Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively including an interaction with an input element rendered at a user device and a request for a resource associated with the input element. The example method includes obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Abstract: A system and method is disclosed for extracting a user persona from a video and embedding that persona into a background feed that may have other content, such as text, graphics, or additional video content. The extracted video and background feed are combined to create a composite video that comprises the display in a videoconference. Embodiments cause the user persona to be embedded at preset positions, or in preset formats, or both, depending on the configuration, position, or motion of the user's body.

Abstract: An RGB color image and an infrared intensity image of a live video are received. The RGB color image is converted to a colorspace image comprising a channel corresponding to a brightness value. Each pixel of the converted colorspace image is evaluated to determine whether the brightness channel of the pixel exceeds a threshold value. If the brightness channel of the pixel exceeds the threshold value, the infrared intensity value of a corresponding pixel from the infrared intensity image is mixed into the pixel's channel value that corresponds to brightness. The converted colorspace image is converted back to an RGB color image.

Abstract: A system and method is disclosed for extracting a user persona from a video and embedding that persona into a background feed that may have other content, such as text, graphics, or additional video content. The extracted video and background feed are combined to create a composite video that comprises the display in a videoconference. Embodiments cause the user persona to be embedded at preset positions, or in preset formats, or both, depending on the configuration, position, or motion of the user's body.

Abstract: A method for detecting and localizing multiple anatomical landmarks in medical images, including: receiving an input requesting identification of a plurality of anatomical landmarks in a medical image; applying a multi-landmark detector to the medical image to identify a plurality of candidate locations for each of the anatomical landmarks; for each of the anatomical landmarks, applying a landmark-specific detector to each of its candidate locations, wherein the landmark-specific detector assigns a score to each of the candidate locations, and wherein candidate locations having a score below a predetermined threshold are removed; applying spatial statistics to groups of the remaining candidate locations to determine, for each of the anatomical landmarks, the candidate location that most accurately identifies the anatomical landmark; and for each of the anatomical landmarks, outputting the candidate location that most accurately identifies the anatomical landmark.

Abstract: An RGB color image and an infrared intensity image of a live video are received. The RGB color image is converted to a colorspace image comprising a channel corresponding to a brightness value. Each pixel of the converted colorspace image is evaluated to determine whether the brightness channel of the pixel exceeds a threshold value. If the brightness channel of the pixel exceeds the threshold value, the infrared intensity value of a corresponding pixel from the infrared intensity image is mixed into the pixel's channel value that corresponds to brightness. The converted colorspace image is converted back to an RGB color image.

Abstract: A method for detecting and localizing multiple anatomical landmarks in medical images including: receiving an input requesting identification of a plurality of anatomical landmarks in a medical image; applying a multi-landmark detector to the medical image to identify a plurality of candidate locations for each of the anatomical landmarks; for each of the anatomical landmarks, applying a landmark-specific detector to each of its candidate locations, wherein the landmark-specific detector assigns a score to each of the candidate locations, and wherein candidate locations having a score below a predetermined threshold are removed; applying spatial statistics to groups of the remaining candidate locations to determine, for each of the anatomical landmarks, the candidate location that most accurately identifies the anatomical landmark; and for each of the anatomical landmarks, outputting the candidate location that most accurately identifies the anatomical landmark.