Abstract: Methods, systems, and computer programs encoded on computer storage medium, for receiving a request for instrumentation data describing one or more devices in the computing environment, the request received from a resource management interface; retrieving the instrumentation data describing the one or more devices from a resource database; identifying a schema associated with the resource management interface; converting the instrumentation data describing the one or more devices based on the identified schema associated with the resource management interface; and transmitting the instrumentation data describing the one or more devices to the resource management interface.

Abstract: Run a computerized numerical partial differential equation solver on at least one partial differential equation representing at least one physical constraint of a physical system, to generate a training data set. A true potential corresponds to an exact solution to the at least one partial differential equation. Using a computerized machine learning system, learn, from the training data set, a surrogate of a gradient of the true potential. Using the computerized machine learning system, apply Langevin sampling to the learned surrogate of the gradient, to obtain a plurality of samples corresponding to candidate designs for the physical system. Make the plurality of samples available to a fabrication entity.

Abstract: Methods, systems, and computer programs encoded on computer storage medium, for receiving a request for instrumentation data describing one or more devices in the computing environment, the request received from a resource management interface; retrieving the instrumentation data describing the one or more devices from a resource database; identifying a schema associated with the resource management interface; converting the instrumentation data describing the one or more devices based on the identified schema associated with the resource management interface; and transmitting the instrumentation data describing the one or more devices to the resource management interface.

Abstract: A natural input image is upscaled, first by interpolation. Second, edges in the interpolated image are sharpened by a lion-parametric patch transform. The result is decomposed into an edge layer and a detail layer. Only pixels in the detail layer enhanced, and the enhanced detail layer is merged with the edge layer to produce a high resolution version of the input image.

Abstract: A natural input image is upscaled, first by interpolation. Second, edges in the interpolated image are sharpened by a lion-parametric patch transform. The result is decomposed into an edge layer and a detail layer. Only pixels in the detail layer enhanced, and the enhanced detail layer is merged with the edge layer to produce a high resolution version of the input image.