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

Various versions of binary decision diagrams (BDDs) have been proposed in the past, differing in the reduction rule needed to give meaning to edges skipping levels. The most widely adopted, fully-reduced BDDs and zero-suppressed BDDs, excel at encoding different types of boolean functions (if the function contains subfunctions independent of one or more underlying variables, or it tends to have value zero when one of its arguments is nonzero, respectively). Recently, new classes of BDDs have been proposed that, at the cost of some additional complexity and larger memory requirements per node, exploit both cases. We introduce a new type of BDD that we believe is conceptually simpler, has small memory requirements in terms of node size, tends to result in fewer nodes, and can easily be further extended with additional reduction rules. We present a formal definition, prove canonicity, and provide experimental results to support our efficiency claims.

Binary Decision Diagrams with Edge-Specified Reductions