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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this paper, we have developed an ellipsoid radial basis function neuralnetwork (ERBFNN) and algorithm for sparse representing of a molecular shape. Toevaluate a sparse representation of the molecular shape model, the Gaussiandensity map of molecule is approximated by ERBFNN with a relatively smallnumber of neurons. The deep learning models were trained by optimizing anonlinear loss function with $L_1$ regularization. Experimental resultsdemonstrate that the original molecular shape is able to be represented withgood accuracy by much fewer scale of ERBFNN by our algorithm. And our networkin principle can be applied to multi-resolution sparse representation ofmolecular shape and coarse-grained molecular modeling.

Molecular Sparse Representation by 3D Ellipsoid Radial Basis Function Neural Networks via $L_1$ Regularization