Abstract: Data analytics systems are described herein which may provide requests for file tiering to one or more file servers. The data analytics systems may receive metadata and/or event data from one or more file servers and may utilize the metadata and/or event data to select files for tiering. In some examples, files may be selected using a sliding window methodology. In some examples, files may be selected in part based on user behavior with the files in the file system. In some examples, file analytics systems may send requests to retry tiering operations which failed. The retry requests may be sent in a manner that is based on the error which caused the failure.

Abstract: A method includes receiving an input video stream and providing, to a convolutional neural network (CNN), multiple image frames of the video stream including a target pair of consecutive frames, a frame immediately preceding the target pair, and a frame immediately following the target pair. The method includes generating, by the CNN, multiple interpolated image frames by performing 3D space-time convolution on the multiple image frames and outputting a video stream in which the interpolated image frames are inserted between the frames of the target pair. The convolution may include passing a 3D filter over the multiple image frames in common width and height dimensions, and in a depth dimension representing the number of frames. Generating the interpolated image frames may include generating image data for multiple color channels in respective convolutional layers. The CNN may be trained to predict non-linear movements that occur over multiple image frames.