Abstract: Generally, implementations of the present invention include devices, systems, and methods for tracking intermittent motion. Such systems can be used to calibrate inertial measurement units (IMUs), which can be used to obtain acceleration, linear and angular velocities, orientation, and position of a moving body. Such systems also can be used to account and compensate for errors and imperfections present in an IMU prior to and during operation.

Abstract: A system and method for automatically classifying documents using an annotated topic tree is provided. A set of topics may be extracted from a document corpus such that each document in the document corpus is associated with a topic model. A sample set of documents may be selected from the document corpus during a current sampling round. The topic models associated with the sample set of documents may be annotated by human reviewers with coding information. Each coded document may be coded as ‘responsive’, ‘non-responsive’, ‘arguably responsive’, ‘null’, and/or for other codes or issues, which are related to the topic model associated with that document. An annotated topic tree may be formed based on the annotated topic model. One or more machine learning algorithms may be used to project the information in the annotated topic tree to the rest of the document corpus.

Abstract: Generally, implementations of the present invention include devices, systems, and methods for tracking intermittent motion. Such systems can be used to calibrate inertial measurement units (IMUs), which can be used to obtain acceleration, linear and angular velocities, orientation, and position of a moving body. Such systems also can be used to account and compensate for errors and imperfections present in an IMU prior to and during operation.