3 results about "Tree representation" patented technology

Systems and methods of graphics processing in which property values for sub-regions of primitives are defined by micromaps. A property value defined for a sub-region of a primitive by a micromap is used during ray tracing to determine whether and / or how a ray interacts with the sub-region of the primitive. A tree representation of a micromap is generated, and a property value for a primitive sub-region is determined by traversing the tree representation of the micromap.

This invention discloses a code incremental injection method, apparatus, electronic device, and storage medium, relating to the field of computer software development technology. The method includes: obtaining a syntax tree representation of target code; receiving a functional incremental description, wherein the functional incremental description defines the behavioral characteristics of the function to be injected; determining the injection position in the syntax tree representation based on the behavioral characteristics; generating a structured code fragment based on the code context corresponding to the injection position and the code template associated with the functional incremental description; integrating the structured code fragment into the injection position to obtain an updated syntax tree representation; and outputting the updated target code based on the updated syntax tree representation. This invention is applicable to any software development scenario requiring automated incremental expansion of existing source code, and can solve the problem that existing technologies cannot automatically determine appropriate positions in the source code and inject adaptable code according to functional requirements while maintaining the integrity of existing handwritten code.

The present application relates to the field of recommendation system, and discloses a point of interest recommendation method based on tree representation trajectory and multi-granularity contrast learning, comprising: obtaining user historical check-in data set and preprocessing; dividing user check-in into day level, time period level and original check-in level node according to check-in time, and constructing multi-time granularity trajectory tree structure; constructing a recommendation model integrating global enhancement and contrast learning, training and testing the model using the data set, and obtaining the prediction result. In feature extraction, a global graph is introduced for the trajectory tree structure, and graph convolution network is used to initialize the features of leaf nodes, so as to strengthen the model's ability to capture global dependency; contrast learning strategy is introduced at each granularity node level, and the model is guided to learn semantic consistent trajectory representation through positive and negative sample constraints. The present application balances local spatio-temporal constraints and global transfer rules while mining user multi-time granularity preferences, and significantly improves the accuracy and personalization of point of interest recommendation.