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We used the observed/expected Hi-C maps, which we calculated from the 20 kb iteratively corrected interaction maps of cis-interactions by dividing each diagonal of a matrix by its chromosome-wide average value.
It makes sense now to talk about the diagonal of a matrix, which consists of those elements for which the row number equals the column number ( the elements a11, a22, up to ann).
i.e., the entries of the main diagonal of a matrix are equal.
This theorem was mainly used to describe how well the elements in the diagonal of a matrix approximate their eigen values.
If so, then dominance is symmetric: dom(X) is defined as dom.sym(X) = vec upper.tri(X + XT)), where upper.tri returns only elements above the diagonal of a matrix, and zero-centered effects vector γ has length J(J − 1)/2.
We apply this operator to the cortically projected fMRI clusters, such that: The source priors can then be formed either by creating "covariance" components via the outer product: (3a) or by creating a "variance" component: (3b) where the diag(x) operator here retains only the leading diagonal of a matrix.
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There are 2 n + 1 anti-diagonals on a matrix of size (n + 1 × n+1).
For the SHAKE algorithm [ 64], only the diagonal elements of the A matrix are considered to reduce the complexity of the calculation, while for the MSHAKE algorithm [ 65] the whole matrix is solved with the LU decomposition.
The function diag constructs a diagonal matrix by selecting the diagonal elements of a matrix.
The inbreeding coefficient for each dog equals the diagonal element of the A matrix by subtracting one.
where diag denotes the diagonal part of a matrix.
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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