Abstract: Methods, systems, and media for generating a summarized video using frame rate modification are provided. In some embodiments, a method for editing a video is provided, the method comprising: receiving, using a hardware processor, a video file; identifying a plurality of frames at a playback frame rate from the video file; generating an edited video file by extracting a subset of the plurality of frames, wherein the extracted frames in the subset of the plurality of frames are separated by a predetermined length in the plurality of frames and wherein extracting the subset of the plurality frames modifies a frame rate of at least a portion of the video file to an effective frame rate; storing the edited video file; and presenting the edited video file at the playback frame rate.

Abstract: A computer memory compression method involves analyzing computer memory content with respect to occurrence of duplicate memory objects as well as value redundancy of data values in unique memory objects. The computer memory content is encoded by eliminating the duplicate memory objects and compressing each remaining unique memory object by exploiting data value locality of the data values thereof. Metadata is provided to represent the memory objects of the encoded computer memory content. The metadata reflects eliminated duplicate memory objects, remaining unique memory objects as well as a type of compression used for compressing each remaining unique memory object. A memory object in the encoded computer memory content is located using the metadata.

Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

Abstract: An imaging apparatus to which an attachable and detachable device attachable to and detachable from the imaging apparatus can be attached includes a control unit configured to cause the attachable and detachable device attached to the imaging apparatus to start an image analysis of a predetermined image, a determination unit configured to determine whether the image analysis of the predetermined image is completed, and a determining unit configured to, based on a result of the determination by the determination unit, determine a waiting time from when the imaging apparatus causes the attachable and detachable device to start an image analysis of a captured image captured by the imaging apparatus to when the imaging apparatus requests the attachable and detachable device to acquire a result of the image analysis.