Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

Abstract: Methods and apparatus for identifying features that may have a high potential impact on key application metrics. These methods rely on observational data to estimate the importance of application features, and use causal inference tools such as Double Machine Learning (double ML) or Recurrent Neural Networks (RNN) to estimate the impacts of treatment features on key metrics. These methods may allow developers to estimate the effectiveness of features without running online experiments. These methods may, for example, be used to effectively plan and prioritize online experiments. Results of the online experiments may be used to optimize key metrics of mobile applications, web applications, websites, and other web-based programs.

Abstract: The disclosure relates to an aerial transportation vehicle including a vehicle frame.

Abstract: Systems and methods for the simultaneous localization and mapping of autonomous vehicle systems are provided. A method includes receiving a plurality of input image frames from the plurality of asynchronous image devices triggered at different times to capture the plurality of input image frames. The method includes identifying reference image frame(s) corresponding to a respective input image frame by matching the field of view of the respective input image frame to the fields of view of the reference image frame(s). The method includes determining association(s) between the respective input image frame and three-dimensional map point(s) based on a comparison of the respective input image frame to the one or more reference image frames. The method includes generating an estimated pose for the autonomous vehicle the one or more three-dimensional map points. The method includes updating a continuous-time motion model of the autonomous vehicle based on the estimated pose.

Abstract: In one embodiment, a computing device includes processing circuitry to predict outcomes of unresolved customer support cases. For example, the processing circuitry: receives a request to predict an outcome of an unresolved customer support case; extracts a set of features from a case record for the unresolved case, including a set of categorical features and a set of textual features; encodes the set of categorical and textual features into numerical representations; predicts the outcome of the unresolved customer support case using a trained case prediction model, wherein the trained case prediction model generates a predicted outcome based on the encoded categorical and textual features; and performs a corresponding customer support action based on the predicted outcome of the unresolved customer support case.

Abstract: A method of generating a personalized video for a user of a social networking service. For a plurality of images associated with the user, respective first metrics are determined for respective images of the plurality of images based on one or more predefined image-content criteria, a first set of images is selected from the plurality of images based at least in part on the respective first metrics, and the personalized video is populated with the first set of images. The method further includes providing the personalized video to the user for viewing.

Abstract: A method of generating a personalized video for a user of a social networking service. For a plurality of images associated with the user, respective first metrics are determined for respective images of the plurality of images based on one or more predefined image-content criteria, a first set of images is selected from the plurality of images based at least in part on the respective first metrics, and the personalized video is populated with the first set of images. The method further includes providing the personalized video to the user for viewing.

Abstract: A liquid composite high-intensity sweetener, comprises a sweet substance and a solvent, wherein the sweet substance is one or combination of a plurality of the following components: aspartame, sucralose, acesulfame and neotame; and the solvent comprises water and one or combination of a plurality of the following components: ethanol, propylene glycol, phosphoric acid, citric acid, lactic acid, acetic acid, malic acid, ascorbic acid, glucose acid, tartaric acid, acetate, lactate, citrate, phosphate, sorbic acid, sorbate, benzoic acid and benzoate. In addition, the present invention further claims a method for preparing the liquid composite high-intensity sweetener. The liquid composite high-intensity sweetener not only has sweet taste similar to sucrose and good quality, but also is mixed uniformly without particles remained; the actual requirements of production and consumption are well met.