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Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 763-769页

作者： Benxin Zhang Zhibin Zhu School of Electronic Engineering and Automation and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology

In this study, we propose a linearized proximal alternating direction method with variable stepsize for solving total variation image reconstruction problems. Our method uses a linearized technique and the proximal function such that the closed form solutions of the subproblem can be easily *** the subproblem, we apply a variable stepsize, that is like Barzilai-Borwein stepsize, to accelerate the algorithm. Numerical results with parallel magnetic resonance imaging demonstrate the efficiency of the proposed algorithm.

关键词： Alternating direction method Barzilai-Borwein stepsize parallel magnetic resonance imaging total variation image reconstruction

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arXiv

引用

arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Ultra-scalable spectral clustering and ensemble clustering

arXiv

引用

arXiv 2019年

作者： Huang, Dong Wang, Chang-Dong Wu, Jian-Sheng Lai, Jian-Huang Kwoh, Chee-Keong College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Information Engineering Nanchang University Nanchang China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://***/publication/330760669. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Time-aware conversion prediction

引用

Frontiers of Computer Science 2017年第4期11卷 702-716页

作者： Wendi JI Xiaoling WANG Feida ZHU Shanghai Key Laboratory of Trustworthy Computing Institute for Data Science and Engineering East China Normal University Shanghai 200062 China School of Information Systems Singapore Management University Singapore 188065 Singapore

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.

关键词： conversion time survival analysis product rec-ommendation advertising

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Feature extraction for hyperspectral imagery: The evolution from shallow to deep

arXiv

引用

arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

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Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach 2

Privacy Preserving Scheme for Location Based Services Using ...

引用

2nd IEEE Symposium on Privacy-Aware computing, PAC 2018

作者： Pu, Yuwen Luo, Jin Wang, Ying Hu, Chunqiang Huo, Yan Zhang, Jiong School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Ningbo Branch of Agricultural Bank of China Ningbo China

ISBN: (纸本)9781538684429

Location based services pose strong security and privacy requirements but achieving privacy protection at the client side is a non-trial problem. In this paper, we propose an efficient and reliable location privacy preserving scheme using cryptographic approach. It can preserve users' privacy data by making their identity be anonymity for LBS provider and fog server. For communication, both AES with One-Time-Pad keys and IBE are employed to guarantee confidentiality and integrity of request and response data. Besides, we also provide security analysis to demonstrate that our scheme can resist internal attack, external attack and colluding attack. © 2018 IEEE.

关键词： Location based services

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User Heterogeneity and Individualized Recommender

引用

Chinese Physics Letters 2017年第6期34卷 135-138页

作者：王庆先张君杰史晓雨尚明生 School of Information and Software Engineering University of Electronic Science and Technology of China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences

Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at an individual level. In this study, we propose an individualized recommender that can satisfy every user with a customized parameter. Experimental results on four benchmark datasets demonstrate that the individualized recommender can significantly improve the accuracy of recommendation. The work highlights the importance of the user heterogeneity in recommender design.

关键词： User Heterogeneity and Individualized Recommender

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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Online Test Paper Composition Based on Genetic Algorithm

Online Test Paper Composition Based on Genetic Algorithm

引用

2018 3rd International Conference on Modelling, Simulation and Applied Mathematics(MSAM 2018)

作者： Wanli Song School of Information Engineering Nanjing Xiaozhuang University Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing XiaoZhuang University

The application of online examination is used more and more widely. Test paper composition is the core function in this application. Online examination system requires that the test paper should be quick and flexible, and the composition of questions should be reasonable and random. Test paper should not only meet the specific needs of users, ensure fairness, impartiality, but also be accurate and efficient. The intelligent test paper composition algorithm based on genetic algorithm is studied in this paper. Then this method is used to organize the structure of the test paper automatically, composites the examination content. Proposals are put forward to examines the degree of students' mastery of knowledge based on this method. This algorithm can automatically composite test papers according to the conditions of difficulty degree, knowledge coverage, and the proportion of questions. It can automatically composite test papers for online assessment. The practice of online testing system based on this algorithm shows that the algorithm is scientific and effective. On the premise of guaranteeing the quality of the test paper, it greatly improves the efficiency and pertinence of online testing. It is beneficial to improve students' learning efficiency and intellectualization of education.

关键词： test paper composition education intellectualization genetic algorithm E-learning

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