Your English writing platform
Discover LudwigSuggestions(5)
Exact(14)
The algorithm is initialized by a point represented by the column vector,, a unit-norm direction vector,, and a boundary definition for the parameter space.
The coded/transmitted symbols on packets, represented by the column vector, are calculated at the transmitter (encoder) using the following linear system of equations: (1).
end{aligned} (3.6) For (i = 1,ldots,n), let (A^{i}) be the matrix formed by replacing the ith column of A by the column vector b.
The population for each group of households is provided by multiplying the row vector of number of individuals in the households by the column vector of expansion factors for each household.
As a result, a large fixed-length column vector is built, denoted by the column vector v a, n = v n τ 1 v n τ 2 … v n τ C T, where C represents the number of HACs.
The overall codeword of length (m s + m r )L is expressed by the column vector x = s 1 T … s m s T r 1 T … r m s T … r m r T T. (3).
Similar(46)
So, at j th time step, the regression is performed by using the column vector related to the same time step.
If j > i, then M j can be obtained by inserting the column vector -(1/ u j - u i ))1M-2after the (j - 2 th column of the matrix diag ( T ̄ ( j - 1 ) 1 ( 1 M - 1 ⊘ ( T i u ) ) ).
As a result, the corresponding linear transformation T i of size (M - 1) × M can be obtained by inserting the column vector -1M-1after i-1 th-1)th column of IM-1, which fulfills T i 1 M = 0M-1, i ∈ {1,..., M}.
By rearranging the column vector x into a matrix with the size of K×K, we obtain the sparse time-frequency distribution matrix of the filtered out Bragg peak s.
When the j th anchor is chosen as a reference and j < i, M j can be obtained by inserting the column vector -(1/ u j - u i ))1M-2after the (j - 1 th column of the matrix diag ( T ̄ j 1 ( 1 M - 1 ⊘ ( T i u ) ) ), where ∅ is element-wise division.
More suggestions(16)
by the galactic vector
by the column chromatography
by the force vector
by the column label
by the column inch
by the pathway vector
by the column vortex
by the support vector
by the column bus
by the column diameter
by the search vector
by the column type
by the shRNA vector
by the column space
by the column stacking
by the gravity vector
Write better and faster with AI suggestions while staying true to your unique style.
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