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The improved Hagedorn wavepacket method for semiclassical Schrödinger equation

International Journal of Modeling, Simulation, and Scientific computing 2014年第4期5卷

作者： Xueyang Li Aiguo Xiao Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan P. R. China

The Hagedorn wavepacket method is an important numerical method for solving the semiclassical time-dependent Schrödinger equation. In this paper, a new semi-discretization in space is obtained by wavepacket operator. In a sense, such semi-discretization is equivalent to the Hagedorn wavepacket method, but this discretization is more intuitive to show the advantages of wavepacket methods. Moreover, we apply the multi-time-step method and the Magnus-expansion to obtain the improved algorithms in time-stepping computation. The improved algorithms are of the Gauss–Hermite spectral accuracy to approximate the analytical solution of the semiclassical Schrödinger equation. And for the given accuracy, the larger time stepsize can be used for the higher oscillation in the semiclassical Schrödinger equation. The superiority is shown by the error estimation and numerical experiments.

关键词： High oscillation semiclassical Schrödinger equation Hagedorn wavepacket multi-time-step method Magnus-expansion

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Uncertain subgraph query processing over uncertain graphs

Uncertain subgraph query processing over uncertain graphs

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15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Ruan, Wenjing Wang, Chaokun Han, Lu Peng, Zhuo Bai, Yiyuan School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University China Department of Computer Science and Technology Tsinghua University China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642374005

In some real-world applications, data cannot be measured accurately. Uncertain graphs emerge when this kind of data is modeled by graph data structures. When the graph database is uncertain, our query is highly possibly uncertain too. All of the prior works do not consider the uncertainty of the query. In this paper, we propose a new algorithm called Mutual-Match to solve the problem of uncertain subgraph query under the situation where both the graph dataset and the query are uncertain. Considering that the subgraph isomorphism verification is an NP-hard problem, and it will be more complex on uncertain graph data, the Mutual-Match algorithm uses MapReduce processes to find out all the subgraph matches and can adapt to dynamic graphs well. Experimental results on both real-world and synthetic datasets show the correctness and effectiveness of our proposed methods. © 2013 Springer-Verlag.

关键词： MapReduce

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Bilingual lexical cohesion trigger model for document-level machine translation

Bilingual lexical cohesion trigger model for document-level ...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Ben, Guosheng Xiong, Deyi Teng, Zhiyang Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China Centre for Next Generation Localisation Dublin City University Ireland

ISBN: (纸本)9781937284510

In this paper, we propose a bilingual lexical cohesion trigger model to capture lexical cohesion for document-level machine translation. We integrate the model into hierarchical phrase-based machine translation and achieve an absolute improvement of 0.85 BLEU points on average over the baseline on NIST Chinese-English test sets. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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A Combined Structure Optimization Method in Community Detection

A Combined Structure Optimization Method in Community Detect...

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第二届中国互联网学术年会

作者： Yuzhong Chen Xiaohui Qiu Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou 350108 China

Community Detection in social networks is usually considered as an objective optimization *** to the objective function,the global optimum cannot describe the real partition well,and it is time *** this paper,a layered optimization framework is designed to improve the optimization process,reduce the scale of network and increase the quality of *** framework consists of three parts: finding cores in networks,repairing isolated nodes and optimization in a new constructed weighted network which is a compressed network of the origin ***,the equivalency of modularity optimization in the new compressed weighted network and the original one is ***,a combined algorithm of community Detection named DBPSO including similarity-based clustering,isolated nodes repairing strategies and a modified particle swarm optimization is proposed according to the layered optimization *** addition,a suitable mutation strategy for particle swarm optimization (PSO) is introduced to guarantee the convergence and global search ***,the experiments are conducted to evaluate the proposed algorithm by using the synthetic and real-world network *** results show that the proposed algorithm can effectively extract the intrinsic community structure of social networks.

关键词： Layered Optimization Framework Community Detection Particle Swarm Optimization Modularity

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A heterogeneous-aware cooperative MIMO transmission scheme in WSN

Journal of Software

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Journal of Software 2013年第3期8卷 639-644页

作者： Pei, Tingrui Xie, Da Li, Zhetao Tu, Dengbiao Choi, Youngjune College of Information Engineering Xiangtan University Xiangtan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China School of Computer National University of Defense Technology Changsha 410073 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Department of Information and Computer Engineering Ajou University Suwon 443749 Korea Republic of

A heterogeneous-aware cooperative MIMO transmission scheme (HAMS) is proposed to optimize the network lifetime and save energy for energy heterogeneous wireless sensor networks (WSN). This scheme extends the traditional low-energy adaptive clustering hierarchy (LEACH) protocol to enable the cooperative MIMO transmission between the sink and clusters. Through the adaptive selection of cooperative nodes and the cooperative MIMO transmission, HAMS can gain effective performance improvement in terms of taking advantage of the presence of node heterogeneity. Based on the energy consumption model developed in this paper, the optimal parameter to minimize the overall energy consumption is found. Simulation results exhibit that HAMS can effectively save energy and prolong the network time. Furthermore, HAMS displays more resilient in different degree of heterogeneous wireless sensor networks. © 2013 ACADEMY PUBLISHER.

关键词： Energy utilization

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A model of three points for location estimation in wireless sensor networks

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2013 2nd International Symposium on Materials Science and Engineering Technology, ISMSET 2013

作者： Xie, Shao Guo Hu, Yan Jun Wang, Yi Jiang, Fang Liu, Jing Jing Wang, Ying Guan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education AnHui University Hefei China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China

ISBN: (纸本)9783037858028

In this paper, we propose a novel model of three points named TP for location estimation in wireless sensor networks (WSNs) with random deployment of anchor nodes. In this model, we select three anchor nodes which have the strongest received signal strength (RSS) for location estimation, the centroid algorithm and the method of intersection of judgment are used to estimate the location of unknown nodes. To further exploit three nearest intersection points in TP, the enhanced TP (ETP) is proposed. The simulation results show that the proposed models outperform MMSE and BML in terms of the localization accuracy for WSNs. Moreover, the localization accuracy of the proposed models in scenario 2 with random deployment of anchor nodes are better than in scenario 1 with planned deployment of anchor nodes. Additionally, compared with MMSE and BML, ETP and TP can reduce the environmental impact on location estimation. © (2013) Trans Tech Publications, Switzerland.

关键词： Location

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A knowledge-based heuristic particle swarm optimization approach with the adjustment strategy for the weighted circle packing problem

A knowledge-based heuristic particle swarm optimization appr...

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作者： Li, Ziqiang Tian, Zhuojun Xie, Yanfang Huang, Rong Tan, Jiyang School of Information and Engineering Xiangtan University Xiangtan Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Ministry of Education Xiangtan Hunan 411105 China School of Mathematics and Computer Science Xiangtan University Xiangtan Hunan 411105 China

The weighted circle packing problem is a kind of important combination optimization problem and has the NP-hard property. Inspired by the No Free Lunch Theorem, a knowledge-based heuristic particle swarm optimization approach with the adjustment strategy (KHPSOA) is developed for this problem. The knowledge (e.g., the relation between its weight matrix and better solution) is obtained from this problem itself and existing layout scheme diagrams and applied to form more effective ordering and positioning rules of the proposed heuristic method. A better layout scheme of larger circles is obtained through the heuristic method. The optimal solution of this problem is obtained by inserting a few residual smaller circles into the gaps of the better layout scheme and further optimizing it through the proposed particle swarm optimization with the adjustment strategy. The numerical experiments show that KHPSOA is superior to the existing algorithms in the performances. © 2013 Elsevier Ltd. All rights reserved.

关键词： Heuristic methods

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A new automatic analysis system for risk behaviors

A new automatic analysis system for risk behaviors

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2013 International Conference on information and Communication Technology for Education (ICTE 2013)

作者： Chan Xu Xin Liu Jian Wu BoYu OuYang Key Laboratory of Intelligent Computing and Information Processing (Xiangtan University) Ministry of Education Institute of Information Engineering Xiangtan University

At present, the software's type forms present diversity, and how to automatically analyze the software's risk behaviors become an urgent problem. This paper used some software behavior crawlers and dynamic analysis method, crawled many representative kinds of malware's behavior, then mapped the behavior as a training data, and combined with BP neural network, established an automated analysis system for risk behaviors. Experiment shows that the system can automatically analyze whether a software is malware, which has better results and a strong practical intelligence.

关键词： risk behavior dynamic analysis malware BP neural network dangerous behaviors intelligence

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Bilingually-guided monolingual dependency grammar induction

Bilingually-guided monolingual dependency grammar induction

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Liu, Kai Lü, Yajuan Jiang, Wenbin Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland University of Chinese Academy of Sciences China

ISBN: (纸本)9781937284503

This paper describes a novel strategy for automatic induction of a monolingual dependency grammar under the guidance of bilingually-projected dependency. By moderately leveraging the dependency information projected from the parsed counterpart language, and simultaneously mining the underlying syntactic structure of the language considered, it effectively integrates the advantages of bilingual projection and unsupervised induction, so as to induce a monolingual grammar much better than previous models only using bilingual projection or unsupervised induction. We induced dependency grammar for five different languages under the guidance of dependency information projected from the parsed English translation, experiments show that the bilinguallyguided method achieves a significant improvement of 28.5% over the unsupervised baseline and 3.0% over the best projection baseline on average. © 2013 Association for Computational Linguistics.

关键词： Syntactics

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Classification of big velocity data via cross-domain Canonical Correlation Analysis

Classification of big velocity data via cross-domain Canonic...

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IEEE International Conference on Big Data

作者： Bo Zhang Zhong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Many classification techniques work well only under a common assumption that the training and test data are drawn from the same feature space and the same distribution. However, big velocity data usually show disobedience of this assumption. For example, in the field of web-document classification, new document is continuously emerging every day. Transfer learning aims at leveraging the knowledge in labeled source domains to predict the unlabeled data in a target domain, where the distributions are different in domains. As one of the important research directions of transfer learning, one kind of approaches focus on the correspondence between pivot features and all the other specific features from different domains, to extract some relevant features that may reduce the difference between the domains, have attracted wide attention and study. However, the limitation caused by the vague meanings in different domains prevents these algorithms from further improvement. To tackle this problem, we propose a cross-domain canonical correlation analysis algorithm called CD-CCA by applying Canonical Correlation Analysis (CCA) to transfer learning. CD-CCA can learn a semantic space of multi-view correspondences from different domains respectively and transfer the knowledge by dimensionality reduction in a multi-view way. Experimental results on the 144×6 classification problems in 20Newsgroups, show that CD-CCA can significantly improve the prediction accuracy.

关键词： Correlation Algorithm design and analysis Classification algorithms Feature extraction Semantics Vectors Mutual information

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