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The probability that a non-outlier is misidentified by our algorithms is analyzed using bounds based on Rademacher complexity.

Our findings confirm that the quality of partition produced by our algorithms is stable as the number of processors is increased while being competitive with those produced by a state-of-the-art serial multilevel partitioning tool.

Investigations with a more statistical nature can also provide an insight to this discussion as the data analyzed by our algorithms is generated by Monte-Carlo simulations.

Mean fixation duration returned by our algorithms is 506 ms for the clean data, 480 ms for the flicker simulation, and 424 ms for the low-precision simulation (in contrast to 472 ms/240 ms/367 ms for the standard dispersal algorithms shown in Fig.  5).

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Estimates generated by our algorithms were then compared with the times captured from the video recordings.

Experimental results obtained by our algorithms are promising in terms of true positive rate (TPR) and false positive rate (FPR).

We require all the iterative points U k and V k generated by our algorithms are feasible, namely (7b) are always satisfied.

All mapping candidates generated by our algorithms were exported into a spreadsheet.

Therefore, we also implemented a number of post hoc validation criteria to check whether the fixations identified by our algorithms were genuine or artifactual.

Our analysis also reveals that the important genes (IL1A, IL1B, IL1R, IL6, TNFA, IL17, IL8, IL1R, TLR4 and TNFR) detected by our algorithms are vital for the inflammatory response because they are more connected during inflammation than in normal conditions.

The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.

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