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I recommend dual form NRT because there is very strong evidence that this gives better results than using one form of NRT by itself.
This gives better performance than a feedforward neural network.
This gives better step coverage during structural material deposition and patterning.
This gives better agreement with noise data compared to Goldstein et al. (2013) for Strouhal numbers less than the peak jet-surface interaction noise.
We have used the square root of the spectral power of the equalizer input for our normalization rather than the spectral power considered in [14] as we found that this gives better performance.
This gives better resolution and better overview of protein spots across the entire chosen pH window.
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This gave better performance than the Euclidian distance, but also provides a linear response upon vector element reweighting (e.g. in machine learning applications).
This gave better results than predictions with direct geographic distances between the sampled populations (as in an isolation-by-distance model).
On the other hand, this phoneme gives better performance in the other experiments.
It was shown that this approach gives better performance than the single solution-vector [13, 14].
This approach gives better results than one employing a constant ratio of turbulent timescale and the scalar covariance ∼c′′Z′′.
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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