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For almost all ML algorithms studied, increasing the number of negative training instances was found to decrease recall and increase both precision and MCC values.
For all the analyzed scenarios (target/ML method/fingerprint) and sizes of used test set, the preferable ratio of positive to negative training instances was found to be approximately 1 9 to 1 10.
An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments.
The coracohumeral ligament in all instances was found to be thickened and frozen in a shortened position.
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New best solutions for many benchmark instances are found.
The hardest instances were found close to the transition between regions of satisfiable and unsatisfiable instances; furthermore, unsatisfiable instances were found harder to solve than satisfiable ones.
Note that in square brackets, you can see the subcategories that no instances were found during the annotation.
In addition, the new best-known solutions for 17 out of 420 small instances and 589 out of 720 large instances are found.
Most instances are found difficult to solve optimally while our metaheuristic can find the best known or close to the best known solution in a short time.
Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found.
The list of the employed visual actions as well as their subcategories are presented in Table 5, where in italics the additionally included actions can be seen and in square brackets the actions that no instances were found.
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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