The set of similarities within templates is represented as I = { S ( F i, F j ) ∣ F i, F j ∈ T } (6).
Most biometric templates are represented as fixed length real vectors and they can be binarized easily using locality sensitive hashing (LSH) techniques and the most natural and widely used distance in the hash space is the Hamming distance.
Approximately 90% of the possible unique DG sequences flanking FseI sites were represented in DG templates, indicating that ~5-10% of the FseI sites flanked by a unique DG sequence were methylated.
Two (Y155 and K159 in 1jtv) were represented in both the reciprocal template of the target and the source template (Y162 and K166 in 1xkq).
Although naming schemas have varied across studies, networks corresponding to the spatial pattern of these four cortical templates have been represented in multiple, independent, and large-sample parcellations (Biswal et al., 2010; Smith et al., 2009; Yeo et al., 2011).
To ascertain the sensitivity and accuracy of the selected templates, FUGUE [ 38] program was used to perform sequence-structure comparison between the target and the template and was represented by JOY annotation program [ 39].
Template set is represented as T = { F i ∣ i = 1, 2, …, n } (5).
When the template frames are represented by GMM indices, the Euclidean and Mahalanobis distances are no longer suitable.
The inelastic dynamic characteristics of the template designs investigated are represented by equivalent single degree of freedom systems and their seismic deformation demands are calculated using a set of 100 strong ground motion records.
Still, successful segmentation allowed template generation, so each image was represented by a template that could be compared.
