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This is also inferred from some of the sequencing experiments of a large number of field isolates [54].
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By means of experiments on a large database, we demonstrate that the performance of our method is quite impressive from the viewpoints of the accuracy and robustness.
We performed different sets of experiments on a large real-world dataset, which validated our approach and showed its capability to produce effective segments.
The effectiveness of the proposed method is demonstrated by experiments on a large number of images.
The effectiveness of the multi-expert approach has been demonstrated in experiments on a large number of images.
Extensive experiments on a large number of SDFGs demonstrate that our approach is effective and robust.
Parallel experiments on a large collection of irregular industrial problems validate our approach.
EPR is statically set to 4.5 by experimenting on a large number of frames of different condition videos.
However, experiments of a larger scale under greenhouse conditions or in the field should support this potential in a more applicable way.
In total, as much as 100.000 repetitions of the same experiment are not uncommon to achieve sufficient signal-to-noise ratio in a time-resolved step-scan experiment of a large protein [ 13].
However, the design, analysis, and experiments of such a large and complex system often suffer from the lack of appropriate experimental infrastructures.
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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