Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high power yet cannot be understood by humans. This limits the ability of humans to use these models derive insights and understanding underlying failure mechanisms, also degree confidence can placed in such a system perform well on future data. We consider task predicting hard drive d...

Abstract The hard Lefschetz property (HLP) is an important which has been studied in several categories of the symplectic world. For Sasakian manifolds, this duality satisfied by basic cohomology (so, it a transverse property), but new version HLP recently given terms manifold itself [1]. Both properties were proved to be equivalent (see [2]) case K-contact flows. In paper, we extend both versi...