March 2025 arXiv papers — page 93

Yuting Guo, Abeed Sarker

The application of large language models (LLMs) to healthcare information extraction has emerged as a promising approach. This study evaluates the classification performance of five open-source LLMs: GEMMA-3-27B-IT, LLAMA3-70B, LLAMA4-109B, DEEPSEEK-R1-DISTILL-LLAMA-70B, and DEEPSEEK-V3-0324-UD-Q2_K_XL, across six healthcare-related classification tasks invo

Javier Del Ser, Jesus L. Lobo, Heimo Müller, Andreas Holzinger

World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the structured, adaptive representations that even young children intuitively develop. Advancing beyond pattern recognition requires dynamic, interpretable frameworks inspired by Piag

Fujian Yan, Hui Li, Hongsheng He

Object affordance and volumetric information are essential in devising effective grasping strategies under task-specific constraints. This paper presents an approach for inferring suitable grasping strategies from limited partial views of an object. To achieve this, a recurrent generative adversarial network (R-GAN) was proposed by incorporating a recurrent

Àlex Pujol Vidal, Sergio Escalera, Kamal Nasrollahi, Thomas B. Moeslund

Machine unlearning methods have become increasingly important for selective concept removal in large pre-trained models. While recent work has explored unlearning in Euclidean contrastive vision-language models, the effectiveness of concept removal in hyperbolic spaces remains unexplored. This paper investigates machine unlearning in hyperbolic contrastive l

Marjory Mwanza

We investigate Cayley graphs of graph products by showing that graph products with vertex groups that have isomorphic Cayley graphs yield isomorphic Cayley graphs.

Dehui Yang, Feng Xi

This paper is concerned with the fundamental problem of estimating chirp parameters from a mixture of linear chirp signals. Unlike most previous methods, which solve the problem by discretizing the parameter space and then estimating the chirp parameters, we propose a gridless approach by reformulating the inverse problem as a constrained two-dimensional ato

Yves Rychener, Daniel Kuhn, Yifan Hu

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probability metrics between distributions across all clients and are not naturally separable by client. To add

Ulf-G. Meißner, Bernard Ch. Metsch, Helen Meyer

We discuss the fine-tunings of nuclear reactions in the Big Bang and in stars and draw some conclusions on the emergence of the light elements and the life-relevant elements carbon and oxygen. We also stress how to improve these calculations in the future. This requires a concerted effort of different communities, especially in nuclear reaction theory, latti

Yang Li, Soumya Snigdha Kundu, Maxence Boels, Toktam Mahmoodi

Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Only Look Once (YOLO) is challenged by limited data, privacy constraints, and inter-institutional variability. Federated learning (FL) enables collaborative training without sharing r

Yoonsang Lee

Nonlinear Bayesian update for a prior ensemble is proposed to extend traditional ensemble Kalman filtering to settings characterized by non-Gaussian priors and nonlinear measurement operators. In this framework, the observed component is first denoised via a standard Kalman update, while the unobserved component is estimated using a nonlinear regression appr

David Bate, Phoebe Valentine

We prove that in a complete metric space $X$, $1$-rectifiability of a set $E\subset X$ with $\mathcal{H}^1(E)<\infty$ and positive lower density $\mathcal{H}^1$-a.e. is implied by the property that all tangent spaces are connected metric spaces.

Yu Zhou, Qiao Shi, Zhengchun Zhou, Zilong Liu

This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications and sensing. We minimize the integrated sidelobe level (ISL) of the mismatch filter output for the transmitted waveform and the integrated leve

Jinsong Yang, Cong Zhang, Yongge Ma

An algebraic framework was introduced in our previous works to address the covariance issue in spherically symmetric effective quantum gravity. This paper extends the framework to the electrovacuum case with a cosmological constant. After analyzing the notion of covariance in the classical theory, we propose an effective Hamiltonian for the electromagnetic f

Joost Luijmes, Alexander Gielisse, Roman Knyazhitskiy, Jan van Gemert

Implicit neural representations (INRs) encode signals in neural network weights as a memory-efficient representation, decoupling sampling resolution from the associated resource costs. Current INR image classification methods are demonstrated on low-resolution data and are sensitive to image-space transformations. We attribute these issues to the global, ful

D. S. Cabral, A. F. Santos, R. Bufalo, N. B. Xavier

In this paper we examine the thermal effects into the $e^{+}e^{-}\to \ell^{+}\ell^{-}$ scattering in a non-hermitian extension of QED. We compute the thermal contributions to this scattering cross-section within the Thermo Field Dynamics approach. In order to highlight the non-hermitian effects we have considered some limits of interest: i) zero-temperature

Lucas Machado Moschen, Maria Soledad Aronna

This paper investigates optimal vaccination strategies in a metapopulation epidemic model. We consider a linear cost to better capture operational considerations, such as the total number of vaccines or hospitalizations, in contrast to the standard quadratic cost assumption on the control. The model incorporates state and mixed control-state constraints, and

Lech Raczynski, Wojciech Krzemien, Konrad Klimaszewski

Positronium Imaging requires two classes of events: double-coincidences originated from pair of back-to-back annihilation photons and triple-coincidences comprised with two annihilation photons and one additional prompt photon. The standard reconstruction of the emission position along the line-of-response of triple-coincidence event is the same as in the ca

Matúš Letko, Milan Pokorný

This paper examines the solvability of the equation $\mathrm{div} \ \mathbf{u} = f$ with a zero Dirichlet boundary condition for $\mathbf{u}$. A classical result establishes that for a bounded domain $\Omega \subset \mathbb{R}^N$ with a Lipschitz boundary and for $f \in L^p(\Omega)$ with zero mean value there exists a solution $\mathbf{u} \in (W_0^{1, p}(\Om

Luca Briani, Marco Cicalese, Leonard Kreutz

We investigate the formation of singularities in a baby Skyrme type energy model, which describes magnetic solitons in two-dimensional ferromagnetic systems. In presence of a diverging anisotropy term, which enforces a preferred background state of the magnetization, we establish a weak compactness of its topological charge density, which converges to an ato

Yan Wang, Jiapeng Liu, Milosz Kadziński, Xiuwu Liao

We present a novel preference learning framework to capture participant preferences efficiently within limited interaction rounds. It involves three main contributions. First, we develop a variational Bayesian approach to infer the participant's preference model by estimating posterior distributions and managing uncertainty from limited information. Second,

Baozhuo Su, Qingli Dou, Kang Liu, Zhengxian Qu

AI computation in healthcare faces significant challenges when clinical datasets are limited and heterogeneous. Integrating datasets from multiple sources and different equipments is critical for effective AI computation but is complicated by their diversity, complexity, and lack of representativeness, so we often need to join multiple datasets for analysis.

Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz, Alexandre Tkatchenko

Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model sp

Yogenraj Patil, Gaurav Chopra, Shruti Tandon, B. N. Goswami

The livelihood and food security of more than a billion people depend on the Indian monsoon (IM). Yet, a universal definition of the large-scale season and progress of IM is missing. Even though IM is a planetary-scale convectively coupled system arising largely from seasonal migration of the Intertropical Convergence Zone (ITCZ), the definitions of its onse

Cyrille Chevalier, Vincent Mathieu

Both positive and negative charge conjugation glueball spectra are computed with a constituent gluon approach. We first compute the spectrum of the Hamiltionian describing two-gluon bound states having $C=+$, before tackling the three-gluon bound states having $C=-$. We review the construction of two and three particle helicity states in order to build total

Jingjing Zhao, Qingyi Huang, Kaiquan Cai, Quan Zhou

A point-to-point movable element (ME) enabled reconfigurable intelligent surface (ME-RIS) communication system is investigated, where each element position can be flexibly adjusted to create favorable channel conditions. For maximizing the communication rate, an efficient ME position optimization approach is proposed. Specifically, by characterizing the casc

Xing He, Zhe Zhu, Liangliang Nan, Honghua Chen

Conventional methods for point cloud completion, typically trained on synthetic datasets, face significant challenges when applied to out-of-distribution real-world scans. In this paper, we propose an effective yet simple source-free domain adaptation framework for point cloud completion, termed \textbf{PointSFDA}. Unlike unsupervised domain adaptation that

Bahar Cennet Okumusoglu, Burak Kocuk

Gas transportation and storage has become one of the most relevant and important optimization problems in energy systems. This problem inherently includes highly nonlinear and nonconvex aspects due to gas physics, and discrete aspects due to the control decisions of active network elements. Obtaining even locally optimal solutions for this problem presents s

Simone Dovetta, Enrico Serra, Lorenzo Tentarelli

We provide a non-uniqueness result for normalized ground states of nonlinear Schr\"odinger equations with pure power nonlinearity on polygons with homogeneous Neumann boundary conditions, defined as global minimizers of the associated energy functional among functions with prescribed mass. Precisely, for nonlinearity powers slightly smaller than the $L^2$-cr

Nikola Đukić, Tim Lebailly, Tinne Tuytelaars

Object-centric representation learning has recently been successfully applied to real-world datasets. This success can be attributed to pretrained non-object-centric foundation models, whose features serve as reconstruction targets for slot attention. However, targets must remain frozen throughout the training, which sets an upper bound on the performance ob

Nuria Senar, Aeilko H. Zwinderman, Michel H. Hof and

Canonical Correlation Analysis, CCA, is a widely used multivariate method in omics research for integrating high dimensional datasets. CCA identifies hidden links by deriving linear projections of features maximally correlating datasets. For standard CCA, observations must be independent of each other. As a result, it cannot properly deal with repeated measu

Katarzyna Walczewska-Szewc, Jakub Rydzewski

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, pose a growing global health burden. Prolyl oligopeptidase (PREP) has emerged as a potential therapeutic target in these diseases. Recent studies have shown that direct interaction between PREP and pathological proteins, such as $\alpha$-synuclein and Tau, influences protein aggregation and neu

Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutions either rely on extensive manual scripting/editing or prioritize single-shot fidelity over cross-scene continuity, limiting their practicality for movie-like content. We introduc

Antonio Alarcon, Jorge Hidalgo

In this paper we develop the theory of approximation for holomorphic null curves in the special linear group ${\rm SL}_2(\mathbb{C})$. In particular, we establish Runge, Mergelyan, Mittag-Leffler, and Carleman type theorems for the family of holomorphic null immersions $M\to{\rm SL}_2(\mathbb{C})$ from any open Riemann surface $M$. Our results include jet in

Hoomaan Maskan, Konstantinos C. Zygalakis, Armin Eftekhari, Alp Yurtsever

Recent work on high-resolution ordinary differential equations (HR-ODEs) captures fine nuances among different momentum-based optimization methods, leading to accurate theoretical insights. However, these HR-ODEs often appear disconnected, each targeting a specific algorithm and derived with different assumptions and techniques. We present a unifying framewo

Jinhui Li, Wenjia Xie, Luis Seco

This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market i

Thierry Dana-Picard

Constructions and exploration of plane algebraic curves has received a new push with the development of automated methods, whose algorithms are continuously improved and implemented in various software packages. We use them to explore the pedal curves of conics. This provides a construction of interesting geometric loci, given at first by parametric represen

Gabriele Cenedese, Samuel T. Mister, Mauro Antezza, Giuliano Benenti

Discrete-Time Crystals (DTC) are a non-equilibrium phase of matter characterized by the breaking of time-translation symmetry in periodically driven quantum systems. In this work, we present a detailed thermodynamic analysis of a DTC in a one-dimensional spin-1/2 chain coupled to a thermal bath. We derive a master equation from the microscopic model, and we

Christina Zorenböhmer, Sebastian Schmidt, Bernd Resch

While sentiment analysis has advanced from sentence to aspect-level, i.e., the identification of concrete terms related to a sentiment, the equivalent field of Aspect-based Emotion Analysis (ABEA) is faced with dataset bottlenecks and the increased complexity of emotion classes in contrast to binary sentiments. This paper addresses these gaps, by generating

Lai Tien Minh, Trinh Tuan

In this paper, we introduce a family of integral transforms, denoted by \(\mathcal{O}_{\alpha}\), and constructed via kernel fusion of the fractional Fourier transform (FRFT) with angle \(\alpha \notin \pi \mathbb{Z}\). We demonstrate that the \(\mathcal{O}_{\alpha}\)-transformation constitutes a well-defined integral operator by establishing its basic opera

Carmen Escribano, Raquel Gonzalo

The main aim of this work is to apply the matrix approach of ortho\-gonal polynomials associated with infinite Hermitian definite positive matrices in relation with an important question regarding the location of zeros of Sobolev orthogonal polynomials via the study of the boundedness of multiplication operator. We apply the notion of bounded point evaluatio

Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu

Despite strong results on many tasks, multimodal large language models (MLLMs) still underperform on visual mathematical problem solving, especially in reliably perceiving and interpreting diagrams. Inspired by human problem-solving, we hypothesize that the ability to extract meaningful information from diagrams is pivotal, as it directly conditions subseque

Linus Mårtensson, Jonas M. D. Enander, Udaya B. Rongala, Henrik Jörntell

In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to a

Man Fai Wong, Chee Wei Tan

This paper studies how AI-assisted programming and large language models (LLM) improve software developers' ability via AI tools (LLM agents) like Github Copilot and Amazon CodeWhisperer, while integrating human feedback to enhance reinforcement learning (RLHF) with crowd-sourced computation to enhance text-to-code generation. Additionally, we demonstrate th

Dominik Macko, Robert Moro, Ivan Srba

Since the proliferation of LLMs, there have been concerns about their misuse for harmful content creation and spreading. Recent studies justify such fears, providing evidence of LLM vulnerabilities and high potential of their misuse. Humans are no longer able to distinguish between high-quality machine-generated and authentic human-written texts. Therefore,

Tommaso Van Der Meer, Andrea Garulli, Antonio Giannitrapani, Renato Quartullo

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This paper presents a comparative study of two popula

Haoyu Ji, Bowen Chen, Weihong Ren, Wenze Huang

Skeleton-based Temporal Action Segmentation (STAS) aims to segment and recognize various actions from long, untrimmed sequences of human skeletal movements. Current STAS methods typically employ spatio-temporal modeling to establish dependencies among joints as well as frames, and utilize one-hot encoding with cross-entropy loss for frame-wise classification

Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis, Urbain Vaes

In this paper, we present a spectral optimal control framework for Fokker-Planck equations based on the standard ground state transformation that maps the Fokker-Planck operator to a Schrodinger operator. Our primary objective is to accelerate convergence toward the (unique) steady state. To fulfill this objective, a gradient-based iterative algorithm with P

Wenjia Xie, Jinhui Li, Kai Zong, Luis Seco

This paper presents a comprehensive study leveraging Support Vector Machine (SVM) regression and Principal Component Regression (PCR) to analyze carbon dioxide emissions in a global dataset of 62 countries and their dependence on idiosyncratic, country-specific parameters. The objective is to understand the factors contributing to carbon dioxide emissions an

Da Ma, Gonghu Shang, Zhi Chen, Libo Qin

Instruction tuning improves the ability of large language models (LLMs) to follow diverse human instructions, but achieving strong performance on specific target tasks remains challenging. A critical bottleneck is selecting the most relevant data to maximize task-specific performance. Existing data selection approaches include unstable influence-based method

Korbinian Kuhn, Verena Kersken, Gottfried Zimmermann

Despite advances in Automatic Speech Recognition (ASR), transcription errors persist and require manual correction. Confidence scores, which indicate the certainty of ASR results, could assist users in identifying and correcting errors. This study evaluates the reliability of confidence scores for error detection through a comprehensive analysis of end-to-en

Paul Kiefer

We generalize the notions of locally and polar harmonic Maass forms to general orthogonal groups of signature $(2, n)$ with singularities along real analytic and algebraic cycles. We prove a current equation for locally harmonic Maass forms and recover the Fourier expansion of the Oda lift involving cycle integrals. Moreover, using the newly defined polar ha

Rostyslav Kozhan, Marcus Vaktnäs

We prove a criterion on the possible locations of zeros of type I and type II multiple orthogonal polynomials in terms of normality of degree $1$ Christoffel transforms. We provide another criterion in terms of degree $2$ Christoffel transforms for establishing zero interlacing of the neighbouring multiple orthogonal polynomials of type I and type II. We app

Carlos Lozano, Jorge Ponsin

We obtain the analytic adjoint solution for two-dimensional (2D) incompressible potential flow for a cost function measuring aerodynamic force using the connection of the adjoint approach to Green's functions and also by establishing and exploiting its relation to the adjoint incompressible Euler equations. By comparison with the analytic solution, it is sho

Korbinian Kuhn, Verena Kersken, Gottfried Zimmermann

Communication access real-time translation (CART) is an essential accessibility service for d/Deaf and hard of hearing (DHH) individuals, but the cost and scarcity of trained personnel limit its availability. While Automatic Speech Recognition (ASR) offers a cheap and scalable alternative, transcription errors can lead to serious accessibility issues. Real-t

Elena M. De Diego, Paula Gordaliza, Jesús Lopez-Fidalgo

The so-called algorithmic bias is a hot topic in the decision making process based on Artificial Intelligence, especially when demographics, such as gender, age or ethnic origin, come into play. Frequently, the problem is not only in the algorithm itself, but also in the biased data feeding the algorithm, which is just the reflection of the societal bias. Th

Kewin Pączek, Damian Jelito, Marcin Pitera, Agnieszka Wyłomańska

This paper explores the applications of the 20/60/20 rule-a heuristic method that segments data into top-performing, average-performing, and underperforming groups-in mathematical finance. We review the statistical foundations of this rule and demonstrate its usefulness in risk management and portfolio optimization. Our study highlights three key application

Tai-Ping Sun, Zhao-Yun Chen, Yun-Jie Wang, Cheng Xue

Efficient simulation of large-scale quantum algorithms is pivotal yet challenging due to the exponential growth of the state space inherent in both Sch\"odinger-based and Feynman-based methods. While Feynman-based simulators can be highly efficient when the quantum state is sparse, these simulators often do not fully support the simulation of large-scale, co

Shichen Li, Zhongqing Wang, Zheyu Zhao, Yue Zhang

Model editing aims at selectively updating a small subset of a neural model's parameters with an interpretable strategy to achieve desired modifications. It can significantly reduce computational costs to adapt to large language models (LLMs). Given its ability to precisely target critical components within LLMs, model editing shows great potential for effic

Xuan Wang, Tuvi Etzion, Denis Krotov, Minjia Shi

The maximum size of $t$-intersecting families is one of the most celebrated topics in combinatorics, and its size is known as the Erd\H{o}s-Ko-Rado theorem. Such intersecting families, also known as constant-weight anticodes in coding theory, were considered in a generalization of the well-known sphere-packing bound. In this work we consider the maximum size

Itai Bankier, Lotan Attias, Alex Levchenko, Maxim Khodas

We study the superconducting diode effect (SDE) in an Ising superconductor with broken basal mirror symmetry in a parallel magnetic field. We show that in the presence of a small Rashba spin splitting, $\Delta_R$, the dominant Ising spin-orbit coupling ($\Delta_I >> \Delta_R$) dramatically enhances the SDE efficiency compared to a Rashba superconductor with

Alejandro Almodóvar, Adrián Javaloy, Juan Parras, Santiago Zazo

We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate causal inference on continuous variables under the presence of hidden confounders. Specifically, we extend previous results on causal estimation under hidden confounding to show tha

Benjamin Estermann, Roger Wattenhofer

Large Language Models (LLMs) have demonstrated remarkable text generation capabilities, and recent advances in training paradigms have led to breakthroughs in their reasoning performance. In this work, we investigate how the reasoning effort of such models scales with problem complexity. We use the infinitely scalable Tents puzzle, which has a known linear-t

Shang Liu, Yao Lu, Wenji Fang, Mengming Li

The automated generation of design RTL based on large language model (LLM) and natural language instructions has demonstrated great potential in agile circuit design. However, the lack of datasets and benchmarks in the public domain prevents the development and fair evaluation of LLM solutions. This paper highlights our latest advances in open datasets and b

Changlong Shi, Jinmeng Li, He Zhao, Dandan Guo

In Federated Learning (FL), weighted aggregation of local models is conducted to generate a new global model, and the aggregation weights are typically normalized to 1. A recent study identifies the global weight shrinking effect in FL, indicating an enhancement in the global model's generalization when the sum of weights (i.e., the shrinking factor) is smal

Zinqin Huang, Gu Wang, Chenyangguang Zhang, Ruida Zhang

Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from instance-level tasks has demonstrated strong performance. H

Shuai Li, Shenglong Zhou, Ziyan Luo

Quadratically constrained quadratic programming (QCQP) has long been recognized as a computationally challenging problem, particularly in large-scale or high-dimensional settings where solving it directly becomes intractable. The complexity further escalates when a sparsity constraint is involved, giving rise to the problem of sparse QCQP (SQCQP), which make

Mohamed Salim Aissi, Clemence Grislain, Mohamed Chetouani, Olivier Sigaud

While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruction-based planning remains a widely open problem. In this paper, we introduce VIPER, a novel framework for multimodal instruction-based planning that integrates VLM-based percepti

Amir Hamza, Andrea Caraffa, Davide Boscaini, Fabio Poiesi

Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptors, GeDi has demonstrated strong zero-shot 6D pose estimation capabilities but remains computationally impractical for real-world applications due to its expensive inference process

Leonard Schmitz, Marcel Wack

Non-commutative Gr\"obner bases of two-sided ideals are not necessarily finite. Motivated by this, we provide a closed-form description of a finite and reduced Gr\"obner bases for the two-sided ideal used in the construction of Wangs quantum symmetric group. In particular, this proves that the word problem for quantum symmetric groups is decidable.

Pawel Dlotko, Davide Gurnari, Radmila Sazdanovic

Data science offers a powerful tool to understand objects in multiple sciences. In this paper we utilize concept of data science, most notably topological data analysis, to extend our understanding of knot theory. This approach provides a way to extend mathematical exposition of various invariants of knots towards understanding their relations in statistical

Fridolin Spitzer, Christoph Burkhardt, Thomas S. Kruijer, Thorsten Kleine

Isotope anomalies in meteorites reveal a fundamental dichotomy between non-carbonaceous- (NC) and carbonaceous-type (CC) planetary bodies. Until now, this dichotomy is established for the major meteorite groups, representing about 36 distinct parent bodies. Ungrouped meteorites represent an even larger number of additional and so far mostly unexplored parent

Pankaj Thorat, Adnan Qidwai, Adrija Dhar, Aishwariya Chakraborty

Data profiling is critical in machine learning for generating descriptive statistics, supporting both deeper understanding and downstream tasks like data valuation and curation. This work addresses profiling specifically in the context of code datasets for Large Language Models (code-LLMs), where data quality directly influences tasks such as code generation

Joan A. Ruiz-de-Azua, Francesc Betorz, Hossein Rouzegar, Joan F. Munoz-Martin

The emergence of the Non-Terrestrial Network (NTN) concept in the last years has revolutionized the space industry. This novel network architecture composed of aircraft and spacecraft is currently being standardized by the 3GPP. This standardization process follows dedicated phases in which experimentation of the technology is needed. Although some missions

Cristina Fiani, Pejman Saeghe, Mark McGill, Mohamed Khamis

Social Virtual Reality (VR), where people meet in virtual spaces via 3D avatars, is used by children and adults alike. Children experience new forms of harassment in social VR where it is often inaccessible to parental oversight. To date, there is limited understanding of how parents and non-parent adults within the child social VR ecosystem perceive the app

A. V. Shapovalov, S. A. Siniukov

In this paper, we propose an approach for constructing quasiparticle-like asymptotic solutions within the weak diffusion approximation for the generalized population Fisher--Kolmogorov--Petrovskii--Piskunov (Fisher--KPP) equation, which incorporates nonlocal quadratic competitive losses and a fractal time derivative of non-integer order $\alpha$, where $0<\a

Lillian Maria Eagan, Jacob Young, Jesse Bering, Tobias Langlotz

Immersive technologies are capable of transporting people to distant or inaccessible environments that they might not otherwise visit. Practitioners and researchers alike are discovering new ways to replicate and enhance existing tourism experiences using virtual reality, yet few controlled experiments have studied how users perceive virtual tours of real-wo

Wieb Bosma, Alex Brouwers

Adapting Cantor set methods that were used by Hall and Hlawka for regular continued fractions, we prove that every real number can be obtained as the sum of two real numbers for which the partial fractions in their nearest integer continued fraction expansion do not exceed 5 (with the zeroth partial fraction as the only possible exception). Furthermore, we p

Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai

The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to significant progress on multiple video tasks. However, two critical challenges remain: 1) Without human annotations, the random temporal sampling introduces uncertainty, increasing

Stelios Zarifis, Ioannis Kordonis, Petros Maragos

We propose Diffusion-Informed Model Predictive Control (D-I MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable stochastic systems by integrating diffusion-based time series forecasting models in Model Predictive Control algorithms. In our approach, a diffusion-based time series forecasting model is used to

YaHui Jin, Hui Liu, KaiFan Ji, ZhenYu Jin

Existing methods for obtaining flat field rely on observed data collected under specific observation conditions to determine the flat field. However, the telescope pointing and the column fixed pattern noise of the CMOS detector change during actual observations, causing residual signals in real time observation data after flat field correction, such as inte

Zonghao Ying, Guangyi Zheng, Yongxin Huang, Deyue Zhang

This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their generated content. Our evaluation encompasses DeepSeek's latest generation of large language models, multimodal large language models, and text-to-image models, systematically examining their performance regardin

Yao Cheng, Zhe Han, Fengyang Jiang, Huaizhen Wang

This paper addresses the high demand in advanced intelligent robot navigation for a more holistic understanding of spatial environments, by introducing a novel system that harnesses the capabilities of Large Language Models (LLMs) to construct hierarchical 3D Scene Graphs (3DSGs) for indoor scenarios. The proposed framework constructs 3DSGs consisting of a f

Judd Harrison

We present an update of HPQCD's lattice QCD determination of the $B_c\to J/\psi$ vector and axial-vector form factors, and provide new results for the tensor form factors. We use the Highly Improved Staggered Quark action for all valence quarks, together with the second generation MILC $n_f=2+1+1$ HISQ gluon field configurations. This calculation includes tw

Achmad Ginanjar, Xue Li, Priyanka Singh, Wen Hua

Out-of-distribution (OOD) prediction remains a significant challenge in machine learning, particularly for tabular data where traditional methods often fail to generalize beyond their training distribution. This paper introduces Tabular Continual Contrastive Learning (TCCL), a novel framework designed to address OOD challenges in tabular data processing. TCC

Alex Bols, Wojciech De Roeck, Michiel De Wilde, Bruno de O. Carvalho

We consider the action of a finite group $G$ by locality preserving automorphisms (quantum cellular automata) on quantum spin chains. We refer to such group actions as ``symmetries''. The natural notion of equivalence for such symmetries is \emph{stable equivalence}, which allows for stacking with factorized group actions. Stacking also endows the set of equ

Christoph Griesbacher, Christian Fruhwirth-Reisinger

Accurate 3D object detection is a critical component of autonomous driving, enabling vehicles to perceive their surroundings with precision and make informed decisions. LiDAR sensors, widely used for their ability to provide detailed 3D measurements, are key to achieving this capability. However, variations between training and inference data can cause signi

Enrico Formenti, Faizal Hafiz, Amelia Kunze, Davide La Torre

Classical Cellular Automata (CCAs) are a powerful computational framework widely used to model complex systems driven by local interactions. Their simplicity lies in the use of a finite set of states and a uniform local rule, yet this simplicity leads to rich and diverse dynamical behaviors. CCAs have found applications in numerous scientific fields, includi

Aashiya Anitha Shaji, Katherine Rawlins, Pranshu Kurel

Damped Lyman-$\alpha$ absorbers (DLAs) with molecular hydrogen have been probed in detail through both spectroscopic observations and numerical modelling. However, such H$_2$ absorbers are quite sparse at very high redshifts. We identify six of the most distant known H$_2$-DLAs (redshift between 3 and 4.5), with medium/high-resolution spectroscopic observati

Zhengyan Liu, Ji-an Jiang, Wen Zhao

The discovery of the kilonova (KN) AT 2017gfo, accompanying the gravitational wave event GW170817, provides crucial insight into the synthesis of heavy elements during binary neutron star (BNS) mergers. Following this landmark event, another KN was detected in association with the second-brightest gamma-ray burst (GRB) observed to date, GRB 230307A, and subs

Mhamed Essafri, Luca Calatroni, Emmanuel Soubies

Regularization using the L0 pseudo-norm is a common approach to promote sparsity, with widespread applications in machine learning and signal processing. However, solving such problems is known to be NP-hard. Recently, the L0 Bregman relaxation (B-rex) has been introduced as a continuous, non-convex approximation of the L0 pseudo-norm. Replacing the L0 term

Le Ma, Ziyu Meng, Tengyu Liu, Yuhan Li

Humanoid robots are anticipated to acquire a wide range of locomotion capabilities while ensuring natural movement across varying speeds and terrains. Existing methods encounter a fundamental dilemma in learning humanoid locomotion: reinforcement learning with handcrafted rewards can achieve agile locomotion but produces unnatural gaits, while Generative Adv

K. Minker, B. Carry, F. Vachier, P. Scheirich

The very wide binary asteroid (VWBA) population is a small subset of the population of known binary and multiple asteroids made of systems with very widely orbiting satellites and long orbital periods, on the order of tens to hundreds of days. The origin of these systems is debatable, and most members of this population are poorly characterized. We have comp

Suemin Lee, Ruiyu Wang, Lukas Herron, Pratyush Tiwary

Predicting and characterizing phase transitions is crucial for understanding generic physical phenomena such as crystallization, protein folding and others. However, directly observing phase transitions is not always easy, and often one has limited observations far from the phase boundary and measured under some specific thermodynamic conditions. In this stu

Ke Zhang, Chenxi Zhang, Chong Wang, Chi Zhang

Automated testing for REST APIs has become essential for ensuring the correctness and reliability of modern web services. While existing approaches primarily focus on detecting server crashes and error codes, they often overlook logical issues that arise due to evolving business logic and domain-specific requirements. To address this limitation, we propose L

Ziqiu Zeng, Siyuan Luo, Fan Shi, Zhongkai Zhang

We present a GPU-friendly framework for real-time implicit simulation of elastic material in the presence of frictional contacts. The integration of hyperelasticity, non-interpenetration contact, and friction in real-time simulations presents formidable nonlinear and non-smooth problems, which are highly challenging to solve. By incorporating nonlinear compl

Zhouzhou Song, Weiyun Xu, Marcos A. Valdebenito, Matthias G. R. Faes

Surrogate models are extensively employed for forward and inverse uncertainty quantification in complex, computation-intensive engineering problems. Nonetheless, constructing high-accuracy surrogate models for complex dynamical systems with limited training samples continues to be a challenge, as capturing the variability in high-dimensional dynamical system

Lynnyngs Kelly Arruda, Dmitry E. Pelinovsky

We characterize a general traveling periodic wave of the defocusing mKdV (modified Korteweg--de Vries) equation by using a quotient of products of Jacobi's elliptic theta functions. Compared to the standing periodic wave of the defocusing NLS (nonlinear Schr\"{o}dinger) equation, these solutions are special cases of Riemann's theta function of genus two. Bas

I. Kudrov, V. Bornyakov

We continue our study of the Gribov copies effrcts in the Maximal Abelian gauge in lattice $SU(3)$ gluodynamics. Our computations were completed for four values of the lattice spacing with physical lattice size $L \approx 2$ fm. It is demonstrated that when one uses the effective simulated annealing algorithm to fix the gauge the obtained Gribov copies produ

Marius Faiß, Burooj Ghani, Dan Stowell

Automatic recognition of insect sound could help us understand changing biodiversity trends around the world -- but insect sounds are challenging to recognize even for deep learning. We present a new dataset comprised of 26399 audio files, from 459 species of Orthoptera and Cicadidae. It is the first large-scale dataset of insect sound that is easily applica

Samira Silva, Ricardo Caldas, Patrizio Pelliccione, Antonia Bertolino

The growing need to test systems post-release has led to extending testing activities into production environments, where uncertainty and dynamic conditions pose significant challenges. Field testing approaches, especially Self-Adaptive Testing in the Field (SATF), face hurdles like managing unpredictability, minimizing system overhead, and reducing human in