Abstract: An online system obtains a composite prediction associated with a content item indicating a likelihood that a viewing user of the online system will perform a type of conversion associated with the content item via one or more paths of events leading to the type of conversion. The online system obtains the composite prediction based on a tree data structure describing the path(s) of events. Upon identifying an opportunity to present content to the viewing user, the online system identifies the tree data structure corresponding to the type of conversion from multiple tree data structures maintained in the online system and obtains a composite prediction associated with the content item by evaluating and performing operations associated with nodes of the tree data structure while traversing the nodes. The online system then determines whether to present the content item to the viewing user based on the composite prediction.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that use multi-point optimization for delivery of digital content by way of a digital content distribution platform. In particular, one or more embodiments described herein receive a content item from a content provider to be displayed to users of the platform. The embodiments optimize delivery to obtain a first target event and determine metrics that delivery of the content item is expected to satisfy. If the actual metrics of delivery fail to satisfy the expected metrics, delivery is re-optimized to obtain either the first target event or a second target event.

Abstract: A bidding system determines values for impression opportunities on an online system. Values are determined by a set of models. Each model of the set of models is associated with a user response and predicts the likelihood that the associated user response will occur following an impression. The models are ordered based on a predicted chronological ordering of user actions that lead towards a conversion. Each model is weighted based on its relevance to conversion and the accuracy of the model relative to the other models in the set of models. Predictions of the probability of user action generated by each model, as well as the model weights, are used to determine a value for impression opportunities. Data from impression opportunities are then used to further train the models and update the weights assigned to each model for use in determining values for subsequent impression opportunities.

Abstract: A closed-loop IC design optimization process by automatically or manually creating design-specific cells with desired characteristics (e.g., performance, area, power, noise, etc.), which will be then implemented as a standard cell (also known hereafter as metacell), from a set of post-layout patterns. A post-layout pattern represents a part or whole of a standard cell and contains information regarding the pattern including, but not limited to, layout, timing, area, power and noise. As the metacells are created from post-layout patterns, the inaccuracies of prior dynamic library techniques are easily avoided. Such metacells, being design-specific, are optimized to satisfy the constraints imposed by the design context, thus bringing the powerful design-specific customization to standard cell-based design methodology.

Abstract: A closed-loop IC design optimization process by automatically or manually creating design-specific cells with desired characteristics (e.g., performance, area, power, noise, etc.), which will be then implemented as a standard cell (also known hereafter as metacell), from a set of post-layout patterns. A post-layout pattern represents a part or whole of a standard cell and contains information regarding the pattern including, but not limited to, layout, timing, area, power and noise. As the metacells are created from post-layout patterns, the inaccuracies of prior dynamic library techniques are easily avoided. Such metacells, being design-specific, are optimized to satisfy the constraints imposed by the design context, thus bringing the powerful design-specific customization to standard cell-based design methodology.