Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes.

It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables'one-shot' compression of single

The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts.