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This method obtains gradient information through correlations of ensemble members.
It is clear that only our method obtains desirable results.
First, the proposed method obtains the virtual reference sequence by constructing the Hankel data matrix.
The proposed method obtains promising results compared with 5 classical feature extraction algorithms.
Next, we demonstrate that our method obtains competitive performance compared to the state of the art.
Moreover, our method obtains better results for the complex Icam application.
The method obtains the VTM with training data of multiple subjects from multiple view angles.
The proposed method obtains the space diversity by using the multi-relay cooperative communications.
In all the given situations, our method obtains satisfied detection performance.
It can be noticed that the proposed method obtains higher precision and recall.
As shown in the second row of Figure 6, the proposed method obtains satisfactory results visually.
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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