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We conducted a comparative experiment with the translation approach developed in the defense domain.
We have chosen most four representative algorithms of them: neural networks, NB, SVM, and AdaBoost for the comparative experiment with LDABoost.
Data from a comparative experiment with ADI clearly show its greater damaging action on CHO than HeLa cells.
The weak signal of these mutants likely arose from intermolecular interactions, as shown by a comparative experiment with a single labeled sample.
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Some comparative experiments with traditional ANN-based methods are conducted.
Comparative experiments with typical desired trajectories are carried out in the test rig.
The effectiveness and superiorities of the proposed method are demonstrated through several comparative experiments with other three manifold learning methods.
In order to prove the stability of the experiment, six groups of comparative experiments with different unlabeled testing sample sets are operated.
This observation was strengthened by comparative experiments with helium and argon, where the diluent effect was even stronger for helium, caused by its higher thermal conductivity.
Comparative experiments with 14 datasets and 6 other online algorithms show that FTRL-ADP is most advantageous in noisy environments with real drifts.
We also give theoretical analysis on the algorithm, and evaluate the performance through comparative experiments with conventional algorithms by artificial and real datasets.
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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