New Supermicro Portfolio Aims to Improve Performance While Boosting Efficiency

At the conclusion of 2021, researchers at UNC Charlotte and Duke University Medical Center published results of work done to use transfer learning methods to generate fluence maps for radiation therapy, aimed at providing medical professionals with more capability and information in fighting adrenal cancers. The technique uses a deep transfer learning model trained on a much larger dataset that can be applied to a smaller data set for a specific application.

• The initial model was trained on pancreas treatment plans, then retuned and applied to a smaller set of data points on adrenal cancers. The output of the model generates a fluence map for specific IMRT beam-based treatments for adrenal cancers.
• According to the researchers, this approach is meant to supplement but not replace human expertise in the field and is reliant on human expertise to finetune and improve the AI model.

Two recent announcements highlight a growing trend towards partnership and innovation aimed at space-based technical computing and storage infrastructure. The former seeks a 100X increase in computational power via a High-Performance Spaceflight Computing (HPSC) processor, and the latter is exploring appropriate storage media for low-earth orbit satellite focal planes and RF sensor data.