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arXiv 2023年

作者： Song, Zhiyun Du, Penghui Yan, Junpeng Li, Kailu Shou, Jianzhong Lai, Maode Fan, Yubo Xu, Yan The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Urology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Chaoyang District Beijing100021 China The Department of Pathology School of Medicine Zhejiang University Zhejiang Provincial Key Lab Disease Proteomics Alibaba-Zhejiang University Joint Research Center of Future Healthcare Hangzhou310053 China

Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://***/zhiyuns/UNITPathSSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective

arXiv

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arXiv 2022年

作者： Zhang, Jiawei Wang, Xiang Bai, Xiao Wang, Chen Huang, Lei Chen, Yimin Gu, Lin Zhou, Jun Harada, Tatsuya Hancock, Edwin R. School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China SKLSDE Institute of Artificial Intelligence Beihang University Beijing China RIKEN AIP Tokyo Japan The University of Tokyo Japan Griffith University Australia University of York United Kingdom

Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo matching networks, which has not been adequately considered. Here we address this issue by proposing a simple pixel-wise contrastive learning across the viewpoints. The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points. A stereo selective whitening loss is further introduced to better preserve the stereo feature consistency across domains, which decorrelates stereo features from stereo viewpoint-specific style information. Counter-intuitively, the generalization of feature consistency between two viewpoints in the same scene translates to the generalization of stereo matching performance to unseen domains. Our method is generic in nature as it can be easily embedded into existing stereo networks and does not require access to the samples in the target domain. When trained on synthetic data and generalized to four real-world testing sets, our method achieves superior performance over several state-of-the-art networks. The code is available online. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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Performance Analysis of the 2D-Unitary ESPRIT Algorithm for 5G mmWave Networks

Performance Analysis of the 2D-Unitary ESPRIT Algorithm for ...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Grigoriy Fokin Ilya Grishin Alexander Sinilnikov Vladimir Sevidov Software Defined Radio Laboratory The Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Department of Communication Networks The Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Center for Satellite Communication Federal State Budgetary Institution Order of the Red Banner of Labor Radio Research and Development Institute named after M.I. Krivosheev Saint Petersburg Russia Department of Radio Engineering S.M. Budyonny VAS Saint Petersburg Russia

ISBN: (数字)9798350364989

ISBN: (纸本)9798350364996

This paper analyzes the 2d-unitary ESPRIT algorithm and investigates its feasibility in 5G networks. The content of this paper can be divided into three parts. In the first two parts of the paper, the mathematical model of the direction of arrival (DoA) algorithm is given. The third part of the paper describes the simulation results of beamforming scenarios considering DoA location in 5G mmWave ultra-dense networks (UDNs). The contribution of this study is the investigation of the dependence of the resolution threshold and accuracy of the 2d unitary ESPRIT algorithm on the number of elements in the antenna array and the value of SINR.

关键词： Accuracy 5G mobile communication Simulation Mathematical models Telecommunications Performance analysis Millimeter wave communication

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An Open-Source Robotic Chinese Chess Player

An Open-Source Robotic Chinese Chess Player

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shan An Guangfu Che Jinghao Guo Yanjie Xu Guoxin Wang Konstantinos A. Tsintotas Fukai Zhang Junjie Ye Changhong Fu Haogang Zhu Hong Zhang JD Health International Inc. Beijing China *** Inc Beijing China Puncture (Shanghai) Intelligent Medical Technology Co. Ltd Shanghai China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Citrus Research Institute Southwest University Chongqing China School of Mechanical Engineering Tongji University Shanghai China State Key Laboratory of Software Development Environment Beihang University Beijing China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://***/Star-Robot/chinese-chess-robot.

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Image super-resolution via dynamic network

arXiv

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arXiv 2023年

作者： Tian, Chunwei Zhang, Xuanyu Zhang, Qi Yang, Mingming Ju, Zhaojie School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Research & Development Institute Northwestern Polytechnical University Shenzhen China School of Economics and Management Harbin Institute of Technology at Weihai Weihai264209 China Tencent AI lab Shenzhen China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Convolutional neural networks depend on deep network architectures to extract accurate information for image super-resolution. However, obtained information of these convolutional neural networks cannot completely express predicted high-quality images for complex scenes. In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super-resolution. To enhance robustness of obtained super-resolution model for complex scenes, a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super-resolution model for varying scenes. To prevent interference of components in a wide enhancement block, a refinement block utilizes a stacked architecture to accurately learn obtained features. Also, a residual learning operation is embedded in the refinement block to prevent long-term dependency problem. Finally, a construction block is responsible for reconstructing high-quality images. Designed heterogeneous architecture can not only facilitate richer structural information, but also be lightweight, which is suitable for mobile digital devices. Experimental results show that our method is more competitive in terms of performance, recovering time of image super-resolution and complexity. The code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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You Can Wash Hands Better: Accurate Daily Handwashing Assessment with a Smartwatch

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Fei Zhang, Tingting Wu, Xilei Wang, Pengcheng Wang, Xin Ding, Han Shi, Jingang Han, Jinsong Huang, Dong Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Kuaishou Technology Inc. Local Life Research and Development Center Beijing100085 China Xi'an Jiaotong University School of Computer Science and Technology Xi'an710049 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China Carnegie Mellon University Robotics Institute Pittsburgh15213 United States

Hand hygiene is among the most effective daily practices for preventing infectious diseases such as influenza, malaria, and skin infections. While professional guidelines emphasize proper handwashing to reduce the risk of viral infections, surveys reveal that adherence to these recommendations remains low. To address this gap, we propose UWash, a wearable solution leveraging smartwatches to evaluate handwashing procedures, aiming to raise awareness and cultivate high-quality handwashing habits. We frame the task of handwashing assessment as an action segmentation problem, similar to those in computer vision, and introduce a simple yet efficient two-stream UNet-like network to achieve this goal. Experiments involving 51 subjects demonstrate that UWash achieves 92.27% accuracy in handwashing gesture recognition, an error of © 2002-2012 IEEE.

关键词： Gesture recognition

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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Behavior Recognition Based on the Integration of Multigranular Motion Features

arXiv

引用

arXiv 2022年

作者： Zhang, Lizong Wang, Yiming Hui, Bei Zhang, Xiujian Liu, Sijuan Feng, Shuxin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Artificial Intelligence Measurement and Standards for State Market Regulation Beijing Aerospace Institute for Metrology and Measurement Technology Beijing100076 China Research Institute of Social Development Southwestern University of Finance and Economics Chengdu610074 China

The recognition of behaviors in videos usually requires a combinatorial analysis of the spatial information about objects and their dynamic action information in the temporal dimension. Specifically, behavior recognition may even rely more on the modeling of temporal information containing short-range and long-range motions;this contrasts with computer vision tasks involving images that focus on the understanding of spatial information. However, current solutions fail to jointly and comprehensively analyze short-range motion between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behavior recognition method based on the integration of multigranular (IMG) motion features. In particular, we achieve reliable motion information modeling through the synergy of a channel attention-based short-term motion feature enhancement module (CMEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Integration

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Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network:A Large Multicenter Study

引用

Congenital Heart Disease 2021年第6期16卷 529-549页

作者： Yanping Ruan Xiangyu Liu Haogang Zhu Yijie Lu Xiaowei Liu Jiancheng Han Lin Sun Ye Zhang Xiaoyan Gu Ying Zhao Lei Li Suzhen Ran Jingli Chen Qiong Yu Yan Xu Hongmei Xia Yihua He Department of Echocardiography Maternal-Fetal Medicine Research Consultation CenterBeijing Anzhen HospitalCapital Medical UniversityBeijing100029China School of Biological Science and Medical Engineering Beihang UniversityBeijing100083China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing100191China Department of Ultrasound Chongqing Health Center for Women and ChildrenChongqing400013China Department of Ultrasound Urumqi Maternal and Child Health HospitalUrumqi830001China Department of Ultrasound Pingxiang Maternal and Child CarePingxiang337055China Department of Ultrasound People’s Hospital of RizhaoRizhao276800China Department of Ultrasound Xinqiao Hospital Army Medical UniversityChongqing400038China

Background:Current studies have confirmed that fetal congenital heart diseases(CHDs)are caused by various ***,the quantitative risk of CHD is not clear given the combined effects of multiple ***:This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios(RRs).Methods:Pregnant women who underwent fetal echocardiography(N=16,086 including 3,312 with CHD fetuses)were ***-six maternal and fetal factors were obtained.A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome,and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm,so as to obtain RR for fetal CHD under different exposure factor *** into account the effect of gestational week on the accuracy of model prediction,we conducted sensitivity analysis on gestational week ***:The single-factor analysis showed that the RRs for the numbers of births,spontaneous abortions,and parental smoking were 1.50,1.38,and 1.11(P<0.001),*** risk gradually increased with the synergistic effect of ranging from one to more environmental factors *** risk was higher among subjects with five synergistic factors,including the number of births,upper respiratory tract infection during early pregnancy,anemia,and mental stress as well as a history of spontaneous abortions or parental smoking,than in those with less than 5 factors(RR=2.62 or 2.28,P<0.001).This result was consistent across the participants grouped by ***:We identified six factors that were directly associated with fetal CHD.A higher number of these factors led to a higher risk of *** findings suggest that it is important to strengthen healthcare and prenatal counseling for women

关键词： Congenital heart diseases bayesian network risk ratio factor

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