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Corrigendum to "Generation of a local lung respiratory motion model using a weighted sparse algorithm and motion prior-based registration" [Comput. Biol. Med. 123 (2020) 103913]

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computers in biology and medicine 2021年 140卷 104271页

作者： Dong Chen Hongzhi Xie Lixu Gu Jing Liu Liang Tian College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Shijiazhuang China Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Department of Cardiothoracic Surgery Peking Union Medical College Hospital Beijing China. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: liujing01@***. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: tianliang@***.

Efficient Fast Updated Frequent Pattern tree algorithm and its parallel implementation 2

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Efficient Fast Updated Frequent Pattern tree algorithm and i...

2nd International Conference on Image, Vision and Computing, ICIVC 2017

作者： Lv, Detao Fu, Bo Sun, Xiao Qiu, Hang Liu, Xiaobing Yanlong, Zhang Department of Computer Science and Engineering UESTC Chengdu China Big Data Research Center UESTC Chengdu China Shulianyikang Science and Technology Co. LTD Chengdu China

ISBN: (纸本)9781509062379

With the rapid expansion of data, incremental update mining for associated rule learning in large scale volume of data processing is concerned. The support count or support ratio are often used as a threshold for the associated rule learning. The support ratio is sensitive to the threshold, especially, adapted to build anomaly detection models learned with unbalanced samples. It might result in missing anomalies or data failures. However, the support count with the characteristic of good explanatory is easy to use. Therefore, the support ratio always needs to be converted into the support count. In order to mine large-scale frequent patterns, based on the FUFP algorithm, an efficient algorithm of incremental frequent item mining is proposed in this paper, named EFUFP (Efficient Fast Updated Frequent Pattern) tree algorithm. The minimum support count and a small item cache are used to effectively detect unbalanced abnormal data and improve the efficiency of query access. The parallelization of EFUFP is implemented on the Spark environment. The experimental results show that the EFUFP algorithm is suitable for the current large scale frequent item mining. © 2017 IEEE.

关键词： Trees (mathematics)

Erratum to 'Relation Between Mitral Valve Prolapse and Erectile Dysfunction (from a Nationwide Case-Control Study)': The American Journal of Cardiology 2019 Nov 15;124(10):1590-1593./ doi: 10.1016/***.2019.08.010

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The American journal of cardiology 2021年 142卷 161页

作者： Shiu-Dong Chung Ju-Chi Liu Tsai-Ning Lou Ben-Chang Shia Herng-Ching Lin Li-Ting Kao Division of Urology Department of Surgery Far Eastern Memorial Hospital Taipei Taiwan Department of Computer Science and Engineering and Graduate Program in Biomedical Informatics College of Informatics Yuan-Ze University Chung-Li Taiwan Sleep Research Center Taipei Medical University Hospital Taipei Taiwan. Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Cardiovascular Medicine Department of Medicine Medical University Hospital-Shuang Ho Hospital TaipeiTaiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. Big Data Research Center Taipei Medical University Taipei Taiwan. Sleep Research Center Taipei Medical University Hospital Taipei Taiwan School of Health Care Administration Taipei Medical University Taipei Taiwan Graduate Institute of Life Sciences National Defense Medical Center Taipei Taiwan Department of Pharmacy Practice Tri-Service General Hospital Taipei Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan.. Electronic address: kaoliting@***.

Efficient nonlinear precoding for massive MIMO downlink systems with 1-bit DACs

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arXiv 2018年

作者： Chu, Lei Wen, Fei Li, Lily Qiu, Robert Department of Electrical Engineering Shanghai Jiaotong University Shanghai China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States Department of Electrical Engineering Research Center for Big Data and Artificial Intelligence Engineering and Technologies Shanghai Jiaotong University Shanghai200240 China

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multipleoutput (MU-MIMO) base station (BS). Using 1-bit DACs can significantly reduce the power consumption. This paper addresses the precoding problem for the massive narrow-band MU-MIMO downlink system equipped with 1-bit DACs at each BS. In such a system, the precoding problem plays a central role as the precoded symbols are affected by extra distortion introduced by 1-bit DACs. In this paper, we develop a highly-efficient nonlinear precoding algorithm based on the alternative direction method framework. Unlike the classic algorithms, such as the semidefinite relaxation (SDR) and squared-infinity norm Douglas-Rachford splitting (SQUID) algorithms, which solve convex relaxed versions of the original precoding problem, the new algorithm solves the original nonconvex problem directly. The new algorithm is guaranteed to globally converge under some mild conditions. A sufficient condition for its convergence has been derived. Experimental results in various conditions demonstrated that, the new algorithm can achieve state-of-the-art performance comparable to the SDR algorithm, while being much more efficient (e.g., more than 300 times faster than the SDR algorithm). Copyright © 2018, The Authors. All rights reserved.

关键词： Electric power utilization

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

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arXiv 2019年

作者： Wang, Le-Zhi Zhao, Zhi-Dan Jiang, Jun-Jie Guo, Bing-Hui Wang, Xiao Huang, Zi-Gang Lai, Ying-Cheng School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Web Sciences Center Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China School of Mathematics Beihang University Beijing100191 China School of Biological and Health Systems Engineering Arizona State University TempeAZ85287 United States School of Life Science and Technology Xi'An Jiaotong University Xi'an710049 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu730000 China Department of Physics Arizona State University TempeAZ85287 United States

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an \S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

ProbeSim: Scalable single-source and top-k SimRank computations on dynamic graphs 44th

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ProbeSim: Scalable single-source and top-k SimRank computati...

44th International Conference on Very Large data Bases, VLDB 2018

作者： Liu, Yu Zheng, Bolong He, Xiaodong Wei, Zhewei Xiao, Xiaokui Zheng, Kai Lu, Jiaheng School of Information Renmin University of China China School of Data and Computer Science Sun Yat-sen University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering and Big Data Research Center University of Electronic Science and Technology of China China Department of Computer Science University of Helsinki Finland

Single-source and top-k SimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection, etc. A plethora of techniques have been proposed for these two types of queries, but very few can efficiently support similarity search over large dynamic graphs, due to either significant preprocessing time or large space overheads. This paper presents ProbeSim, an index-free algorithm for single-source and top-k SimRank queries that provides a nontrivial theoretical guarantee in the absolute error of query results. ProbeSim estimates SimRank similarities without precomputing any indexing structures, and thus can naturally support real-time SimRank queries on dynamic graphs. Besides the theoretical guarantee, ProbeSim also offers satisfying practical efficiency and effectiveness due to non-trivial optimizations. We conduct extensive experiments on a number of benchmark datasets, which demonstrate that our solutions outperform the existing methods in terms of efficiency and effectiveness. Notably, our experiments include the first empirical study that evaluates the effectiveness of SimRank algorithms on graphs with billion edges, using the idea of pooling. © 2018 VLDB.

关键词： Efficiency

The effect of non-Markovian recovery on reversible failure propagation on networks

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arXiv 2019年

作者： Lin, Zhaohua Feng, Mi Tang, Ming Liu, Zonghua Hui, Pak Ming Lai, Ying-Cheng Department of Physics East China Normal University Shanghai200062 China School of Information Science Technology East China Normal University Shanghai200241 China Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Physics Chinese University of Hong Kong Shatin Hong Kong School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Compared with the classical irreversible failure propagation, the spontaneous recovery model can better depict the failure propagation on network systems such as financial and transportation networks. In this paper, we study how the non-Markovian recovery process influences cascading failure dynamics in the spontaneous recovery systems. To this end, we compare two kinds of failure-recovery models: the non-Markovian recovery (NMR) model and the corresponding Markovian recovery (MR) model. We first develop a Pairwise approximation theory for the MR and NMR models, which can predict the time evolution, steady state, and hysteresis behaviour of failure-recovery systems more accurately than the classical mean-field (MF) method. We find that although the non-Markovian recovery mechanism does not essentially affect the steady state and hysteresis behaviour of failure-recovery systems, the two models exhibit distinct evolution processes. When investigating the effect of initial conditions on the phase transition and hysteresis behavior, we find in the hysteresis region, the NMR model exhibits a non-monotonic growth characteristic: with the increase of initial failed nodes, the steady state staying in relative low-failure phase first turns into relative high-failure phase, and then into relative low-failure phase again. We then use the MF theory to explain the phenomenon qualitatively and point out when considering non-Markovian dynamics, we should study the steady state not only from the perspective of the variation of parameters, but also from the initial conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Hysteresis

Sysevr: A framework for using deep learning to detect software vulnerabilities

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

Electricity Sales Prediction Model of Electricity Market of the Non-linear Regression Combination with the Optimal Weight

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Journal of computers (Taiwan) 2019年第5期30卷 75-87S页

作者： Qu, Zhao-Yang Xie, Shu-Ya Feng, Rong-qiang Qu, Nan Lv, Hong-bo Chi, Zuo-Wei Qu, Chong Department of computer science and technology of the Institute of Information Engineering Northeast Electric Power University Jilin Jilin132012 China Intelligent Electric Power Big Data Processing Jilin Engineering Technology Research Center Jilin Jilin132012 China Maintenance Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China Guizhou Power Grid Co. Ltd. Guiyang Baiyun Power Supply Bureau GuiYang550014 China

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction. © 2019 computer Society of the Republic of China. All rights reserved.

关键词： Sales