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: This application relates to a point cloud segmentation method, a computer-readable storage medium, and a computer device. The method includes encoding a to-be-processed point cloud to obtain a shared feature, the shared feature referring to a feature shared at a semantic level and at an instance level; decoding the shared feature to obtain a semantic feature and an instance feature respectively; adapting the semantic feature to an instance feature space and fusing the semantic feature with the instance feature, to obtain a semantic-fused instance feature of the point cloud, the semantic-fused instance feature representing an instance feature fused with the semantic feature; dividing the semantic-fused instance feature of the point cloud, to obtain a semantic-fused instance feature of each point in the point cloud; and determining an instance category to which each point belongs according to the semantic-fused instance feature of each point.

Abstract: Provided herein are materials and methods related to scaffolds and uses thereof. In some embodiments, provided herein are poly (octamethylene-octanol citrates) (POOC) scaffolds with native mechanical properties that are comparable to urinary bladder tissue. In some embodiments, provided herein are methods for the synthesis of POOC scaffolds with native mechanical properties that are comparable to urinary bladder tissue by including octanol in the polycondensation synthesis of POC. In some embodiments, provided herein are methods of use of the disclosed scaffolds, including for urinary bladder tissue regeneration.

Abstract: This application relates to a point cloud segmentation method, a computer-readable storage medium, and a computer device. The method includes encoding a to-be-processed point cloud to obtain a shared feature, the shared feature referring to a feature shared at a semantic level and at an instance level; decoding the shared feature to obtain a semantic feature and an instance feature respectively; adapting the semantic feature to an instance feature space and fusing the semantic feature with the instance feature, to obtain a semantic-fused instance feature of the point cloud, the semantic-fused instance feature representing an instance feature fused with the semantic feature; dividing the semantic-fused instance feature of the point cloud, to obtain a semantic-fused instance feature of each point in the point cloud; and determining an instance category to which each point belongs according to the semantic-fused instance feature of each point.

Abstract: The invention belongs to the technical field of organic chemistry, in particular being a method for preparing (+)-tricyclic hydroxyl lactone. The preparation of the (+)-tricyclic hydroxyl lactone compound in the prior art has lengthy steps, low stereoselectivity and high costs. The (+)-tricyclic hydroxyl lactone of the invention is obtained by an asymmetric oxidation reaction of prochiral tricyclic lactones in an organic solvent with an optically active Davis oxidant in the presence of an organic base. The method of the invention uses easily available raw materials, has low costs, good selectivity, and is suitable for large-scale preparation.