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Experimental results obtained from several volumetric datasets demonstrate that it was possible to accelerate the marching cubes algorithm by a factor of approximately 18 times when compared to the CPU approach.
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In general, SIP-FS and mRMR perform best among all comparison methods on the three datasets, demonstrating that it can maintain good predictability.
These models in turn have been validated with additional non-overlapping datasets, demonstrating that it is possible to use publically accessible data to find novel in vitro active antituberculars.
Genetic correlations between environments were very close to unity, except between very extreme environments for all breeds, models, and datasets, demonstrating that reranking of animals for production traits across environments does not exist in France.
Furthermore, the use of a validation dataset demonstrated that it is possible to reach better performances than the ones presented by batch learning.
Results on multiple datasets demonstrate that the proposed feature representation outperforms existing motion features, and importantly it does so independently of the classifier.
Extensive experiments on real-world datasets demonstrate that our approach obtains significant gains.
The experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state-of-the-art works.
Experiments performing the clustering and classification on time series datasets demonstrate that the performance of the proposed method outperforms PVQA.
The experimental results on several challenging datasets demonstrate that our approach outperforms the state-of-the-art methods.
Detailed evaluations using the Meetup datasets demonstrate that our prediction frameworks achieves high prediction performance with the proposed features.
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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