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The classification method produced comparable results to spectrometric band-based methods using only grayscale images, but was slower and had lower classification resolution.
Limitations of previous analyses include inadequate statistical methods using only bivariate correlations or poorly specified multiple regression models.
Herein, we propose a general idea for how one can formulate multiscale finite-volume methods using only a primal coarse partition.
Methods using only image processing rely, similar to grammar-based approaches, on prior knowledge and assumptions about the facades' appearance.
In existing methods using only the best matched examples, the best matched patch is selected by monitoring errors in the known neighboring areas around the missing areas.
We show that using such a "polydesign" framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design.
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Because these methods use only a color image, it is unclear which reason explains the modeling failure in the situation.
Image-based methods use only the spatial information of single frames for video content analysis and are generally fast.
Hilbert and Bernays (1939) suggest that the methods used only provides a consistency proof for first-order arithmetic with open induction.
Moreover, some methods use only several tens to hundreds of functional categories in the prediction process which resulted in more generic rather than specific functional assignments.
Current computational target prediction methods use only the miRNA sequence as input.
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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