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These terms are defined by a confusion matrix as given in Table 4.
Moreover, it can be considered a circular block-wise permutation matrix as given in Example 3.3.
Calculate the weighted normalized decision matrix as given: V_{ij} = left[ {r_{ij} } right]_{, m times n} times left[ {W_{j} } right]_{, n times m}^{text{diagonal}} (16).
The TG results were generated using the despreader coefficients generated as given in (9) and the covariance matrix as given in (10).
The elements of the adjoint matrix as given in the RHS of (15) can be found by running the algorithm described previously.
Random index (RI) is the consistency of the randomly generated pair wise matrix and is dependent on the size of the matrix as given in Table 2.
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However, the bottleneck is the construction of an expression matrix of TDFs (rows) per time points (columns) of HiCEP electrophoretic data due to the problem of imperfect alignment, though most microarray analysis uses such matrices as given data (e.g., [ 17, 18]).
The normalized matrix thus obtained is represented in matrix N as given below.
Taking the terms with y to one side, these equations can be written in a matrix form as given in Eq. 3.
According to the above definition, one can easily establish a number of important properties of the matrix permanent, as given in the following lemma [28].
It is defined in terms of the adjacency matrix A as given by (k_{i}^{in}=sum A_{textit {ij}}.) For directed network total degree is sum of in-degree of node and out-degree of node given with Eqs. 1 to 3. k_{i}^{in}=sumlimits_{jin N}A_{ij} (1).
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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