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When no random matrices had higher NODF than the real matrix, we defined P<0.001.
As a starting predictor matrix, we defined the minimum proportion of usable cases is at least 0.4 and the minimum correlation is at least 0.1.
Within this matrix, we defined knowledge gaps as the least-connected pairs, i.e. cells for which the connectivity value (no. refined articles/no. keywords involved in the search syntax) was less than the tenth percentile.
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For the matrix, we define the vector-induced -norm by (4.5).
We introduce pattern mining mechanisms and, based on a QoS-SWP matrix, we define SWP workload configurations for required QoS.
For the specific case in which the input to the channel is Gaussian with covariance matrix, we define.
Based on the network adjacency matrix, we define the following dissimilarity measure between the samples: dissA= 1-adjacency.
In the above-mentioned n * m matrix we define ∑ c z = ∑ y 0, z + y 1, z + y 2, z + … + y n, z as the sum of all the elements in the column z.
To define the probable structures and alignments, and thus determine the 'relevant' entries of the matrix, we define several probabilities of structure elements in the structure ensemble of a sequence A. For defining these probabilities, we assume that the structures of an RNA sequence A are Boltzmann-distributed in the structure ensemble, where RNA energy is given by a loop-based energy model.
Thus, similar to comparing the energies of two undirected graphs with respect to their adjacency matrices, we define the quasi-ordering relation '⪯' for oriented graphs as follows.
With the above four transition matrices, we define and further normalize every row of this matrix to obtain the transition matrix of the heterogeneous network W = (w ij), where.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com