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We have developed an efficient implementation to compute the 2D fast Fourier transform (FFT) on a new very long instruction word programmable mediaprocessor.
Part of the reason for the lack of parallel implementation of SpMM in generic software packages is that the best implementation for computing SpMM for a particular matrix A depends on the sparsity pattern of A. In this paper, we present a simple yet effective implementation to compute SpMM for a matrix A that represents an undirected network.
The first algorithm has an easy implementation to compute all maximal cliques in c-max-tolerance graphs, but it has a theoretical runtime of O(n).
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The VC-1 transform process also allows fast algorithm implementations to compute the inverse transforms.
The workflow from in silico SNP discovery in the Sce_Assembly02 to selection of high confidence SNP candidates was a three-step procedure: First, the tool GigaBayes V0.4.1 [ 41] was applied with parameter settings given in Additional file 3. Second, characteristics for discovered SNPs were extracted by in-house implementations to compute defined selection criteria for candidate SNPs.
The enhanced performance of the parallel implementation allows to compute a solution closer to the maximum quality of the alignment.
Furthermore, we propose a hardware/software co-design of a system-on-chip implementation to efficiently compute modular exponentiations.
An algorithm is presented with an implementation in MATLAB to compute the thermodynamic minimum air flow rate for the desired cooling.
Based on these observations, in what follows we only run the LP-based implementation with fastFVA to compute the FBIDs corresponding to the different genes and networks investigated for phenotypic prediction.
Our implementation is able to compute the consensus set of domains for the human fibroblast cell line and extract the consensus set in 24 minutes when run on a personal computer with 2.3GHz Intel Core i5 processor and 8Gb of RAM.
where all the sums used are points in the neighborhood N. In this section, the implementation of the Lucas-Kanade algorithm proposed by Correia [44, 45] is described because this implementation has been used to compute the optical flow prior to being segmented.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com