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Let be a matrix containing all pathway vectors for positive examples from task.
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A Latin square is a matrix containing the same number of rows and columns.
W is a matrix containing all the mapping vectors as column vectors.
where E ∈ ℂ M×N is a matrix containing N sample of the noise vector e(n).
The auto-correlation matrix can be decomposed as R U = T Λ T TT, where Λ is a diagonal matrix containing the eigenvalues for the entire network and T is a matrix containing the eigenvectors corresponding to these eigenvalues.
where B is a matrix containing the basis functions obtained from the training data set, and w is a weight matrix.
where G d f ( s ) is a matrix containing the rational component of the system, H ( s ) = ( H i j ( s ) ) = ( e − h i j s ), for i, j = 1, 2, …, n, is a matrix containing only delays, and denotes the Schur (or component-wise) product [28].
Any initial matrix A can be represented as ({mathbf{A}} = {mathbf{P}} - {mathbf{Q}}) where P is a matrix of positive entries and Q is a matrix containing the absolute values of negative entries.
where is a matrix containing shifted sinc functions, is a vector with entries the samples of the signal, and are the expansion coefficients of the projection in terms of the sinc functions.
refers to the target source and is a subset of capturing the remaining interfering sources. is a row of which captures the transfer functions from the target source to the sensors and is a matrix containing the remaining rows of.
where R is an N b ×N b upper-triangular matrix with all positive diagonal elements, Q is an (N d + N b )×N b semi-unitary matrix with Q H Q = I N b, Q ‾ is a matrix containing the first N d rows of Q, and Q ─ contains the last N b rows of Q.
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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