Abstract: Disclosed are a silicon nitride ceramic sintered body and preparation method thereof. The silicon nitride ceramic sintered body includes a sintered bulk and a hard surface layer having a thickness of 10-1000 ?m, formed on a surface of the sintered bulk, wherein the sintered bulk comprises a first silicon nitride crystalline phase and a first grain boundary phase; the hard surface layer comprises a second silicon nitride crystalline phase and a second grain boundary phase; the first grain boundary phase comprises a metal tungsten phase being tungsten elementary substance and/or a tungsten alloy; the second grain boundary phase comprises tungsten carbide particles; tungsten element in the metal tungsten phase accounts for 80-100 wt % of total tungsten element in the first grain boundary phase; and tungsten element in the tungsten carbide particles accounts for 60-100 wt % of total tungsten element in the second grain boundary phase.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

Abstract: Disclosed are a silicon nitride ceramic sintered body and a-preparation method thereof. The silicon nitride ceramic sintered body includes a sintered bulk and a hard surface layer having a thickness of 10-1000 ?m, formed on a surface of the sintered bulk, wherein the sintered bulk comprises a first silicon nitride crystalline phase and a first grain boundary phase; the hard surface layer comprises a second silicon nitride crystalline phase and a second grain boundary phase; the first grain boundary phase comprises a metal tungsten phase being tungsten elementary substance and/or a tungsten alloy; the second grain boundary phase comprises tungsten carbide particles; tungsten element in the metal tungsten phase accounts for 80-100 wt % of total tungsten element in the first grain boundary phase; and tungsten element in the tungsten carbide particles accounts for 60-100 wt % of total tungsten element in the second grain boundary phase.

Abstract: Disclosed are a silicon nitride ceramic sintered body and a preparation method thereof. The silicon nitride ceramic sintered body has a content of a silicon nitride crystalline phase of not less than 98 wt %, a relative density of not less than 99%, a porosity of not larger than 1%, a grain boundary phase including Li, O, N, and Si elements, and a total content of C, F, Al, Mg, K, Ca, Na and rare-earth metals elements of less than 0.1 wt %.

Abstract: A neural network control system for controlling an agent to perform a task in a real-world environment, operates based on both image data and proprioceptive data describing the configuration of the agent. The training of the control system includes both imitation learning, using datasets generated from previous performances of the task, and reinforcement learning, based on rewards calculated from control data output by the control system.

Abstract: A method of generating an output image having an output resolution of N pixels×N pixels, each pixel in the output image having a respective color value for each of a plurality of color channels, the method comprising: obtaining a low-resolution version of the output image; and upscaling the low-resolution version of the output image to generate the output image having the output resolution by repeatedly performing the following operations: obtaining a current version of the output image having a current K×K resolution; and processing the current version of the output image using a set of convolutional neural networks that are specific to the current resolution to generate an updated version of the output image having a 2K×2K resolution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

Abstract: A system for performing real-time quantum-safe computation of a digital transaction using in a blockchain consensus protocol, comprising a plurality of permissioned verification servers being a plurality of distributed participants that are adapted to create common randomization to all of said participants which remains unrevealed until being used by said participants, by assigning to each participant a unique polynomial having a maximal degree being common to all participants; allowing each participant to select a random value; allowing each participant to send his selected random value to all other participants using a secret sharing scheme based on points on his unique polynomial, such that said secret hides the details of said selected random value and all other participants that receive shares of said selected random value will not be able to reconstruct said selected random value from the received shares; create a pool of all shares of all participants; build a quantum-safe consensus of honest participa

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.

Abstract: Disclosed are a method and system for temporary coating supporting and permanent bolt supporting. Tunnel excavation construction operation is composed of a plurality of operation cycle units, and each of the operation cycle units includes: excavating by at least one row pitch to form a new tunnel; spraying a coating material onto a surface of the new tunnel to form a sealing coat on a surface of surrounding rock, wherein the sealing coat has an adhesive property and a sealing property both meeting preset conditions, and has a tensile strength and a toughness needed for supporting; and inserting bolts into the new tunnel to perform permanent supporting.

Abstract: Disclosed herein is a hybrid polymeric material comprising a tropoelastin and a copolymer of a polyol monomer and a polycarboxylic acid monomer. The hybrid polymeric material is suitable for use as a tissue scaffold.

Abstract: The present invention relates to a method for predicting and monitoring the severity of COVID-19 disease following infection of a subject with the SARS-CoV-2 virus. It also relates to a method for the treatment of a subject with COVID-19 disease. It also relates to kits for use in the methods of the invention.

Abstract: A method of generating an output image having an output resolution of N pixels×N pixels, each pixel in the output image having a respective color value for each of a plurality of color channels, the method comprising: obtaining a low-resolution version of the output image; and upscaling the low-resolution version of the output image to generate the output image having the output resolution by repeatedly performing the following operations: obtaining a current version of the output image having a current K×K resolution; and processing the current version of the output image using a set of convolutional neural networks that are specific to the current resolution to generate an updated version of the output image having a 2K×2K resolution.

Abstract: A method is proposed of training a neural network to generate action data for controlling an agent to perform a task in an environment. The method includes obtaining, for each of a plurality of performances of the task, one or more first tuple datasets, each first tuple dataset comprising state data characterizing a state of the environment at a corresponding time during the performance of the task; and a concurrent process of training the neural network and a discriminator network. The training process comprises a plurality of neural network update steps and a plurality of discriminator network update steps.

Abstract: A method of generating an output image having an output resolution of N pixels×N pixels, each pixel in the output image having a respective color value for each of a plurality of color channels, the method comprising: obtaining a low-resolution version of the output image; and upscaling the low-resolution version of the output image to generate the output image having the output resolution by repeatedly performing the following operations: obtaining a current version of the output image having a current K×K resolution; and processing the current version of the output image using a set of convolutional neural networks that are specific to the current resolution to generate an updated version of the output image having a 2K×2K resolution.

Abstract: A high-sensitivity, high-selectivity and portable detection method for trace uranyl ion is described. The method has an ultralow detection limit of 11 pM/2.6 ppt and is useful in precise monitoring of the uranium content in agricultural and sideline products, foods, environments and so on. The test instrument is miniaturized and low in cost to achieve high-precision portable measurement in the field. A conjugated polymer with aggregation-induced emission (AIE) activity is synthesized, and prepared into Pdots, and a uranyl-responsive electrochemiluminescence (ECL) probe is developed by modifying the Pdots with DNA or RNA, which serves as an adsorption ligand of uranyl ion. The probe exhibits good biocompatibility. The ECL technology can be used in uranyl ion detection and the method has extremely high sensitivity. A uranyl ion probe with AIE activity is also disclosed, which can be applied in portable precise monitoring of trace uranyl ion by means of the ECL technology.

Abstract: A GIS mechanical fault diagnosis method and the device are disclosed. The method includes: collecting vibration signals to be measured of various excitation sources of GIS in mechanical operation; performing wavelet packet-feature entropy vector extraction on the vibration signals to be measured, when it is determined that the vibration signals to be measured are abnormal according to standard vibration signals in the normal state; inputting the extracted wavelet packet-feature entropy vectors into the pre-trained BP neural network for GIS mechanical fault identification, and outputting the corresponding fault. The disclosure integrates the vibration signals under the action of various excitation sources, extracts the feature entropy vectors according to the entropy theory, and constructs and trains a BP neural network that can classify and recognize various GIS mechanical faults, so as to perform comprehensive and effective GIS mechanical faults diagnose.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

Abstract: A method of generating an output image having an output resolution of N pixels×N pixels, each pixel in the output image having a respective color value for each of a plurality of color channels, the method comprising: obtaining a low-resolution version of the output image; and upscaling the low-resolution version of the output image to generate the output image having the output resolution by repeatedly performing the following operations: obtaining a current version of the output image having a current K×K resolution; and processing the current version of the output image using a set of convolutional neural networks that are specific to the current resolution to generate an updated version of the output image having a 2K×2K resolution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network. One of the methods includes maintaining a replay memory that stores trajectories generated as a result of interaction of an agent with an environment; and training an action selection neural network having policy parameters on the trajectories in the replay memory, wherein training the action selection neural network comprises: sampling a trajectory from the replay memory; and adjusting current values of the policy parameters by training the action selection neural network on the trajectory using an off-policy actor critic reinforcement learning technique.