Hewlett Packard Enterprise Wednesday dramatically expanded its artificial intelligence-machine learning (AI/ML) market reach with a standalone release of its Ezmeral Data Fabric. The new standalone Ezmeral edge-to-cloud data fabric brings the fast growing cloud native AI/ML platform to a new multibillion-dollar market where the data fabric offering can be used in multiple enterprise big data buildouts on its own. HPE made the decision to establish a separate standalone version of the data fabric in direct response to customers, said HPE Chief Technology Officer and Head of Software Kumar Sreekanti (pictured above). "It's a huge market opportunity," he said. "Customers have asked for this because it is a very proven platform with phenomenal scale. Many customers want to first deploy the data platform and later on bring in the Ezmeral container platform."

We present a concept for a machine-learning classification of hard X-ray (HXR) emissions from solar flares observed by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), identifying flares that are either occulted by the solar limb or located on the solar disk. Although HXR observations of occulted flares are important for particle-acceleration studies, HXR data analyses for past observations were time consuming and required specialized expertise. Machine-learning techniques are promising for this situation, and we constructed a sample model to demonstrate the concept using a deep-learning technique. Input data to the model are HXR spectrograms that are easily produced from RHESSI data. The model can detect occulted flares without the need for image reconstruction nor for visual inspection by experts. A technique of convolutional neural networks was used in this model by regarding the input data as images. Our model achieved a classification accuracy better than 90 %, and the ability for the application of the method to either event screening or for an event alert for occulted flares was successfully demonstrated.