Sentence examples for information normalized from inspiring English sources

These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters.

The quantitative information (normalized amplitudes of mass signals) did not affect the similarity patterns and therefore can be neglected in diversity surveys.

The next set of input features (six in total) provide coevolution information for each pair of alignment columns, and are as follows: mutual information, normalized mutual information (Dunn et al., 2008), mean contact potential, PSICOV score, mfDCA score and CCMpred score.

They considered mutual information normalized by the joint entropy MI j,k /H j,k) and set a threshold of Z-score>4.0 for accepting pairs of positions.

Past information of normalized permeate flux and salt passage were introduced as unique input variables along with process operating parameters to capture short-term plant performance variability.

Since our interest is in comparing two vectors and ( being the desired response), it is useful to use a modified definition for the mutual information, the normalized mutual information (NMI) which can be defined as (see also [25, page 47]) (16).

Fig. 11 Mutual information between normalized postsynaptic response and preceding ISIs for the control and muscarine parameter sets stimulated by Poisson spike train with mean frequencies ranging from 0.1 to 100 Hz.

Supplementary information contains normalized ratios (Dataset S2), z-scores relative to the unselected library (Dataset S3), z-scores relative to the enrichments performed in the media without antibiotics (Dataset S4), combined z-scores (Dataset S5), and the combined z-scores considered significant (Dataset S1).

GRX: Glutaredoxin; HGT: horizontal gene transfer; LCA: Last common ancestor; MI: Mutual information; NMI: Normalized mutual information; TRX: Thioredoxin.

Both the associated level information and normalized section strings were also encoded as additional lexicon features.

If the interactive refinement is done, the transformed image and the reference image can be evaluated using kinds of metrics, such as SSD, mutual information, and normalized correlation.