakar, Arnab Mandal

The notion of the quantum automorphism group of a graph was introduced by J. Bichon in 2003 and T. Banica in 2005 respectively. This article explores primarily the quantum automorphism group of a graph $\Gamma$, denoted by $QAut_{Bic}(\Gamma)$, in Bichon's framework. First, we provide a sufficient condition for non-commutativity of Bichon's quantum automorph

Nayoung Oh, Dohyun Kim, Junhyeong Bang, Rohan Paul

Space grounding refers to localizing a set of spatial references described in natural language instructions. Traditional methods often fail to account for complex reasoning -- such as distance, geometry, and inter-object relationships -- while vision-language models (VLMs), despite strong reasoning abilities, struggle to produce a fine-grained region of outp

The Tien Mai

In high-dimensional statistics, the Lasso is a cornerstone method for simultaneous variable selection and parameter estimation. However, its reliance on the squared loss function renders it highly sensitive to outliers and heavy-tailed noise, potentially leading to unreliable model selection and biased estimates. To address this limitation, we introduce the

Anqi Wang, Zhengyi Li, Xin Tong, Pan Hui

Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent formulation, while LLMs treat inputs as complete specifications. This challenges design intent formulation, where designers must progressively refine understanding through exploration. Existing tools either sacr

Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme, Nadja Klein

Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable feature selection. Tasks such as identifying motif interactions in genomics exemplify these difficulties, as only a small subset of biologically relevant features (e.g., motifs) are t

Kamel Ibn Aziz Derouiche Jihbed

This article focuses on the mathematical publications of Zygmunt Janiszewski (1888-1920), a major figure in Polish science at the beginning of the twentieth century. Serving in the Polish Legion between 1914 and 1920 in the struggle for national independence, Janiszewski was not only one of the founders of the Polish School of Mathematics, but also the initi

Huseyin Goksu

Spectral Graph Neural Networks (GNNs) suffer from two critical limitations: poor performance on "heterophilic" graphs and performance collapse at high polynomial degrees (K), known as over-smoothing. Both issues stem from the static, low-pass nature of standard filters (e.g., ChebyNet). While adaptive polynomial filters, such as the discrete MeixnerNet, have

Huseyin Goksu

Spectral Graph Neural Networks (GNNs) based on polynomial filters, such as ChebyNet, suffer from two critical limitations: 1) performance collapse on "heterophilic" graphs and 2) performance collapse at high polynomial degrees (K), known as over-smoothing. Both issues stem from the static, low-pass nature of standard filters. In this work, we propose `Krawtc

Clemens Hutter, Valentin Abadie, Helmut Bölcskei

Classical neural network approximation results take the form: for every function $f$ and every error tolerance $\epsilon > 0$, one constructs a neural network whose architecture and weights depend on $\epsilon$. This paper introduces a fundamentally different approximation paradigm that reverses this quantifier order. For each target function $f$, we constru

Nikolaos Samaras

Milgromian Dynamics (MOND) has been particularly successful in predicting scaling relations for galactic systems, namely the baryonic Tully-Fisher for spirals, the Faber-Jackson for ellipticals and the Radial Acceleration Relation for late-type galaxies. Its essential tenet is the modification of the gravity law at low accelerations. Nevertheless, despite MO

Atharva Pandey, Abhilash Neog, Gautam Jajoo

Time-series Foundation Models (TSFMs) have recently emerged as a universal paradigm for learning across diverse temporal domains. However, despite their empirical success, the internal mechanisms by which these models represent fundamental time-series concepts remain poorly understood. In this work, we undertake a systematic investigation of concept interpre

Youwei Xiao, Yuyang Zou, Yun Liang

Hardware synthesis from high-level descriptions remains fundamentally limited by the sequential optimization of interdependent design decisions. Current methodologies, including state-of-the-art high-level synthesis (HLS) tools, artificially separate implementation selection from scheduling, leading to suboptimal designs that cannot fully exploit modern FPGA

Zahra Farzadpour, Masoumeh Azghani

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistan

Vishal Kachhad, Amit Joshi, Luigi Glielmo

This article proposes an optimization problem formulation to find the optimal sizes of Photovoltaics (PV) and Battery Energy Storage Systems (BESS) for individual participants within the context of the Renewable Energy Community (REC). An optimization problem considered the dynamic nature of electricity pricing, solar irradiation levels, financial aspects su

Shu Tamano, Yui Tomo

General Bayesian updating replaces the likelihood with a loss scaled by a learning rate, but posterior uncertainty can depend sharply on that scale. We propose a simple post-processing that aligns generalized posterior draws with their asymptotic target, yielding uncertainty quantification that is invariant to the learning rate. We prove total-variation conv

Dario Spirito

We introduce smooth sequences of integral domains as well-ordered ascending chains that behave well at limit ordinals. Subsequently, we use this notion to give some conditions on the freeness of kernels of extension maps between groups of invertible ideals of Pr\"ufer domains. We also define overring operators to construct smooth sequences in a recursive way

Plouton Grammatikos, Ali Mohamed Ali, Fabrizio Sossan

This paper describes a method for computing price signals for prosumers, incentivizing them to adjust their consumption according to the constraints of the distribution grids to which they are connected, thereby preventing voltage violations and line congestion. The proposed method leverages an interpretation of the Alternating Direction Method of Multiplier

Zhihan Ren, Lijun He, Jiaxi Liang, Xinzhu Fu

Split DNNs enable edge devices by offloading intensive computation to a cloud server, but this paradigm exposes privacy vulnerabilities, as the intermediate features can be exploited to reconstruct the private inputs via Feature Inversion Attack (FIA). Existing FIA methods often produce limited reconstruction quality, making it difficult to assess the true e

Abdelhamid Ezzerg, Ilija Bogunovic, Jeremias Knoblauch

Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded cumulative corruption budget, which makes them defenseless against even a single corruption of sufficient magnitude. To address this, we introduce a new adversary whose budget is only bounded in the frequency of corruptions, not in

Ziyang You, Wenhui Huang, Libo Zhang, Song Liu

Although known for negatively impacting the operation of superconducting qubits, thermal baths are shown to exert qubit control in a positive way, provided they are properly engineered. We demonstrate an experimental method to engineer the transduction of microwave driving into heat flow through a leaky resonator. Given the precise conversion, a qubit receiv

Zhi-Qiang Ding, Xin-Qiao Li, Da-Li Zhang, Zheng-Hua An

Accurate spectral analysis of high-energy astrophysical sources often relies on comparing observed data to incident spectral models convolved with the instrument response. However, for Gamma-Ray Bursts and other high-energy transient events observed at high count rates, significant distortions (e.g., pile-up, dead time, and large signal trailing) are introdu

Mauro Larrat, Claudomiro Sales

Unmanned aerial vehicle (UAV) detection and aerial object recognition are critical for modern surveillance and security, prompting a need for robust systems that overcome limitations of single-modality approaches. This research addresses these challenges by designing and rigorously evaluating a novel multimodal Transformer model that integrates diverse data

Mehran Tamjidi, Hamidreza Dastmalchi, Mohammadreza Alimoradijazi, Ali Cheraghian

3D Vision-Language Foundation Models (VLFMs) have shown strong generalization and zero-shot recognition capabilities in open-world point cloud processing tasks. However, these models often underperform in practical scenarios where data are noisy, incomplete, or drawn from a different distribution than the training data. To address this, we propose Uni-Adapte

Thomas Drant, Ella Sciamma-O'Brien, Lora Jovanovic, Zoé Perrin

Previous observations of Titan, Pluto and Solar System gas giants along with recent observations of exoplanet atmospheres with the James Webb Space Telescope taught us that photochemical hazes are ubiquitous and form in a variety of temperature, gas composition and irradiation environments. Despite being crucial to understand their impact on observations and

Jyun-Heng Lin, Chin-Fei Lee, Zhi-Yun Li, Yueh-Ning Lee

Magnetic braking can drive angular momentum loss in star formation and influence disk evolution. A previous study of HH 111 VLA1 suggested a decrease in rotation velocity in a region between the infalling envelope and rotating disk. Using ALMA C$^{18}$O ($J = 2-1$) data, we analyzed the gas motion within 6000 au and found clear deviations from the simplest e

Yan Xia, Letian Shi, Yilin Di, Joao F. Henriques

We tackle the problem of localizing 3D point cloud submaps using complex and diverse natural language descriptions, and present Text2Loc++, a novel neural network designed for effective cross-modal alignment between language and point clouds in a coarse-to-fine localization pipeline. To support benchmarking, we introduce a new city-scale dataset covering bot

Yuejie Zhang, Jinjun Ding, Tao Liu, Xiaofei Yang

We report that due to the orbital Hall effect, orbital pumping effects can occur in materials with weak spin-orbit coupling. Moreover, there is a positive correlation between the strength of the orbital Hall effect and the size of spin-pumping. During the spin-pumping, with the enhancement of the orbital Hall effect, the resonant absorption of orbital curren

Purvi Gupta, Rudranil Sahu

Given a compact smooth totally real immersed $n$-submanifold $M\subset\mathbb C^n$ with only finitely many transverse double points, it is known that if $M$ is Lagrangian with respect to some K{\"a}hler form on $\mathbb C^n$, then it is rationally convex in $\mathbb C^n$ (Gayet, 2000), but the converse is not true (Mitrea, 2020). We show that $M$ is Lagrangi

Michel Crouzeix

We describe the set of inner functions of finite order in a multi-connected domain, then we consider an optimization formulation of the Pick-Nevanlinna interpolation problem, and we generalize it to Hermite type interpolation.

Piercosma Bisconti, Matteo Prandi, Federico Pierucci, Francesco Giarrusso

We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for Large Language Models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poet

Radek Machulka, Václav Michálek, Ondřej Haderka, Jan Peřina

In this paper, we address the calibration of the quantum efficiency of single-photon cameras using radioluminescent light sources. The proposed methods are subsequently compared with absolute calibration techniques based on the detection of correlated photon pairs. Furthermore, we propose a method for transferring absolute calibration using the aforementione

Stelios Negrepontis, Athanase Papadopoulos

Even though Plato's philosophy in ancient times was always closely associated with mathematics, modern Platonic scholarship, during the last five centuries, has moved steadily toward de-mathematization. The present work aims to outline a radical re-interpretation of Plato's philosophy, according to which the Platonic Idea, that is, the intelligible Being, ha

Rayen Dhahri, Steffen Urban

Specialized edge accelerators rely on low-bit quantization, but vendor compilers differ in scaling, clipping, and kernel support, often as black boxes. The same floating-point (FP) checkpoint can therefore yield inconsistent accuracy across backends, forcing practitioners to tweak flags or refactor models to vendor-friendly operator subsets. We introduce Qua

Jialong Sun, Hongguang Zhu, Weizhe Liu, Yunda Sun

Training prohibited item detection models requires a large amount of X-ray security images, but collecting and annotating these images is time-consuming and laborious. To address data insufficiency, X-ray security image synthesis methods composite images to scale up datasets. However, previous methods primarily follow a two-stage pipeline, where they impleme

Aymeric Guillot, Julien Gauthier, Jeanne Lejoncour, Franck Di Rienzo

There is now ample evidence that Motor Imagery (MI) contributes to improve motor performance. Previous studies provided evidence that its effectiveness remains dependent upon specific guidelines and recommendations. The body posture, as well as the context in which MI is performed, are notably critical and should be carefully considered. The present study in

Sourav Ghosh

We consider the rescaled flow associated with a mean curvature flow that develops a compact singularity of multiplicity one. We prove that the ``decay order'' of such a rescaled flow is uniformly bounded. As a consequence, we prove a unique continuation result.

Shabbir Anees, Anshuman, Ayush Chaurasia, Prathmesh Bogar

It is well known that fraudulent reviews cast doubt on the legitimacy and dependability of online purchases. The most recent development that leads customers towards darkness is the appearance of human reviews in computer-generated (CG) ones. In this work, we present an advanced machine-learning-based system that analyses these reviews produced by AI with re

Thomas Kronland-Martinet, Stéphane Viollet, Laurent U Perrinet

In the context of spiking neural networks, temporal coding of signals is increasingly preferred over the rate coding hypothesis due to its advantages in processing speed and energy efficiency. In temporal coding, synaptic delays are crucial for processing signals with precise spike timings, known as spiking motifs. Synaptic delays are however bounded in the

Badrinath Ramakrishnan, Akshaya Balaji

Retrieval-augmented generation (RAG) systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled AI agents and propose a multi-layered defense framework. Our b

Alexandr Klimchik, Anatol Pashkevich, Damien Chablat

The paper presents a systematic approach for stiffness modeling of manipulators with complex and hybrid structures using matrix structural analysis. In contrast to previous results, it is suitable for mixed architectures containing closed-loops, flexible links, rigid connections, passive and elastic joints with external loadings and preloadings. The proposed

Jia Li, Zhi Jin, Huangzhao Zhang, Kechi Zhang

Software development automation is a long-term goal in software engineering. With the development of artificial intelligence (AI), more and more researchers are exploring approaches to software automation. They view AI systems as tools or assistants in software development, still requiring significant human involvement. Another initiative is ``vibe coding'',

Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie

Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adversarial attack frameworks face certain limitations, e.g., impracticality due to the requirement of white-box information or high control authority, and a lack of stealthiness or ef

Randa Zarnoufi

Sentiment analysis of Arabic dialects presents significant challenges due to linguistic diversity and the scarcity of annotated data. This paper describes our approach to the AHaSIS shared task, which focuses on sentiment analysis on Arabic dialects in the hospitality domain. The dataset comprises hotel reviews written in Moroccan and Saudi dialects, and the

Nicolas Gautier, Yves Guillermit, Mathieu Porez, David Lemoine

This study presents a methodology for determining the optimal base placement of a Fanuc CRX10iA/L collaborative robot for a desired trajectory corresponding to an industrial task. The proposed method uses a particle swarm optimization algorithm that explores the search space to find positions for performing the trajectory. An $\alpha$-shape algorithm is then

Daniele Bartolucci, Aleks Jevnikar, Juncheng Wei, Ruijun Wu

The qualitative behavior of the Rabinowitz unbounded continuum of subcritical Gelfand problems is well known on balls in any dimension. We don't know of any such sharp and detailed description otherwise, which is our motivation to look for a new approach to the problem. The underlying idea is to describe solutions of Gelfand problems via suitably defined con

Yanni Ma, Hao Liu, Yulan Guo, Theo Gevers

3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adopt object-centric graph neural networks, where relation edge features are iteratively updated by aggregating messages from connected object nodes. However, this design inherently re

Seongyeon Kim, Ki-Hyun Kim, Shenghui Cui, Jae-Jung Jung

Under asymmetrical faults, analyzing the transient stability of grid-forming voltage-source converters (GFM-VSCs) becomes essential because their behavior fundamentally differs from that under symmetrical faults. When current limiting is activated under asymmetrical faults, the point-of-common-coupling voltage of a GFM-VSC contains both positive- and negativ

Laura Baldelli, Norihisa Ikoma

This paper concerns the existence of normalized solutions to a class of $(2,q)$-Laplacian equations with a power type nonlinearity in the intermediate regime between the two mass critical exponents $2(1+2/N)$, $q(1+2/N)$. More precisely, we prove the existence of solutions with negative energy obtained through a global minimization procedure, and of solution

Jonas De Maeyer, Hossein Yarahmadi, Moharram Challenger

Path planning in dynamic environments is a fundamental challenge in intelligent transportation and robotics, where obstacles and conditions change over time, introducing uncertainty and requiring continuous adaptation. While existing approaches often assume complete environmental unpredictability or rely on global planners, these assumptions limit scalabilit

Manuel Asorey, Gastão Krein, Miguel Pardina, Ilya L. Shapiro

The inclusion of higher derivatives is a necessary condition for a renormalizable or superrenormalizable local theory of quantum gravity. On the other hand, higher derivatives lead to classical instabilities and a loss of unitarity at the quantum level. A standard way to detect such issues is by examining the reflection positivity condition and the existence

Ninell Oldenburg, Ruchira Dhar, Anders Søgaard

In this paper, we argue that current AI research operates on a spectrum between two different underlying conceptions of intelligence: Intelligence Realism, which holds that intelligence represents a single, universal capacity measurable across all systems, and Intelligence Pluralism, which views intelligence as diverse, context-dependent capacities that cann

Sarah C. Gillespie, Jérome Gautier, Linde M. van de Ven, Agustin O. Alvarez

Metal halide perovskites exhibit coupled electronic and ionic properties that determine their photovoltaic performance and operational stability. Understanding and quantifying ionic transport are therefore essential for advancing perovskite optoelectronics. Conventional electrical methods such as impedance spectroscopy require fully integrated devices, and t

Pratham Sorte

Modern artificial intelligence systems depend heavily on large datasets for both training and transferring knowledge between models. Knowledge distillation, transfer learning, and dataset distillation have made such transfers more efficient, yet they remain fundamentally data-driven: a teacher must produce examples, logits, or gradients for a student to lear

Sam Adriaensen, Jan De Beule, Jozefien D'haeseleer, Sam Mattheus

DISCLAIMER: Due to an error in the literature, we cannot be sure that the conclusions drawn in this paper are correct. The goal of this note is to connect some interesting results in the literature on algebraic graph theory and finite geometry. In 1999, Weng gave an almost complete classification of classical distance-regular graphs of negative type with dia

Miguel M. Valero, Marcello Meldi

Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterised by statistical stationarity or limited temporal variability. This work proposes a methodology that combines Data Assimilation (DA) and Machine Learning (ML) to predict flow config

Jiashu Yang, Yifan Han, Yucheng Xie, Ning Guo

In embodied AI, visual perception should be active rather than passive: the system must decide where to look and at what scale to sense to acquire maximally informative data under pixel and spatial budget constraints. Existing vision models coupled with fixed RGB-D cameras fundamentally fail to reconcile wide-area coverage with fine-grained detail acquisitio

Nilesh Vyas, Benjamin Zhao, Aygün Baltaci, Gustavo de Carvalho Bertoli

The proliferation of IoT devices in shared, multi-vendor environments like the modern aircraft cabin creates a fundamental conflict between the promise of data collaboration and the risks to passenger privacy, vendor intellectual property (IP), and regulatory compliance. While emerging standards like the Cabin Secure Media-Independent Messaging (CSMIM) proto

Jorge Fariña-Asategui, Paul-Henry Leemann, Tatiana Nagnibeda

For a weakly branch group $G$ acting on a regular enough rooted tree, we provide two constructions of continuous families of distinct subgroups that are not closed in the profinite topology on $G$. On the one hand, we construct a continuous family of distinct non-closed subgroups such that each $H$ in the family is not ERF, that is, contains subgroups not cl

Hyeongheon Cha, Dong Min Kim, Hye Won Chung, Taesik Gong

Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computational cost, making them unsuitable for resource-constrained edge environments. To address this, we propose SNAP, a sparse TTA framework that reduces adaptation frequency and data usa

Wei-Jian Guo, Victoria A. Fawcett, Małgorzata Siudek, Yan-Rong Li

From a parent catalog of 561 changing-look active galactic nuclei (CL-AGNs) identified by Guo et al. (2025), we investigate the evolutionary sequence of broad emission lines using a redshift-selected subset (0.35 < z < 0.45) of 54 CL-AGNs whose Dark Energy Spectroscopic Instrument (DESI) spectra simultaneously cover the H{\alpha}, H\b{eta}, and Mg II emissio

Alexander Boldachev

This paper compares two distinct approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO), implemented through the boldsea framework. BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules. We demon

Alexander Stotsky

This report describes a new regularization approach based on segmentation of the forgetting profile in sliding window least squares estimation. Each segment is designed to enforce specific desirable properties of the estimator such as rapidity, desired condition number of the information matrix, accuracy, numerical stability, etc. The forgetting profile is d

Nilesh Vyas, Konstantin Baier

The security of future large-scale IoT networks is critically threatened by the ``Harvest Now, Decrypt Later'' (HNDL) attack paradigm. Securing the massive, long-lived data streams from these systems requires protocols that are both quantum-resistant and highly scalable. Existing solutions are insufficient: post-quantum classical protocols rely on computatio

Loveneet Saini, Hasan Tercan, Tobias Meisen

Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional grid and sequence-based convolutional and transformer detectors. This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar a

Chih-Pin Tan, Hsuan-Kai Kao, Li Su, Yi-Hsuan Yang

Recent advances in AI-based music generation have focused heavily on text-conditioned models, with less attention given to reference-based generation such as song adaptation. To support this line of research, we introduce LargeSHS, a large-scale dataset derived from SecondHandSongs, containing over 1.7 million metadata entries and approximately 900k publicly

Mohammad Mortezaei Nobahari

We investigate the nonequilibrium topological phases of monolayer 1T$^\prime$--MoS$_2$ under high-frequency circularly polarized driving using a low-energy $k\!\cdot\!p$ Hamiltonian combined with a van Vleck expansion. The off-resonant field generates spin- and valley-dependent mass corrections that reshape the Berry curvature profile and shift the condition

Marco Bonetti, Gudrun Heinrich, Stephen Jones, Matthias Kerner

Constraining the Higgs boson self-interaction is one of the main goals for the high luminosity phase of the LHC. A promising channel to this aim is the simultaneous production of two Higgs bosons from gluon fusion. For the interpretation of the data, precise theoretical predictions, also for differential cross sections, are needed. Following current projecti

Liangyu Chen, Yichen Xu, Jianzhe Ma, Yuqi Liu

Chart editing reduces manual effort in visualization design. Typical benchmarks limited in data diversity and assume access to complete chart code, which is seldom in real-world scenarios. To address this gap, we present ChartEditVista, a comprehensive benchmark consisting of 7,964 samples spanning 31 chart categories. It encompasses diverse editing instruct

Marco Grandis, Robert Paré

We construct various multiple categories, based on generalised Ehresmann quintets. The main construction is a multiple category whose objects are all the `lax' multiple categories; the transversal arrows are their strict multiple functors while the arrows in a positive direction are multiple functors of a `mixed laxity', varying from the lax ones (in directi

Yudong Wang, Zhe Yang, Wenhan Ma, Zhifang Sui

While reinforcement learning has unlocked unprecedented complex reasoning in large language models, it has also amplified their propensity for hallucination, creating a critical trade-off between capability and reliability. This work confronts this challenge by introducing a targeted RL framework designed to mitigate both intrinsic and extrinsic hallucinatio

Zhengyan Wu

We study the scaling limit behavior of a family of conservative SPDEs as the fluctuating Ising-Kac-Kawasaki dynamics. Precisely, we show that there exists a sequence of the one-dimensional rescaled fluctuating Ising-Kac-Kawasaki equation converges to the solution of the stochastic Cahn-Hilliard equation. This solves a simple version of the conjecture concern

Tomas Espana, Yadh Hafsi, Fabrizio Lillo, Edoardo Vittori

We investigate the use of Reinforcement Learning for the optimal execution of meta-orders, where the objective is to execute incrementally large orders while minimizing implementation shortfall and market impact over an extended period of time. Departing from traditional parametric approaches to price dynamics and impact modeling, we adopt a model-free, data

Chaohua Duan, Yan Jiang, Hongyu Liu, Wenjian Peng

This paper introduces a novel wave front tracking framework for reconstructing unknown flux functions in $2\times 2$ hyperbolic conservation laws, extending beyond the well-studied scalar case. By analyzing Riemann solutions at fixed observation times, we develop explicit reconstruction formulas that handle arbitrary combinations of shock and rarefaction wav

Sowmya Vajjala

In this paper, we report the results of the TeamNRC's participation in the BHASHA-Task 1 Grammatical Error Correction shared task https://github.com/BHASHA-Workshop/IndicGEC2025/ for 5 Indian languages. Our approach, focusing on zero/few-shot prompting of language models of varying sizes (4B to large proprietary models) achieved a Rank 4 in Telugu and Rank 2

Philipp Wiesner, Daniel W. O'Neill, Francesca Larosa, Odej Kao

AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing's global energy footprint has been stabilized by sustained efficiency gains and natural saturation thresholds in demand. But as efficiency improvements are approaching physical li

Yitong Yang, Yinglin Wang, Changshuo Wang, Yongjun Zhang

Disentangling image content and style is essential for customized image generation. Existing SDXL-based methods struggle to achieve high-quality results, while the recently proposed Flux model fails to achieve effective content-style separation due to its underexplored characteristics. To address these challenges, we conduct a systematic analysis of Flux and

Hiep Hong Trinh, Federico Ciccozzi, Abu Naser Masud, Marjan Sirjani

Complex software-driven systems often interleave distributed, concurrent computation processes with physical interactions with the environment. Developing these systems more efficiently and safely can be achieved by employing actionable, software-based models. From a high-level system model, engineers often need to derive multiple specialized models for diff

Yanchen Xu, Ziheng Jiao, Hongyuan Zhang, Xuelong Li

The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical applications such as DeepSeek-R1. It raises a question whether GRPO can be generalized to representation learning models. In this paper, we propose Group Relative Policy Optimization for Repr

Yassine Hamdi, Aaron B. Wagner, Deniz Gündüz

Realism constraints (or constraints on perceptual quality) have received considerable recent attention within the context of lossy compression, particularly of images. Theoretical studies of lossy compression indicate that high-rate common randomness between the compressor and the decompressor is a valuable resource for achieving realism. On the other hand,

Xiaozhi Liu, Yong Xia

The Extragradient (EG) method stands as a cornerstone algorithm for solving monotone nonlinear equations but faces two important unresolved challenges: (i) how to select stepsizes without relying on the global Lipschitz constant or expensive line-search procedures, and (ii) how to reduce the two full evaluations of the mapping required per iteration to effec

Sirui Chen, Jinsong Zhou, Xinli Xu, Xiaoyu Yang

Effective presentation skills are essential in education, professional communication, and public speaking, yet learners often lack access to high-quality exemplars or personalized coaching. Existing AI tools typically provide isolated functionalities such as speech scoring or script generation without integrating reference modeling and interactive feedback i

Chandra Sekhar Reddy Kolli, Gowtham Polumati, Aleksandra A. Kutuzova, Ekaterina E. Maslova

Engineering nanoscale light matter interaction in mixed dimensional semiconductor heterostructures offers a pathway to mitigate the intrinsic gain bandwidth trade off in photodetectors. Here, we report a broadband, high responsivity 2D and 1D photodetector formed by integrating monolayer p type WSe2 with electrospun p type NiO nanowires. The device photoresp

Kareem Shehada, Yifan Wu, Wyatt D. Feng, Adithya Iyer

Large Language Models (LLMs) have revolutionized automated program repair (APR) but current benchmarks like SWE-Bench predominantly focus on userspace applications and overlook the complexities of kernel-space debugging and repair. The Linux kernel poses unique challenges due to its monolithic structure, concurrency, and low-level hardware interactions. Prio

Chengze Du, Heng Xu, Zhiwei Yu, Bo Liu

Network tomography aims to infer hidden network states, such as link performance, traffic load, and topology, from external observations. Most existing methods solve these problems separately and depend on limited task-specific signals, which limits generalization and interpretability. We present PLATONT, a unified framework that models different network ind

Jin Ye, Lingmei Wang, Shujian Zhang, Haihang Wu

With the global energy transition and rapid development of renewable energy, the scheduling optimization challenge for combined power-heat systems under new energy integration and multiple uncertainties has become increasingly prominent. Addressing this challenge, this study proposes an intelligent scheduling method based on the improved Dual-Delay Deep Dete

Laurent Chaput, Henrique Miranda, Atsushi Togo, Manuel Engel

In this paper, we propose a computational framework, based on the VASP and phono3py computer codes, to obtain the thermoelectric figure of merit from the electron-phonon and phonon-phonon interactions using finite displacements in supercells. Several numerical techniques are developed for efficiency. The method is applied to several thermoelectric materials.

Kai Yang, Xin Xu, Yangkun Chen, Weijie Liu

Long-term training of large language models (LLMs) requires maintaining stable exploration to prevent the model from collapsing into sub-optimal behaviors. Entropy is crucial in this context, as it controls exploration and helps avoid premature convergence to sub-optimal solutions. However, existing reinforcement learning methods struggle to maintain an appr

Antonio Ferrer-Sánchez, Nino Villanueva-Espinosa, Carlos Hernani Morales, Roberto Ruiz de Austri-Bazan

The gravitational collapse of a massless scalar field remains a demanding benchmark for numerical methods in numerical relativity, as it exhibits critical behavior at the boundary between dispersion and black hole formation. In this work we revisit this problem by relying on Physics-Informed Neural Networks (PINNs) as flexible solvers for partial differentia

Tung Giang Le, Xuan Tung Nguyen, Won-Joo Hwang

Increasing wireless network complexity demands scalable resource management. Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Network (QGNN) that implements message passing via Parameterized Quantum Circuits (PQCs). Our Quantum Graph Convolutional Layers (QGCLs) encode

Chuanlei Li, Zhicheng Sun, Jing Xin Yuu, Xuechao Wang

Cross-chain interoperability is a core component of modern blockchain infrastructure, enabling seamless asset transfers and composable applications across multiple blockchain ecosystems. However, the transparency of cross-chain messages can inadvertently expose sensitive transaction information, creating opportunities for adversaries to exploit value through

Fanfan Liu, Haibo Qiu

Million-level token inputs in long-context tasks pose significant computational and memory challenges for Large Language Models (LLMs). Recently, DeepSeek-OCR conducted research into the feasibility of Contexts Optical Compression and achieved preliminary results. Inspired by this, we introduce Context Cascade Compression C3 to explore the upper limits of te

Stéphane Louboutin

We fill the gaps in A. Gica's determination of all the odd positive integers $d$ for which the number of distinct prime divisors of $f_d(x)=d+x^2$ is less than or equal to $2$ for all the positive and odd integers $x\leq\sqrt{d}$. We also determine all the even positive integers $d$ for which the number of distinct prime divisors of $f_d(x)$ is less than or

Gleb Berloff, Natalia G. Berloff

We give a preparation-invariant analytic description of image formation near microlensing caustics. After a local Weierstrass preparation at any multiple image (order $d\ge2$), the lens mapping reduces to a single geode variable $m$ satisfying $m=U\,\varphi(m)$, where $U$ is a prepared source coordinate and $\varphi$ is an image-side kernel. The coefficients

Yuhao Shen, Zhangtianyi Chen, Yuanhao He, Yan Xu

The clinical translation of dermatological AI is hindered by opaque reasoning and systematic performance disparities across skin tones. Here we present SkinGPT-R1, a multimodal large language model that integrates chain-of-thought diagnostic reasoning with a fairness-aware mixture-of-experts architecture for interpretable and equitable skin disease diagnosis