Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object recognition neural network for amodal center prediction. One of the methods includes receiving an image of an object captured by a camera. The image of the object is processed using an object recognition neural network that is configured to generate an object recognition output. The object recognition output includes data defining a predicted two-dimensional amodal center of the object, wherein the predicted two-dimensional amodal center of the object is a projection of a predicted three-dimensional center of the object under a camera pose of the camera that captured the image.

Abstract: Persistent storage contains a plurality of configuration items characterizing attributes of a virtualized architecture and containing representations of relationships between the plurality of configuration items.

Abstract: A method for managing a boost time required for an application launch in an electronic device is provided. The method includes detecting, by the electronic device, a user input to launch the application. Further, the method includes measuring, by the electronic device, real-time system health parameters of the electronic device. Further, the method includes predicting, by the electronic device, an application launch time by inputting the real-time system health parameters to an AI-based application prediction model. Further, the method includes boosting, by the electronic device, at least one hardware of the electronic device based on the predicted application launch time.

Abstract: An example embodiment may involve identifying a virtual software application that is accessible within a managed network. The example embodiment may further involve determining a delivery group associated with accessing the virtual software application within the managed network. The example embodiment may further involve, based on the delivery group, determining a potential-entity count of entities with access to the virtual software application within the managed network. The example embodiment may further involve calculating compliance of a software license associated with the virtual software application based on the potential-entity count.

Abstract: Persistent storage may contain software models defining corresponding software packages and entitlements to a software package, wherein use of the entitlements is constrained by entitlement rules. One or more processors may be configured to: (i) determine, by querying computing devices, a first deployment of the software package; (ii) determine, by querying an interface of a public cloud platform, a second deployment of the software package, wherein use of the entitlements on the public cloud platform is constrained by platform rules; (iii) determine, based on the constraints, an assignment of the entitlements to the first deployment and the second deployment; (iv) determine that the assignment leaves one or more deployed instances of the software package not covered by the entitlements; and (v) determine a modification to the entitlements that: satisfies the constraints and facilitates a further assignment of the entitlements that covers all deployed instances of the software package.

Abstract: An example embodiment may involve identifying a virtual software application that is accessible within a managed network. The example embodiment may further involve determining a delivery group associated with accessing the virtual software application within the managed network. The example embodiment may further involve, based on the delivery group, determining a potential-entity count of entities with access to the virtual software application within the managed network. The example embodiment may further involve calculating compliance of a software license associated with the virtual software application based on the potential-entity count.