February 2025 arXiv papers — page 24

Aline Ramires

We investigate the impact of the presence of altermagnetism on physical observables in the presence of explicit or spontaneous symmetry-breaking fields. We focus on unconventional superconductivity as a potential source of spontaneous symmetry-breaking fields and derive the symmetry considerations for the transmutation of altermagnets from pure to mixed, whi

Mingjian Fu, Hengsheng Chen, Dongchun Jiang, Yanchao Tan

In the era of advancing information technology, recommender systems have emerged as crucial tools for dealing with information overload. However, traditional recommender systems still have limitations in capturing the dynamic evolution of user behavior. To better understand and predict user behavior, especially taking into account the complexity of temporal

Jinzhe Zeng, Duo Zhang, Anyang Peng, Xiangyu Zhang

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integ

Rebekka Görge, Michael Mock, Héctor Allende-Cid

Social categories and stereotypes are embedded in language and can introduce data bias into Large Language Models (LLMs). Despite safeguards, these biases often persist in model behavior, potentially leading to representational harm in outputs. While sociolinguistic research provides valuable insights into the formation of stereotypes, NLP approaches for ste

Xuan Ding, Rui Sun, Yunjian Zhang, Xiu Yan

Depth-wise pruning accelerates LLM inference in resource-constrained scenarios but suffers from performance degradation due to direct removal of entire Transformer layers. This paper reveals ``Patch-like'' redundancy across layers via correlation analysis of the outputs of different layers in reproducing kernel Hilbert space, demonstrating consecutive layers

Yijiang River Dong, Tiancheng Hu, Yinhong Liu, Ahmet Üstün

While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences across users, overlooking diverse human values and minority viewpoints. Although personalized preference learning addresses this by tailoring separate preferences for individual user

Shin Nakamura, Fuminori Okabayashi

We examine the concept of temperature in non-equilibrium steady states. Using the D3-D5 model of gauge/gravity duality, we investigate spontaneous symmetry breaking in a relativistic (2+1)-dimensional defect moving at constant velocity within a (3+1)-dimensional heat bath. We find that the dependence of the order parameter on both the heat bath temperature a

Noureddine Rochdi, Rachid Ahl Laamara, Mohamed Bennai

We study the quantum Rabi model (QRM) in the deep strong coupling (DSC) regime. To capture the full dynamics of the QRM in the DSC regime, we implemented single-qubit rotations combined with integrated digital steps and qubit-bosonic blocks. This approach leads to a paradigm known as digital analog quantum simulations (DAQSs). In this work, we review the enc

Mohsen Fathi

We study the gravitational lensing effects of a static, asymptotically flat black hole with primary scalar hair in Beyond Horndeski gravity, focusing on the strong lensing regime. Recently, in Ref. [1], we placed constraints on the scalar hair parameter by analyzing its thermodynamic stability and the black hole shadow. In this work, we further investigate t

Zeeshan Afzal, Giovanni Gaggero, Mikael Asplund

Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverag

Yuhan Tang, Yudian Wang, Weizhen Li, Ye Yue

Fundus image quality is crucial for diagnosing eye diseases, but real-world conditions often result in blurred or unreadable images, increasing diagnostic uncertainty. To address these challenges, this study proposes RetinaRegen, a hybrid model for retinal image restoration that integrates a readability classifi-cation model, a Diffusion Model, and a Variati

Sunny Pradhan, Jesús Cobos, Enrique Rico, Germán Sierra

We investigate the entanglement properties of the Quantum Six-Vertex Model on a cylinder, focusing on the Shannon-Renyi entropy in the limit of Renyi order $n = \infty$. This entropy, calculated from the ground state amplitudes of the equivalent XXZ spin-1/2 chain, allows us to determine the Renyi entanglement entropy of the corresponding Rokhsar-Kivelson wa

Vijay Kumar Sutrakar, Nikhil Morge, Anjana PK, Abhilash PV

In this paper, deep learning-based approach for the design of radar absorbing structure using resistive frequency selective surface is proposed. In the present design, reflection coefficient is used as input of deep learning model and the Jerusalem cross based unit cell dimensions is predicted as outcome. Sequential neural network based deep learning model w

Ignacio D. Lopez-Miguel, Borja Fernández Adiego, Matias Salinas, Christine Betz

The increased technological complexity and demand for software reliability require organizations to formally design and verify their safety-critical programs to minimize systematic failures. Formal methods are recommended by functional safety standards (e.g., by IEC 61511 for the process industry and by the generic IEC 61508) and play a crucial role. Their s

Jiarong Wu, Songqiang Chen, Jialun Cao, Hau Ching Lo

Existing code generation benchmarks for Large Language Models (LLMs) such as HumanEval and MBPP are designed to study LLMs' end-to-end performance, where the benchmarks feed a problem description in natural language as input and examine the generated code in specific programming languages. However, the evaluation scores revealed in this way provide a little

Zhaowei Zhang, Fengshuo Bai, Qizhi Chen, Chengdong Ma

How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, changing, and diverse regarding culture, values, or time. This leads to the problem that the actual user preferences often do not coincide with those trained by the model developers in

Kiyoshi Igusa

This is my old unpublished paper called "The generalized Grassmann invariant". It shows how "pictures" also known as "Peiffer diagrams" represent elements of $H_3G$ for any group $G$ and shows that $K_3(\mathbb Z [G])$ is isomorphic to a group of deformation classes of pictures for the Steinberg group of $\mathbb Z[G]$. A picture representing an element of o

Ali Caglar Ozen, Shuai Liu, Serhat Ilbey, Michael Bock

Motivation: Conventional echo planar imaging(EPI) based functional MRI(fMRI) uses the BOLD contrast to map activity changes in human brains. Introducing an efficient ZTE sequence for functional brain mapping can help address limitations of EPI and demonstrate the feasibility of using T1 related changes as a surrogate marker of brain activity. Goals: To test

Pierre Peigne-Lefebvre, Mikolaj Kniejski, Filip Sondej, Matthieu David

As AI agents are increasingly adopted to collaborate on complex objectives, ensuring the security of autonomous multi-agent systems becomes crucial. We develop simulations of agents collaborating on shared objectives to study these security risks and security trade-offs. We focus on scenarios where an attacker compromises one agent, using it to steer the ent

Péter Füri, Árpád Farkas, Werner Hofmann, Balázs G. Madas

Variability in radiation-related health risk and genetic susceptibility to radiation effects within a population is a key issue for radiation protection. Besides differences in the health and biological effects of the same radiation dose, individual variability may also affect dose distribution and its consequences for the same exposure. As exposure to radon

Daniel A. A. Pelsmaeker, Aron Zwaan, Casper Bach, Arjan J. Mooij

Modern Integrated Development Environments (IDEs) offer automated refactorings to aid programmers in developing and maintaining software. However, implementing sound automated refactorings is challenging, as refactorings may inadvertently introduce name-binding errors or cause references to resolve to incorrect declarations. To address these issues, previous

Abdalla Ibrahim, Johannes Rosenberger, Boulat A. Bash, Christian Deppe

The problem of identification over a discrete memoryless wiretap channel is examined under the criterion of semantic effective secrecy. This secrecy criterion guarantees both the requirement of semantic secrecy and of stealthy communication. Additionally, we introduce the related problem of combining approximation-of-output statistics and transmission. We de

Lucas Reis

Let $q$ be a power of a prime $p$, let $\mathbb F_q$ be the finite field with $q$ elements and, for each nonconstant polynomial $F\in \mathbb F_{q}[X]$ and each integer $n\ge 1$, let $s_F(n)$ be the degree of the splitting field (over $\mathbb F_q$) of the iterated polynomial $F^{(n)}(X)$. In 1999, Odoni proved that $s_A(n)$ grows linearly with respect to $n

Shashwat Anand, Tara P. Mishra, Peichen Zhong, Yunyeong Choi

Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformation of Ti-containing Mn-rich disordered rock-salt cathodes into a phase -- named $\delta$ -- with nanoscale spinel-like domains has been shown to increase energy density, capacity

Y. He, D. Liu, Y. Wang

In this paper, we classify the simple Harish-Chandra modules over the superconformal current algebra $\widehat{\frak g}$, which is the semi-direct sum of the $N=1$ superconformal algebra with the affine Lie superalgebra $\dot{\frak g} \otimes \mathcal{A}\oplus \mathbb CC_1$, where $\dot{\frak g}$ is a finite-dimensional simple Lie algebra, and $\mathcal{A}$

Xianghan Cui, Clancy James, Di Li, Chengmin Zhang

Fast Radio Bursts (FRBs), a class of millisecond-scale, highly energetic phenomena with unknown progenitors and radiation mechanisms, require proper statistical analysis as a key method for uncovering their mysteries. In this research, we build upon the bias correction method using pulse injections for the first CHIME/FRB catalog, to include correlations bet

Piotr Szańkowski

Dynamical maps are the principal subject of the open system theory. Formally, the dynamical map of a given open quantum system is a density matrix transformation that takes any initial state and sends it to the state at a later time. Physically, it encapsulates the system's evolution due to coupling with its environment. Hence, the theory provides a flexible

A. Flores, R. de Lamare

Cell-free (CF) multiuser multiple-input multiple-output (MU-MIMO) systems are an emerging technology that provides service simultaneously to multiple users but suffers from multiuser interference (MUI). In this work, we propose a robust transmit scheme based on rate-splitting (RS) for CF MU-MIMO systems in the presence of imperfect channel state information

Tianxiang Gou, Xiaoan Shen

In this paper, we consider the existence, orbital stability/instability and regularity of bound state solutions to nonlinear Schr\"odinger equations with super-quadratic confinement in two and three spatial dimensions for the mass supercritical case. Such solutions, which are given by time-dependent rotations of a non-radially symmetric spatial profile, corr

Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du

Time series data are foundational in finance, healthcare, and energy domains. However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as forecasting or anomaly detection. To bridge this gap, we introduce Time Series Multi-Task Question Answering (Time-MQA), a unified framework that enables natural language queries acros

Manish Kumar, Rishav Saha, Johanna Potyka, Kathrin Schulte

Droplet impact onto heated surfaces is a widespread process in industrial applications, particularly in the context of spray cooling techniques. Therefore, it is essential to study the complex phenomenon of droplet spreading, heat removal from a hot surface, and flow distribution during the impact. This study focuses on Direct Numerical Simulation (DNS) of t

Shuai Ma, Junling Wang, Yuanhao Zhang, Xiaojuan Ma

Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering in

Isaac Y. Miranda-Valdez, Tero Mäkinen, Juha Koivisto, Mikko J. Alava

Inferring viscoelasticity parameters is a key challenge that often leads to non-unique solutions when fitting rheological data. In this context, we propose a machine learning approach that utilizes Bayesian optimization for parameter inference during curve-fitting processes. To fit a viscoelastic model to rheological data, the Bayesian optimization maps the

Jiaheng Lu

Modern database systems face a significant challenge in effectively handling the Variety of data. The primary objective of this paper is to establish a unified data model and theoretical framework for multi-model data management. To achieve this, we present a categorical framework to unify three types of structured or semi-structured data: relation, XML, and

Lars Benedikt Kaesberg, Jonas Becker, Jan Philip Wahle, Terry Ruas

Much of the success of multi-agent debates depends on carefully choosing the right parameters. The decision-making protocol stands out as it can highly impact final model answers, depending on how decisions are reached. Systematic comparison of decision protocols is difficult because many studies alter multiple discussion parameters beyond the protocol. So f

Stanislav Tiagulskyi, Roman Yatskiv, Marta Sobanska, Karol Olszewski

Self-assembled GaN nanowires are typically grown on Si substrates with convenient nucleation layers. Light-emitting devices based on arrays of GaN nanowires require that the nucleation layer is electrically conductive and optically nontransparent to prevent the absorption of generated light in the Si substrate. This study reports the molecular beam epitaxial

Junlong Ren, Hao Wu, Hui Xiong, Hao Wang

The cross-modal 3D retrieval task aims to achieve mutual matching between text descriptions and 3D shapes. This has the potential to enhance the interaction between natural language and the 3D environment, especially within the realms of robotics and embodied artificial intelligence (AI) applications. However, the scarcity and expensiveness of 3D data constr

Siyuan Zhang, Yichi Zhang, Yinpeng Dong, Hang Su

Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of factual hallucinations, which can be difficult to detect and mislead users without relevant knowledge. Although post-training techniques have been employed to mitigate the issue, existing methods usually suffer from poor generalization and tra

A. V. Kotikov, A. V. Lipatov

We present analytical expressions for the Transverse Momentum Dependent (TMD, or unintegrated) gluon and quark densities in a proton derived at leading order of QCD running coupling and valid at both small and large x. The calculations are performed using the Kimber-Martin-Ryskin/Watt-Martin-Ryskin prescription (in both differential and integral formulations

Ziyuan Luo, Anderson Rocha, Boxin Shi, Qing Guo

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerati

Changhong Li

Big bounce cosmology provides a solution to the Universe's initial singularity, and stochastic gravitational wave background (SGWB) searches offer a promising avenue for testing this paradigm. In this work, we establish an analytical relation between the bouncing energy scale, $\rho_{s\downarrow}^{1/4}$, and SGWB spectrum, $\Omega_\mathrm{GW}(f)h^2$, for big

Behnaz Elhaminia, Abdullah Alsalemi, Esha Nasir, Mostafa Jahanifar

Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to iden

Sebastian Chwilczyński, Dariusz Brzezinski

With predictive models becoming prevalent, companies are expanding the types of data they gather. As a result, the collected datasets consist not only of simple numerical features but also more complex objects such as time series, images, or graphs. Such multi-modal data have the potential to improve performance in predictive tasks like outlier detection, wh

Wenbin Xuan, Wenguang Nan

We simulated the cohesive particle flow in an impeller-based rheometer using Discrete Element Method (DEM), and we focus on the dynamics of particles around the constriction between the blade and its surrounding vessel wall. The results show that mechanical jamming could transiently and intermittently occur in the constriction, but it is limited in a narrow

Ricardo Reyes

We propose a reduced basis method to solve time-dependent partial differential equations based on the Laplace transform. Unlike traditional approaches, we start by applying said transform to the evolution problem, yielding a time-independent boundary value problem that depends on the complex Laplace parameter. First, in an offline stage, we appropriately sam

Guikun Chen, Xu Zhang, Xiaolin Hu, Yong Liu

Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the c

Iago Fernández Llovo, Guillermo Díaz-Camacho, Natalia Costas Lago, Andrés Gómez Tato

Distributed quantum computing relies on coordinated operations between remote quantum processing units (QPUs), yet most existing work either assumes full connectivity, unrealistic for large networks, or relies on entanglement swapping. To mitigate the overhead of communication, we propose a scheme for the distribution of collective quantum operations among r

Zhihui Liu, Jie Shen

We construct a class of novel tamed schemes for superlinear stochastic partial differential equations (SPDEs), including the stochastic Allen--Cahn equation driven by either multiplicative or additive noise. The schemes preserve the same Lyapunov structure as the original system, and we rigorously establish their longtime unconditional stability. Furthermore

Quentin Mazouni, Helge Spieker, Arnaud Gotlieb, Mathieu Acher

In recent years, following tremendous achievements in Reinforcement Learning, a great deal of interest has been devoted to ML models for sequential decision-making. Together with these scientific breakthroughs/advances, research has been conducted to develop automated functional testing methods for finding faults in black-box Markov decision processes. Pang

Bojana Bašaragin, Darija Medvecki, Gorana Gojić, Milena Oparnica

This study introduces a novel natural language processing pipeline that enhances customer service efficiency at Telekom Srbija, a leading Serbian telecommunications company, through automated email topic detection and labeling. Central to the pipeline is BERTopic, a modular framework that allows unsupervised topic modeling. After a series of preprocessing an

Dominik Walter, Marita Halm, Daniel Seidel, Indrayudh Ghosh

Increasing demands for computing power also propel the need for energy-efficient SoC accelerator architectures. One class for such accelerators are so-called processor arrays, which typically integrate a two-dimensional mesh of interconnected processing elements (PEs). Such arrays are specifically designed to accelerate the execution of multidimensional nest

Thomas Nussle, Stam Nicolis, Iason Sofos, Joseph Barker

In this work, we propose a path integral-inspired formalism for computing the quantum thermal expectation values of spin systems, when subject to magnetic fields that can be time-dependent and can accommodate the presence of Heisenberg exchange interactions between the spins. This is done by deriving an effective magnetic field from the quantum partition fun

Shihui Feng, David Gibson, Dragan Gasevic

Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning analytic methods

Avoy Jana

We present a proof of the generalized Kramers-Pasternack relation using the hyper-radial equation approach. Following Kramers' method, we manipulate the radial equation by multiplying it with an expression closely related to terms in the hyper-virial theorem. Through successive integrations by parts, we systematically derive the second Pasternack formula, ex

Eugenia Ellis, Emanuel Rodríguez Cirone, Gisela Tartaglia

We make an exposition of the proof of the Baum-Connes conjecture for the infinite dihedral group following the ideas of Higson and Kasparov.

Zhengping Jiang, Anqi Liu, Benjamin Van Durme

Language model outputs are not always reliable, thus prompting research into how to adapt model responses based on uncertainty. Common approaches include: \emph{abstention}, where models refrain from generating responses when uncertain; and \emph{linguistic calibration}, where models hedge their statements using uncertainty quantifiers. However, abstention c

Zhuan Shi, Patrick Ohl, Boi Faltings

Federated learning (FL) allows machine learning models to be trained on distributed datasets without directly accessing local data. In FL markets, numerous Data Consumers compete to recruit Data Owners for their respective training tasks, but budget constraints and competition can prevent them from securing sufficient data. While existing solutions focus on

Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma

Instant messaging with texts and stickers has become a widely adopted communication medium, enabling efficient expression of user semantics and emotions. With the increased use of stickers conveying information and feelings, sticker retrieval and recommendation has emerged as an important area of research. However, a major limitation in existing literature h

Dejan Grba

Generative artificial intelligence (generative AI) has entered the mainstream culture and become a subject of extensive academic investigation. However, the character and background of its impact on art require subtler scrutiny and more nuanced contextualization. This paper summarizes a broader study of the roles that AI's conceptual and ideological substrat

Tianle Yang, Luyao Chang, Jiadong Yan, Juntao Li

As industrial products become abundant and sophisticated, visual industrial defect detection receives much attention, including two-dimensional and three-dimensional visual feature modeling. Traditional methods use statistical analysis, abnormal data synthesis modeling, and generation-based models to separate product defect features and complete defect detec

Ross Pallister, Natasha L. S. Jeffrey, Morgan Stores

Non-thermal particle acceleration in the solar corona is evident from both remote hard X-ray (HXR) sources in the chromosphere and direct in-situ detection in the heliosphere. Correlation of spectral indices between remote and in-situ energy spectra presents the possibility of a common source acceleration region within the corona, however the properties and

Michelle Kappl

We present WinoMTDE, a new gender bias evaluation test set designed to assess occupational stereotyping and underrepresentation in German machine translation (MT) systems. Building on the automatic evaluation method introduced by arXiv:1906.00591v1, we extend the approach to German, a language with grammatical gender. The WinoMTDE dataset comprises 288 Germa

Siwei Wu, Yizhi Li, Xingwei Qu, Rishi Ravikumar

Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understood and evaluated. Our analysis reveals that current LLMs struggle with length requirements and information density in long-text generation, with performance deteriorating as text le

Edward G. A. Henderson, Marcel van Herk, Andrew F. Green, Eliana M. Vasquez Osorio

We propose an anatomically-informed initialisation method for interpatient CT non-rigid registration (NRR), using a learning-based model to estimate correspondences between organ structures. A thin plate spline (TPS) deformation, set up using the correspondence predictions, is used to initialise the scans before a second NRR step. We compare two established

Fernando Alonso-Marroquin

This study introduces a pore morphology algorithm that emphasizes the central role of topology in multiphase flow through porous media. Analysis of drainage in lattice-based pore networks identifies two key quantities, the percolation threshold and residual saturation, as topological invariants. These descriptors, which are based solely on connectivity rathe

Ziyang Liu, Zekai Chen, Changxiong Zheng, Phil Surman

Our group is developing a multi-user eye-tracked 3D display, an evolution of the single-user eye-tracked 3D display that we have already successfully developed. This display utilizes a slanted lenticular setup, where multiple perspective views are shown across the viewing field. Due to the constraints of the lenticular lens parameters, identical views are re

Erica Cau, Valentina Pansanella, Dino Pedreschi, Giulio Rossetti

Understanding how opinions evolve is crucial for addressing issues such as polarization, radicalization, and consensus in social systems. While much research has focused on identifying factors influencing opinion change, the role of language and argumentative fallacies remains underexplored. This paper aims to fill this gap by investigating how language - al

Mykola Kozlenko, Ihor Lazarovych, Valerii Tkachuk, Vira Vialkova

In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation

Christophe Charlier, Tom Claeys

We introduce a new method for studying gap probabilities in a class of discrete determinantal point processes with double contour integral kernels. This class of point processes includes uniform measures of domino and lozenge tilings as well as their doubly periodic generalizations. We use a Fourier series approach to simplify the form of the kernels and to

Samuele Pasini, Gianluca Maragliano, Jinhan Kim, Paolo Tonella

Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulnerable to adversarial attacks due to the discontinuous nature of the mapping between the input (i.e., the attack) and the output (i.e., the prediction of the model whether an input i

Andrey Kuzminskiy, Yuri Ozhigov

The article addresses the important and relevant task of remote induction of quantum dynamic scenarios. This involves transferring such scenarios from donor atoms to a target atom. This induction is based on the enhancement of quantum transitions in the presence of multiple photons of the same transition. We use the quantum master equation for the Tavis-Cumm

Zongzhen Yang, Binhang Qi, Hailong Sun, Wenrui Long

Model merging based on task vectors, i.e., the parameter differences between fine-tuned models and a shared base model, provides an efficient way to integrate multiple task-specific models into a multitask model without retraining. Recent works have endeavored to address the conflicts between task vectors, one of the significant challenges faced by model mer

Xiyao Mei, Yu Zhang, Chaofan Yang, Rui Shi

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historic

Netzer Moriya

We present a novel application of the Flexible Foil Mesh Generation (FFMG) method to model the $3D$ Focal Body generated by a spherical mirror collecting light from an infinitely distant source on its optical axis. The study addresses the challenge of accurately representing highly concave structures formed by the focusing effect. Through theoretical analysi

Humza Sami, Mubashir ul Islam, Samy Charas, Asav Gandhi

Recent advancements in Large Language Models (LLMs) have substantially evolved Multi-Agent Systems (MASs) capabilities, enabling systems that not only automate tasks but also leverage near-human reasoning capabilities. To achieve this, LLM-based MASs need to be built around two critical principles: (i) a robust architecture that fully exploits LLM potential

Qingyao Tian, Huai Liao, Xinyan Huang, Bingyu Yang

Endoscopic video-based tasks, such as visual navigation and surgical phase recognition, play a crucial role in minimally invasive surgeries by providing real-time assistance. While recent video foundation models have shown promise, their applications are hindered by (1) computational inefficiencies and (2) suboptimal performance caused by limited data for pr

Adalberto Perez, Siavash Toosi, Tim Felle Olsen, Stefano Markidis

PySEMTools is a Python-based library for post-processing simulation data produced with high-order hexahedral elements in the context of the spectral element method in computational fluid dynamics. It aims to minimize intermediate steps typically needed when analyzing large files. Specifically, the need to use separate codebases (like the solvers themselves)

Lorenzo Valentini, Diego Forlivesi, Marco Chiani

In the implementation of quantum information systems, one type of Pauli error, such as phase-flip errors, may occur more frequently than others, like bit-flip errors. For this reason, quantum error-correcting codes that handle asymmetric errors are critical to mitigating the impact of such impairments. To this aim, several asymmetric quantum codes have been

Masaaki Miki

In the field of computer graphics, conformal surface flattening has been widely studied for tasks such as texture mapping, geometry processing, and mesh generation. Typically, existing methods aim to flatten a given input geometry while preserving conformality as much as possible, meaning the result is only as conformal as possible. By contrast, this study f

Sergio Morales-Tejera, Victor E. Ambruş, Maxim N. Chernodub

A rigidly-rotating body in unbounded space is usually considered a pathological system since it leads to faster-than-light velocities and associated breaches of causality. However, numerical results on chiral symmetry breaking in rotating plasmas of interacting fermions reveal surprisingly close correspondence in predictions between the rigorous bounded and

Stefano Damato, Dario Azzimonti, Giorgio Corani

We adopt Gaussian Processes (GPs) as latent functions for probabilistic forecasting of intermittent time series. The model is trained in a Bayesian framework that accounts for the uncertainty about the latent function. We couple the latent GP variable with two types of forecast distributions: the negative binomial (NegBinGP) and the Tweedie distribution (Twe

Pengzhou Cheng, Zheng Wu, Zongru Wu, Aston Zhang

Autonomous graphical user interface (GUI) agents powered by multimodal large language models have shown great promise. However, a critical yet underexplored issue persists: over-execution, where the agent executes tasks in a fully autonomous way, without adequate assessment of its action confidence to compromise an adaptive human-agent collaboration. This po

L. Dodell, M. Neumann, M. Osenberg, A. Hilger

Organic electrode-active materials (OAMs) not only enable a variety of charge and storage mechanisms, but are also safer for the environment and of lower cost compared to materials in commonly used lithium-ion batteries. Cross-linked Poly(3)-vinyl-N-methylphenothiazine (X-PVMPT) is a p-type OAM which shows high performance and enables fast and reversible ene

Narasimha Kumar, Dwipanjana Shit

Let $\mathbb{F}_q$ be a finite field with $q$ elements, where $q$ is a prime power and let $A:= \mathbb{F}_{q}[T]$. By~\cite{PR09}, the adelic image of the Galois representation attached to a rank $2$ Drinfeld $A$-module $\varphi$ is open, and determining when it is surjective remains a subtle problem. To resolve this question, in this article, we study the

Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly, Jessica He

The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack of interoperability between them creates challenges for researchers, practitioners, and policymakers seeking to operationalise AI governance. To address this gap, we introduce the A

Shourya Dutta, Janet van Niekerk, Haavard Rue

Approximate Bayesian inference for the class of latent Gaussian models can be achieved efficiently with integrated nested Laplace approximations (INLA). Based on recent reformulations in the INLA methodology, we propose a further extension that is necessary in some cases like heavy-tailed likelihoods or binary regression with imbalanced data. This extension

JaeWon Kim, Robert Wolfe, Ramya Bhagirathi Subramanian, Mei-Hsuan Lee

Adolescents heavily rely on social media to build and maintain close relationships, yet current platform designs often make self-disclosure feel risky or uncomfortable. Through a three-part study involving 19 teens aged 13-18, we identify key barriers to meaningful self-disclosure on social media. Our findings reveal that while these adolescents seek casual,

Fraser Birks, Matthew Nutter, Thomas D Swinburne, James R Kermode

Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods we present ML-MIX, a CPU- and GPU-compatible LAMMPS package to accelerate simulations by spatially mixing interat

Cristina Catalá-Lahoz, Jose Capmany

Programmable integrated photonics has emerged as a powerful platform for implementing diverse optical functions on a single chip through software-driven reconfiguration. At the core of these processors, photonic waveguide meshes enable flexible light routing and manipulation. However, recirculating waveguide meshes are fundamentally limited by the fixed dime

Jaroslav Hancl, Mathias L. Laursen, Simon Kristensen

We give conditions on a finite set of series of rational numbers to ensure that they are algebraically independent. Specialising our results to polynomials of lower degree, we also obtain new results on irrationality and $mathbb{Q}$-linear independence of such series.

Yiheng Yang, Yujie Wang, Chi Ma, Lei Yu

Dense large language models(LLMs) face critical efficiency bottlenecks as they rigidly activate all parameters regardless of input complexity. While existing sparsity methods(static pruning or dynamic activation) address this partially, they either lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. Inspired

Xiangming Du, Shuowen Zhang, Francis C. -M. Lau

In this letter, we study a cellular-connected unmanned aerial vehicle (UAV) which aims to complete a mission of flying between two pre-determined locations while maintaining satisfactory communication quality with the ground base stations (GBSs). Due to the potentially long distance of the UAV's flight, frequent handovers may be incurred among different GBSs

Jiamin Luo, Jingjing Wang, Junxiao Ma, Yujie Jin

Prior studies on Visual Sentiment Understanding (VSU) primarily rely on the explicit scene information (e.g., facial expression) to judge visual sentiments, which largely ignore implicit scene information (e.g., human action, objection relation and visual background), while such information is critical for precisely discovering visual sentiments. Motivated b

Aloka Fernando, Nisansa de Silva, Menan Velyuthan, Charitha Rathnayake

Parallel Data Curation (PDC) techniques aim to filter out noisy parallel sentences from web-mined corpora. Ranking sentence pairs using similarity scores on sentence embeddings derived from Pre-trained Multilingual Language Models (multiPLMs) is the most common PDC technique. However, previous research has shown that the choice of the multiPLM significantly

Jeffrey C. Lagarias, Wijit Yangjit

This paper extends Bhargava's theory of $\mathfrak{p}$-orderings of subsets $S$ of a Dedekind ring $R$ valid for prime ideals $\mathfrak{p}$ in $R$. Bhargava's theory defines for integers $k\ge1$ invariants of $S$, the generalized factorials $[k]!_S$, which are ideals of $R$. This paper defines $\mathfrak{b}$-orderings of subsets $S$ of a Dedekind domain $D$

Dongwei Xu, Yutao Zhu, Yao Lu, Youpeng Feng

With the rapid advancements in wireless communication technology, automatic modulation recognition (AMR) plays a critical role in ensuring communication security and reliability. However, numerous challenges, including higher performance demands, difficulty in data acquisition under specific scenarios, limited sample size, and low-quality labeled data, hinde

Haoyang Li, Li Bai, Qingqing Ye, Haibo Hu

Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to reconstruct realistic label-level private data, such as the general appearance of a target person from all training images labeled on him. Beyond label-level privacy, in this paper w

Pedro J. Freitas

It is well known that Charles Hermite kept an intense correspondence with many of the word's leading mathematicians of his time. This paper focuses on Hermite's letters to Francisco Gomes Teixeira, a Portuguese mathematician, who exchanged letters with Hermite for more than twenty years.

Huiqiang Wang, Mingchen Song, Guoqiang Zhong

Currently, restoring clean images from a variety of degradation types using a single model is still a challenging task. Existing all-in-one image restoration approaches struggle with addressing complex and ambiguously defined degradation types. In this paper, we introduce a dynamic degradation decomposition network for all-in-one image restoration, named D$^

Ujjwal Singh, Aditi Sharma, Nikhil Gupta, Deepakshi

Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation from natural language prompts, revolutionizing software development workflows. As we advance towards agent-based development paradigms, these models form the cornerstone of next-generation software development lifecycles. However, current benchmarks for evaluating multi