选手们,北马想对你说important Tips
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Brief Introduction of Xiyuan Hospital of China Academy of Chinese Medical Sciences and Our Intent for Future Cooperation
2014 APEC Seminar on the Application of Traditional Chinese Medicine(TCM) in Airborne Diseases Containment October 17-18 2014 Beijing · China Welcome Distinguished professors: The Asia-Pacific Economic Cooperation (APEC) is the premier Asia-Pacific economic forum, and is also the highest level of official collaboration in the Asia-Pacific region. APEC pays great attention to public health, and provides strong and continuous support for work in this field. Seminar on the Application of Traditional Chinese Medicine(TCM) in Airborne Diseases Containment, as the first TCM-related project supported by APEC Health Working Group, will be held on 17th ~ 18th October, 2014, in Beijing (China). The seminar aims to establish an international platform for academic exchanges among APEC economies, and therefore builds collaborations among multi-disciplinary like clinical and basic medicines, disease control and prevention, and also medical researches and drug development. It is to promote the transition of scientific research achievements to practical products and the use of such TCM-related products against airborne diseases in APEC regions, which leads to the enhancement of public health capacity and responses to such issues. The seminar will have experts with different backgrounds presenting depth information in relation to the TCM theories, TCM drug developments, marketing TCM-based products, and regulations for the application of TCM in different APEC economies. In the beautiful golden autumn of Beijing, We would like to show sincerely welcome -
Beijing Subway Map
Beijing Subway Map Ming Tombs North Changping Line Changping Xishankou 十三陵景区 昌平西山口 Changping Beishaowa 昌平 北邵洼 Changping Dongguan 昌平东关 Nanshao南邵 Daoxianghulu Yongfeng Shahe University Park Line 5 稻香湖路 永丰 沙河高教园 Bei'anhe Tiantongyuan North Nanfaxin Shimen Shunyi Line 16 北安河 Tundian Shahe沙河 天通苑北 南法信 石门 顺义 Wenyanglu Yongfeng South Fengbo 温阳路 屯佃 俸伯 Line 15 永丰南 Gonghuacheng Line 8 巩华城 Houshayu后沙峪 Xibeiwang西北旺 Yuzhilu Pingxifu Tiantongyuan 育知路 平西府 天通苑 Zhuxinzhuang Hualikan花梨坎 马连洼 朱辛庄 Malianwa Huilongguan Dongdajie Tiantongyuan South Life Science Park 回龙观东大街 China International Exhibition Center Huilongguan 天通苑南 Nongda'nanlu农大南路 生命科学园 Longze Line 13 Line 14 国展 龙泽 回龙观 Lishuiqiao Sunhe Huoying霍营 立水桥 Shan’gezhuang Terminal 2 Terminal 3 Xi’erqi西二旗 善各庄 孙河 T2航站楼 T3航站楼 Anheqiao North Line 4 Yuxin育新 Lishuiqiao South 安河桥北 Qinghe 立水桥南 Maquanying Beigongmen Yuanmingyuan Park Beiyuan Xiyuan 清河 Xixiaokou西小口 Beiyuanlu North 马泉营 北宫门 西苑 圆明园 South Gate of 北苑 Laiguangying来广营 Zhiwuyuan Shangdi Yongtaizhuang永泰庄 Forest Park 北苑路北 Cuigezhuang 植物园 上地 Lincuiqiao林萃桥 森林公园南门 Datunlu East Xiangshan East Gate of Peking University Qinghuadongluxikou Wangjing West Donghuqu东湖渠 崔各庄 香山 北京大学东门 清华东路西口 Anlilu安立路 大屯路东 Chapeng 望京西 Wan’an 茶棚 Western Suburban Line 万安 Zhongguancun Wudaokou Liudaokou Beishatan Olympic Green Guanzhuang Wangjing Wangjing East 中关村 五道口 六道口 北沙滩 奥林匹克公园 关庄 望京 望京东 Yiheyuanximen Line 15 Huixinxijie Beikou Olympic Sports Center 惠新西街北口 Futong阜通 颐和园西门 Haidian Huangzhuang Zhichunlu 奥体中心 Huixinxijie Nankou Shaoyaoju 海淀黄庄 知春路 惠新西街南口 芍药居 Beitucheng Wangjing South望京南 北土城 -
2017 IEEE International Conference on Image Processing Conference At
2017 IEEE International Conference on Image Processing Conference at a Glance Sept. 17-20, 2017 CNCC, Beijing, China SCHEDULE AT A GLANCE Sunday, Sept. 17, 2017 09:00 – 12:00 Tutorials (T1, T2, T3) 13:30 – 16:30 Tutorials (T4, T5, T6, T7) 18:30 – 20:30 Welcome Reception Monday, Sept. 18, 2017 08:00 - 09:00 Opening Ceremony 09:00 - 10:00 Plenary: PLEN-1: Michael Elad 10:00 - 10:30 Coffee Break 10:30 - 12:30 Lecture Sessions, Special Sessions 10:30 - 12:00 Poster Sessions, Demo Session 12:30 - 14:00 Industry Workshop (Netflix), Lunch Time, 14:00 - 16:00 Lecture Sessions, Special Sessions 14:30 - 16:00 Poster Sessions 14:00 - 18:30 Challenge Session I, Challenge Session II 16:00 - 16:30 Coffee Break 16:30 - 18:10 Lecture Sessions 16:30 - 18:00 Poster Sessions Tuesday Sept. 19, 2017 09:00 - 10:00 Plenary: PLEN-2: Song-Chun Zhu 10:00 - 10:30 Coffee Break 10:30 - 12:30 Lecture Sessions, Special Sessions, Industry Workshop (MathWorks) 10:30 - 12:00 Poster Sessions, Doctoral Student Symposium 12:30 - 14:00 Industry Workshop (Wolfram), Lunch Time 14:00 - 16:00 Lecture Sessions, Special Sessions 14:00 - 15:30 Industry Keynotes 14:30 - 16:00 Poster Sessions 16:00 - 16:30 Coffee Breaks 16:30 - 18:10 Lecture Sessions, Industry Panels 16:30 - 18:00 Poster Sessions 18:30 - 21:30 Awards Banquet Wednesday, Sept. 20, 2017 09:00 - 10:00 Plenary: PLEN-3: Kari Pulli 10:00 - 10:30 Coffee Break 10:30 - 12:30 Lecture Sessions, Special Sessions 10:30 - 12:00 Poster Sessions, Challenge Session III, Challenge Session IV 12:30 - 14:00 Industry Workshop (Google), Lunch Time 14:00 - 16:00 Lecture Sessions, Special Sessions 14:30 - 16:00 Poster Sessions 16:00 - 16:30 Coffee Break 16:30 - 18:10 Lecture Sessions 16:30 - 18:00 Poster Sessions 2 SCHEDULE AT A GLANCE 3 REGISTRATION AND LUNCH ICIP2017 Registration and Reception Centre is located in the Main Lobby on CNCC level one in the delegates accesses C1-C3. -
HARROW BEIJING Welcome to Harrow Beijing
HARROW BEIJING Welcome to Harrow Beijing A Harrow Beijing education embraces the challenges of pursuing academic excellence, participation and fulfillment through a broad range of extra- curricular activities, and developing strong values to help guide our students in an increasingly complex world. At the heart of our unique educational philosophy is the importance of sympathetic and committed relationships, led by the expertise of our teaching staff. We aim to ensure every student is known, nurtured, and encouraged to turn their abilities into achievements. Whether you are looking for a day school or a residential learning experience for your child, everything we do at Harrow Beijing encourages our students to develop themselves holistically, so they become rounded and balanced, ethical, moral and involved citizens. Be inspired by what a Harrow education can do for your child. Stet Fortuna Domus, Mr. David A. Shinkfield Head Master 1 History and Tradition Every student is part of a global Harrow family. Every student has the opportunity to contribute to the tradition of Harrow. We honour our connection to the Harrow family through shared symbols, terminology and a commitment to a high quality education. We are an Academic School We are academically selective. We support every student in achieving academic excellence. Our students go on to the world’s top universities. We celebrate academic achievement. All English as additional language learners will achieve native English fluency levels. Holistic Education Every student will develop a portfolio of participation, passions and interests through our Leadership in Action activities. Every student will be encouraged to develop values, morals and conduct in line with the traditions of Harrow and befitting global citizenship. -
Analysis and Evaluation of the Beijing Metro Project Financing Reforms
Advances in Social Science, Education and Humanities Research, volume 291 International Conference on Management, Economics, Education, Arts and Humanities (MEEAH 2018) Analysis and Evaluation of the Beijing Metro Project Financing Reforms Haibin Zhao1,a, Bingjie Ren2,b, Ting Wang3,c 1Ministry of Transport Research Institute, Chaoyang, Beijing, China,100029; 2Beijing Urban Construction Design & Development Group Co., Limited, Xicheng, Beijing, China,100037; 3School of Civil Engineering, Beijing Jiaotong University, Haidian, Beijing, China, 100044. [email protected], [email protected], [email protected] Keywords: metro; financing; marketisation; reform Abstract. The construction and operation of a metro system are costly, and the sustainable development of a metro system is difficult using government funding alone, particularly for developing countries. The main source for metro system financing in China is, currently, government budget and bank debt. Many cities have begun to seek new ways to attract funds from finance markets, which is increasing the need for the evaluation of metro financing. This study uses Beijing as a case study that utilises various financing modes with impressive results. As participants of the financing reform, the authors collected all the relative government documents and interviewed stakeholders to accomplish this work. This article reviews the development of financing modes for the Beijing Metro system during the last four decades and analyses the role of the government in the reformed financing system within the Chinese social political environment. The study addresses the advantages and challenges of the reforms in this context. To further analyses the technical processes of typical financing modes, the public-private partnership mode of Line 4, the BT mode of Olympic Branch Line, the insurance claim mode of Line 10 and the failure of the market oriented financing for Capital Airport Line are analysed and evaluated in detail. -
A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data
Hindawi Complexity Volume 2018, Article ID 3412070, 14 pages https://doi.org/10.1155/2018/3412070 Research Article A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data Kai Lu ,1 Alireza Khani,2 and Baoming Han 1 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA Correspondence should be addressed to Kai Lu; [email protected] and Baoming Han; [email protected] Received 18 December 2017; Revised 2 June 2018; Accepted 15 July 2018; Published 28 August 2018 Academic Editor: Shuliang Wang Copyright © 2018 Kai Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. -
Title of Paper in 14 Point Arial Font*
August 18th - 23rd, 2017 The Conference Booklet Beijing, China AIRAPT 26 joint with ACHPR 8 & CHPC 19 The 26th International Conference on High Pressure Science & Technology (AIRAPT 26) The 8th Asian Conference on High Pressure Research (ACHPR 8) The 19th China High Pressure Conference (CHPC 19) THE CONFERENCE BOOKLET Organized by Institute of Physics, Chinese Academy of Sciences Center for High Pressure Science & Technology Advanced Research Southern University of Science and Technology August 18th - 23rd, 2017 Beijing, China www.airapt26.org August 18th - 23rd, 2017 The Conference Booklet Beijing, China AIRAPT 26 joint with ACHPR 8 & CHPC 19 August 18th - 23rd, 2017 The Conference Booklet Beijing, China AIRAPT 26 joint with ACHPR 8 & CHPC 19 Table of Contents FOREWORD ...................................................................................................................................... 1 INTRODUCTION ................................................................................................................................ 2 TOPICS ..................................................................................................................................... 2 COMMITTEE ............................................................................................................................. 4 TALKS & POSTERS PRESENTATION INSTRUCTIONS .................................................................. 9 MAPS INFORMATION ............................................................................................................. -
Congestion Avoidance Routing Based on Large-Scale Social Signals Kun He, Zhongzhi Xu, Pu Wang, Lianbo Deng, and Lai Tu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 17, NO. 9, SEPTEMBER 2016 2613 Congestion Avoidance Routing Based on Large-Scale Social Signals Kun He, Zhongzhi Xu, Pu Wang, Lianbo Deng, and Lai Tu Abstract—The emergence of large-scale social signal data has Fundamentally, congestion is caused by an imbalance be- provided unprecedented opportunities to develop techniques for tween network capacity and travel demand, and it can be improving transportation systems. In this paper, we use two types alleviated by enhancing a network’s capacity [6], [7], reducing of social signal data, namely, mobile phone data and subway card data, to investigate congestion avoidance routing methodologies the volume of travel demand [1], [8]–[10], and by intelligently in the Beijing subway and San Francisco road networks. The inducing travelers to use proper routes [11], [12]. Studies have social signal data were used to estimate detailed travel demand shown that increasing the capacity of important backbones information and to target sources of congestion, in order to or removing specific segments of transportation networks can develop intelligent routing models. We study two fundamental improve their transport efficiency [6], [11]. It has also been routing scenarios, namely, the shortest path (SP) scenario and the minimum cost (MC) scenario, and propose a hybrid routing model found that traffic congestion in road networks can be effec- that combines SP routing and MC routing. The hybrid model tively mitigated by intelligently reducing a small fraction of requires only a small fraction of travelers to take MC routes, travel demand [1]. These studies, which focus on improving but achieves nearly the same effect as MC routing. -
Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations
Received July 31, 2020, accepted August 24, 2020, date of publication September 14, 2020, date of current version September 24, 2020. Digital Object Identifier 10.1109/ACCESS.2020.3022625 Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations LIYING ZHANG 1,2, TAO PEI 2,3, BIN MENG4, YUANFENG LIAN1, AND ZHOU JIN1 1College of Information Science and Engineering, China University of Petroleum, Beijing 102249, China 2State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 4College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China Corresponding author: Tao Pei ([email protected]) This work was supported in part by the National Natural Science Foundation of China under Grant 41525004, Grant 41421001, Grant 41877523, and Grant 41671165; in part by the State Key Laboratory of Resources and Environmental Information System; and in part by the Science Foundation of China University of Petroleum, Beijing, under Grant ZX20200100. ABSTRACT Consider the problem of clustering objects with temporally changing multivariate variables, for instance, in the classification of cities with several changing socioeconomic indices in geographical research. If the changing multivariate can be recorded simultaneously as a multivariate time series, in which the length of each subseries is equal and the subseries can be correlated, the problem is transformed into a multivariate time series clustering problem. The available methods consider the correlations between distinct time series but overlook the shape of each time series, which causes multivariate time series with similar correlations and opposite shapes to be clustered into the same class. -
第 28 届国际天文学联合会大会 Programme Book
IAU XXVIII GENERAL ASSEMBLY 20-31 AUGUST, 2012 第 28 届国际天文学联合会大会 PROGRAMME BOOK 1 Table of Contents Welcome to IAU Beijing General Assembly XXVIII ........................... 4 Welcome to Beijing, welcome to China! ................................................ 6 1.IAU EXECUTIVE COMMITTEE, HOST ORGANISATIONS, PARTNERS, SPONSORS AND EXHIBITORS ................................ 8 1.1. IAU EXECUTIVE COMMITTEE ..................................................................8 1.2. IAU SECRETARIAT .........................................................................................8 1.3. HOST ORGANISATIONS ................................................................................8 1.4. NATIONAL ADVISORY COMMITTEE ........................................................9 1.5. NATIONAL ORGANISING COMMITTEE ..................................................9 1.6. LOCAL ORGANISING COMMITTEE .......................................................10 1.7. ORGANISATION SUPPORT ........................................................................ 11 1.8. PARTNERS, SPONSORS AND EXHIBITORS ........................................... 11 2.IAU XXVIII GENERAL ASSEMBLY INFORMATION ............... 14 2.1. LOCAL ORGANISING COMMITTEE OFFICE .......................................14 2.2. IAU SECRETARIAT .......................................................................................14 2.3. REGISTRATION DESK – OPENING HOURS ...........................................14 2.4. ON SITE REGISTRATION FEES AND PAYMENTS ................................14 -
The Modeling of Attraction Characteristics Regarding Passenger Flow in Urban Rail Transit Network Based on Field Theory
RESEARCH ARTICLE The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory Man Li1,2, Yanhui Wang1,2,3*, Limin Jia1,2,3 1 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China, 2 School of traffic and transportation, Beijing Jiaotong University, Beijing, China, 3 Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Beijing Jiaotong University, Beijing, China * [email protected] a1111111111 a1111111111 Abstract a1111111111 a1111111111 Aimed at the complicated problems of attraction characteristics regarding passenger flow in a1111111111 urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collec- tion and distribution of passenger flow and the topology of network. As for the computation OPEN ACCESS methods of field strength, an optimum path concept is proposed to define betweenness cen- Citation: Li M, Wang Y, Jia L (2017) The modeling trality parameter. Regarding the computation of potential energy, Compound Simpson's of attraction characteristics regarding passenger Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a flow in urban rail transit network based on field theory. PLoS ONE 12(9): e0184131. https://doi. practical example, an analysis of simulation and verification is conducted, and the results org/10.1371/journal.pone.0184131 shows in the following ways. Firstly, the bigger field strength value between two stations is, Editor: Xiaolei Ma, Beihang University, CHINA the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. -
1. Iau Executive Committee, Host Organisations, Partners, Sponsors and Exhibitors
1. IAU EXECUTIVE COMMITTEE, HOST ORGANISATIONS, PARTNERS, SPONSORS AND EXHIBITORS 1.1. IAU EXECUTIVE COMMITTEE President Robert Williams, United States President-Elect Norio Kaifu, Japan General Secretary Ian F. Corbett, United Kingdom Assistant General Secretary Thierry Montmerle, France Vice-President Matthew Colless, Australia Vice-President Martha P. Haynes, United States Vice-President George K. Miley, Netherlands Vice-President Jan Palouš, Czech Republic Vice-President Marta G. Rovira, Argentina Vice-President Giancarlo Setti, Italy Advisor Catherine J. Cesarsky, France Advisor Karel A. van der Hucht, Netherlands 1.2. IAU SECRETARIAT IAU - UAI Secretariat 98-bis Boulevard Arago Institut d'Astrophysique de Paris F–75014 Paris France Phone: +33 (0) 1 43 25 83 58 Fax: +33 (0) 1 43 25 26 16 Email:[email protected] www.iau.org 1.3. HOST ORGANISATIONS Chinese Astronomical Society (CAS) http://english.astronomy.pmo.cas.cn/ National Astronomical Observatories, Chinese Academy of Sciences (NAOC) http://english.nao.cas.cn 1.4. NATIONAL ADVISORY COMMITTEE Chair DENG, Nan (President, China Association for Science and Technology) Vice-Chairs CAO, Jianlin (Vice Minister, Ministry of Science and Technology) ZHAN, Wenlong (Vice President, Chinese Academy of Sciences) SHEN, Wenqing (Vice Director, National Natural Science Foundation of China) LIN, Huiqing (Assistant Minister, Ministry of Education) Members WANG, Shouguan (Honorary President, Chinese Astronomical Society) YE, Shuhua (Honorary President, Chinese Astronomical Society) QU, Qinyue (Honorary