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Each 4 × 4 luma block is vectorized as a one-dimensional structure and saved on two consecutive columns on the memory.
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The buffer is vectorized as well and stores messages, with the maximum CN degree in the code.
The normalized channel matrix, (bar {mathbf {H}}_{ki}, ; forall k,; forall i), is vectorized as (bar {mathbf {h}}_{ki} = vec(bar {mathbf {H}}_{ki})) where (bar {mathbf {h}}_{ki}in mathbb {C}^{N_{T_{k}}N_{R_{k}}times 1}).
Following the formulation in [13], the autocorrelation matrix is vectorized as: z ω 0 = vec R x x ω 0 = A ˜ ω 0 p ω 0 + σ n 2 ω 0 i ˜, (5).
The operation of writing the new reference frame to the memory does not add extra delay as the blocks are vectorized by reordering, which on a hardware platform can be performed without penalties.
Both of these alternatives can be vectorized as a consequence of the simple form of our objective function.
The computation of the frequency vectors is vectorized and quickly performed thanks to the binary format of the seed flanking regions.
First, the two-dimensional image is vectorized into a one-dimensional vector to fulfill the one-dimensional EMD decomposition.
Basically, I draw it by hand, and then it is vectorized with software, and the it goes into a machine.
The metrics are vectorized for each run, represented as ; initially reducing the dimension of each run from p peptides to q metrics with the resulting dataset dimensionality of (n× q) where n is the number of LC-MS runs.
Using the condition of having standardized SNPs, we can rewrite the RSR for SNP p as β ^ p = x p ⊤ y, where x p is the standardized genotype vector of SNP p. This expression can be vectorized over all SNPs as β ^ RSR = X ⊤ y.
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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