Abstract: Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Abstract: Techniques for pharmacovigilence adverse-event processing include receiving data comprising medical narrative text and generating, based on the received data, using a recurrent neural network encoder, a fixed-length context vector representation of the medical narrative text. The fixed-length context vector representation may then be queried, using a recurrent neural network decoder, to generate one or more hidden states. A first set of the one or more hidden states may be processed to generate an assessment of seriousness represented by the medical narrative text, and a second set of the one or more hidden states may be processed to generate a plurality of respective assessments of whether respective individual words of the medical narrative text correspond to one or more adverse events.

Abstract: Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Abstract: A system for vehicle resource management with a memory, controller, and vehicle. The memory includes instructions. The controller executes the instructions. The vehicle includes a vehicle system, controls device, and can communicate with the controller. The vehicle system generates resource data for a replenishable resource. The controls device commands the vehicle to perform tasks. Moreover, the instructions enable the controller to: receive resource data after a first task; provide a first status for the vehicle when the resource data reflect the resource meets a threshold requirement, or otherwise provide a second status; generate and communicate a first output after the first status is provided, the first output instructing the vehicle to reposition itself to perform a second task; and generate and communicate a second output after the second status is provided, the second output instructing the vehicle to reposition itself to replenish the resource to meet the threshold requirement.

Abstract: A system for vehicle resource management with a memory, controller, and vehicle. The memory includes instructions. The controller executes the instructions. The vehicle includes a vehicle system, controls device, and can communicate with the controller. The vehicle system generates resource data for a replenishable resource. The controls device commands the vehicle to perform tasks. Moreover, the instructions enable the controller to: receive resource data after a first task; provide a first status for the vehicle when the resource data reflect the resource meets a threshold requirement, or otherwise provide a second status; generate and communicate a first output after the first status is provided, the first output instructing the vehicle to reposition itself to perform a second task; and generate and communicate a second output after the second status is provided, the second output instructing the vehicle to reposition itself to replenish the resource to meet the threshold requirement.