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arXiv 2018年

作者： Zhang, Rongqing Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University CO United States Shenzhen Research Institute of Big Data Shenzhen Guangdong China

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to- EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EVprovider- optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs. Copyright © 2018, The Authors. All rights reserved.

关键词： Electric vehicles

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Attentive relational networks for mapping images to scene graphs

arXiv

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arXiv 2018年

作者： Qi, Mengshi Li, Weijian Yang, Zhengyuan Wang, Yunhong Luo, Jiebo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Department of Computer Science University of Rochester United States

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Online Robust Lagrangian Support Vector Machine against Adversarial Attack

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Procedia Computer Science 2018年 139卷 173-181页

作者： Yue Ma Yiwei He Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

In adversarial environment such as intrusion detection and spam filtering, the adversary-intruder or spam advertiser may attempt to produce contaminate training instance and manipulate the learning of classifier. In order to keep good classification performance, many robuster learning methods have been proposed to deal with the adversarial attack. Support Vector Machines(SVMs) is a kind of successful approach in the adversarial classification tasks and the investigation of robust SVMs is very popular. However, in many real application, the data including stain instance is coming dynamically. Batch learning which needs retraining when encountering new samples, will consume more computing resources. In this paper, we propose a robust Lagrangian support vector machine (RLSVM) with modified kernel matrix and explore the online learning algorithm on it. The experimental results show the robustness of RLSVM against label noise produced by adversaries under the online adversarial environment.

关键词： adversarial attack poison attack label noise online learning Lagrangian SVM

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Corrigendum to "Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario" [Radiother. Oncol. 161 (2021) 230-240]

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Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2021年 163卷 119-120页

作者： Ruijie Yang Xueying Yang Le Wang Dingjie Li Yuexin Guo Ying Li Yumin Guan Xiangyang Wu Shouping Xu Shuming Zhang Maria F Chan Lisheng Geng Jing Sui Department of Radiation Oncology Peking University Third Hospital Beijing China. School of Physics Beihang University Beijing China. Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China. Department of Radiation Therapy Henan Cancer Hospital Zhengzhou China. Department of Radiation Oncology The First Affiliated Hospital of Zhengzhou University Zhengzhou China. Department of Oncology The First Affiliated Hospital of Chongqing Medical University Chongqing China. Department of Radiation Therapy Yantai Yuhuangding Hospital Yantai China. Department of Radiotherapy Shanxi Provincial Cancer Hospital Xi'an China. Department of Radiation Oncology General Hospital of People's Liberation Army Beijing China. Department of Radiation Oncology Peking University Third Hospital Beijing China Department of Ultrasound Beijing Hospital Beijing China. Medical Physics Department Memorial Sloan Kettering Cancer Center New York NY United States. School of Physics Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing China. Electronic address: lisheng.geng@***. School of Artificial Intelligence University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Electronic address: jsui@***.

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Max-min fairness user scheduling and power allocation in full-duplex OFDMA systems

arXiv

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arXiv 2018年

作者： Zhang, Xiaozhou Chang, Tsung-Hui Liu, Ya-Feng Shen, Chao Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization

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Angular distribution measurement for ^12C fragmentation via ^12C ＋ ^12C scattering at 100 MeV/u

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Chinese Physics C 2018年第7期42卷 30-36页

作者： Wei-Wei Qu Gao-Long Zhang Guo-Yu Tian Zhi-Qiang Chen Shou-Ping Xu Royichi Wada State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and ProtectionSoochow UniversitySuzhou 215123China Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Suzhou 215123China School of Physics and Nuclear Energy Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing100083China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China Department of Radiation Oncology PLA General HospitalBeijing 100853China Cyclotron Institute Texas A&M UniversityCollege StationTexas 77843USA

The angular distributions and energy spectra of 11B, 10B, and 9Be fragments of 12C in the angular range from 1.0° to 7.5° at 100 MeV/u were obtained via 12C ＋ 12C scattering. Detailed comparisons are presented between the experimental data and the modified antisymmetrized molecular dynamics （AMD-FM）, binary intranuclear cascade model （BIC） and Liège intranuclear cascade model （INCL＋＋）. The experimental angular distributions and energy spectra are well reproduced by the AMD-FM calculations but fail to be reproduced by the physical models installed in the Geant4 program, including the BIC and INCL＋＋ models.

关键词： breakup angular distribution AMD Geant4 simulation

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Correction to: Performance study of trial M-THGEM

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Radiation Detection Technology and Methods 2020年第1期4卷 83-83页

作者： Shao-Qing Deng Wen-Qi Yan Dao-Jin Hong Bo-Xiang Yu Gao-Long Zhang Yu-Guang Xie School of Physics and Nuclear Energy Engineering Beihang University Beijing China Institute of High Energy Physics Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Beijing China University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China

Upon request from the authors, the sequence of grant numbers are corrected as: Nos. 11575221, 11975040, U1832130, 11475013.

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Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer Science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

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Spatiotemporal data organization and application research

Spatiotemporal data organization and application research

引用

ISPRS Geospatial Week 2017

作者： Tan, Chengguo Yan, Suiyu Big Data Innovation Center State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Wuda Geoinformatics Co. Ltd. Wuhan China

Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications, this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle. © Authors 2017. CC BY 4.0 License.

关键词： Smart city

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How China Deals with big data

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Annals of data Science 2017年第4期4卷 433-440页

作者： Shi, Yong Shan, Zhiguang Li, Jianping Fang, Yufei School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Informatization Research Department State Information Center Beijing100045 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China School of Computer and Communication Engineering University of Science and Technology Beijing100083 China Informatization Research Department State Information Center Beijing100045 China

On September 5, 2015, the state Council of Chinese Government, China’s cabinet formally announced its Action Framework for Promoting big data (***, 2015). This is the milestone for China to catch up the global wave of big data. Since 2012 big data became a hot issue for scientific communities as well as the governments of many countries (Lazer et al. in Science 343:1203–1205, 2014;Einav et al. in Science 345:715, 2014;Cate in Science 346:818, 2014;Khoury and Ioannidis in Science 346:1054–1055, 2014). At the 2013 G8 Summit, the leaders of Canada, France, Germany, Italy, Japan, Russia, U.S.A. and United Kingdom agreed on an "open government plan" (***/government/publications/open-data-charter/g8-open-data-charter-and-technical-annex, 2013). China’s framework, however, mainly emphasizes the integration of all trans-departmental data and establishes a number of government-driven national big data platforms so as to provide big data services to research, public and enterprises. The framework not only demonstrates a strong commitment of the Chinese government on big data, but also covers a wide range of governmental branches, enterprises and institutions far more than that of other countries. In addition, the framework shows an interpretation of big data that differs from other countries. If its objective is achieved, China would become a strong "big data country". © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： big data

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