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: In particular embodiments, a computing system may capture a first image of a scene using a first camera of an artificial reality device. The system may capture a second image of the scene using a second camera and one or more optical elements of the artificial reality device. The second image may include an overlapping portion of multiple shifted copies of the scene. The system may generate an upsampled first image by applying a particular sampling technique to the first image. The system may generate a tiled image comprising a plurality of repeated second images by applying a tiling process to the second image. The system may generate an initial output image by processing the upsampled first image and the tiled image using a machine learning model. The system may generate a final output image by normalizing the initial output image using the upsampled first image.

Abstract: A distributed imaging system for augmented reality devices is disclosed. The system includes a computing module in communication with a plurality of spatially distributed sensing devices. The computing module is configured to process input images from the sensing devices based on performing a local feature matching computation to generate corresponding first output images. The computing module is further configured to process the input images based on performing an optical flow correspondence computation to generate corresponding second output images. The computing module is further configured to computationally combine first and second output images to generate third output images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

Abstract: Embodiments described herein provide thin film transistors (TFTs) and processes to reduce plasma induced damage in TFTs. In one embodiment, a buffer layer is disposed over a substrate and a semiconductor layer is disposed over the buffer layer. A gate dielectric layer is disposed over the semiconductor layer. The gate dielectric layer contacts the semiconductor layer at an interface. The gate electrode 204 is disposed over the gate dielectric layer. The gate dielectric layer has a Dit of about 5e10 cm?2eV?1 to about 5e11 cm?2eV?1 and a hysteresis of about 0.10 V to about 0.30 V improve performance capability of the TFT while having a breakdown field between about 6 MV/cm and about 10 MV/cm.

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: A method and apparatus for checking power grid measurement data, a device, a storage medium and a program product. The method includes: a feature factor of power grid measurement data is extracted; power balance of a set time scale is checked based on the measurement data and the feature factor to obtain a check result; a classification rule base of abnormal problems of measurement data is built based on the check result; and an abnormal problem in target measurement data is checked based on the classification rule base of abnormal problems of measurement data, to obtain a measurement data quality report.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating computer code using neural networks. One of the methods includes receiving description data describing a computer programming task; receiving a first set of inputs for the computer programming task; generating a plurality of candidate computer programs by sampling a plurality of output sequences from a set of one or more generative neural networks; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.

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: 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: 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: 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: 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: 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 a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

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. 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: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for satisfiability solving for Boolean formulas. One of the methods includes receiving a request to determine whether an input Boolean formula is satisfiable according to a set of satisfiability criteria; processing the input Boolean formula using a formula model that is configured to receive, as input, the input Boolean formula, and to generate, as output, a satisfiability probability distribution over assignments of values to the literals in the clauses of the input Boolean formula; determining whether the input Boolean formula is satisfiable; sampling, using the satisfiability probability distribution, an assignment of values to the literals in the clauses of the input Boolean formula, and determining whether the assignment of values satisfies the set of satisfiability criteria.

Abstract: Methods for determining a self-driving technology-based resource and communications are provided. One example method includes that a server determines a first time-frequency resource and a first location of the first vehicle; determines a first area range in which the first vehicle is located; when a second vehicle exists in the first area range, and a second time-frequency resource of the second vehicle is the same as the first time-frequency resource, determines, from time-frequency resources in the first area range, a third time-frequency resource including an idle time-frequency resource; and the server sends the third time-frequency resource to the first vehicle, and the first vehicle performs laser signal transmission by using the third time-frequency resource.

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.