Sentence examples for matrices we use from inspiring English sources

To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function.

In symmetric block matrices, we use an asterisk ∗ to represent a term induced by symmetry.

For the transpose, transpose conjugate, and conjugate matrices, we use T, H, and ∗, respectively.

In symmetric block matrices, we use an asterisk " " to represent a term that is induced by symmetry and diag stands for a block-diagonal matrix.

For and, denote by the family of all -measurable, -valued random variables such that, where stands for the mathematical expectation operator with respect to the given probability measure P. In symmetric block matrices, we use an asterisk " " to represent a term that is induced by symmetry and diag stands for a block-diagonal matrix.

To describe the matrices, we use 16-element binary series which are built of matrix elements row by row: begin{aligned} left[ begin{array}{llll} 1 & quad 0 & quad 0 & quad 0 0 & quad 0 & quad 1 & quad 0 0 & quad 0 & quad 0 & quad 1 0 & quad 1 & quad 0 & quad 0 end{array} right] rightarrow 1000|0010|0001|0100 rightarrow 1000001000010100.

To check the consistency of the fuzzy pairwise comparison matrices, we used the consistency test auxiliary linear programming mentioned in Eqs.

To compute the train-train and train-test kernel matrices, we used the parameter free Χ 2 kernel for all features.

Given two genes in one of our expression matrices we used different quantitative measures of coexpression to construct independent sets of RCGs.

For JASPAR matrices we used log-likelihood ratio scores.

To find instances of motif matrices we used a procedure similar to Habib et al. (2012).