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Numerical examples demonstrate that our method supports the theoretical results.
Experimental results demonstrate that our method is effective.
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Experimental results demonstrate that our method can increase the recognition accuracy specifically in noisy environments.
The comparisons demonstrate that our method produces physically accurate results for this class of problems.
Experimental results demonstrate that our method yields near-optimal result with a short running time.
The experimental results demonstrate that our method can achieve better performance compared to several baseline methods.
Experimental results demonstrate that our method can accurately detect players under challenging conditions.
The comparison results demonstrate that our method outperforms the others in object recognition tasks.
We demonstrate that our method provides better reproducibility with a significant difference (p < 0.01).
Experiments using numerous real and synthetic images demonstrate that our method can match multi-modal images robustly.
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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