Abstract Using genetic algorithm (GA), this study proposes a portfolio optimization scheme for index fund management. Index fund is one of popular strategies in portfolio management that aims at matching the performance of the benchmark index such as the S&P 500 in New York and the FTSE 100

similarly to vector computers and heterogeneity of the system by computing both on GPU and CPU. This study includes detailed benchmarking of the GPU memory system that reveals sizes and latencies of caches and TLB. We present a couple of algorithmic optimizations aimed at increasing parallelism

consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an “automatic” or “benchmark” prior structure that can be used in such cases. We focus

in 1999 and 2001. Achieve helps states raise academic standards, measure performance against those standards, establish clear accountability for results and strengthen public confidence in our education system. To do this, we: ■ help states benchmark their standards, assessments and accountability systems

due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features

. Starting 30 years ago, research efforts have aimed at understanding NPD practices and identifying those which are deemed “best practices. ” During the past five years, pursuit of this goal has produced numerous privately available reports and two research efforts sponsored by the PDMA. Abbie Griffin

-of-the-art performance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components

To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here

CONSPECTUS: The past 15 years have witnessed an explosion of activity in the field of fragment-based quantum chemistry, whereby ab initio electronic structure calculations are performed on very large systems by decomposing them into a large number of relatively small subsystem calculations and then reassembling the subsystem data in order to approximate supersystem properties. Most of these methods are based, at some level, on the so-called many-body (or “n-body”) expansion, which ultimately requires calculations on monomers, dimers,..., n-mers of fragments. To the extent that a low-order n-body expansion can reproduce supersystem properties, such methods replace an intractable supersystem calculation with a large number of easily distributable subsystem calculations. This holds great promise for performing, for example, “gold standard ” CCSD(T) calculations on large molecules, clusters, and condensed-phase systems. The literature is awash in a litany of fragment-based methods, each with their own working equations and terminology, which

prediction accuracy, and cache behavior with the programs from MediaBench. Although the aimed architectures are similar for MediaBench and NetBench suites, we show that these workloads have significantly different characteristics. Hence a separate benchmarking suite for network processors is a necessity