March 2025 arXiv papers — page 160

Nazar Pyvovar, Sadi Ayhan, Carl Pfeiffer, Igor Anisimov

Magnetic materials are crucial in nonreciprocal electromagnetic devices, such as isolators, circulators, and nonreciprocal phase shifters. However, their use is often limited by the need for a uniform bias magnetic field and nonuniform demagnetizing fields, resulting in the restricted aperture of free-space devices, poor temperature stability, and incompatib

E Harshith Kumar Yadav, Rahul Narava, Anshika, Shashi Shekher Jha

Managing equal charge levels in active cell balancing while charging a Li-ion battery is challenging. An imbalance in charge levels affects the state of health of the battery, along with the concerns of thermal runaway and fire hazards. Traditional methods focus on safety assurance as a trade-off between safety and charging time. Others deal with battery-spe

Kazem Rezazadeh

Taking into account the temperature corrections of the energy equipartition law for the bits of information that are coarse-grained on the holographic screen leads to a modification of Einstein's gravitational field equations. In the very high-temperature limit, which corresponds to strong gravitational fields, the modified gravitational equations reduce to

Jiayu He, Matteo G. A. Paris

Multiparameter quantum estimation theory plays a crucial role in advancing quantum metrology. Recent studies focused on fundamental challenges such as enhancing precision in the presence of incompatibility or sloppiness, yet the relationship between these features remains poorly understood. In this work, we explore the connection between sloppiness and incom

Alexander Pütz

Quiver Grassmannians of equioriented type $\texttt{A}$ and nilpotent equioriented type $\tilde{\texttt{A}}$ quiver representations are GKM-varieties. In particular, they have a cellular decomposition and admit a torus action with finitely many fixed points and one-dimensional orbits (i.e. skeletal action). We examine the case of string representations and pr

Seppo Borenius, Pekka Kekolahti, Petri Mähönen, Matti Lehtonen

This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyse the evolution of power generation capacity mix, assess the likelihood of different grid managem

Antoine Picard-Weibel, Eugenio Clerico, Roman Moscoviz, Benjamin Guedj

We discuss necessary conditions for a PAC-Bayes bound to provide a meaningful generalisation guarantee. Our analysis reveals that the optimal generalisation guarantee depends solely on the distribution of the risk induced by the prior distribution. In particular, achieving a target generalisation level is only achievable if the prior places sufficient mass o

E. Flament, N. Ombredane, F. Arrouas, D. Ronco

In this paper, we design and experimentally implement various robust quantum unitary transformations (gates) acting on $d$-dimensional vectors (qudits) by tuning a single control parameter using optimal control theory. The quantum state is represented by the momentum components of a Bose-Einstein condensate (BEC) placed in an optical lattice, with the lattic

Ao Li, Zongfang Liu, Xinhua Li, Jinghui Zhang

Large pre-trained vision-language models (VLMs) offer a promising approach to leveraging human language for enhancing downstream tasks. However, VLMs such as CLIP face significant limitation: its performance is highly sensitive to prompt template design. Although prompt learning methods can address the sensitivity issue by replacing natural language prompts

Federico Di Menna, Luca Traini, Gabriele Bavota, Vittorio Cortellessa

Code optimization is the process of enhancing code efficiency, while preserving its intended functionality. This process often requires a deep understanding of the code execution behavior at run-time to identify and address inefficiencies effectively. Recent studies have shown that language models can play a significant role in automating code optimization.

T. Thuillier

A general method to generate a centrosymmetric matrix associated with the solving of partial differential equation (PDE) on an irreducible domain by means of a linear equation system is proposed. The method applies to any PDE for which both the domain to solve and the boundary condition (BC) type accept a planar symmetry, while no conditions are required on

Esther Chiramal, Kelvin Soh Boon Kai

Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text a

Haozhen Cheng, Verena Buccoliero, Alexander Kocher, Veit Hagenmeyer

Classical heating of residential areas is very energy-intensive, so alternatives are needed, including renewable energies and advanced heating technologies. Thus, the present paper introduces a new methodology for comprehensive variant analysis for future district heating planning, aiming at optimizing emissions and costs. For this, an extensive Modelica-bas

Dongbin Zhang, Yunfei Liu, Lijian Lin, Ye Zhu

Reconstructing animatable and high-quality 3D head avatars from monocular videos, especially with realistic relighting, is a valuable task. However, the limited information from single-view input, combined with the complex head poses and facial movements, makes this challenging. Previous methods achieve real-time performance by combining 3D Gaussian Splattin

Chao Zhou, Wei Pu, Miguel Rodrigues

Hyperspectral image (HSI) unmixing is a challenging research problem that tries to identify the constituent components, known as endmembers, and their corresponding proportions, known as abundances, in the scene by analysing images captured by hyperspectral cameras. Recently, many deep learning based unmixing approaches have been proposed with the surge of m

Tao Shen, Didi Zhu, Ziyu Zhao, Zexi Li

The remarkable success of foundation models has been driven by scaling laws, demonstrating that model performance improves predictably with increased training data and model size. However, this scaling trajectory faces two critical challenges: the depletion of high-quality public data, and the prohibitive computational power required for larger models, which

Haegu Lee, Yitaek Kim, Victor Melbye Staven, Christoffer Sloth

The strength of the human hand lies in its ability to manipulate small objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle small objects effectively. This is why many automation tasks remain unsolved by robots. This paper presents an optimization-based framework for in-hand manipulation with a robotic ha

Junbin Xiao, Nanxin Huang, Hao Qiu, Zhulin Tao

We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (MLLMs). EgoBlind comprises 1,392 first-person videos from the daily lives of blind and visually impaired individuals. It also features 5,311 questions directly posed or verified by

Cheng Luo, Luping Xiang, Jie Hu, Kun Yang

Deep learning (DL) has emerged as a powerful tool for addressing the intricate challenges inherent in communication and sensing systems, significantly enhancing the intelligence of future sixth-generation (6G) networks. A substantial body of research has highlighted the promise of DL-based techniques in these domains. However, in addition to improving accura

Kaiqiang Xiong, Rui Peng, Zhe Zhang, Tianxing Feng

Unsupervised Multi-View Stereo (MVS) methods have achieved promising progress recently. However, previous methods primarily depend on the photometric consistency assumption, which may suffer from two limitations: indistinguishable regions and view-dependent effects, e.g., low-textured areas and reflections. To address these issues, in this paper, we propose

Sara Maria Pagotto, Federico Tognoni, Matteo Rossi, Dario Bovio

Wearable measurements, such as those obtained by photoplethysmogram (PPG) sensors are highly susceptible to motion artifacts and noise, affecting cardiovascular measures. Chest-acquired PPG signals are especially vulnerable, with signal degradation primarily resulting from lower perfusion, breathing-induced motion, and mechanical interference from chest move

Kaiqiang Xiong, Ying Feng, Qi Zhang, Jianbo Jiao

3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models for guidance, optimizing a 3D representation via Score Distillation Sampling(SDS) or generating one back-view image for facilitating reconstruction. However, these methods tend to pr

Guangting Zheng, Jiajun Deng, Xiaomeng Chu, Yu Yuan

Recently, 3D Gaussian Splatting (3DGS) has reshaped the field of photorealistic 3D reconstruction, achieving impressive rendering quality and speed. However, when applied to large-scale street scenes, existing methods suffer from rapidly escalating per-viewpoint reconstruction costs as scene size increases, leading to significant computational overhead. Afte

Georges Dubourg, Divyasree Prabhakaran, Harry Dawson, Vasa Radonic

Recent advances in microphysiological systems have underscored the need for novel sensing and monitoring systems specifically designed for three-dimensional (3D) cell culture. In this article, an original architecture of a cell-culture multiwell plate embedding an impedance spectroscopy monitoring system is presented alongside a fast and straightforward fabr

Beitao Chen, Xinyu Lyu, Lianli Gao, Jingkuan Song

Despite their success, Large Vision-Language Models (LVLMs) remain vulnerable to hallucinations. While existing studies attribute the cause of hallucinations to insufficient visual attention to image tokens, our findings indicate that hallucinations also arise from interference from instruction tokens during decoding. Intuitively, certain instruction tokens

Haozhen Cheng, Jan Stock, André Xhonneux, Hüseyin K. Çakmak

Improving energy efficiency by monitoring system behavior and predicting future energy scenarios in light of increased penetration of renewable energy sources are becoming increasingly important, especially for energy systems that distribute and provide heat. On this background, digital twins of cities become paramount in advancing urban energy system planni

Maksym Shamrai

Concatenating matrices is a common technique for uncovering shared structures in data through singular value decomposition (SVD) and low-rank approximations. The fundamental question arises: How does the singular value spectrum of the concatenated matrix relate to the spectra of its individual components? In the present work, we develop a perturbation techni

Yitaek Kim, Iñigo Iturrate, Christoffer Sloth, Hansoul Kim

There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily focused on detecting and analyzing objects or robots, with limited attention to ensuring safety, which is critical for clinical applications, where accidents can be caused by unsaf

Nandakishor M

In this paper, I present our work on DeepRAG, a specialized embedding model we built specifically for Hindi language in RAG systems. While LLMs have gotten really good at generating text, their performance in retrieval tasks still depends heavily on having quality embeddings - something that's been lacking for Hindi despite being one of the world's most spok

H. Faustino Vieira, A. Duarte-Cabral, M. W. L. Smith, D. Colombo

The study of molecular clouds in galaxies beyond the Local Group is limited by the need to efficiently sample diverse galactic environments across galactic discs, typically resulting in a loss of resolution. Using a high-resolution dust extinction technique, we image the dust (and gas) of 4 nearby galaxies (<18 Mpc; NGC 4689, NGC 628, NGC 1566, and NGC 4321)

Gerth Stølting Brodal, Casper Moldrup Rysgaard, Rolf Svenning

We present an optimal partially-persistent external-memory search tree with amortized I/O bounds matching those achieved by the non-persistent $B^{\varepsilon}$-tree by Brodal and Fagerberg [SODA 2003]. In a partially-persistent data structure each update creates a new version of the data structure, where all past versions can be queried, but only the curren

Konstantin Gaul, Lei Cong, Dmitry Budker

A measurement of parity violation in the hyperfine structure of $^{138}$Ba$^{19}$F [E. Altunta\c{s} et al. Phys. Rev. Lett. 120, 142501 (2018)] is reinterpreted with electronic structure calculations in terms of beyond Standard Model vector boson mediated electron-nucleus interactions. Our results set constraints on previously unexplored, new boson mediated

Jukka-Pekka Humaloja, Nikolaos Bekiaris-Liberis

We develop a non-collocated, observer-based output-feedback law for a class of continua of linear hyperbolic PDE systems, which are viewed as the continuum version of $n+m$, general heterodirectional hyperbolic systems as $n\to\infty$. The design relies on the introduction of a novel, continuum PDE backstepping transformation, which enables the construction

Jack Langerman, Denys Rozumnyi, Yuzhong Huang, Dmytro Mishkin

"What cannot be measured cannot be improved" while likely never uttered by Lord Kelvin, summarizes effectively the driving force behind this work. This paper presents a detailed discussion of automated metrics for evaluating structured 3D reconstructions. Pitfalls of each metric are discussed, and an analysis through the lens of expert 3D modelers' preferenc

Denan Li, Jiyuan Yang, Xiangkai Chen, Lintao Yu

Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired by foundational large language models, recent years have seen considerable progress in developing foundational atomistic models, sometimes referred to as universa

Zhenyu Deng, Tao Zhou, Yilin Bi

Hypergraph, which allows each hyperedge to encompass an arbitrary number of nodes, is a powerful tool for modeling multi-entity interactions. Hyperedge prediction is a fundamental task that aims to predict future hyperedges or identify existent but unobserved hyperedges based on those observed. In link prediction for simple graphs, most observed links are tr

Claire Burrin, Seul Bee Lee, Stefano Marmi

The Brjuno and Wilton functions bear a striking resemblance, despite their very different origins; while the Brjuno function $B(x)$ is a fundamental tool in one-dimensional holomorphic dynamics, the Wilton function $W(x)$ stems from the study of divisor sums and self-correlation functions in analytic number theory. We show that these perspectives are unified

Yiheng Yu, Sheng Liu, Yuan Feng, Min Xu

The primary challenge in continuous sign language recognition (CSLR) mainly stems from the presence of multi-orientational and long-term motions. However, current research overlooks these crucial aspects, significantly impacting accuracy. To tackle these issues, we propose a novel CSLR framework: Orientation-aware Long-term Motion Decoupling (OLMD), which ef

S. Bhatia, A. Ahmad, M. Zeeshan, S. Kaur

The interplay between lattice distortions, magnetism, and spin-orbit coupling in 5d transition-metal halides offers a fertile platform for exploring correlated spin-lattice dynamics. Here, we investigate the impact of structural symmetry breaking on lattice vibrations and local spin environments in the antifluorite compound K2IrBr6 using temperature dependen

Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn

Supervised contrastive learning (SupCL) has emerged as a prominent approach in representation learning, leveraging both supervised and self-supervised losses. However, achieving an optimal balance between these losses is challenging; failing to do so can lead to class collapse, reducing discrimination among individual embeddings in the same class. In this pa

Cheng Luo, Jie Hu, Luping Xiang, Kun Yang

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information

Xuanhan Wang, Huimin Deng, Lianli Gao, Jingkuan Song

Human-centric visual perception (HVP) has recently achieved remarkable progress due to advancements in large-scale self-supervised pretraining (SSP). However, existing HVP models face limitations in adapting to real-world applications, which require general visual patterns for downstream tasks while maintaining computationally sustainable costs to ensure com

Wei Shi, Sihang Li, Tao Liang, Mingyang Wan

Mechanistic interpretability of large language models (LLMs) aims to uncover the internal processes of information propagation and reasoning. Sparse autoencoders (SAEs) have demonstrated promise in this domain by extracting interpretable and monosemantic features. However, prior works primarily focus on feature extraction from a single layer, failing to effe

Miao Zhang, Zhenlong Fang, Tianyi Wang, Qian Zhang

Traditional Reinforcement Learning (RL) suffers from replicating human-like behaviors, generalizing effectively in multi-agent scenarios, and overcoming inherent interpretability issues.These tasks are compounded when deep environment understanding, agent coordination and dynamic optimization are required. While Large Language Model (LLM) enhanced methods ha

Cheng Luo, Jie Hu, Luping Xiang, Kun Yang

Recently, a novel structures of reconfigurable intelligent surface (RIS) integrating both passive and active elements, termed reconfigurable intelligent sensing surface (RISS), efficiently addresses challenges in RIS channel estimation and mitigates issues related to multiplicative path loss by processing the signal at the RISS. In this paper, we propose a s

Yanyan Cai, Xiaowei Deng, Libo Zhang, Zhongchu Ni

Quantum squeezed states, with reduced quantum noise, have been widely utilized in quantum sensing and quantum error correction applications. However, generating and manipulating these nonclassical states with a large squeezing degree typically requires strong nonlinearity, which inevitably induces additional decoherence that diminishes the overall performanc

G. Guiglion

Machine-learning is playing an increasing role in helping the astronomical community to face data analysis challenges, in particular in the field of Galactic Archaeology and large scale spectroscopic surveys. We present recent developments in the field of convolutional neural-networks (CNNs) for stellar abundances in the context of the Galactic spectroscopic

Wenlong Meng, Fan Zhang, Wendao Yao, Zhenyuan Guo

Large language models (LLMs) have demonstrated significant utility in a wide range of applications; however, their deployment is plagued by security vulnerabilities, notably jailbreak attacks. These attacks manipulate LLMs to generate harmful or unethical content by crafting adversarial prompts. While much of the current research on jailbreak attacks has foc

Mohammad Pouranvari

We investigate the directional localization properties of wave-functions in a two-dimensional tight-binding model with uniform hopping and correlated random on-site energies. By controlling the disorder correlation strength with a parameter $\alpha$, we explore the effects of disorder on wave-function localization using Single Particle Entanglement Entropy (

Rui Xu, MingYu Wang, XinTao Wang, Dakuan Lu

Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in many tasks for LLMs, the internal thinking processes of RPLAs remain unexplored. Understanding characters' inner thoughts is crucial for developing advanced RPLAs. In this paper, we

Alhassan Abdelhalim, Michaela Regneri

Violence descriptions in literature offer valuable insights for a wide range of research in the humanities. For historians, depictions of violence are of special interest for analyzing the societal dynamics surrounding large wars and individual conflicts of influential people. Harvesting data for violence research manually is laborious and time-consuming. Th

Valentin Mouet, Guillaume Michel, François Pétrélis, Stephan Fauve

Rayleigh-B{\'e}nard convection is investigated with sulfur hexafluoride (SF$_6$) in the vicinity of its critical point. In the supercritical domain, direct measurements of the heat flux $Q$ as a function of the temperature difference $\Delta T$ are consistent with the usual scaling laws of single-phase turbulent convection. Along the liquid-vapor coexistence

Yael Naze, Gregor Rauw

Most (or possibly all) massive stars reside in multiple systems. From stellar evolution models, numerous systems with an OB star coupled to a black hole would be expected to exist. There have been several claimed detections of such pairs in recent years and this is notably the case of HD96670. Using high-quality photometry and spectroscopy in the optical ran

Xinyan Wang, Jinshuo Liu, Kaijian Xie, Meng Wang

Current Continual Knowledge Graph Embedding (CKGE) methods primarily rely on translation-based embedding approaches, leveraging previously acquired knowledge to initialize new facts. While these methods often integrate fine-tuning or continual learning strategies to enhance efficiency, they compromise prediction accuracy and lack support for complex relation

Gonzalo Santamaría Gómez, Guillem García Subies, Pablo Gutiérrez Ruiz, Mario González Valero

Large Language Models (LLMs) have become a key element of modern artificial intelligence, demonstrating the ability to address a wide range of language processing tasks at unprecedented levels of accuracy without the need of collecting problem-specific data. However, these versatile models face a significant challenge: both their training and inference proce

Ali Gholami, Silvia Gazzola

Full waveform inversion (FWI) is a challenging, ill-posed nonlinear inverse problem that requires robust regularization techniques to stabilize the solution and yield geologically meaningful results, especially when dealing with sparse data. Standard Tikhonov regularization, though commonly employed in FWI, applies uniform smoothing that often leads to overs

Hector Bouton, Laurent Desvillettes, Helge Dietert

We prove the existence of global strong solutions to the triangular Shigesada-Kawasaki-Teramoto (SKT) cross-diffusion system with Lokta-Volterra reaction terms in three dimensions. A key part is the independent careful study of the parabolic equation $a\partial_t w - \Delta w = f$ with a rough coefficient $a$, homogeneous Neumann boundary conditions, and the

Anna Ben-Hamou

We consider a Markov chain on invertible $n\times n$ matrices with entries in $\mathbb{Z}_2$ which moves by picking an ordered pair of distinct rows and add the first one to the other, modulo $2$. We establish a logarithmic Sobolev inequality with constant $n^2$, which yields an upper bound of $O(n^2\log n)$ on the mixing time.

Graeme Andrew Stewart, Alexander Moreno Briceño, Philippe Gras, Benedikt Hegner

Julia is a mature general-purpose programming language, with a large ecosystem of libraries and more than 12000 third-party packages, which specifically targets scientific computing. As a language, Julia is as dynamic, interactive, and accessible as Python with NumPy, but achieves run-time performance on par with C/C++. In this paper, we describe the state o

M. Koumans, J. L. M. van Mechelen

We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network. We validate and analyze its

Philipp Straubinger, Marvin Kreis, Stephan Lukasczyk, Gordon Fraser

Large Language Models (LLMs) can generate plausible test code. Intuitively they generate this by imitating tests seen in their training data, rather than reasoning about execution semantics. However, such reasoning is important when applying mutation testing, where individual tests need to demonstrate differences in program behavior between a program and spe

ATLAS Collaboration

This paper presents the measurement of charged-hadron and identified-hadron ($K^\mathrm{0}_\mathrm{S}$, $\Lambda$, $\Xi^\mathrm{-}$) yields in photo-nuclear collisions using 1.7 $\mathrm{nb^{-1}}$ of $\sqrt{s_\mathrm{NN}} = 5.02$ TeV Pb+Pb data collected in 2018 with the ATLAS detector at the Large Hadron Collider. Candidate photo-nuclear events are selected

Minyue Dai, Ke Fan, Bin Ji, Haoran Xu

Styled motion in-betweening is crucial for computer animation and gaming. However, existing methods typically encode motion styles by modeling whole-body motions, often overlooking the representation of individual body parts. This limitation reduces the flexibility of infilled motion, particularly in adjusting the motion styles of specific limbs independentl

Zicheng Ma, Chuanliu Fan, Zhicong Wang, Zhenyu Chen

Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. However, many fundament

Nils Hausbrandt, Stefan Ruzika

In this article, we investigate the multi-parametric matroid problem. The weights of the elements of the matroid's ground set depend linearly on an arbitrary but fixed number of parameters, each of which is taken from a real interval. The goal is to compute a minimum weight basis for each possible combination of the parameters. For this problem, we propose a

Lennart Huth, Heiko Augustin, Lucas Dittmann, Sebastian Dittmeier

With increasing demands by future and current upgrades of particle physics experiments on rate capabilities and time resolution, the requirements on test beams are also increasing. The current infrastructure at the DESY II test beam facility includes particle tracking telescopes with long integration times, no additional timing but excellent spatial resoluti

Maxim A. Korolev

This version corrects minor inaccuracies and missprints. One drawing is changed. We continue to study some arithmetical properties of Farey sequences by the method introduced by F.Boca, C.Cobeli and A.Zaharescu (2001). Let $\Phi_{Q}$ be the classical Farey sequence of order $Q$. Having the fixed integers $D\geqslant 2$ and $0\leqslant c\leqslant D-1$, we col

Zitong Shi, Guancheng Wan, Wenke Huang, Guibin Zhang

LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive domains, they face emerging privacy protection challenges. In this paper, we introduce the concept of Federated MAS, highlighting the fundamental differences between Federated MAS

Veronica Bot, Zheyuan Xu

Large language models (LLMs) have demonstrated unprecedented capability in reasoning with natural language. Coupled with this development is the emergence of embodied AI in robotics. Despite showing promise for verbal and written reasoning tasks, it remains unknown whether LLMs are capable of navigating complex spatial tasks with physical actions in the real

Yuan Tian, Kaiyuan Ji, Rongzhao Zhang, Yankai Jiang

Medical image re-identification (MedReID) is under-explored so far, despite its critical applications in personalized healthcare and privacy protection. In this paper, we introduce a thorough benchmark and a unified model for this problem. First, to handle various medical modalities, we propose a novel Continuous Modality-based Parameter Adapter (ComPA). Com

Yoann Offret, Sergey Dovgal

We introduce and develop the concept of Maximal Entropy Random Walks (MERWs) on Weighted Bratteli Diagrams (WBDs), maximizing entropy production along paths as a natural criterion for choosing random walks on networks. Initially defined for irreducible finite graphs, MERWs were recently extended to the infinite setting in [1]. Bratteli Diagrams model various

Shahar Hod

It has recently been proved that, in the presence of a static absorbing trap, Sisyphus random walkers with a restart mechanism are characterized by {\it exponentially} decreasing asymptotic survival probability functions. Interestingly, in the present compact paper we prove analytically that, in the presence of a moving trap whose velocity approaches zero as

Dongyue Li, Daisuke Deguchi, Hiroshi Murase

Visual Place Recognition (VPR) aims to estimate the location of the given query image within a database of geo-tagged images. To identify the exact location in an image, detecting landmarks is crucial. However, in some scenarios, such as urban environments, there are numerous landmarks, such as various modern buildings, and the landmarks in different cities

Victor Dominguez

We propose, analyze, and implement a quadrature method for evaluating integrals of the form $\int_0^2 f(s)\exp(zs)\, {\rm d}s$, where $z$ is a complex number with a possibly large negative real part. The integrand may exhibit exponential decay, highly oscillatory behavior, or both simultaneously, making standard quadrature rules computationally expensive. Ou

Miao Zhang, Jun Yin, Pengyu Zeng, Yiqing Shen

Deep learning-based image enhancement methods show significant advantages in reducing noise and improving visibility in low-light conditions. These methods are typically based on one-to-one mapping, where the model learns a direct transformation from low light to specific enhanced images. Therefore, these methods are inflexible as they do not allow highly pe

Salvatore Capozziello, Sara Cesare, Carmen Ferrara

Extensions of equivalent representations of gravity are discussed in the metric-affine framework. First, we focus on: (i) General Relativity, based upon the metric tensor whose dynamics is given by the Ricci curvature scalar $R$; (ii) the Teleparallel Equivalent of General Relativity, based on tetrads and spin connection whose dynamics is given by the torsio

Jiaxuan Zhu, Hao Tang

Representing and rendering dynamic scenes from 2D images is a fundamental yet challenging problem in computer vision and graphics. This survey provides a comprehensive review of the evolution and advancements in dynamic scene representation and rendering, with a particular emphasis on recent progress in Neural Radiance Fields based and 3D Gaussian Splatting

Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang

We introduce AvatarForge, a framework for generating animatable 3D human avatars from text or image inputs using AI-driven procedural generation. While diffusion-based methods have made strides in general 3D object generation, they struggle with high-quality, customizable human avatars due to the complexity and diversity of human body shapes, poses, exacerba

Ivan Shestakov, Efim Zelmanov

The purpose of this paper is a partial progress towards classification of simple infinite dimensional Jordan superalgebras. First, we prove that the only simple infinite dimensional Jordan superalgebras with finite dimensional even parts are the superalgebras of superforms. Then we consider the superalgebras whose even parts are infinite dimensional algebras

Jiawen Wei, Aniruddha Bora, Vivek Oommen, Chenyu Dong

Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in numerical weather prediction and artificial intelligence tools, extreme weather still present challenges. More specifically, identifying the pre

Kangan Qian, Ziang Luo, Sicong Jiang, Zilin Huang

Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems. While end-to-end models trained on large datasets perform well in standard driving scenarios, they struggle with complex low-frequency events. Recent Large Language Models (LLMs) and Vision Language Models (VLMs) advancements offer enhanced reasoning but suffer from

Zuchen Gao, Zizheng Zhan, Xianming Li, Erxin Yu

Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous training primarily relies on optimizing the InfoNCE loss by comparing positive natural language (NL)-code pairs with in-batch negatives. However, due to the sparse nature of code contexts

Zheng-Long Ban, Xiao-Jun Gao, Jinsong Yang

In this paper, we mainly investigate the shadow of a rotating charged black hole with a cosmological constant immersed in perfect fluid dark matter. We first obtain the charged spherically symmetric black hole with a cosmological constant solution immersed in perfect fluid dark matter by using the gravitational decoupling method. Based on the mass function s

Letian Zhang, Quan Cui, Bingchen Zhao, Cheng Yang

The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due to privacy concerns. The expensive and labor-intensive process of collecting multi-modal data further exacerbates the problem. Is it possible to synthesize multi-modal training data

Alexandre Santerne, Héloïse Meheut, Didier Barret, Olivier Berné

During the 2024's quinquennial scientific roadmap of CNES, a specific group worked on setting recommendations to decrease the environmental footprint of space science activities. This correspondence to Nature Astronomy highlights the efforts of the french space research to move towards sustainability. It relies on two complementary methods: decarbonisation a

Taojie Kuang, Qianli Ma, Athanasios V. Vasilakos, Yu Wang

In recent years, deep learning techniques have made significant strides in molecular generation for specific targets, driving advancements in drug discovery. However, existing molecular generation methods present significant limitations: those operating at the atomic level often lack synthetic feasibility, drug-likeness, and interpretability, while fragment-

Jianan Li, Zhikun Wang, Susheng Ding, Shiliang Guo

This paper addresses the multi-robot pursuit problem for an unknown target, encompassing both target state estimation and pursuit control. First, in state estimation, we focus on using only bearing information, as it is readily available from vision sensors and effective for small, distant targets. Challenges such as instability due to the nonlinearity of be

Maria Mihaela Trusca, Liesbeth Allein

Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity, we can anticipate misunderstandings and reveal hidden toxic meanings. Our proposed decoding strateg

Krzysztof Langner, Elena Martellato, Robert Luther, Francesco Marzari

We investigate the effects of low--velocity impacts of rocks and boulders, originally released after the DART impact, on the surface of Didymos and the dynamics of dust particles released by those impacts. We determine if any of those effects can be observed by the Hera mission. The iSALE-2D shock physics code was used to simulate the re-impacts of boulders

Zhanjie Zhang, Ao Ma, Ke Cao, Jing Wang

Ultra-high quality artistic style transfer refers to repainting an ultra-high quality content image using the style information learned from the style image. Existing artistic style transfer methods can be categorized into style reconstruction-based and content-style disentanglement-based style transfer approaches. Although these methods can generate some ar

Yufan Chen, Ching Ting Leung, Jianwei Sun, Yong Huang

Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research; however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are not available in machine-readable form, limiting the broader application of AI in this field. The extraction

Okan Koç, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama

Current machine learning systems are brittle in the face of distribution shifts (DS), where the target distribution that the system is tested on differs from the source distribution used to train the system. This problem of robustness to DS has been studied extensively in the field of domain adaptation. For deep neural networks, a popular framework for unsup

Tian Jin, Enjun Du, Changwei Wang, Wenhao Xu

Parameter-efficient transfer learning (PETL) aims to reduce the scales of pretrained models for multiple downstream tasks. However, as the models keep scaling up, the memory footprint of existing PETL methods is not significantly reduced compared to the reduction of learnable parameters. This limitation hinders the practical deployment of PETL methods on mem

Jing Wang, Ao Ma, Ke Cao, Jun Zheng

Recent rapid advancements in text-to-video (T2V) generation, such as SoRA and Kling, have shown great potential for building world simulators. However, current T2V models struggle to grasp abstract physical principles and generate videos that adhere to physical laws. This challenge arises primarily from a lack of clear guidance on physical information due to

Chengzhi Ma, Kunqian Li, Shuaixin Liu, Han Mei

Indiscernible marine object counting encounters numerous challenges, including limited visibility in underwater scenes, mutual occlusion and overlap among objects, and the dynamic similarity in appearance, color, and texture between the background and foreground. These factors significantly complicate the counting process. To address the scarcity of video-ba

Takuya Machida

Quantum walks are quantum counterparts of random walks and their probability distributions are different from each other. A quantum walker distributes on a Hilbert space and it is observed at a location with a probability. The finding probabilities have been investigated and some interesting things have been analytically discovered. They are, for instance, b

Sonaldeep Halder, Kartikey Anand, Rahul Maitra

Generative machine learning models like the Restricted Boltzmann Machine (RBM) provide a practical approach for ansatz construction within the quantum computing framework. This work introduces a method that efficiently leverages RBM and many-body perturbative measures to build a compact chemistry-inspired ansatz for determining accurate molecular energetics.

Hanbyul Lee, Juneho Yi

Recently, images that distort or fabricate facts using generative models have become a social concern. To cope with continuous evolution of generative artificial intelligence (AI) models, model attribution (MA) is necessary beyond just detection of synthetic images. However, current deep learning-based MA methods must be trained from scratch with new data to

Zhifeng Xie, Qile He, Youjia Zhu, Qiwei He

In this work, we implement music production for silent film clips using LLM-driven method. Given the strong professional demands of film music production, we propose the FilmComposer, simulating the actual workflows of professional musicians. FilmComposer is the first to combine large generative models with a multi-agent approach, leveraging the advantages o

Graeme Andrew Stewart. Sanmay Ganguly, Sattwamo Ghosh, Philippe Gras, Atell Krasnopolski

Jet reconstruction remains a critical task in the analysis of data from HEP colliders. We describe in this paper a new, highly performant, Julia package for jet reconstruction, JetReconstruction.jl, which integrates into the growing ecosystem of Julia packages for HEP. With this package users can run sequential reconstruction algorithms for jets. In particul