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As it is clear from the figures, our method outperforms the other methods in total.
From the figures, our method consistently outperformed the private SVM at different sampling rates.
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According to this figure, our method cost almost as half as the preamble consumption of Wang's method.
As can be seen from the figure, our method still maintains a relative high level of classification accuracy under the influence of errors in label proportions.
As denoted in the figure, our method successfully detected the four fold bifurcation points, A, B, D, and F which are indicated with green stars.
As shown in the figures, our proposed method provides highly accurate factor estimation results, and more than 95% of the estimated factors are in the ±0.05 scope of the reference ones.
We show from these figures that our method can accurately track each source trajectory.
Additionally, as our figures have shown (cf. Figure 6), our method is able to produce maps apt for patch dynamics visualization to monitor population density [23].
Figure 9 Our method: MSE (top) versus distance to the destination (bottom).
As shown in Figure 3, our method 'SD-CRBM' performed the best of all the methods for each case.
For images I to VIII in Figure 8, our method segments the skin regions with high precision.
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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