February 2025 arXiv papers — page 7

Andrea Cipriani, Alessandro De Santis, Giorgio Di Russo, Alfredo Grillo

The recent increase in computational resources and data availability has led to a significant rise in the use of Machine Learning (ML) techniques for data analysis in physics. However, the application of ML methods to solve differential equations capable of describing even complex physical systems is not yet fully widespread in theoretical high-energy physic

Hu Gao, Depeng Dang

Image deblurring aims to restore high-quality images from blurred ones. While existing deblurring methods have made significant progress, most overlook the fact that the degradation degree varies across different regions. In this paper, we propose AIBNet, a network that adaptively identifies the blurred regions, enabling differential restoration of these reg

Björn Braun, Rayan Armani, Manuel Meier, Max Moebus

Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior inside it, including motions, activities, and interactions. We argue that egocentric systems must additionally detect physiological states to capture a person's attention and situational responses, which are critical for context-aware behavior modeling. In this pap

Chencheng Xu, Saber Jafarpour, Chengcheng Zhao, Zhiguo Shi

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold $\mathrm{SO}(3) \times \mathbb{R}^3$ and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the R

Haozhong Sun, Zhongsen Li, Chenlin Du, Haokun Li

Quantitative magnetic resonance imaging (qMRI) requires multi-phase acqui-sition, often relying on reduced data sampling and reconstruction algorithms to accelerate scans, which inherently poses an ill-posed inverse problem. While many studies focus on measuring uncertainty during this process, few explore how to leverage it to enhance reconstruction perform

Daniel Alvestad, Alexander Rothkopf, Dénes Sexty

Real time evolution of a scalar field theory is investigated. The severe sign problem is circumvented using the Complex Langevin equation. The naive application of the method breaks down for extended real times due to the appearance of boundary terms. We use the kernel freedom of the complex Langevin equation to push the breakdown to larger real-times. We se

Vasudevarao Allu, Satyajit Sahoo

We characterize the weighted composition-differentiation operators $D_{\mfn,\psi,\varphi}$ acting on $\mathcal{H}_\gamma(\mathbb{D}^d)$ over the polydisk $\mathbb{D}^d$ which are complex symmetric with respect to the conjugation $\mathcal{J}$. We obtain necessary and sufficient conditions for $D_{\mfn,\psi,\varphi}$ to be self-adjoint. We also investigate co

Valentina Grazian, Chris Parker, Jason Semeraro, Martin van Beek

Let $q$ be a power of a fixed prime $p$. We classify up to isomorphism all simple saturated fusion systems on a certain class of $p$-groups constructed from the polynomial representations of $\mathrm{SL}_2(q)$, which includes the Sylow $p$-subgroups of $\mathrm{GL}_3(q)$ and $\mathrm{Sp}_4(q)$ as special cases. The resulting list includes all Clelland--Parke

Tiago Martins, Alessandra Vizzaccaro, Daniel Rixen

This work presents a framework for parametric reduction in FEM, where geometry is controlled by a parameter without altering material properties or stress states. The inverse determinant in the weak form is expanded as a power series, with explicit expressions for the zeroth and first-order terms. External forcing and parameter dependence are incorporated in

Yurii Averboukh, Ekaterina Kolpakova

This paper focuses on the value function in the time-optimal problem for a continuity equation in the space of probability measures. We derive the dynamic programming principle for this problem. In particular, we prove that the Kruzhkov transform of the value function is a unique discontinuous viscosity solution to the corresponding Dirichlet problem for the

Chunlin Zhong, Shuang Hao, Junhua Wu, Xiaona Chang

With the rapid development of computational pathology, many AI-assisted diagnostic tasks have emerged. Cellular nuclei segmentation can segment various types of cells for downstream analysis, but it relies on predefined categories and lacks flexibility. Moreover, pathology visual question answering can perform image-level understanding but lacks region-level

Lei Yang, Renren Jin, Ling Shi, Jianxiang Peng

With reasoning language models such as OpenAI-o3 and DeepSeek-R1 emerging, large language models (LLMs) have entered a new phase of development. However, existing benchmarks for coding evaluation are gradually inadequate to assess the capability of advanced LLMs in code reasoning. To bridge the gap for high-level code reasoning assessment, we propose ProBenc

Haiyun Zhang, Dahai Yan, Jianeng Zhou, Li Zhang

We apply a Gaussian process method to the extreme $\gamma$-ray flares of 3C 454.3 and 3C 279 to discover the variable patterns and then to investigate the physical origins of the giant flares. The kernels of stochastically driven damped simple harmonic oscillator (SHO), the damped random-walk (DRW), and Mat$\acute{\rm e}$rn-3/2 are respectively used to descr

Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares

While LLMs excel in zero-shot tasks, their performance in linguistic challenges like syntactic parsing has been less scrutinized. This paper studies state-of-the-art open-weight LLMs on the task by comparing them to baselines that do not have access to the input sentence, including baselines that have not been used in this context such as random projective t

Xin Xu, Shi Dai, Qijun Zhi, Juntao Bai

We present the discovery and timing results of 15 pulsars discovered in a high Galactic latitude survey conducted with the Five-hundred-meter Aperture Spherical Telescope (FAST). The survey targeted a region as close as possible to the Galactic Center, encompassing an area near the Galactic Bulge. The newly discovered pulsars consist of eleven normal pulsars

Jeff Yang, Duy-Khanh Vu, Minh-Tien Nguyen, Xuan-Quang Nguyen

This paper introduces layout-aware graph modeling for multimodal RAG. Different from traditional RAG methods that mostly deal with flat text chunks, the proposed method takes into account the relationship of multimodalities by using a graph structure. To do that, a graph modeling structure is defined based on document layout parsing. The structure of an inpu

Mohammad Rifqi Farhansyah, Iwan Darmawan, Adryan Kusumawardhana, Genta Indra Winata

The Javanese language features a complex system of honorifics that vary according to the social status of the speaker, listener, and referent. Despite its cultural and linguistic significance, there has been limited progress in developing a comprehensive corpus to capture these variations for natural language processing (NLP) tasks. In this paper, we present

Domagoj Bradač, Zach Hunter, Benny Sudakov

We prove that, for all $k \ge 3,$ and any integers $\Delta, n$ with $n \ge \Delta,$ there exists a $k$-uniform hypergraph on $n$ vertices with maximum degree at most $\Delta$ whose $4$-color Ramsey number is at least $\mathrm{tw}_k(c_k \Delta) \cdot n$, for some constant $c_k > 0$, where $\mathrm{tw}_k$ denotes the tower function. For $k \ge 4,$ this is tigh

Shaoming Li, Qing Cai, Songqi Kong, Runqing Tan

Reconstructing 3D shapes from a single image plays an important role in computer vision. Many methods have been proposed and achieve impressive performance. However, existing methods mainly focus on extracting semantic information from images and then simply concatenating it with 3D point clouds without further exploring the concatenated semantics. As a resu

Lei Zhang, Markus Stricker

The discovery and optimization of high-performance materials is the basis for advancing energy conversion technologies. To understand composition-property relationships, all available data sources should be leveraged: experimental results, predictions from simulations, and latent knowledge from scientific texts. Among these three, text-based data sources are

Yifan Duan, Yihong Tang, Xuefeng Bai, Kehai Chen

Large language models (LLMs) excel in both closed tasks (including problem-solving, and code generation) and open tasks (including creative writing), yet existing explanations for their capabilities lack connections to real-world human intelligence. To fill this gap, this paper systematically investigates LLM intelligence through the lens of ``human simulati

Xiaochuan Liu, Xin Cheng, Yuchong Sun, Xiaoxue Wu

Imitating how humans move their gaze in a visual scene is a vital research problem for both visual understanding and psychology, kindling crucial applications such as building alive virtual characters. Previous studies aim to predict gaze trajectories when humans are free-viewing an image, searching for required targets, or looking for clues to answer questi

Hyeonuk Nam, Yong-Hwa Park

Sound event detection (SED) has significantly benefited from self-supervised learning (SSL) approaches, particularly masked audio transformer for SED (MAT-SED), which leverages masked block prediction to reconstruct missing audio segments. However, while effective in capturing global dependencies, masked block prediction disrupts transient sound events and l

Ge Yan, Lipeng Zhu, Rui Zhang

The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). How

Jonathan Drechsel, Anja Reusch, Steffen Herbold

Mathematical formulas are a fundamental and widely used component in various scientific fields, serving as a universal language for expressing complex concepts and relationships. While state-of-the-art transformer models excel in processing and understanding natural language, they encounter challenges with mathematical notation, which involves a complex stru

Xujie Yuan, Yongxu Liu, Shimin Di, Shiwen Wu

The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigating hallucinations and improving model accuracy. However, a systematic understanding and comparative analysis of the rapidly emerging KG-RAG methods are still lacking. This paper se

Anjali Abirami Kugarajh

In this review we look into the gauge-dependence of scalar-induced gravitational waves (SIGWs) that are second-order tensors produced by first-order scalar-modes. The method includes deriving the background, first- and second-order Einstein field equations without imposing a gauge. We address the gauge-invariant approach and study the source-term of SIGWs in

Yuxiang Chen, Haocheng Xi, Jun Zhu, Jianfei Chen

Pre-training Transformers in FP4 precision is becoming a promising approach to gain substantial speedup, but it comes with a considerable loss of accuracy. Microscaling (MX) data format provides a fine-grained per-group quantization method to improve the representation ability of the FP4 format and is supported by the next-generation Blackwell GPU architectu

Rongchang Lu, Bingcheng Liao, Haowen Hou, Jiahang Lv

Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the challenges such as long scan times and expensive equipment by enhancing image resolution from low-quality inputs acquired in shorter scan times in clinical settings. However, current SR techniques still have problems such as limited ability to capture both local and global static patterns e

Aurélien Drezet, Arnaud Amblard

In this work celebrating the centenary of quantum mechanics, we review the principles of de Broglie Bohm theory, also known as pilot-wave theory and Bohmian mechanics. We assess the most common reading of it (the Nomological interpretation based on the notion of primitive ontology in tridimensional space) and defend instead a more causal and pluralistic appr

Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim, Jin Tae Kwak

Recent advances in vision-language models (VLMs) have shown remarkable potential in bridging visual and textual modalities. In computational pathology, domain-specific VLMs, which are pre-trained on extensive histopathology image-text datasets, have succeeded in various downstream tasks. However, existing research has primarily focused on the pre-training pr

Karolina Gutmańska, Agnieszka Podborska, Andrzej Sławek, Ramesh Sivasamy

Thiazolothiazoles (TzTz) represent a class of compounds with distinctive structural motifs and exceptional optical properties, positioning them as promising candidates for breakthroughs in optoelectronic and sensing technologies. X-ray crystallographic analyses of TzTz units symmetrically substituted with functional groups such as imidazole, o-vanillin, p-va

Robin Nolte, Mihai Pomarlan, Ayden Janssen, Daniel Beßler

Background: Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents but remains a fragmented field: diverse theories, terminologies, and design choices have led to disjointed developments and limited comparability across systems. Existing overviews remain at a conceptual level that is undisce

Karolina Gutmańska, Agnieszka Podborska, Tomasz Mazur, Andrzej Sławek

Cyanothiazoles, small and quite overlooked molecules, possess remarkable optical properties that can be fine-tuned through coordination with transition metals. In this study, we investigate a promising application of cyanothiazoles, where their combination with copper(I) iodide forms a new class of complexes exhibiting outstanding optical properties. X-ray c

Qinwei Ma, Jingzhe Shi, Can Jin, Jenq-Neng Hwang

Direct Preference Optimization (DPO) has been proposed as a promising alternative to Proximal Policy Optimization (PPO) based Reinforcement Learning with Human Feedback (RLHF). However, empirical evaluations consistently reveal suboptimal performance in DPO compared to common RLHF pipelines. In this work, we conduct a systematic analysis of DPO's training dy

Lingxiao Jin, Zinuo Cai, Zebin Chen, Hongyu Zhao

Serverless computing is increasingly adopted for its ability to manage complex, event-driven workloads without the need for infrastructure provisioning. However, traditional resource allocation in serverless platforms couples CPU and memory, which may not be optimal for all functions. Existing decoupling approaches, while offering some flexibility, are not d

Shi Meng, Bin Tian, Xiaotong Zhang

Efficient truck dispatching via Reinforcement Learning (RL) in open-pit mining is often hindered by reliance on complex reward engineering and value-based methods. This paper introduces Curriculum-inspired Adaptive Direct Policy Guidance, a novel curriculum learning strategy for policy-based RL to address these issues. We adapt Proximal Policy Optimization (

Elira Shaska, Tony Shaska

This paper presents a neurosymbolic approach to classifying Galois groups of polynomials, integrating classical Galois theory with machine learning to address challenges in algebraic computation. By combining neural networks with symbolic reasoning we develop a model that outperforms purely numerical methods in accuracy and interpretability. Focusing on sext

Yoonyoung Cho, Junhyek Han, Jisu Han, Beomjoon Kim

For robots to operate in general environments like households, they must be able to perform non-prehensile manipulation actions such as toppling and rolling to manipulate ungraspable objects. However, prior works on non-prehensile manipulation cannot yet generalize across environments with diverse geometries. The main challenge lies in adapting to varying en

Jean B Lasserre

We consider the integral v(y) = Ky f (x)dx on a domain Ky = {x $\in$ R d\,: g(x) $\le$ y}, where g is nonnegative and Ky is compact for all y $\in$ [0, +$\infty$). Under some assumptions, we show that for every y $\in$ (0, $\infty$) there exists a distinguished scalar $\lambda$y $\in$ (0, +$\infty$) such that which is the counterpart analogue for integration

Xiaoyu Tang, Chaojie Hao, Jing Li, Zhengzhou Yan

M giants, with their distinctive properties such as high luminosity, serve as excellent indicators for mapping the structure of the Milky Way. The distance to distant M giants can be determined by using the color-magnitude relation (CMR), which is derived from color-magnitude diagrams of specific systems in previous studies. In this work, we aimed to achieve

Gérard Roquier

The minimum void ratio of sand-fine mixtures as a function of fines content is an important property to know in geotechnical engineering. In this paper, a mathematical model is proposed to estimate it. It takes into account both the insertion of fines into the granular skeleton of the sand when the size ratio allows it, and the way in which the particles are

Maxim Dvornikov

We study the evolution of interacting large scale magnetic and axionic fields. Based on the new induction equation accounting for the contribution of spatially inhomogeneous axions, we consider the evolution of a magnetized spherical axion structure. Using the thin layer approximation, we derive the system of the nonlinear ordinary differential equations for

Long Chen, Xianchao Xiu

Sparse principal component analysis (PCA) is a well-established dimensionality reduction technique that is often used for unsupervised feature selection (UFS). However, determining the regularization parameters is rather challenging, and conventional approaches, including grid search and Bayesian optimization, not only bring great computational costs but als

Martin Mickan, Ulf Helmersson, David Horwat

Al-doped ZnO thin films were deposited using reactive high power impulse magnetron sputtering at substrate temperatures between room temperature and 600 $\bullet$ C. Two sample series with different oxygen partial pressures were studied. The films with the lowest resistivity of 3 x 10 -4 $\Omega$cm were deposited at the highest substrate temperature of 600 $

Stanislaw Baranski, Julian Szymanski

Distributed Key Generation (DKG) underpins threshold cryptography in many systems, including decentralized wallets, validator key ceremonies, cross-chain bridges, threshold signatures, secure multiparty computation, and internet voting. Classical ($t$,$n$)-DKG assumes a fixed group of n parties and a global threshold $t$, requiring full and timely participat

Zhaoyi Li, Gangwei Jiang, Chenwang Wu, Ying Wei

Despite the rising prevalence of neural language models, recent empirical evidence suggests their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data augmentation, which aims to introduce additional compositional inductive bias. However, existing handcrafted augmentation strategies offer lim

Tingwei Meng, Siting Liu, Samy Wu Fung, Stanley Osher

Hamilton-Jacobi partial differential equations (HJ PDEs) play a central role in many applications such as economics, physics, and engineering. These equations describe the evolution of a value function which encodes valuable information about the system, such as action, cost, or level sets of a dynamic process. Their importance lies in their ability to model

Byeoksong Lee, Minki Lee, Joongoo Kang

Vertically stacked layers derived from non-ferroelectric monolayers offer a promising route to two-dimensional (2D) ferroelectrics, where polarization switching occurs via interlayer sliding at sub-unit cell scales. Here, we develop a theory of slidetronics based on the notion that sliding-induced switching $P \rightarrow P'$ can also be achieved by applying

Raymond W. Yeung

In the past over two decades, very fruitful results have been obtained in information theory in the study of the Shannon entropy. This study has led to the discovery of a new class of constraints on the Shannon entropy called non-Shannon-type inequalities. Intimate connections between the Shannon entropy and different branches of mathematics including group

Haoran Li, Zhenzhou Yuan, Rui Yue, Guangchuan Yang

This study introduces a dynamic bus lane (DBL) strategy, referred to as the dynamic bus priority lane (DBPL) strategy, designed for mixed traffic environments featuring both manual and automated vehicles. Unlike previous DBL strategies, this approach accounts for partially connected and autonomous vehicles (CAVs) capable of autonomous trajectory planning. By

Francis Granger, Edith Bellet-Amalric, Kuntheak Kheng, Gilles Nogues

The emission properties of a localized solid-state emitter are strongly influenced by its environment. The coupling to acoustic phonons impacts the coherence of the emitter and its temperature dependence, and also results in the apparition of phonon sidebands besides the sharp zero-phonon line. Here, we present a method for measuring the absolute temperature

Hideaki Takahashi

The development of kinetic energy functional (KEF) is known as one of the most difficult subjects in the electronic density functional theory (DFT). In particular, the sound description of chemical bonds using a KEF is a matter of great significance in the field of theoretical physics and chemistry. It can be readily confirmed that the famous Thomas-Fermi (T

Nicolas Nagel

We investigate $L_2$-discrepancies of what we call weak Latin hypercubes. In this case it turns out that there is a precise equivalence between the extreme and periodic $L_2$-discrepancy which follows from a much broader result about generalized energies for weighted point sets. Motivated by this we study the asymptotics of the optimal $L_2$-discrepancy of w

Yusuf Yigit Pilavci, Jérémie Boulanger, Pierre-Antoine Thouvenin, Pierre Chainais

We propose two formulations to leverage the geometric properties of bivariate signals for dealing with the denoising problem. In doing so, we use the instantaneous Stokes parameters to incorporate the polarization state of the signal. While the first formulation exploits the statistics of the Stokes representation in a Bayesian setting, the second uses a ker

Zelong Sun, Dong Jing, Zhiwu Lu

Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images by integrating information from a composed query (reference image and modification text) without training samples. Existing methods primarily combine caption models and large language models (LLMs) to generate target captions based on composed queries but face various issues such as i

Ahmad Faraz Khan, Azal Ahmad Khan, Anas Mohamed, Haider Ali

Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions lack adaptability and require extensive manual tuning, leading to inefficiencies and misconfigurations. We introduce LADs, the first LLM-driven framework designed to tackle these cha

Fadeel Sher Khan, Joshua Ebenezer, Hamid Sheikh, Seok-Jun Lee

Smartphone cameras have become ubiquitous imaging tools, yet their small sensors and compact optics often limit spatial resolution and introduce distortions. Combining information from multiple low-resolution (LR) frames to produce a high-resolution (HR) image has been explored to overcome the inherent limitations of smartphone cameras. Despite the promise o

Jiawen Li, Jiali Hu, Qiehe Sun, Renao Yan

The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. Traditionally, weakly supervised fine-tuning via multiple instance learning (MIL) has been the primary method for adapting foundation models to WSIs. However, in this work we presen

Roberto Auzzi, Stefano Bolognesi, Giuseppe Nardelli, Gianni Tallarita

We study soliton and black hole solutions with scalar hair in AdS$_3$ in a theory with a Maxwell field and a charged scalar field with double trace boundary conditions, which can trigger the dual boundary theory to become a superconductor. We investigate the phase diagram as a function of the temperature $T$ and of the double trace coupling $\kappa$ and we f

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $4.5$ fb$^{-1}$ of $e^{+}e^{-}$ collision data accumulated with the BESIII detector at center-of-mass energies ranging from $4599.53$ MeV to $4698.82$ MeV, we report the measurement of the absolute branching fraction (BF) of the inclusive decay $\Lambda_{c}^{+} \to K_{S}^{0} X$ using the double-tag technique. The result is $\mathcal{B}(\Lambda_{

Jiangwei Xu, Jinchen Jiang, Heng Xu, Bojun Wang

We present polarization pulse profiles for 56 millisecond pulsars (MSPs) monitored by the Chinese Pulsar Timing Array (CPTA) collaboration using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The observations centered at 1.25 GHz with a raw bandwidth of 500 MHz. Due to the high sensitivity ($\sim$16 K/Jy) of the FAST telescope and our long

Guo Liang, Guangwu Liu, Kun Zhang

Gradient-based methods are well-suited for derivative-free optimization (DFO), where finite-difference (FD) estimates are commonly used as gradient surrogates. Traditional stochastic approximation methods, such as Kiefer-Wolfowitz (KW) and simultaneous perturbation stochastic approximation (SPSA), typically utilize only two samples per iteration, resulting i

Shu Liu, Xiangxi Mo, Moshik Hershcovitch, Henric Zhang

Modern applications span multiple clouds to reduce costs, avoid vendor lock-in, and leverage low-availability resources in another cloud. However, standard object stores operate within a single cloud, forcing users to manually manage data placement across clouds, i.e., navigate their diverse APIs and handle heterogeneous costs for network and storage. This i

Mingyang Yang, Zeyu Sha, Feitian Zhang

Underwater vehicles have emerged as a critical technology for exploring and monitoring aquatic environments. The deployment of multi-vehicle systems has gained substantial interest due to their capability to perform collaborative tasks with improved efficiency. However, achieving precise localization of a leader underwater vehicle within a multi-vehicle conf

Angelo Milfont, Alvaro Veiga

We aim to develop a time series modeling methodology tailored to high-dimensional environments, addressing two critical challenges: variable selection from a large pool of candidates, and the detection of structural break points, where the model's parameters shift. This effort centers on formulating a least squares estimation problem with regularization cons

Yu Nakayama

The extended Majorana Nicolai model is one of the simplest models of supersymmetry realized on a fermionic chain in $1+1$ dimensions. Within a certain parameter region, the extended theory breaks the supersymmetry spontaneously, but it has a distinguished feature that the general counting rule of the Nambu-Goldstone mode does not apply: we observe twice as m

Bach-Thuan Bui, Huy-Hoang Bui, Yasuyuki Fujii, Dinh-Tuan Tran

In this paper, we present a new approach for improving 3D point and line mapping regression for camera re-localization. Previous methods typically rely on feature matching (FM) with stored descriptors or use a single network to encode both points and lines. While FM-based methods perform well in large-scale environments, they become computationally expensive

Grigori Olshanski

The classical Jacobi polynomials on the interval $[-1,1]$ are eigenfunctions of a second order differential operator. It is well known that this operator generates a diffusion process on $[-1,1]$. Further, this fact admits an extension to $N$ dimensions (Demni (2010), Remling-R\"osler (2011)) leading to a $3$-parameter family of diffusion processes $X_N$ on

Shenao Wang, Yanjie Zhao, Yinglin Xie, Zhao Liu

The rapid growth of Large Language Models (LLMs) and AI-driven applications has propelled Vector Database Management Systems (VDBMSs) into the spotlight as a critical infrastructure component. VDBMS specializes in storing, indexing, and querying dense vector embeddings, enabling advanced LLM capabilities such as retrieval-augmented generation, long-term memo

Xiao Wang, Jingyun Hua, Weihong Lin, Yuanxing Zhang

Recent Multi-modal Large Language Models (MLLMs) have made great progress in video understanding. However, their performance on videos involving human actions is still limited by the lack of high-quality data. To address this, we introduce a two-stage data annotation pipeline. First, we design strategies to accumulate videos featuring clear human actions fro

Hongmei Hu

The present paper is devoted to extend parabolic presentations, depending on an arbitrary composition of M+N and an arbitrary 01-sequence, of the super Yangian Y(M|N) to a field of positive characteristic.

Ruoyu Wang, Yuxiang Gao, Hongyu Wu, Zhicheng Zhong

Universal force fields generalizable across the periodic table represent a new trend in computational materials science. However, the applications of universal force fields in material simulations are limited by their slow inference speed and the lack of first-principles accuracy. Instead of building a single model simultaneously satisfying these characteris

Peijie Wang, Zhong-Zhi Li, Fei Yin, Xin Yang

Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encountered in real-world mathematical applications. To address th

Jiawei Wang, Kai Wang, Shaojie Lin, Runze Wu

With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teachers, and programmers. In this paper, we develop an application-level testbed based on the open-source strategy game "Unciv", which has millions of active players, to enable researche

Faisal Mohammad, Duksan Ryu

In recent years, the rise of autonomous driving technologies has highlighted the critical importance of reliable software for ensuring safety and performance. This paper proposes a novel approach for just-in-time software defect prediction (JIT-SDP) in autonomous driving software systems using multimodal learning. The proposed model leverages the multimodal

L. J. Dong, Y. G. Zheng, S. J. Kang, C. Y. Yang

The very high energy (VHE, E $>$ $100 \mathrm~{GeV}$) $\gamma$-ray observations offer a possibility of indirectly detecting the presence of axion-like particles (ALPs). The paper focuses on detecting photon-ALP oscillations on $\gamma$-ray spectra from distant sources in astrophysical magnetic fields. Strong evidence indicates that: (1) the photon-ALP oscill

Masoumeh Chapariniya, Hossein Ranjbar, Teodora Vukovic, Sarah Ebling

In the age of AI-driven generative technologies, traditional biometric recognition systems face unprecedented challenges, particularly from sophisticated deepfake and face reenactment techniques. In this study, we propose a Two-Stream Spatial-Temporal Transformer Framework for person identification using upper body keypoints visible during online conversatio

Jinbao Long, Zhongkai Wang, Huanfa Peng, Wei Sun

Microresonator-based Kerr frequency combs ("Kerr microcombs") constitute chip-scale frequency combs of broad spectral bandwidth and repetition rate ranging from gigahertz to terahertz. An appealing application exploiting microcombs' coherence and large repetition rate is microwave and millimeter-wave generation. Latest endeavor applying two-point optical fre

Faizuddin Ahmed, İzzet Sakallı, Ahmad Al-Badawi

In this paper, we theoretically investigate gravitational lensing within the space-time framework of traversable wormholes, focusing on the combined effects of a global monopole and a cosmic string. Specifically, we examine the Ellis-Bronnikov-Morris-Thorne wormhole metric and analyze how these topological defects influence photon trajectories. By considerin

M. Doohan, M. Millea, S. Raghunathan, F. Ge

We estimate the magnitude of the bias due to non-Gaussian extragalactic foregrounds on the optimal reconstruction of the cosmic microwave background (CMB) lensing potential and temperature power spectra. The reconstruction is performed using a Bayesian inference method known as the marginal unbiased score expansion (MUSE). We apply MUSE to a minimum variance

Yutaka Konomi

Let $p$ be a prime and $\mathbb{Z}_p$ the ring of $p$-adic integers. Let $G$ denote the simple group of order 168, 504 or 360. In this paper, we study the structure of the $\chi$-part of a $\mathbb{Z}_p[\mathrm{Im}(\chi)][G]$-module come from ideal class groups, Artin $L$-functions and Iwasawa theory.

Longfei Chang, Zhendong Li, Wei-Hai Fang

Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for addressing this challenge. Inspired by the quantum-enhanced Markov chain Monte Carlo (QeMCMC) algorithm [Nature, 619, 282-287 (2023)], which was originally designed for sampling th

Junxing Zhou, Junxiang Huang, Xin Chang, Toru Okuda

Using two-dimensional hydrodynamic simulations, we investigate the stability of shocked accretion flows around black holes under non-axisymmetric perturbations. By systematically exploring the parameter space of specific energy and angular momentum that permits shock formation in advective accretion flows, we demonstrate that quasi-periodic oscillations (QPO

Rob den Teuling, Ritesh Das, Artem V. Bondarenko, Elena V. Tartakovskaya

We derive analytical expressions for the spin wave frequencies and precession amplitudes in monolayer and antiferromagnetically coupled bilayer CrSBr under in-plane external magnetic fields. The analysis covers the antiferromagnetic, ferromagnetic, and canted phases, demonstrating that the spin wave frequencies in all phases are tunable by the applied magnet

Bing Xu, Rui Liu, Hongliang Wo, Zhiyu Liao

The heavy fermion (HF) state of $d$-electron systems is of great current interest since it exhibits various exotic phases and phenomena that are reminiscent of the Kondo effect in $f$-electron HF systems. Here, we present a combined infrared spectroscopy and first-principles band structure calculation study of the $3d$-electron HF compound YFe$_2$Ge$_2$. The

Kuang-Da Wang, Teng-Ruei Chen, Yu Heng Hung, Guo-Xun Ko

Aligning Large Language Models (LLMs) with human preferences through finetuning is resource-intensive, motivating lightweight alternatives at test time. We address test-time alignment through the lens of sequential decision making, a perspective that reveals two fundamental challenges. When actions are defined at the token level, as in guided decoding, align

Haobo Chang, Zhuangzhuang Tian, Xin Lv, Mengna Yang

The coherence time of an optically trapped neutral atom is a crucial parameter for quantum technologies. We found that optical dipole traps with higher-order spatial forms inherently offer lower decoherence rates compared to those with lower-order spatial forms. We formulated the decoherence rate caused by the variance of the differential energy shift and ph

Amir Jahangiri, Tatiana Agback, Ulrika Brath, Vladislav Orekhov

In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that a

Yukang Liang, Qinxia Wang, Zhihui Wang, Shijun Guan

High-sensitivity measurements of the microwave electric field are important in applications of communication and metrology. \replaced{The sensitivity of traditional Rydberg superheterodyne receivers in free space is effectively determined by the signal-to-noise ratio (SNR), which is often considered equivalent to sensitivity in practical sensing applications

Xiaoqun Liu, Jiacheng Liang, Qiben Yan, Jiyong Jang

The exponential growth of cyber threat knowledge, exemplified by the expansion of databases such as MITRE-CVE and NVD, poses significant challenges for cyber threat analysis. Security professionals are increasingly burdened by the sheer volume and complexity of information, creating an urgent need for effective tools to navigate, synthesize, and act on large

Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu

Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through

Adiba Mahbub Proma, Neeley Pate, James Druckman, Gourab Ghoshal

While Large Language Models (LLMs) can amplify online misinformation, they also show promise in tackling misinformation. In this paper, we empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering political misinformation. We implement a two-step, chain-of-thought prompting approach, where models first identify credible

Yixuan Li, Xuesong Wang, Tianyi Wang, Qian Liu

To date, hundreds of crashes have occurred in open road testing of automated vehicles (AVs), highlighting the need for improving AV reliability and safety. Pre-crash scenario typology classifies crashes based on vehicle dynamics and kinematics features. Building on this, characteristics analysis can identify similar features under comparable crashes, offerin

Silius M. Vandeskog, Magne Aldrin, Daniel Howell, Edvin Fuglebakk

The stock assessment model SAM contains a large number of age-dependent parameters that must be manually grouped together to obtain robust inference. This can make the model selection process slow, non-extensive and highly subjective, while producing unrealistic looking parameter estimates with discrete jumps. We propose to model age-dependent SAM parameters

Lin Cheng, Zhiyuan Xiong, Shuaishuai Hou, Yijia Sun

We report the phenomena of electromagnetically induced transparency (EIT) and electromagnetically induced absorption (EIA) using two identical beams in rubidium atomic vapor. The {\Lambda}-type EIT configuration is employed to examine the EIT spectrum for the D1 line in 87Rb F=2 characteristics16 by varying parameters such as frequency detuning, Iprobe/Ipump

Yuhang Zhang, Minghui Song

In contrast to ordinary stochastic differential equations (SDEs), the numerical simulation of McKean-Vlasov stochastic differential equations (MV-SDEs) requires approximating the distribution law first. Based on the theory of propagation of chaos, particle approximation method is widely used. Then, a natural question is to investigate the convergence rate of

Hyewon Jeon, Jay-Yoon Lee

Automated fact-checking aims to assess the truthfulness of textual claims based on relevant evidence. However, verifying complex claims that require multi-hop reasoning remains a significant challenge. We propose GraphCheck, a novel framework that transforms claims into entity-relationship graphs for structured and systematic fact-checking. By explicitly mod

Tao Chen, Chenhui Wang, Zhihao Chen, Hongming Shan

While deep learning has significantly advanced medical image segmentation, most existing methods still struggle with handling complex anatomical regions. Cascaded or deep supervision-based approaches attempt to address this challenge through multi-scale feature learning but fail to establish sufficient inter-scale dependencies, as each scale relies solely on

Yuezhou Hu, Weiyu Huang, Zichen Liang, Chang Chen

Serving Large Language Models (LLMs) is costly. However, post-training weight quantization can address this problem by both compressing their sizes for limited memory and saving bandwidth for acceleration. As not all weight dimensions are equally important, those methods typically rely on a sensitivity metric, which indicates the element-wise influence of we