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National Economic Status Inference from the World Trade Network

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National Economic Status Inference from the World Trade Netw...

International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： YAN-LI LEE YAN FU CompleX Lab University of Electronic Science and Technology of China Chengdu China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Quantifying the economic status of nations is significant for economic development. Apart from the traditional economic indicators, many new, granular and real-time economic indicators based on various socioeconomic and economic datasets are reported. In this paper, by employing the world trade data, we characterize the economic status from three dimensions, including the monetary dimension, the network structure dimension and the trade diversification dimension. The results show that network centrality indicators can explain up to 85.4% of the variance of the GDP, which perform best compared with indicators of the other two dimensions. The results show the importance of the network structure in inferencing the economic status of nations, and provide a new perspective for characterizing and understanding the national economic status.

关键词： Economic indicators big data Real-time systems data mining Remote sensing History

eGPS 1.0: comprehensive software for multi-omic and evolutionary analyses

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National science Review 2019年第5期6卷 867-869页

作者： Dalang Yu Lili Dong Fangqi Yan Hailong Mu Bixia Tang Xiao Yang Tao Zeng Qing Zhou Feng Gao Zhonghuang Wang Ziqian Hao Hongen Kang Yi Zheng Hongwei Huang Yuzhang Wei Wei Pan Yaochen Xu Junwei Zhu Shilei Zhao Ciran Wang Pengyu Wang Long Dai Mushan Li Li Lan Yiwei Wang Hua Chen Yi-Xue Li Yun-Xin Fu Zhen Shao Yiming Bao Fangqing Zhao Luo-Nan Chen Guo-Qing Zhang Wenming Zhao Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesUniversity of Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences BIG Data Center Beijing Institute of GenomicsChinese Academy of Sciences Shanghai Center for Bioinformation Technology Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences CAS Laboratory of Genomic and Precision Medicine Beijing Institute of Genomics Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Department of Biostatistics and Data Science School of Public Health University of Texas Health Science Center at Houston Computational Genomics Lab Beijing Institutes of Life Science Chinese Academy of Sciences Shanghai Research Center for Brain Science and Brain-Inspired Intelligence

It has become increasingly challenging for researchers to analyze the exponentially expanding amount of multi-omic ***, we describe multi-functional software named evolutionary GenotypePhenotype Systems (eGPS) that en... 详细信息

关键词： GPS eGPS 1.0 comprehensive software for multi-omic and evolutionary analyses

MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

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Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

data-DRIVEN MODEL SET DESIGN FOR MODEL AVERAGED PARTICLE FILTER

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arXiv 2019年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model;it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained data set. The average performance of these model components is guaranteed since each one’s parameter value is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained data set, respectively. Computer simulations are used to demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

Adaptive gradient methods with dynamic bound of learning rate

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arXiv 2019年

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University College of Information Science and Electronic Engineering Zhejiang University Department of Computer Science University of Southern California Center for Data Science Beijing Institute of Big Data Research Peking University

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

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N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia 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 Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

Tensor Decompositions for Hyperspectral data Processing in Remote Sensing: A Comprehensive Review

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arXiv 2022年

作者： Wang, Minghua Hong, Danfeng Han, Zhu Li, Jiaxin Yao, Jing Gao, Lianru Zhang, Bing Chanussot, Jocelyn The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing10094 China The College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The International Research Center of Big Data for Sustainable Development Goals Beijing100094 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing10094 China

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications, while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3-D HS inherent structure, tensor decomposition has aroused widespread concern and research in HS data processing tasks over the past decades. In this article, we aim at presenting a comprehensive overview of tensor decomposition, specifically contextualizing the five broad topics in HS data processing, and they are HS restoration, compressed sensing, anomaly detection, superresolution, and spectral unmixing. For each topic, we elaborate on the remarkable achievements of tensor decomposition models for HS RS with a pivotal description of the existing methodologies and a representative exhibition on the experimental results. As a result, the remaining challenges of the follow-up research directions are outlined and discussed from the perspective of the real HS RS practices and tensor decomposition merged with advanced priors and even with deep neural networks. This article summarizes different tensor decomposition-based HS data processing methods and categorizes them into different classes from simple adoptions to complex combinations with other priors for the algorithm beginners. We also expect this survey can provide new investigations and development trends for the experienced researchers who understand tensor decomposition and HS RS to some extent. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Towards Multi-Pose Guided Virtual Try-On Network

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Towards Multi-Pose Guided Virtual Try-On Network

International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bochao Wang Hanjiang Lai Jia Zhu Zhiting Hu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China School of Intelligent Systems Engineering Sun Yat-sen University ByteDance AI Lab. School of Computer Science South China Normal University Guangzhou Key Laboratory of Big Data and Intelligent Education Carnegie Mellon University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

关键词： Shape Three-dimensional displays Generators Hair Task analysis Face Gallium nitride

A regularized approach to sparse optimal policy in reinforcement learning

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arXiv 2019年

作者： Li, Xiang Yang, Wenhao Zhang, Zhihua School of Mathematical Sciences Peking University Beijing China Center for Data Science Peking University Beijing China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China

We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term. The extant entropy-regularized MDPs can be cast into our framework. Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning. In particular, we present sufficient and necessary conditions that induce a sparse optimal policy. We also conduct a full mathematical analysis of the proposed regularized MDPs, including the optimality condition, performance error, and sparseness control. We provide a generic method to devise regularization forms and propose off-policy actor critic algorithms in complex environment settings. We empirically analyze the numerical properties of optimal policies and compare the performance of different sparse regularization forms in discrete and continuous environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes