Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. A token value allocation engine optimizes the allocation of content consumption revenue to publisher systems and recommendation source systems (e.g., the systems that provide the recommended content for presentation with the publisher system's native content) based on a model calculating and applying a life time or long term value (LTV) metric. The LTV metric for revenue is based on a detection of a how often a user system engages with content derived from or associated with a publisher system.

Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. A token value allocation engine optimizes the allocation of content consumption revenue to publisher systems and recommendation source systems (e.g., the systems that provide the recommended content for presentation with the publisher system's native content) based on a model calculating and applying a life time or long term value (LTV) metric. The LTV metric for revenue is based on a detection of a how often a user system engages with content derived from or associated with a publisher system.

Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.

Abstract: Methods and systems detect changes occurring over time between synthetic aperture sonar (SAS) images. A processor performs coarse navigational alignment, fine-scale co-registration and local co-registration between current image data and historical image data. Local co-registration includes obtaining correlation peaks for large neighborhood non-overlapping patches. Relative patch translations are estimated and parameterized into error vectors. Interpolation functions formed from the vectors re-map the current image onto the same grid as the historical image and the complex correlation coefficient between images is calculated. The resulting interferogram is decomposed into surge and sway functions used to define the argument of a phase function, which is multiplied by the current image to remove the effects of surge and sway on the interferogram. Based on the aforementioned computations, a canonical correlation analysis is performed to detect scene changes between the historical and new SAS images.

Abstract: Methods and systems detect changes occurring over time between synthetic aperture sonar (SAS) images. A processor performs coarse navigational alignment, fine-scale co-registration and local co-registration between current image data and historical image data. Local co-registration includes obtaining correlation peaks for large neighborhood non-overlapping patches. Relative patch translations are estimated and parameterized into error vectors. Interpolation functions formed from the vectors re-map the current image onto the same grid as the historical image and the complex correlation coefficient between images is calculated. The resulting interferogram is decomposed into surge and sway functions used to define the argument of a phase function, which is multiplied by the current image to remove the effects of surge and sway on the interferogram. Based on the aforementioned computations, a canonical correlation analysis is performed to detect scene changes between the historical and new SAS images.