English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Data generated from a system of interest typically consists of measurementsfrom an ensemble of subjects across multiple response and covariate features,and is naturally represented by one response-matrix against onecovariate-matrix. Likely each of these two matrices simultaneously embracesheterogeneous data types: continuous, discrete and categorical. Here a matrixis used as a practical platform to ideally keep hidden dependency among/betweensubjects and features intact on its lattice. Response and covariate dependencyis individually computed and expressed through mutliscale blocks via a newlydeveloped computing paradigm named Data Mechanics. We propose a categoricalpattern matching approach to establish causal linkages in a form of informationflows from patterned response dependency to structured covariate dependency.The strength of an information flow is evaluated by applying the combinatorialinformation theory. This unified platform for system knowledge discovery isillustrated through five data sets. In each illustrative case, an informationflow is demonstrated as an organization of discovered knowledge loci viaemergent visible and readable heterogeneity. This unified approachfundamentally resolves many long standing issues, including statisticalmodeling, multiple response, renormalization and feature selections, in dataanalysis, but without involving man-made structures and distributionassumptions. The results reported here enhance the idea that linking patternsof response dependency to structures of covariate dependency is the truephilosophical foundation underlying data-driven computing and learning insciences.

From patterned response dependency to structured covariate dependency: categorical-pattern-matching