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Experimental results show that the proposed method achieves high recognition rate up to 89.2%.
In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.
We evaluate the algorithm against three datasets with the result that this method achieves high precision and good performance under various conditions.
Experimental results showed that the proposed method achieves high accuracy and is feasible to replace the conventional positron source based methods.
Empirical results demonstrate that our molecular hierarchical abstraction method achieves high quality rendering results and performs better than other existing methods.
Experimental results confirm that the effectiveness of the three changes, and the proposed method achieves high rank on the benchmark dataset.
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The method achieves high-order accuracy using Discontinuous Galerkin (DG) discretization of phase space and strong stability-preserving, Runge Kutta (SSP-RK) time integration.
Results from numerical experiments — including one example in spherical symmetry adopting the Schwarzschild metric — demonstrate that the method achieves high-order accuracy and that the distribution function satisfies the maximum principle.
The proposed method achieves higher accuracy on face recognition and verification than the existing techniques.
Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort.
With average dice metric (DM) > 0.9690 on the test set, our experimental results demonstrate that our method achieves higher aorta segmentation accuracy than existing methods.
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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