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Let us highlight that the considered data are not spatially sparse.
As for the score functions g, let us highlight one attack in particular, the interleaving attack.
Before we get there, let us highlight a couple of initiatives headed in this direction.
Rather than viewing the entire diagram, let us highlight a couple of interesting stories.
Let us highlight the most promising results for each hierarchy: Regarding the Disorder sub-hierarchy, we obtained the translation of 21.41% of the terms (see Table 9).
Let us highlight the main differences between 2-D gel data and microarray data before we proceed to describe the methods in detail: Running differences between gels add a source of errors for spot matching, whereas in microarray data, matching is trivial because every gene is spotted at a known row and column.
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Let us now highlight some important sources of healthcare data in the Sect.
Let us finally highlight an emerging hypothesis; one that deals with stigma and norm susceptibility.
Let us again highlight one resulting decoder in particular, the one for the interleaving attack.
To get the nodal one, let us first highlight that (h([0,1]) cap overline{mathcal{A}}_{0} = emptyset).
Let us survey some highlights.
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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