October 2025 arXiv papers — page 35

Hiroaki Hashida, Boya Di

Interference management is a central bottleneck in dense multi-antenna wireless networks. Therefore, in this study, we present a digital precoding-free hierarchical rate-splitting multiple access (HRSMA) architecture assisted by a stacked intelligent metasurface (SIM) to achieve high spectral efficiency and user fairness with reduced hardware complexity. In

Lysander Miller, Airlie Chapman, James Kennedy, Richard Hebden

Advances in scintillation crystal and Silicon PhotoMultiplier (SiPM) technologies have enabled the development of compact, lightweight, and low-power radiation detectors that are suitable for integration with Unmanned Aerial Vehicles (UAVs). This integration enables efficient and cost-effective large-area radiation monitoring while minimising occupational ex

Yeor Hafouta

We prove Local Central Limit Theorems (LLT) for partial sums of the form $S_n=\sum_{j=0}^{n-1}f_j(...,X_{j-1},X_j,X_{j+1},...)$, where $(X_j)$ is a Markov chains with equicontinuous conditional probabilities satisfying contraction conditions close in spirit to Dobrushin's, and some ``physicality" assumptions and $f_j$ are equicontinuous functions. Our condit

Clément Bénard

Tree ensembles have demonstrated state-of-the-art predictive performance across a wide range of problems involving tabular data. Nevertheless, the black-box nature of tree ensembles is a strong limitation, especially for applications with critical decisions at stake. The Hoeffding or ANOVA functional decomposition is a powerful explainability method, as it b

Duc Nguyen Dao, Haibin Zhang, Andre B. J. Kokkeler, Yang Miao

In this paper, we consider a full-duplex (FD) Integrated Sensing and Communication (ISAC) system, in which the base station (BS) performs downlink and uplink communications with multiple users while simultaneously sensing multiple targets. In the scope of this work, we assume a narrowband and static scenario, aiming to focus on the beamforming and power allo

Zihan Li, Jiahao Yang, Yuxin Zhang, Zhe Chen

Large vision-language models (LVLMs) have recently demonstrated great potential in remote sensing (RS) tasks (e.g., disaster monitoring) conducted by low Earth orbit (LEO) satellites. However, their deployment in real-world LEO satellite systems remains largely unexplored, hindered by limited onboard computing resources and brief satellite-ground contacts. W

Zixian Zhang, Takfarinas Saber

Code clone detection is a fundamental task in software engineering that underpins refactoring, debugging, plagiarism detection, and vulnerability analysis. Existing methods often rely on singular representations such as abstract syntax trees (ASTs), control flow graphs (CFGs), and data flow graphs (DFGs), which capture only partial aspects of code semantics.

Edward Markai, Sina Molavipour

Temporal Knowledge Graphs have emerged as a powerful way of not only modeling static relationships between entities but also the dynamics of how relations evolve over time. As these informational structures can be used to store information from a real-world setting, such as a news flow, predicting future graph components to a certain extent equates predictin

Maja Szymczak, Jan Hočevar, Jernej Iskra, Darja Lisjak

In this work, we demonstrate the use of CQDs synthesized from lignin - currently one of the most abundant and underutilized by-products of paper and pulp production - for advanced pH monitoring applications. The presented approach integrates green chemistry principles with an operator-friendly, low-cost, and practical solution for spatial and temporal pH mea

Jinting Wang, Jun Wang, Hei Victor Cheng, Li Liu

Unlike existing methods that rely on source images as appearance references and use source speech to generate motion, this work proposes a novel approach that directly extracts information from the speech, addressing key challenges in speech-to-talking face. Specifically, we first employ a speech-to-face portrait generation stage, utilizing a speech-conditio

Teague McMillan, Gabriele Dominici, Martin Gjoreski, Marc Langheinrich

Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especially problematic: explanations that omit salient clinical cues or mask spurious shortcuts can undermine clinician trust and lead to unsafe decision support. We study how inference a

Ai Jian, Jingqing Ruan, Xing Ma, Xiaoyun Zhang

Reward models (RMs) are central to reinforcement learning from human feedback (RLHF), providing the critical supervision signals that align large language models (LLMs) with human preferences. Generative reward models (GRMs) provide greater interpretability than traditional scalar RMs, but they come with a critical trade-off: pairwise methods are hindered by

Ludovic Schwartz, Hamish Flynn, Gergely Neu

Many high-dimensional online decision-making problems can be modeled as stochastic sparse linear bandits. Most existing algorithms are designed to achieve optimal worst-case regret in either the data-rich regime, where polynomial dependence on the ambient dimension is unavoidable, or the data-poor regime, where dimension-independence is possible at the cost

Antoine Szatkownik, Aurélien Decelle, Beatriz Seoane, Nicolas Bereux

Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or protected content. This raises critical concerns around privacy-preserving synthetic data, and more specifically around privacy leakage, an issue closely tied to overfitting. Existing methods almost exclusively rel

Qing Zhao, Weijian Deng, Pengxu Wei, ZiYi Dong

To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration -- an issue that remains underexplore

Waseem Shariff, Timothy Hanley, Maciej Stec, Hossein Javidnia

Microsaccades are small, involuntary eye movements vital for visual perception and neural processing. Traditional microsaccade studies typically use eye trackers or frame-based analysis, which, while precise, are costly and limited in scalability and temporal resolution. Event-based sensing offers a high-speed, low-latency alternative by capturing fine-grain

David Gontier, Thaddeus Roussigné, Éric Séré

We investigate a conventional tight-binding model for graphene, where distortion of the honeycomb lattice is allowed, but penalized by a quadratic energy. We prove that the optimal 3-periodic lattice configuration has Kekul\'e O-type symmetry, and that for a sufficiently small elasticity parameter, the minimizer is not translation-invariant. Conversely, we p

Surender Kumar, Stefan Velja, Muhammad Sufyan Ramzan, Caterina Cocchi

The control of localized magnetic domains at the nanoscale holds great promise for next-generation spintronic applications. Colloidal transition metal dichalcogenides nanostructures are experimentally accessible and chemically tunable platforms for spintronics, deserving dedicated research to assess their potential. Here, we investigate from first principles

Luis Romero-Ben, Paul Irofti, Florin Stoican, Vicenç Puig

The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalma

Senuri Wijenayake, Joanne Gray, Asangi Jayatilaka, Louise La Sala

The growing prevalence of negative experiences in online spaces demands urgent attention from the human-computer interaction (HCI) community. However, research on online safety remains fragmented across different HCI subfields, with limited communication and collaboration between disciplines. This siloed approach risks creating ineffective responses, includi

Shuichi Hirahara, Naoto Ohsaka, Tatsuhiro Suga, Akira Suzuki

In reconfiguration problems, we are given two feasible solutions to a graph problem and asked whether one can be transformed into the other via a sequence of feasible intermediate solutions under a given reconfiguration rule. While earlier work focused on modifying a single element at a time, recent studies have started examining how different rules impact c

Christian Dustmann, Sebastian Otten, Uta Schönberg, Jan Stuhler

Most studies on the labor market effects of immigration use repeated cross-sectional data to estimate the effects of immigration on regions. This paper shows that such regional effects are composites of effects that address fundamental questions in the immigration debate but remain unidentified with repeated cross-sectional data. We provide a unifying empiri

Yichen Gao, Daniel D. B. Koll, Feng Ding

Hycean planets are hypothetical exoplanets characterized by $H_2O$ oceans and $H_2$-rich atmospheres. These planets are high-priority targets for biosignature searches, as they combine abundant surface liquid water with easy-to-characterize $H_2$-rich atmospheres. Perhaps their most unusual climate feature is convective inhibition, which can dramatically alt

Mahmut Kemal Ercan, Alireza Pourafzal, Musa Furkan Keskin, Sinan Gezici

We study uplink orthogonal frequency-division multiplexing (OFDM) pilot distortion to deliberately obfuscate time-of-arrival (ToA) estimation at a single base station while preserving communication performance. We design a complex per-subcarrier distortion vector that increases sidelobes of the mismatched ambiguity function (MAF) relative to its mainlobe, us

Adi Simhi, Jonathan Herzig, Itay Itzhak, Dana Arad

Hallucinations in LLMs present a critical barrier to their reliable usage. Existing research usually categorizes hallucination by their external properties rather than by the LLMs' underlying internal properties. This external focus overlooks that hallucinations may require tailored mitigation strategies based on their underlying mechanism. We propose a fram

Naoya Ando, Masaaki Umehara

We investigate the relationship between the Hopf differentials and the curvature line flows on time-like constant mean curvature (CMC) surfaces in Lorentzian 3-space forms. In particular, when the Hopf differential is non-degenerate, the index of a curvature line flow at an umbilic point depends precisely on the remainder of its order modulo four.

Doan Trung Cuong, Toshinori Kobayashi

We investigate local rings in which a syzygy of the residue field occurs as a direct summand of another syzygy of the field. This class of local rings includes Golod rings, Burch rings and non-trivial fiber products of local rings. For such rings, we prove that the Betti sequence of any finitely generated module is eventually periodically non-decreasing. As

Alexey Khartov

We study a new class of so-called rational-infinitely (or quasi-infinitely) divisible probability laws on the real line. The characteristic functions of these distributions are ratios of the characteristic functions of classical infinitely divisible laws and they admit L\'evy--Khinchine type representations with ``signed spectral measures''. This class is ra

Alisher Turubayev, Anna Shopova, Fabian Lange, Mahmut Kamalak

As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the dat

Fan Xu, Hao Wu, Kun Wang, Nan Wang

In dynamical system modeling, traditional numerical methods are limited by high computational costs, while modern data-driven approaches struggle with data scarcity and distribution shifts. To address these fundamental limitations, we first propose SPARK, a physics-guided quantitative augmentation plugin. Specifically, SPARK utilizes a reconstruction autoenc

Jinhong Deng, Wen Li, Joey Tianyi Zhou, Yang He

Multimodal Large Language Models (MLLMs) typically process a large number of visual tokens, leading to considerable computational overhead, even though many of these tokens are redundant. Existing visual token pruning methods primarily focus on selecting the most salient tokens based on attention scores, resulting in the semantic incompleteness of the select

Haoxin Yang, Yihong Lin, Jingdan Kang, Xuemiao Xu

Face anonymization aims to conceal identity information while preserving non-identity attributes. Mainstream diffusion models rely on inference-time interventions such as negative guidance or energy-based optimization, which are applied post-training to suppress identity features. These interventions often introduce distribution shifts and entangle identity

E. Diego Mercerat, Martijn P. A. van den Ende, Anthony Sladen, Vanessa Carrillo-Barra

Distributed Acoustic Sensing (DAS) of ambient vibrations is a promising technique in the context of structural health monitoring of civil engineering structures. The methodology uses Rayleigh backscattered light from small deformations at different locations of the sensed fiber-optic cable, turning it into a large array of equally distributed strain sensors.

Jinting Wang, Chenxing Li, Li Liu

Dance-to-music (D2M) generation aims to automatically compose music that is rhythmically and temporally aligned with dance movements. Existing methods typically rely on coarse rhythm embeddings, such as global motion features or binarized joint-based rhythm values, which discard fine-grained motion cues and result in weak rhythmic alignment. Moreover, tempor

Junhyuk So, Hyunho Kook, Chaeyeon Jang, Eunhyeok Park

Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrained by the slow inference speed of per-token generation, which often requires thousands of steps to produce a single sample. While several Speculative Decoding (SD)-based methods have been proposed to solve this p

Xiangyu Zhao, Yuqing Liu, Jingzhu Shao, Longsheng Fang

Infrared (IR) spectroscopy has emerged as a pivotal tool in biomedical diagnostics, offering label-free spectral biomarkers for the detection of numerous diseases, particularly in the fingerprint region. However, the lack of rapid and sensitive IR spectroscopic techniques for analyzing complex fluid analytes remains a critical challenge in clinical practice.

Cao H. Nam

We represent a new approach to exploring the thermodynamic topology of black holes, without introducing the nonphysical variable $\Theta\in[0,\pi]$ considered in previous studies, where black holes can exchange both energy and matter with the environment, leading to a thermal and chemical equilibrium. We construct a conserved topological tensor based on the

Narendra Dev, Varun Kulkarni, Sivakumar Deivandren

Droplets collide in several complex spray environments ranging from sea sprays to combustion chambers, altering their size and velocity characteristics. The present work offers a systematic investigation of such collisions within the interacting region formed by three hollow-cone sprays, termed the combined spray, at two elevated liquid sheet Weber numbers (

Rob van Glabbeek

This paper shows that guarded systems of recursive equations have unique solutions up to strong bisimilarity for any process algebra with a structural operation semantics in the ready simulation format. A similar result holds for simulation equivalence, for ready simulation equivalence and for the (ready) simulation preorder. As a consequence, these equivale

Telmo Ribeiro, José Proença, Mário Florido

Concurrent systems are often complex and difficult to design. Choreographic languages, such as Multiparty Session Types (MPST), allow the description of global protocols of interactions by capturing valid patterns of interactions between participants. Many variations of MPST exist, each one with its rather specific features and idiosyncrasies. Here we propos

Renato Neves

We present an adequacy theorem for a concurrent extension of probabilistic GCL. The underlying denotational semantics is based on the so-called mixed powerdomains, which combine non-determinism with probabilistic behaviour. The theorem itself is formulated via M. Smyth's idea of treating observable properties as open sets of a topological space. The proof hi

Lukas Bartl, Julian Linne, Kirstin Peters

Multiparty session types are designed to abstractly capture the structure of communication protocols and verify behavioural properties. One important such property is progress, i.e., the absence of deadlock. Distributed algorithms often resemble multiparty communication protocols. But proving their properties, in particular termination that is closely relate

Anshul Kaushal, Kunal Jangid, Vinod K. Kurmi

Accurate polyp and cardiac segmentation for early detection and treatment is essential for the diagnosis and treatment planning of cancer-like diseases. Traditional convolutional neural network (CNN) based models have represented limited generalizability, robustness, and inability to handle uncertainty, which affects the segmentation performance. To solve th

David Kazhdan, Maarten Solleveld, Yakov Varshavsky

For a reductive group G over a non-archimedean local field, we compare smooth representations over C with smooth representations over Qbar (an algebraic closure of Q). We show that an elliptic G-representation (in the sense of Arthur) can be realized over Qbar if and only if its central character takes values in Qbar. That applies in particular to all essent

Alexander Bakarsky, Dimitar I. Dimitrov, Maximilian Baader, Martin Vechev

Federated Learning has seen an increased deployment in real-world scenarios recently, as it enables the distributed training of machine learning models without explicit data sharing between individual clients. Yet, the introduction of the so-called gradient inversion attacks has fundamentally challenged its privacy-preserving properties. Unfortunately, as th

Loek van Everdingen, Jaimy Plugge, Tim Fuchs, Guido van de Stolpe

Highly coherent mechanical resonators are invaluable to ultrasensitive detection techniques by enabling detection of small forces. Studying mechanical resonators in a thermal equilibrium state at millikelvin temperatures provides a promising path to increase their coherence time. Here, we passively cool a 700 Hz massive (1.5 ng) mechanical cantilever down to

Yijing Cui, Yu Song Cai, Xuchen Wang, Xiang Chen

The photophysical properties of deoxyribonucleic acid (DNA) are fundamental to life sciences and biophotonics. While previous studies have generally been restricted to fluorescence, attributing it to pi-pi* transitions and charge transfer within nucleobases in dilute solution, these understandings fail to explain the pronounced visible emission in physiologi

Heinrich T. Roth, Philipp Gebhart, Karl A. Kalina, Thomas Wallmersperger

In this work, we develop a neural network-based, data-driven, decoupled multiscale scheme for the modeling of structured magnetically soft magnetorheological elastomers (MREs). On the microscale, sampled magneto-mechanical loading paths are imposed on a representative volume element containing spherical particles and an elastomer matrix, and the resulting bo

Jijun Yang, Ruize Li, Chang Cai, Guocai Chen

In this paper, we present the design and characterization of a photosensor system developed for the RELICS experiment. An extended dynamic range base was designed to mitigate photomultiplier tube (PMT) saturation caused by intense cosmic muon backgrounds in the surface-level RELICS detector. The system employs dual readout from the anode and the seventh dyno

Phi-Hung Hoang, Nam-Thuan Trinh, Van-Manh Tran, Thi-Thu-Hong Phan

Assessing fish freshness is vital for ensuring food safety and minimizing economic losses in the seafood industry. However, traditional sensory evaluation remains subjective, time-consuming, and inconsistent. Although recent advances in deep learning have automated visual freshness prediction, challenges related to accuracy and feature transparency persist.

Ziqi Zhou, Yifan Hu, Yufei Song, Zijing Li

Recent studies reveal the vulnerability of the image segmentation foundation model SAM to adversarial examples. Its successor, SAM2, has attracted significant attention due to its strong generalization capability in video segmentation. However, its robustness remains unexplored, and it is unclear whether existing attacks on SAM can be directly transferred to

Ev Zisselman, Mirco Mutti, Shelly Francis-Meretzki, Elisei Shafer

Behavioral cloning is a simple yet effective technique for learning sequential decision-making from demonstrations. Recently, it has gained prominence as the core of foundation models for the physical world, where achieving generalization requires countless demonstrations of a multitude of tasks. Typically, a human expert with full information on the task de

Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang

The advancements in Automatic Modulation Classification (AMC) have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless communication environments. To protect user privacy in communication links, we have optimized

Lei Wu, Zuoli Xiao

Overshoot of high-speed transitional skin-friction and heat-transfer values over their fully turbulent levels is well documented by numerous direct numerical simulations (DNS) and experimental studies. However, this high-speed-specific overshoot phenomenon remains a longstanding challenge in Reynolds-averaged Navier-Stokes (RANS) transition models. In this p

Isabelle Krauss, Victor G. Lopez, Matthias A. Müller

In this paper, we propose a sample-based moving horizon estimation (MHE) scheme for general nonlinear systems to estimate the current system state using irregularly and/or infrequently available measurements. The cost function of the MHE optimization problem is suitably designed to accommodate these irregular output sequences. We also establish that, under a

Hong Niu, Jiancheng An, Chau Yuen

Stacked intelligent metasurfaces (SIMs) have recently gained attention as a paradigm for wave-domain signal processing with reduced reliance on costly radio-frequency (RF) chains. However, conventional SIMs rely on uniform inter-layer spacing and require deep stacking to ensure processing capability, resulting in severe power attenuation in practice. To addr

Ayan Chatterjee, Sahil Devdutt, Avirup Ghosh

We extend the isolated horizon formalism to include rotating black holes arising in five dimensional Einstein-Gauss-Bonnet (EGB) theory of gravity, and derive the laws of black hole mechanics. This result allows us to show that the first law of black hole mechanics is modified, due to the Gauss-Bonnet term, so as to include corrections to (i) the area of hor

César Santos, Ermeson Andrade, Roberto Natella

Automatically generated software, especially code produced by Large Language Models (LLMs), is increasingly adopted to accelerate development and reduce manual effort. However, little is known about the long-term reliability of such systems under sustained execution. In this paper, we experimentally investigate the phenomenon of software aging in application

Rupjyoti Chutia, Dibya Jyoti Bora

Digital image processing involves the systematic handling of images using advanced computer algorithms, and has gained significant attention in both academic and practical fields. Image enhancement is a crucial preprocessing stage in the image-processing chain, improving image quality and emphasizing features. This makes subsequent tasks (segmentation, featu

Kwadwo Mensah Obeng Afrane, Yang Miao, André B. J. Kokkeler

In-band Full-duplex joint communication and sensing systems require self interference cancellation as well as decoupling of the mutual interference between UL communication signals and radar echoes. We present sub-band full-duplex as an alternative duplexing scheme to achieve simultaneous uplink communication and target parameter estimation in a cell-free ma

Amandip Sangha

We introduce an intrinsic deformation of the algebra of smooth functions on a compact Riemannian manifold using only the Laplace spectral decomposition. The construction twists the canonical multiplication-projection channels by unimodular phases, producing a well-defined bilinear product on the finite spectral core with values in $L^2(M)$. We give a simple

Bardia Panahbehagh, Mehdi Mohebbi, Amir Mohammad HosseiniNasab

Well-spread samples are desirable in many disciplines because they improve estimation when target variables exhibit spatial structure. This paper introduces an integrated methodological framework for spreading samples over the population's spatial coordinates. First, we propose a new, translation-invariant spreadness index that quantifies spatial balance wit

Judith Rousseau, Vincent Rivoirard, Déborah Sulem

In this paper we study the frequentist properties of Bayesian approaches in linear high dimensional Hawkes processes in a sparse regime where the number of interaction functions acting on each component of the Hawkes process is much smaller than the dimension. We consider two types of loss function: the empirical $L_1$ distance between the intensity function

Jianbing Xiahou, Weixi Zhai, Xu Cui

We propose NanyinHGNN, a heterogeneous graph network model for generating Nanyin instrumental music. As a UNESCO-recognized intangible cultural heritage, Nanyin follows a heterophonic tradition centered around the pipa, where core melodies are notated in traditional notation while ornamentations are passed down orally, presenting challenges for both preserva

Arpita Kundu, Joyita Chakraborty, Anindita Desarkar, Aritra Sen

The surge of audiovisual content on streaming platforms and social media has heightened the demand for accurate and accessible subtitles. However, existing subtitle generation methods primarily speech-based transcription or OCR-based extraction suffer from several shortcomings, including poor synchronization, incorrect or harmful text, inconsistent formattin

Iván Martínez-Murillo, Paloma Moreda, Elena Lloret

This paper explores the influence of external knowledge integration in Natural Language Generation (NLG), focusing on a commonsense generation task. We extend the CommonGen dataset by creating KITGI, a benchmark that pairs input concept sets with retrieved semantic relations from ConceptNet and includes manually annotated outputs. Using the T5-Large model, w

Aaron Scott, Maike Züfle, Jan Niehues

Sarcasm is a complex form of figurative language in which the intended meaning contradicts the literal one. Its prevalence in social media and popular culture poses persistent challenges for natural language understanding, sentiment analysis, and content moderation. With the emergence of multimodal large language models, sarcasm detection extends beyond text

L. M. Pismen

We review inadequacy of existing nematodynamic theories and suggest a novel way of establishing relations between nematic orientation and flow based on the \emph{local} symmetry between simultaneous rotation of nematic alignment and flow, which establishes energy exchange between the the two without reducing the problem to near-equilibrium conditions and inv

Tatiana Nizkaia, Philipp Groppe, Valentin Müller, Jens Harting

The reduction of 4-nitrophenol (4-NiP) with sodium borohydride is widely used to benchmark heterogeneous catalysts, yet its kinetics are commonly oversimplified as pseudo-first-order. In reality, borohydride hydrolysis and hydrogenation by dissolved hydrogen proceed concurrently, making hydrogen transport a decisive factor in shaping apparent activity. Re-ex

Yun-Shi Dai, Peng-Fei Dai, Stéphane Goutte, Duc Khuong Nguyen

With escalating macroeconomic uncertainty, the risk interlinkages between energy and food markets have become increasingly complex, posing serious challenges to global energy and food security. This paper proposes an integrated framework combining the GJRSK model, the time-frequency connectedness analysis, and the random forest method to systematically inves

Binbin Li, Guimiao Yang, Zisen Qi, Haiping Wang

Recent lightweight retrieval-augmented image caption models often utilize retrieved data solely as text prompts, thereby creating a semantic gap by leaving the original visual features unenhanced, particularly for object details or complex scenes. To address this limitation, we propose $DualCap$, a novel approach that enriches the visual representation by ge

Yiheng Du, Aditi S. Krishnapriyan

Computationally resolving turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive, motivating data-driven machine learning alternatives. In this work, we propose EddyFormer, a Transformer-based spectral-element

Changjian Xie, Cheng Wang

We propose and implement a third-order accurate numerical scheme for the Landau-Lifshitz-Gilbert equation, which describes magnetization dynamics in ferromagnetic materials under large damping parameters. This method offers two key advantages: (1) It solves only constant-coefficient linear systems, enabling fast solvers and thus achieving much higher numeric

Abhiram Kaushik, Heikki Mäntysaari, Jani Penttala

We compute the $q\bar{q}g$ contribution to the diffractive structure functions in high-energy deep inelastic scattering. The obtained result corresponds to a finite part of the next-to-leading-order contribution to the diffractive cross section. Previous phenomenological applications have included this contribution only in the high-$Q^2$ or high-$M_X^2$ limi

Hong Wang, Jie Wang, Minghao Ma, Haoran Shao

Matrix preconditioning is a critical technique to accelerate the solution of linear systems, where performance heavily depends on the selection of preconditioning parameters. Traditional parameter selection approaches often define fixed constants for specific scenarios. However, they rely on domain expertise and fail to consider the instance-wise features fo

Vikas, Rahul Marathe, Anjan Roy

It is well known that the different cell-division models, such as Timer, Sizer, and Adder, can be distinguished based on the correlations between different single-cell-level quantities such as birth-size, division-time, division-size, and division-added-size. Here, we show that other statistical properties of these quantities can also be used to distinguish

Weihua Cheng, Junming Liu, Yifei Sun, Botian Shi

Multimodal Large Language Models (MLLMs) have significantly advanced GUI agents, yet long-horizon automation remains constrained by two critical bottlenecks: context overload from raw sequential trajectory dependence and architectural redundancy from over-engineered expert modules. Prevailing End-to-End and Multi-Agent paradigms struggle with error cascades

Yoonhak Nam, Kazuyuki Sekizawa

Neutron stars provide unique laboratories for probing physics of dense nuclear matter under extreme conditions. Their thermal and luminosity evolution reflects key internal properties such as the equation of state (EoS), nucleon superfluidity and superconductivity, envelope composition, and magnetic field, and so on. Recent observations [\textit{e.g.}, V. Ab

Xin Yang, Yuhang Zhang, Wei Li, Xin Lin

Motion planning is a critical component of autonomous vehicle decision-making systems, directly determining trajectory safety and driving efficiency. While deep learning approaches have advanced planning capabilities, existing methods remain confined to single-dataset training, limiting their robustness in planning. Through systematic analysis, we discover t

Kengo Fukunaga, Tadashi Ochiai

The theory of admissible distributions over a weight-space of one-variable was studied by Amice--V\'{e}lu and played important roles in the cyclotomic Iwasawa theory of non-ordinary p-adic Galois representations. In this article, we discuss the multi-variable generalization of the theory of admissible distributions over a weight-space of several variables. A

Zhenghao Luo, Yi Li, Xingyu Zhao, Zihan Xie

The Unruh effect predicts that an accelerated observer perceives the Minkowski vacuum as a thermal bath, but its direct observation requires extreme accelerations beyond current experimental reach. Foundational theory [Olson & Ralph, Phys. Rev. Lett. 106, 110404 (2011)] shows that an equivalent thermal response, known as the timelike Unruh effect, can occur

Wentao Tan, Bowen Wang, Heng Zhi, Chenyu Liu

Multimodal large language models (MLLMs) have advanced vision-language reasoning and are increasingly deployed in embodied agents. However, significant limitations remain: MLLMs generalize poorly across digital-physical spaces and embodiments; vision-language-action models (VLAs) produce low-level actions yet lack robust high-level embodied reasoning; and mo

Aiqing Zhu, Beatrice W. Soh, Grigorios A. Pavliotis, Qianxiao Li

Complex dissipative systems appear across science and engineering, from polymers and active matter to learning algorithms. These systems operate far from equilibrium, where energy dissipation and time irreversibility govern their behavior but are difficult to quantify from data. Here, we introduce a universal and identifiable neural framework that learns dis

Keiya Hirashima, Shingo Nozaki, Naoto Harada

Stellar mass is a fundamental quantity that determines the properties and evolution of stars. However, estimating stellar masses in star-forming regions is challenging because young stars are obscured by dense gas and the regions are highly inhomogeneous, making spherical dynamical estimates unreliable. Supervised machine learning could link such complex str

Junsoo Oh, Jerry Song, Chulhee Yun

Weak-to-strong generalization refers to the phenomenon where a stronger model trained under supervision from a weaker one can outperform its teacher. While prior studies aim to explain this effect, most theoretical insights are limited to abstract frameworks or linear/random feature models. In this paper, we provide a formal analysis of weak-to-strong genera

Jisong Gao, Qiaoxiao Zhao, Wenbo Liu, Dong Li

The development of extreme ultraviolet sources is critical for advancing angleresolved photoemission spectroscopy (ARPES), a powerful technique for probing the electronic structure of materials. Here, we report the construction of a tabletop 10.8-eV femtosecond laser through cascaded third-harmonic generation, which operates at a repetition rate of 1 MHz and

Rohit Rajendra Dhage

The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product is widely used for global fire monitoring, yet its confidence classification scheme exhibits an undocumented systematic pattern. Through analysis of 21,540,921 fire detections spanning one year (January 2023 - January 2024), I demonstrate a complete absence of low-confidence classifica

M. Obradovic, N. Tuneski

In this paper a survey is given of application of a method based on Grunsky coefficients for obtaining different estimates (some sharp) for the general class of univalent functions where no analytical characterisation exists. More precisely, estimates are given for the modulus of the third and the fourth logarithmic coefficients, for the modulus of the secon

Sarai Dery Folkestad, Eirik F. Kjønstad, Alexander C. Paul, Rolf H. Myhre

The eT program is an open-source electronic structure program with emphasis on performance and modularity. As its name suggests, the program features extensive coupled cluster capabilities, performing well compared to other electronic structure programs, and, in some cases, outperforming commercial alternatives. However, eT is more than a coupled cluster pro

Kun Huang, Shi Pu

We propose Local Momentum Tracking (LMT), a novel distributed stochastic gradient method for solving distributed optimization problems over networks. To reduce communication overhead, LMT enables each agent to perform multiple local updates between consecutive communication rounds. Specifically, LMT integrates local updates with the momentum tracking strateg

Francesco Mainardi, Andrea Mentrelli, Juan Luis Gonza'les Santander

In the study of linear dispersive media it is of primary interest to gain knowledge of the impulse response of the material. The standard approach to compute the response involves a Laplace transform inversion, i.e., the solution of a Bromwich integral, which can be a notoriously troublesome problem. In this paper we propose a novel approach to the calculati

Ning Gao, Didi Li, Na Liu, Yankui Chang

Purpose: We presented a GPU-based MC framework, ARCHER-EPID, specifically designed for EPID transit dosimetry, with improving accuracy and efficiency. Methods: A comprehensive MC framework was developed to perform full radiation transport simulations through three distinct zones: a detailed linear accelerator head model, a CT-based patient/phantom geometry,

Yuya Takada, Kiyoshi Izumi

National statistical institutes are beginning to use non-traditional data sources to produce official statistics. These sources, originally collected for non-statistical purposes, include point-of-sales(POS) data and mobile phone global positioning system(GPS) data. Such data have the potential to significantly enhance the usefulness of official statistics.

Aodi Wu, Xubo Luo

This technical report presents our solution for the RoboSense Challenge at IROS 2025, which evaluates Vision-Language Models (VLMs) on autonomous driving scene understanding across perception, prediction, planning, and corruption detection tasks. We propose a systematic framework built on four core components. First, a Mixture-of-Prompts router classifies qu

Bingsen Qiu, Zijian Liu, Xiao Liu, Bingjie Wang

Building training-ready multi-hop question answering (QA) datasets that truly stress a model's retrieval and reasoning abilities remains highly challenging recently. While there have been a few recent evaluation datasets that capture the characteristics of hard-to-search but easy-to-verify problems -- requiring the integration of ambiguous, indirect, and cro

Chanwoo Park, Suyoung Park, JiA Kang, Jongyeon Park

We present Ko-MuSR, the first benchmark to comprehensively evaluate multistep, soft reasoning in long Korean narratives while minimizing data contamination. Built following MuSR, Ko-MuSR features fully Korean narratives, reasoning chains, and multiple-choice questions verified by human annotators for logical consistency and answerability. Evaluations of four

Petr M. Korusenko, Olga V. Petrova, Anatoliy A. Vereshchagin, Oleg V. Levin

A detailed study of poly-[Ni(Salen)] polymer in its oxidized (Ox) and reduced (Red) states was conducted using X-ray photoelectron (XPS) and ultraviolet photoemission (UV PES) spectroscopy, near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, and quantum-chemical calculations. XPS analysis revealed significant energy shifts (-1.5 to -1.8 eV) and

Giao Ky Duong, Thi Minh Thao Le, Phan Thành Nam, Phuoc-Tai Nguyen

We establish the existence of finite-rank operators for an interpolation version of the Lieb--Thirring inequality in the mass--supercritical case, thereby extending a result of Hong, Kwon, and Yoon in 2019 to the full parameter regime. Our method also applies to the Hardy--Lieb--Thirring inequality, where the existence of optimizers faces additional difficul

Manivannan Saminathan, Prakash Govindaraj, Hern Kim, Kowsalya Murugan

Double cation chalcohalide have recently been emerged as the interesting candidates for sustainable energy conversion applications, owing to their intrinsic chemical tunability, suitable band gap, and low thermal conductivity. With this motivation, the current study is designed to explore the structural, electron and phonon transport mechanism, and thermoele

Anurag Adityanarayan Ray, Sreejita Bhaduri, Swetarka Das, Ashoke De

OpenFOAM is a widely used computational fluid dynamics (CFD) framework based on the finite volume method for solving a wide range of flow problems. However, its default numerical schemes, particularly the Kurganov-Noelle-Petrova (KNP) method used for shock capturing, are only low-order accurate. This work presents the implementation of modern high-order Riem