Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing data about a medical encounter using neural networks. One of the methods includes obtaining features for a medical encounter associated with the patient, each feature representing a corresponding health event associated with the medical encounter and each of the plurality of features belonging to a vocabulary of possible features that each represent a different health event; and generating respective final embeddings for each of the features for the medical encounter by applying a sequence of one or more self-attention blocks to the features for the medical encounter, wherein each of the one or more self-attention blocks receives a respective block input for each of the features and applies self-attention over the block inputs to generate a respective block output for each of the features.

Abstract: Devices, systems, and methods for remote detection and ranging are described. In an embodiment, a remote sensing method includes obtaining data points that are spatially distributed and have respective intensity values, by performing a remote detection and ranging operation, determining a spatial autocorrelation of a set of data points, out of the data points, based on a difference in distances between data points in the data points, determining an intensity weight multiplier based on a reference intensity value of the data points and an average intensity value of the data points, and determining a quality score of the set of data points by applying the intensity weight multiplier to the spatial autocorrelation of the set of data points; and identifying, based on the quality score, whether the set of data points includes one or more data points that are created by a noise source.

Abstract: The present disclosure relates to compositions and methods for targeting vesicles to specific tissue and cell types. Also disclosed are compositions and methods for delivering therapeutic molecules, including nucleic acids and nucleic acid derivatives, to specific cells or tissues using vesicles with cell and tissue-specific targeting moieties expressed on their surfaces.

Abstract: The present disclosure provides a grinding robot for an inside wall of a small diameter pipe. The grinding robot includes a grinding device, a transmission device, and a driving device. By arranging the grinding robot into the above three portions, the overall bending pipe passability of the robot can be increased, which is convenient for the grinding robot to grind the small diameter pipe. A first gimbal and two second gimbals provided in the transmission device enable the grinding robot to flexibly pass through bends of the pipe, and enable a grinding driving force to be variably transmitted to the grinding device. When the grinding body rotates and contacts a pipe wall, a reaction force of the pipe wall on the grinding body is balanced by an adjusting spring adjustment force in a balance adjusting device and a self-weight of a grinding body.

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: The present disclosure provides a grinding robot for an inside wall of a small diameter pipe. The grinding robot includes a grinding device, a transmission device, and a driving device. By arranging the grinding robot into the above three portions, the overall bending pipe passability of the robot can be increased, which is convenient for the grinding robot to grind the small diameter pipe. A first gimbal and two second gimbals provided in the transmission device enable the grinding robot to flexibly pass through bends of the pipe, and enable a grinding driving force to be variably transmitted to the grinding device. When the grinding body rotates and contacts a pipe wall, a reaction force of the pipe wall on the grinding body is balanced by an adjusting spring adjustment force in a balance adjusting device and a self-weight of a grinding body.

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: 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 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 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.