March 2025 arXiv papers — page 69

Vincent Knibbeler

For a finite subgroup $G$ of $SU(2)$ and one of its ground forms $P\in\mathbb{C}[X,Y]$, we show that the space of invariants $\mathbb{C}[X,Y,P^{-1}]^{G}_k$ of degree $k\in2\mathbb{Z}$ is a cyclic module over the algebra of invariants of degree zero. We find a generator for this module, uniformly for all finite subgroups of $SU(2)$. Then we construct a unifor

Hao Jin, Tamitake Itoh, Yuko S. Yamamoto

In this study, we combined the surface-enhanced Raman scattering (SERS) with density functional theory (DFT) calculations to investigate the SERS spectra of lanthanide (Ln)-citrate complexes (Ln = La, Ce, Pr, Nd, Sm, Eu, and Gd) under 488, 532, and 660 nm laser excitations. Detailed vibrational analysis and peak assignments were performed based on SERS spect

Yuhang Jiang, Ramakanth Kavuluru

Relation extraction (RE) is a standard information extraction task playing a major role in downstream applications such as knowledge discovery and question answering. Although decoder-only large language models are excelling in generative tasks, smaller encoder models are still the go to architecture for RE. In this paper, we revisit fine-tuning such smaller

Dilshod Nematov, Mirabbos Hojamberdiev

The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their

Zexu Huang, Min Xu, Stuart Perry

Recent developments in 3D reconstruction and neural rendering have significantly propelled the capabilities of photo-realistic 3D scene rendering across various academic and industrial fields. The 3D Gaussian Splatting technique, alongside its derivatives, integrates the advantages of primitive-based and volumetric representations to deliver top-tier renderi

Chaoyu Liu, Davide Murari, Lihao Liu, Yangming Li

Partial Differential Equation (PDE) problems often exhibit strong local spatial structures, and effectively capturing these structures is critical for approximating their solutions. Recently, the Fourier Neural Operator (FNO) has emerged as an efficient approach for solving these PDE problems. By using parametrization in the frequency domain, FNOs can effici

Nuojin Cheng, Alireza Doostan

Estimating failure probability is a key task in the field of uncertainty quantification. In this domain, importance sampling has proven to be an effective estimation strategy; however, its efficiency heavily depends on the choice of the biasing distribution. An improperly selected biasing distribution can significantly increase estimation error. One approach

Russelle Guadalupe

We derive new Gosper-type Lambert series identities of levels $12$ and $16$ using certain sums of generalized $\eta$-quotients on the genus zero congruence subgroups $\Gamma_0(12)$ and $\Gamma_0(16)$.

Ketan Suhaas Saichandran, Xavier Thomas, Prakhar Kaushik, Deepti Ghadiyaram

Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of Coarse-to-fine Prompt Embeddings), a training-free method to improve text-to-image alignment by progressively refining the input p

Codefuse, Ling Team, :, Wenting Cai

Recent advancements in code large language models (LLMs) have demonstrated remarkable capabilities in code generation and understanding. It is still challenging to build a code LLM with comprehensive performance yet ultimate efficiency. Many attempts have been released in the open source community to break the trade-off between performance and efficiency, su

Lingyun Deng, Litong Liu, Dong Wang, Xiao-Ping Wang

Variational models are widely used in image segmentation, with various models designed to address different types of images by optimizing specific objective functionals. However, traditional segmentation models primarily focus on the visual attributes of the image, often neglecting the topological properties of the target objects. This limitation can lead to

Zachary L. Clements, Patrick B. Ellis, Iain Goodridge, Matthew J. Murrian

This paper presents an analysis and experimental demonstration of single-satellite single-pass geolocation of a terrestrial broadcast Global Navigation Satellite System (GNSS) spoofer from Low Earth Orbit (LEO). The proliferation of LEO-based GNSS receivers offers the prospect of unprecedented spectrum awareness, enabling persistent GNSS interference detecti

Lutfu S. Sua, Haibo Wang, Jun Huang

A novel spatiotemporal framework using diverse econometric approaches is proposed in this research to analyze relationships among eight economy-wide variables in varying market conditions. Employing Vector Autoregression (VAR) and Granger causality, we explore trade policy effects on emerging manufacturing hubs in China, India, Malaysia, Singapore, and Vietn

N. Heidari, A. A. Araújo Filho, Iarley P. Lobo

In this work, we propose a new black hole solution, namely, a Hayward-like metric incorporating corrections due to non-commutativity by taking into account $\partial_r\wedge\partial_\theta$ Moyal twist. We begin by deriving this solution using the non-commutative gauge theory framework. The general properties of the metric are then analyzed, including the ev

Gaoge Han, Yongkang Cheng, Zhe Chen, Shaoli Huang

Two-hand reconstruction from monocular images is hampered by complex poses and severe occlusions, which often cause interaction misalignment and two-hand penetration. We address this by decoupling the problem into 2D structural alignment and 3D spatial interaction alignment, each handled by a tailored component. For 2D alignment, we pioneer the attempt to un

Kulsuma Begum, Abhijeet Das, Dinesh O. Shah, Sanjeev Kumar

Effects of chain length compatibility and molecular ratio on the two-dimensional crystallization of a binary mixed surfactant system with non-identical molecular size and its consequence on retardation to water evaporation are described via Langmuir Blodgett films. The mixed monolayers corresponding to 1:3 exhibit minimal area per molecule owing to identical

Tommaso Di Noto, Sofyan Jankowski, Francesco Puccinelli, Guillaume Marie

Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We ai

Xueying Shao, He Gao

Internal-Collision-induced Magnetic Reconnection and Turbulence (ICMART) model is a widely accepted model for explaining how high-magnetization jets produce gamma-ray burst (GRB) prompt emissions. In previous works, we show that this model can produce: 1) light curves with a superposition of fast and slow components; 2) a Band-shaped spectrum whose parameter

Xiaodan Zhang, Yanzhao Shi, Junzhong Ji, Chengxin Zheng

The automatic generation of brain CT reports has gained widespread attention, given its potential to assist radiologists in diagnosing cranial diseases. However, brain CT scans involve extensive medical entities, such as diverse anatomy regions and lesions, exhibiting highly inconsistent spatial patterns in 3D volumetric space. This leads to biased learning

Nguyen Phuc Tran, Brigitte Jaumard, Oscar Delgado

Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high ener

Hyungyu Choi, Young Kyun Jang, Chanho Eom

Vision-language models like CLIP have shown impressive capabilities in aligning images and text, but they often struggle with lengthy and detailed text descriptions because of their training focus on short and concise captions. We present GOAL (Global-local Object Alignment Learning), a novel fine-tuning method that enhances CLIP's ability to handle lengthy

D. V. Alekseevsky, P. Osipov

E.B. Vinberg developed a theory of homogeneous convex cones $C \subset V= \mathbb{R}^n$, which has many applications. He gave a construction of such cones in terms of non-associative rank $n$ matrix T-algebras $\cal{T}$, that consist of vector-valued $n \times n$ matrices $X = ||x_{ij}||, \, x_{ij} \in V_{ij} $ where $V_{ij}$ are Euclidean vector spaces. The

Junpeng Hu, Jinglai Li, Lei Zhang, Shi Jin

Gaussian Process Regression (GPR) is a nonparametric supervised learning method, widely valued for its ability to quantify uncertainty. Despite its advantages and broad applications, classical GPR implementations face significant scalability challenges, as they involve matrix operations with a cubic complexity in relation to the dataset size. This computatio

Martin Cederwall, Jakob Palmkvist

Over-extended Kac-Moody algebras contain so-called gradient structures - a gl(d)-covariant level decomposition of the algebra contains strings of modules at different levels that can be interpreted as spatial gradients. We present an algebraic origin for this phenomenon, based on the recently introduced Lie algebra extension of an over-extended Kac-Moody alg

Matteo Milazzo, Federico Musciotto, Jyrki Piilo, Rosario N. Mantegna

We study the long term dynamics of the stock portfolios owned by single Finnish legal entities in the Helsinki venue of the Nasdaq Nordic between 2001 and 2021. Using the Herfindahl-Hirschman index as a measure of concentration for the composition of stock portfolios, we investigate the concentration of Finnish household portfolios both at the level of each

Lei Guo, Wei Chen, Yuxuan Sun, Bo Ai

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwid

Zewen Zhang, Qinyuan Zheng, Eduardo Ibarra-Garcia-Padilla, Richard T. Scalettar

The advent of ultracold alkaline-earth atoms in optical lattices has established a platform for investigating correlated quantum matter with SU($N$) symmetry, offering highly tunable model parameters that allow experiments to access phenomena that are unavailable in conventional materials. Understanding the ground-state physics of SU($N$) Fermi-Hubbard model

Felipe Linares, Dequin Zhou

We consider the long time behavior of the solutions of the coupled Schr\"odinger-KdV systems \begin{eqnarray*} \left\{ \begin{array}{llll}i\partial_tu+\partial^2_xu=\alpha uv+\beta u|u|^2,\hskip30pt (x,t)\in \mathbb{R}\times \mathbb{R}^{+},\\ \partial_tv+\partial^3_xv+v\partial_xv=\gamma \partial_x(|u|^2), \hskip20pt (x,t)\in \mathbb{R}\times \mathbb{R}^{+},

Xin Mao, Anqi Dong, Ziqin He, Yidan Mei

Numerous complex real-world systems, such as those in biological, ecological, and social networks, exhibit higher-order interactions that are often modeled using polynomial dynamical systems or homogeneous polynomial dynamical systems (HPDSs). However, identifying system parameters and analyzing key system-theoretic properties remain challenging due to their

Reinier Sorgdrager

Let $p>3$ be a prime number, $f\geq1$ an integer. We consider a certain full subcategory $\mathcal C$ of the category of smooth admissible mod $p$ representations of either $\text{GL}_2\mathbf Q_{p^f}$ or of the group of units of the quaternion algebra over $\mathbf Q_{p^f}$. This category was introduced in the context of the mod $p$ Langlands program by Bre

Antonio Piccolomini d'Aragona

Proof-theoretic semantics (PTS) is normally understood today as Base-Extension Semantics (B-eS), i.e., as a theory of proof-theoretic consequence over atomic proof systems. Intuitionistic logic (IL) has been proved to be incomplete over a number of variants of B-eS, including a monotonic one where introduction rules play a prior role (miB-eS). In its origina

Natalie R. Rees, Robert G. Izzard, David D. Hendriks

Mass changes due to strong stellar winds and binary mass transfer have a dramatic impact on the consequent evolution of stars. This is generally not accounted for in population synthesis codes which are built using single star evolution models from full stellar evolution codes. We produce a new grid of models using the 1D stellar evolution code \textit{MESA}

Triveni Magadum, Sanjana Murgod, Kartik Garg, Vivek Yadav

In this research, renewable energy expansion in South America up to 2050 is predicted based on machine learning models that are trained on past energy data. The research employs gradient boosting regression and Prophet time series forecasting to make predictions of future generation capacities for solar, wind, hydroelectric, geothermal, biomass, and other re

Yixiang Huang, Jianhua Pei, Luocheng Chen, Zhenchang Du

The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their p

Karen Meagher, Andriaherimanana Sarobidy Razafimahatratra

The \emph{intersection density} of a transitive permutation group $G\leq \operatorname{Sym}(\Omega)$ is the ratio between the largest size of a subset of $G$ in which any two agree on at least one element of $\Omega$, and the order of a point-stabilizer of $G$. In this paper, we determine the intersection densities of the automorphism group of the arc-transi

Chen Song, Vladimir Cvetkovic, Rong Su

Socio-psychological studies have identified a common phenomenon where an individual's public actions do not necessarily coincide with their private opinions, yet most existing models fail to capture the dynamic interplay between these two aspects. To bridge this gap, we propose a novel agent-based modeling framework that integrates opinion dynamics with a de

Juan Di Mauro, Eduardo Salazar, Hugo D. Scolnik

The aim of this paper is to present a new design for a pseudorandom number generator (PRNG) that is cryptographically secure, passes all of the usual statistical tests referenced in the literature and hence generates high quality random sequences, that is compact and easy to implement in practice, of portable design and offering reasonable execution times. O

Jin-Jun Geng, Ding-Fang Hu, Hao-Xuan Gao, Yi-Fang Liang

Gamma-ray bursts (GRBs) are luminous stellar explosions characterized by the ejection of relativistic jets. This work proposes a novel paradigm to study these GRB jets. By analyzing the timing information of prompt pulses and X-ray flares, in conjunction with the multi-wavelength afterglow observations, we identify three distinct jets in the extraordinary GR

Jin-Jun Geng, Ying-Kang Zhang, Hao-Xuan Gao, Fan Xu

The long-term evolution of relativistic jets in gamma-ray bursts (GRBs), particularly from days to months post-burst, remains a fundamental puzzle in astrophysics. Here, we report our very long baseline interferometry observation of the brightest GRB 221009A from 5 to 26 days post-burst. Combined with released data, we uncover a remarkable two-stage evolutio

Rodrigo San-José

We generalize the Brouwer-Zimmermann algorithm, which is the most efficient general algorithm for computing the minimum distance of a random linear code, to the case of generalized Hamming weights. We also adapt this algorithm to compute the relative generalized Hamming weights of a nested pair of linear codes. In the package GHWs we provide an implementatio

Lorenzo Leuzzi, Simon Jones, Sabine Hauert, Davide Bacciu

Adapting to task changes without forgetting previous knowledge is a key skill for intelligent systems, and a crucial aspect of lifelong learning. Swarm controllers, however, are typically designed for specific tasks, lacking the ability to retain knowledge across changing tasks. Lifelong learning, on the other hand, focuses on individual agents with limited

Adeline Crépieux, Emile Pangburn, Shuqiu Wang, Kuanysh Zhussupbekov

We compute the (0-11) surface spectral function, the surface density of states (DOS), and the quasiparticle interference (QPI) patterns, both in the normal state and superconducting (SC) state of UTe$_2$. We consider all possible non-chiral and chiral order parameters (OPs) that could in principle describe the superconductivity in this compound. We describe

Shuqiu Wang, Kuanysh Zhussupbekov, Joseph P. Carroll, Bin Hu

Although no known material exhibits intrinsic topological superconductivity, wherein spin-triplet odd-parity electron pairing occurs, UTe2 is now the leading representative of this class. Conventionally, the parity of the superconducting order parameter may be established by using Bogoliubov quasiparticle interference (QPI) imaging. However, odd-parity super

Zeyu Liu, Zanlin Ni, Yeguo Hua, Xin Deng

Discrete visual tokenizers transform images into a sequence of tokens, enabling token-based visual generation akin to language models. However, this process is inherently challenging, as it requires both compressing visual signals into a compact representation and discretizing them into a fixed set of codes. Traditional discrete tokenizers typically learn th

Guoding Liu, Zhenyu Du, Zi-Wen Liu, Xiongfeng Ma

Efficient and high-performance quantum error correction is essential for achieving fault-tolerant quantum computing. Low-depth random circuits offer a promising approach to identifying effective and practical encoding strategies. In this work, we rigorously prove through information-theoretic analysis that one-dimensional logarithmic-depth random Clifford en

Si Wang, Guoqiang Xiao

Sparse arrays with $N$-sensors can provide up to $O(N^2)$ degrees of freedom (DOF) by second-order cumulants. However, these sparse arrays like minimum-/low-redundancy arrays (MRAs/LRAs), nested arrays and coprime arrays can only provide limited DOF and array aperture with the same number of physical sensors. However, further increasing DOF would increase co

Hao Xu

Inspired by quantum gravity frameworks predicting Planck-scale deviations from Lorentz invariance, we probe Lorentz symmetry violation via modified dispersion relations $\omega_{|\textbf{k}|}$. Departing from conventional approaches, we employ an Unruh-DeWitt detector to probe energy-dependent modifications to the dispersion relations. Two key methodological

Abdullah Al-Khatib, Abdullah Ahmed, Klaus Moessner, Holger Timinger

Onsite bandwidth reservation requests often face challenges such as price fluctuations and fairness issues due to unpredictable bandwidth availability and stringent latency requirements. Requesting bandwidth in advance can mitigate the impact of these fluctuations and ensure timely access to critical resources. In a multi-Mobile Network Operator (MNO) enviro

Sharan Maiya, Yinhong Liu, Ramit Debnath, Anna Korhonen

Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs' latent knowledge and extract more accurate preferences. Through

Aiden Kelly, Rémi Daou, Joel Daou, Vadim N. Kurdyumov

This study investigates the critical conditions for flame propagation in channels with cold walls. We analyze the impact of the Lewis number and flow amplitude ($A$) on the minimum channel width required to sustain a premixed flame. Our results span a wide range of Lewis numbers, encompassing both aiding and opposing flow conditions. Results are presented fo

Hojun Cho, Donghu Kim, Soyoung Yang, Chan Lee

Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient ar

R. D. Lin, Pengcheng Weng, Yinqiao Wang, Han Ding

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., on

Yi Ouyang, Qimin Song, Chenhao Zhang

In this paper, we establish two finiteness results and propose a conjecture concerning the Pythagoras number $P(A)$ of a finitely generated real algebra $A$. Let $X \hookrightarrow \mathbb{P}^n$ be an integral projective surface over $\mathbb{R}$, let $\widetilde{X}$ be the normalization of $X$, and let $s \in \Gamma(X,\mathcal{O}_X(1))$ be a nonzero section

Houqiang Zhong, Shaocheng Shen, Ke Cai, Zhenglong Wu

Fine-tuning large pre-trained vision foundation models in a parameter-efficient manner is critical for downstream vision tasks, considering the practical constraints of computational and storage costs. Low-rank adaptation (LoRA) is a well-established technique in this domain, achieving impressive efficiency by reducing the parameter space to a low-rank form.

Michael Temkin

An extension $K/k$ of analytic (i.e. real valued complete) fields is called small if it is topologically-algebraically generated by finitely many elements. We prove that this property is inherited by subextensions and hence topological generating degree of such extensions is monotonic. Much more detailed results are obtained in the case of degree one. Let $k

Filipe R. do Amaral, Petrônio A. S. Nogueira, Igor A. Maia, André V. G. Cavalieri

We study the hydrodynamic and acoustic fields of turbulent jets issuing from nozzles modified by the addition of cylindrical tabs on the inner surface, one diameter upstream of the exit. The tabs are designed to promote significant growth of steady streaks in the nozzle turbulent boundary layer. A baseline smooth nozzle is also studied for comparison. Acoust

Yohei Ito

In [arXiv:2109.13991], the author explained a relation between enhanced ind-sheaves and enhanced subanalytic sheaves. In particular, a relation between [Thm.9.5.3, Andrea D'Agnolo and Masaki Kashiwara, Riemann-Hilbert correspondence for holonomic $\mathcal{D}$-modules, 2016] and [Thm.6.3, Masaki Kashiwara, Riemann-Hilbert correspondence for irregular holonom

Ergun Simsek

This study presents a finite-difference-based numerical solver designed for the electric field formulation of vector wave equations in optically linear, non-magnetic, dielectric waveguides. We construct a generalized eigenvalue problem by incorporating all three components of the electric field into a self-consistent formulation. This ensures accurate enforc

Ergun Simsek, Alioune Niang, Raonaqul Islam, Logan Courtright

We present an easy-to-implement numerical method for analyzing electromagnetic wave propagation in dielectric rings. Our approach employs a finite-difference-based solver in cylindrical coordinates, solving a mixed electric-magnetic field formulation to accurately enforce boundary conditions and compute resonant modes. The method avoids geometric transformat

Faguo Zhou, Shunde Li, Rong Xue, Lingkun Bu

Three-dimensional neutron transport calculations using the Method of Characteristics (MOC) are highly regarded for their exceptional computational efficiency, precision, and stability. Nevertheless, when dealing with extensive-scale computations, the computational demands are substantial, leading to prolonged computation times. To address this challenge whil

Ao Wu, Di Cheng, Xinyun Wang, Meng Zeng

The hexagonal MnTe is a prime material candidate for altermagnets, an emerging class of magnetic compounds characterized by the nontrivial interplay of antiparallel spin arrangements with their underlying crystal structures. Recognizing precise knowledge of crystal symmetry as the cornerstone of the spin-group classification scheme, we report here a native i

Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu

Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C's high performance. However, vastly different features between C and Rust--e.g., distinct definitions and usages of pointers and references--pose significant challenges beyond mere syntactic translation. Existing automated translation to

Martin Brokate, Constantin Christof

We study directional differentiability properties of solution operators of rate-independent evolution variational inequalities with full-dimensional convex polyhedral admissible sets. It is shown that, if the space of continuous functions of bounded variation is used as the domain of definition, then the most prototypical examples of such solution operators

Chatrine Qwaider, Bashar Alhafni, Kirill Chirkunov, Nizar Habash

Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. The lack of annotated essay datasets inhibits the development of Arabic AES systems. This paper leverages Large Language Models (LLMs) and Transformer models to generate synthetic Arabic essays for A

Junli Wang, Wanyue Cao, Jinyan Huang, Yu Zhou

Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ su

Robert Millar, Jinglai Li

Optimal portfolio allocation is often formulated as a constrained risk problem, where one aims to minimize a risk measure subject to some performance constraints. This paper presents new Bayesian Optimization algorithms for such constrained minimization problems, seeking to minimize the conditional value-at-risk (a computationally intensive risk measure) und

Yiming Zhao, Yu Zeng, Yukun Qi, YaoYang Liu

Large Vision-Language Models (LVLMs) have made significant strides in the field of video understanding in recent times. Nevertheless, existing video benchmarks predominantly rely on text prompts for evaluation, which often require complex referential language and diminish both the accuracy and efficiency of human model interaction in turn. To address this li

Zhiqiang Yuan, Ting Zhang, Peixiang Luo, Ying Deng

Recently, significant advancements have been achieved in video generation technology, but applying it to resource-constrained downstream tasks like multi-frame animated sticker generation (ASG) characterized by low frame rates, abstract semantics, and long tail frame length distribution-remains challenging. Parameter-efficient fine-tuning (PEFT) techniques (

Sudhanshu Shekhar, Bhabani Prasad Mandal, Anirban Dutta

In this article, we employ the transfer matrix method (TMM) to analytically explore the impact of uniaxial strain on electron scattering in graphene under locally periodic and super-periodic electrostatic potential. Our study reveals that strain significantly influences electron transmission through the merging parameter $(\delta)$, which modulates the Dirac

Bin Fu, Jialin Li, Bin Zhang, Ruiping Wang

3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation, demonstrating strong potential for robotic applications. However, 3DGS-based methods in robotics primarily focus on static scenes, with limited attention to the dynamic scene changes essential for long-term service robots. T

Xiaodi Yu, Zhijie Qu, Zheng Cai, Jiangtao Li

As part of the HIERACHY program, we collect the high-SN and high-spectral resolution optical spectra of 25 quasars at $z\approx4-5$ to constrain the C IV evolution at $z\approx 3-5$. In this paper, we report a catalog of 626 (1263) C IV absorption systems (components) at $z\approx3-5$ with a 50% completeness column density of log$(N_{\rm CIV}/\rm cm^{-2}) \a

Sungphill Moon, Hyeontae Son, Dongcheol Hur, Sangwook Kim

We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large nu

Francisco J. Rodríguez Lera, Yoana Pita Lorenzo, David Sobrín Hidalgo, Laura Fernández Becerra

Cybersecurity in robotics stands out as a key aspect within Regulation (EU) 2024/1689, also known as the Artificial Intelligence Act, which establishes specific guidelines for intelligent and automated systems. A fundamental distinction in this regulatory framework is the difference between robots with Artificial Intelligence (AI) and those that operate thro

G. Lamura, D. Tay, R. Khasanov, P. Gentile

The intermetallic quasi-one-dimensional binary superconductor V$_2$Ga$_5$ was recently found to exhibit a topologically nontrivial normal state, making it a natural candidate for a topological superconductor (TSC). By combining dc-magnetization, nuclear magnetic resonance (NMR), and muon-spin rotation ({$\mu$SR) measurements on high-quality V$_2$Ga$_5$ singl

Yongjin Choi, Chanhun Park, Seung Jun Baek

Recent advances in text-to-image diffusion models spurred research on personalization, i.e., a customized image synthesis, of subjects within reference images. Although existing personalization methods are able to alter the subjects' positions or to personalize multiple subjects simultaneously, they often struggle to modify the behaviors of subjects or their

F. Ahmadova, K. André, N. Armesto, G. Azuelos

The LHeC is the project for delivering electron-nucleon collisions at CERN using the HL-LHC beams. An Energy Recovery Linac in racetrack configuration will provide 50 GeV electrons to achieve centre-of-mass energies around 1 TeV/nucleon and instantaneous luminosities around $10^{34}$ cm$^{-2}$s$^{-1}$. The LHeC program elaborated in the CDR of 2021 included

Ali Forootani

Mathematical reasoning and optimization are fundamental to artificial intelligence and computational problem-solving. Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning, theorem proving, and optimization techniques. This survey explores the evolution of mathematical problem-solving in AI, from ear

M. Królikowska, P. Kankiewicz, P. Wajer

We investigate the future 100 kyr evolution of six selected HTCs to show their basic commonalities and differences in dynamical behaviour. This includes estimating the probability of sungrazing and flipping. We combined three complementary numerical methods to study the dynamical features: the numerical integrations forwards in time, the Lyapunov time estima

Jie Zhang, Zhongqi Wang, Shiguang Shan, Xilin Chen

Backdoor attacks targeting text-to-image diffusion models have advanced rapidly. However, current backdoor samples often exhibit two key abnormalities compared to benign samples: 1) Semantic Consistency, where backdoor prompts tend to generate images with similar semantic content even with significant textual variations to the prompts; 2) Attention Consisten

Felix Haag

The desirable properties of explanations in information systems have fueled the demands for transparency in artificial intelligence (AI) outputs. To address these demands, the field of explainable AI (XAI) has put forth methods that can support human decision-making by explaining AI outputs. However, current empirical works present inconsistent findings on w

Gargi Das, Bhabani Prasad Mandal

In this work, we consider a two level $P\sigma_{z}$ pseudo-Hermitian system in contact with a thermal bath to study various thermodynamic properties. The system is realized in terms of infinitely many invariant subspaces. We find explicit solution in each subspace analytically. The quantum system undergoes a $P\sigma_{z}$ phase transition in each invariant s

Suketu P Patni, Vikram M Gadre

This work focuses on estimating the number of terms of a Fourier-Bessel series of order $p'$ required to get within a certain error of a Bessel function of a fixed order $p$ where $p \neq p'$. Our approach consists of two steps: one, constructing an invariant over $n$ of the $n^{\text{th}}$ order Hankel transform; and two, observing the effect of expanding a

Yongxiao Li, Feroz Khan, Manzoor Ahmed, Aized Amin Soofi

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C\&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C\&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a

Robin Quade, Michael Potthoff

Exchangeless braiding of Majorana modes is studied in minimal networks of weakly hybridized Kitaev chains of finite length using a rigorous many-body framework. In particular, for two coupled chains it is shown that exchangeless braiding is achieved by $2\pi$ rotations of the phase $\phi$ of the superconducting order parameter of one of the chains. This brai

Luigi Brugnano, Gianmarco Gurioli, Felice Iavernaro, Mikk Vikerpuur

Recently, the class of Runge-Kutta type methods named Fractional HBVMs (FHBVMs) has been introduced for the numerical solution of initial value problems of fractional differential equations, and a corresponding Matlab software has been released. Though an error analysis has already been given, a corresponding linear stability analysis is still lacking. We he

Songjie Yang, Zihang Wan, Yue Xiu, Boyu Ning

Movable antennas offer new potential for wireless communication by introducing degrees of freedom in antenna positioning, which has recently been explored for improving sum rates. In this paper, we aim to fully leverage the capabilities of movable antennas (MAs) by assuming that both the transmitter and receiver can optimize their antenna positions in multi-

Yu Wang, Junxian Mu, Hongzhi Huang, Qilong Wang

Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers. In this paper, we discuss the aforementioned

Tim Alpherts, Sennay Ghebreab, Nanne van Noord

Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims to capture changes in street scenes using computer vision and can help raise awareness of changes that make it possible to better understand the city and its residents. Traditionall

Abtin Pourhadi, Paul Swoboda

We introduce the Normalized Matching Transformer (NMT), a deep learning approach for efficient and accurate sparse semantic keypoint matching between image pairs. NMT consists of a strong visual backbone, geometric feature refinement via SplineCNN, followed by a normalized Transformer for computing matching features. Central to NMT is our hyperspherical norm

Huynh Viet Khanh, Nguyen Duc Anh Khoa

Let $K$ be a field equipped with a Henselian valuation, and let $D$ be a tame central division algebra over the field $K$. Denote by $\mathrm{TK}_1(D)$ the torsion subgroup of the Whitehead group ${\rm K}_1(D) = D^*/D'$, where $D^*$ is the multiplicative group of $D$ and $D'$ is its derived subgroup. Let ${\bf G}$ be the subgroup of $D^*$ such that $\mathrm{

Benjamin Zhou

Let $(X,E)$ be a smooth log Calabi-Yau pair consisting of a smooth Fano surface $X$ and a smooth anticanonical divisor $E$. We obtain certain higher genus local Gromov-Witten invariants from the projectivization of the canonical bundle $Z := \mathbb{P}(K_X \oplus \mathcal{O}_X)$, using the degeneration formula for stable log maps [KLR]. We evaluate an invari

Heng Gao, Zhuolin He, Shoumeng Qiu, Xiangyang Xue

Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles that may jeopardize the safety of autonomous driving systems. Based on our experiments, we find that current uni-modal anomaly segmentation frameworks tend to produce high anomaly sc

Hisaaki Iida, Junichiro Sugihara, Kazuki Sugihara, Haruki Kozuka

In previous research, various types of aerial robots equipped with perching mechanisms have been developed to extend operational time. However, most existing perching methods adopt either an upward or downward approach, making it difficult to perch near walls with surrounding obstacles. Additionally, perching hands are typically designed solely for attachmen

Yizhou Zhou

The rapid advancements in Large Language Models (LLMs) have revolutionized educational technology, enabling innovative approaches to automated and personalized content creation. This paper introduces Slide2Text, a system that leverages LLMs to transform PowerPoint presentations into customized textbooks. By extracting slide content using OCR, organizing it i

Yuchen Sun, Shanhui Zhao, Tao Yu, Hao Wen

GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limitin

Yanan Ma, Zhengru Fang, Longzhi Yuan, Yiqin Deng

Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edge computing (VEC) has emerged as a solution, offloading data-intensive tasks to roadside servers or other vehicles is hindered by large obstacles like trucks/buses and the surge in

Chongpeng Liu, Xiaojian Liao, Hancheng Liu, Limin Xiao

This paper presents PipeBoost, a low-latency LLM serving system for multi-GPU (serverless) clusters, which can rapidly launch inference services in response to bursty requests without preemptively over-provisioning GPUs. Many LLM inference tasks rely on the same base model (e.g., LoRA). To leverage this, PipeBoost introduces fault-tolerant pipeline paralleli

V. A. Reshetov

The Jaynes-Cummings model with degenerate atomic levels and polarization-degenerate field mode is considered. The general expression for the system evolution operator is derived. The analytical expressions for such operators in the case of low values ($J \leq 3/2$) of atomic angular momentum are obtained. The polarization properties of the photon emitted int

Javed Mohd, Debopam Das

Buoyant plumes are encountered in both natural and artificial scenarios, ranging from volcanic ash clouds and wildfires to smoke from chimneys and industrial pollutant discharge to rivers and lakes. These plumes are driven by the buoyancy forces arising from the density differences between the plume and the ambient fluids. Measurements of three dimensional d

Liang Jiang, Yuzhou Cheng, Kun Luo, Jianren Fan

Physics-informed neural networks (PINNs) demonstrate promising potential in parameterized engineering turbulence optimization problems but face challenges, such as high data requirements and low computational accuracy when applied to engineering turbulence problems. This study proposes a framework that enhances the ability of PINNs to solve parametric turbul