Abstract: A map of IP addresses of a distributed cloud computing network to one or more groupings is stored. The IP addresses are anycast IP addresses for which compute servers of the distributed cloud computing network share. These IP addresses are to be used as source IP addresses when transmitting traffic to destinations external to the cloud computing network. The map is made available to external destinations. Traffic is received at the distributed cloud computing network that is destined to an external destination. An IP address is selected based on the characteristic(s) applicable for the traffic and the map. The distributed cloud computing network transmits the traffic to the external destination using the selected IP address.

Abstract: A quantum computing chip device provides an edge based capacitive, intra-chip connection. A first chip includes a first signal line with a distal end positioned proximate to or on an edge of the first chip and a proximal end positioned away from the edge of the first chip. A second chip includes a second signal line with a distal end positioned proximate to or on an edge of the second chip and a proximal end positioned away from the edge of the second chip. The first signal line and the second signal line are configured to conduct a signal. The second signal line of the second chip is disposed in alignment for a capacitive bus connection to the first signal line of the first chip.

Abstract: An electronic system for a drug delivery device is provided, the electronic system comprising at least one user interface member configured to be manipulated by a user for performing a dose operation, e.g.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using simulated local demonstration data for robotic demonstration learning. One of the methods includes receiving perceptual data of a workcell of a robot to be configured to execute a task according to a skill template, wherein the skill template specifies one or more subtasks required to perform the skill, wherein at least one of the subtasks is a demonstration subtask that relies on learning visual characteristics of the workcell. A virtual model is generated of a portion of the workcell. A training system generates simulated local demonstration data from the virtual model of the portion of the workcell and tunes a base control policy for the demonstration subtask using the simulated local demonstration data generated from the virtual model of the portion of the workcell.

Abstract: A scanning element, for measuring a position along a measuring direction, includes a multilayer printed circuit board. The printed circuit board has a first receiver track that includes a first receiver circuit trace. The printed circuit board also has a connecting line that includes a first conductor trace and a second conductor trace, the connecting line crossing the first receiver track. In at least one first section of the connecting line, the first conductor trace is arranged offset to the second conductor trace in the positive measuring direction, and, in at least one second section of the connecting line, the second conductor trace is arranged offset to the first conductor trace in the positive measuring direction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving, from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reducing latency in network communications and data presentation. In one aspect, a user session is initiated in which data related to an account is presented to the user. A user group to which the given user has been assigned is identified. A first dataset related to the account is selected based on the user group. A second dataset related to the account is selected based on types of data previously requested by various other users in the user group. A user interface for the account is updated to present at least a portion of the first dataset. Latency in updating the user interface is reduced when presenting additional portions of the first dataset or the second dataset by providing, to the client device, the second dataset prior to receiving a request for the second dataset.

Abstract: A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

Abstract: A breathing assistance apparatus has a housing with an engagement feature and an electrical component in the housing and a removable component. The electrical component has a receptacle. The removable component has an electrical connector that is a close or tight fit in the receptacle of the electrical component to assist with holding the removable component in connection with the electrical component. The removable component has a tab with a terminal end portion that can be flexed relative to the rest of the removable component. An engagement feature is provided on the terminal end portion of the tab and engages with the engagement feature of the housing to inhibit disconnection of the removable component from the housing in the absence of actuating the terminal end portion of the removable component to flex the tab.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using skill templates for robotic demonstration learning. One of the methods includes receiving a skill template for a task to be performed by a robot, wherein the skill template defines a state machine having a plurality of subtasks and one or more respective transition conditions between one or more of the subtasks. Local demonstration data for a demonstration subtask of the skill template is received, where the local demonstration data is generated from a user demonstrating how to perform the demonstration subtask with the robot. A machine learning model is refined for the demonstration subtask and the skill template is executed on the robot, causing the robot to transition through the state machine defined by the skill template to perform the task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.