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As we showed in later sections, the amount of crosstalk introduced by our methods is small, while the quality remains good for low bitrate applications.
The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%.
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From Table 3, it can be seen that numerical results obtained by our methods are in good agreement with Srivastava, Bahadir and Jain's methods.
In addition to the novel predicted conditional exons, examples of known conditional exons which were predicted by our methods were shown in Supplementary Figure S1.
We also show that the experimental results discovered by our methods are superior to those by Zhang's algorithm.
The confidence of similarity assessments evaluated by our method is then compared to some popularly-used traditional assessment methods.
The distribution of the generated particle samples by our method is better than that of the traditional techniques.
This study indicates that the hollow LDH microsphere prepared by our method is a promising material for supercapacitors.
Our evaluation shows that the quality of the quadrangulations generated by our method is as good as, if not better than, those from other methods, achieving about four times fewer singularities than Instant Meshes.
We show numerically that the accuracy of the numerical solutions computed by our method is significantly higher than that computed by the IPDG method using polynomials.
It is demonstrated numerically that the tip velocity obtained by our method is in good agreement with the previously published results.
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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