Parallel computing is increasingly exposed to the development and challenges of distributed systems, such as asynchrony, long latencies, failures, network partitions, mobility, heterogeneity, malicious and selfish beh...
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Modern multicore and manycore systems offer impressive performance for various applications. However, achieving this performance is a challenging task. While multicore and manycore processors alleviate several problem...
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The increasing trend to distribute computing over large-scale parallel and distributed platforms, such as clouds, grids and large clusters, often combined with the use of multicore processors and hardware accelerators...
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This topic deals with architecture design and and compilation for high performance systems. The areas of interest range from microprocessors to large-scale parallel machines;from general-purpose platforms to specializ...
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The way for performing multiple sequence alignment is based on the criterion of the maximum scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same ...
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ISBN:
(纸本)9783642219306
The way for performing multiple sequence alignment is based on the criterion of the maximum scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same highest score leading to ambiguities in selecting the best alignment. This paper addresses this issue by introducing the concept of joint weight matrix to eliminate the randomness in selecting the best alignment of multiple sequences. Alignments with equal scores are iteratively re-scored with joint weight matrix of increasing level (nucleotide pairs, triplets and so on) until one single best alignment is eventually found. This method can be easily implemented to algorithms using weight matrix for scoring such as those based on the widely used Gibbs sampling method.
Developing parallel or distributed applications is a hard task and it requires advanced algorithms, realistic modeling, efficient design tools, high-level programming abstractions, high-performance implementations, an...
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The recent years have seen great research interest in exploiting GPUs and accelerators for computations, as shown by the latest TOP500 editions, whose very top entries are fully based on their use. Their potential com...
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Peer-to-peer (P2P) systems enable computers to share information and other resources with their networked peers in large-scale distributed computing environments. The resulting overlay networks are inherently decentra...
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One could define vagueness as the existence of borderline cases and characterise the philosophical debate on vagueness as being about the nature of these. The prevalent theories of vagueness can be divided into three ...
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Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investmen...
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ISBN:
(纸本)9783319121574;9783319121567
Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investments for all competitive F1 teams. The F1 world and teams invest vast amounts in developing high-fidelity simulators;however, driving games have seldom been associated with uncovering certain natural abilities. Beyond nature and nurture to attain success at the top level, certain motor-cognitive aspects are paramount for proficiency. One method of potentially finding talent is studying the behavioral and cognitive patterns associated with learning. Here, an F1 simulation game was used to demonstrate how learning had taken place. The indicative change of interest is from cognitive to motor via more skilled autonomous driving style -a skill synonymous with expert driving and ultimately winning races. Our data show clear patterns of how this skill develops.
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