May 2025 arXiv papers — page 112

Yanwen Hong, Marwa Al Moussawi, Fabio Ambrosino, Antonio Anastasio

The MUon RAdiography of VESuvius (MURAVES) project aims to use muography imaging techniques to study the internal structure of the summit of the Mt. Vesuvius, an active volcano near Naples, Italy. This paper presents recent advancements in both data analysis and simulation tools that enhance the quality and reliability of the experiments results. A new track

Xi Chen, Shen Yan, Juelin Zhu, Chen Chen

We introduce Land-MoE, a novel approach for multispectral land cover classification (MLCC). Spectral shift, which emerges from disparities in sensors and geospatial conditions, poses a significant challenge in this domain. Existing methods predominantly rely on domain adaptation and generalization strategies, often utilizing small-scale models that exhibit l

Ziyi Chang, He Wang, George Alex Koulieris, Hubert P. H. Shum

Generating large-scale multi-character interactions is a challenging and important task in character animation. Multi-character interactions involve not only natural interactive motions but also characters coordinated with each other for transition. For example, a dance scenario involves characters dancing with partners and also characters coordinated to new

Davide Picca

This paper challenges the prevailing tendency to frame Large Language Models (LLMs) as cognitive systems, arguing instead for a semiotic perspective that situates these models within the broader dynamics of sign manipulation and meaning-making. Rather than assuming that LLMs understand language or simulate human thought, we propose that their primary functio

Martin Buhmann, Joaquín Jódar, Miguel L. Rodríguez

In this paper a new class of radial basis functions based on hyperbolic trigonometric functions will be introduced and studied. We focus on the properties of their generalised Fourier transforms with asymptotics. Therefore we will compute the expansions of these Fourier transforms with an application of the conditions of Strang and Fix in order to prove poly

Pengyan Zhu, Tingting Yang

Emerging intelligent service scenarios in 6G communication impose stringent requirements for low latency, high reliability, and privacy preservation. Generative large language models (LLMs) are gradually becoming key enablers for the integration of semantic communication and computation. However, due to the limited computational resources of edge devices and

Nataliia G. Shchukina, Javier Trujillo Bueno, Supriya Hebbur Dayananda, Rafael Manso Sainz

A major challenge in solar physics is to obtain empirical information on the magnetic field of the million-degree plasma of the solar corona. To this end, we need observables of the solar radiation sensitive to the coronal magnetic field. The most familiar observables are the polarization signals of visible and near-infrared forbidden lines of highly ionized

Davit Gondauri

Today, the economy is greatly influenced by Artificial General Intelligence (AGI). The purpose of this paper is to determine the impact of the quantitative relations of AGI on the country's economic parameters. The authors use the analysis of historical data in the research, develop a new mathematical algorithm that refers to the level of AGI development, an

Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco

This paper addresses the covariate shift problem in the context of nonparametric regression within reproducing kernel Hilbert spaces (RKHSs). Covariate shift arises in supervised learning when the input distributions of the training and test data differ, presenting additional challenges for learning. Although kernel methods have optimal statistical propertie

Tunde Joseph Taiwo

We consider the solution of PT symmetry Hamiltonians using the technique of tridiagonal representation approach. This methodology provides more accurate results and proper depiction of the Hamiltonian energy level and wavefunctions. It is well know that PT symmetry condition of a Hamiltonian ensure that its spectra are real and positive even if the Hamiltoni

Aswini Bala, Dhruva K. S

Here we discuss the construction of Sp$(4;\mathbb{R})$ invariant objects in the twistor space for three dimensional conformal field theories. The Sp$(4;\mathbb{R})$ invariant projective delta function, alongside the Twistor symplectic dot product invariants form the basis for conformal Wightman functions involving conserved currents and $\Delta=1$ scalars. F

Luca Ballotta, Nicola Bastianello, Riccardo M. G. Ferrari, Karl H. Johansson

In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of heterogeneous agents to learn local models tailored to their own data and tasks, while still generalizing well; on the other hand, the learning process must be resilient to cyberattac

Jarek Duda

Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln

Franziska Sofia Hafner, Ana Valdivia, Luc Rocher

Language models encode and subsequently perpetuate harmful gendered stereotypes. Research has succeeded in mitigating some of these harms, e.g. by dissociating non-gendered terms such as occupations from gendered terms such as 'woman' and 'man'. This approach, however, remains superficial given that associations are only one form of prejudice through which g

Weihong Du, Wenrui Liao, Binyu Yan, Hongru Liang

Large language model (LLM) based agents have shown great potential in following human instructions and automatically completing various tasks. To complete a task, the agent needs to decompose it into easily executed steps by planning. Existing studies mainly conduct the planning by inferring what steps should be executed next starting from the agent's initia

Eloise Minder, Sylvain Fleury, Solène Neyret, Jean-Rémy Chardonnet

Understanding which factors could influence co-presence in Virtual Reality could help develop more qualitative social interactions, or social interactions that generate similar sensations, emotions and feelings than the ones generated during Face-to-Face interactions. Co-presence is studied since the beginning of Virtual Reality (VR); though, no consensus is

Freija Beirnaert, Archisman Ghosh, Gergely Dálya

In this paper, we explore the possibility of using galaxy cluster catalogues to provide redshift support for a gravitational-wave dark standard siren measurement of the Hubble constant $H_0$. We adapt the cosmology inference pipeline gwcosmo to handle galaxy cluster catalogues. Together with binary black holes from the GWTC-3, we use galaxy cluster data from

Felix Finster, Albert Much

The fermionic relative entropy in two-dimensional Rindler spacetime is studied using both modular theory and the reduced one-particle density operators. The methods and results are compared. A formula for the relative entropy for general Gaussian states is derived. As an application, the relative entropy is computed for a class of non-unitary excitations.

Jannis N. Ahlers, Lorenzo D'Amico, Henriette Bast, Lucy F. Costello

Propagation-based phase-contrast X-ray imaging is a promising technique for in~vivo medical imaging, offering lower radiation doses than traditional attenuation-based imaging. Previous studies have focused on X-ray energies below 50 keV for small-animal imaging and mammography. Here, we investigate the feasibility of high-energy propagation-based computed to

Owais Mujtaba Khanday, Pablo Rodroguez San Esteban, Zubair Ahmad Lone, Marc Ouellet

Understanding how neural activity encodes speech and language production is a fundamental challenge in neuroscience and artificial intelligence. This study investigates whether embeddings from large-scale, self-supervised language and speech models can effectively reconstruct high-gamma neural activity characteristics, key indicators of cortical processing,

Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang

This paper investigates the underlying mechanisms of toxicity generation in Large Language Models (LLMs) and proposes an effective detoxification approach. Prior work typically considers the Feed-Forward Network (FFN) as the main source of toxicity, representing toxic regions as a set of toxic vectors or layer-wise subspaces. However, our in-depth analysis r

Soroush Hashemifar, Sherry Sahebi

Despite advances in deep learning for education, student knowledge tracing and behavior modeling face persistent challenges: limited personalization, inadequate modeling of diverse learning activities (especially non-assessed materials), and overlooking the interplay between knowledge acquisition and behavioral patterns. Practical limitations, such as fixed-

Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu

Steering methods have emerged as effective and targeted tools for guiding large language models' (LLMs) behavior without modifying their parameters. Multimodal large language models (MLLMs), however, do not currently enjoy the same suite of techniques, due in part to their recency and architectural diversity. Inspired by this gap, we investigate whether MLLM

Feiyu Duan, Xuemiao Zhang, Sirui Wang, Haoran Que

The performance of Large Language Models (LLMs) is intrinsically linked to the quality of its training data. Although several studies have proposed methods for high-quality data selection, they do not consider the importance of knowledge richness in text corpora. In this paper, we propose a novel and gradient-free High-Knowledge Scorer (HKS) to select high-q

Wenlin Zhang, Xiangyang Li, Kuicai Dong, Yichao Wang

Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information. However, traditional RAG systems are limited by static workflows and lack the adaptability required for multistep reasoning and complex task management. To address these limitations, agen

Zhenyu Li, Tianyi Shang, Pengjie Xu, Zhaojun Deng

Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop closure in Simultaneous Localization and Mapping (SLAM) and long-term navigation under varying environmental conditions. In this survey, we c

Janine Schneider, Immanuel Lautner, Denise Moussa, Julian Wolf

To avoid the disclosure of personal or corporate data, sanitization of storage devices is an important issue when such devices are to be reused. While poor sanitization practices have been reported for second-hand hard disk drives, it has been reported that data has been found on original storage devices based on flash technology. Based on insights into the

Tomas Bueno Momcilovic, Barbara Gallina, Ingmar Kessler, Jule Hendricks

Assurance cases (ACs) are a common artifact for building and maintaining confidence in system properties such as safety or robustness. Constructing an AC can be challenging, although existing tools provide support in static, document-centric applications and methods for dynamic contexts (e.g., autonomous driving) are emerging. Unfortunately, managing ACs rem

Michael Keiblinger, Mario Sieg, Jack Min Ong, Sami Jaghouar

This report presents the Prime Collective Communications Library (PCCL), a novel fault-tolerant collective communication library designed for distributed ML workloads over the public internet. PCCL introduces a new programming model that enables dynamic peer joining and failure recovery. The library implements efficient collective operations like all-reduce

Cosmin I. Bercea, Jun Li, Philipp Raffler, Evamaria O. Riedel

In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-world recognition flags such inputs to ensure the system remains robust as ever-emerging, previously $unknown$ categories appear and must be a

Stefano Berrone, Andrea Borio, Gioana Teora, Fabio Vicini

This paper introduces PolyDiM, an open-source C++ library tailored for the development and implementation of polytopal discretization methods for partial differential equations. The library provides robust and modular tools to support advanced numerical techniques, with a focus on the Virtual Element Method in both 2D and 3D settings. PolyDiM is designed to

Bo Li, Haoke Xiao, Lv Tang

Vision Mamba offers linear complexity for long visual sequences, yet its performance depends critically on how a two-dimensional patch grid is serialized into a one-dimensional state-space recurrence. Raster-style scans disrupt spatial continuity, and the mismatch between 2D locality and 1D state propagation becomes increasingly severe when the inference res

Michael Jaber, Vinayak M. Kumar, David Zuckerman

We prove that hashing $n$ balls into $n$ bins via a random matrix over $\mathbf{F}_2$ yields expected maximum load $O(\log n / \log \log n)$. This matches the expected maximum load of a fully random function and resolves an open question posed by Alon, Dietzfelbinger, Miltersen, Petrank, and Tardos (STOC '97, JACM '99). More generally, we show that the maxim

Anna Volpara, Alessandro Lupoli, Emma Perracchione

We address the problem of approximating parametric Fourier imaging problems via interpolation/ extrapolation algorithms that impose smoothing constraints across contiguous values of the parameter. Previous works already proved that interpolating via Variably Scaled Kernels (VSKs) the scattered observations in the Fourier domain and then defining the sought a

Hao Feng, Shu Wei, Xiang Fei, Wei Shi

Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decen

Ziad Adwan, Nicola Sottocornola

Copulas are essential tools in statistics and probability theory, enabling the study of the dependence structure between random variables independently of their marginal distributions. Among the various types of copulas, Ratio-Type Copulas have gained significant attention due to their flexibility in modeling joint distributions. This paper focuses on Separa

Yu Cui, Feng Liu, Jiawei Chen, Xingyu Lou

Click-through rate (CTR) prediction is a fundamental task in modern recommender systems. In recent years, the integration of large language models (LLMs) has been shown to effectively enhance the performance of traditional CTR methods. However, existing LLM-enhanced methods often require extensive processing of detailed textual descriptions for large-scale i

Yubo Peng, Luping Xiang, Bingxin Zhang, Kun Yang

Traditional single-modal sensing systems-based solely on either radio frequency (RF) or visual data-struggle to cope with the demands of complex and dynamic environments. Furthermore, single-device systems are constrained by limited perspectives and insufficient spatial coverage, which impairs their effectiveness in urban or non-line-of-sight scenarios. To o

Daniel O. Siebadji Tchuimeni, Marc Ziegler, Olivier Crégut, Pierre Gilliot

We investigate the ultrafast dynamics of excitons in a 2.6 nm-thick $\mathrm{ZnO/Zn_{0.84}Mg_{0.16}O}$ quantum well grown on a c-axis sapphire substrate, using non-degenerate time-resolved pump-probe spectroscopy. A pump pulse at 266 nm generates photocarriers within the ZnMgO barriers, and their dynamics is monitored through time-resolved differential refle

Alireza Fadakar, Musa Furkan Keskin, Hui Chen, Henk Wymeersch

Reconfigurable intelligent surfaces (RISs) have the potential to significantly enhance the performance of integrated sensing and communication (ISAC) systems, particularly in line-of-sight (LoS) blockage scenarios. However, as larger RISs are integrated into ISAC systems, mutual coupling (MC) effects between RIS elements become more pronounced, leading to a

Simone Cecchini, Bernhard Hanke, Thomas Schick, Lukas Schoenlinner

Using the index theory for twisted Dirac operators acting on sections of Lipschitz bundles over non-compact manifolds, we prove Llarull-type comparison results in scalar curvature geometry. They apply to spin Riemannian manifolds with cone-type singularities and Lipschitz comparison maps to spheres. We use the language of abstract cone operators which are in

Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin

The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Traditional testing approaches often prioritize either natural scenario sampling or safety-critical scenario generation, resulting in overly simplistic or unrealistic hazardous tests. In pr

Wei Jiang, Anying Fu, Youling Zhang

Model pruning is a performance optimization technique for large language models like R1 or o3-mini. However, existing pruning methods often lead to significant performance degradation or require extensive retraining and fine-tuning. This technique aims to identify and remove neurons, connections unlikely leading to the contribution during the human-computer

Antoine Collas, Ce Ju, Nicolas Salvy, Bertrand Thirion

Generating realistic brain connectivity matrices is key to analyzing population heterogeneity in brain organization, understanding disease, and augmenting data in challenging classification problems. Functional connectivity matrices lie in constrained spaces, such as the set of symmetric positive definite or correlation matrices, that can be modeled as Riema

Gregor Pasemann, Markus Reiß

We consider statistics for stochastic evolution equations in Hilbert space with emphasis on stochastic partial differential equations (SPDEs). We observe a solution process under additional measurement errors and want to estimate a real or functional parameter in the drift. Main targets of estimation are the diffusivity, transport or source coefficient in a

An-Dan Nguyen, Quang-Khoi Ta, Duy-Anh Vo

Algorithmic trading has long been an opaque, fragmented domain, guarded by secrecy and built around proprietary systems. In contrast to the open, collaborative evolution in fields like machine learning or software engineering, the algorithmic trading ecosystem has been slow to adopt reproducibility, standardization, and shared infrastructure. This paper intr

Yibo Gao, Hangqi Zhou, Zheyao Gao, Bomin Wang

The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-

Silvia Crafa, Teresa Scantamburlo

This paper elaborates on the concept of moral exercises as a means to help AI actors cultivate virtues that enable effective human oversight of AI systems. We explore the conceptual framework and significance of moral exercises, situating them within the contexts of philosophical discourse, ancient practices, and contemporary AI ethics scholarship. We outlin

I. A. Makarov, S. G. Ovchinnikov

The effect of the the $c$ axis compression on the electronic structure and superconducting properties of the HTSC cuprate La$_{2-x}$Sr$_x$CuO$_4$ at different doping is investigated. The electronic structure of quasiparticle excitations is obtained within the effective five-band Hubbard model using the equation of motion method for Green's functions builded

Jihye Hwang, Kate Pattle, Chang Won Lee, Janik Karoly

Magnetic fields play a significant role in star-forming processes on core to clump scales. We investigate magnetic field orientations and strengths in the massive star-forming clump P2 within the filamentary infrared dark cloud G28.34+0.06 using dust polarization observations made using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as part of the B-fiel

Duncan Adamson

Temporal graphs are a class of graphs defined by a constant set of vertices and a changing set of edges, each of which is known as a timestep. These graphs are well motivated in modelling real-world networks, where connections may change over time. One such example, itself the primary motivation for this paper, are public transport networks, where vertices r

Yingli Shen, Wen Lai, Shuo Wang, Ge Gao

Continued pretraining and instruction tuning on large-scale multilingual data have proven to be effective in scaling large language models (LLMs) to low-resource languages. However, the unaligned nature of such data limits its ability to effectively capture cross-lingual semantics. In contrast, multi-way parallel data, where identical content is aligned acro

Luyao Tang, Kunze Huang, Chaoqi Chen, Cheng Chen

Generalized category discovery (GCD) is essential for improving deep learning models' robustness in open-world scenarios by clustering unlabeled data containing both known and novel categories. Traditional GCD methods focus on minimizing intra-cluster variations, often sacrificing manifold capacity, which limits the richness of intra-class representations. I

Qianqian Zhang, WeiJun Wang, Yunxing Liu, Li Zhou

Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational complexity of the Transformer architecture increases quadratically with image resolution, while Convolutional Neural Networks (CNN) architectures are forced to stack deeper convolutio

Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

Adversarial training is one of the most effective defenses against adversarial attacks, but it incurs a high computational cost. In this study, we present the first theoretical analysis suggesting that adversarially pretrained transformers can serve as universally robust foundation models -- models that can adapt robustly to diverse downstream tasks with onl

Andreas Debrouwere

We provide a geometric characterization of the closed sets $K \subseteq \mathbb{R}^d$ such that every real $d$-sequence is the moment sequence of some Schwartz function on $\mathbb{R}^d$ with support in $K$. We obtain a similar result for Gelfand-Shilov spaces. Several illustrative examples are discussed. Our work is inspired by a recent result of Schm\"udge

Jingyun Zhang, Hao Peng, Li Sun, Guanlin Wu

Research on Graph Structure Learning (GSL) provides key insights for graph-based clustering, yet current methods like Graph Neural Networks (GNNs), Graph Attention Networks (GATs), and contrastive learning often rely heavily on the original graph structure. Their performance deteriorates when the original graph's adjacency matrix is too sparse or contains no

Luca Pellegrini, Massimiliano Ghiotto, Edoardo Centofanti, Luca Franco Pavarino

Ionic models, described by systems of stiff ordinary differential equations, are fundamental tools for simulating the complex dynamics of excitable cells in both Computational Neuroscience and Cardiology. Approximating these models using Artificial Neural Networks poses significant challenges due to their inherent stiffness, multiscale nonlinearities, and th

Xinzhe Zheng, Sijie Ji, Jiawei Sun, Renqi Chen

Mental health risk is a critical global public health challenge, necessitating innovative and reliable assessment methods. With the development of large language models (LLMs), they stand out to be a promising tool for explainable mental health care applications. Nevertheless, existing approaches predominantly rely on subjective textual mental records, which

Nicholas Hu, Mark A. Iwen, Deanna Needell, Rongrong Wang

The alternating least squares (ALS/AltLS) method is a widely used algorithm for computing the CP decomposition of a tensor. However, its convergence theory is still incompletely understood. In this paper, we prove explicit quantitative local convergence theorems for CP-AltLS applied to orthogonally decomposable and incoherently decomposable tensors. Specific

Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon, Dongwoo Lee

Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of

Shiyao Cui, Qinglin Zhang, Xuan Ouyang, Renmiao Chen

Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but conveys hazard when combined. Multimodal implicit toxicity appears not only as formal statements in social platforms but also prompts that can lead to toxic dialogs from Large Vision-Lan

Tong Tang, Songmei Qin, Jing Zhong, Yueyue Jiang

Reliable stellar atmospheric parameters are essential for probing stellar structure and evolution, and for stellar population studies. However, various deviations appear in comparisons with different ground-based spectroscopic surveys. We aim to select high-quality open cluster members and employ the atmospheric parameters provided by the theoretical isochro

Guoming Li, Jian Yang, Yifan Chen

Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various graph-related tasks. Conventional methods adopt a graph-wise filtering paradigm, imposing a uniform filter across all nodes, yet recent findings suggest that this rigid paradigm strug

Yifan Wang, Mai Tan, Carlos Fernandez-Granda, Peter A. Crozier

Electron energy-loss spectroscopy (EELS) coupled with scanning transmission electron microscopy (STEM) is a powerful technique to determine materials composition and bonding with high spatial resolution. Noise is often a limitation especially with the increasing sophistication of EELS experiments. The signal characteristics from direct electron detectors pro

Sunghyo Chung, Hyeon Jeon, Sungbok Shin, Md Naimul Hoque

A large portion of texts is written in English, but readers who see English as a Foreign Language (EFL) often struggle to read texts accurately and swiftly. EFL readers seek help from professional teachers and mentors, which is limited and costly. In this paper, we explore how an intelligent reading tool can assist EFL readers. We conducted a case study with

Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang, Xuanbing Xie

Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action trajectories. While recent benchmarks have advanced VLA research in domestic tasks, professional science-oriented domains remain underexplored. We introduce AutoBio, a simulation fram

Laura-Sophia von Hirschhausen, Jannes S. Magnusson, Mykyta Kovalenko, Fredrik Boye

Deep learning has transformed computer vision for precision agriculture, yet apple orchard monitoring remains limited by dataset constraints. The lack of diverse, realistic datasets and the difficulty of annotating dense, heterogeneous scenes. Existing datasets overlook different growth stages and stereo imagery, both essential for realistic 3D modeling of o

Ye Wang, Ruiqi Liu, Jiang Lin, Fei Liu

In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style categories, each enhanced with textual descriptions and instruction prompts. We show that OmniStyle-1M can not only enable efficient and scalable of style transfer models through su

Yifan Zeng

As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing high-dimensional, complex traffic patterns and imbalanced data cat

Naohiro Matsumoto, Ryusuke Endo, Mitsuharu Uemoto, Tomoya Ono

Two processes have been proposed to fabricate graphene/NiFe alloy interfaces for tunneling magnetoresistance devices. One is the transfer of graphene and the other is the evaporation of alloys onto graphene. The formation energy of a NiFe alloy substrate and the adsorption energy of graphene on the NiFe alloy substrate are investigated by a density functiona

Yuxuan Pang, Xue-Bing Wu, Yuming Fu, Rui Zhu

Slitless spectroscopy is a traditional method for selecting quasars. In this paper, we develop a procedure for selecting quasars (QSOs) using the 3D-HST G141 slitless spectra. We initially identify over 6,000 sources with emission lines broader than those typically found in emission line galaxies (ELGs) by analyzing the 1D spectra. These ``broad'' emission l

Di Wu, Qian Li, Heng Yang, Yong Han

Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by sharing only their local models, ensuring data privacy. However, FL is vulnerable to untargeted attacks that aim to degrade the global model's performance on the underlying data distribution. Existing defense mechanisms attempt to improve FL

Debam Biswas, Yulin Cai

We associate a concave transform to any compactified S-metrized divisor on a quasi-projective variety over an adelic curve. Then we show a Hilbert-Samuel type formula for relatively nef compactified S-metrized YZ-divisors.

Chenghuan Huang, Zhigeng Xu, Chong Sun, Chen Li

Multi-scale deformable attention (MSDA) is a flexible and powerful feature extraction mechanism for visual tasks, but its random-access grid sampling strategy poses significant optimization challenges, especially on domain-specific accelerators such as NPUs. In this work, we present a co-design approach that systematically rethinks memory access and computat

Amirhossein Bagheri, Mark Simons, Ryan S. Park, Alexander Berne

Enceladus is among the most intriguing bodies in the solar system due to its astrobiological potential. Determining the extent and duration of habitability (i.e., sustained habitability) requires characterizing the interior properties and the level and distribution of tidal heating in Enceladus. Inferring the intensity of geophysical activity in the core has

Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

Although adversarial training is known to be effective against adversarial examples, training dynamics are not well understood. In this study, we present the first theoretical analysis of adversarial training in random deep neural networks without any assumptions on data distributions. We introduce a new theoretical framework based on mean field theory, whic

Hao Dong, Ziyue Qiao, Zhiyuan Ning, Qi Hao

Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts occur. TKG extrapolation aims to infer possible future facts based on known history, which has garnered significant attention in recent years. Some existing methods treat TKG as a

Pedro E. A. da Costa, André V. Giannini, Victor P. Goncalves, Bruno D. Moreira

A comprehensive analysis of the photoproduction of $J/\Psi$ and $\Upsilon$ mesons in peripheral $PbPb$ collisions at the center - of - mass energies of the Large Hadron Collider (LHC) is performed, considering distinct assumptions for the modeling of the nuclear photon flux, photon - nucleus cross - section, overlap function and dipole - proton scattering am

R. Krithika, Pranabendu Misra, Prafullkumar Tale

For a collection $\mathcal{F}$ of graphs, the $\mathcal{F}$-\textsc{Contraction} problem takes a graph $G$ and an integer $k$ as input and decides if $G$ can be modified to some graph in $\mathcal{F}$ using at most $k$ edge contractions. The $\mathcal{F}$-\textsc{Contraction} problem is \NP-Complete for several graph classes $\mathcal{F}$. Heggerners et al.

Hong Huang, Dapeng Wu

Large language models (LLMs) have made exciting achievements across various domains, yet their deployment on resource-constrained personal devices remains hindered by the prohibitive computational and memory demands of task-specific fine-tuning. While quantization offers a pathway to efficiency, existing methods struggle to balance performance and overhead,

Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes, Adrian Dalca

Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) thickness estimation. However, most tools for estimating cortical surfaces work exclusively on scans with at least 1 mm isotropic resolution and are tuned to a specific magnetic resonance (MR) contrast, often T1-w

Shunjing Zhao, Xian Shi, Hanlun Lei

Cometary activity is a compelling subject of study, with thermophysical models playing a pivotal role in its understanding. However, traditional numerical solutions for small body thermophysical models are computationally intensive, posing challenges for investigations requiring high-resolution or repetitive modeling. To address this limitation, we employed

Tai D. Nguyen, Long H. Pham, Jun Sun

The rapid advancement of domain-specific large language models (LLMs) in fields like law necessitates frameworks that account for nuanced regional legal distinctions, which are critical for ensuring compliance and trustworthiness. Existing legal evaluation benchmarks often lack adaptability and fail to address diverse local contexts, limiting their utility i

Zelin Zhang, Tao Zhang, KediLI, Xu Zheng

Recent efforts have explored multimodal semantic segmentation using various backbone architectures. However, while most methods aim to improve accuracy, their computational efficiency remains underexplored. To address this, we propose EGFormer, an efficient multimodal semantic segmentation framework that flexibly integrates an arbitrary number of modalities

Nobuhisa Fujita, Komajiro Niizeki

We present a novel variant of a planar quasiperiodic tiling with tenfold symmetry, employing the same thick and thin rhombuses as the celebrated rhombic Penrose tiling. Despite its distinct visual appearance, this new tiling shares several key features with its predecessor, including similar vertex environments, polygonal acceptance domains based on regular

Anna-Mariya Otsetova, Jonas M. Tölle

We investigate the well-posedness and long-time behavior of a general continuum neural field model with Gaussian noise on possibly unbounded domains. In particular, we give conditions for the existence of invariant probability measures by restricting the solution flow to an invariant subspace with a nonlocal metric. Under the assumption of a sufficiently lar

Yifei Jin, Xin Zheng, Lei Guo

Existing research on judicial sentencing prediction predominantly relies on end-to-end models, which often neglect the inherent sentencing logic and lack interpretability-a critical requirement for both scholarly research and judicial practice. To address this challenge, we make three key contributions:First, we propose a novel Saturated Mechanistic Sentenci

Pu Wang, Pengwen Dai, Chen Wu, Yeying Jin

In this paper, we propose an efficient visual transformer framework for ultra-high-definition (UHD) image dehazing that addresses the key challenges of slow training speed and high memory consumption for existing methods. Our approach introduces two key innovations: 1) an \textbf{a}daptive \textbf{n}ormalization mechanism inspired by the nGPT architecture th

Yuxuan Yao, Shuqi Liu, Zehua Liu, Qintong Li

Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to integrate the diverse capabilities of different Large Langu

Zhidan Liu, Chengtang Yao, Jiaxi Zeng, Yuwei Wu

In this paper, we present a multi-label stereo matching method to simultaneously estimate the depth of the transparent objects and the occluded background in transparent scenes.Unlike previous methods that assume a unimodal distribution along the disparity dimension and formulate the matching as a single-label regression problem, we propose a multi-label reg

Zeyu Liu, Xianghua Kong, Zhidan Li, Zewen Wu

Two-dimensional (2D) moir\'e superlattices have emerged as a versatile platform for uncovering exotic quantum phases, many of which arise in bilayer systems exhibiting Archimedean tessellation patterns such as triangular, hexagonal, and kagome lattices. Here, we propose a strategy to engineer semiregular tessellation patterns in untwisted bilayer graphene by

Jiangtao Li

Let (X, g, J, f ) be a non-compact gradient shrinking Kahler-Ricci soliton. We prove that if the scalar curvature of X satisfies a mild assumption, then OP (X), the ring of holomorphic functions with polynomial growth on X, is finitely generated. This gives a partial confirmation to a conjecture of Munteanu and Wang (cf.[MW14]).

Han Zhang, Yan Wang, Guanfeng Liu, Pengfei Ding

To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has emerged. However, two major limitations severely degrade the performance and hinder the generalizability of existing XGNN methods: they (a) fail to capture the complete decision logi

Kunyun Wang, Bohan Li, Kai Yu, Minyi Guo

Diffusion models have emerged as a powerful class of generative models across various modalities, including image, video, and audio synthesis. However, their deployment is often limited by significant inference latency, primarily due to the inherently sequential nature of the denoising process. While existing parallelization strategies attempt to accelerate

Averi Banerjee, Syeda Rafisa Rahaman, Nilanjan Bondyopadhaya

We study electrical, thermal and thermoelectric transport in a hybrid device consisting of a long-range Kitaev chain coupled to two metallic leads at two ends. Electrical and thermal currents are calculated in this device under both voltage and thermal bias conditions. We find that the transport characteristics of the long-range Kitaev chain are distinguisha

Nicolai Taufertshöfer, Corinna Burri, Rok Venturini, Iason Giannopoulos

In-operando characterization of van der Waals (vdW) devices using surface-sensitive methods provides critical insights into phase transitions and correlated electronic states. Yet, integrating vdW materials in functional devices while maintaining pristine surfaces is a key challenge for combined transport and surface-sensitive experiments. Conventional litho

Wei Zhao, Tao Luo

We present a unified convergence theory for gradient-based training of neural network methods for partial differential equations (PDEs), covering both physics-informed neural networks (PINNs) and the Deep Ritz method. For linear PDEs, we extend the neural tangent kernel (NTK) framework for PINNs to establish global convergence guarantees for a broad class of

Sterre Lutz, Matthijs T. J. Spaan, Anna Lukina

Autonomous systems operating in the real world encounter a range of uncertainties. Probabilistic neural Lyapunov certification is a powerful approach to proving safety of nonlinear stochastic dynamical systems. When faced with changes beyond the modeled uncertainties, e.g., unidentified obstacles, probabilistic certificates must be transferred to the new sys

Liat Kessler, Nikolas Wardenski

Following Gonzales, we answer the question of whether the isomorphism type of a semi-free Hamiltonian $S^1$-manifold of dimension six is determined by certain data on the critical levels. We first give counter examples showing that Gonzales' assumptions are not sufficient for a positive answer. Then we prove that it is enough to further assume that the reduc