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Our list of data sets is far from complete; we view this list as an initial step towards building a comprehensive collection of benchmarking data sets for PH.
Though the thresholds are empirical, they function well according to the results in our large collection of benchmarking tests.
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We evaluated the performance of known snapshot algorithms for a collection of benchmarks on a simulated distributed shared-memory multiprocessor.
Computational experiments on a collection of benchmark classification problems shows improvement on the original HERF, and other state-of-the-art approaches.
First, a collection of benchmark cases is presented and used to carefully validate the radiation model against literature results and to analyse the model prediction limits.
Experimental evaluations on a wide collection of benchmark graphs show that the proposed approach not only competes very favorably with the two well-known partitioning packages METIS and CHACO, but also improves more than two thirds of the best balanced partitions ever reported in the literature.
The performance evaluation is included in "Performance evaluation", based on the experiments with a rich collection of benchmark data.
The problems included in this collection of benchmarks do not make use of constraints (6– 7).
An overview of the collection of benchmark problems is presented in Table 1.
In this section, we describe the collection of benchmark datasets and the construction of the predictive model.
We show how our collection of benchmark problems can be used by reporting selected results using several parameter estimation methods.
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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