Abstract: A standard conformance testing system and method for a CIM/E model of a power system and a storage medium include: converting the standards “Power Grid Common Model Description Specification (GB/T 30149)” and “Power Grid Operating Model Data Exchange Specification (DL/T 1380)” into practically operable testing rules, and performing automatic generation and verification of a CIM/E standard model and defect model. The testing system and method can improve the comprehensiveness and standardization of a CIM/E model standard compliance test, improving testing efficiency, promoting the level of standardization according to the CIM/E model in a power system software development process, and real time data exchange and interoperation between respective application systems, effectively supporting the safe and stable operation of a power grid.

Abstract: Disclosed herein are systems and methods for contactless sleep monitoring. The contactless sleep monitoring system collects patient data from a plurality of sensors, including thermal, radar, and audio sensors. The data is then processed using various signal processing techniques. Machine learning algorithms then convert the thermal data, audio data, and radar data into latent representations, preserving the features of each type of data but enabling them to be combined together for analysis. Finally, the system fuses the representations and then predicts sleep states by performing machine learning analysis on the fused data. Sleep states include sleep stages and sleep conditions.

Abstract: A computer-implemented method for determining a heart rate and respiratory rate from a radio frequency signal comprises inputting a radio frequency signal obtained from a test subject into a neural network. The method further comprises training the neural network using the radio frequency signal and extracting a heart rate and a respiratory rate from the radio frequency signal using the neural network. Further, the method comprises comparing the heart rate and the respiratory rate extracted from the radio frequency signal to a verifiable heart rate and verifiable respiratory rate for the test subject to compute an error measure. Finally, the method comprises using the error measure to apply back propagation to adjust front end parameters for one or more layers of the neural networks to improve a prediction accuracy of the neural network.

Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.

Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.

Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.

Abstract: A computer-implemented method for computing node embeddings of a sparse graph that is an input of a sparse graph neural network is described. Each node embedding corresponds to a respective node of the sparse graph and represents feature information of the respective node and a plurality of neighboring nodes of the respective node.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a schedule for a computation graph. One of the methods includes obtaining data representing an input computation graph; processing the data representing the input computation graph using a graph neural network to generate one or more instance-specific proposal distributions; and generating a schedule for the input computation graph by performing an optimization algorithm in accordance with the one or more instance-specific proposal distributions.

Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.

Abstract: There is described a neural network system for generating a graph, the graph comprising a set of nodes and edges. The system comprises one or more neural networks configured to represent a probability distribution over sequences of node generating decisions and/or edge generating decisions, and one or more computers configured to sample the probability distribution represented by the one or more neural networks to generate a graph.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining a graph of nodes and edges that represents an interaction history of the agent with the environment; generating an encoded representation of the graph representing the interaction history of the agent with the environment; processing an input based on the encoded representation of the graph using an action selection neural network, in accordance with current values of action selection neural network parameters, to generate an action selection output; and selecting an action from a plurality of possible actions to be performed by the agent using the action selection output generated by the action selection neural network.

Abstract: Various techniques implement an electronic design with hybrid analysis techniques. An activity map is identified or generated for an electronic design. The electronic design is reduced into a reduced electronic design at least by applying a plurality of reduction processes to different portions of the electronic design based in part or in whole upon the activity map. Transient behaviors of the electronic design may be determined or predicted at least by performing one or more transient analyses on a representation of the electronic design with a simulation start point based in part or in whole upon the activity map. The electronic design may then be implemented for manufacturing at least by modifying or correcting the electronic design based at least in part upon the transient behaviors.

Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.

Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.

Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

Abstract: A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

Abstract: Various techniques implement an electronic design with electrical analyzes with compensation circuit components. A power pin of a power net may be identified in an electronic design. The electronic design may be reduced into a reduced electronic design at least by applying one or more circuit reduction techniques to at least a portion of the electronic design. At least one load device of a plurality of load devices in the reduced electronic design may be transformed into a transformed load device. One or more design closure tasks may be performed on the electronic design using at least the reduced electronic design and the transformed load device.

Abstract: A standard conformance testing system and method for a CIM/E model of a power system and a storage medium include: converting the standards “Power Grid Common Model Description Specification (GB/T 30149)” and “Power Grid Operating Model Data Exchange Specification (DL/T 1380)” into practically operable testing rules, and performing automatic generation and verification of a CIM/E standard model and defect model. The testing system and method can improve the comprehensiveness and standardization of a CIM/E model standard compliance test, improving testing efficiency, promoting the level of standardization according to the CIM/E model in a power system software development process, and real time data exchange and interoperation between respective application systems, effectively supporting the safe and stable operation of a power grid.

Abstract: A “Natural Motion Controller” identifies various motions of one or more parts of a user's body to interact with electronic devices, thereby enabling various natural user interface (NUI) scenarios. The Natural Motion Controller constructs composite motion recognition windows by concatenating an adjustable number of sequential periods of inertial sensor data received from a plurality of separate sets of inertial sensors. Each of these separate sets of inertial sensors are coupled to, or otherwise provide sensor data relating to, a separate user worn, carried, or held mobile computing device. Each composite motion recognition window is then passed to a motion recognition model trained by one or more machine-based deep learning processes. This motion recognition model is then applied to the composite motion recognition windows to identify a sequence of one or more predefined motions. Identified motions are then used as the basis for triggering execution of one or more application commands.