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Taking the best performing variant, we compared its performance with that of OSCAR and ChemSpot by also running their latest versions (OSCAR4.1 and ChemSpot 2.0) on each corpus of interest.
We performed the rMd-PAV analysis, and compared its performance with t correlation alone to identify statistical outliers (runs at the peptide abundance level) via a receiver operating characteristic (ROC) curve analysis.
We compared its performance with three state-of-the-art load balancing algorithms.
They have compared its performance with that of the performance of the conventional location updating algorithm.
We examined the intermediate steps of the eHMM and compared its performance to the baseline HMM.
We implemented the algorithm in Qualnet network simulator and compared its performance to existing TCP versions.
We empirically compared its performance with other well-known perceptual metrics.
Pawarat Kitmanomai and Prachya Lalitnorasate created a neural network classifier and compared its performance to the OpenDragon maximum likelihood classification.
Parker et al. [23] also chose MongoDB and compared its performance with MS SQL Server using only one server instance.
They compared its performance with and without a shaft between the end plates at different gap ratios.
We re-implemented the state-of-the-art method proposed in [49] and compared its performance with our method.
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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