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Phase diagrams of interacting spreading dynamics in complex networks

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Physical Review Research 2020年第2期2卷 023233-023233页

作者： Liming Pan Dan Yang Wei Wang Shimin Cai Tao Zhou Ying-Cheng Lai School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu 210023 China Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Cybersecurity Research Institute Sichuan University Chengdu 610065 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

Epidemic spreading processes in the real world can interact with each other in a cooperative, competitive, or asymmetric way, requiring a description based on coevolution dynamics. Rich phenomena such as discontinuous outbreak transitions and hystereses can arise, but a full picture of these behaviors in the parameter space is lacking. We develop a theory for interacting spreading dynamics on complex networks through spectral dimension reduction. In particular, we derive from the microscopic quenched mean-field equations a two-dimensional system in terms of the macroscopic variables, which enables a full phase diagram to be determined analytically. The diagram predicts critical phenomena that were known previously but only numerically, such as the interplay between discontinuous transition and hysteresis as well as the emergence and role of tricritical points.

关键词： Complex systems Epidemic Epidemic prevalence Network structure Epidemic spreading Spreading models

Lecture Notes in Computer Science (including subseries Lectu...

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Preface

Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13469 LNAI卷 v-vi页

作者： Bringas, Pablo García García, Hilde Pérez de Pisón, Francisco Javier Martínez Flecha, José Ramón Villar Lora, Alicia Troncoso de la Cal, Enrique A. Herrero, Álvaro Álvarez, Francisco Martínez Psaila, Giuseppe Quintián, Héctor Corchado, Emilio University of Deusto Bilbao Spain University of León León Spain University of La Rioja La Rioja Logroño Spain University of Oviedo Oviedo Spain Data Science and Big Data Analytics Lab Pablo de Olavide University Sevilla Spain Department of Computer Science University of Oviedo Oviedo Spain Applied Computational Intelligence University of Burgos Burgos Burgos Spain Universidad Pablo de Olavide Seville Spain DIGIP University of Bergamo Dalmine Bergamo Italy Department of Industrial Engineering University of A Coruña Ferrol Spain University of Salamanca Salamanca Spain

RECURRENT NEURAL NETWORKS WITH STOCHASTIC LAYERS FOR ACOUSTIC NOVELTY DETECTION

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arXiv 2019年

作者： Nguyen, Duong Kirsebom, Oliver S. Frazão, Fábio Fablet, Ronan Matwin, Stan IMT Atlantique Lab-STICC UBL Brest France Institute for Big Data Analytics Dalhousie University Halifax Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland

In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Because the learned distribution can be calculated explicitly in terms of probability, we can evaluate how likely an observation is then detect low-probability events as novel. The model is robust, highly unsupervised, end-to-end and requires minimum preprocessing, feature engineering or hyperparameter tuning. An experiment on a benchmark dataset shows that our model outperforms the state-of-the-art acoustic novelty detectors. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

Tracking the consumption junction: Temporal dependencies between articles and advertisements in dutch newspapers

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arXiv 2019年

作者： Wevers, Melvin Gao, Jianbo Nielbo, Kristoffer L. Digital Humanities Lab KNAW Humanities Cluster Netherlands Institute of Complexity Science and Big Data Technology Guangxi University China Center for Humanities Computing Aarhus Aarhus University Denmark

Historians have regularly debated whether advertisements can be used as a viable source to study the past. Their main concern centered on the question of agency. Were advertisements a reflection of historical events and societal debates, or were ad makers instrumental in shaping society and the ways people interacted with consumer goods? Using techniques from econometrics (Granger causality test) and complexity science (Adaptive Fractal Analysis), this paper analyzes to what extent advertisements shaped or reflected society. We found evidence that indicate a fundamental difference between the dynamic behavior of word use in articles and advertisements published in a century of Dutch newspapers. Articles exhibit persistent trends that are likely to be reflective of communicative memory. Contrary to this, advertisements have a more irregular behavior characterized by short bursts and fast decay, which, in part, mirrors the dynamic through which advertisers introduced terms into public discourse. On the issue of whether advertisements shaped or reflected society, we found particular product types that seemed to be collectively driven by a causality going from advertisements to articles. Generally, we found support for a complex interaction pattern dubbed the consumption junction. Finally, we discovered noteworthy patterns in terms of causality and long-range dependencies for specific product groups. All in, this study shows how methods from econometrics and complexity science can be applied to humanities data to improve our understanding of complex cultural-historical phenomena such as the role of advertising in society. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical tests

Temporal data fusion at the edge

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arXiv 2019年

作者： Yang, Linfu Liu, Bin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

—As an enabler technique, data fusion has gained great attention in the context of Internet of things (IoT). In traditional settings, data fusion is done at the cloud server. So the data to be fused should be transferred from the sensor nodes to the cloud server before data fusion. Such an application mode of data fusion inherits disturbing concerns from the cloud computing framework, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption. We take into account how to do temporal data fusion at the edge to bypass the above issues. We present a Gaussian process based temporal data fusion (GPTDF) method targeted for the problem of sequential online prediction at the edge. The GPTDF method fits the edge computing framework and thus inherits desirable properties from edge computing, such as privacy-preserving, low latency between data capture and computation, and tiny bandwidth consumption. Through a real-data experiment using archived traffic datasets from the Caltrans Performance Measurement System (PeMS), we demonstrate that the application of GPTDF can provide more timely and accurate real-time predictions at the network edge. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

Temporal data Fusion at the Edge

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Temporal Data Fusion at the Edge

IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Linfu Yang Bin Liu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728152103

As an enabler technique, data fusion has gained great attention in the context of Internet of things (IoT). In traditional settings, data fusion is done at the cloud server. So the data to be fused should be transferred from the sensor nodes to the cloud server before data fusion. Such an application mode of data fusion inherits disturbing concerns from the cloud computing framework, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption. We take into account how to do temporal data fusion at the edge to bypass the above issues. We present a Gaussian process based temporal data fusion (GPTDF) method targeted for the problem of sequential online prediction at the edge. The GPTDF method fits the edge computing framework and thus inherits desirable properties from edge computing, such as privacy-preserving, low latency between data capture and computation, and tiny bandwidth consumption. Through a real-data experiment using archived traffic datasets from the Caltrans Performance Measurement System (PeMS), we demonstrate that the application of GPTDF can provide more timely and accurate real-time predictions at the network edge.

关键词： data privacy Gaussian processes Internet of Things sensor fusion data fusion Gaussian processes Internet of things data integration data privacy Sensor fusion Edge of a figure or solid data capture Privacy

Deep learning for image and point cloud fusion in autonomous driving: A review

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arXiv 2020年

作者： Cui, Yaodong Chen, Ren Chu, Wenbo Chen, Long Tian, Daxin Li, Ying Cao, Dongpu The Waterloo CogDrive Lab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada Research Institute Co. Ltd. Beijing100176 China School of Data and Computer Science Sun Yat-sen University Zhuhai519082 China Waytous Inc. Qingdao266109 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

Energy Saving Strategy of Power System Cluster Based on Container Virtualization

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Energy Saving Strategy of Power System Cluster Based on Cont...

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Deep lidar odometry 4

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Deep lidar odometry

4th ISPRS Geospatial Week 2019

作者： Li, Q. Wang, C. Chen, S. Li, X. Wen, C. Cheng, M. Li, J. Fujian Key Lab. of Sensing and Comp. for Smart City and the Sch. of Info. Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Geometric and Visual Computing Group Louisiana State University LA70808 United States GeoSTARS Lab Department of Geography and Environmental Management University of Waterloo Canada

Most existing lidar odometry estimation strategies are formulated under a standard framework that includes feature selection, and pose estimation through feature matching. In this work, we present a novel pipeline called LO-Net for lidar odometry estimation from 3D lidar scanning data using deep convolutional networks. The network is trained in an end-to-end manner, it infers 6-DoF poses from the encoded sequential lidar data. Based on the new designed mask-weighted geometric constraint loss, the network automatically learns effective feature representation for the lidar odometry estimation problem, and implicitly exploits the sequential dependencies and dynamics. Experiments on benchmark datasets demonstrate that LO-Net has similar accuracy with the geometry-based approach. © Authors 2019.

关键词： Motion estimation