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Below are two matrices representing the DCT input and DCT output blocks from a gray-scale image.
Consequently the matrices representing the differential operators (on the finest scale) have block-diagonal structure.
Given two constituent words, their composed representation (the compound) is computed by using matrices representing the abstract properties of constituent roles (modifier vs. head).
W 0, W 1, and V are the interconnection matrices representing the weight coefficients of the neurons.
F d and G d are b × b matrices, representing the precoder and decoder in the virtual channel.
From the data structure representing a cellular mesh, that is a regular cell complex, we can deduce the incidence matrices representing the boundary operators.
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These matrices represent the factors that underlie consideration of how and whether a technology should be developed.
The covariance matrices represent the whole ensemble and the Kalman gain K e is derived from these.
For example, 2-by-2 rotation matrices represent the multiplication with some complex number of absolute value 1, as above.
Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution.
However, only the R1 random matrices represented the same combination of nucleotide preferences, though present in distinct permutations as compared to the original matrices.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com