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HAS-QA: Hierarchical answer spans model for open-domain question answering

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arXiv 2019年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Su, Lixin Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Statistics University of California Berkeley

This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection;2) multiple answer spans may exist within one given paragraph;3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines. Copyright © 2019, The Authors. All rights reserved.

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Design of Target Behavior Intelligent Analysis System Based on Distributed Computing

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Design of Target Behavior Intelligent Analysis System Based ...

Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Xinlong Pan Xueqi Cheng Libo Yao Zhenqiu Zhu Biao Ding Tiantian Tang Minbo Li Chuanhui Liu CAS Key Laboratory of Network Data Science & Technology Inistitute of Computing Technology Beijing China Institute of Information Fusion Naval Aviation University Yantai China School of Software Fudan University Shanghai China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems.

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NKT cells in liver diseases

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Frontiers of Medicine 2018年第3期12卷 249-261页

作者： Shasha Zhu Huimin Zhang Li Bai CAS Key Laboratory of Innate Immunity and Chronic Disease CAS Center for Excellence in Molecular Cell Science School of Life Sciences and Medical Center University of Science and Technology of China Hefei 230027 China Innovation Center for Cell Signaling Network Hefei National Laboratory for Physical Sciences at Microscale Hefei 230027 China

Natural killer T cells are innate-like and tissue-resident lymphocytes, which recognize lipid antigens and are enriched in the liver. Natural killer T cells play important roles in infections, tumors, autoimmune diseases, and metabolic diseases. In this study, we summarize recent findings on biology of natural killer T cells and their roles in hepatitis B virus and hepatitis C virus infection, autoimmune liver diseases, alcoholic liver disease, nonalcoholic fatty liver disease, and hepatocellular carcinoma. Controversial results from previous studies are discussed, and indicate the dynamic alteration in the role of natural killer T cells during the progression of liver diseases, which might be caused by changes in natural killer T subsets, factors skewing cytokine responses, and intercellular crosstalk between natural killer T cells and CDld-expressing cells or bystander cells.

关键词： natural killer T cells hepatitis B virus and hepatitis C virus infection autoimmune liver diseases alcoholic liverdisease nonalcoholic fatty liver disease hepatocellular carcinoma

Controlling risk of web question answering

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arXiv 2019年

作者： Su, Lixin Guo, Jiafeng Fan, Yixing Lan, Yanyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users’ search experience by providing a direct answer to users’ information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

M2-specific reduction of CD1d switches NKT cell-mediated immune responses and triggers metaflammation in adipose tissue

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Cellular & Molecular Immunology 2018年第5期15卷 506-517页

作者： Huimin Zhang Rufeng Xue Shasha Zhu Sicheng Fu Zuolong Chen Rongbin Zhou Zhigang Tian Li Bai CAS Key Laboratory of Innate Immunity and Chronic Disease CAS Center for Excellence in Molecular Cell ScienceSchool of Life Sciences and Medical CenterUniversity of Science and Technology of ChinaHefei 230027China Innovation Center for Cell Signaling Network Hefei National Laboratory for Physical Sciences at MicroscaleHefei 230027China

Metaflammation is responsible for several metabolic syndromes,such as type 2 ***,the mechanisms by which metabolic disorders trigger metaflammation remain *** identified a cell type-specific downregulation of CD1d expression in M2 macrophages during the progression of obesity prior to the onset of inflammation in visceral adipose tissues.A reduction in CD1d expression influenced the ability of M2 macrophages to present antigens and caused a change in antigen-presenting cells from M2 macrophages to M1 *** CD1d conditional knockout(KO)mice,we further demonstrated that natural killer T(NKT)cell activation by M2 macrophages inhibited metaflammation and insulin resistance by promoting Th2 responses and M2 polarization in visceral adipose tissues of obese mice,whereas NKT cell activation by M1 macrophages exacerbated metaflammation and insulin resistance by promoting Th1 responses and inhibiting M2 *** results suggest that an M2-specific reduction of CD1d is an initiating event that switches NKT cell-mediated immune responses and disrupts the immune balance in visceral adipose tissues in obese mice.

关键词： CD1d macrophage metaflammation NKT cells

A unified adaptive recoding framework for Batched network coding

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arXiv 2021年

作者： Yin, Hoover H.F. Tang, Bin Ng, Ka Hei Yang, Shenghao Wang, Xishi Zhou, Qiaoqiao The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The School of Computer and Information Hohai University Nanjing China The Department of Physics The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong

Batched network coding is a variation of random linear network coding which has low computational and storage costs. In order to adapt to random fluctuations in the number of erasures in individual batches, it is not optimal to recode and transmit the same number of packets for all batches. Different distributed optimization models, which are called adaptive recoding schemes, were formulated for this purpose. The key component of these optimization problems is the expected value of the rank distribution of a batch at the next network node, which is also known as the expected rank. In this paper, we put forth a unified adaptive recoding framework with an arbitrary recoding field size. We show that the expected rank functions are concave when the packet loss pattern is a stationary stochastic process, which covers but not limited to independent packet loss and Gilbert-Elliott packet loss model. Under this concavity assumption, we show that there always exists a solution which not only can minimize the randomness on the number of recoded packets but also can tolerate rank distribution errors due to inaccurate measurements or limited precision of the machine. We provide an algorithm to obtain such an optimal optimal solution, and propose tuning schemes that can turn any feasible solution into a desired optimal solution. © 2021, CC BY-NC-ND.

关键词： Packet loss

Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

Travel Time Prediction for Multi-Airport Systems Via Multiclass Queuing networks

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Travel Time Prediction for Multi-Airport Systems Via Multicl...

Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Kailin Chen Shaoyu Wang Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Guangdong China Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

In this paper, we consider predicting travel time for aircraft operated in multi-airport systems by modeling and simulating a multiclass queuing network, which can systematically capture the complicated coupling relationship among multiple airports and terminal airspace and the complex nature of flight trajectories following different traffic flow patterns. In this multiclass queuing network model, each class of queuing network, named a class of customers, is modeled with the data of a traffic flow pattern, which is identified for a cluster of flight trajectories. Airports and airspace sectors are correspondingly modeled as networked servers with nonhomogeneous and time-varying arrival rate, service rate and server capacity to serve those classes of customers following their specific routing probabilities. Then, all of the parameters for setting up the multiclass queuing network model can be properly estimated using historical 4D flight trajectory data. To illustrate the superiority of this model, both average travel time for each class of customers, i.e., aircraft following a particular flow pattern, and the arrival time for an individual flight are predicted via simulations of a multiclass queuing network, and furthermore, compared with the real travel time. A typical example of a multi-airport system, the Guangdong-Hong Kong-Macau Greater Bay Area in China, is utilized to showcase the prediction performance of the proposed multiclass queuing network simulation model. The simulation experiments of the case study demonstrate that the proposed model well fits this multi-airports system. For most of the time periods, the percentage error (PE) of simulated average travel time and real average travel time is less than 5%. The travel time prediction for a random individual flight can achieve around 1% of the percentage error in terms of point estimation.

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Analysis and Defense of network Attacking Based on the Linux Server

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Analysis and Defense of Network Attacking Based on the Linux...

2019国际计算机前沿大会

作者： Dapeng Lang Wei Ding Yuhan Xiang Xiangyu Liu College of Computer Science and Technology Harbin Engineerning University Key Laboratory of Network Assessment Technology CAS(Institute of Information EngineeringChinese Academy of Sciences)

The kernel of the Linux server is analyzed to find out the main cause of the server's denial of service when it is *** the kernel,when the connection request information memory is full,the new connection request is ***,the printk function was used to alert the kernel output log when the memory was full,the processing of discarding the connection request in the kernel was changed,and the function tcpyn_floodction was applied to full memory *** the function tcpyn_floodction,the free function was used to release the memory according to the condition,so that the new connection request has a storage space,thereby offering the server's normal ***,the proposed defense technology is verified to be effective.

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