March 2025 arXiv papers — page 224

Aaron Liberman, Anton Golovanov, Slava Smartsev, Sheroy Tata

The use of structured light to control the phase velocity of the wake in laser-wakefield accelerators has generated significant interest for its ability to mitigate electron dephasing. Combining the diffraction-free properties of Bessel beams with spatio-temporal shaping of the pulse promises to enable acceleration with an unprecedented combination of long a

Xin Guan, Yiyuan Li, Xu Liu, Jinhong You

Motivated by the inherent heterogeneity observed in many functional or imaging datasets, this paper focuses on subgroup learning in functional or image responses. While change-plane analysis has demonstrated empirical success in practice, the existing methodology is confined to scalar or longitudinal data. In this paper, we propose a novel framework for esti

Jingru Zhang, Shengjie Zhang, Christopher W Jones, Mathias Basner

Advancements in data collection have led to increasingly common repeated observations with complex structures in biomedical studies. Treating these observations as random objects, rather than summarizing features as vectors, avoids feature extraction and better reflects the data's nature. Examples include repeatedly measured activity intensity distributions

Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé

We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of LLMs. While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable

Fan Yang, Zheng-Cheng Gu, Fei Zhou

In standard studies of quantum critical points (QCPs), the dynamic critical exponent $z$ is introduced as a fundamental parameter along with global symmetries to identify universality classes. Often, the dynamic critical exponent $z$ is set to be one as the most natural choice for quantum field theory representations, which further implies emergence of highe

Rafael M. Francisco, D. S. Rosa, T. Frederico, M. T. Yamashita

As resonantly interacting trimers of the type AAB are progressively squeezed from $D=3$ to $D=2$, unatomic states emerge. We calculated the contacts from the high momentum tail of the single particle densities. The sharp increase of the contacts serves as a signature of the transition between the Efimov and unatomic regimes, characterized by the emergence of

Alexander Baranov, Anna Palatkina, Yulia Makovka, Pavel Braslavski

We present KoWit-24, a dataset with fine-grained annotation of wordplay in 2,700 Russian news headlines. KoWit-24 annotations include the presence of wordplay, its type, wordplay anchors, and words/phrases the wordplay refers to. Unlike the majority of existing humor collections of canned jokes, KoWit-24 provides wordplay contexts -- each headline is accompa

Teemu Säilynoja, Andrew R. Johnson, Osvaldo A. Martin, Aki Vehtari

A key step in the Bayesian workflow for model building is the graphical assessment of model predictions, whether these are drawn from the prior or posterior predictive distribution. The goal of these assessments is to identify whether the model is a reasonable (and ideally accurate) representation of the domain knowledge and/or observed data. There are many

Yao Wang, Mingxuan Cui, Arthur Jiang, Jun Yan

In the pursuit of Artificial General Intelligence (AGI), automating the generation and evaluation of novel research ideas is a key challenge in AI-driven scientific discovery. This paper presents Relative Neighbor Density (RND), a domain-agnostic algorithm for novelty assessment in research ideas that overcomes the limitations of existing approaches by compa

Ramkrishna Acharya

This study reviews popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning, play a crucial role in finding better model parameters. Hence, this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes tha

Xiangyu Xi, Deyang Kong, Jian Yang, Jiawei Yang

Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity

Carlos Gomes, Isabelle Wittmann, Damien Robert, Johannes Jakubik

Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth System Models (ES

Chao Qi, Tobias Koch

This paper investigates the maximum coding rate at which data can be transmitted over a noncoherent, multiple-input, multiple-output (MIMO) Rayleigh block-fading channel using an error-correcting code of a given blocklength with a block-error probability not exceeding a given value. A high-SNR normal approximation is derived that becomes accurate as the sign

Alessandra Bianchi, Marco Lenci, Françoise Pène

We introduce a continuous-time random walk model on an infinite multilayer structure inspired by transportation networks. Each layer is a copy of $\mathbb{R}^d$, indexed by a non-negative integer. A walker moves within a layer by means of an inertial displacement whose speed is a deterministic function of the layer index and whose direction and duration are

Diego Vallarino

Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consu

Jürgen Rossmann

The author proves the existence of strong solutions of the Dirichlet problem for the nonstationary Stokes system in polygonal domain. Here, the solutions are elements of weighted Sobolev spaces, where the weight function is a power of the distance from the corner points.

Are Austad, David Kyed

We study the quantum metric structure arising from length functions on quantum groups and show that for coamenable quantum groups of Kac type, the quantum metric information is captured by the algebra of central functions. Using this, we provide the first examples of length functions on (genuine) quantum groups which give rise to compact quantum metric space

Nicola Rossberg, Celina L. Li, Simone Innocente, Stefan Andersson-Engels

Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signals require algorithmic processing, as they are often difficult for the human eye to distinguish. The implementation of machine learning models for this task has demonstrated high le

Kazunori Matsuda, Ryosuke Sato, Yuichi Yoshida

Let ind-match$(G)$, min-match$(G)$ and match$(G)$ denote the induced matching number, minimum matching number and matching number of a graph $G$, respectively. It is known that ind-match$(G) \leq $ min-match$(G) \leq {\rm match}(G) \leq$ 2min-match$(G)$ holds. In the present paper, we investigate the minimum number of vertices and edges of connencted simple

Richard E. Cannon, Agata Rozek, Marina Brozović, Petr Pravec

Contact binaries are found throughout the solar system. The recent discovery of Selam, the satellite of MBA (152830) Dinkinesh, by the NASA LUCY mission has made it clear that the term `contact binary' covers a variety of different types of bi-modal mass distributions and formation mechanisms. Only by modelling more contact binaries can this population be pr

Nader Motee, Qiyu Sun

This paper presents Carleman-Fourier linearization for analyzing nonlinear real dynamical systems with periodic vector fields. Using Fourier basis functions, this novel framework transforms such dynamical systems into equivalent infinite-dimensional linear dynamical systems. In this paper, we establish the exponential convergence of the primary block in the

Ramkrishna Acharya

In this paper, we propose a novel approach for air drawing that uses image processing techniques to draw on the screen by moving fingers in the air. This approach benefits a wide range of applications such as sign language, in-air drawing, and 'writing' in the air as a new way of input. The approach starts with preparing ROI (Region of Interest) background i

Disen Lan, Weigao Sun, Jiaxi Hu, Jusen Du

Transformers with linear recurrent modeling offer linear-time training and constant-memory inference. Despite their demonstrated efficiency and performance, pretraining such non-standard architectures from scratch remains costly and risky. The linearization of large language models (LLMs) transforms pretrained standard models into linear recurrent structures

Matteo Gasparin, Aaditya Ramdas

Vovk (2015) introduced cross-conformal prediction, a modification of split conformal designed to improve the width of prediction sets. The method, when trained with a miscoverage rate equal to $\alpha$ and $n \gg K$, ensures a marginal coverage of at least $1 - 2\alpha - 2(1-\alpha)(K-1)/(n+K)$, where $n$ is the number of observations and $K$ denotes the num

Luke Major Krauth, Jordy Davelaar, Zoltán Haiman, John Ryan Westernacher-Schneider

The multi-messenger combination of gravitational waves (GWs) from merging massive black hole binaries (MBHBs) and the electromagnetic (EM) counterpart from the surrounding circumbinary disk (CBD) will open avenues to new scientific pursuits. In order to realize this science, we need to correctly localize the host galaxy of the merging MBHB. Multi-wavelength,

Fajri Koto, Rituraj Joshi, Nurdaulet Mukhituly, Yuxia Wang

Llama-3.1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh. Sherkala-Chat (8B) aims to enhance the inclusivity of LLM advancements for Kazakh speakers. Adapted from the LLaMA-3.1-8B model, Sherkala-Chat (8B) is trained on 45.3B tokens across Kazakh, Engli

José A. Cañizo, Alejandro Gárriz, Fernando Quirós

We give large-time asymptotic estimates, both in uniform and $L^1$ norms, for solutions of the Dirichlet heat equation in the complement of a bounded open set of $\mathbb{R}^d$ satisfying certain technical assumptions. We always assume that the initial datum has suitable finite moments (often, finite first moment). All estimates include an explicit rate of a

Yufeng Yuan, Yu Yue, Ruofei Zhu, Tiantian Fan

Reinforcement learning (RL) is pivotal for enabling large language models (LLMs) to generate long chains of thought (CoT) for complex tasks like math and reasoning. However, Proximal Policy Optimization (PPO), effective in many RL scenarios, fails in long CoT tasks. This paper identifies that value initialization bias and reward signal decay are the root cau

Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu

Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effective clarification. However, existing interactive clarification approaches face two critical limitations: reliance on manually constructed datasets, which inherently constrains train

Xueyang Feng, Bo Lan, Quanyu Dai, Lei Wang

In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents often heavily rely on large-scale LLMs. Meanwhile, although fine-tuning methods significantly enhance the capabilities of smaller LLMs, the fine-tuned agents often lack the potentia

Michele Ancona, Damien Gayet

In this paper, we provide a lower bound for the Cheeger constant and the spectral gap for random complex curves in $\C P^2$. The complex curve is endowed with the restriction of the ambient Fubini-Study metric, and the probability measure is the Gaussian measure induced by the $\mathscr{L}^2$-Hermitian product on the space of complex homogeneous polynomialso

Yifan Niu, Ziqi Gao, Tingyang Xu, Yang Liu

Exploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading off multiple properties due to the conflicting or correlated nature of chemical properties. To tackle this issue, we introduce InversionGNN framework, an effective yet sample-effi

Simone Naldi, Mohab Safey El Din, Adrien Taylor, Weijia Wang

We consider linear matrix inequalities (LMIs) $A = A_0 + x_1 A_1 + ... + x_n A_n \succeq 0$ with the $A_i$'s being $m \times m$ symmetric matrices, with entries in a ring $\mathcal{R}$. When $\mathcal{R} = \mathbb{R}$, the feasibility problem consists in deciding whether the $x_i$'s can be instantiated to obtain a positive semidefinite matrix. When $\mathcal

Wei-Han Li

We examine the relation between inter-particle interactions and real-time equilibration in one-dimensional lattice systems with hard-core constraints. Focusing on the roles of interactions, our results demonstrate that in the presence of interaction tails, any power-law exponent (including the limit ones) can encode the random particle configurations to the

Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu

We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low a

Antonio Strippoli

This Master's Thesis in Computer Science dives into the design and creation of a user-friendly interface for VoxLogicA, an image analysis tool using spatial model checking with a focus on neuroimaging. The research tackles the problem of existing tools being too complex, which makes them hard for medical professionals and researchers to use. By using spatial

Julien Verges

Consider the continuous greedy paths model: given a $d$-dimensional Poisson point process with positive marks interpreted as masses, let $\mathrm P(\ell)$ denote the maximum mass gathered by a path of length $\ell$ starting from the origin. It is known that $\mathrm P(\ell)/\ell converges a.s.\ to a deterministic constant $\mathrm P$. We show that the lower-

Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski, Evangelos Eleftheriou, Martino Dazzi

One of the challenges of quantizing a large language model (LLM) is the presence of outliers. Outliers often make uniform quantization schemes less effective, particularly in extreme cases such as 4-bit quantization. We introduce KurTail, a new post-training quantization (PTQ) scheme that leverages Kurtosis-based rotation to mitigate outliers in the activati

Héber H. Arcolezi, Sébastien Gambs

Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms that achieve an optimal trade-off between privacy, utility and robustness to adversarial inference and integrity attacks remains challenging. In this work, we introduce a general mul

Zhenwei Ni, Chang Xu, Zhihang Qin, Ceng Zhang

Soft robotic grippers gently and safely manipulate delicate objects due to their inherent adaptability and softness. Limited by insufficient stiffness and imprecise force control, conventional soft grippers are not suitable for applications that require stable grasping force. In this work, we propose a soft gripper that utilizes an origami-inspired structure

Veljko Askovic, Emmanuel Trélat, Hasnaa Zidani

In this paper we consider an optimal control problem in large time horizon and solve it numerically. More precisely, we are interested in an aerial vehicle guidance problem: launched from a ground platform, the vehicle aims at reaching a ground/sea target under specified terminal conditions while minimizing a cost modelling some performance and constraint cr

Hang Yang, Zhijie Qu, Joel N. Bregman, Li Ji

A major challenge in CGM studies is determining the three-dimensional (3D) properties from the observed projected observations. Here, we decompose the 3D gas density and spatial distribution of cool clouds by fitting a cool CGM model with the absorption observations, including the cool gas density, Ly$\alpha$, and Mg II equivalent widths. The clumpiness in t

Lu Dai, Yijie Xu, Jinhui Ye, Hao Liu

Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG). Despite the potential of this approach, existing studies evaluate RAG effectiveness by 1) assessing retrieval and generation components jointly, which obscures retrieval's dis

Linjun Li, Pengfei Huang, Zi-Xiang Hu, Yu-Yu Zhang

We present Meissner-like photon currents in a quantum Rabi zigzag chain under staggered synthetic magnetic fields. The ground state of the Meissner superradiant phase hosts persistent chiral edge currents in a sequence of cancellation of antiparallel vortex pairs, akin to surface currents of the Meissner effect in superconductors. The Meissner phase displays

Patrick Halder, Hannes Homburger, Lothar Kiltz, Johannes Reuter

Formulating the intended behavior of a dynamic system can be challenging. Signal temporal logic (STL) is frequently used for this purpose due to its suitability in formalizing comprehensible, modular, and versatile spatiotemporal specifications. Due to scaling issues with respect to the complexity of the specifications and the potential occurrence of non-dif

Ahmed Dawoud, Shravan Talupula

Root Cause Analysis (RCA) is becoming ever more critical as modern systems grow in complexity, volume of data, and interdependencies. While traditional RCA methods frequently rely on correlation-based or rule-based techniques, these approaches can prove inadequate in highly dynamic, multi-layered environments. In this paper, we present a pathway-tracing pack

Zhihai Bi, Kai Chen, Chunxin Zheng, Yulin Li

Interactive navigation is crucial in scenarios where proactively interacting with objects can yield shorter paths, thus significantly improving traversal efficiency. Existing methods primarily focus on using the robot body to relocate large obstacles (which could be comparable to the size of a robot). However, they prove ineffective in narrow or constrained

Florian Surmont

This article presents an alternative approach to finite elements for modeling and analyzing 3D static mooring lines using string theory and the shooting method (SM) to solve two-point boundary value problems (TPBVPs) for 3D nonlinear static string equations with various boundary condition (BC) types relevant to offshore slender system assemblies.The two-poin

Ya Fan, Rongling Lang

Detector-based and detector-free matchers are only applicable within their respective sparsity ranges. To improve adaptability of existing matchers, this paper introduces a novel probabilistic reweighting method. Our method is applicable to Transformer-based matching networks and adapts them to different sparsity levels without altering network parameters. T

Mihir Kulkarni, Welf Rehberg, Kostas Alexis

This paper contributes the Aerial Gym Simulator, a highly parallelized, modular framework for simulation and rendering of arbitrary multirotor platforms based on NVIDIA Isaac Gym. Aerial Gym supports the simulation of under-, fully- and over-actuated multirotors offering parallelized geometric controllers, alongside a custom GPU-accelerated rendering framewo

Ben Bucknall, Robert F. Trager, Michael A. Osborne

The external evaluation of AI systems is increasingly recognised as a crucial approach for understanding their potential risks. However, facilitating external evaluation in practice faces significant challenges in balancing evaluators' need for system access with AI developers' privacy and security concerns. Additionally, evaluators have reason to protect th

Hao Deng, Haibo Xing, Kanefumi Matsuyama, Yulei Huang

Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role. Classical representation methods integrate item features using concatenation or neural networks to generate homogeneous representation sequences. While straightforward, these metho

Abdullah Akgül, Gulcin Baykal, Manuel Haußmann, Melih Kandemir

Continuous control of non-stationary environments is a major challenge for deep reinforcement learning algorithms. The time-dependency of the state transition dynamics aggravates the notorious stability problems of model-free deep actor-critic architectures. We posit that two properties will play a key role in overcoming non-stationarity in transition dynami

Jens Emil Christensen, Søren Fuglede Jørgensen, Andreas Pavlogiannis, Jaco van de Pol

Computing a minimum-size circuit that implements a certain function is a standard optimization task. We consider circuits of CNOT gates, which are fundamental binary gates in reversible and quantum computing. Algebraically, CNOT circuits on $n$ qubits correspond to $GL(n,2)$, the general linear group over the field of two elements, and circuit minimization r

Behzad Azmi, Nicolas Schlosser

We analyze a class of general nonlinear epidemic models with age and space structure, including a nonlocal infection term depending on age and space. After establishing the well-posedness of the state partial differential equation, we introduce a control parameter interpreted as a vaccination rate. Under certain conditions, we show that an optimal control ex

Danying Yu, Wange Song, Luojia Wang, Rohith Srikanth

The concept of synthetic dimensions has emerged as a powerful framework in photonics and atomic physics, enabling the exploration of high-dimensional physics beyond conventional spatial constraints. Originally developed for quantum simulations in high dimensions, synthetic dimensions have since demonstrated advantages in designing novel Hamiltonians and mani

Boris Nazarov, Darya Frolova, Yackov Lubarsky, Alexei Gaissinski

Fine-tuning of Large Language Models (LLMs) for downstream tasks, performed on domain-specific data has shown significant promise. However, commercial use of such LLMs is limited by the high computational cost required for their deployment at scale. On the other hand, small Language Models (LMs) are much more cost effective but have subpar performance in a s

Zhixiong Nan, Xianghong Li, Jifeng Dai, Tao Xiang

Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features. However, the

A. Tajja, A. Aghabiglou, E. Tolley, J-P. Kneib

Recently, the R2D2 paradigm, standing for ''Residual-to-Residual DNN series for high-Dynamic-range imaging'', was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R2D2 are limited to planar imaging on small fields of view, failing to meet the spherical-imaging requi

Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni

Large Reasoning Models(LRMs) such as OpenAI o1 and DeepSeek-R1 have shown remarkable reasoning capabilities by scaling test-time compute and generating long Chain-of-Thought(CoT). Distillation--post-training on LRMs-generated data--is a straightforward yet effective method to enhance the reasoning abilities of smaller models, but faces a critical bottleneck:

Li-Shuai Wang, Qiong-Tao Xie, Li-Yang Chen

We investigate an inflationary model with a non-minimal derivative coupling, where the coupling function contains both constant and periodic components. On large scales, the model is in excellent agreement with the latest Planck-ACT-LiteBIRD-BICEP/Keck 2018 (P-ACT-LB-BK18) observations. On small scales, the periodic component induces a sound-speed resonance

Hanyu Wang, Emmanuel K. Tsinda, Anthony J. Dunn, Francis Chikweto

Motivation: PCR is more economical and quicker than Next Generation Sequencing for detecting target organisms, with primer design being a critical step. In epidemiology with rapidly mutating viruses, designing effective primers is challenging. Traditional methods require substantial manual intervention and struggle to ensure effective primer design across di

Xu Wan, Chao Yang, Cheng Yang, Jie Song

Although multi-agent reinforcement learning (MARL) has shown its success across diverse domains, extending its application to large-scale real-world systems still faces significant challenges. Primarily, the high complexity of real-world environments exacerbates the credit assignment problem, substantially reducing training efficiency. Moreover, the variabil

Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier

Although Transformers-based architectures excel at processing textual information, their naive adaptation for tabular data often involves flattening the table structure. This simplification can lead to the loss of essential inter-dependencies between rows, columns, and cells, while also posing scalability challenges for large tables. To address these issues,

Matthew Adeoye, Xavier Didelot, Simon EF Spencer

The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the structured population. Here we present new generally applicable methodology to perform this task. We introduce a parsimonious representation of seasonality and a biologically i

Xi He, Max A. Little

We present an axiomatic framework for analyzing the algorithmic properties of decision trees. This framework supports the classification of decision tree problems through structural and ancestral constraints within a rigorous mathematical foundation. The central focus of this paper is a special class of decision tree problems-which we term proper decision tr

Xiaojuan Chen, Qiang Tu, Ni Xiang

In this paper, we consider a class of Hessian equations associated to the $L_p$-dual Christoffel-Minkowski problem for the case $p\geq q$. By combining the tools of constant rank theorem, the a priori estimates and the continuity method, we obtain the existence and uniqueness for strictly spherical convex solutions to the $L_p$-dual Christoffel-Minkowski pro

Pankaj Choudhury, Yogesh Aggarwal, Prabhanjan Jadhav, Prithwijit Guha

Most existing works in image caption synthesis use computation heavy deep neural networks and generates image descriptions in English language. This often restricts this important assistive tool for widespread use across language and accessibility barriers. This work presents AC-Lite, a computationally efficient model for image captioning in low-resource Ass

Amon Furuichi, Sung Hak Lim, Mihoko M. Nojiri

The state-of-the-art deep learning (DL) models for jet classification use jet constituent information directly, improving performance tremendously. This draws attention to interpretability, namely, the decision-making process, correlations contributing to the classification, and high-level features (HLFs) representing the difference between signal and backgr

Tiancheng Li, Jingyuan Wang, Guchong Li, Dengwei Gao

This is the third part of a series of studies that model the target trajectory, which describes the target state evolution over continuous time, as a sample path of a stochastic process (SP). By adopting a deterministic-stochastic decomposition framework, we decompose the learning of the trajectory SP into two sequential stages: the first fits the determinis

Antonio M. Mercado-Martínez, Beatriz Soret, Antonio Jurado-Navas

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails finding the subset of observation targets to be scheduled along the satellite's orbit while meeting operational constraints of time, energy and memory. The problem of deciding what and when to observe is inherently complex, and becomes even more challenging when considering several iss

Xiang He, Zuoqin Wang

Let $M$ be a compact connected smooth manifold with smooth boundary, and let $\rho$ be a positive continuous function on the boundary which is served as the Robin parameter. In this paper, we study three problems concerning the prescription of finite Robin spectrum: (1) Prescribing finitely many Robin eigenvalues and the volume. (2) Within a fixed conformal

Ting Zhang, Chengran Yang, Yindu Su, Martin Weyssow

Recent advancements in generative AI have led to the widespread adoption of large language models (LLMs) in software engineering, addressing numerous long-standing challenges. However, a comprehensive study examining the capabilities of LLMs in software vulnerability detection (SVD), a crucial aspect of software security, is currently lacking. Existing resea

Xiangjun Tang, Biao Zhang, Peter Wonka

Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate modeling of clothing-body interactions. To tackle this challenge, we build upon Geometry distributions, a recently proposed representation that can model a single human geometry with high fidelity using a flow ma

Pablo Capuzzi, Zehra Akdeniz, Patrizia Vignolo

The study of the long-time dynamics of quantum systems can be a real challenge, especially in systems like ultracold gases, where the required timescales may be longer than the lifetime of the system itself. In this work, we show that it is possible to access the long-time dynamics of a strongly repulsive atomic gas mixture in shorter times. The shortcut-to-

Elham Ghobadpour, Max Kolb, Ivan Junier, Ralf Everaers

The statistics of randomly branching double-folded ring polymers are relevant to the secondary structure of RNA, the large-scale branching of plectonemic DNA (and thus bacterial chromosomes), the conformations of single-ring polymers migrating through an array of obstacles, as well as to the conformational statistics of eukaryotic chromosomes and melts of cr

Farehe Soheil, Kirill Simonov, Tobias Friedrich

$\kC$ clustering is a fundamental classification problem, where the task is to categorize the given collection of entities into $k$ clusters and come up with a representative for each cluster, so that the maximum distance between an entity and its representative is minimized. In this work, we focus on the setting where the entities are represented by binary

Kay Heider, Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen

The management of timing constraints in a real-time operating system (RTOS) is usually realized through a global tick counter. This counter acts as the foundational time unit for all tasks in the systems. In order to establish a connection between a tick and an amount of elapsed time in the real world, often this tick counter is periodically incremented by a

Fu-Yuan Zhang, Qi Huang, Li-Ming Wang

The exploration of exotic mesons, which transcend the conventional quark-antiquark framework, is pivotal for advancing our understanding of QCD and the strong interaction. Among these, states possessing the quantum numbers $J^{PC}=1^{-+}$, such as $\pi_1(1600)$, $\pi_1(2015)$, and the recently discovered $\eta_1(1855)$, have attracted significant attention d

Vaishali Aggarwal, Sachin Thukral, Krushil Patel, Arnab Chatterjee

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, de

Dan Garber

We consider the problem of minimizing a smooth and convex function over the $n$-dimensional spectrahedron -- the set of real symmetric $n\times n$ positive semidefinite matrices with unit trace, which underlies numerous applications in statistics, machine learning and additional domains. Standard first-order methods often require high-rank matrix computation

Hyungho Na, Kwanghyeon Lee, Sumin Lee, Il-Chul Moon

In the context of multi-agent reinforcement learning, generalization is a challenge to solve various tasks that may require different joint policies or coordination without relying on policies specialized for each task. We refer to this type of problem as a multi-task, and we train agents to be versatile in this multi-task setting through a single training p

Yushan Liu, Shilong Mu, Xintao Chao, Zizhen Li

Robotic manipulation in complex scenes demands precise perception of task-relevant details, yet fixed or suboptimal viewpoints often impair fine-grained perception and induce occlusions, constraining imitation-learned policies. We present AVR (Active Vision-driven Robotics), a bimanual teleoperation and learning framework that unifies head-tracked viewpoint

Zhiheng Li, Yubo Cui, Ningyuan Huang, Chenglin Pang

Recently, 4D millimetre-wave radar exhibits more stable perception ability than LiDAR and camera under adverse conditions (e.g. rain and fog). However, low-quality radar points hinder its application, especially the odometry task that requires a dense and accurate matching. To fully explore the potential of 4D radar, we introduce a learning-based odometry fr

Théo Vincent, Tim Faust, Yogesh Tripathi, Jan Peters

Recent works have successfully demonstrated that sparse deep reinforcement learning agents can be competitive against their dense counterparts. This opens up opportunities for reinforcement learning applications in fields where inference time and memory requirements are cost-sensitive or limited by hardware. Until now, dense-to-sparse methods have relied on

Fatima Ahmed, Parag Biswas, Abdur Rashid, Md. Khaliluzzaman

Falls are a common cause of fatal injuries and hospitalization. However, having fall detection on person, in particular for senior citizens can prove to be critical. Presently,there are handheld, ambient detector and vision-based detection techniques being utilized for fall detection. However, the approaches have issues with accuracy and cost. In this regard

Joseph J. Webber, Thomas D. Montenegro-Johnson

Thermo-responsive hydrogels are smart materials that rapidly switch between hydrophilic (swollen) and hydrophobic (shrunken) states when heated past a threshold temperature, resulting in order-of-magnitude changes in gel volume. Modelling the dynamics of this switch is notoriously difficult, and typically involves fitting a large number of microscopic materi

Shu Pan, Ziyang Hong, Zhangrui Hu, Xiandong Xu

Visual degradation in underwater environments poses unique and significant challenges, which distinguishes underwater SLAM from popular vision-based SLAM on the ground. In this paper, we propose RUSSO, a robust underwater SLAM system which fuses stereo camera, inertial measurement unit (IMU), and imaging sonar to achieve robust and accurate localization in c

Piotr Kalaczyński

The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced b

Dan Garber

Second-order methods are of great importance for composite convex optimization problems due to their local super-linear convergence rates (under appropriate assumptions). However, the presence of even a simple nonsmooth function in the model most often renders the subproblems in proximal Newton methods computationally difficult to solve in high dimensions. W

Max Eissler, Tim Korjakow, Stefan Ganscha, Oliver T. Unke

Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are increasingly replaced by general-purpose architectures trained on vast datasets. In line with this trend, several recent stu

Birger Moell, Fredrik Sand Aronsson, Per Östberg, Jonas Beskow

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an opportunity to harness these patterns for accurate disease diagnosis. Objective: This review synthesized findings from studies exploring ML's capability in leveraging speech for the diagnosis of neurological, laryn

Huimin Li, Yuya Yamakawa, Ellen H. Fukuda, Nobuo Yamashita

Most numerical methods developed for solving nonlinear programming problems are designed to find points that satisfy certain optimality conditions. While the Karush-Kuhn-Tucker conditions are well-known, they become invalid when constraint qualifications (CQ) are not met. Recent advances in sequential optimality conditions address this limitation in both fir

Paul Disberg, Nicola Gaspari, Andrew J. Levan

The magnitudes of the velocity kicks that neutron stars (NSs) obtain at their formation have long been a topic of discussion. In previous work, a novel method was proposed to determine kicks based on the eccentricity of Galactic trajectories. We applied this method to the isolated pulsars with known parallax in order to kinematically constrain the NS natal k

Archana S. Morye

Origami is the art of paper folding, and it borrows its name from two Japanese words \emph{ori} and \emph{kami}. In Japanese, {ori} means folding, and the paper is called {kami}. While origami is just a hobby to most, there is a lot more to it. If you fold a square sheet of paper into any of the traditional origami model (for example the flapping bird) and u

Naifu Xue, Zhaoyang Jia, Jiahao Li, Bin Li

Recent studies in extreme image compression have achieved remarkable performance by compressing the tokens from generative tokenizers. However, these methods often prioritize clustering common semantics within the dataset, while overlooking the diverse details of individual objects. Consequently, this results in suboptimal reconstruction fidelity, especially

Xueling Huang, Olivier Goubet, Jie Shen

We study the properties of a semi-implicit Euler scheme that is widely used in time discretization of Keller-Segel equations both in the parabolic-elliptic form and the parabolic-parabolic form. We prove that this linear, decoupled, first-order scheme preserves unconditionally the important properties of Keller-Segel equations at the semi-discrete level, inc

Shubin Fu, Lina Zhao

In this paper, we develop a novel control volume method that is locally conservative and locking-free for linear elasticity problem on quadrilateral grids. The symmetry of stress is weakly imposed through the introduction of a Lagrange multiplier. As such, the method involves three unknowns: stress, displacement and rotation. To ensure the well-posedness of

Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng

Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents

Sylwia Cichacz, Štefko Miklavič

A $\Gamma$\emph{-distance magic labeling} of a graph $G = (V, E)$ with $|V| = n$ is a bijection $\ell$ from $V$ to an Abelian group $\Gamma$ of order $n$, for which there exists $\mu \in \Gamma$, such that the weight $w(x) =\sum_{y\in N(x)}\ell(y)$ of every vertex $x \in V$ is equal to $\mu$. In this case, the element $\mu$ is called the \emph{magic constant