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arXiv 2023年

作者： Schaffer, Sebastian Schrefl, Thomas Oezelt, Harald Kovacs, Alexander Breth, Leoni Mauser, Norbert J. Suess, Dieter Exl, Lukas Research Platform MMM Mathematics - Magnetism - Materials University of Vienna Vienna Austria Wolfgang Pauli Institute Vienna Austria Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning University for Continuing Education Krems Wr. Neustadt Austria Department of Integrated Sensor Systems University for Continuing Education Krems Wr. Neustadt Austria Faculty of Physics University of Vienna Vienna Austria

We study the full 3d static micromagnetic equations via a physics-informed neural network (PINN) ansatz for the continuous magnetization configuration. PINNs are inherently mesh-free and unsupervised learning models. In our approach we can learn to minimize the total Gibbs free energy with additional conditional parameters, such as the exchange length, by a single low-parametric neural network model. In contrast, traditional numerical methods would require the computation and storage of a large number of solutions to interpolate the continuous spectrum of quasi-optimal magnetization configurations. We also consider the important and computationally expensive stray field problem via PINNs, where we use a basically linear learning ansatz, called extreme learning machines (ELM) within a splitting method for the scalar potential. This reduces the stray field training to a linear least squares problem with precomputable solution operator. We validate the stray field method by means of numerical example comparisons from literature and illustrate the full micromagnetic approach via the NIST µMAG standard problem #*** Codes 62P35, 68T05, 65Z05 Copyright © 2023, The Authors. All rights reserved.

关键词： Gibbs free energy

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Dispatching and control Information Freshness-Aware Federated learning for Simplified Power IoT

Dispatching and Control Information Freshness-Aware Federate...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

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Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation

Self-supervised Monocular Depth Estimation for All Day Image...

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International Conference on Computer Vision (ICCV)

作者： Lina Liu Xibin Song Mengmeng Wang Yong Liu Liangjun Zhang Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China Huzhou Institue of Zhejiang University China

ISBN: (纸本)9781665428132

Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time images, while their performance degrades for all-day images due to large domain shift and the variation of illumination between day and night images. To relieve these limitations, we propose a domain-separated network for self-supervised depth estimation of all-day images. Specifically, to relieve the negative influence of disturbing terms (illumination, etc.), we partition the information of day and night image pairs into two complementary sub-spaces: private and invariant domains, where the former contains the unique information (illumination, etc.) of day and night images and the latter contains essential shared information (texture, etc.). Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected. Meanwhile, the reconstruction and photometric losses are utilized to estimate complementary information and depth maps effectively. Experimental results demonstrate that our approach achieves state-of-the-art depth estimation results for all-day images on the challenging Oxford RobotCar dataset, proving the superiority of our proposed approach. Code and data split are available at https://***/LINA-lln/ADDS-DepthNet.

关键词： Computer vision Codes Estimation Lighting Generative adversarial networks Feature extraction Data mining

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Self-supervised monocular depth estimation for all day images using domain separation

arXiv

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arXiv 2021年

作者： Liu, Lina Song, Xibin Wang, Mengmeng Liu, Yong Zhang, Liangjun Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China Huzhou Institue of Zhejiang University China National Engineering Laboratory of Deep Learning Technology and Application China

关键词： Textures

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Laser speckle contrast imaging with principal component and entropy analysis: a novel approach for depth-independent blood flow assessment

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Frontiers of Optoelectronics 2025年第1期18卷 1-19页

作者： Yu.Surkov P.Timoshina I.Serebryakova D.Stavtcev I.Kozlov G.Piavchenko I.Meglinski A.Konovalov D.Telyshev S.Kuznetcov E.Genina V.Tuchin Institution of Physics Saratov State UniversitySaratov 410012Russia Scientific Medical Center Saratov State UniversitySaratov 410012Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State UniversityTomsk 634050Russia Institute for Bionic Technologies and Engineering I.M.Sechenov First Moscow State Medical UniversityMoscow 119991Russia Institute of Biomedical Systems National Research University of Electronic TechnologyZelenogradMoscow 124498Russia Department of Human Anatomy and Histology Cytology and EmbryologyInstitute of Clinical Medicine N.V.SklifosovskyI.M.Sechenov First Moscow State Medical UniversityMoscow 119991Russia Aston Institute of Materials Research School of Engineering and Applied ScienceAston UniversityBirmingham B47ETUK Burdenko Neurosurgery Institute Moscow 125047Russia

Current study presents an advanced method for improving the visualization of subsurface blood vessels using laser speckle contrast imaging (LSCI), enhanced through principal component analysis (PCA) filtering. By combining LSCI and laser speckle entropy imaging with PCA filtering, the method effectively separates static and dynamic components of the speckle signal, significantly improving the accuracy of blood flow assessments, even in the presence of static scattering layers located above and below the vessel. Experiments conducted on optical phantoms, with the vessel depths ranging from 0.6 to 2 mm, and in vivo studies on a laboratory mouse ear demonstrate substantial improvements in image contrast and resolution. The method’s sensitivity to blood flow velocity within the physiologic range (0.98-19.66 mm/s) is significantly enhanced, while its sensitivity to vessel depth is minimized. These results highlight the method’s ability to assess blood flow velocity independently of vessel depth, overcoming a major limitation of conventional LSCI techniques. The proposed approach holds great potential for non-invasive biomedical imaging, offering improved diagnostic accuracy and contrast in vascular imaging. These findings may be particularly valuable for advancing the use of LSCI in clinical diagnostics and biomedical research, where high precision in blood flow monitoring is essential.

关键词： Laser speckle imaging Speckle contrast Entropy Principal component analysis Blood flow velocity Vascular imaging

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Analysis of the research Status of Information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis Databases Education Bibliometrics Educational technology Market research Internet

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Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation

arXiv

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arXiv 2022年

作者： Goel, Yash Vaskevicius, Narunas Palmieri, Luigi Chebrolu, Nived Stachniss, Cyrill The laboratory for Photogrammetry and Robotics University of Bonn Germany Robert Bosch GmbH Germany Robert Bosch GmbH Corporate Research Stuttgart Germany The Dynamic Robot Systems Group University of Oxford United Kingdom The University of Bonn Germany The Department of Engineering Science the University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

We investigate the task of object goal navigation in unknown environments where the target is specified by a semantic label (e.g. find a couch). Such a navigation task is especially challenging as it requires understanding of semantic context in diverse settings. Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. We propose a deep neural network architecture and loss function to predict dense cost maps that implicitly contain semantic context and guide the robot towards the semantic goal. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The estimated cost maps are then used by a sampling-based model predictive controller (MPC) for generating continuous robot actions. The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 7 percentage points. Copyright © 2022, The Authors. All rights reserved.

关键词： Navigation

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Overview of Modern Forecasting Methods and Models

Overview of Modern Forecasting Methods and Models

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Vera Ivanyuk Anatoliy Tsvirkun Anna Sunchalina Tatiana Goroshnikova Andrey Sunchalin Galina Zholobova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of the Development of Large-Scale Systems V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Faculty of International Economic Relations Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods.

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Design of a novel flexible assistive hand exoskeleton

Design of a novel flexible assistive hand exoskeleton

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Guotao Li Long Cheng Zhengrun Gao Jilong Lu Yanjiang Zhao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The National Clinical Research Center for Aging and Medicine Fudan University Shanghai China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin China Intelligent Machine Institute Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781665465373

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient’s grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance.

关键词： Exoskeletons Thumb Metals Stroke (medical condition) Three-dimensional printing Motion capture Paralysis

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