Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: A bayonet connector device including a female coupler including an annular wall defining a female coupler opening and a central axis, and a plurality of female coupler engagement structures extending radially inward from the annular wall toward the central axis, and a male coupler sized to be received within the female coupler opening and including a plurality of male coupler engagement structures shaped to mesh with the female coupler engagement structures. The female coupler engagement structures and the male coupler engagement structures define an inner length in an axial direction parallel with the central axis, and an outer length in the axial direction at a position radially farther away from the central axis than the inner length. The outer length is greater than the inner length.

Abstract: System logs are processed to identify and report anomalies in execution of processes of a log-generating system such as a data storage system. Log messages of system logs are vectorized to generate log-message vectors; long short-term memory (LSTM) neural network processing is applied to the log-message vectors to generate an LSTM output sequence representing a production flow of the processes; and second-level neural network processing is applied to a combination of the LSTM output sequence and a training sequence to generate an analysis sequence containing a representation of anomalies in the production flow of the processes, where the training sequence is generated from a non-anomalous training flow. An anomaly report is generated and provided to a report consumer for taking further action with respect to the anomalies represented in the analysis sequence.

Abstract: System logs are processed to identify and report anomalies in execution of processes of a log-generating system such as a data storage system. Log messages of system logs are vectorized to generate log-message vectors; long short-term memory (LSTM) neural network processing is applied to the log-message vectors to generate an LSTM output sequence representing a production flow of the processes; and second-level neural network processing is applied to a combination of the LSTM output sequence and a training sequence to generate an analysis sequence containing a representation of anomalies in the production flow of the processes, where the training sequence is generated from a non-anomalous training flow. An anomaly report is generated and provided to a report consumer for taking further action with respect to the anomalies represented in the analysis sequence.