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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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A Novel Acceleration Method for DGTD Algorithm on Sunway TaihuLight

A Novel Acceleration Method for DGTD Algorithm on Sunway Tai...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Geng Chen Lei Zhao Wenhua Yu Hu Ren Haohuan Fu National Supercomputing Center in Wuxi China Jiangsu Key Lab of Education Big Data Science and Engineering Jiangsu Normal University China National Supercomputing Center in Wuxi Tsinghua University China

In this paper, a novel acceleration method for dis-continuous Galerkin time domain (DGTD) algorithm on Sunway platform is proposed with a multi-level speedup technique. In the proposed method, the message passing interface (MPI) is used to connect each core-group (CG) and the registor level massaging passing interface (RL-MPI) is used to connect the slave core on each computing processing element (CPE) cluster, which efficiently overcomes the bottleneck caused by discrete memory accessing. Numerical results show that the performance of DGTD on SW26010 CPU can be dramatically improved by using RL-MPI.

关键词： Acceleration Registers Time-domain analysis Electronic mail Method of moments Computer architecture Transforms

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25th IEEE International Conference on Network Protocols, ICNP 2017

作者： Chen, Hanhua Zhang, Fan Jin, Hai Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781509065011

Real-world stream data with skewed distribution raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a 'one size fits all' design, which leverage either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this paper, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalable in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose and implement a differentiated distributed stream processing system, call DStream, which assigns the popular keys using shuffle grouping while assigns unpopular ones using key grouping. We design a novel efficient and light-weighted probabilistic counting scheme for identifying the current hot keys in dynamic real-time streams. Two factors contribute to the power of this design: 1) the probabilistic counting scheme is extremely computation and memory efficient, so that it can be well integrated in processing instances in the system;2) the scheme can adapt to the popularity changes in the dynamic stream processing environment. We implement the DStream system on top of Apache Storm. Experiment results using large-scale traces from real-world systems show that DStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64% compared to state-of-the-art designs. © 2017 IEEE.

关键词： Higher order statistics

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

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arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Information filtering via trust relationship for better personalized recommendation

Information filtering via trust relationship for better pers...

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2017 International Conference on Computer Technology, Electronics and Communication, ICCTEC 2017

作者： Chen, Lingjiao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781538657843

Recommender system is one of the most promising techniques with widest application in solving the problem of information overload. Aimed at providing better performance personalized recommendation, this paper improves a new hybrid recommender method based on social trust relationship and benchmark information filtering algorithm. Epinions and Friendfeed show that the hybrid trust method has better performance with high accuracy. Moreover, trust relationship has positive effect on the personalized recommendation performance. © 2017 IEEE.

关键词： Information filtering

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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Effect Analysis of Factors Based on Neural Network in Non-Contact Electrostatic Discharge 5

Effect Analysis of Factors Based on Neural Network in Non-Co...

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The 5th International Symposium on Electromagnetic Compatibility

作者： Huang Jun Ruan Fangming Su Ming Wang Heng YangXiangdong David Pommerenke Department of Big Data and Electronic Sciences Guizhou Normal University Beijing Oriental Institute of Metrology and Test EMC Lab of Missouri University of Science & Technology

ISBN: (纸本)9781509051861;9781509051854

Discharge parameters in non-contact electrostatic dtscharge(ESD) are affected fay various factors,including electrode moving speed to the target,gas pressure,temperature,*** of non-contact electrostatic discharge was analyzed based on a neural network model and compared to current waveform in non-contact electrostatic discharge measured with new measurement system of electrostatic *** network method was used to adjust the weight of discharge parameters in non-contact electrostatic discharge based on discharge current waveform affected by electrode moving speed,gas pressure,temperature and humidity,so as to compare with the experiment results waveforms met to the requirement of international standard IEC61000-4-2,and to analyze the main parameters that affect discharge currents in non-contact electrostatic discharge events.

关键词： component Electrostatic Discharge Gas Pressure ElectrodeMovingSpeed Neural Network Discharge Parameters

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Growth Onset Rather Than Photosynthesis Strongly Regulates Autumn Senescence Termination Besides Climate Change

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Global Ecology and Biogeography 2025年第5期34卷

作者： Shuping Ji Shilong Ren Yann Vitasse Constantin M. Zohner Yongshuo H. Fu Xiaoqiu Chen Xiaoyang Zhang Charlotte Grossiord Huiying Liu Matthias Peichl Dailiang Peng Shuai An Yating Li Maihe Li Lei Fang Jinyue Chen Xinfeng Wang Qingzhu Zhang Guoqiang Wang Qiao Wang Big Data Research Center for Ecology and Environment Environment Research Institute Shandong University Qingdao China WSL Swiss Federal Institute for Forest Snow and Landscape Research Birmensdorf Switzerland Institute of Integrative Biology ETH Zurich (Swiss Federal Institute of Technology) Zurich Switzerland College of Water Sciences Beijing Normal University Beijing China College of Urban and Environmental Sciences Laboratory for Earth Surface Processes of the Ministry of Education Peking University Beijing China Geospatial Science Center of Excellence Department of Geography and Geospatial Sciences South Dakota State University Brookings South Dakota USA Plant Ecology Research Laboratory (PERL) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Tiantong National Station for Forest Ecosystem Research Center for Global Change and Ecological Forecasting The Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration School of Ecological and Environmental Sciences East China Normal University Shanghai China Department of Forest Ecology and Management Swedish University of Agricultural Sciences (SLU) Umeå Sweden Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Environmental Sciences University of Basel Basel Switzerland Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Jilin China College of Life Science Hebei University Baoding Hebei China Center for Geodata and Analysis Faculty of Geographical Science Beijing Normal University Beijing China

Aim Plant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how environmental and developmental factors drive autumn senescence and evaluate whether woody and herbaceous plants exhibit divergent responses to these drivers. Location Eurasia. Time Period 1982–2014. Major Taxa Studied Woody and herbaceous species. Methods Using 120,833 long-term ground phenological observations, we employed partial correlation analysis to investigate the influence of environmental and developmental factors on senescence termination. Experimental records from literature and pasture survey observations from China were separately utilised to further validate the influence of developmental factors on senescence termination. Structural equation modelling was applied to analyse the pathways of growth onset affecting senescence termination. Additionally, multiple linear regression was used to examine the tendency of the sensitivity of senescence termination to plant development rate. Results We find that earlier growth onset primarily leads to earlier senescence termination directly in herbaceous plants, but indirectly in woody plants by accelerating early-season development. The sensitivity of senescence termination to plant development rate shows a declining trend, particularly in early-season negative effects on woody plants and late-season positive effects on herbaceous plants, suggesting diminished impacts of future warming on senescence timing. The impact of growing season photosynthetic activity on senescence termination is not pronounced for both woody and herbaceous plants. Main Conclusions The results demonstrate that growth onset may affect woody and herbaceous senescence termination through different pathways, whereas the carry-over effects of growing season photosynthetic activity are not wi

关键词： global warming growth onset herbaceous plants photosynthesis plant senescence woody plants

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Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

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Corrigendum to “Promotion and resignation in employee networks” [Physica A 444 (2016) 442–447]

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Physica A: Statistical Mechanics and its Applications 2020年 555卷

作者： Jia Yuan Qian-Ming Zhang Jian Gao Linyan Zhang Xue-Song Wan Xiao-Jun Yu Tao Zhou Chengdu Institute of Public Administration Chengdu 610110 People’s Republic of China School of Management and Economics University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China CompleX Lab Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China Center for Polymer Studies Department of Physics Boston University Boston 02215 United States Hire Big Data (Chengdu) LTD. Chengdu 611731 People’s Republic of China Beijing Strong Union Technology Co. Ltd. Beijing 100086 People’s Republic of China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China

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