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S. , K. Sakharkar, A. Krishnan, A. B. M. Ridwan, M. Chitre, and Zhu H. Grid Cellware: The first Grid-enabled tool for modeling and simulating cellular processes. Bioinformatics, 2004. 3. J. D. Murray. Mathematical biology II: Spatial models and biomedical applications. Springer-Verlag, 2003. 4. B. Slepchenko et al. Computational cell biology: Spatiotemporal simulation of cellular events. Ann. Rev. Biophys. Biomol. , 31:423–441, 2002. Data Reduction in Human Vision System Kwang-Baek Kim1, Jae-Hyun Nam2, and Am-Suk Oh3 1 Dept.

Assuming heterogeneity in the performance of processors, allows us (i) to model interference from other users’ jobs, and (ii) to incorporate a cost penalty for migrations. In the case of heterogeneous processors we used a modified list scheduling algorithm based on that proposed by Davis and Jaffe [1]. The algorithm is known to give a solution at most times worse than the optimal for processors. Fig. 2. Scheduling. For each time step, the Master instructs the Medium to run the diffusion algorithm.

Fig. 2(a) shows the response time of mediator against other integration approaches. Fig. 2. Response Time for Querying Organism “Human Coronavirus” from Swiss-Prot for various integration methods; Comparison of Response Time for mediator and SRS for searching with various no of parameters The results shown that GIM [5] and SRS have faster response time compared to mediator-based approach. The response time is slower in the mediator-based because of the delay from query translation, the process of parsing and filtering the results in the wrapper components and the network propagation time for submitting the query and getting back the result.

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Bayesian analysis of case control polygenic etiology studies with missing data by Lee M.-L.T.

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