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arXiv 2024年

作者： Bauer-Fasching, B. Bernhard, K. Brandli, E. Burger, H. Eisele, B. Hummerich, S. Neuhold, J. Paunzen, E. Piecka, M. Ratzenbock, S. Prišegen, M. Department of Astrophysics Vienna University Turkenschanzstrase 17 Vienna1180 Austria Berlin Germany Cambridge United States Department of Theoretical Physics and Astrophysics Faculty of Science Masaryk University Kotlarska 2 Brno611 37 Czech Republic University of Vienna Research Network Data Science at Uni Vienna Kolingasse 14-16 Vienna1090 Austria Advanced Technologies Research Institute Faculty of Materials Science and Technology in Trnava Slovak University of Technology in Bratislava Bottova 25 Trnava917 24 Slovakia

Context. The manifestation of surface spots on magnetic chemically peculiar (mCP) stars is most commonly explained by the atomic diffusion theory, which requires a calm stellar atmosphere and only moderate rotation. While very successful and well described, this theory still needs to be revised and fine-tuned to the observations. Aims. Our study aims to enlarge the sample of known photometrically variable mCP stars (ACV variables) to pave the way for more robust and significant statistical studies. We derive accurate physical parameters for these objects and discuss our results in the framework of the atomic diffusion theory. Methods. We studied 1314 candidate ACV variables that were selected from the Zwicky Transient Factory catalogue of periodic variables based on light curve characteristics. We investigated these objects using photometric criteria, a colour-magnitude diagram, and spectroscopic data from the LAMOST and Gaia missions to confirm their status as ACV variables. Results. We present a sample of 1232 new ACV variables, including information on distance from the Sun, mass, fractional age on the main sequence, fraction of the radius between the zero-age and terminal-age main sequence, and the equatorial velocity and its ratio to the critical velocity. Conclusions. Our results confirm that the employed selection process is highly efficient for detecting ACV variables. We have identified 38 stars with vequ in excess of 150 km s-1(with extreme values up to 260 km s-1). This challenges current theories that cannot explain the occurrence of such fast-rotating mCP stars. © 2024, CC BY-NC-ND.

关键词： Stars

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An accelerated minimax algorithm for convex-concave saddle point problems with nonsmooth coupling function

arXiv

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arXiv 2021年

作者： Boţ, Radu Ioan Csetnek, Ernö Robert Sedlmayer, Michael Faculty of Mathematics Research Network Data Science @ Uni Vienna University of Vienna Austria

In this work we aim to solve a convex-concave saddle point problem, where the convex-concave coupling function is smooth in one variable and nonsmooth in the other and not assumed to be linear in either. The problem is augmented by a nonsmooth regulariser in the smooth component. We propose and investigate a novel algorithm under the name of OGAProx, consisting of an optimistic gradient ascent step in the smooth variable coupled with a proximal step of the regulariser, and which is alternated with a proximal step in the nonsmooth component of the coupling function. We consider the situations convex-concave, convex-strongly concave and strongly convex-strongly concave related to the saddle point problem under investigation. Regarding iterates we obtain (weak) convergence, a convergence rate of order O(K1 ) and linear convergence like O(θK) with θ K1 ), O(K12 ) and O(θK) with θ © 2021, CC BY.

关键词： Support vector machines

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An Overview on Machine Learning Methods for Partial Differential Equations: From Physics Informed Neural networks to Deep Operator Learning

arXiv

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arXiv 2024年

作者： Gonon, Lukas Jentzen, Arnulf Kuckuck, Benno Liang, Siyu Riekert, Adrian von Wurstemberger, Philippe Department of Mathematics Imperial College London United Kingdom School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Mathematisches Institut Ludwig-Maximilians-Universität München Germany Risklab Department of Mathematics ETH Zurich Switzerland

The approximation of solutions of partial differential equations (PDEs) with numerical algorithms is a central topic in applied mathematics. For many decades, various types of methods for this purpose have been developed and extensively studied. One class of methods which has received a lot of attention in recent years are machine learning-based methods, which typically involve the training of artificial neural networks (ANNs) by means of stochastic gradient descent type optimization methods. While approximation methods for PDEs using ANNs have first been proposed in the 1990s they have only gained wide popularity in the last decade with the rise of deep learning. This article aims to provide an introduction to some of these methods and the mathematical theory on which they are based. We discuss methods such as physics-informed neural networks (PINNs) and deep BSDE methods and consider several operator learning approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Partial differential equations

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Evaluation of China’s Digital Economy: A Case Study using Entropy and TOPSIS

引用

Procedia computer science 2024年 242卷 1256-1262页

作者： Junjie Liu Boya Wang Jiayu Xue Yi Qu Yong Shi Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo Zhejiang 315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth; Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy; Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces.

关键词： Digital economy Multi-source information Evaluation TOPSIS

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Cooperative Visual Inertial Odometry for Heterogeneous Swarm of Drones Navigating in Noisy Environments

Cooperative Visual Inertial Odometry for Heterogeneous Swarm...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kamil Canberk Atik Enes Erdogan Ahmet Yusuf Yahsi Feyzullah Kara Baris Yalcin Gü rkan Cetin Nazim Kemal Ure Faculty of Aeronautics and Astronautics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Havelsan Ankara Turkey

This paper presents a methodology for enhancing the localization performance of drone swarms by collaborative propagation of the estimates of drones equipped with higher cost sensors to the drones with lower-cost sensors. Although recent advances in visual-inertial odometry (VIO) enabled good performance in GPS denied environments, the localization performance is still highly dependent on the accuracy of inertial sensors. Moreover, environmental effects that corrupt visual inputs also contribute to the error rate of VIO estimates thus localization in noisy environments with low-grade sensors is still widely an open problem. We show that this problem can be alleviated when drones are navigating in the form of a swarm, and the relative distance measurements between drones are available through ultra-wideband (UWB) signals. In particular, we develop a collaborative Kalman filtering and optimization framework, where the drones with lower grade sensors receive updates from drones that have access to higher-quality measurements. We validate our approach in a simulation environment with realistic visual representations and show that the proposed methodology can significantly improve the localization performance, especially for the scenarios where the camera measurements are corrupted with high noise. We further present results in simplistic flight tests to demonstrate the applicability of our approach to real hardware.

关键词： Location awareness Visualization Costs Navigation Surveillance Teamwork Trajectory

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of data Mining Competition Results 18

Cybersecurity Threat Detection in the Behavior of IoT Device...

引用

18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Czerwinski, Michal Michalak, Marcin Biczyk, Piotr Adamczyk, Blazej Iwanicki, Daniel Kostorz, Iwona Brzeczek, Maksym Janusz, Andrzej Hermansa, Marek Wawrowski, Lukasz Kozlowski, Artur Qed Software Mazowiecka 11/49 Warsaw0-052 Poland Ukasiewicz Research Network - Institute of Innovative Technologies Emag Leopolda 31 Katowice40-189 Poland Efigo Sp. Z O.o. M.Kopernika 8/6 Katowice40-064 Poland Faculty of Mathematics University of Warsaw Banacha 2 Warsaw02-097 Poland Department of Computer Networks and Systems Faculty of Automatic Control University of Technology Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems. © 2023 Polish Information Processing Society.

关键词： Quality control

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Correction:Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering

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Intelligent Automation & Soft Computing 2024年第5期39卷 985-986页

作者： Jing Geng Huali Yang Shengze Hu National Engineering Research Center of Educational Big Data Central China Normal UniversityWuhan430079China Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430079China

In the article“Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering”by Jing Geng,Huali Yang and Shengze Hu(Intelligent Automation&Soft Computing,2023,Vol.37,No.2,***:10.32604/iasc.2023.038481),the References[1-2],[4-12],and[23-29]were not appropriately aligned with the context of the main text.

关键词： Deep Automation app

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CMM PLN at MentalRiskES: A Traditional Machine Learning Approach for Detection of Eating Disorders and Depression in Chat Messages

CMM PLN at MentalRiskES: A Traditional Machine Learning Appr...

引用

2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Guerra, Rodrigo Pizarro, Benjamín Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Dunstan, Jocelyn Center for Mathematical Modeling Universidad de Chile Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile National Center for Artificial Intelligence Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Millenium Institute Foundational Research on Data Chile

This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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