Sentence examples for metric between a from inspiring English sources

The value of this metric between a pair of nodes can be determined based on the history of contacts between the nodes.

In this procedure, the production data objective function is calculated by the conventional least squares misfit between the historical data and simulation predictions, while the seismic objective function uses the Current measurement metric between a binary image of saturation change.

Given two intervals (A=[a^,a^]), (B=[b^,b^]inmathcal{K}), the Hausdorff-Pompeiu metric between A and B is defined by (H A,B =max{vert a^-b^vert,vert a^-b^vert }).

Especially, if A and B are simplified into two singleton sets ({a} ) and ({b}), respectively, then we can infer from equality (1) that (d_{infty}(chi_{{a}},chi_{{b}})=d a,b)), where d denotes the usual metric between a and b.

The logarithm of the cosine distance, as in the B-L2F-Four phone log-likelihood ratio feature + DTW-based fusion QbE STD system, is employed as a distance metric between a query and an utterance frame to build a cost matrix.

KM requires a distance metric between a sample and a cluster center (prototype).

The Hausdorff metric H between A and B is defined by H ( A, B ) = max { e ( A, B ), e ( B, A ) }, where e ( A, B ) = sup a ∈ A d ( a, B ) with d ( a, B ) = inf b ∈ B ∥ a − b ∥.

The Hausdorff metric H between A and B is defined by H ( A, B ) = max { H ∗ ( A, B ), H ∗ ( B, A ) }, where H ∗ ( A, B ) = sup a ∈ A d ( a, B ) with d ( a, B ) = inf b ∈ B ∥ a − b ∥.

The shaded boxes indicate the cost of the path selected by each routing metric between node A and nodes D and F. As shown in Figures 16 and 17, the average hop count of the flows does not monotonically increase as the distance between sender and receiver increases, due to the radio signal propagation issues mentioned in Section 7.5.1.

This paper defines two somewhat dual Maximal Design Diversity (MDD) problems, namely, (1) Primal -MDD: maximizing a design-variable diversity metric between architectures given a margin for compromising Pareto optimality, and (2) Dual -MDD: minimizing the compromise of Pareto optimality of architectures given a lower bound for design-variable diversity.

Finally, the last row/column of each of the within- and between-covariance matrices illustrate the remaining paradox, in that the attitudinal scales consistently show a remaining positive correlation with the achievement metric between countries, but a negative correlation within countries.