Overall, moderately fragmented landscapes and those surrounded by matrices with intermediate indexes of temporal modification (i.e., crop plantations, forestry) are related to high densities of this species.
The DI can be automatically computed at the sensor as well as at the source level (Fig. 2) on a large matrix of spatial-temporal data, without having to select particular peaks, or components.
In this work, we implemented the DI because it allows to quantify the differences between TMS-evoked potentials starting directly from the entire matrix of spatial-temporal data, without requiring a priori information, and to synthesize them into a single number.
Consequently, to estimate the perceptual saliency of the figure segment in our stimuli, we computed the correlation matrix simultaneously for a range of temporal resolutions, and then reported the largest correlation values as explained in more detail below and in 'Materials and methods'.
The problem of off-line simultaneous estimation of temporal O-D matrices is tackled in this paper adopting a simulation approach, which avoids introducing assignment matrices [9].
Despite the importance of regenerating secondary forests for tropical biodiversity conservation, studies of temporal effects of matrix regeneration on species responses in fragmented landscapes are scarce.
An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from to, where is the number of emitted signals, the data vector length, and the dimension of the signal subspace.
We then acquired individual temporal correlation matrices of the ninety brain regions by computing the correlation coefficients between the time-courses of every pair of regions (Figure 1A).
Although they did not explicitly mention, this type of temporal process peculiar to the log-odd matrix in protein evolution is fully encoded in the transition matrices of JTT, WAG, LG, and KHG.
Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices.
