Sentence examples for a stochastic pattern of from inspiring English sources

In neural networks, a stochastic pattern of synaptic weights is generated after a learning session is completed which can be used as keys.

The other six genes show a stochastic pattern of monoallelic expression in some NSC lines and bi-allelic expression in others.

Instead, they will follow a stochastic pattern of distribution in the cells of the developing organism, where they will remain a small minority compared to the population of egg mitochondria or may even be lost from the organism.

Therefore it is likely that while origin spacing appears to be short when averaged across the population, this most likely reflects a stochastic pattern of origin activation at larger intervals in individual cells rather than unique pattern shared in all cells.

(5.10) From the deterministic theory, we know that the system undergoes a Turing instability (stationary patterns) rather than a Turing Hopf instability (oscillatory patterns) so we can set (varOmega=0) and determine conditions under which (S k,0)) has a peak at a non-zero, finite value of k, which is an indication of a stochastic pattern.

As reported (Leonardy et al, 2010; Zhang et al, 2010), mglB+ cells containing YFP MglA+ occasionally reversed in a stochastic pattern with an average reversal period of 13.8 min (Table I).

Interestingly, the heatmaps of the prospective and pre-invasive cancer series also revealed the existence of a striking bi-modality within the cases only, with a small subset of cases exhibiting a highly correlated pattern of CpG methylation, in contrast to the more stochastic pattern of methylation seen in the majority of samples.

In this paper, we explore the power of these features to deal with the stochastic pattern of corrosion for damage detection in metallic surfaces.

The stochastic pattern of AKT serine 473 phosphorylation is therefore unlikely to be due to variations in Raf ER expression or ERK1/2 activity, but it does depend on ERK activation.

The paper presents a simulation and parameter-estimation approach for evaluating stochastic patterns of population growth and spread of an annual forest herb, Melampyrum pratense (Orobanchaceae).

To our knowledge, this study is the first to mathematically formalize pathogen environments that are more realistic in terms of their diversity and the stochastic patterns of exposure than previously conceived in theoretical studies.