Sentence examples for matrices we computed from inspiring English sources
To formally characterize the similarities in the substitution process across the eight matrices, we computed a neighbor-joining tree on the Markov processes defined by each matrix using the total variation metric (TVM) [31].
To establish differences between the resultant coherence matrices, we computed the maximum value of the cross-correlation across all time points.
Based on these matrices, we computed a class difference matrix and a squared Mahalanobis class distance matrix, that is, the squared difference between the two class element values, divided by the average of the two class variances for that particular GLEM element (Nielsen and Danielsen, 2006).
To quantify differences between matrices, we compute the basic reproductive number (R_{0}) defined as the dominant eigenvalue of the next generation matrix [5], which is equivalent to the dominant eigenvalue of the contact matrix up to a constant.
For the inferred network probability matrices, we compute their true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) at a given threshold.
On the basis of these matrices, we compute a 4D class difference matrix and a 4D Mahalanobis class distance matrix in the same manner as described for 2D matrices.
For example with a graph size of 7 Gbit composed of 70,000 vertices and 3.6 billion of data recording in its adjacent matrix, we computed the shortest path in 2939 s (00 26:40) with our (MRA^*) approach versus 177,948 s (49 25 48 (approx ) 2 days 27 min) with (A^*).
Using the same distance matrix, we computed diversity and haplotype connectivity scores for each of the 10 sampling locations as explained in the Methods section (using the measure AD for diversity).
With the information contained in the Leontief inverse matrix, we compute a measure of the diffusion properties of a sector based on the Herfindahl concentration index (Herfindahl 1950).
Using this payoff matrix, we compute the optimal reordering of the arrivals by utilizing the well-known solution to the assignment problem, which maximizes the benefit in a computationally efficient fashion.
With a real interaction matrix, we compute A simply as the (weighted) sum of each row of the matrix.
