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International Joint Conference on Neural Networks

作者： Xiaojia Pu Gangshan Wu Chunfeng Yuan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China

ISBN: (纸本)9781509061839

With the rapid growth of opinionated contents, e.g. product reviews, sentiment analysis has drawn much attention from the researchers. The most fundamental task of sentiment analysis is document sentiment classification which aims to predict the overall sentiment (e.g. positive or negative) towards the opinion target in a review. There are usually various opinion sentences towards different aspects with different sentiments. Among them, the overall opinion towards the whole target should be more deterministic in document sentiment prediction. However, most existing methods treat all the sentences equally, thus, they may encounter difficulty especially when the sentiments of most aspect opinion sentences differ from the overall sentiment. To address this, we propose a novel method for document sentiment classification which adequately explores the effect of overall opinion sentences. The method is extended from structural SVM, and the overall opinion sentences are taken as the hidden variables for document sentiment. Experiments on several standard product review datasets show the effectiveness of our method.

关键词： Support vector machines Pragmatics Cameras Sentiment analysis Learning systems Predictive models Probability

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A flexible I/O arbitration framework for netCDF-based big data processing workflows on high-end supercomputers

A flexible I/O arbitration framework for netCDF-based big da...

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作者： Liao, Jianwei Gerofi, Balazs Lien, Guo-Yuan Miyoshi, Takemasa Nishizawa, Seiya Tomita, Hirofumi Liao, Wei-Keng Choudhary, Alok Ishikawa, Yutaka College of Computer and Information Science Southwest University of China Chongqing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China RIKEN Advanced Institute for Computational Science Kobe Japan Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

On the verge of the convergence between high-performance computing and Big Data processing, it has become increasingly prevalent to deploy large-scale data analytics workloads on high-end supercomputers. Such applications often come in the form of complex workflows with various different components, assimilating data from scientific simulations as well as from measurements streamed from sensor networks, such as radars and satellites. For example, as part of the Flagship 2020 (post-K) supercomputer project of Japan, RIKEN is investigating the feasibility of a highly accurate weather forecasting system that would provide a real-time outlook for severe guerrilla rainstorms. One of the main performance bottlenecks of this application is the lack of efficient communication among workflow components, which currently takes place over the parallel file *** this paper, we present an initial study of a direct communication framework designed for complex workflows that eliminates unnecessary file I/O among components. Specifically, we propose an I/O arbitration layer that provides direct parallel data transfer (both synchronous and asynchronous) among job components that rely on the netCDF interface for performing I/O operations. Our solution requires only minimal modifications to application code. Moreover, we propose a configuration file–based approach that allows users to specify the desired data transfer pattern among workflow components, offering a general solution for different application contexts. We present a preliminary evaluation of the proposed framework on the K computer (running on up to 4800 compute nodes) using RIKEN's experimental weather forecasting workflow as a case study. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Supercomputers

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An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

An energy-aware virtual machine scheduling method for servic...

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作者： Dou, Wanchun Xu, Xiaolong Meng, Shunmei Zhang, Xuyun Hu, Chunhua Yu, Shui Yang, Jian State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical & Computer Engineering The University of Auckland Auckland New Zealand School of Computer and Information Engineering Hunan University of Commerce Hunan China School of Information Technology Deakin University Melbourne Australia Jiangsu Second Normal University Nanjing China

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Quality of service

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Quantum network coding for multi-unicast problem based on 2D and 3D cluster states

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science China(Information sciences) 2016年第4期59卷 5-19页

作者： Jing LI Xiubo CHEN Xingming SUN Zongpeng LI Yixian YANG Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications Department of Computer Science University of Calgary School of Computer and Software Nanjing University of Information Science & Technology

We mainly consider quantum multi-unicast problem over directed acyclic network, where each source wishes to transmit an independent message to its target via bottleneck channel. Taking the advantage of global entanglement state 2D and 3D cluster states, these problems can be solved efficiently. At first, a universal scheme for the generation of resource states among distant communication nodes is provided. The corresponding between cluster and bigraph leads to a constant temporal resource cost. Furthermore, a new approach based on stabilizer formalism to analyze the solvability of several underlying quantum multi-unicast networks is presented. It is found that the solvability closely depends on the choice of stabilizer generators for a given cluster state. And then, with the designed measurement basis and parallel measurement on intermediate nodes, we propose optimal protocols for these multi-unicast sessions. Also, the analysis reveals that the resource consumption involving spatial resources, operational resources and temporal resources mostly reach the lower bounds.

关键词： quantum information network coding multi-unicast cluster state stabilizer formalism

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Cardiac Cavity Tracking: CACCT: An Automated Tool of Detecting Complicated Cardiac Malformations in Mouse Models (Adv. Sci. 8/2020)

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Advanced science 2020年第8期7卷 2070042-2070042页

作者： Qing Chu Haobin Jiang Libo Zhang Dekun Zhu Qianqian Yin Hao Zhang Bin Zhou Wenzhang Zhou Zhang Yue Hong Lian Lihui Liu Yu Nie Shengshou Hu State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Disease Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100037 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100089 China Heart Center and Shanghai Institution of Pediatric Congenital Heart Diseases Shanghai Children's Medical Center National Children's Medical Center Shanghai Jiao Tong University School of Medicine Shanghai 200127 China State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences (CAS) University of Chinese Academy of Sciences Shanghai 200031 China Department of Cardiovascular Surgery Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 China

In article number https://***/10.1002/advs.201903592 , Yu Nie, Shengshou Hu, and co‐workers establish an automated tool based on micro‐computed tomography (CT) images known as CACCT to identify complicated cardiac malformations in mice. With CACCT, researchers, even those without expert training or diagnostic experience of congenital heart diseases, can identify murine complicated cardiac malformations effectively, including transposition of the great arteries, double‐outlet right ventricle and atypical ventricular septal defect, whose accuracy is equivalent to senior fetal cardiologists.

关键词： computer-assisted diagnosis congenital heart disease double-outlet right ventricle micro-CT ventricular septal defects

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Separability-entanglement classifier via machine learning

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Physical Review A 2018年第1期98卷 012315-012315页

作者： Sirui Lu Shilin Huang Keren Li Jun Li Jianxin Chen Dawei Lu Zhengfeng Ji Yi Shen Duanlu Zhou Bei Zeng Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada N2L 3G1 Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China Department of Mathematics & Statistics University of Guelph Guelph Ontario Canada N1G 2W1 Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Centre for Quantum Software and Information School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada N2L 3G1 Institute of Physics Chinese Academy of Sciences Beijing 100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing. Despite the many methods—such as the positive partial transpose criterion and the k-symmetric extendibility criterion—to tackle this problem, none of them enables a general, practical solution due to the problem's NP-hard complexity. Explicitly, separable states form a high-dimensional convex set of vastly complicated structures. In this work, we build a different separability-entanglement classifier underpinned by machine learning techniques. We use standard tools from machine learning to learn the entanglement feature of arbitrary given quantum states. We perform substantial numerical tests on two-qubit and two-qutrit systems, and the results indicate that our method can outperform the existing methods in generic cases in terms of both speed and accuracy. This opens up avenues to explore quantum entanglement via the machine learning approach.

关键词： Machine learning Quantum entanglement

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Multi-view Common Component Discriminant Analysis for Cross-view Classification

arXiv

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arXiv 2018年

作者： You, Xinge Xu, Jiamiao Yuan, Wei Jing, Xiao-Yuan Tao, Dacheng Zhang, Taiping School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Software Engineering School of Computer Wuhan University China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science Chongqing University Chongqing400030 China

Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. A promising approach to handle this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Despite the satisfactory results achieved by existing methods, the performance of previous work will be dramatically degraded when multi-view data lies on nonlinear manifolds. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. We develop a kernel method of MvCCDA to further boost the performance of MvCCDA. Beyond kernel extension, optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority. Copyright © 2018, The Authors. All rights reserved.

关键词： Discriminant analysis

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ParaGraphE: A library for parallel knowledge graph embedding

arXiv

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arXiv 2017年

作者： Niu, Xiao-Fan Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Knowledge graph embedding aims at translating the knowledge graph into numerical representations by transforming the entities and relations into con- tinuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implemen- tations of them are time-consuming for large-scale knowledge graphs. Here, we design a unified parallel framework to parallelize these methods, which achieves a significant time reduction without inuencing the accuracy. We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. The source code can be downloaded from https: //***/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge graph

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Sketch-based stroke generation in Chinese flower painting

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science China(Information sciences) 2016年第7期59卷 237-239页

作者： Lijie YANG Tianchen XU Jixiang DU Enhua WU College of Computer Science and Technology Huaqiao University Department of Computer Science and Engineering Hong Kong University of Science and Technology Faculty of Science and Technology University of Macau State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences

Dear editor,Flower painting,which is one of the major categories of Chinese painting,represents the key artistic characteristics of oriental *** other words,its typical usages of stroke and ink color contribute to the unique appeal of Chinese painting,since the strokes may be straight or curved,hard or soft,thick or thin,pale or dark,and the ink colors may be dry or running,blending or ***,the complexity and variety of

关键词： In Sketch-based stroke generation in Chinese flower painting real work

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Physical origins of ruled surfaces on the reduced density matrices geometry

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science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 1-8页

作者： Ji-Yao Chen Zhengfeng Ji Zheng-Xin Liu Xiaofei Qi Nengkun Yu Bei Zeng Duanlu Zhou State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Department of Physics Renmin University of China Beijing 100872 China Department of Mathematics Shanxi University Taiyuan 030000 China Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2W1 Canada Institute of Physics Chinese Academy of Sciences Beijing 100190 China

The reduced density matrices （RDMs） of many-body quantum states form a convex set. The boundary of low dimensional projections of this convex set may exhibit nontrivial geometry such as ruled surfaces. In this paper, we study the physical origins of these ruled surfaces for bosonic systems. The emergence of ruled surfaces was recently proposed as signatures of symmetry- breaking phase. We show that, apart from being signatures of symmetry-brealdng, ruled surfaces can also be the consequence of gapless quantum systems by demonstrating an explicit example in terms of a two-mode Ising model. Our analysis was largely simplified by the quantum de Finetti＇s theorem--in the limit of large system size, these RDMs are the convex set of all the symmetric separable states. To distinguish ruled surfaces originated from gapless systems from those caused by symmetry- breaking, we propose to use the finite size scaling method for the corresponding geometry. This method is then applied to the two-mode XY model, successfully identifying a ruled surface as the consequence of gapless systems.

关键词： ruled surfaces reduced density matrices bosonic system geometry

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