Dialysis systems comprising actuators that cooperate to perform dialysis functions and sensors that cooperate to monitor dialysis functions are disclosed. According to one aspect, such a hemodialysis system comprises a user interface model layer, a therapy layer, below the user interface model layer, and a machine layer below the therapy layer. The user interface model layer is configured to manage the state of a graphical user interface and receive inputs from a graphical user interface. The therapy layer is configured to run state machines that generate therapy commands based at least in part on the inputs from the graphical user interface. The machine layer is configured to provide commands for the actuators based on the therapy commands.

Dialysis systems comprising actuators that cooperate to perform dialysis functions and sensors that cooperate to monitor dialysis functions are disclosed. According to one aspect, such a hemodialysis system comprises a user interface model layer, a therapy layer, below the user interface model layer, and a machine layer below the therapy layer. The user interface model layer is configured to manage the state of a graphical user interface and receive inputs from a graphical user interface. The therapy layer is configured to run state machines that generate therapy commands based at least in part on the inputs from the graphical user interface. The machine layer is configured to provide commands for the actuators based on the therapy commands.

In examples provided herein, upon receiving notification of a computational task requested by a package to provide an experience to a user, a remote node management engine identifies computing nodes for performing the computational task and determining available processing resources for each computing node, where a computing node resides at networked wearable devices associated with the user. The remote node management engine further selects one of the computing nodes as a primary controller to distribute portions of the computational task to one or more of the other computing nodes and receive results from performance of the portions of the computational task by the other computing nodes, and provides to the selected computing node information about available processing resources at each computing node.

In examples provided herein, upon receiving notification of a computational task requested by a package to provide an experience to a user, a remote node management engine identifies computing nodes for performing the computational task and determining available processing resources for each computing node, where a computing node resides at networked wearable devices associated with the user. The remote node management engine further selects one of the computing nodes as a primary controller to distribute portions of the computational task to one or more of the other computing nodes and receive results from performance of the portions of the computational task by the other computing nodes, and provides to the selected computing node information about available processing resources at each computing node.

Systems and methods for performance benchmarking-based selection of processor for generating graphic primitives. An example method comprises: initializing, by a computer system comprising a plurality of processors of a plurality of processor types, a current value of a graphic primitive parameter; for each processor type of the plurality of processor types, computing a corresponding value of a performance metric by generating, using at least one processor of a currently selected processor type, a corresponding graphic primitive of a specified graphic primitive type, wherein the graphic primitive is characterized by the current value of the graphic primitive parameter; and estimating, based on the computed performance metric values, a threshold value of the graphic primitive parameter.

A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; determining if the task request is appropriate for the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on content of the task request or a context of the autonomous vehicle; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the determination; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; determining if the task request is appropriate for the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on content of the task request or a context of the autonomous vehicle; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the determination; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

A method for enabling automation templates as a service for data processing includes: receiving a selection of an automation template among automation templates available for performing an automation request; retrieving, from a cloud system, an inputs form template corresponding to the selected automation template; receiving inputs to be inputted to the inputs form template; submitting, to the cloud system, the inputs form to trigger an automation execution based on the selected automation template and the inputs form; performing data ingestion based on input data sources specified in the inputs form and pre-defined set of rules specified in the selected automation template; executing an automation process based on a pre-defined set of calculations, transformations, and/or arrangements specified in the automation template; and pushing results of the executing based on destination information specified in the inputs form and the pre-defined set of rules specified in the automation template.

A data aggregation implementation includes self-configuring drivers. From the viewpoint of a Network Operation Center (NOC), a plurality of heterogenous content sources provide content that may be of a variety of different types and formats. All of this content must be ingested and stored for retrieval and reporting, analysis, and/or presentation despite many differences in their collection, format, transmission, and quality. In some embodiments, the NOC includes or cooperates with one or more servers to, among other functions, receive content from content sources, request object reflection by the driver of each content source, receive driver attributes in response, and map the metadata of the content for each content source to a universal schema, thereby self-configuring the driver.

An accelerating boot time system includes a memory and a processor. The memory is configured to pre-store a boot process to be performed on the first boot. The processor is configured to directly read the boot process from the memory and execute the boot process when the first boot is performed. Also, the processor executes a monitoring process to monitor a plurality of hardware usage rates of the plurality of devices each time the device is powered up, and inserts the hardware usage rates into a machine learning algorithm to determine whether a particular process supported by the devices is abnormal.