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Zaharia et al. [10] proposed a prediction model for sub-tasks of Hadoop job, rather than the entire job.
In fact, the model provides an interface for the distribution of sub-tasks and gathering of outputs.
Similarly to a multi-task learning problem [2], dynamic network node vaccination, or removal, has a main task, namely the optimal solution and then sub tasks to calculate and determine the overall benefit for top-k vaccination order subgroups.
For load balancing in a heterogeneous environment the important decision parameters are granularity of sub-task, application requirements, computation, communication resources available and task dependency.
The results indicated medium to high-risk REBA scores in at least one sub task for each sample preparation job task.
Similarly, the intermediate deadline for the sub-tasks in ω 2 is computed and the result is (d^{(2)}_{s}).
Thus, designing suitable and independent uniform random number generators for the sub-tasks has become a very important issue in large-scale parallel simulations.
11 By considering the worst-case execution time for each sub-task in the experiments we are evaluating the benefits of using work-stealing in the worst possible scenario.
As our algorithms return exactly the same outputs as original algorithms, we report only performance results for various sub-tasks.
For all sub-tasks, relevant ROIs were street, road signs, rear view mirror, side mirror left, side mirror right, while environment was considered as non-relevant ROI.
Video recordings were later analyzed in laboratory to quantify (i) Borg CR-10 force ratings for each sub-task, (ii) verbal anchor HAL ratings [ 48], (iii) total frequency of exertion, (iv) frequency of forceful exertions, (v) total percent duration of exertion, (vi) percent duration of forceful exertions, and (vii) posture and speed of work used to calculate SI scores.
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