Abstract: Described herein is a compliance dataflow management system. The system may include a plurality of different databases. The system may be configured to provide for workflow that are in conformance of regulations and/or requirements for specific jurisdictions. Furthermore, aspects of the system may allow for automatic updating of compliance systems in a manner that allows for resource savings for automated compliance software. Additionally, the system may include automated auditing systems, allowing for compliance to be determined in a quick and resource efficient manner while allowing for privacy and security to be maintained.

Abstract: Described herein is a compliance dataflow management system. The system may include a plurality of different databases. The system may be configured to provide for workflow that are in conformance of regulations and/or requirements for specific jurisdictions. Furthermore, aspects of the system may allow for automatic updating of compliance systems in a manner that allows for resource savings for automated compliance software. Additionally, the system may include automated auditing systems, allowing for compliance to be determined in a quick and resource efficient manner while allowing for privacy and security to be maintained.

Abstract: Described herein is a compliance dataflow management system. The system may include a plurality of different databases. The system may be configured to provide for workflow that are in conformance of regulations and/or requirements for specific jurisdictions. Furthermore, aspects of the system may allow for automatic updating of compliance systems in a manner that allows for resource savings for automated compliance software. Additionally, the system may include automated auditing systems, allowing for a compliance rating to be determined in a quick and resource efficient manner while allowing for privacy and security to be maintained.

Abstract: Described herein is a compliance dataflow management system. The system may include a plurality of different databases. The system may be configured to provide for workflow that are in conformance of regulations and/or requirements for specific jurisdictions. Furthermore, aspects of the system may allow for automatic updating of compliance systems in a manner that allows for resource savings for automated compliance software. Additionally, the system may include automated auditing systems, allowing for compliance to be determined in a quick and resource efficient manner while allowing for privacy and security to be maintained.

Abstract: A method for using piecewise forecasts involves obtaining, by a model discovery service, a plurality of models and generating, by a demand prediction service, a plurality of values for a time series variable. The plurality of values corresponding to a plurality of days to be predicted. The method further involves inputting the plurality of values for the time series variable as part of a piecewise forecast to a headcount estimation service and generating, by the headcount estimation service with the piecewise forecast, an estimated headcount from the time series variable.

Abstract: An ordered combination of machine-learning models may be used to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. For example, a first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: Systems described herein apply an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. A first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: A putting training device having the same size and feel of a standard golf ball yet adapted to exaggeratedly deviate from an expected line of action when mishit relative to a predefined line of action. The putting training device may have one or more inner cavities wherein an inner spherical object rolls along a tracked, concentric first curvature when the putting training device is struck with a putter accurately along the predefined line of action, and wherein the inner spherical object moves to a side portion of the one or more cavities upon the putting training device being mishit, resulting in the exaggerated deviation that affords the user a visual feedback they can learn from and recalibrate their future putts to.

Abstract: Systems described herein apply an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. A first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: An ordered combination of machine-learning models may be used to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. For example, a first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: A method for using piecewise forecasts involves obtaining, by a model discovery service, a plurality of models and generating, by a demand prediction service, a plurality of values for a time series variable. The plurality of values corresponding to a plurality of days to be predicted. The method further involves inputting the plurality of values for the time series variable as part of a piecewise forecast to a headcount estimation service and generating, by the headcount estimation service with the piecewise forecast, an estimated headcount from the time series variable.

Abstract: An optical beam delivery device includes: a first length of fiber having a first refractive index profile (RIP) to enable modification of one or more beam characteristics of an optical beam having a first beam shape; an a second length of fiber having at least one beam-shaping confinement region and situated to receive the optical beam from the first length of fiber, wherein the at least one beam shape-modifying confinement region has a quadrilateral cross-section.

Abstract: A retrofittable submerged terrarium for an aquarium comprising a bottle with a horizontal passageway tube and an upwardly extending access tube with an upper end extending above the water level. Alternatively, two bottles are attached to opposite ends of a passageway tube and an upwardly extending access tube extends above the water level that is attached either to the passageway tube or to one of the bottles. Preferably, an airway tube is provided into a bottle so that air can be circulated freely throughout the terrarium. Brackets are preferably provided to retain the apparatus near the bottom of the aquarium.