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检索条件"任意字段=6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015"

共 237 条记录，以下是71-80 订阅

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10th international Doctoral Workshop on Mathematical and Engineering methods in Computer Science, MEMICS 2015

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10th international Doctoral Workshop on Mathematical and Engineering methods in Computer Science, MEMICS 2015

ISBN: (纸本)9783319298160

the proceedings contain 13 papers. the special focus in this conference is on Mathematical and Engineering methods in Computer Science. the topics include: Programming support for future parallel architectures;flexible interpolation for efficient model checking;understanding transparent and complicated users as instances of preference learning for recommender systems;span-program-based quantum algorithms for graph bipartiteness and connectivity;fitting aggregation operators;practical exhaustive generation of small multiway cuts in sparse graphs;self-adaptive architecture for multi-sensor embedded vision system;exceptional configurations of quantum walks with grover’s coin;performance analysis of distributed stream processing applications through colored petri nets;GPU-accelerated real-time mesh simplification using parallel half edge collapses;classifier ensemble by semi-supervised learning;the challenge of increasing safe response of antivirus software users and weak memory models as LLVM-to-LLVM transformations.

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Efficient subformula orders for real quantifier elimination of non-prenex formulas 6th

Efficient subformula orders for real quantifier elimination ...

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6th international conference on Mathematical Aspects of Computer and Information Sciences, MACIS 2015

作者： Kobayashi, Munehiro Iwane, Hidenao Matsuzaki, Takuya Anai, Hirokazu University of Tsukuba Tsukuba Japan Fujitsu Laboratories Ltd. Kawasaki Japan National Institute of Informatics Chiyoda Japan Nagoya University Nagoya Japan Kyushu University Fukuoka Japan

ISBN: (纸本)9783319328584

In this paper we study speeding up real quantifier elimination (QE) methods for non-prenex formulas. Our basic strategy is to solve non-prenex first-order formulas by performing QE for subformulas constituting the input non-prenex formula. We propose two types of methods (heuristic methods/machine learning based methods) to determine an appropriate ordering of QE computation for the subformulas. then we empirically examine their effectiveness through experimental results over more than 2,000 non-trivial example problems. Our experiment results suggest machine learning can save much effort spent to design effective heuristics by trials and errors without losing efficiency of QE computation. © Springer international Publishing Switzerland 2016.

关键词： Heuristic methods

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On the computational complexity of positive linear functionals on C[0;1] 6th

On the computational complexity of positive linear functiona...

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6th international conference on Mathematical Aspects of Computer and Information Sciences, MACIS 2015

作者： Férée, Hugo Ziegler, Martin Department of Mathematics TU Darmstadt Schlossgartenstr. 7 Darmstadt64289 Germany Université de Lorraine LORIA UMR 7503 Vandœuvre-lès-Nancy54506 France Inria Villers-lès-Nancy54600 France CNRS LORIA UMR 7503 Vandœuvre-lès-Nancy54506 France KAIST School of Computing 291 Daehak-ro Daejeon34141 Korea Republic of

ISBN: (纸本)9783319328584

the Lebesgue integration has been related to polynomial counting complexity in several ways, even when restricted to smooth functions. We prove analogue results for the integration operator associated with the Cantor measure as well as a more general second-order #P#P-hardness criterion for such operators. We also give a simple criterion for relative polynomial time complexity and obtain a better understanding of the complexity of integration operators using the Lebesgue decomposition theorem. © Springer international Publishing Switzerland 2016.

关键词： Domain decomposition methods

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6th international Euro-Mediterranean conference on Digital Heritage, EuroMed 2016

6th International Euro-Mediterranean Conference on Digital H...

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6th international Euro-Mediterranean conference on Digital Heritage, EuroMed 2016

ISBN: (纸本)9783319484952

the proceedings contain 73 papers. the special focus in this conference is on 3D Reconstruction, 3D Modelling, Heritage Building Information models, Innovative methods on Risk Assesment, Monitoring, Intangible Cultural Heritage Documentation, Non-destructive Techniques in Cultural Heritage Conservation, Digital Cultural Heritage in Education, Learning and Training. the topics include: Implementation and usage scenarios of a participatory platform for cultural environments;proportional systems in the design of the cathedral of St. George of the Greeks, Cyprus;multi-scale 3D modelling of damaged cultural sites;low cost technique for accurate geometric documentation of complex monuments by non-experts;implementation analysis and design for energy efficient intervention on heritage buildings;historic BIM in the cloud;parameterizing the geometry and visualizing the lighting method of byzantine church domes;a study of 3D digital simulation analysis of fire charring degree of wood construction of Chinese traditional architecture;diagnostic activities for the planned and preventive conservation of mosaic pavements;researching knowledge concerns in virtual historical architecture;surveying illusory architectures painted on vaulted surfaces;internal 3D printing of intricate structures;digital preservation of cultural heritage;debate and considerations on using videos for cultural heritage from social media for 3D modelling;investigations of low-cost systems for 3D reconstruction of small objects;from SfM to semantic-aware BIM objects of architectural elements;an interdisciplinary study on the ancient Egyptian wines;towards the design of a user-friendly and trustworthy mobile system for museums and contextualizing 3D cultural heritage.

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Efficient pseudo-relevance feedback methods for collaborative filtering recommendation 38th

Efficient pseudo-relevance feedback methods for collaborativ...

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38th European conference on Information Retrieval Research, ECIR 2016

作者： Valcarce, Daniel Parapar, Javier Barreiro, Álvaro Information Retrieval Lab Computer Science Department University of A Coruña A Coruña Spain

ISBN: (纸本)9783319306704

Recently, Relevance-Based Language models have been demonstrated as an effective Collaborative Filtering approach. Nevertheless, this family of Pseudo-Relevance Feedback techniques is computationally expensive for applying them to web-scale data. Also, they require the use of smoothing methods which need to be tuned. these facts lead us to study other similar techniques with better trade-offs between effectiveness and efficiency. Specifically, in this paper, we analyse the applicability to the recommendation task of four well-known query expansion techniques with multiple probability estimates. Moreover, we analyse the effect of neighbourhood length and devise a new probability estimate that takes into account this property yielding better recommendation rankings. Finally, we find that the proposed algorithms are dramatically faster than those based on Relevance-Based Language models, they do not have any parameter to tune (apart from the ones of the neighbourhood) and they provide a better trade-off between accuracy and diversity/ novelty. © Springer international Publishing Switzerland 2016.

关键词： Collaborative filtering

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8th international conference on Computational Collective Intelligence, ICCCI 2016

作者： Davis, Alexandre Veloso, Adriano Universidade Federal de Minas Gerais Avenida Presidente Antônio Carlos 6627 Belo HorizonteMG31270-901 Brazil

ISBN: (纸本)9783662495209

Efficiently retrieving and understanding messages from social media is challenging, considering that shorter messages are strongly dependent on context. Assuming that their audience is aware of background and real world events, users can shorten their messages without compromising communication. However, traditional data mining algorithms do not account for contextual information. We argue that exploiting context can lead to advancements in the analysis of social media messages. Recall rate increases if context is taken into account, leading to context-aware methods for filtering messages without resorting only to keywords. A novel approach for subject classification of social media messages, using computational linguistics techniques, is proposed, employing both textual and extra-textual (or contextual) information. Experimental analysis over sports-related messages indicates over 50% improvement in retrieval rate over text-based approaches due to the use of contextual information. © Springer-Verlag Berlin Heidelberg 2016.

关键词： Computational linguistics

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algorithms for regularized linear discriminant analysis 6

Algorithms for regularized linear discriminant analysis

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6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Kalina, Jan Tebbens, Jurjen Duintjer Dept. of Medical Informatics and Biostatistics Institute of Computer Science AS CR Prague Czech Republic Dept. of Computational Methods Institute of Computer Science AS CR Prague Czech Republic Dept. of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Králové Charles University in Prague Hradec Králové Czech Republic

ISBN: (纸本)9789897580703

this paper is focused on regularized versions of classification analysis and their computation for high-dimensional data. A variety of regularized classification methods has been proposed and we critically discuss their computational aspects. We formulate several new algorithms for regularized linear discriminant analysis, which exploits a regularized covariance matrix estimator towards a regular target matrix. Numerical linear algebra considerations are used to propose tailor-made algorithms for specific choices of the target matrix. Further, we arrive at proposing a new classification method based on L2-regularization of group means and the pooled covariance matrix and accompany it by an efficient algorithm for its computation.

关键词： Covariance matrix

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Fast hoeffding drift detection method for evolving data streams 1

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15th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Pesaranghader, Ali Viktor, Herna L. Faculty of Engineering School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (数字)9783319462271

ISBN: (纸本)9783319462264

Decision makers increasingly require near-instant models to make sense of fast evolving data streams. Learning from such evolving environments is, however, a challenging task. this challenge is partially due to the fact that the distribution of data often changes over time, thus potentially leading to degradation in the overall performance. In particular, classification algorithms need to adapt their models after facing such distributional changes (also referred to as concept drifts). Usually, drift detection methods are utilized in order to accomplish this task. It follows that detecting concept drifts as soon as possible, while resulting in fewer false positives and false negatives, is a major objective of drift detectors. To this end, we introduce the Fast Hoeffding Drift Detection Method (FHDDM) which detects the drift points using a sliding window and Hoeffding’s inequality. FHDDM detects a drift when a significant difference between the maximum probability of correct predictions and the most recent probability of correct predictions is observed. Experimental results confirm that FHDDM detects drifts with less detection delay, less false positive and less false negative, when compared to the state-of-the-art. © Springer international Publishing AG 2016.

关键词： Decision making

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Towards a unified Named Entity Recognition system disease mention identification 6

Towards a unified Named Entity Recognition system disease me...

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6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Munkhdalai, Tsendsuren Li, Meijing Batsuren, Khuyagbaatar Ryu, Keun Ho Database/Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju Korea Republic of

ISBN: (纸本)9789897580703

Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biomedical text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. In this study, we take a step towards a unified NER system in biomedical, chemical and medical domain. We evaluate word representation features automatically learnt by a large unlabeled corpus for disease NER. the word representation features include brown cluster labels and Word Vector Classes (WVC) built by applying k-means clustering to continuous valued word vectors of Neural Language Model (NLM). the experimental evaluation using Arizona Disease Corpus (AZDC) showed that these word representation features boost system performance significantly as a manually tuned domain dictionary does. BANNER-CHEMDNER, a chemical and biomedical NER system has been extended with a disease mention recognition model that achieves a 77.84% F-measure on AZDC when evaluating with 10-fold cross validation method. BANNER-CHEMDNER is freely available at: https://***/tsendeemts/banner-chemdner.

关键词： Supervised learning

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PERFECTOS-APE: Predicting regulatory functional effect of SNPs by approximate P-value estimation 6

PERFECTOS-APE: Predicting regulatory functional effect of SN...

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6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Vorontsov, Ilya E. Kulakovskiy, Ivan V. Khimulya, Grigory Nikolaeva, Daria D. Makeev, Vsevolod J. Department of Computational Systems Biology Vavilov Institute of General Genetics Gubkina Str. 3 Moscow Russia Laboratory of Bioinformatics and Systems Biology Engelhardt Institute of Molecular Biology Vavilova Str. 32 Moscow Russia Faculty of Bioengineering and Bioinformatics Lomonosov Moscow State University Moscow Russia Department of Medical and Biological Physics Moscow Institute of Physics and Technology Moscow Region Russia

ISBN: (纸本)9789897580703

Single nucleotide polymorphisms (SNPs) and variants (SNVs) are often found in regulatory regions of human genome. Nucleotide substitutions in promoter and enhancer regions may affect transcription factor (TF) binding and alter gene expression regulation. Nowadays binding patterns are known for hundreds of human TFs. thus one can assess possible functional effects of allele variations or mutations in TF binding sites using sequence analysis. We present PERFECTOS-APE, the software to PrEdict Regulatory Functional Effect of SNPs by Approximate P-value Estimation. Using a predefined collection of position weight matrices (PWMs) representing TF binding patterns, PERFECTOS-APE identifies transcription factors whose binding sites can be significantly affected by given nucleotide substitutions. PERFECTOS-APE supports both classic PWMs under the position independency assumption, and dinucleotide PWMs accounting for the dinucleotide composition and correlations between nucleotides in adjacent positions within binding sites. PERFECTOS-APE uses dynamic programming to calculate PWM score distribution and convert the scores to P-values with an optional binary search mode using a precomputed P-value list to speed-up the computations. Software is written in Java and is freely available as standalone program and online tool: http://***/perfectosape/. We have tested our algorithm on several disease associated SNVs as well as on a set of cancer somatic mutations occurring in intronic regions of the human genome.

关键词： Binding sites

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