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The feasibility in a reasonable computation time and the interest of the dense correlation measurements of this fast correlation technique are illustrated on HR SAR images which can be regularly acquired by repeated satellite passes.
For networks containing cliques that are locally dense, correlation networks might have an advantage compared to the sparsity imposed by the graphical Lasso algorithm with a single penalty term, as used in this study.
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\tilde {\Sigma }$\end{document} Σ ~ is the dense correlation matrix, which describes the covariance structure of ξ.
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For obtaining an accurate dense map of correlations between two stereo images, time-consuming stereo algorithms are usually required.
About two thirds of the negative correlation between dense and non-dense area was explained by the same genetic factors influencing both traits, but in opposing directions [ 8].
The following algorithms will be briefly explained: brain network, correlation, classification, dense matrix multiplication, fast Fourier transform 1D, fast Fourier transform 2D, motion estimation, hybrid coin miner, high speed packet capture, breast mammogram ROI extraction, linear regression, and n-body simulation.
There was a weak positive and significant correlation for dense area (r = 0.14, P = 0.03).
Therefore, we expect the mAs value to have a positive correlation with dense area and also with PD.
These results suggest a positive correlation between dense methylation of the TGFBI promoter and locoregional invasiveness in human prostate cancer.
These results suggest a positive correlation between dense methylation of the TGFBI promoter and metastasis in human lung cancer.
Next, we examined the association and correlation between PD, dense area, and non-dense area and the four acquisition parameters.
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