Sentence examples for matrices of connectivity from inspiring English sources

Animations of larval movement near the Gascoyne canyon CMR, dispersal surfaces over depth and time for CMRs and Key Ecological Features in the northwest, as well as matrices of connectivity values among CMRs are shown.

Such models simulate the probability of larval dispersal between populations, which can be used to generate matrices of connectivity and which underpin the 'seascape genetics' approach (Galindo et al. 2006).

This method required a boolean matrix of connectivity in which nodes are either connected to a phenotype or not.

This is feasible because in general the matrix of connectivity W in Eq. (1) is sparse [ 19].

In a previous study using repeated k-means clustering, we showed that applying hard clustering to the cross-correlation matrix of connectivity patterns is undesirable in the insula: repeated application of k-means clustering reveals that in comparison with the mPMC, a significantly higher proportion of the middle insula cannot be reliably categorized using this method [Nanetti et al., 2009].

A pairwise matrix of directional connectivity resulted from this data.

(2) An Euclidean anatomical distance matrix was added to the correlation/similarity matrix of the connectivity maps to penalize the similarity between seed voxels, which are very close in the anatomy.

We can construct an adjacency matrix of the connectivity graph as follows: g i, j =1 if MI i, j ≥ mMI c or otherwise 0, where MI i, j is mutual information between m i and m j, and m MI c is a threshold value.

Such a ratio is a natural measure of connectivity matrix overlap, ranging from 0 for matrices with no common links to 1 for identical matrices.

Such a finding seems to indicate that, in order to find a consensus matrix which captures well the inherent structure of the dataset, one needs a sensible number of connectivity matrices.

The results for a subsampling value of p = 66% confirms that the number of connectivity matrices one needs to compute is more relevant than the percentage of the data matrix actually used to compute them.