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Exact(5)
In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function.
Every dyad-level variable can be constructed as a matrix composed of the elements 1 and 0. The rows and columns of these matrices represent companies, and every element in these matrices represents the network relation between two companies.
In the matrices, rows represent the number of days in a year and columns the hours in a given day; each element inside the matrices represents the wind speed at a given hour of a particular day in the year.
The upper left corner of the matrices represents the synchronization level between brain regions in the right hemisphere, while the left hemisphere internal synchronicity is represented in the lower right corner.
One of the matrices represents the clusters of the trans-factors, while the other, the UTRs in terms of their interactions within the clusters.
Similar(55)
Consequently the matrices representing the differential operators (on the finest scale) have block-diagonal structure.
F d and G d are b × b matrices, representing the precoder and decoder in the virtual channel.
The covariance matrices represent the whole ensemble and the Kalman gain K e is derived from these.
W 0, W 1, and V are the interconnection matrices representing the weight coefficients of the neurons.
The spatial correlation matrices represent the second-order statistics of the channels which vary slowly and can be precisely estimated.
For example, 2-by-2 rotation matrices represent the multiplication with some complex number of absolute value 1, as above.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com