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After we get our desired clusters, we will start allocating VMs from the cluster which have maximum VMs on it.
If we do not control the amount of clusters, we will have a very large number of audio-visual candidates when compared to phoneme-based units.
Since the stream clustering process should provide a greater level of importance to recent clusters, we will provide a time-sensitive weight to each micro-cluster.
Also, although we used SNP data to construct genetic clusters, we will merge them by the associated genes for the subsequent cross-platform comparisons and function analysis.
Using a two-sided alpha of 0,05, a power of 80%, an assumed between-clusters ICC of 0,01 [ 32], and a minimum cluster size of 35 with 2×6 clusters, we will be able to detect an effect size of >0,32, which is sufficient to detect even small differences [ 33, 34].
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So in first cluster we will have VMs with CPU utilization of 700 and 800 MHz and in the second cluster we will have VMs with CPU utilization of 1100, 1150 and 1250 MHz.
Finally, the origin has a Hopf bifurcation in which the E and I cells separately cluster (we will refer to this as the "E/I" limit cycle).
Where analysis has been adjusted for clustering, we will take the reported point estimate and CIs.
After describing the data sets and the methods to quantify the result of a given clustering, we will now describe the results.
If we include cluster-randomised trials and insufficient information is available to adjust for clustering, we will use individuals as the unit of analysis and perform sensitivity analyses to assess the potential bias that may have occurred as a result of inadequately controlling for the effects of clustering.
For studies that have used a cluster-RCT design (e.g. randomisation occurs at a village or household level), when the analyses have not adjusted for clustering, we will attempt to adjust the results by multiplying the standard errors of the estimates by the square root of the design effect, where the design effect is calculated as DEff=1+ m-1)*ICC.
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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