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The experimental results demonstrate that dynamic allocation of text pointers and hybrid methods achieve better performance than the two original ones.
We demonstrate these new CB methods achieve better ensemble decision accuracy than methods which apply fixed rules in combining classifier decisions.
Experimental results show that the proposed methods achieve better performance than the existing state-of-the-art methods in terms of both embedding capacity and the quality of the rendered virtual views.
Generally, the results show that the proposed methods achieve better approximation accuracy than other methods, especially for the long time domain.
It shows that the proposed methods achieve better estimations of A and cost less flops and CPU time than ACDC LU +.
In relation to the classical separation metrics (i.e., SDR, SAR, SIR and ISR, in decibels), the default method is limited by the interferences between the different instruments (SIR) while the other methods achieve better SIR results.
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The approach is compared to other two significant natural scene segmentation methods, achieving better results in a global evaluation.
In contrast, the patch-based denoising methods achieved better results when denoising the natural scene images.
As noticed in Table 2, the new EPSTRA methods achieved better results than existing automatic approaches, almost reaching the performance of human experts.
Clearly all the participating methods achieved better scores for an in silico network than for either one of in vivo networks.
The results reveal that the data obtained with DIC method achieve better linearity than ESPI.
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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