Sentence examples for pattern representations are from inspiring English sources

The pattern representations are classified into nine levels, from −4 (extremely under-represented), through 0 (normally represented) to +4 (extremely over-represented) based on the P-value and the observed/expected ratio (Supplementary Table S8).

The simulated properties of epidemic spread, based on different contact pattern representations, are quantified and compared by means of several indicators that we compute over an ensemble of stochastic realizations of the epidemic process.

Complete prokaryotic genomes were downloaded from the National Center for Biotechnology Information (NCBI) ftp server (ftp://ftp.ncbi.nih.gov/genomes/Bacteria/). Pattern representations were assessed for the complete genomes, as well as for 1 Mb segments randomly selected from each genome.

The data related to the pattern representation are included in the data related to OvGraph nodes and will be considered below.

Pattern representation is the first step toward clustering or classification.

Time series pattern representation is the basis of time series similarity matching research, while similarity matching is the core of their research.

Pattern representation is often used to extract the most descriptive and discriminatory features in the original data set; then these features can be used exclusively in subsequent analyses [22].

Currently, the typical pattern representation is Discrete Wavelet Transform (DWT) [6], Singular Value Decomposition (SVD) [7], Piecewise Aggregate Approximation (PAA) [8, 9], and Symbolic Aggregate Approximation (SAX) [10] etc.

Efficiency of the CS sampling pattern representation is more important as the number of patterns required for a scan increase, such as if different patterns are used for different 2D slices or 3D slabs, successive signal averages, dynamic repetitions, diffusion b-values, cardiac phase delays, MR echo times, or any other of many possible parameters that may be arrayed in a single MR imaging series.

From the perspective of theoretical neuroscience, mental representations are patterns of neural activity, and inference is transformation of such patterns.

It is not known whether these anatomical connectivity patterns are 'bi-modal', and only distinguish central and peripheral space, or are part of a topography where connectivity patterns with intermediate eccentricity representations are distinguishable from central and peripheral connectivity profiles.