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生物化学与生物物理进展 2012年第10期39卷 979-980页

作者：罗海涛赵屹 Bioinformatics Research Group Advanced Computing Research LaboratoryKey Laboratory of Intelligent Information ProcessingInstitute of Computing TechnologyChinese Academy of Sciences Research Center of Bioinformatics Kunshan RNAi Institute

MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as

关键词：肾癌 IL-8 上皮细胞-间质细胞转化蛋白激酶C

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Zhou, Jianlong Xiao, Chun Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Ministry of Education Hunan China Janelia Farm Research Campus Howard Hughes Medical Institute VA United States

ISBN: (纸本)9781467300223

A contour tree is a topological abstraction of a scalar field. Contour tree simplification (CTS) removes branches corresponding to noise, while making the size of the tree small enough for maintaining essential structure of data. This paper proposes a similarity-driven multi-level partial CTS (PCTS) approach. The PCTS preserves branches corresponding to structures of interest or specific objects specified by users, while removing other branches of the contour tree. A clustering method (e.g. k-means clustering) is used to cluster branch nodes into groups based on their similarities (i.e., similar locations) in the attribute space. As a result, the contour tree is simplified with multi-levels based on different clustering groups. Furthermore, various interfaces and rendering windows are provided and synchronized, which makes the simplification process more meaningful and efficient compared with traditional simplification methods using slide-bar based approaches. The proposed approach can be generalized to process branches with more than three measures. © 2012 IEEE.

关键词： Trees (mathematics)

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Dynamic logic for the semantic web 1

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Chang, Liang Zhang, Qicheng Gu, Tianlong Shi, Zhongzhi Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

The propositional dynamic logic PDL is one of the most successful variants of modal logic;it plays an important role in many fields of computer science and artificial intelligence. As a logical basis for the W3C-recommended Web ontology language OWL, description logic provides considerable expressive power going far beyond propositional logic as while as the reasoning is still decidable. In this paper, we bring the power and character of description logic into PDL and present a dynamic logic ALC-DL for the semantic Web. The logic ALC-DL inherits the knowledge representation ability of both the description logic ALC and the logic PDL. With an approach based on Buchi tree automaton, we prove that the satisfiability problem of ALC-DL formulas is still decidable and is EXPTIME-complete. The logic ALC-DL is suitable for modeling and reasoning about dynamic knowledge in the semantic Web environment. © 2012 IFIP International Federation for information processing.

关键词： Knowledge representation

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On the support of ad-hoc semantic web data sharing 1

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhou, Jing Yang, Kun Shi, Lei Shi, Zhongzhi School of Computer Science Communication University of China Beijing 100024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Sharing Semantic Web datasets provided by different publishers in a decentralized environment calls for efficient support from distributed computing technologies. Moreover, we argue that the highly dynamic ad-hoc settings that would be pervasive for Semantic Web data sharing among personal users in the future pose even more demanding challenges for enabling technologies. We propose an architecture that is based upon the peer-to-peer (P2P) paradigm for ad-hoc Semantic Web data sharing and identify the key technologies that underpin the implementation of the architecture. We anticipate that our (current and future) work will offer powerful support for sharing of Semantic Web data in a decentralized manner and becomes an indispensable and complementary approach to making the Semantic Web a reality. © 2012 IFIP International Federation for information processing.

关键词： Digital storage

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Adaptive associative classification with emerging frequent patterns

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High Technology Letters 2012年第1期18卷 38-44页

作者： Wang Xiaofeng Zhang Dapeng Shi Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China Graduate University of Chinese Academy of Sciences Beijing 100049P.R.China Institute of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China

In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

关键词： associative classification rule frequent pattern mining emerging frequent pattern supportvector machine （SVM）

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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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Hierarchical chunk-to-string translation

Hierarchical chunk-to-string translation

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Feng, Yang Zhang, Dongdong Li, Mu Zhou, Ming Liu, Qun Department of Computer Science University of Sheffield Sheffield United Kingdom Microsoft Research Asia China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China

ISBN: (纸本)9781937284244

We present a hierarchical chunk-to-string translation model, which can be seen as a compromise between the hierarchical phrasebased model and the tree-to-string model, to combine the merits of the two models. With the help of shallow parsing, our model learns rules consisting of words and chunks and meanwhile introduce syntax cohesion. Under the weighed synchronous context-free grammar defined by these rules, our model searches for the best translation derivation and yields target translation simultaneously. Our experiments show that our model significantly outperforms the hierarchical phrasebased model and the tree-to-string model on English-Chinese Translation tasks. © 2012 Association for Computational Linguistics.

关键词： Context free grammars

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A novel granular support vector machine based on mixed kernel function

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第20期6卷 484-492页

作者： Huang, Huajuan Ding, Shifei Jin, Fengxiang Yu, Junzhao Han, Youzhen School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.

关键词： Support vector machines

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A heuristic particle swarm optimization with quasi-human strategy for weighted circles packing problem

A heuristic particle swarm optimization with quasi-human str...

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International Conference on Natural Computation (ICNC)

作者： Ziqiang Li Yanfang Xie Zuijun Tian Zhou Lichao School of Information and Engineering Xiangtan University China 411105 Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan 411105 China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan China

The weighted circles layout problem belongs to the layout optimization problem with performance constraints. Due to its NP-hard property, it is difficult to solve in polynomial time. In this paper, a heuristic particle swarm optimization approach with quasi-human strategy (HQHPSA) is presented for this problem. Its layout scheme is constructed through the proposed heuristic method: that both circular radius and the norm of row vector of the matrix and sub-vector are taken as the probability factors of the roulette selection and the circles are located by arranging round existing circles in peripheral with counterclockwise. The complexity of the proposed heuristic method is only O(n) for one layout scheme. The better layout solution obtained through the proposed heuristic method is taken as the elite particle individual. The PSO with quasi-human strategy is used to optimize the elite particle into the optimal solution. The numerical experiments show that the performance of proposed algorithm is superior to the existing algorithms.

关键词： Layout Algorithm design and analysis Particle swarm optimization Optimization Computers Genetic algorithms Educational institutions

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Iterative Annotation Transformation with Predict-Self Reestimation for Chinese Word Segmentation 12

Iterative Annotation Transformation with Predict-Self Reesti...

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Conference on Empirical Methods in Natural Language processing

作者： Wenbin Jiang Fandong Meng Qun Liu Yajuan Lue Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781622765034

In this paper we first describe the technology of automatic annotation transformation, which is based on the annotation adaptation algorithm (Jiang et al., 2009). It can automatically transform a human-annotated corpus from one annotation guideline to another. We then propose two optimization strategies, iterative training and predict-self reestimation, to further improve the accuracy of annotation guideline transformation. Experiments on Chinese word segmentation show that, the iterative training strategy together with predict-self reestimation brings significant improvement over the simple annotation transformation baseline, and leads to classifiers with significantly higher accuracy and several times faster processing than annotation adaptation does. On the Penn Chinese Treebank 5.0, it achieves an F-measure of 98.43%, significantly outperforms previous works although using a single classifier with only local features.

关键词： ANNOTATIONS Classifiers Iterative Word Chinese Chinese philosophy Chinese art AUTOMATIC

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