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Local search algorithms exploit moves on an adjacency graph of the search space.
As observed in (1), margins are defined on an adjacency basis.
As a case study, we focused on an adjacency matrix obtained by using a multi-regional input output analysis (Kagawa et al. 2015).
The higher F(A), the better A CF - I approximates A - I. Approximate factorizability is a very strong structural assumption on an adjacency matrix.
Since the matrix TopOverlap = [ TopOverlap ij ] is symmetric and its entries lie in [0, 1], it satisfies our assumptions on an adjacency matrix.
For module detection, we used average linkage hierarchical clustering of a topological overlap matrix based on an adjacency matrix that is comprised of power-transformed correlations between gene expression profiles [ 31].
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A κ-neighbor of p ∈ Z n is a point of Z n which is κ-adjacent to p, where κ is an adjacency relation defined on Z n.
For instance, if κ is an adjacency relation on a digital image Y, then f : [ a, b ] Z → Y is ( 2, κ ) -connected if and only if for every { c, c + 1 } ⊂ [ a, b ] Z, either f ( c ) = f ( c + 1 ) or f ( c ) and f ( c + 1 ) are κ-adjacent.
We only included phrase constructs in our search strategy which imposes an adjacency condition on search terms, e.g. 'left ventricular systolic dysfunction' or 'LV dysfunction'.
Let κ i be an adjacency relation defined on Z n i, i ∈ { 0, 1 }.
Suppose that κ is an adjacency relation defined on Z n.
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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