Abstract: There is provided a the system configured to receive a pretrained model configured to output a plurality of representations based at least in part on an input comprising a graph representing an environment through which a vehicle is traversing, a node of the graph comprising, as an embedding, a vector encoding; receive a task comprising: a function to append to the pretrained model, a dataset, a loss function; and update, based at least in part on the task, the pretrained model to generate an updated machine learned model; and deploy the updated machine learned model to a vehicle computing system associated with a vehicle configured to be controlled based at least in part on an output of the updated machine learned model.

Abstract: There is provided a system configured to receive data associated with a vehicle operating within an environment; generate, based at least in part on the data, a graph comprising a plurality of nodes, a node of the plurality associated with one or more of a vehicle operating in the environment, a road feature, an additional vehicle, or a pedestrian; input the graph into a self-supervised machine learned model comprising an encoder, wherein the machine learned model is trained to output a representation associated with the node; receive, from the self-supervised machine learned model, a representation associated with the node; and transmit the representation to a downstream machine learned model trained to output control data based at least in part on the representation, wherein the control data is configured to control the vehicle or another vehicle.

Abstract: A device may receive and process a change request, work items, and IT data, to generate processed data. The device may transform the processed data into vectorized data, and may select similarity analytics models, regression models, and a classification model. The device may process the vectorized data, with the similarity analytics models, to determine an estimated effort, a user story, and IT requirements, and may process the vectorized data, with the regression models, to determine a schedule overrun, a defect rate, and a sprint velocity. The device may process the vectorized data, with the classification model, to determine a story point, and may calculate a resource capacity. The device may generate an impact analysis based on the estimated effort, the user story, the IT requirements, the schedule overrun, the defect rate, the sprint velocity, the story point, or the resource capacity, and may perform actions based on the impact analysis.

Abstract: An apparatus for preventing noise from occurring due to cavitation in a power steering pump during low temperature start-up includes the addition of a multi-passage orifice element for adding wetted surface resistance to high viscous fluid flow downstream from a single passage outlet orifice element of a fluid flow control device and upstream of an associated steering gear or steering assist valve.

Abstract: An apparatus for preventing noise from occurring due to cavitation in a power steering pump during low temperature start-up includes the addition of a multi-passage orifice element for adding wetted surface resistance to high viscous fluid flow downstream from a single passage outlet orifice element of a fluid flow control device and upstream of an associated steering gear or steering assist valve.

Abstract: A method for installing a bearing into a component using an insert includes aligning the insert with an opening within the component that has a dimension that is less than a complimentary dimension of the insert, aligning the bearing with the insert, inserting the insert at least partially into the opening such that at least a portion of a surface defining the opening is deformed by the insert, and inserting the bearing into the opening after the insert such that the bearing is positioned at least partially with the opening and is at least partially retained within the opening by a portion of the surface defining the opening.