(3) to (11) the diagonal matrices of value-added coefficients, (V_{t}), by diagonal matrices of labor coefficients (, {text{L}}_{t}), which are obtained from dividing the total amount of persons engaged in production by industry and country of origin between the gross product of the respective industry.
Second, we used DISTLM forward (McArdle and Anderson 2001) to perform multivariate multiple regression with permutation tests on locus-specific pairwise FST distance matrices versus pairwise matrices of values for physical variables (Carmichael et al. 2007; Olsen et al. 2010; Hess et al. 2011; Matala et al. 2011).
Let S ∗ be the similarity matrix of value pairs in V i.
It can be visualized as a 3D matrix of values called weights (W).
(V_{t}) is a diagonal matrix of value-added coefficients, and (F_{t}) is the diagonal matrix of final demand by country of origin/destination.
What's interesting about deep learning is that while there's nothing magical or genie-like about it — as Geordie Wood points out in Wired, "it's really just simple math executed on an enormous scale" — its "programs" are ultimately matrixes of values that have been trained, rather than lines of code which are written (although many lines of traditional code go into the training, of course).
And then the resulting collection of functions of the single variable y, i.e., ∀ai: Φ ai, y), can be reduced to a "matrix" of values by "considering" the function for all possible values of "individuals" bi substituted in place of variable y: :∀bj∀ai: Φ ai, bj).
This matrix of values can naturally be viewed as a weighted graph, in which the nodes are residues and the links between the residues are assigned weights according to the results of the pair analysis.
An unrooted neighbour-joining cladogram was constructed from the genetic distance matrix of values for all pairs of breeds using the R package APE [ 34].
The package takes as input a matrix of values for genes versus samples, and sets up the pipeline for the learning process: initialisation, training and many auxiliary functions.
The remaining 121 features comprised an 11 × 11 matrix of values, centred at location i, j which are taken from the relevant outputs of the first stage (summarized in Supplementary Table S3).
