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The dependency in each source vector is retained by adopting a multivariate source prior instead of a univariate source prior.
The substrate is nonheated from any external heat source prior, during and after the plasma process.
For good science journalism to happen, journalists must try to stay at arm's length from their sources and not, for example, run their story past their source prior to publication.
Experimental results on real speech signals confirm the advantage of adopting the proposed source prior on three types of IVA algorithm.
In this paper, a multivariate generalized Gaussian distribution is adopted as the source prior which can exploit frequency domain energy correlation within each source vector.
First, the generic principle of thermal propulsion is outlined: a propellant is directly heated by a power source prior to being expanded which creates a thrusting force on the rocket.
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In this study, we extend previous approaches [3] by allowing the source priors to be a mixture of multivariate Gaussian distributions for each pixel.
This suggests that such source priors do not exactly match the distribution of speech signals and that the IVA performance for speech separation can be improved by finding better dependency models [22, 23].
The general lack of any improvement with the addition of fMRI priors suggests that the MSP source priors are already optimal (i.e., a superset of the fMRI priors).
Despite the additional model complexity (i.e., six vs. two source priors), local priors produced a highly reliable increase in F for MEG magnetometers and gradiometers, T(12) > 4.88, P < 0.001.
Journalists' dealings with sources prior to publication or broadcast are not binary, yes/no relationships.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com