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

This paper introduces a novel framework for modeling temporal events withcomplex longitudinal dependency that are generated by dependent sources. Thisframework takes advantage of multidimensional point processes for modeling timeof events. The intensity function of the proposed process is a mixture ofintensities, and its complexity grows with the complexity of temporal patternsof data. Moreover, it utilizes a hierarchical dependent nonparametric approachto model marks of events. These capabilities allow the proposed model to adaptits temporal and topical complexity according to the complexity of data, whichmakes it a suitable candidate for real world scenarios. An online inferencealgorithm is also proposed that makes the framework applicable to a vast rangeof applications. The framework is applied to a real world application, modelingthe diffusion of contents over networks. Extensive experiments reveal theeffectiveness of the proposed framework in comparison with state-of-the-artmethods.

HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Media