Automatic peptide identification from collision-induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by t...
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Automatic peptide identification from collision-induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by the fuzzy nature of constraints from noisy data and by the existence of diverse but equally justifiable probabilistic models of peak matching. Here, two different scoring functions are combined into a parallel multi-objective optimization framework. It is shown how multi-objective optimization can be used to empirically test for independence between distinct scoring functions. The loss of selection pressure during the evolution of a population of putative peptide sequences by a Pareto-driven genetic algorithm is addressed by alternating between two definitions of fitness according to a numerical threshold. Copyright (c) 2005 John Wiley & Sons, Ltd.
Speeded up robust features (SURFs) are considered to be the most efficient feature extraction algorithm and it has been implemented in powerful hardware for real-time operation due to its characteristics of data-inten...
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Speeded up robust features (SURFs) are considered to be the most efficient feature extraction algorithm and it has been implemented in powerful hardware for real-time operation due to its characteristics of data-intensive computation of high complexity. Especially, the computational load of the descriptor extraction procedure is very significant and the overall performance of SURF can be improved by speeding up the descriptor extraction step with increasing parallel hardware accelerators. However, simply increasing the hardware accelerators is burdensome because of causing significant area and power consumption. Therefore, a reconfigurable hardware architecture is proposed that enables achieving the maximum performance of the descriptor extraction step with making the best use of the existing accelerators without any additional ones. Experimental results show that the proposed architecture improves the performance of the descriptor extraction step by 24.77-47.45% with negligible area overheads when compared with the existing hardware implementations of the SURF algorithm.
Traversing huge graphs is a crucial part of many real-world problems, including graph databases. We show how to apply Fixed Length lightweight compression method for traversing graphs stored in the GPU global memory. ...
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ISBN:
(纸本)9783319105178
Traversing huge graphs is a crucial part of many real-world problems, including graph databases. We show how to apply Fixed Length lightweight compression method for traversing graphs stored in the GPU global memory. This approach allows for a significant saving of memory space, improves data alignment, cache utilization and, in many cases, also processing speed. We tested our solution against the state-of-the-art implementation of BFS for GPU and obtained very promising results.
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