The Experts below are selected from a list of 1635 Experts worldwide ranked by ideXlab platform
Evan Odell - One of the best experts on this subject based on the ideXlab platform.
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evanodell/hansard: v0.5.0
2017Co-Authors: Evan OdellAbstract:New features All Functions have a Wrapper Function with the same name, but with hansard_ prefixed. Existing names have remained untouched. Addition of house and answering_body parameters to all_answered_questions() Function. Bug Fixes Fixed bug in lord_vote_record() which produced an error if requesting both lobbies, but a peer had only voted in one of the lobbies
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evanodell/hansard: v0.4.9
2017Co-Authors: Evan OdellAbstract:hansard 0.4.9 New features All Functions have a Wrapper Function with the same name, but with hansard_ prefixed. Party name columns in election_candidates() and election_results(all_data=TRUE) are now in alphabetical order
Diptikalyan Saha - One of the best experts on this subject based on the ideXlab platform.
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service mining from legacy database applications
International Conference on Web Services, 2015Co-Authors: Diptikalyan SahaAbstract:As software consumption is shifting to mobile platforms, enterprises are looking for efficient ways to reuse their existing legacy systems by exposing their Functionalities as services. Mining services from legacy code is therefore an important problem for the enterprises. In this paper we present a technique for mining service candidates from the database applications. Central to our mining technique is the specification and identification of data-access patterns which specify how a program interacts with the databases. In addition to finding service candidates which are internal Functions in the source code, we also provide an algorithm to expose the Function as a stateless service by generating a Wrapper Function around the internal Function. We demonstrate the effectiveness of our technique on two open source applications and twelve industrial applications.
Hua Zhou - One of the best experts on this subject based on the ideXlab platform.
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svt: Singular Value Thresholding in MATLAB
'Foundation for Open Access Statistic', 2017Co-Authors: Hua ZhouAbstract:Many statistical learning methods such as matrix completion, matrix regression, and multiple response regression estimate a matrix of parameters. The nuclear norm regularization is frequently employed to achieve shrinkage and low rank solutions. To minimize a nuclear norm regularized loss Function, a vital and most time-consuming step is singular value thresholding, which seeks the singular values of a large matrix exceeding a threshold and their associated singular vectors. Currently MATLAB lacks a Function for singular value thresholding. Its built-in svds Function computes the top r singular values/vectors by Lanczos iterative method but is only efficient for sparse matrix input, while aforementioned statistical learning algorithms perform singular value thresholding on dense but structured matrices. To address this issue, we provide a MATLAB Wrapper Function svt that implements singular value thresholding. It encompasses both top singular value decomposition and thresholding, handles both large sparse matrices and structured matrices, and reduces the computation cost in matrix learning algorithms
Zee, Benny Chung-ying - One of the best experts on this subject based on the ideXlab platform.
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Enhancing power of rare variant association test by Zoom-Focus Algorithm (ZFA) to locate optimal testing region
2016Co-Authors: Wang, Maggie Haitian, Weng Haoyi, Sun Rui, Zee, Benny Chung-yingAbstract:Motivation: Exome or targeted sequencing data exerts analytical challenge to test single nucleotide polymorphisms (SNPs) with extremely small minor allele frequency (MAF). Various rare variant tests were proposed to increase power by aggregating SNPs within a fixed genomic region, such as a gene or pathway. However, a gene could contain from several to thousands of markers, and not all of them may be related to the phenotype. Combining Functional and non-Functional SNPs in arbitrary genomic region could impair the testing power. Results: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region, as a Wrapper Function to be applied in conjunction with rare variant association tests. In the first Zooming step, a given genomic region is partitioned by order of two, and the best partition is located within all partition levels. In the next Focusing step, boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially enhanced the statistical power of rare variant tests by over 10 folds, including the WSS, SKAT and W-test. The algorithm is applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorder that are undiscoverable by testing using full gene. The proposed algorithm is an efficient and powerful tool to increase the effectiveness of rare variant association tests for exome sequencing datasets of complex disorder.Comment: Main paper: 13 pages, 2 figures, 3 tables, 3 diagrams; Submitted to Bioinformatics, and the 27th International Conference on Genome Informatic
Wang, Maggie Haitian - One of the best experts on this subject based on the ideXlab platform.
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Enhancing power of rare variant association test by Zoom-Focus Algorithm (ZFA) to locate optimal testing region
2016Co-Authors: Wang, Maggie Haitian, Weng Haoyi, Sun Rui, Zee, Benny Chung-yingAbstract:Motivation: Exome or targeted sequencing data exerts analytical challenge to test single nucleotide polymorphisms (SNPs) with extremely small minor allele frequency (MAF). Various rare variant tests were proposed to increase power by aggregating SNPs within a fixed genomic region, such as a gene or pathway. However, a gene could contain from several to thousands of markers, and not all of them may be related to the phenotype. Combining Functional and non-Functional SNPs in arbitrary genomic region could impair the testing power. Results: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region, as a Wrapper Function to be applied in conjunction with rare variant association tests. In the first Zooming step, a given genomic region is partitioned by order of two, and the best partition is located within all partition levels. In the next Focusing step, boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially enhanced the statistical power of rare variant tests by over 10 folds, including the WSS, SKAT and W-test. The algorithm is applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorder that are undiscoverable by testing using full gene. The proposed algorithm is an efficient and powerful tool to increase the effectiveness of rare variant association tests for exome sequencing datasets of complex disorder.Comment: Main paper: 13 pages, 2 figures, 3 tables, 3 diagrams; Submitted to Bioinformatics, and the 27th International Conference on Genome Informatic
