Abstract: A method to predict and fit an APC of a LEO satellite considering attitude information is provided, which includes: transforming a spacecraft body-fixed coordinate system, obtaining a transformation matrix from orbital coordinate system to transformed spacecraft body-fixed coordinate system, obtaining rotation angles during a rotation process, obtaining rotation angles in orbit determination time period to predict rotation angles in orbit prediction time period, obtaining a transformation matrix from spacecraft body-fixed coordinate system to Earth-fixed coordinate system of each prediction moment, obtaining a correction vector from a CoM to a phase center of a downlink signal antenna in the Earth-fixed coordinate system at each prediction moment, and obtaining an orbit of the phase center of the downlink signal antenna of a LEO satellite, and performing an ephemeris parameter fitting on the orbit of the phase center of the downlink signal antenna of the LEO satellite in a fitting time period.

Abstract: A method for evaluating an on-site desorption effect of activated coke. An on-site desorption effect of activated coke is evaluated in combination with a laboratory simulation desorption process, an activated-coke desorption rate R is calculated by means of measuring the sulfur content of activated-coke samples before and after on-site desorption and the sulfur content of an activated-coke sample after laboratory simulation desorption, and then the on-site desorption effect of activated coke is evaluated, so as to ensure the recycling effect of activated coke. Therefore, the problem of it being impossible to accurately evaluate the on-site desorption effect of activated coke is solved, and an important guidance is provided for on-site process optimization control.

Abstract: A wireless device can include a first radio configured to transmit over a first link and a second link of a first wireless band. The first radio can be configured to detect an OBSS communication on the first link of the first wireless band. The wireless device can perform coordinated link-switch (CLS) operations. The wireless device can be configured to operate as an access point (AP) for one or more stations of a wireless network or a station.

Abstract: In various examples, image-based three-dimensional occupant assessment for in-cabin monitoring systems and applications are provided. An evaluation function may determine a 3D representation of an occupant of a machine by evaluating sensor data comprising an image frame from an optical image sensor. The 3D representation may comprise at least one characteristic representative of a size of the occupant, (e.g., a 3D pose and/or 3D shape), which may be used to derive other characteristics such as, but not limited to weight, height, and/or age. A first processing path may generate a representation of one or more features corresponding to at least a portion of the occupant based on optical image data, and a second processing path may determine a depth corresponding to the one or more features based on depth data derived from the optical image data and ground truth depth data corresponding to the interior of the machine.

Abstract: The present invention discloses a human-robot collaboration method based on a multi-scale graph convolutional neural network. The method includes the following steps: S1, data acquisition: acquiring a dataset of a human skeleton in human-robot collaboration scenes, and performing pre-processing to obtain pre-processed data; S2, model training: loading the pre-processed data, and obtaining a human behavior recognition network model by training a multi-scale graph convolutional neural network; S3, human behavior recognition: predicting human behaviors through a trained deep learning network model; and S4, human-robot interaction: sending predicted information to a robot system through a communication algorithm, and enabling a robot to make action plans based on the human behaviors. By the human-robot collaboration method based on a multi-scale graph convolutional neural network disclosed by the present invention, a robot can predict human behaviors and intents in real scenes and make correct interaction.

Abstract: Disclosed is a Bayesian classification recognition system based on an industrial PaaS platform, comprising: an IaaS infrastructure service layer, a G-PaaS graph neural network processing layer, an O-PaaS docking service layer and an SaaS system application layer. The G-PaaS graph neural network processing layer is configured for point cloud feature generation, point cloud feature learning, point cloud structure estimation and point cloud model classification; and the recognition accuracy of a workpiece point cloud model is improved through the Bayesian classification recognition system based on the industrial PaaS platform.

Abstract: A method for deep learning. The method includes: receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples; adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model; and assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.

Abstract: A switch control device includes a casing, a motor, a control unit, and a push arm. The casing includes a mounting wall adapted to be mounted on a mounting surface, and a surrounding wall extending transversely from the mounting wall. The motor is disposed in the casing and has a rotary shaft extending through the surrounding wall. The control unit is connected to the motor. The push arm is disposed outside of the surrounding wall and is coupled to the rotary shaft so as to be driven to rotate relative to the casing without touching the casing, between a pushing state and a release state, thereby controlling the switch.

Abstract: A method for generating a set of adversarial patches for an image. The method includes segmenting the image into a plurality of regions; selecting a set of target regions that satisfies an attacking criterion by discretely searching of the plurality of regions; and generating a set of adversarial patches by using the set of target regions.

Abstract: A method for visual reasoning. The method includes: providing a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of a set of outputs corresponding to the set of inputs based on visual information on the set of inputs, and wherein the network comprising a Probabilistic Generative Model (PGM) and a set of modules; determining a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs; and applying domain knowledge as one or more posterior regularization constraints on the determined posterior distribution.

Abstract: Provided is an engineered bacterium for producing nervonic acid and/or grease. The genome of the engineering bacterium is integrated with an expression cassette expressing a protein encoded by 3-ketoacyl-CoA synthase (KCS) gene and/or an exterase gene.

Abstract: The present invention discloses a resource recovery method and a resource recovery system of desulfurized ash. The resource recovery method comprises: washing desulfurized ash with water, and performing solid-liquid separation to obtain solid residues rich in calcium sulfite and calcium sulfate and a solution rich in calcium hydroxide; preparing the solution into desulfurized slurry; and roasting the solid residues under the action of a reducing agent to obtain flue gas rich in sulfur dioxide and residues rich in calcium oxide. Therefore, the recovery of sulfur and calcium in the desulfurized ash is realized, and no solid waste, liquid waste, gas waste, etc. are produced in the process.

Abstract: A method for training a deep neural network (DNN) capable of adversarial detection. The DNN is configured with a plurality of sets of weights candidates. The method includes inputting training data selected from training data set to the DNN. The method further includes calculating, based on the training data, a first term for indicating a difference between a variational posterior probability distribution and a true posterior probability distribution of the DNN. The method further includes perturbing the training data to generate perturbed training data; and calculating a second term for indicating a quantification of predictive uncertainty on the perturbed training data. The method further includes updating the plurality of sets of weights candidates of the DNN based on augmenting the summation of the first term and the second term.

Abstract: A method for generating adversarial examples for a Graph Neural Network (GNN) model. The method includes: determining vulnerable features of target nodes in a graph based on querying the GNN model, wherein the graph comprising nodes including the target nodes and edges, each of the edges connecting two of the nodes; grouping the target nodes into a plurality of clusters according to the vulnerable features of the target nodes; and obtaining the adversarial examples based on the plurality of clusters.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources that include person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on a person corpus derived from at least one data source private to the person, obtaining a second set of suggestions based on information from an additional data source, ranking the first and second sets of suggestions to generate a ranked list of suggestions, and presenting at least some of the ranked suggestions.

Abstract: A lock assembly includes a casing, a blocking unit, and a lock unit. The blocking unit includes a blocking member operable to move between an open position and a block position. The lock unit includes an engaging member, an electric unlock module, and a manual unlock module. The engaging member has a main body having first and second abutment portions, and is movable between an engaging position for engaging the blocking member when the blocking member is in the block position, and an unlocked position for disengaging from the blocking member. One of the electric unlock module and the manual unlock module is operable to abut a push member thereof against a corresponding one of the first and second abutment portions to linearly move the engaging member.

Abstract: A location-aware search assist capability identifies location-aware search query suggestions using location information associated with the location-aware search query suggestions. A user's search query input and location and a location associated with each location-aware search query suggestion candidates may be used to identify a set of search query suggestions for presentation to the user. Location-aware search query suggestion candidates may be ranked in accordance with a closeness of each one's location to the user's location. The ranking may be performed using a score, such as a popularity score associated with each search query suggestion candidate. The location-aware search query suggestion candidates having a location closer to the user's location may be promoted by adjusting each candidate's popularity score upward, and the search query suggestion candidates that are farther away from the user's location may be demoted by adjusting each such candidate's popularity score downward.

Abstract: A method for training neural networks based on energy-based latent variable models (EBLVMs) includes bi-level optimizations based on a score matching objective. The lower-level optimizes a variational posterior distribution of the latent variables to approximate the true posterior distribution of the EBLVM, and the higher-level optimizes the neural network parameters based on a modified SM objective as a function of the variational posterior distribution. The method is used to train neural networks based on EBLVMs with nonstructural assumptions.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources including person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on information from a first data source, presenting the first set of suggestions to the person, and presenting, to the person, a second set of suggestions obtained from a person corpus when a portion of the person corpus relevant to the input is accessible. The person corpus may be from a second data source that is private to the person.