Application run time estimation: a quality of service metric for web-based data mining services.

leads to an inprovement (in terms of run-time and chip area) of the whole mixed HW/SW system.

The hydrophobic-hydrophilic (H-P) model for protein folding was introduced by Dill et al. [6]. A problem instance consists of a sequence of amino acids, each labeled as either hydrophobic (H) or hydrophilic (P). The sequence must be placed on a 2D or 3D grid without overlapping, so that adjacent amino acids in the sequence remain adjacent in the grid. The goal is to minimize the energy, which in the simplest variation corresponds to maximizing the number of adjacent hydrophobic pairs. Although the model is extremely simple, it captures the main features of the problem. The protein folding problem in the H-P model is NP-hard in both 2D and 3D. Recently, Fu and Wang [9] proved an exp(O(n ln n) algorithm for d-dimensional protein folding simulation in the HP-model. Our preliminary results on stochastic search applied to protein folding utilize complete move sets proposed by Lesh et al. [15] and Blazewicz et al. [3]. We obtain that after (n/# ) Markov chain transitions, the probability to be in a minimum energy conformation is at least 1 where n is the length of the instance, # is the maximum value of the minimum escape height from local minima of the underlying energy landscape, and c is a (small) constant.

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joint analysis considering three metrics. A case study is given and corresponding results indicate that well known DAG scheduling algorithms designed for single DAG and single machine settings are not well suited for Grid scheduling scenarios, where user run time estimates are available. We show

Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media B.V.. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication.

run-time of our method is Õ(d/(λɛ)), where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non

to be shortest paths. The algorithm and its analysis are simple and intuitive, yet the algorithm runs as fast as any other known method on dense. graphs, achieving an O(n³) time bound on an n-vertex graph. By incorporating the dynamic tree data structure of Sleator and Tarjan, we obtain a version

phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil

quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detec-tion of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach