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Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA

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arXiv 2024年

作者： Shaojie, Tang Penpen, Miao Xingyu, Gao Yu, Zhong Dantong, Zhu Haixing, Wen Zhihui, Xu Qiuyue, Wei Hongping, Yao Xin, Huang Rui, Gao Chen, Zhao Weihua, Zhou School of Automation Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Xi'an Key Laboratory of Advanced Control and Intelligent Process Shaanxi Xi’an710121 China Second Clinical Medical School of Nanjing Medical University Jiangsu Nanjing210011 China Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing210000 China Department of Pharmacy The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Cardiology The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Nuclear Medicine The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Applied Computing Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ventricle (LV) epicardial regions (LVERs) in SPECT and CTA images were segmented by using different U-Net neural networks trained to generate the point clouds of the LV epicardial contours (LVECs). Secondly, according to the characteristics of cardiac anatomy, the special points of anterior and posterior interventricular grooves (APIGs) were manually marked in both SPECT and CTA image volumes. Thirdly, we developed an in-house program for coarsely registering the special points of APIGs to ensure a correct cardiac orientation alignment between SPECT and CTA images. Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images. Finally, the image fusion between SPECT and CTA was realized after the fine registration. The experimental results showed that the cardiac orientation was aligned well and the mean distance error of the optimal registration method (CPD with affine transform) was consistently less than 3 mm. The proposed method could effectively fuse the structures from cardiac CTA and SPECT functional images, and demonstrated a potential in assisting in accurate diagnosis of cardiac diseases by combining complementary advantages of the two imaging modalities. © 2024, CC BY.

关键词： Image fusion

Surface Charging Phenomena on HVDC Spacers for Compressed SF_(6) Insulation and Charge Tailoring Strategies

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CSEE Journal of Power and Energy systems 2020年第1期6卷 83-99页

作者： Chuanyang Li Yuan Xu Chuanjie Lin Geng Chen Youping Tu Yao Zhou Zhipeng Lei Tao Han Simone Vincenzo Suraci Jian Wang Weidong Liu M.Tariq Nazir Shun He Andrea Cavallini Giovanni Mazzanti Davide Fabiani Jinliang He Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA. China Electric Power Research Institute Beijing 100192China Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”University of BolognaViale Risorgimento 2Bologna40136Italy State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206China Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control National and Provincial Joint EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Smart Grid of Education Ministry School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Mechanical and Manufacturing Engineering University of New South Wales SydneyNSW 2052Australia Electric Power Research Institute Yunnan Electric Power Grid Co.ltd.KunmingYunnan 650217China

Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover *** paper discusses the laws regarding distribution patterns of surface charges and the related *** field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous *** addition,the main surface charge control methods for epoxy resin are summarized and *** potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.

关键词： GIL HVDC insulator SF6 surface charge accumulation surface flashover

TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

3D Face Reconstruction System From A Single Photo Based On Regression Neural Network

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IFAC-PapersOnLine 2020年第5期53卷 71-76页

作者： Yatai Ji Keqiang Li Huaiyu Wu Gang Xiong Zhen Shen Xiuqin Shang Bangwen Xi The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Automation Tsinghua University Beijing 100084 China University of Chinese Academy of Sciences Beijing 100049 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

3D human face modeling is one of the most popular research directions in the field of computer stereo vision. On the one hand, the complex physiological structure of human face as well as various expressions and attitude changes bring great challenges to three-dimensional modeling, which makes the modeling method of human face have high research value and reference significance. On the other hand, 3D face reconstruction has a broad application prospect, and the application demand attracts more researchers to invest in it. Based on the 3DMM and 3DDFA method, this paper utilizes regression neural network to realize end-to-end face reconstruction. After that, we build a visualization program with the training model to realize face modeling based on a single face image of any pose or expression. It’s a 3D face reconstruction system which has functions such as face alignment, face rotation and so on.

关键词： face reconstruct 3DMM 3DDFA regression network

Randomised benchmarking for characterizing and forecasting correlated processes

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

Learning to Detect Unacceptable Machine Translations for Downstream Tasks

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arXiv 2020年

作者： Zhang, Meng Jiang, Xin Liu, Yang Liu, Qun Huawei Noah’s Ark Lab Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology

The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided language translation

Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

The Prediction of the Epidemic Trend of COVID-19 Using Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 857-862页

作者： Jing Yang Zhen Shen Xisong Dong Xiuqin Shang Wei Li Gang Xiong J. Yang is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with College of Information Sciences & Technology Beijing University of Chemical Technology Beijing 100029 China. Z. Shen is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao Shandong 266113 China. X. Dong X. Shang and W. Li are with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China. G. Xiong is with Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan Guangdong 523808 China.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

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