Abstract: One example method includes adjusting overall resource usage in a digital twin network that includes a dynamically informed digital twin of a near-edge node and a dynamically informed digital twin of far-edge nodes. Operational conditions of a dynamically informed digital twin are evaluated based on contextual variables that represent operating properties of the dynamically informed digital twin. Updated information levels are received from an orchestration service of the dynamically informed digital twin. The updated information levels define an amount of resources the dynamically informed digital twin will use in the performance of primary tasks. The updated information levels are parsed. A physical entity associated with the dynamically informed digital twin is informed to adjust sampling properties or increase or decrease its activity and the dynamically informed digital twin is informed to modify information processing methods to thereby adjust the overall resource usage in the digital twin network.

Abstract: Systems and methods for decoupling information into multiple levels of detail based on semantics while providing a compact global representation structure. A tree-based graph represents objects in an environment. The top node provides complete environment information at the lowest level of detail. Leaves of the tree-based graph complement the parent nodes with more detailed local information. This achieved efficient processing, communication, and data compaction.

Abstract: Techniques are provided for evaluation of machine learning models using agreement scores. One method comprises obtaining two or more of: (i) a first set of quantitative features characterizing model parameters of a machine learning model; (ii) a second set of quantitative features characterizing a training process used to train the machine learning model; and (iii) a third set of quantitative features characterizing a training dataset used to train the machine learning model; generating a score based on an aggregation of at least portions of the two or more of the first set, the second set and the third set, wherein the score is based on an agreement of the machine learning with designated characteristics; and initiating an automated action based on the score. The automated action may comprise updating the machine learning model; generating a notification in connection with an audit; and/or selecting a machine learning model for deployment.

Abstract: Systems and methods for decoupling information into multiple levels of detail based on semantics while providing a compact global representation structure. A tree-based graph represents objects in an environment. The top node provides complete environment information at the lowest level of detail. Leaves of the tree-based graph complement the parent nodes with more detailed local information. This achieved efficient processing, communication, and data compaction. Steps for updating the tree-based graph are performed at both a far-edge node and a near-edge node.

Abstract: One example method includes identifying features for operational actions that are to be performed at an edge node of an edge computing system. The features include information used by the edge node to execute the operational actions. A first set of the features is received during an input processing stage. The first set of the features are used in the execution of the operational actions. In response to the execution of the operational actions, a second set of the one or more features are obtained, In response to retrieving the first set of the features or in response to obtaining the second set of the features, a local map is updated to include the features. The local map includes a subset of a global map, where the local map and the global map represent a shared representation of a map structure.