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检索条件"任意字段=14th Symposium on Large Data Analysis and Visualization"

共 540 条记录，以下是291-300 订阅

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Demonstrating a linked data platform for finite element biosimulations of cochlear mechanics

Demonstrating a linked data platform for finite element bios...

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ISWC 2015 Posters and Demonstrations Track, ISWC-P and D 2015 - co-located with the 14th International Semantic Web Conference, ISWC 2015

作者： Mehdi, Muntazir Khan, Yasar Freitas, Andre Jares, Joao Hasapis, Panagiotis Sahay, Ratnesh Insight Centre for Data Analytics NUI Galway Ireland Computer Science Department University of Passau Germany INTRASOFT International Luxembourg Luxembourg

Biosimulations employ Finite Element Method (FEM) to simulate complex biological systems in order to understand different aspects of human organs. the applications of FEM biosimulations range from human ear cochlear mechanics, cardiovascular, to neurovascular research. Performing these large Finite Element (FE) simulations, comparison and analysis of multiple simulation experiment results, and visualization of large FEM numerical data is a time consuming hectic job for domain experts-mainly because simulations are performed in an isolated environment. In this paper, we demonstrate a linked data platform to dispatch simulations on biosimulation models for the inner-ear (cochlear) mechanics in an integrated non-isolated environment. Copyright © 2015 for the individual papers by the papers' authors.

关键词： Linked data

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Reorganization of gene network of Bacillus subtilis for amylase production and secretion under different conditions

Reorganization of gene network of Bacillus subtilis for amyl...

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the 14th China-Japan-Korea Joint symposium on Enzyme Engineering

作者： Shaomin Yan Guang Wu State Key Laboratory of Non-food Biomass Enzyme Technology National Engineering Research Center for Non-food BiorefineryGuangxi Biomass Industrialization Engineering InstituteGuangxi Key Laboratory of BiorefineryGuangxi Academy of Sciences

As an important cell factory,*** can produce large amounts of designed proteins,for which many efforts have been made to improve its properties in order to meet the demand for industrial *** has a sophisticated genetic system to regulate and control its protein production and *** ***,about 150 genes have been identified in response to amylase overproduction and super-secretion,meanwhile these genes can function differently under different conditions because no single gene acts alone without interaction with other *** it is necessary to study how these genes behave under different conditions in order to better understand this sophisticated genetic *** way to study how genes behave under different conditions is to conduct the transcriptome studies,whose data have been documented in Gene Expression Omnibus(GEO).In this study,we apply network analysis to investigating the transcriptome data of *** from GEO including platforms GPL 188,GPL343,GPL6257,GPL 15025,GPL 19890,*** different conditions,and construct gene networks focusing on the abovementioned genes related to amylase production and *** results show that gene networks are different under different experimental conditions in terms of gene clusters,which consequently lead the genes related to amylase production and secretion to change their *** changes do not act alone but are associated with different genes,thus the network analysis reveals what types of genes are more likely to follow amylase production and secretion genes to change their ***,gene hierarchical structures are built,and relevant implications are *** our knowledge,this is the first study to use network analysis to integrate all *** genes together to get a whole picture of the gene relationships for amylase production and secretion,which provides information on better understanding of the mechanisms as suggested by Big

关键词： Amylase Bacillus subtilis Gene Network analysis Overproduction Reorganization Hyper-secretion

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Efficient Model Selection for Regularized Classification by Exploiting Unlabeled data 1

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14th International symposium on Intelligent data analysis (IDA)

作者： Balikas, Georgios Partalas, Ioannis Gaussier, Eric Babbar, Rohit Amini, Massih-Reza Univ Grenoble Alpes St Martin Dheres France Viseo R&D Grenoble France Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (数字)9783319244655

ISBN: (纸本)9783319244655

Hyper-parameter tuning is a resource-intensive task when optimizing classification models. the commonly used k-fold cross validation can become intractable in large scale settings when a classifier has to learn billions of parameters. At the same time, in real-world, one often encounters multi-class classification scenarios with only a few labeled examples;model selection approaches often offer little improvement in such cases and the default values of learners are used. We propose bounds for classification on accuracy and macro measures (precision, recall, F1) that motivate efficient schemes for model selection and can benefit from the existence of unlabeled data. We demonstrate the advantages of those schemes by comparing them with k-fold cross validation and hold-out estimation in the setting of large scale classification.

关键词： Artificial intelligence

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Revealing the Structure and Function of P-pastoris Metabolic Network using Petri nets 14

Revealing the Structure and Function of <i>P-pastoris</i> Me...

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International symposium Distributed Computing and Applications Business Engineering and Science

作者： Wang, Yufang Ding, Dewu Jingchu Univ Technol Dept Math & Phys Jingmen Peoples R China Chizhou Coll Dept Math & Comp Sci Chizhou Peoples R China

ISBN: (纸本)9781467365932

Due to the limitations of detailed experimental data for large scale metabolic networks, structural analysis of these networks is a growing research topic in bioinformatics field. Among several available structure-oriented approaches, Petri nets has proved to be particular efficient tool, and thus is employed for studying P. pastoris metabolic network in this study. After a brief introduction about the conceptual framework of Petri nets analysis, we give the first in silico Petri nets model of P. pastoris metabolic network, we also calculate, analyze and classify the p, t invariants in the model based on their structural capability and biological significance.

关键词： invariant analysis metabolic model systems biology

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14th International symposium on Intelligent data analysis, IDA 2015

14th International Symposium on Intelligent Data Analysis, I...

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14th International symposium on Intelligent data analysis, IDA 2015

ISBN: (纸本)9783319244648

the proceedings contain 29 papers. the special focus in this conference is on Intelligent data analysis. the topics include: data analytics and optimisation for assessing a ride sharing system;constraint-based querying for bayesian network exploration;efficient model selection for regularized classification by exploiting unlabeled data;segregation discovery in a social network of companies;a first-order-logic based model for grounded language learning;a parallel distributed processing algorithm for image feature extraction;a probabilistic graphical model based approach;diversity-driven widening of hierarchical agglomerative clustering;batch steepest-descent-mildest-ascent for interactive maximum margin clustering;time series classification with representation ensembles;simultaneous clustering and model selection for multinomial distribution;on binary reduction of large-scale multiclass classification problems;probabilistic active learning in datastreams;implicitly constrained semi-supervised least squares classification;diagonal co-clustering algorithm for document-word partitioning;an attributed graph clustering method;class-based outlier detection;using metalearning for prediction of taxi trip duration using different granularity levels;using entropy as a measure of acceptance for multi-label classification;investigation of node deletion techniques for clustering applications of growing self organizing maps;exploratory topic modeling with distributional semantics;assigning geo-relevance of sentiments mined from location-based social media posts and continuous and discrete deep classifiers for data integration.

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the data problem in data mining 14th

The data problem in data mining

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14th International symposium on Intelligent data analysis, IDA 2015

作者： Zimmermann, Albrecht INSA de Lyon France

ISBN: (纸本)9783319244648

Computer science is essentially an applied or engineering science, creating tools. In data Mining, those tools are supposed to help humans understand large amounts of data, and produce actionable insight. In this talk, I argue that for all the progress that has been made in data Mining, in particular Pattern Mining, we are lacking understanding of key aspects of the performance and results of pattern mining algorithms. I will focus particularly on the difficulty of deriving actionable knowledge from patterns. I trace the lack of progress regarding those questions to a lack of data with varying, controlled properties, and argue that we will need to make a science of digital data generation, and use it to develop guidance to data practitioners. © Springer International Publishing Switzerland 2015.

关键词： data mining

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Performance analysis for Fast Parallel Recomputing Algorithm under DTA 14

Performance Analysis for Fast Parallel Recomputing Algorithm...

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International symposium Distributed Computing and Applications Business Engineering and Science

作者： Dou, Wanfeng Miao, Shoushuai Nanjing Normal Univ Dept Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781467365932

With the rapid increasing of spatial data resolution, the huge volume of datasets makes the geo-computation more time-consuming especially in operating some complex algorithms. Parallel computing is regarded as an efficient solution by utilizing more computing resource. the stable and credible services play an irreplaceable role in parallel computing, especially when an error occurs in the large-scale science computing. In this paper, a master/ slave approach of implementing the fast parallel recomputing is proposed based on redundancy mechanism. Once some errors in application layer are detected, the original data block with computation errors is further partitioned into several sub-blocks which are recomputed by the surviving processes concurrently to improve the efficiency of failure recovery. the multi-thread strategy in main process is adopted to distribute data block, detect errors and start recomputing procedure concurrently. the experimental results show that the proposed method can achieve better performance efficiency with fewer additional overhead.

关键词： parallel computing digital terrain analysis fault tolerance fast parallel recomputing

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A Semantic-Based data Model for the Manipulation of Trajectories: Application to Urban Transportation 14th

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14th International symposium on Web and Wireless Geographic Information Systems (W2GIS)

作者： Zheni, Donia Frihida, Ali Claramunt, Christophe Ben Ghezala, Henda Univ Manouba ISAMM Manouba Tunisia Univ Tunis Al Manar ENIT CONTOS2 Tunis Tunisia Naval Acad Res Inst Brest France Univ Manouba ENSI RIADI Manouba Tunisia

ISBN: (纸本)9783319182513;9783319182506

Nowadays, the increasing development of positioning and wireless communication technologies favors a better real-time integration and manipulation of large spatial databases. this offers many new opportunities for the development of trajectory databases, but a number of research challenges are still open as the generated information is often unstructured, continuous, large and sometimes unpredictable. the research presented in this paper develops a modeling approach that integrates the semantic, spatial and temporal dimensions when representing spatial trajectories at the abstract and logical levels. A data manipulation language that supports the querying and analysis of large trajectory databases is also proposed. the spatial database model is based on algebraic data types, and a prototype is developed on top of the DBMS PostgreSQL/PostGIS. the whole approach and the prototype development have been experimented and applied to benchmark transportation data derived from an origin-destination survey in the region of Quebec in Canada.

关键词： Trajectory modeling Moving object databases Abstract data type Temporal GIS

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Scalable Massively Parallel Learning of Multiple Linear Regression Algorithm with MapReduce 14

Scalable Massively Parallel Learning of Multiple Linear Regr...

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Joint 14th IEEE Int Conf on Trust, Secur and Privacy in Comp and Commun / 13th IEEE Int symposium on Parallel and Distributed Proc with Applications / 9th IEEE Int Conf on Big data Science and Engineering (IEEE TrustCom-ISPA-BigdataSE)

作者： Adjout Rehab, Moufida Boufares, F. Paris 13 Novedia VISEO Grp Lab LIPN UMR CNRS 7030 F-93430 Villetaneuse France

ISBN: (纸本)9781467379526

the large volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes training of very large scale of data a challenging task. However, this information cannot be practically analyzed on a single machine due to the sheer size of the data to fit in memory. For this purpose, the process of such data requires the use of high-performance analytical systems running on distributed environments. To this end standard analytics algorithms need to be adapted to take advantage of cloud computing models which provide scalability and flexibility. this paper introduces a new distributed training method, which combines the widely used framework, MapReduce, for Multiple Linear Regression which will be based on the QR decomposition and the ordinary least squares method adapted to MapReduce. Our platform is deployed on Cloud Amazon EMR service. Experimental results demonstrate that our parallel version of the Multiple Linear Regression can efficiently handle very large datasets with different parameter settings (number, size and structure of machines).

关键词： Big data Cloud Computing data mining Hadoop MapReduce Multiple Linear regression Predictive analysis big data Cloud Computing data mining Linear Regression Scalability Individual Needs machine

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Based on rough sets and the associated analysis of KNN text classification research 14

Based on rough sets and the associated analysis of KNN text ...

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International symposium Distributed Computing and Applications Business Engineering and Science

作者： Guo Aizhang Tao, Yang Qilu Univ Technol Jinan 250353 Peoples R China

ISBN: (纸本)9781467365932

With the rapid development of network information technology, the text is as a basic information carrier and begins to present exponential growth. the existing text classification methods haven't got information from the vast amounts of information resources timely and accurately. In order to solve the problem, the paper puts forward a new method about text categorization. It is a KNN algorithm based on rough set and correlation analysis. Firstly, we introduce the concept of rough set. In the training set of text vector space, we divide all kinds of text vector spaces into certain and uncertain areas. For certain areas, we can directly judge its category. For uncertain areas, we determine the type of text vector through KNN text classification algorithm based on correlation analysis. Experimental results show that the KNN text classification algorithm based on rough sets and the associated analysis have greatly improved the efficiency and accuracy of text categorization. It can meet the requirements of processing large amounts of text data.

关键词： text classification k-NearestNeighbo Correlation analysis the rough set

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