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url: =81. Genomics and expression analysis of DHHC-cysteine-rich domain S-acyl transferase protein family in apple SCIE\n82. Plasma microRNA panels to diagnose pancreatic cancer: Results from a multicenter study SCOPUS SCIE\n83. Adrenalectomy improves hypertension control in patients with non-functional adrenal adenomas without causing endocrinological changes SCOPUS SCIE\n84. Magnetic gold nanoparticle-mediated small interference RNA silencing Bag-1 gene for colon cancer therapy SCIE\n作者：Huang, Wenbai; Liu, Zhan'ao; Zhou, Guanzhou; Tian, Ailing; Sun, Nianfeng\n作者机构：[Huang, Wenbai; Liu, Zhan'ao; Zhou, Guanzhou; Tian, Ailing; Sun, Nianfeng] Shandong Univ, Qilu Hosp, Dept Gen Surg, Jinan 200012, Shandong, Peoples R China.\n85. Alfven wings in the lunar wake: The role of pressure gradients SCIE\n作者：Zhang, H.; Khurana, K. K.; Kivelson, M. G.; Fatemi, S.; Holmstrom, M.; Angelopoulos, V.; Jia, Y. D.; Wan, W. X.; Liu, L. B.; Chen, Y. D.; Le, H. J.; Shi, Q. Q.; Liu, W. L.\n作者机构：[Zhang, H.; Wan, W. X.; Liu, L. B.; Chen, Y. D.; Le, H. J.] Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China.; [Zhang, H.; Wan, W. X.; Liu, L. B.; Chen, Y. D.; Le, H. J.] Chinese Acad Sci, Beijing Natl Observ Space Environm, Inst Geol & Geophys, Beijing, Peoples R China.; [Khurana, K. K.; Kivelson, M. G.; Angelopoulos, V.; Jia, Y. D.] Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA USA.; [Kivelson, M. G.] Univ Michigan, Climate & Atmospheres & Space Sci & Engn Dept, Ann Arbor, MI 48109 USA.; [Fatemi, S.] Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA.; [Holmstrom, M.] Swedish Inst Space Phys, Kiruna, Sweden.; [Shi, Q. Q.] Shangdong Univ Weihai, Sch Space Sci & Phys, Weihai, Peoples R China.; [Liu, W. L.] Beihang Univ, Sch Astronaut, Beijing, Peoples R China.\n来源：JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS,2016,Vol.121,Issue.11,10698-10711\n86. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma SCOPUS SCIE\n87. Imbalance Between Th17 Cells and Regulatory T Cells During Monophasic Experimental Autoimmune Uveitis SCOPUS SCIE\n88. Whole mitochondrial genome sequence of a rat rheumatoid arthritis E3 strain SCIE\n作者：Ji, Chang-Jiao; Duan, Xiang-Dong; Nie, Lin\n作者机构：[Ji, Chang-Jiao; Duan, Xiang-Dong; Nie, Lin] Shandong Univ, Qilu Hosp, Dept Orthoped, 44 Wenhua Xi Rd, Jinan 250012, Peoples R China.; [Ji, Chang-Jiao; Duan, Xiang-Dong] Univ Tradit Chinese Med, Affiliated Hosp Shandong, Dept Orthoped, Jinan, Peoples R China.\n来源：MITOCHONDRIAL DNA PART A,2016,Vol.27,Issue.3,2004-2005\n89. Functional Role and Mechanism of microRNA-28b in Atrial Myocyte in a Persistent Atrial Fibrillation Rat Model SCIE\n作者：Wang, Yongbin; Kang, Weiqiang; Wang, Xu; Chen, Meina; Qin, Qiaoji; Guo, Minglei; Ge, Zhiming\n作者机构：[Wang, Yongbin; Ge, Zhiming] Shandong Univ, Qilu Hosp, Dept Cardiol, Jinan, Shandong, Peoples R China.; [Wang, Yongbin; Wang, Xu; Chen, Meina; Qin, Qiaoji; Guo, Minglei] Qingdao Univ, Affiliated Hosp, Dept Emergency Med, Qingdao, Shandong, Peoples R China.; [Kang, Weiqiang] Qingdao Municipal Hosp, Dept Cardiol, Qingdao, Shandong, Peoples R China.\n来源：MEDICAL SCIENCE MONITOR,2016,Vol.22,3073-3078\n90. TET3 and BRCA1 co-repress EZH2 to inhibit the aggressive behavior of breast cancer cells SCIE\n作者：Wang, Mingxin; Yang, Qifeng; Yuan, Qingzhong; Yi, Xinhao; Wang, Qi; Tan, Panli; Li, Guoqiang\n作者机构：[Wang, Mingxin; Yang, Qifeng] Shandong Univ, Qilu Hosp, Jinan 250012, Shandong, Peoples R China.; [Wang, Mingxin; Yuan, Qingzhong; Yi, Xinhao; Wang, Qi; Li, Guoqiang] Shengli Oilfield Cent Hosp, Dongying 257034, Shandong, Peoples R China.; [Tan, Panli] Zhejiang Chinese Med Univ, Affiliated Hosp 1, Dept Lab Med, Hangzhou 31006, Zhejiang, Peoples R China.\n来源：INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE,2016,Vol.9,Issue.2,1585-1593\n91. Ultrasound-targeted microbubble destruction of calcium channel subunit alpha 1D siRNA inhibits breast cancer via G protein-coupled receptor 30 SCOPUS SCIE\n作者：Ji, Yanlei; Han, Zhen; Shao, Limei; Zhao, Yuehuan\n作者机构：[Ji, Yanlei; Shao, Limei; Zhao, Yuehuan] Shandong Univ, Shandong Acad Med Sci, Dept Special Diag, Shandong Canc Hosp, Jinan 250117, Shandong, Peoples R China.; [Han, Zhen] Jinan Second Peoples Hosp, Dept Internal Med, Jinan 250117, Shandong, Peoples R China.\n92. MicroRNA-544a Regulates Migration and Invasion in Colorectal Cancer Cells via Regulation of Homeobox A10 SCOPUS SCIE\n93. Effect of p38 Mitogen Activated Protein Kinase Inhibitor on Temporomandibular Joint Synovitis Induced by Occlusal Alteration SCOPUS SCIE\n作者：Jiang, Linhong; Lin, Xuefen; Ji, Ping\n作者机构：[Ji, Ping] Shandong Univ, Stomatol Hosp, 44 Wen Hua Xi Rd, Jinan 250012, Shandong, Peoples R China.; Shandong Prov Key Lab Oral Tissue Regenerat, Jinan, Shandong, Peoples R China.\n来源：JOURNAL OF ORAL AND MAXILLOFACIAL SURGERY,2016,Vol.74,Issue.6,1131-1139\n94. Treatment of late-onset epilepsy after cerebral hemorrhage by integrated traditional Chinese medicine and western medicine. SCOPUS SCIE\n作者：Liu, Renjing; Jiang, Zhonghua; Wu, Changyan; Chen, Jiaquan; Tao, Suai\n作者机构：[Liu, Renjing; Jiang, Zhonghua; Wu, Changyan; Chen, Jiaquan; Tao, Suai] Shandong Univ, Yantai Tradit Chinese Med Hosp, Dept Encephalopathy, Jinan 250100, Peoples R China.\n95. The Association Between Body Size and Breast Cancer in HanWomen in Northern and Eastern China SCOPUS SCIE\n96. LncRNA PVT1 overexpression is a poor prognostic biomarker and regulates migration and invasion in small cell lung cancer SCOPUS SCIE\n97. A Proposal for Combination of Lymph Node Ratio and Anatomic Location of Involved Lymph Nodes for Nodal Classification in Non-Small Cell Lung Cancer SCOPUS SCIE\n作者：Ding, Xiao; Hui, Zhouguang; Dai, Honghai; Fan, Chengcheng; Men, Yu; Ji, Wei; Liang, Jun; Lv, Jima; Zhou, Zongmei; Feng, Qinfu; Xiao, Zefen; Chen, Dongfu; Zhang, Hongxing; Yin, Weibo; Lu, Ning; He, Jie; Wang, Luhua\n作者机构：[Ding, Xiao; Dai, Honghai] Shandong Univ, Shandong Prov Hosp, Ctr Canc, Jinan, Peoples R China.; [Ding, Xiao; Hui, Zhouguang; Dai, Honghai; Fan, Chengcheng; Men, Yu; Ji, Wei; Liang, Jun; Lv, Jima; Zhou, Zongmei; Feng, Qinfu; Xiao, Zefen; Chen, Dongfu; Zhang, Hongxing; Yin, Weibo; Wang, Luhua] Chinese Acad Med Sci, Canc Hosp & Inst, Dept Radiat Oncol, Pan Jia Yuan Nan Li 17, Beijing 100021, Peoples R China.; [Ding, Xiao; Hui, Zhouguang; Dai, Honghai; Fan, Chengcheng; Men, Yu; Ji, Wei; Liang, Jun; Lv, Jima; Zhou, Zongmei; Feng, Qinfu; Xiao, Zefen; Chen, Dongfu; Zhang, Hongxing; Yin, Weibo; Lu, Ning; He, Jie; Wang, Luhua] Peking Union Med Coll, Pan Jia Yuan Nan Li 17, Beijing 100021, Peoples R China.; [Lu, Ning] Chinese Acad Med Sci, Canc Hosp & Inst, Dept Pathol, Beijing, Peoples R China.; [He, Jie] Chinese Acad Med Sci, Canc Hosp & Inst, Dept Thorac Surg, Beijing, Peoples R China.\n来源：JOURNAL OF THORACIC ONCOLOGY,2016,Vol.11,Issue.9,1565-1573\n98. Curcumin exerts antitumor effects in retinoblastoma cells by regulating the JNK and p38 MAPK pathways SCOPUS SCIE\n作者：Yu, Xiaoming; Zhong, Jingtao; Yan, Li; Li, Jie; Wang, Hui; Wen, Yan; Zhao, Yu\n作者机构：[Yu, Xiaoming; Yan, Li; Wang, Hui; Wen, Yan] Shandong Jiaotong Hosp, Dept Ophthalmol, 12 Wuyingshan Middle Rd, Jinan 250031, Shandong, Peoples R China.; [Zhong, Jingtao; Li, Jie] Shandong Univ, Qianfoshan Hosp, Dept Gen Surg, Jinan 250014, Shandong, Peoples R China.; [Zhao, Yu] Shandong Jiaotong Hosp, Dept Otolaryngol, Jinan 250031, Shandong, Peoples R China.\n来源：INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE,2016,Vol.38,Issue.3,861-868\n99. Over-expression of lncRNA SBF2-AS1 is associated with advanced tumor progression and poor prognosis in patients with non-small cell lung cancer SCOPUS SCIE\n100. Dynamic mechanical response and a constitutive model of Fe-based high temperature alloy at high temperatures and strain rates SCOPUS SCIE\n作者：Su, Xiang; Wang, Gang; Li, Jianfeng; Rong, Yiming\n作者机构：[Su, Xiang; Li, Jianfeng] Shandong Univ, Minist Educ, Key Lab High Efficiency & Clean Mech Manufacture, Jinan 250001, Peoples R China.; [Su, Xiang; Wang, Gang; Rong, Yiming] Beijing Key Lab Precis Ultra Precis Mfg Equipment, Beijing 100084, Peoples R China.; [Rong, Yiming] South Univ Sci & Technol China, Mech & Energy Engn Dept, Shenzhen 518055, Peoples R China.


url: =【(晉昌冥安)】 唐璿 (休璟) (627 - 712) - Genealogy\nTang Xiujing 唐休璟\nIs your surname 唐?\nResearch the 唐 family\nTang Xiujing 唐休璟's Geni Profile\n【(晉昌冥安)】 唐璿 (休璟)\nChinese: 宋忠公 【(晉昌冥安)】 唐璿(一) (休璟)\nDeath: 712 (84-86)\nSon of Tang Xie 唐諧\nFather of TAng Xianshen 唐先慎; Tang Xianze 唐先擇; Tang Xianshen 唐先眘; Tang Youzhong 唐脩忠; Tang Youxiao 唐脩孝 and 1 other; and Tang Lüzhi 唐履直 « less\nBrother of Tang Mou 唐某 and 唐休珪\nTAng Xianshen 唐先慎\nTang Xianze 唐先擇\nTang Xianshen 唐先眘\nTang Youzhong 唐脩忠\nTang Youxiao 唐脩孝\nTang Lüzhi 唐履直\nTang Xie 唐諧\nTang Mou 唐某\nAbout Tang Xiujing 唐休璟\nTang Xiujing 唐休璟  Zaixiang of Zhongzong. His mingzi was Rui (璿), but he used his zi. QTW, 189.8a-8b, 257.17a-21b. JTS, 93.2978-80. Literary fragments preserved in QTW, where he is listed by his mingzi. — RMH\nTang Xuan 唐璿 cf. 全唐文 257:2605-2607 — RMH\n宋忠公 唐璿(一) (休璟)生平 (中文)\nTang Xiujing 唐休璟's Timeline\nBirth of Tang Xiujing 唐休璟\nDeath of Tang Xiujing 唐休璟\nBirth of Tang Xianshen 唐先眘\nBirth of Tang Youxiao 唐脩孝\nBirth of Tang Lüzhi 唐履直\nBirth of TAng Xianshen 唐先慎\nBirth of Tang Xianze 唐先擇\nBirth of Tang Youzhong 唐脩忠


url: =Summary of Wavelet Theory--《Science & Technology Information》2013年11期\n《Science & Technology Information》 2013-11\nSummary of Wavelet Theory\nJIN Yu-zhu(Qiqihar University Network Center,Qiqihar University,Qiqihar Heilongjiang,161006,China)\nWavelet transform is a rapidly developing new areas in the applied mathematics and engineering disciplines currently.Wavelet transform has a good localized nature in the time domain and frequency domain,so it has a profound theory and a wide range of application.Firstly,the basic concepts of wavelet function is introduced,and the characteristics of wavelet transform is discussed.Secondly,the applications of the wavelet transform is summarized in all directions.Finally,the applications of wavelet transform are prospected.\n1 Wang Wenfu Zhang Yujuan(Heilongjiang Engineering Institute);THE APPLICATION OF WAVELET TRANFORM IN TREATING REMOTE SENSING IMAGE[J];Natural Science Journal of Harbin Normal University;2009-04\n2 WANG Yun-liang,SUN Feng-jie (School of Electrical Engineeing,Tianjin University of Technology,Tianjin 300384,Chian);Application and the development of wavelet theory in power harmonic analysis[J];Journal of Tianjin University of Technology;2009-04\n1 SU Guo-you1,ZHU Ming-xing2,HU Ting-fang2(1.Maanshan Iron & Steel Company Ltd,Maanshan 243003,Anhui,China.2.Anhui University Power Quality Engineering Research Center Ministry of Education,Hefei 230601,China);Research of Harmonic Analysis in the Electric Arc Furnace[J];Electrical Measurement & Instrumentation;2013-03\n2 Ren Zhen, Huang Wenying (South China University of Technology,510641,Guangzhou,China) He Jianjun, Shi Zhiqiang, Yang Hua, Yang Hao (Chongqing University,630044,Chongqing,China);WAVELET ANALYSIS AND APPLICATIONS TO POWER SYSTEMS Part One Introduction[J];AUTOMATION OF ELECTRIC POWER SYSTEMS;1997-01\n3 CHEN Ming hua,YANG Yang,ZHANG Ang ran,SHI Xu gang (Zhejiang Provincial Key Lab of Optical Communication, Zhejiang University of Technology, Hangzhou 310014,China);The quasisynchronous sampling and its application in the measurement of harmonies[J];Telecommunications For Electric Power System;2002-05\n4 WANG Jing1, SHU Hong-chun2, CHEN Xue-yun1 (1. Harbin Institute of Technology, Harbin 150001, Heilongjiang Province, China; 2. Kunming University of Science and Technology, Kunming 650001, Yunnan Province, China);A SURVEY OF WAVELETS TRANSFORM APPLYING TO POWER SYSTEM ENGINEERING[J];Power System Technology;2003-06\n6 XIE Yong-hua, FU De-sheng(Department of Computer Science and Technology , NIM Nanjing 210044, China);Multi-Source Remote Sensing Image Data FusionBased on Wavelet Transformation[J];Remote Sensing Technology and Application;2001-04\n7 QIN Yi QIN Shu-ren MAO Yong-fang (Department of Mechanical Engineering,Chongqing University Chongqing 400030,China);APPLICATION OF IMPROVED HILBERT-HUANG TRANSFORM IN TRANSIENT FEATURE EXTRACTION[J];Journal of Vibration and Shock;2008-11\n8 HE Bin,QI Jia-jie,LI Ming-he(Department of Control Science and Engineering,Tongji University,Shanghai 200092,China);Application and research of wavelet analysis in fault diagnosis of rolling bearings[J];Journal of Zhejiang University(Engineering Science);2009-07\n9 Zhang Fusheng Xian Jiaotong University Xian,710049 China Geng Zhongxing Research Center for Aviation Engineering and Technology,Beijing 100076 China Ge Yaozhong Xian Jiaotong University Xian,710049 China;FFT ALGORITHM WITH HIGH ACCURACY FOR HARMONIC ANALYSIS IN POWER SYSTEM[J];Proceedings of the Csee;1999-03


url:
=Jing sha da qi hang (Work) - Vancouver Public Library\nJing sha da qi hang\nThe work Jing sha da qi hang represents a distinct intellectual or artistic creation found in Vancouver Public Library. This resource is a combination of several types including: Work, Language Material, Books.\nThe Resource Jing sha da qi hang\nMali Bo Aosiben (Mary Pope Osborne) wen ; Wu Jianfeng tu ; Xie Peiwen yi\nKu xiao shuo, 75\nShen qi shu wu, 53\nTraditional Chinese text accompanied with zhu yin fu hao, and English\nKu xiao shuo\nShen qi shu wu =\nContext of Jing sha da qi hang\nJing sha da qi hang, Mali Bo Aosiben (Mary Pope Osborne) wen ; Wu Jianfeng tu ; Xie Peiwen yi\n
 Data from Jing sha da qi hang - Vancouver Public Library
\nData Citation of the Work Jing sha da qi hang


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url: =Zhi-Wei Wei,1 Meng Liu,1,2 Shu-Xian Ming,1 Ai-Ping Luo,1 Wen-Cheng Xu,1 and Zhi-Chao Luo1,*\nZhi-Wei Wei, Meng Liu, Shu-Xian Ming, Ai-Ping Luo, Wen-Cheng Xu, and Zhi-Chao Luo, "Pulsating soliton with chaotic behavior in a fiber laser," Opt. Lett. 43, 5965-5968 (2018)


url: =Harazuru Hotel | Taoyuan City | Jetstar Hotels Australia\n1/39 Interior Detail\nWith a stay at Harazuru Hotel in Taoyuan City (Taoyuan City Centre), you'll be within a 10-minute drive of Taoyuan Night Market and Taoyuan Shrine. This hotel is 6.2 mi (9.9 km) from Yingge Ceramics Museum and 9.8 mi (15.7 km) from Taoyuan International Baseball Stadium.\nTaoyuan Night Market - 1.7 km / 1 mi\nTaoyuan Arena - 2.7 km / 1.7 mi\nTaoyuan City Stadium - 2.9 km / 1.8 mi\nTaoyuan Chinese Furniture Museum - 3.1 km / 1.9 mi\nKimlan Foods Museum - 4.6 km / 2.8 mi\nThe Jiangs Cultural Park - 4.6 km / 2.8 mi\nTudigongkeng Shan - 8.6 km / 5.3 mi\nOrient Golf and Country Club - 8.7 km / 5.4 mi\nYingge Ceramics Museum - 9.9 km / 6.2 mi\nNiujiaopo - 10.4 km / 6.4 mi\nNational Taiwan Sport University - 11.7 km / 7.3 mi\nCaoziqi - 12 km / 7.5 mi\nGloria Outlets - 14.6 km / 9.1 mi\nTaipei (TPE-Taoyuan Intl.) - 18.7 km / 11.6 mi\nThe preferred airport for Harazuru Hotel is Taipei (TPE-Taoyuan Intl.).


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url: =Conferences of Pesticide Toxicology Iran\nConferences of Pesticide Toxicology\n1. 2nd National Conference on Food Science and Technology\nRelated Science Fields: Life Science, Immunology, Toxicology , Pesticide Toxicology , Life Sciences, Agricultural and Biological Sciences, Agricultural Science, Plant Science, Environmental Sciences , Environmental Health\nConference Date: 2013-04-29 untill 2013-04-29\nVenue: : Province Khorasan Razavi - City Quchan\nAccepting Papers: 2013-04-08\nRegistration Result: 2013-04-14\n2. The 1st national conference on solutions to access sustainable development in agriculture,natural resources and the environment\nRelated Science Fields: Life Science, Pesticide Toxicology , Agricultural and Biological Sciences, Aquatic Science, Forest Science, Soil Science, Agricultural Science, Plant Science, Geology, Environmental Sciences , Environmental Health , Environmental Sciences (General) , Environmental Technology, Policy and Management , Environmental Engineering\nConference Date: 2013-03-10\nAccepting Papers: 2013-01-19\nRegistration Result: 2013-03-06\n3. The First National Conference on Planning and Environmental Protection\nRelated Science Fields: Life Science, Immunology, Basic Immunology, Applied Immunology, Toxicology , Toxicology Testing and Evaluation , Pesticide Toxicology , Toxicology (General) , Life Sciences, Life Sciences (General), Agricultural and Biological Sciences, Forest Science, Plant Science, Environmental Sciences , Ecology and Conservation , Environmental Health , Environmental Sciences (General) , Environmental Technology, Policy and Management , Environmental Engineering, Agricultural and Natural Resource Economics\nConference Date: 2013-02-21\nAccepting Papers: 2013-02-08\nRegistration Result: 2013-02-13\nUniversity of Applied Science: 16\nFifth International Conference on Interdisciplinary Studies in Iran\n3rd International Conference on Jurisprudence, Law and Religious Research\nFourth International Conference on Physics, Mathematics and Basic Science Development\nFourth International Conference on Information Technology, Computer and Telecommunication Engineering of Iran\nNinth International Conference on Management Criticism and Analysis in the Third Millennium\n9th International Conference on Accounting, Management and Innovation in Business\n3rd International Conference on Modern Research in Electrical, Computer, Mechanical and Mechatronics Engineering in Iran and Islamic World\n9th International Conference on Modern Research Achievements in jurisprudence, law and Humanities\n9th International Conference on Modern Research Achievements in Education Science, Psychology and Social Science\n6th International Conference on Religious Research, Islamic Science, jurisprudence and law in Iran and Islamic World


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url: =1 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification Kenta Oono, Taiji Suzuki link\n2 Estimating Gradients for Discrete Random Variables by Sampling without Replacement Wouter Kool, Herke van Hoof, Max Welling link\n3 Intensity-Free Learning of Temporal Point Processes Oleksandr Shchur, Marin Bilo�, Stephan G�nnemann link\n4 PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, Hongkai Xiong link\n5 Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao, Peilin Zhong, Changxi Zheng link\n6 Encoding word order in complex embeddings Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen link\n7 Ridge Regression: Structure, Cross-Validation, and Sketching Sifan Liu, Edgar Dobriban link\n8 Influence-Based Multi-Agent Exploration Tonghan Wang*, Jianhao Wang*, Yi Wu, Chongjie Zhang link\n9 Duration-of-Stay Storage Assignment under Uncertainty Michael Lingzhi Li, Elliott Wolf, Daniel Wintz link\n10 The Logical Expressiveness of Graph Neural Networks Pablo Barcel�, Egor V. Kostylev, Mikael Monet, Jorge P�rez, Juan Reutter, Juan Pablo Silva link\n11 Disentangling neural mechanisms for perceptual grouping Junkyung Kim, Drew Linsley, Kalpit Thakkar, Thomas Serre link\n12 Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song link\n13 Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning Hengyuan Hu, Jakob N Foerster link\n14 Network Deconvolution Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Thomas Goldstein, James A. Yorke, Cornelia Fermuller, Yiannis Aloimonos link\n15 NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search Xuanyi Dong, Yi Yang link\n16 Behaviour Suite for Reinforcement Learning Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt link\n17 And the Bit Goes Down: Revisiting the Quantization of Neural Networks Pierre Stock, Armand Joulin, R�mi Gribonval, Benjamin Graham, Herv� J�gou link\n18 Model Based Reinforcement Learning for Atari Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski link\n19 Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Jascha Sohl-Dickstein, Samuel S. Schoenholz link\n20 Differentiation of Blackbox Combinatorial Solvers Marin Vlastelica Pogancic, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolinek link\n21 Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization Michael Volpp, Lukas Froehlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel link\n22 Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning Dexter R.R. Scobee, S. Shankar Sastry link\n23 Spectral Embedding of Regularized Block Models Nathan De Lara, Thomas Bonald link\n24 What Can Neural Networks Reason About? Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka link\n25 Training individually fair ML models with sensitive subspace robustness Mikhail Yurochkin, Amanda Bower, Yuekai Sun link\n26 Learning from Rules Generalizing Labeled Exemplars Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi link\n27 Directional Message Passing for Molecular Graphs Johannes Klicpera, Janek Gro�, Stephan G�nnemann link\n28 At Stability’s Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry link\n29 Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps Tri Dao, Nimit Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra link\n30 Neural Arithmetic Units Andreas Madsen, Alexander Rosenberg Johansen link\n31 Depth-Width Trade-offs for ReLU Networks via Sharkovsky’s Theorem Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang link\n32 Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang link\n33 BackPACK: Packing more into Backprop Felix Dangel, Frederik Kunstner, Philipp Hennig link\n34 Data-dependent Gaussian Prior Objective for Language Generation Zuchao Li, Rui Wang, Kehai Chen, Masso Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao link\n35 Contrastive Learning of Structured World Models Thomas Kipf, Elise van der Pol, Max Welling link\n36 Optimal Strategies Against Generative Attacks Roy Mor, Erez Peterfreund, Matan Gavish, Amir Globerson link\n37 GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng link\n38 Comparing Fine-tuning and Rewinding in Neural Network Pruning Alex Renda, Jonathan Frankle, Michael Carbin link\n39 Understanding and Robustifying Differentiable Architecture Search Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter link\n40 Implementation Matters in Deep RL: A Case Study on PPO and TRPO Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry link\n41 Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks Donghyun Na, Hae Beom Lee, Hayeon Lee, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang link\n42 Reformer: The Efficient Transformer Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya link\n43 Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer link\n44 Restricting the Flow: Information Bottlenecks for Attribution Karl Schulz, Leon Sixt, Federico Tombari, Tim Landgraf link\n45 Building Deep Equivariant Capsule Networks Sairaam Venkatraman, S. Balasubramanian, R. Raghunatha Sarma link\n46 SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference Lasse Espeholt, Rapha�l Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski? link\n47 Sharing Knowledge in Multi-Task Deep Reinforcement Learning Carlo D’Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters link\n48 Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data Sergei Popov, Stanislav Morozov, Artem Babenko link\n49 Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification Yixiao Ge, Dapeng Chen, Hongsheng Li link\n50 Federated Adversarial Domain Adaptation Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko link\n51 LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning Fan-Keng Sun, Cheng-Hao Ho, Hung-Yi Lee link\n52 Distance-Based Learning from Errors for Confidence Calibration Chen Xing, Sercan Arik, Zizhao Zhang, Tomas Pfister link\n53 To Relieve Your Headache of Training an MRF, Take AdVIL Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang link\n54 Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, Lei Zhang link\n55 Weakly Supervised Clustering by Exploiting Unique Class Count Mustafa Umit Oner, Hwee Kuan Lee, Wing-Kin Sung link\n56 Scalable and Order-robust Continual Learning with Additive Parameter Decomposition Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang link\n57 On Mutual Information Maximization for Representation Learning Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic link\n58 Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation Suraj Nair, Chelsea Finn link\n59 Multi-agent Reinforcement Learning for Networked System Control Tianshu Chu, Sandeep Chinchali, Sachin Katti link\n60 FSPool: Learning Set Representations with Featurewise Sort Pooling Yan Zhang, Jonathon Hare, Adam Pr�gel-Bennett link\n61 Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng link\n62 Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang*, Kun Xu*, Jun Zhu link\n63 Measuring Compositional Generalization: A Comprehensive Method on Realistic Data Daniel Keysers, Nathanael Sch�rli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet link\n64 Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu link\n65 Learning the Arrow of Time for Problems in Reinforcement Learning Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio link\n66 Robust Local Features for Improving the Generalization of Adversarial Training Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft link\n67 Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification Bennet Breier, Arno Onken link\n68 Learning Disentangled Representations for CounterFactual Regression Negar Hassanpour, Russell Greiner link\n69 Logic and the 2-Simplicial Transformer James Clift, Dmitry Doryn, Daniel Murfet, James Wallbridge link\n70 Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn link\n71 Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chen, Zhenyu Zhong, Tao Wei link\n72 Accelerating SGD with momentum for over-parameterized learning Chaoyue Liu, Mikhail Belkin link\n73 Progressive Memory Banks for Incremental Domain Adaptation Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang link\n74 Few-shot Text Classification with Distributional Signatures Yujia Bao, Menghua Wu, Shiyu Chang, Regina Barzilay link\n75 Adversarial Policies: Attacking Deep Reinforcement Learning Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell link\n76 VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma link\n77 GLAD: Learning Sparse Graph Recovery Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song link\n78 Editable Neural Networks Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, Artem Babenko link\n79 LEARNING EXECUTION THROUGH NEURAL CODE FUSION Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi link\n80 FasterSeg: Searching for Faster Real-time Semantic Segmentation Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang link\n81 Difference-Seeking Generative Adversarial Network–Unseen Sample Generation Yi Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu link\n82 Stochastic AUC Maximization with Deep Neural Networks Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang link\n83 MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang link\n84 Adversarial Example Detection and Classification with Asymmetrical Adversarial Training Xuwang Yin, Soheil Kolouri, Gustavo K Rohde link\n85 Variational Recurrent Models for Solving Partially Observable Control Tasks Dongqi Han, Kenji Doya, Jun Tani link\n86 Black-Box Adversarial Attack with Transferable Model-based Embedding Zhichao Huang, Tong Zhang link\n87 Action Semantics Network: Considering the Effects of Actions in Multiagent Systems Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao link\n88 Gap-Aware Mitigation of Gradient Staleness Saar Barkai, Ido Hakimi, Assaf Schuster link\n89 Counterfactuals uncover the modular structure of deep generative models Michel Besserve, Arash Mehrjou, Remy Sun, Bernhard Schoelkopf link\n90 NAS evaluation is frustratingly hard Antoine Yang, Pedro M. Esperan�a, Fabio M. Carlucci link\n91 Efficient and Information-Preserving Future Frame Prediction and Beyond Wei Yu, Yichao Lu, Steve Easterbrook, Sanja Fidler link\n92 A Fair Comparison of Graph Neural Networks for Graph Classification Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli link\n93 SAdam: A Variant of Adam for Strongly Convex Functions Guanghui Wang, Shiyin Lu, Quan Cheng, Weiwei Tu, Lijun Zhang link\n94 FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES Jatin Chauhan, Deepak Nathani, Manohar Kaul link\n95 A TARGET-AGNOSTIC ATTACK ON DEEP MODELS: EXPLOITING SECURITY VULNERABILITIES OF TRANSFER LEARNING Shahbaz Rezaei, Xin Liu link\n96 Generative Ratio Matching Networks Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton link\n97 Option Discovery using Deep Skill Chaining Akhil Bagaria, George Konidaris link\n98 On the Variance of the Adaptive Learning Rate and Beyond Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Jiawei Han link\n99 Language GANs Falling Short Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin link\n100 Sign Bits Are All You Need for Black-Box Attacks Abdullah Al-Dujaili, Una-May O’Reilly link\n101 Deep Semi-Supervised Anomaly Detection Lukas Ruff, Robert A. Vandermeulen, Nico G�rnitz, Alexander Binder, Emmanuel M�ller, Klaus-Robert M�ller, Marius Kloft link\n102 Adaptive Structural Fingerprints for Graph Attention Networks Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang link\n103 Pure and Spurious Critical Points: a Geometric Study of Linear Networks Matthew Trager, Kathl�n Kohn, Joan Bruna link\n104 Neural Text Generation With Unlikelihood Training Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston link\n105 Dynamic Time Lag Regression: Predicting What & When Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michele Sebag link\n106 Overlearning Reveals Sensitive Attributes Congzheng Song, Vitaly Shmatikov link\n107 Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle link\n108 Learning Space Partitions for Nearest Neighbor Search Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner link\n109 Toward Amortized Ranking-Critical Training For Collaborative Filtering Sam Lobel, Chunyuan Li, Jianfeng Gao, Lawrence Carin link\n110 The function of contextual illusions Drew Linsley, Junkyung Kim, Alekh Ashok, Thomas Serre link\n111 Fast is better than free: Revisiting adversarial training Eric Wong, Leslie Rice, J. Zico Kolter link\n112 Four Things Everyone Should Know to Improve Batch Normalization Cecilia Summers, Michael J. Dinneen link\n113 Learning to Learn by Zeroth-Order Oracle Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh link\n114 Decentralized Distributed PPO: Mastering PointGoal Navigation Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra link\n115 Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, Edward Suh, Zhiru Zhang link\n116 Span Recovery for Deep Neural Networks with Applications to Input Obfuscation Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang link\n117 Learn to Explain Efficiently via Neural Logic Inductive Learning Yuan Yang, Le Song link\n118 Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou link\n119 Composition-based Multi-Relational Graph Convolutional Networks Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar link\n120 Gradient-Based Neural DAG Learning S�bastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien link\n121 Composing Task-Agnostic Policies with Deep Reinforcement Learning Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip link\n122 Convergence Behaviour of Some Gradient-Based Methods on Bilinear Zero-Sum Games Guojun Zhang, Yaoliang Yu link\n123 Learning from Explanations with Neural Module Execution Tree Yujia Qin, Ziqi Wang, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Xiang Ren, Leonardo Neves, Zhiyuan Liu link\n124 Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality Saurabh Khanna, Vincent Y. F. Tan link\n125 TOWARDS STABILIZING BATCH STATISTICS IN BACKWARD PROPAGATION OF BATCH NORMALIZATION Junjie Yan, Ruosi Wan, Xiangyu Zhang, Wei Zhang, Yichen Wei, Jian Sun link\n126 Transformer-XH: Multi-hop question answering with eXtra Hop attention Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul Bennett, Saurabh Tiwary link\n127 A Closer Look at the Optimization Landscapes of Generative Adversarial Networks Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien link\n128 Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators Reinhard Heckel and Mahdi Soltanolkotabi link\n129 Generative Models for Effective ML on Private, Decentralized Datasets Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas link\n130 Inductive representation learning on temporal graphs da Xu, chuanwei ruan, evren korpeoglu, sushant kumar, kannan achan link\n131 Learning representations for binary-classification without backpropagation Mathias Lechner link\n132 HiLLoC: lossless image compression with hierarchical latent variable models James Townsend, Thomas Bird, Julius Kunze, David Barber link\n133 Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou link\n134 PairNorm: Tackling Oversmoothing in GNNs Lingxiao Zhao, Leman Akoglu link\n135 Unsupervised Clustering using Pseudo-semi-supervised Learning Divam Gupta, Ramachandran Ramjee, Nipun Kwatra, Muthian Sivathanu link\n136 Quantum Algorithms for Deep Convolutional Neural Networks Iordanis Kerenidis, Jonas Landman, Anupam Prakash link\n137 Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers Junjie LIU, Zhe XU, Runbin SHI, Ray C. C. Cheung, Hayden K.H. So link\n138 GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation Chence Shi*, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang link\n139 Graph Convolutional Reinforcement Learning Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu link\n140 Multi-Agent Interactions Modeling with Correlated Policies Minghuan Liu, Ming Zhou, Weinan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu link\n141 Implementing Inductive bias for different navigation tasks through diverse RNN attrractors Tie XU, Omri Barak link\n142 Plug and Play Language Model: A simple baseline for controlled language generation Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu link\n143 Pad� Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks Alejandro Molina, Patrick Schramowski, Kristian Kersting link\n144 Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency Piyush Gupta, Nikaash Puri, Sukriti Verma, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, Sameer Singh link\n145 Meta Dropout: Learning to Perturb Latent Features for Generalization Hae Beom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang link\n146 Learning transport cost from subset correspondence Ruishan Liu, Akshay Balsubramani, James Zou link\n147 Variance Reduction With Sparse Gradients Melih Elibol, Lihua Lei, Michael I. Jordan link\n148 Weakly Supervised Disentanglement with Guarantees Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole link\n149 Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft link\n150 Fantastic Generalization Measures and Where to Find Them Yiding Jiang*, Behnam Neyshabur*, Dilip Krishnan, Hossein Mobahi, Samy Bengio link\n151 Tensor Decompositions for Temporal Knowledge Base Completion Timoth�e Lacroix, Guillaume Obozinski, Nicolas Usunier link\n152 Hyper-SAGNN: a self-attention based graph neural network for hypergraphs Ruochi Zhang, Yuesong Zou, Jian Ma link\n153 DropEdge: Towards Deep Graph Convolutional Networks on Node Classification Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang link\n154 Masked Based Unsupervised Content Transfer Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano link\n155 U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwang Hee Lee link\n156 Learning Robust Representations via Multi-View Information Bottleneck Marco Federici, Anjan Dutta, Patrick Forr�, Nate Kushman, Zeynep Akata link\n157 Robust anomaly detection and backdoor attack detection via differential privacy Min Du, Ruoxi Jia, Dawn Song link\n158 Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel Xin Qiu, Elliot Meyerson, Risto Miikkulainen link\n159 B-Spline CNNs on Lie groups Erik J Bekkers link\n160 Efficient Probabilistic Logic Reasoning with Graph Neural Networks Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song link\n161 GraphSAINT: Graph Sampling Based Inductive Learning Method Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna link\n162 Projection Based Constrained Policy Optimization Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge link\n163 Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators Daniel Stoller, Sebastian Ewert, Simon Dixon link\n164 Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova*, Tao Lin*, Sebastian U Stich, Martin Jaggi link\n165 On the Relationship between Self-Attention and Convolutional Layers Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi link\n166 Learning-Augmented Data Stream Algorithms Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff link\n167 Structured Object-Aware Physics Prediction for Video Modeling and Planning Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting link\n168 Incorporating BERT into Neural Machine Translation Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tieyan Liu link\n169 Meta-learning curiosity algorithms Ferran Alet*, Martin F. Schneider*, Tomas Lozano-Perez, Leslie Pack Kaelbling link\n170 Lookahead: A Far-sighted Alternative of Magnitude-based Pruning Sejun Park*, Jaeho Lee*, Sangwoo Mo, Jinwoo Shin link\n171 Spike-based causal inference for weight alignment Jordan Guerguiev, Konrad Kording, Blake Richards link\n172 Empirical Bayes Transductive Meta-Learning with Synthetic Gradients Xu Hu, Pablo Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil Lawrence link\n173 Demystifying Inter-Class Disentanglement Aviv Gabbay, Yedid Hoshen link\n174 Mixed-curvature Variational Autoencoders Ondrej Skopek, Gary B�cigneul, Octavian-Eugen Ganea link\n175 BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations Hyungjun Kim, Kyungsu Kim, Jinseok Kim, Jae-Joon Kim link\n176 BayesOpt Adversarial Attack Binxin Ru, Adam Cobb, Arno Blaas, Yarin Gal link\n177 RaPP: Novelty Detection with Reconstruction along Projection Pathway Ki Hyun Kim, Sangwoo Shim, Yongsub Lim, Jongseob Jeon, Jeongwoo Choi, Byungchan Kim, Andre S. Yoon link\n178 Dynamics-Aware Embeddings William Whitney, Rajat Agarwal, Kyunghyun Cho, Abhinav Gupta link\n179 AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing Xingzhe He, Helen Lu Cao, Bo Zhu link\n180 AtomNAS: Fine-Grained End-to-End Neural Architecture Search Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, Jianchao Yang link\n181 Deep Audio Priors Emerge From Harmonic Convolutional Networks Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman link\n182 Expected Information Maximization: Using the I-Projection for Mixture Density Estimation Philipp Becker, Oleg Arenz, Gerhard Neumann link\n183 A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sebastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal link\n184 Latent Normalizing Flows for Many-to-Many Cross Domain Mappings Shweta Mahajan, Iryna Gurevych, Stefan Roth link\n185 Adversarial Lipschitz Regularization D�vid Terj�k link\n186 Compositional Continual Language Learning Yuanpeng Li, Liang Zhao, Kenneth Church, Mohamed Elhoseiny link\n187 HOW THE CHOICE OF ACTIVATION AFFECTS TRAINING OF OVERPARAMETRIZED NEURAL NETS Abhishek Panigrahi, Abhishek Shetty, Navin Goyal link\n188 Lipschitz constant estimation for Neural Networks via sparse polynomial optimization Fabian Latorre, Paul Rolland, Volkan Cevher link\n189 Unrestricted Adversarial Examples via Semantic Manipulation Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David Forsyth link\n190 Differentiable Programming for Physical Simulation Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Fredo Durand link\n191 Sub-policy Adaptation for Hierarchical Reinforcement Learning Alexander Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel link\n192 Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Ocurring in Data David W. Romero Guzm�n, Mark Hoogendoorn link\n193 Information Geometry of Orthogonal Initializations and Training Piotr Aleksander Sok�l, Il Memming Park link\n194 Extreme Classification via Adversarial Softmax Approximation Robert Bamler, Stephan Mandt link\n195 NAS-BENCH-1SHOT1: BENCHMARKING AND DISSECTING ONE-SHOT NEURAL ARCHITECTURE SEARCH Arber Zela, Julien Siems, Frank Hutter link\n196 The Shape of Data: Intrinsic Distance for Data Distributions Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, Emmanuel Mueller link\n197 Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy link\n198 Massively Multilingual Sparse Word Representations G�bor Berend link\n199 Learning The Difference That Makes A Difference With Counterfactually-Augmented Data Divyansh Kaushik, Eduard Hovy, Zachary Lipton link\nPaper Digest: NeurIPS 2019 Highlights\nPaper Digest: ICLR 2020 Highlights


url:
=Paul Huppertz Cloud Computing From System Design To Service Compo…\nData-Need Fit – Towards Data-Driven... by ServDes 450 views\nService Design with Cancer Patients by Juha Kronqvist 7177 views\nTransforming health care with Servi... by Stefan Moritz 9104 views\nService Design Thinking by Katrin Mathis 3232 views\nFrans Verschoor , Director at self-employed\n1. Cloud ComputingFrom system design to service composing
What‘s the cloud
all about?
E-Mail\tPaul.G.Huppertz@servicEvolution.com
Mobile\t+49-1520-9 84 59 62
XING\thttps://www.xing.com/profile/PaulG_Huppertz
CIO Netzwerk\thttp://netzwerk.cio.de/profil/paul_g__huppertz
yasni http://person.yasni.de/paul-g.-huppertz-251032.htm
LinkedIn http://www.linkedin.com/in/paulghuppertz
Paul G. Huppertz
ICT-Consultant & System Architect
Service Composer & Meta Service Provider
\n2. What‘s the cloud all about?Cloud‘s about sourcing service contributions!
service
contributions
service
contributions
service
contributions
Sourcing service contributions
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n3. What‘s the cloud all about?Service contributions aggregated to ICT-based Business Support Service
Aggregating service contributions
ICTBSS
realtime
transaction
service
contributions
service
contributions
service
contributions
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n4. What‘s the cloud all about?ICT-based Business Support Service for triggering service consumer
Service
Consumer
Rendering triggered ICTBSS to service consumer
ICTBSS
service
contributions
service
contributions
service
contributions
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n5. What‘s the cloud all about?Service consumer creates business value in business process
Business process
Business Value
Consuming ICTBSS & creating business value
Service
Consumer
ICTBSS
service
contributions
service
contributions
service
contributions
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n6. What‘s the cloud all about?Service customer commissions ICTBSS from service provider
Business process
Business Value
Service
Customer
Service
Consumer
Commissioning ICTBSS for service consumers
ICTBSS
SLA
Accountable
Service
Provider
service
contributions
service
contributions
service
contributions
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n7. What‘s the cloud all about?Service provider composes ICTBSS and commissions service contributions
Business process
Business Value
Service
Customer
Service
Consumer
Composing ICTBSS from service contributions
ICTBSS
SLA
Accountable
Service
Provider
service
contributions
service
contributions
service
contributions
OLA
UC
Commissioning service contributions from cloud operators
internal
or
external
Service
Supplier
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n8. Workplacesystem
Network system
Backend system
ICT-
good
ICT-
good
ICT-
good
ICT-
good
ICT-
good
ICT-
good
What‘s the cloud all about?Service suppliers, i.e. cloud operators, manage ICT-systems in clouds
Business process
Business Value
Service
Customer
Service
Consumer
ICTBSS
SLA
Accountable
Service
Provider
Clouds are
containing service-relevant ICT systems
service
contributions
service
contributions
service
contributions
OLA
UC
internal
or
external
Service
Supplier
Storage
Cloud
SaaS
Cloud
Network
Cloud
Frontend
Cloud
Platform Cloud
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n9. What‘s the cloud all about?Concl(o)uding ;-)
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n10. What‘s the cloud all about?The clear, complete & concise service (contribution) specification

Clear, complete & concise\n11. All 12 attributes must be jointly considered\n12. Promise of the service provider to any authorized service consumer\n13. Concerns every individual requested service\n14. Per ICTBSS only one entry in the service catalogue\n15. Fits on one to two pages

Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil
\n16. Business Value
ICTBSS-specificbenefits
What‘s the cloud all about?Service delivery model - From business strategy to business value chain
ICT-based Business Support Service
Business Process
Business
Value
Chain
Business
Strategy
Service Contributions
Creating
Business Value
service (contribution) relevant ICT systems
(application, middleware & infrastructur systems),
resources, processes and further means
Service
Supply
Chain
Service
Strategy
Aggregating Service Contributions
Effectuating service contributions
ICT (System)
Strategy
System
Supply
Chain
Assembling ICT products
Sourcing | Aggregating | Rendering | Consuming | Commissioning | Containing | Concl(o)uding | Codicil


url: =- Picture of Pudong Sunshine Hotel Shanghai, Shanghai - TripAdvisor\n- Picture of Pudong Sunshine Hotel Shanghai, Shanghai\nAsia China Shanghai Region Shanghai Shanghai Hotels Pudong Sunshine Hotel Shanghai Pictures\nof Pudong Sunshine Hotel Shanghai\n#1,575 of 5,069 Hotels in Shanghai No.99 Jinxin Road, Pudong New District, Shanghai 201206, China (Formerly Best Western Pudong Sunshine Hotel Shanghai)\nName/address in local language Photo: “餐厅\nNo.99 Jinxin Road, Pudong New District, Shanghai 201206, China (Formerly Best Western Pudong Sunshine Hotel Shanghai)\n150 Reviews | Ranked #1575 of 5069 To help you get around Shanghai, here's the name and address of this business in the local language\nLocal name 浦东圣莎大酒店\nLocal address 201206浦东新区金新路99号\n“Good or business stay, mostly visi...”\t07/12/2016\n“Clean, helpful staff, comfortable”\t11/09/2015\nHelpful? Thank A TripAdvisor China Member Report Been to Pudong Sunshine Hotel Shanghai? Share your experience!


url: =Old newspapers: Changjiang Daily February 1. 1972 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-09-21 37:27:11. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 1. 1972 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 11. 1976 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-05-02 25:00:40. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 11. 1976 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 12. 1972 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2017-01-23 32:60:53. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 12. 1972 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 12. 1975 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2017-01-06 46:33:02. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 12. 1975 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 12. 1976 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-05-09 47:42:36. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 12. 1976 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 13. 1976 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-10-09 52:23:43. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 13. 1976 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 14. 1975 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-09-04 06:09:08. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 14. 1975 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 14. 1976 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2017-06-05 33:27:09. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 14. 1976 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 15. 1972 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2016-02-09 35:53:50. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 15. 1972 (1-4)(Chinese Edition)(Old-Used) book.\nOld newspapers: Changjiang Daily February 16. 1972 (1-4)(Chinese Edition)(Old-Used) PDF By author CHANG JIANG RI BAO SHE last download was at 2017-06-07 55:41:54. This book is good alternative for Old newspapers: Changjiang Daily December 7. 1975 (1-4)(Chinese Edition)(Old-Used). Download now for free or you can read online Old newspapers: Changjiang Daily February 16. 1972 (1-4)(Chinese Edition)(Old-Used) book.


url: =Access: Public / Place: China / Data type: Polygon - University of Texas Libraries GeoData Search Results\nYou searched for: Access: Public ✖Remove constraint Access: Public Place: China ✖Remove constraint Place: China Data type: Polygon ✖Remove constraint Data type: Polygon\n11. International Settlement, China, 1863-1899 Polygon Stanford University Public content\n12. International Settlement, China, 1899-1943 Polygon Stanford University Public content\n13. Manshū oyobi Shiberi heiyō nijūmanbun no ichi zu, Maps Index Polygon Stanford University Public content\n14. Mapping Interstate Territorial Conflict, 1947-2000 (v2) Polygon Stanford University Public content\n15. Nihyakumanbun no ichi kōkūzu, Maps Index Polygon Stanford University Public content\n16. Provincial Boundaries, China, 1820 Polygon Stanford University Public content\n17. Second-level Administrative Divisions, China, 2015 Polygon Stanford University Public content\n18. Shinkoku, Maps Index Polygon Stanford University Public content\n19. Third-level Administrative Divisions, China, 2015 Polygon Stanford University Public content\n20. Yōzu (Kūchū shashin sokuryō) Kita Shina jūmanbun no ichi zu, Maps Index Polygon Stanford University Public content


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url: =Yixian Yanjing Ore Dressing Factory Company Profile | Baoding, Hebei, China | Competitors, Financials & Contacts - Dun & Bradstreet\nYixian Yanjing Ore Dressing Factory\nQiaojiahe, Qiaojiahe Country, Yi County\nBaoding, Hebei, 074206 China\nYixian Yanjing Ore Dressing Factory is located in Baoding, Hebei, China and is part of the Metal Ore Mining Industry. Yixian Yanjing Ore Dressing Factory has 63 employees at this location.\nD&B Hoovers provides sales leads and sales intelligence data on over 120 million companies like Yixian Yanjing Ore Dressing Factory around the world, including contacts, financials, and competitor information. To witness the full depth and breadth of our data and for industry leading sales intelligence tools, take D&B Hoovers for a test drive. Try D&B Hoovers Free\nA D&B Hoovers Subscription is your foot in the door to Yixian Yanjing Ore Dressing Factory contact information.\nWith a Hoovers subscription you can get a comprehensive view of Yixian Yanjing Ore Dressing Factory


url: =27 January 2020 Analysis of the Food Consumption Mode and Its Influencing Factors in Kazakhstan\nLiang Yihang,1,2,* Zhen Lin,2,3,** Hu Yunfeng,2,3 Yan Huimin,2,3 Zhang Changshun2,3\n11. School of Earth Science and Resource, Chang'an University, Xi'an 710000, China\n33. School of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, Ch\nJ. of Resources and Ecology, 11(1):121-127 (2020). https://doi.org/10.5814/j.issn.1674-764x.2020.01.012\nLiang Yihang, Zhen Lin, Hu Yunfeng, Yan Huimin, and Zhang Changshun "Analysis of the Food Consumption Mode and Its Influencing Factors in Kazakhstan," Journal of Resources and Ecology 11(1), 121-127, (27 January 2020). https://doi.org/10.5814/j.issn.1674-764x.2020.01.012\nReceived: 29 June 2019; Accepted: 15 September 2019; Published: 27 January 2020\nLiang Yihang, Zhen Lin, Hu Yunfeng, Yan Huimin, Zhang Changshun "Analysis of the Food Consumption Mode and Its Influencing Factors in Kazakhstan," Journal of Resources and Ecology, 11(1), 121-127, (27 January 2020)


url: =85% de 291 = ...\nMuitas vezes precisamos fazer alguns cálculos básicos em nosso dia-a-dia, caso você precise calcular quanto é 85% de 291 agora você já sabe que é 247.35.\n85% de 291 = 247.35\nOutros cáculos de 85% para valores próximos a 291:\n85% de 301 | 85% de 300 | 85% de 299 | 85% de 298 | 85% de 297 | 85% de 296 | 85% de 295 | 85% de 294 | 85% de 293 | 85% de 292 | 85% de 291 | 85% de 290 | 85% de 289 | 85% de 288 | 85% de 287 | 85% de 286 | 85% de 285 | 85% de 284 | 85% de 283 | 85% de 282 | 85% de 281 |\nFigura 1. Ilustração de 85% de 291 = 247.35


url: =Min Wang, Ying-Ying Huang, Liyi Huang, Tianhong Dai, Michael R. Hamblin, and Long Chiang, "Decacationic fullerenyl-light harvesting chromophore conjugates for efficient photokilling of infectious bacteria (Conference Presentation)," Proc. SPIE 10479, Light-Based Diagnosis and Treatment of Infectious Diseases, 104790Y (Presented at SPIE BiOS: January 31, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2289070.5751486314001.\nAuthors: Min Wang et al.


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url: =On the Application of High-density Method in Testing the Flaws of Hongwei Reservoir Earth-rockfill Dam--《Journal of Logistical Engineering University》2008年02期\n《Journal of Logistical Engineering University》 2008-02\nOn the Application of High-density Method in Testing the Flaws of Hongwei Reservoir Earth-rockfill Dam\nZHOU Jun-long1,JIANG Shi-yong2,WANG Bing1,FEI Wei1(1.Dept.of Architecture & Civil Engineering,LEU,Chongqing 400041,China;2.Dept.of Science & Research,LEU,Chongqing 400016,China)\nHigh-density resistance method is applied extensively in mining and exploration owing to its great density,high efficiency,abundant information,high-differentiation rate and high intelligence.In addition,it has a promising prospect with respect to testing earth-rockfill dams' latent flaws.This paper reviews the current researches home and abroad and introduces the principles of applying high-density resistance method to testing dams' flaws.The principles are formed by 2D finite element method to simulate and picturize the gathered data.By testing Chongqing Hongwei Reservior's dam and analyzing its testing results,this study finds that the testing results nearly accord to the actual results.Relevant suggestions are put forward on the basis of the above-mentioned results.This study concludes that this method is a really good and feasible way which is cost-saving and highly-efficient.\n【Fund】： 重庆市科委攻关资助项目(CSTC 2006AC7057)\n1 ZHANG Huan-ou HU Yu-rui WANG Xu ZHAO Ting-rong (China Aviation Oil Co., Ltd., Yunnan Company, Kunming 650200, China);The High-Density Resistivity Method Apply in Changkou Tunnel Testing[J];Construction Quality;2010-02\n2 Zhang Huanou Hu Yurui(China National Aviation Fuel Yunnan Company, Yunnan Kunming 650200);Application of the High--Density Resistivity Method in Changkou Tunnel[J];Science Mosaic;2009-09\n3 ZHAO Ming-jie,YU Dong,ZHAO Huo-yan (Chongqing Jiaotong University,Chongqing 400074,China);Experimental study on velocity and resistivity combined tomography for diagnosing leakage in earth rock-fill dam[J];Journal of Hydraulic Engineering;2012-01\n2 Xu Fangquan et al;The Application of High-density Resistivity in Detecting of Hidden Defects on dams[J];Equipment for Geotechnical Engineering;2006-04\n3 YANG Fa-jie, Ju Miao-lan, LIU Quan-de (Research Institute of Liaoning Nonferrous Metals Geophysical prospecting, Shenyang, Liaoning 110121,China);HIGH DENSITY RESISTIVITY SURVEYING METHOD AND ITS APPLICATION[J];Mineral Resources and Geology;2004-04\n4 Qin Zheng;Application of high-density resistivity in engineering exploration[J];Eouipment for Geo Physical Prospecting;2005-03\n5 Du Huakun,Yu Zhenhua and Tang Jingtian.;The numerical modeling of monitoring the dam leakage by high-density resistivity[J];Eouipment for Geo Physical Prospecting;2005-04\n3 MENG Gui-xiang1,YAN Jia-yong1,Lü Qing-tian1,ZHANG Kun1,CHEN Wei-min2 1.Institute of Mineral Resources,CAGS,Beijing 100037,China 2.Department of Land and Resources of Xinjiang,Urumqi 830000,China;The Application of Electrical Resistivity Imaging in Stone Mineral Deposits Exploration[J];Journal of Jilin University(Earth Science Edition);2011-02\n5 SUN Fang-yu,XU Shun-qiang,LI Qing-lin,GUO Xin-jing,WANG Ning (The Geophysical Exploration Center of CEA,Zhengzhou 450002,China);The Application to Qinhe Dam Hidden Trouble Exploration with High Density Resistivity Tomography Technology[J];CT Theory and Applications;2011-04\n8 SHI Ming,FENG De-shan,DAI Qian-wei(1.Faculty of Land Resource Engineering Kunming,University of Science and Technology,Kunming 650093,China;2.Nonferrous Metals Geophysical and Geochemical Prospecting Team of Guizhou Province,Duyun 558004,China;3.School of Info-Physics and Geometrics Engineering,Central South University,Changsha 410083,China);Application of integrated geophysical method in quality detection of dikes[J];Progress in Geophysics;2006-04\n1 YAN Yong-Li~1,ZHAO Yong-Gui~1,CHEN Ben-Chi~2,CHEN Yun~1, MA Xiao-Bing~1,KONG Xiang-Ru~1 1 Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China 2 Technical Department,China Petroleum and Chemical Corporation,Beijing 100029,China;Multiple superimposed tomography of probability on second electrical field[A];[C];2009\n2 SONG Hong-zhu (Shandong University of Science and Technology,Qingdao shandong 266510,China);Application of High Density Resistivity in Tumulus Geological Survey and Its Potential Detection in Mined Area[A];[C];2007\n4 SONG Hong-zhu (Shandong University of Science and Technology,Qingdao shandong 266510,China);Application of High Density Resistivity in Tumulus Geological Survey and Its Potential Detection in Mined Area[A];[C];2007\n5 MA Qi-hua,WANG Yi-tai (College of Resources and Environmental Engineering,Shandong University of Science and Technology,Qingdao 266510,China);Application of the high density resistivity method to surrvey huge empty water outside of a coal mine[A];[C];2009\n1 ZHAO Ming-jie,XU Rong,WANG Jun-jie,WANG Peng(School of River & Ocean Engineering,Chongqing Jiaotong University,Chongqing 400074,China);Application of Electrical Resistivity Tomography to Leakage Diagnosis of Earth Rock-Fill Dam[J];Journal of Chongqing Jiaotong University(Natural Science);2009-06\n2 Qiu GuangXin,Ge RuBing(Guangzhou Urban Planning & Design Survey Research Institute Guangdong,Guangzhou 510060);Applying High-Density Resistivity to Detect the Drainage Pipes and Canals[J];Urban Geotechnical Investigation & Surveying;2008-05\n4 XU Zhu-qing1,ZHANG Gui-rong1,ZHENG Jun2(1.Nanjing Hydraulic Research Institute,Nanjing,Jiangsu 210024,China;2.Nanjing Metro Headquaters,Nanjing,Jiangsu 210005,China);Classification of Hidden Troubles and Corresponding Rapid Detection Methods about Sick Earth-rock Dam in China[J];Journal of Water Resources and Architectural Engineering;2010-03\n5 CHEN Ze-lin(Water Affairs Bureau of Wen'an County,Wen'an 065800,China);Application of a covered high-density resistivity detecting system to disk hidden trouble[J];Water Sciences and Engineering Technology;2008-S1\n6 CHEN Liang,WU Yong,CHEN Jian-sheng(Institute of Geotechnical Engineering,Hohai University,Nanjing,JS,210098,China);Study on Temperature Field Inverse Analysis and Experiment of Concentrated Leakage Passage in Dam Base[J];Journal of East China Institute of Technology;2007-04\n7 WANG Zhen-yu~1,LIU Guo-hua~1,ZHANG Min-qiang~2,LIANG Guo-qian~2(1.Zhejiang University,Hangzhou 310027,Zhejiang China;2.Zhejiang Institute of Hydraulics and Estuary,Hangzhou 310020,Zhejiang China);Application of resistivity tomography technique to detecting hiddendanger in embankment[J];Zhejiang Hydrotechnics;2004-02\n8 SONG Xue-dong,WAND Hui,WANG Shao-jie,MA Wen-ming (Water Conservancy and Civil Engineering Colleges of Shandong Agricultural University,Tai'an 271018,China);APPLICATION OF THE HIGH DENSITY RESISTIVITY METHOD IN THE SAFE APPRAISEMENT OF DAM[J];Journal of Shandong Agricultural University(Natural Science Edition);2008-02\n9 LIANG Guoqian1, ZHENG Minsheng1, SUN Boyong1, XU Jiahai1(1. Zhejiang Institute of Hydraulics and Estuary, Hangzhou310020,China);Analysis model and method of seepage observation data for earth rock-fill dams[J];Journal of Hydraulic Engineering;2003-02\n10 SI Chun-di,LIAN Ji-jian,ZHENG Yang(Tianjin University,Tianjin 300072,China);Genetic support vector machine model for seepage safety monitoring of earth-rock dams[J];Journal of Hydraulic Engineering;2007-11\n1 Sun Xiao 1,Zhao Mingjie 1,Wang Kui 1,Xun Yang 2(1.School of River & Ocean Engineering,Chongqing Jiaotong University,Chongqing 400074,China;2.92153 PLA Troops,Ningbo 315809,Zhejiang,China);Research on Application of Wave Theory in Geotechnical Engineering[J];Journal of Chongqing Jiaotong University(Natural Science);2013-01\n2 Fang Chungang, Ge Huaiguang, Lu Ying, Ke Zhiquan, Jia Yongmei (China Institute of Water Resources and Hydro-Power Research, IWHR, Beijing 100038);DETECTION OF HIDDEN DEFECTS AND LEAKAGE IN LEVEES BY TRANSIENT ELECTRO-MAGNETIC SOUNDING[J];Dam Observation and Geotechnical Tests;2001-04


url: =King Yue Mountain (Zijin County, China): Hours, Address - TripAdvisor\nZijin County RECENTLY VIEWED No results found 0Did you know? You can find your recently viewed places here. JOINinSearch About Zijin CountyHotelsHoliday RentalsThings to doFlightsTravel ForumAirlinesTravel GuidesBest of 2017Help CentreLog in Join My Trips Bookings Rental Inbox Browse\nKing Yue Mountain, Zijin County\nAsia China Guangdong Zijin County Things to do in Zijin County King Yue Mountain\n#2 of 4 things to do in Zijin County Mountains, Nature & Parks Guzhu Town, Zijin County 517465, China+ Add phone number+ Add website\nOpens in 5 minSun - Sat07:00 - 18:00All hours Hours Today: 07:00 - 18:00LOCATION Guzhu Town, Zijin County 517465, China\nMore Things to do in Zijin County Updating list... Updating list... Nearby Nearby RestaurantsAjisen Ramen (Zhongshan Road)27.24 km away LongJin Seafood Fang27.80 km away Qing CiLang Sushi27.96 km away FaGuo XiDi Dao Coffee28.04 km away Nearby AttractionsHeyuan Garden14.68 km away Dunzi Natural Reserve1 reviews 17.91 km away Longyuan Onsen Resort2 reviews 18.70 km away Questions & AnswersGet quick answers from King Yue Mountain staff and past visitors.Note: your question will be posted publicly on the Questions & Answers page.Verification: Posting guidelinesSubmit


url: =Jiangxi Province 江西省 Archives - Holachina.com\nCategory: Jiangxi Province 江西省\nJiangxi Province 江西省: Welcome to the beautiful province of Jiangxi 江西省: Discover the stunning villages around Wuyuan. National Parks and Porcelain City, Jingdezhen.\nLikeng Wuyuan Jiangxi Province 江西省\nAlso explore lovely Jinggangshan; rich in Chinese Communist Party (CCP) History where Mao Zidong discovered the power of the peasants and the developed the revolutioanry tactics that would bring the CCP to power in 1949.\nThe quaintest Village in China might be Hongcun 洪村: no tourist here.\nYancun Village: Wuyuan: another beautiful Village in the Wuyuan area\nWuyuan an Introduction more on Wuyuan and its villages 2\nJingdezhen some new photos\nJinggangshan the Red Cradle: home tothe revolution of 1949 and the starting point for the Long March\nChangsha to Jinggangshan A 12 hour long journey on a clapped out bus on diabolical roads\nJingdezhen Porcelain City 1 discover the home of porcelain\nWuyuan and surrounding Villages an introduction 1\nWuyuan and around: In the next few weeks we will be uploading our photos of the villages in the Wuyuan area. We based ourselves in the bucolic and sleepy village of Sixi 思溪村 and spent several days hiking between villages and occasionally hiring a car to those village further afield.\nAuthor AdamPosted on November 24, 2020 January 2, 2022 Categories Jiangxi Province 江西省, Travel 旅游Tags cixi wuyuan, hongcun wuyuan, likeng wuyuan, lower xiaoqi wuyuan, upper xiaoqi wuyuan, Wuyuan, yangcun wuyuanLeave a comment on Wuyuan and around


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url: =Hu Yue-Yue Cheering Thread - Page 17 - TennisForum.com\nAug 6th, 2011, 03:24 AM\nwill she receive CO WC?\nyes. "被挡在四强门外。不过这位世界排名仅552位，从资格赛一路杀入八强的小黑马，也赢得了十月中网女单正赛外 卡的大奖。此外，中网男单外卡颁给了表现最出色的李吉吉。" __________________\nTennisForum.comAdvertisement Aug 6th, 2011, 03:42 AM\nwhat is her new ranking __________________\nAug 6th, 2011, 04:00 AM\nwhat is her new ranking #476\nAug 6th, 2011, 04:03 AM\nhope she'll draw a golden flower in CO R1 this yr __________________\nshe got MD WC or Q WC __________________\nAug 24th, 2011, 11:15 AM\nYing-Ying DUAN (CHN) (1) defeated Wan-Ting LIU (CHN) 7-6(3) 6-2 Miki MIYAMURA (JPN) defeated Etsuko KITAZAKI (JPN) 4-6 6-4 7-5 Maya KATO (JPN) defeated Mari INOUE (JPN) 3-6 6-3 6-0 Kanae HISAMI (JPN) (6) defeated Kazusa ITO (JPN) 6-3 6-2 Miyabi INOUE (JPN) (4) defeated Ai KOGA (JPN) 6-3 6-4 Mami ADACHI (JPN) defeated Tomoko SUGANO (JPN) 6-3 7-5 Wing-Yau Venise CHAN (HKG) defeated Yuri KURASHIMA (JPN) 7-6(5) 6-1 Hirono WATANABE (JPN) (8) defeated Yurina KOSHINO (JPN) 1-6 6-3 7-5 Chihiro TAKAYAMA (JPN) (5) defeated Mana AYUKAWA (JPN) 6-3 6-2 Yi-Fan XU (CHN) defeated Hua-Chen LEE (TPE) 6-1 6-4 Miharu IMANISHI (JPN) defeated Makiho KOZAWA (JPN) 6-3 6-3 Yue-Yue HU (CHN) (3) defeated Mako MIZUNUMA (JPN) 6-1 6-3 Yuka HIGUCHI (JPN) defeated Akari INOUE (JPN) (7) 7-5 6-4 Ayumi OKA (JPN) defeated Ayaka OKUNO (JPN) 6-4 6-0 Miyu KATO (JPN) defeated Yumi NAKANO (JPN) 6-3 7-5 Chen LIANG (CHN) (2) defeated Misa KINOSHITA (JPN) 6-1 7-6(2) __________________\nAug 25th, 2011, 11:48 AM\nYing-Ying DUAN (CHN) (1) defeated Miki MIYAMURA (JPN) 6-2 6-3 Maya KATO (JPN) defeated Kanae HISAMI (JPN) (6) 6-7(5) 7-5 6-3 Mami ADACHI (JPN) defeated Miyabi INOUE (JPN) (4) 6-4 6-1 Wing-Yau Venise CHAN (HKG) defeated Hirono WATANABE (JPN) (8) 6-4 6-3 Chihiro TAKAYAMA (JPN) (5) defeated Yi-Fan XU (CHN) 7-6(3) 6-3 Yue-Yue HU (CHN) (3) defeated Miharu IMANISHI (JPN) 6-1 6-1 Ayumi OKA (JPN) defeated Yuka HIGUCHI (JPN) 7-6(5) 7-5 Chen LIANG (CHN) (2) defeated Miyu KATO (JPN) 6-1 6-1 __________________\nYing-Ying DUAN (CHN) (1) defeated Maya KATO (JPN) 7-5 5-7 6-3 Wing-Yau Venise CHAN (HKG) defeated Mami ADACHI (JPN) 6-0 7-6(4) Yue-Yue HU (CHN) (3) defeated Chihiro TAKAYAMA (JPN) (5) 6-0 6-2 Ayumi OKA (JPN) defeated Chen LIANG (CHN) (2) 7-5 6-2 __________________\nAug 26th, 2011, 06:17 PM\nhelicopter's wife __________________\nAug 28th, 2011, 03:14 AM\n25k Japanese Singles Qualifying Draw (Aug 29)\n(1)Peangtarn Plipuech (THA) v Kotomi Takahata (JPN)\n(10)Akari Inoue (JPN) v (wc)Kanako Morisaki (JPN)\n(2)Chinami Ogi (JPN) v Mari Inoue (JPN)\n(14)Miharu Imanishi (JPN) v Yi-Fan Xu (CHN)\n(3)Mari Tanaka (JPN) v (wc)Nana Nemoto (JPN)\n(15)Makiho Kozawa (JPN) v Shiho Hisamatsu (JPN)\n(4)Chin-Wei Chan (TPE) v Yuka Higuchi (JPN)\n(12)Yurina Koshino (JPN) v Kazusa Ito (JPN)\n(5)Chen Liang (CHN) v (wc)Chihiro Ueki (JPN)\n(13)Yumi Nakano (JPN) v Aki Yamasoto (JPN)\n(6)Yue-Yue Hu (CHN) v Emi Mutaguchi (JPN)\n(11)Ayumi Oka (JPN) v Riko Sawayanagi (JPN)\n(7)Chihiro Takayama (JPN) v Sakiko Shimizu (JPN)\n(9)Hirono Watanabe (JPN) v (wc)Riko Tachibana (JPN)\n(8)Kanae Hisami (JPN) v (wc)Momoko Urakawa (JPN)\n(16)Miki Miyamura (JPN) v Maya Kato (JPN)\nAug 29th, 2011, 04:17 AM\nQR1: (6)Yue-Yue Hu (CHN) d. Emi Mutaguchi (JPN) 62 20 Ret.\nSep 1st, 2011, 12:12 PM\nErika SEMA (JPN) (1) defeated Peangtarn PLIPUECH (THA) 7-5 6-4 Chin-Wei CHAN (TPE) defeated So-Jung KIM (KOR) (7) 6-3 6-3 Sachie ISHIZU (JPN) (4) Vs Misa EGUCHI (JPN) Akiko YONEMURA (JPN) Vs Qiang WANG (CHN) (5)\nTamaryn HENDLER (BEL) defeated Varatchaya WONGTEANCHAI (THA) (8) 6-4 7-6(6) Yue-Yue HU (CHN) defeated Ying-Ying DUAN (CHN) 6-2 6-0 Aiko NAKAMURA (JPN) defeated Shiho AKITA (JPN) (6) 6-1 Retired\nWen-Hsin HSU (TPE) defeated Kanae HISAMI (JPN) 6-4 6-2\nSep 1st, 2011, 03:14 PM\npoor duan __________________\nSep 2nd, 2011, 12:08 PM\n25k QF: Tamaryn Hendler (BEL) d. (Q)Yue-Yue Hu (CHN) 63 63 (didi's ears) __________________


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