Self-supervised learning has witnessed great progress in vision and NLP; recently, it also attracted much attention to various medical imaging modalities such as X-ray, CT, MRI. Existing methods mostly focus on building new pretext self-supervision tasks reconstruction, orientation, masking identification according the properties of images. However, publicly available models are not fully explo...

medicines and of the methods of preparing and prescribing them, with a sketch of their action, physiological and therapeutic. There is a chapter on weights, measures, and pharmaceutical apparatus, another on the sources, selection and adulteration of drugs, including their active principles. The operations of pharmacy, crystallizing, decocting, digesting, distilling, &c., are fully and practica...

We present results of the growth of thin films of crystalline H2O2 and H2O2*2H2O (dihydrate) in ultrahigh vacuum by distilling an aqueous solution of hydrogen peroxide. We traced the process using infrared reflectance spectroscopy, mass loss on a quartz crystal microbalance, and in a few cases ultraviolet-visible reflectance. We find that the different crystalline phases-water, dihydrate, and h...

This paper revisits the problem of analyzing multiple ratings given by different judges. Different from previous work that focuses on distilling the true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic insights into our in-house well-trained judges. We generalize the well-known DawidSkene model (Dawid & Skene, 1979) to a spectrum of probabilistic models under the same “...

Asavas and aristhas are medicines prepared by fermentation. In some medicines alcohol produced by fermentation is distilled and added to the powdered drugs for a fixed period, filtered and the filtrate is used as medicines.Karpurasava is prepared by distilling the alcohol. The quantity of alcohol in the distillate was 9.87% by the textual method while it was 5.77% by glass distillation apparatu...

Researchers have recently proposed plenty of heterogeneous graph neural networks (HGNNs) due to the ubiquity graphs in both academic and industrial areas. Instead pursuing a more powerful HGNN model, this paper, we are interested devising versatile plug-and-play module, which accounts for distilling relational knowledge from pre-trained HGNNs. To best our knowledge, first propose HI gh-order RE...