Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Novel tools and techniques might provide for implementing intent-based network services orchestration. In some embodiments, a computing system might receive, over a network, a request for network services from a customer. The request for network services might include desired performance parameters for the requested network services, without information regarding any of specific hardware, specific hardware type, specific location, or specific network for providing the requested network services. The computing system might allocate network resources from one or more networks, based at least in part on the desired performance parameters. Based on a determination that at least one network can no longer provide at least one network resource having the desired performance parameters, the computing system might allocate at least one other network resource from at least one second network, based at least in part on network performance metrics, and based at least in part on the desired performance parameters.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.

Abstract: Implementations include deploying portions of a model used in a cognitive computing process for execution on respective devices in a distributed system, and include processing a device list to provide a benchmark list that includes an inventory of devices across systems that provide data for execution of the model used in the cognitive computing process, each device being a candidate for executing at least a portion of the model, the benchmark list including the devices, and, for each device, accuracies of the device with respect to the at least a portion of the model, providing a deployment plan based on the device and benchmark lists, the deployment plan specifying tasks to be executed by each device, at least two devices included in respective systems, at least one device providing data that is to be processed based on the at least a portion of the model, and executing the deployment plan.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Novel tools and techniques might provide for implementing intent-based network services orchestration. In some embodiments, a computing system might receive, over a network, a request for network services from a customer. The request for network services might include desired performance parameters for the requested network services, without information regarding any of specific hardware, specific hardware type, specific location, or specific network for providing the requested network services. The computing system might allocate network resources from one or more networks, based at least in part on the desired performance parameters. Based on a determination that at least one network can no longer provide at least one network resource having the desired performance parameters, the computing system might allocate at least one other network resource from at least one second network, based at least in part on network performance metrics, and based at least in part on the desired performance parameters.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for enhancing digital content provided from devices, and actions of providing, by a sensor of a device, primary digital content, providing, by the device, secondary digital content, the secondary digital content indicating a context, within which the primary digital content was generated, generating, by the device, a data package including the primary digital content and the secondary digital content, and transmitting, by the device, the data package to a back-end system over a network.

Abstract: Implementations directed to providing a computer-implemented system for performing an action with a robot comprising receiving command information indicating a command related to performance of an action with a robot, identifying state information for a plurality of active routines that are actively running for the robot, the state information indicating a state for each of the active routines, determining contextual information for the command based on the accessed state information for the plurality of active routines, selecting one of the active routines as a handling routine to service the command based on the contextual information, determining an output module of the robot to perform the action based on the state of the handling routine and the contextual information, and executing one or more instructions to perform the action with the output module.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Novel tools and techniques might provide for implementing intent-based network services orchestration. In some embodiments, a computing system might receive, over a network, a request for network services from a customer. The request for network services might include desired performance parameters for the requested network services, without information regarding any of specific hardware, specific hardware type, specific location, or specific network for providing the requested network services. The computing system might allocate network resources from one or more networks, based at least in part on the desired performance parameters. Based on a determination that at least one network can no longer provide at least one network resource having the desired performance parameters, the computing system might allocate at least one other network resource from at least one second network, based at least in part on network performance metrics, and based at least in part on the desired performance parameters.

Abstract: Novel tools and techniques might provide for implementing intent-based network services orchestration. In some embodiments, a computing system might receive, over a network, a request for network services from a customer. The request for network services might include desired performance parameters for the requested network services, without information regarding any of specific hardware, specific hardware type, specific location, or specific network for providing the requested network services. The computing system might allocate network resources from one or more networks, based at least in part on the desired performance parameters. Based on a determination that at least one network can no longer provide at least one network resource having the desired performance parameters, the computing system might allocate at least one other network resource from at least one second network, based at least in part on network performance metrics, and based at least in part on the desired performance parameters.

Abstract: Implementations include deploying portions of a model used in a cognitive computing process for execution on respective devices in a distributed system, and include processing a device list to provide a benchmark list that includes an inventory of devices across systems that provide data for execution of the model used in the cognitive computing process, each device being a candidate for executing at least a portion of the model, the benchmark list including the devices, and, for each device, accuracies of the device with respect to the at least a portion of the model, providing a deployment plan based on the device and benchmark lists, the deployment plan specifying tasks to be executed by each device, at least two devices included in respective systems, at least one device providing data that is to be processed based on the at least a portion of the model, and executing the deployment plan.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for enhancing digital content provided from devices, and actions of providing, by a sensor of a device, primary digital content, providing, by the device, secondary digital content, the secondary digital content indicating a context, within which the primary digital content was generated, generating, by the device, a data package including the primary digital content and the secondary digital content, and transmitting, by the device, the data package to a back-end system over a network.

Abstract: Novel tools and techniques might provide for implementing intent-based network services orchestration. In some embodiments, a computing system might receive, over a network, a request for network services from a customer. The request for network services might include desired performance parameters for the requested network services, without information regarding any of specific hardware, specific hardware type, specific location, or specific network for providing the requested network services. The computing system might allocate network resources from one or more networks, based at least in part on the desired performance parameters. Based on a determination that at least one network can no longer provide at least one network resource having the desired performance parameters, the computing system might allocate at least one other network resource from at least one second network, based at least in part on network performance metrics, and based at least in part on the desired performance parameters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an open healthcare data platform are disclosed. In one aspect, a method includes receiving a request for access to healthcare related data that is associated with one or more healthcare related parameters. The method further includes determining that a user's personal healthcare related data that is shared with a healthcare provider that provides services to the user corresponds to the one or more healthcare related parameters. The method further includes providing a request for access to the user's personal healthcare related data. The method further includes receiving data indicating an acceptance of the request for access to the user's personal healthcare related data and data indicating a level of abstraction. The method further includes processing the user's personal healthcare related data that is shared with the healthcare provider according to the level of abstraction.

Abstract: A process for forming an electrolyte for a metal-supported solid-oxide fuel cell, the process comprising: a. combining a doped-ceria powder with a sintering aid and solvent to form a slurry; b. applying the slurry to an anode layer; c. drying to form a green electrolyte; and d. firing the green electrolyte to form a sintered electrolyte; wherein the slurry in step b. comprises doped-ceria powder with a physical property selected from bimodal particle size distribution, a BET surface area in the range 15-40 m2/g, a spherical morphology, or combinations thereof together with an electrolyte obtained by the process, a fuel cell and fuel cell stack, comprising the electrolyte, and the use of the fuel in the generation of electrical energy.

Abstract: A process for forming an electrolyte for a metal-supported solid-oxide fuel cell, the process comprising: a. applying a doped-ceria green electrolyte to an anode layer; b. removing any solvents and organic matter from the green electrolyte; c. pressing the green electrolyte to increase green electrolyte density; and d. heating the green electrolyte at a rate of temperature increase whilst in the temperature range 800° C.-1000° C. of in the range 5-20° C./minute to form the electrolyte, together with an electrolyte obtained by the process, a fuel cell and fuel cell stack, comprising the electrolyte, and the use of the fuel in the generation of electrical energy.