Abstract: Techniques are disclosed relating to graphics processor that support ray tracing. In particular, shader circuitry may be configured to adjust a scheduling priority of a single-instruction multiple-data (SIMD) group of a shader program based on a hint that the SIMD group has an upcoming ray intersect command for ray intersect accelerator circuitry and based on a resource usage indication from the ray intersect accelerator circuitry. This may advantageously reduce cache thrashing, e.g., when shaders may allocate memory for ray intersect commands and fill a shared cache faster than the ray intersect accelerator circuitry can process the rays.

Abstract: Systems, methods, and other embodiments associated with adaptive code scanning are described. In one example method, a valid configuration that specifies approved parameters of use for a software component is defined. Software code is scanned to detect that the software component exists in the software code. Where the component is detected, the software code is scanned to identify implemented parameters of use for the software component. The implemented parameters are compared to the approved parameters. Based on the comparison, the software component is determined to be not implemented according to the valid configuration. Where the software component is implemented according to the valid configuration, the method automatically determines to proceed with the automated action based on the implemented parameters. The automated action is performed to adapt to the implemented parameters.

Abstract: Techniques are disclosed relating to graphics processor that support ray tracing. In particular, shader circuitry may be configured to adjust a scheduling priority of a single-instruction multiple-data (SIMD) group of a shader program based on a hint that the SIMD group has an upcoming ray intersect command for ray intersect accelerator circuitry and based on a resource usage indication from the ray intersect accelerator circuitry. This may advantageously reduce cache thrashing, e.g., when shaders may allocate memory for ray intersect commands and fill a shared cache faster than the ray intersect accelerator circuitry can process the rays.

Abstract: Systems, methods, and other embodiments associated with adaptive code scanning are described. In one example method, a valid configuration that specifies approved parameters of use for a software component is defined. Software code is scanned to detect that the software component exists in the software code. Where the component is detected, the software code is scanned to identify implemented parameters of use for the software component. The implemented parameters are compared to the approved parameters. Based on the comparison, the software component is determined to be not implemented according to the valid configuration. Where the software component is implemented according to the valid configuration, the method automatically determines to proceed with the automated action based on the implemented parameters. The automated action is performed to adapt to the implemented parameters.

Abstract: Techniques for suggesting a filter field based on a user input are disclosed. A system trains a machine learning model by obtaining historical data including sets of user search input, including a first search term and a first value for a first filter field. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting a filter field based on a user input are disclosed. A system trains a machine learning model by obtaining historical data including sets of user search input, including a first search term and a first value for a first filter field. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: A computer-implemented method for operating a fantasy sports system permits the drafting of non-active players. The non-active players can be draft alongside active players, or a team or even a league can be comprised of exclusively non-active players. Fantasy points are generated for each of the non-active players by randomly selecting a plurality of historical plays by each non-active player from a database of historical plays. A total fantasy score for each fantasy team is generated based upon the fantasy points for each of the non-active players.