Abstract: An optical element driving mechanism is provided and includes a fixed assembly, a movable assembly, a driving assembly and a stopping assembly. The fixed assembly has a main axis. The movable assembly is configured to connect an optical element, and the movable assembly is movable relative to the fixed assembly. The driving assembly is configured to drive the movable assembly to move relative to the fixed assembly. The stopping assembly is configured to limit the movement of the movable assembly relative to the fixed assembly within a range of motion.

Abstract: In some embodiments, the present disclosure relates to a semiconductor device comprising a source and drain region arranged within a substrate. A conductive gate is disposed over a doped region of the substrate. A gate dielectric layer is disposed between the source region and the drain region and separates the conductive gate from the doped region. A bottommost surface of the gate dielectric layer is below a topmost surface of the substrate. First and second sidewall spacers are arranged along first and second sides of the conductive gate, respectively. An inner portion of the first sidewall spacer and an inner portion of the second sidewall spacer respectively cover a first and second top surface of the gate dielectric layer. A drain extension region and a source extension region respectively separate the drain region and the source region from the gate dielectric layer.

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations.

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations.

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Abstract: Techniques and systems are described that employ survival analysis and classification to predict occurrence of future events by a digital analytics system. Survival analysis involves modeling time to event data. Survival analysis is used by digital analytics systems to analyze an expected duration of time until an event happens. In the techniques described herein, survival analysis is employed as part of a classification technique by a digital analytics system. In one example, a digital analytics system generates training data from a dataset in accordance with a survival analysis technique such that, after generated, the training data is usable to train a classification model.