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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

15th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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

arXiv

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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

Design of Target Behavior Intelligent Analysis System Based ...

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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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A quantum-inspired classical algorithm for separable non-negative matrix factorization

arXiv

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arXiv 2019年

作者： Chen, Zhihuai Li, Yinan Sun, Xiaoming Yuan, Pei Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Centrum Wiskunde & Informatica and QuSoft Science Park 123 Amsterdam1098XG Netherlands

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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A Deep Look into Neural Ranking Models for Information Retrieval

arXiv

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arXiv 2019年

作者： Guo, Jiafeng Fan, Yixing Pang, Liang Yang, Liu Ai, Qingyao Zamani, Hamed Wu, Chen Croft, W. Bruce Cheng, Xueqi University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Center for Intelligent Information Retrieval University of Massachusetts Amherst AmherstMA United States

Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Information retrieval

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Galaxy-galaxy lensing in the VOICE deep survey

arXiv

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arXiv 2022年

作者： Luo, Ruibiao Fu, Liping Luo, Wentao Napolitano, Nicola R. Xie, Linghua Radovich, Mario Liu, Jing Li, Rui Amaro, Valeria Chen, Zhu Liu, Dezi Fan, Zuhui Covone, Giovanni Vaccari, Mattia School of Physics and Astronomy Sun Yat-sen University Zhuhai Campus 2 Daxue Road Tangjia Zhuhai Guangdong519082 China Shanghai Key Lab for Astrophysics Shanghai Normal University Shanghai200234 China CAS Key Laboratory for Research in Galaxies and Cosmology University of Science and Technology of China Anhui Hefei230026 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Anhui Hefei230026 China INAF - Osservatorio Astronomico di Padova via dell'Osservatorio 5 PadovaI-35122 Italy South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650500 China Dipartimento di Fisica E. Pancini Universita degli Studi Federio II NapoliI-80126 Italy INFN Sezione di Napoli NapoliI-80126 Italy INAF - Osservatorio Astronomico di Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Inter-university Institute for Data Intensive Astronomy Department of Astronomy University of Cape Town Rondebosch Cape Town7701 South Africa Inter-university Institute for Data Intensive Astronomy Department of Physics and Astronomy University of the Western Cape Bellville Cape Town7535 South Africa INAF - Istituto di Radioastronomia via Globetti 101 Bologna40129 Italy

The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg2of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshift estimation to be the two essential quantities for weak lensing analysis. The depth of magABis up to 26.1 (5σ limiting) in r-band. We estimate the Excess Surface Density (ESD;ΔΣ) based on galaxy-galaxy measurements around galaxies at lower redshift (0.10l∗=108.31Mo) deviates from the SHMR relation whereas all other three samples agrees well with empirical curves. We interpret this discrepancy as the effect of a low star formation efficiency of the low-mass blue dwarf galaxy population dominated in the VOICE-CDFS area. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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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

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Identifying High Potential Talent: A Neural network Based Dynamic Social Profiling Approach

Identifying High Potential Talent: A Neural Network Based Dy...

引用

IEEE International Conference on data Mining (ICDM)

作者： Yuyang Ye Hengshu Zhu Tong Xu Fuzhen Zhuang Runlong Yu Hui Xiong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

关键词： Social networking (online) Companies Artificial neural networks Germanium Engineering profession

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Erratum: Prototype of readout electronics for GAEA gamma spectrometer of Back-n facility at CSNS

引用

Journal of Instrumentation 2022年第8期17卷 E08002-E08002页

作者： L. Xie P. Cao T. Yu Z. Jiang Q. An J. Li C. Li X. Wu Z. Wang H. Bai J. Bai J. Bao Q. Chen Y. Chen Z. Chen Z. Cui A. Fan R. Fan C. Feng F. Feng K. Gao M. Gu C. Han Z. Han G. He Y. He Y. Hong Y. Hu H. Huang W. Jia H. Jiang W. Jiang Z. Jin L. Kang B. Li G. Li Q. Li X. Li Y. Li J. Liu R. Liu S. Liu G. Luan C. Ning B. Qi J. Ren Z. Ren X. Ruan Z. Song K. Sun Z. Tan J. Tang S. Tang L. Wang P. Wang Z. Wen Y. Yang H. Yi Y. Yu G. Zhang L. Zhang M. Zhang Q. Zhang X. Zhang Y. Zhang Z. Zhang M. Zhao L. Zhou Z. Zhou K. Zhu State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei China Department of Modern Physics University of Science and Technology of China Hefei China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing China Spallation Neutron Source Science Center Dongguan China USTC archaeometry lab University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences Beijing China Northwest Institute of Nuclear Technology Xi'an China University of Chinese Academy of Sciences Beijing China

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A tree search algorithm for sequence labeling

arXiv

引用

arXiv 2018年

作者： Lao, Yadi Xu, Jun Lan, Yanyan Guo, Jiafeng Gao, Sheng Cheng, Xueqi Guo, Jun Beijing University of Posts and Telecommunications CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from le. to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of thewhole sentence are produced.e policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a *** search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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