Some of contractive conditions are based on functions called control functions which alter the distance between two points in a metric space.
It has no explicit limitation, either on the number of stored concepts or on the underlying metric for association, since the metric is based on kernel functions.
In particular, if word meaning can be measured based on a metric system, the same metric system might be useful to measure the meaning of experiences.
This method, based on Riemannian metric comparison, minimizes a cost function constructed from a measure of metric non-conformity that compares two metrics: the metric that transforms a given element into its reference element and a specified Riemannian metric that contains the desired target size and shape of each element.
We propose that centrally located proteins, particularly those involved in sensing functions, may present good therapeutic targets and should be formally evaluated in future studies based on our metric space approach.
In order to identify these repeats, we propose here a novel method based on a metric characterizing amino-acid properties (polarity, secondary structure, molecular volume, codon diversity, electric charge) using five previously derived numerical functions.
Leap was also based on the metric of total shareholder return, made up of share price appreciation and dividends.
