Abstract: Motion forecasting for autonomous systems includes obtaining map data of a geographic region and historical trajectories of agents located in the geographic region. The map data includes map elements. The agents and the map elements have a corresponding physical locations in the geographic region. Motion forecasting further includes building, from the historical trajectories and the map data, a heterogeneous graph for the agents and the map elements. The heterogeneous graph defines the corresponding physical locations of the agents and the map elements relative to each other of the agents and the map elements. Motion forecasting further includes modelling, by a graph neural network, agent actions of an agent of the agents using the heterogeneous graph to generate an agent goal location, and operating an autonomous system based on the agent goal location.

Abstract: Methods, systems, and computer program products for direct assignment of physical devices to confidential virtual machines (VMs). At a first guest privilege context of a guest partition, a direct assignment of a physical device associated with a host computer system to the guest partition is identified. The guest partition includes the first guest privilege context and a second guest privilege context, which is restricted from accessing memory associated with the first guest privilege context. The guest partition corresponds to a confidential VM, such that a memory region associated with the guest partition is inaccessible to a host operating system. It is determined, based on a policy, that the physical device is allowed to be directly assigned to the guest partition. Communication between the physical device and the second guest privilege context is permitted, such as by exposing the physical device on a virtual bus and/or forwarding an interrupt.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.