Abstract: Disclosed is a method including receiving visual input comprising a human within a scene, detecting a pose associated with the human using a trained machine learning model that detects human poses to yield a first output, estimating a shape (and optionally a motion) associated with the human using a trained machine learning model associated that detects shape (and optionally motion) to yield a second output, recognizing the scene associated with the visual input using a trained convolutional neural network which determines information about the human and other objects in the scene to yield a third output, and augmenting reality within the scene by leveraging one or more of the first output, the second output, and the third output to place 2D and/or 3D graphics in the scene.

Abstract: A database is fragmented into a plurality of database fragments using a plurality of fragmentation expressions. Each fragmentation expression corresponds to a database fragment and includes a boolean combination of one or more comparison-predicates. Each comparison-predicate defines a range of a fragmentation dimension basis function of one or more database fields. A database query is processed against the database by: (i) resolving a data selection expression of the database query into a boolean combination of fragment selection comparison-predicates each defining a range of one of the fragmentation dimension basis functions; (ii) identifying one or more eliminated database fragments based on the boolean combination of fragment selection comparison-predicates and the fragmentation scheme; and (iii) processing the database query against database fragments other than the eliminated database fragments.