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As can be seen from the figures, the sum capacity curves for our method and the original method 1 coincide, which again illustrates that our method always achieves the same solution as the original method 1.
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Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms.
Theoretically, one of the adaptive methods always achieves equal headway stability unless the passenger density saturates the system.
When validating the effectiveness of the three local filters, results also show that the combinations of multiple methods always achieve similar or better predictions than any single method.
First, we show that there is no single method that always achieves the best prediction performance for all different datasets, a result consistent with the 'there is no free lunch theorem' [16].
It statistically indicates that in terms of accuracy and Macro-F1 there is no single method that always achieves a consistent rank position for different datasets, which is something similar to the well-known 'no-free lunch theorem' [16].
Results show that the classification method can always achieve acceptable performance (e.g. it can arrange 9,379 pre-miRNAs to 398 families with an accuracy of 97.97%).
The large test error of these supervised methods motivates spending time on interactive annotation, which always achieves 0% training error.
Comparing each of these expressions with the DMT upper bound in (9), we see that the best relay selection method is the only one which can always achieve the DMT bound.
So we follow the method in [17] and set T f = δ + D. This will always achieve as high a data rate as possible without inducing IFI.
Although the MLP method is not disrupted by irrelevant information from outer atoms, MLP does not always achieve superior sensitivity to MLR.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com