Sentence examples for synopsis use from inspiring English sources

Synopsis use BSAT::K_Mismatch; K_Mismatch Text Pattern K Arguments: Text: is a scalar containing the text string; Pattern: is a scalar containing the pattern string; K: is a scalar giving the maximum number of allowed mismatches.

Synopsis use BSAT::FLCS; FLCS X Y Frags Arguments: X: is a scalar containing string X. Y: is a scalar containing string Y. Frags: is a hash reference (see below).

Synopsis use BSAT::Edit_Distance_Gaps; Edit_Distance_Gaps X Y Xw Yw Substitution Arguments: X: is a scalar containing string X; Y: is a scalar containing string Y; Xw: is a hash reference defined below; Yw: is a hash reference defined below; Yw: is a list reference containing the Substitution: is a list reference containing an upper triangular symbol substitution cost matrix.

Synopsis use BATS::Z_Score; Z_Score patters texts organismpath Arguments: patterns: is an array of strings containing the set of patterns; sequences: is an array of strings containing the text strings; organismpath: it is the path to the file containing all probabilistic information for an organism.

Synopsis use BATS::Model_Generation; Model_Generatation strings path organism Arguments: strings: is an array of strings; path: is a scalar containing the string of the output path; organism: points to the string containing the name of the organism; Return values: Model_Generation returns a scalar containing 0 if the computation is completed successfully and 1 otherwise.

Synopsis use BSAT::K_Difference; K_Difference Text Pattern K Arguments: As in function K_Mismatch Return values: As in function K_Mismatch In this section we consider the problem of identifying a longest common subsequence (LCS for short) of two strings X and Y, using a set M of matching fragments.

Other duplicate sensitive statistical aggregates such as COUNT and AVERAGE can also be implemented with ODI synopsis using probabilistic techniques [4, 14].

As a consequence, proper names are likely to jeopardize or, at least, to not contribute to the distance between a transcript and a synopsis when using the tf-idf weighted vector space model.

Second, researchers and research networks should develop field synopses that use meta-analysis to integrate published and unpublished data and evaluate the cumulative evidence.

In the delinearization framework considered, the relations established between a segment's transcript and the synopses are used to validate, and eventually correct, labels resulting from the EPG-based segmentationntation.

Researchers performing synopses have used different thresholds or trigger points for conducting a meta-analysis.