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We measure the specificity of the predicted blocks by computing the proportion of genes from all reported alignments or clusters that are also located in simulated blocks.
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We estimate location of each block by computing the following ratio: (3) .
This allowed us to estimate the significance of the score of each block by computing the probability that a particular block had a score equal to or larger than the observed value, just by chance.
The proposed blocking metric was obtained by computing the pixel differences across the block boundaries.
The likelihood score for a DAG is the product of the likelihood scores for each of the duplication blocks, where a duplication block's likelihood is derived by computing the weighted ensemble of all possible duplication scenarios that could have generated it.
A Lanczos-type algorithm is presented which carries out the reduction by computing the block tridiagonal form of the Hermitian part of U, i.e., of the matrix U+UH.
We subsequently computed the z-scores of SFC values in each block (Jarvis and Mitra, 2001) by computing the distribution of coherence under null hypothesis given the number of degrees of freedom (i.e. the number of spikes * number of tapers), then calculating how many standard deviations the observed coherence differs from zero.
By computing the structural similarity (SSIM) between blocks of inverse halftoning and host content, it is expected that SSIM is higher for blocks where there are no tampers.
Acquisition curves for both the quinine and the water group were obtained by computing the frequency of correct choices during 3 blocks of 5 trials each.
We begin by computing the following quantities during the m th received block: (7).
You measure the opportunity cost by computing the value of the road not taken.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com