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CS theory asserts that, if a signal has a sparse representation in a certain space, one can use the random sampling to obtain the measurements and successfully reconstruct the signal with overwhelming probability by nonlinear algorithms, as stated in Section 'Signal model'.
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In the presented example, we use the random samples of size n = 10 under both methods.
We use the Random Sample Consensus algorithm (RANSAC) (Fischler and Bolles, 1981) to identify outliers.
We use the Random Sample Consensus (RANSAC) (Fischler and Bolles, 1981) to separate true correspondences that behave consistently with respect to a rigid transformation up to a maximal correspondence displacement ϵmax (see Algorithm 1).
The methods proposed construct cross-validation folds deterministically instead of using the random sampling approach.
A fundamental theoretical investigation is conducted for the molecular weight distribution formed in free-radical and living copolymerizations with macromonomers by using the random sampling technique.
For the comparison group, we randomly and proportionally sampled one patient suffering injuries from general assaults (E960-968; E967uding E967 for child/adult abuse) for each case identified within the respective dataset of origin using the random sampling procedure in SAS PROC SURVEYSELECTT).
Selection of random subsets of patients was performed using the random sampling function in Microsoft Excel (Redmond, WA, USA).
We therefore used the random sampling algorithm [ 15] to identify reactions that change both in flux and transcription.
First, the new search strategies developed using the random sampling approach were done using a subset of 15 randomly sampled journals with ≥ 99 pass articles.
The new search strategies developed using the random sampling approach seem to perform better than the new strategies developed using the top journal approach in low-yielding journals.
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