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The option best ensures that only the best mapping for each read is reported; when multiple best mappings are found they are all reported.
Let be an -inverse strongly monotone mapping for each.
where m indexes the mapping for each feature element.
Assume that and define a mapping for each.
Let (T_{m}: D rightarrow D) be a non-expansive mapping for each (m in N).
Highlighted atom pairs mark the highest similarity mapping for each atom.
Let (S_{i} Cto C) be a nonexpansive mapping for each (i=1,ldots,N).
Let be an RKHS on the separable metric space, with a continuous feature mapping for each.
If, where denotes the identity mapping, for each, then Theorem 2.2 is reduced to the following.
By virtue of the closeness of mapping for each, it yields that for each, that is,.
Let be a nonempty closed convex subset of a strictly convex Banach space and a nonexpansive mapping for each.
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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