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We estimate our approach with a SimpleScalar based simulator run on the Mediabench benchmark suite and compare to the trace cache performance on the same benchmarks.
With the exception of THERM and THR, the performance of the best encodings was at least comparable to the mean squared error values for a sophisticated graph kernel given by Fechner et al. [33] on the same benchmarks.
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Three sensitivity analyses were then performed on the same benchmark to evaluate the properties of the model.
We also illustrate on the same benchmark problems that our revision always reports solutions that are feasible.
The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.
However, even if using such a stringent criterion on the same benchmark dataset by the jackknife test, the overall absolute true success rate achieved by iLoc-Euk was 5535/7766 = 71.27%.
Trained on the same benchmark datasets, our models produce obviously better performance for the independent dataset.
Therefore, we train the AM-SVM with the CTF on the same benchmark dataset for comparison.
All of these methods improved the prediction accuracy on the same benchmarking dataset [ 26, 27].
First, based on the sequence similarity principle, we used BLAST [ 109] to conduct the jackknife test on the same benchmark dataset as used by the iSS-PseDNC predictor.
We therefore tested the performance of LoopIng on the same benchmark and show here the comparison of its results with those of FREAD and LEAP (Table 2).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com