Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: An optical system includes an oriented polymeric multilayer optical film having a first reflection band, and a light source configured to produce light in an output band and/or a sensor configured to receive light in an input band. The light source and/or sensor is in optical communication with the oriented polymeric multilayer optical film. In some cases, the first reflection band overlaps the input and/or output band at normal incidence, but not at an oblique incidence angle. In some cases, the first reflection band overlaps the input and/or output band at an oblique incidence angle, but not at normal incidence. The optical system can further include a non-birefringent optical filter having a first blocking band where the first blocking band overlaps the first reflection band for at least one of normal incidence or an oblique incidence angle.

Abstract: An optical stack including an oriented polymeric multilayer optical film and a non-birefringent optical filter is described. The oriented polymeric multilayer optical film has a first reflection band with a first band edge and the non-birefringent optical filter has a first blocking band. In some cases, the first blocking band contains the first band edge and the first blocking band provides a reduction in variation of a band edge of an overall blocking band of the optical stack.

Abstract: An optical stack including an oriented polymeric multilayer optical film and a non-birefringent optical filter is described. The oriented polymeric multilayer optical film has a first reflection band with a first band edge and the non-birefringent optical filter has a first blocking band. In some cases, the first blocking band contains the first band edge and the first blocking band provides a reduction in variation of a band edge of an overall blocking band of the optical stack.