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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The maximal information coefficient (MIC), which measures the amount ofdependence between two variables, is able to detect both linear and non-linearassociations. However, computational cost grows rapidly as a function of thedataset size. In this paper, we develop a computationally efficientapproximation to the MIC that replaces its dynamic programming step with a muchsimpler technique based on the uniform partitioning of data grid. A variety ofexperiments demonstrate the quality of our approximation.

An Information-Theoretic Measure of Dependency Among Variables in Large Datasets