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Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Retraction notice to ‘‘Specification and Verification of Dynamic Evolution of Software Architectures” [SYSARC 56 (10) (2010) 523–533]

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Journal of systems Architecture 2012年第8期58卷 338-338页

作者： Xu Hongzhen Zeng Guosun Department of Computer Science and Technology Tongji University Shanghai 201804 China Department of Computer Science and Technology East China Institute of Technology Fuzhou Jiangxi Province 344000 China Application of Nuclear Technology Engineering Center of Ministry of Education Nanchang Jiangxi Province 330013 China Embedded System and Service Computing Key Lab of Ministry of Education Shanghai 201804 China

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Heterocyclic Microporous Polymers: Hypercrosslinked Aromatic Heterocyclic Microporous Polymers: A New Class of Highly Selective CO2Capturing Materials (Adv. Mater. 42/2012)

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Advanced Materials 2012年第42期24卷

作者： Yali Luo Buyi Li Wei Wang Kangbing Wu Bien Tan Hubei Key Laboratory of Material Chemistry and Service Failure Key Laboratory for Large‐Format Battery Materials and System Ministry of Education School of Chemistry and Chemical Engineering Huazhong University of Science and Technology Wuhan 430074 P. R. China State Key Laboratory of Applied Organic Chemistry College of Chemistry and Chemical Engineering Lanzhou University Lanzhou 730000 P.R. China

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Feng Xiao Cong Shen Yu Chen Tian Yang Shengyong Chen Zhijun Liao Jijun Tang School of Computer Science and Engineering Tianjin University of Technology Tianjin P.R.China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China School of Basic Medical Sciences Fujian Medical University Fuzhou Fujian P.R.China College of Intelligence and Computing Tianjin University Tianjin P.R.China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin P.R.China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong P.R.China University of South Carolina Columbia SC U.S.A.

ISBN: (纸本)9781665429825

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.

关键词： Deep learning Image segmentation Statistical analysis Lung Predictive models Feature extraction Generative adversarial networks

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Analyzing the characteristics of "East Turkistan" activities using text mining and network analysis

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Zidonghua Xuebao/Acta Automatica Sinica 2014年第11期40卷 2456-2468页

作者： Fu, Ju-Lei Liu, Wen-Li Zheng, Xiao-Long Fan, Ying Wang, Shou-Yang College of Information System and Management National University of Defense Technology Changsha410073 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Management and Control Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Industrial Technology Innovation and Incubation Center Chinese Academy of Sciences Dongguan523808 China School of Systems Science Beijing Normal University Beijing100875 China

Open source intelligence, with rich content and sophisticated collection and analytical techniques, is an alternative data source in many anti-terrorism studies and has manifested significant applicable prospects. In this paper, we take the "East Turkistan" separatist activities as the research objects and collect the data from the world wide web by utilizing web crawlers. We adopt text analysis techniques to extract the information of four fundamental elements of "East Turkistan" separatist activities, including person, organization, time and place, and then construct multi-mode networks according to the relations between concepts. To analyze the characteristics of these networks, we first apply the approach of dimensional decomposition to separate the multimode networks into single vertex sub-networks and bipartite sub-networks, distinguish the importances of different types of nodes by measuring the centrality of each sub-network, and synthesize the centrality index of each sub-network to form an importance composite index (ICI). To further identify the core nodes of the four types of nodes in the network, we adopt the k-shell decomposition method to directly decompose the multi-mode network. We find that the results produced by these approaches presented above can be in accord with the actual ones. ©, 2014, Acta Automatica Sinica. All rights reserved.

关键词： Data mining

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A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional Chinese medicines

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Science Bulletin 2021年第9期66卷 884-888,M0003页

作者： Yifei Dai Weijie Qiang Yu Gui Xue Tan Tianli Pei Kequan Lin Siwei Cai Liang Sun Guochen Ning Jianxun Wang Hongyan Guo Yimin Sun Jing Cheng Lan Xie Xun Lan Dong Wang State Key Laboratory of Southwestern Chinese Medicine Resources School of Basic Medical SciencesChengdu University of Traditional Chinese MedicineChengdu 611137China Department of Basic Medical Sciences School of MedicineTsinghua UniversityBeijing 100084China Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100193China School of Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Electronic and Computer Engineering College of EngineeringDrexel UniversityPhiladelphia 19104USA Dongli District Jinqiao Street Community Health Service Center Tianjin 300300China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing 100084China School of Life Sciences Beijing University of Chinese MedicineBeijing 100029China National Engineering Research Center for Beijing Biochip Technology Beijing 102206China State Key Laboratory of Membrane Biology School of MedicineTsinghua UniversityBeijing 100084China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310003China Medical Systems Biology Research Center School of MedicineTsinghua UniversityBeijing 100084China

The application of traditional Chinese medicine(TCM)has made great contributions to the fight against the epidemic of coronavirus disease-2019(COVID-19).Despite the remarkable therapeutic effects of TCM,the molecular mechanisms of TCM formulae inhibiting COVID-19 are still not fully ***,we cφm-bined the automated high throughput sequencing-based high throughput screening(HTS^(2))assay with bioinformatics and computer-aided drug design(CADD)to investigate the molecular mechanisms of TCM-mediated therapeutic effects on COVID-19-related cytokine storm(Fig.1a).

关键词：细胞因子风暴六君子汤理中汤诊疗方案黄连解毒汤中药提取物肺炎分子机制

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Implicit multi-feature learning for dynamic time series prediction of the impact of institutions

arXiv

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arXiv 2020年

作者： Bai, Xiaomei Zhang, Fuli Hou, Jie Xia, Feng Tolba, Amr Elashkar, Elsayed Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Library Anshan Normal University Anshan114007 China Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia Mathematics Department Faculty of Science Menoufia University Shebin El-Kom32511 Egypt Administrative Sciences Department Community College King Saud University Riyadh11437 Saudi Arabia Applied Statistics Department Faculty of Commerce Mansoura University Egypt

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors’ influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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The dominant role of East Asia warming in increasing tropical western North Pacific tropical cyclone intensity

Research Square

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Research Square 2021年

作者： Xu, Jing Zhao, Ping Chan, Johnny C.L. Shi, Mingyuan Yang, Chi Xing, Rui Zhao, Siyu State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong Hong Kong National Meteorological Information Center Beijing China College of Global Change and Earth System Science Beijing Normal University Beijing China Meteorological Service in Binhai New Area Tianjin China University of California at Los Angeles CA United States

The increase in intense tropical cyclone (TC) activity over the western North Pacific (WNP) has often been linked to a warming ocean1-8. Here we show, however, that the TC intensity increasing trend in the tropical WNP during the past three decades are mainly related to the warming of the East Asian continent, especially a warming Tibetan Plateau (TP). The regional weak increasing trend of local sea surface temperature unlikely supplies the necessary energy for this increase in TC intensity. Instead, a weakened vertical wind shear (VWS) appears to be the main contributing factor. Through numerical simulations, we demonstrate that the warming TP strengthens the South Asian high-pressure system, which triggers a wave train toward the tropical WNP, subsequently modifying the upper- and lower-tropospheric zonal winds to reduce the VWS. Applying the high correlation between TC intensity and the local VWS to climate model projection results supports that TCs will likely become stronger, with a significantly increasing rate of 1.0 m s-1/10 years during 2021–2050, due to a further warming of the East Asian continent. Thus, the rims of East Asia and Southeast Asia could face an increasing risk of intense typhoons. © 2021, CC BY.

关键词： Tropical cyclone

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高可扩展的RDF数据存储系统

高可扩展的RDF数据存储系统

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第29届中国数据库学术会议

作者： Yuan Pingpeng 袁平鹏 Liu Pu 刘谱 Zhang Wenya 张文娅 Wu Buwen 吴步文 Key Laboratory of Services Computing Technology and System(Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 服务计算技术与系统教育部重点实验室(华中科技大学) 武汉430074 Key Laboratory of Cluster and Grid Computing of Hubei Province(Huazhong University of Science and Technology)Wuhan 430074 集群与网格计算湖北省重点实验室(华中科技大学) 武汉430074 School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 华中科技大学计算机科学与技术学院武汉430074

由于资源描述框架(resource description framework,RDF)具有表达灵活、简洁等优点,已被接受为表达元数据及万维网上数据互联的规范.近年来,其数据量在以飞快的速度增长.相应地,要求存储RDF数据的系统应具有高扩展性.介绍了一个高可扩展... 详细信息

由于资源描述框架(resource description framework,RDF)具有表达灵活、简洁等优点,已被接受为表达元数据及万维网上数据互联的规范.近年来,其数据量在以飞快的速度增长.相应地,要求存储RDF数据的系统应具有高扩展性.介绍了一个高可扩展的RDF数据存储系统TripleBit.为尽可能降低存储空间消耗,采用了增量压缩和变长整数编码方法.并采用了数据分块的存储方法,既使得存储管理方便又使得存储结构紧凑,加速了数据读取.系统提供了基于启发式规则的动态查询计划生成方法,所产生的查询计划在执行过程中根据中间结果会相应作调整,以保持最优的执行顺序.对于多变量的查询,使用二步执行策略以减少查询过程中产生的中间结果.与目前流行RDF数据存储系统相比较,在存储空间上RDF-3X比TripleBit至少多40％；在查询性能上,比RDF-3X和MonetDB获得数倍的提升.

关键词：数据存储系统资源描述框架数据编码查询处理

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Linked Data Crowdsourcing Quality Assessment based on Domain Professionalism

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Lu Yang Li Huang Zhenzhen Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the rapid development of Internet technology, crowdsourcing, as a flexible, effective and low-cost problem-solving method, has begun to receive more and more attention. The use of crowdsourcing to evaluate the quality of linked data has also become a research hotspot. This paper proposes the concept of Domain Specialization Test (DST), which uses domain professional testing tasks DSTs to evaluate the professionalism of workers, and combines the idea of Mini-batch Gradient Descent (MBGD) to improve the EM algorithm, and the MBEM algorithm is proposed to achieve efficient and accurate evaluation of task results. The experimental results show that the proposed method can screen out the appropriate workers for the linked data crowdsourcing task and improve the accuracy and iteration efficiency of the results.

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