Abstract: Disclosed is a method and a device for calculating a radiation field of satellite-borne array antennas. comprising: calculation of a kernel function of an antenna: calculating the kernel function of a very-low-frequency antenna in an ionospheric environment; calculation of an antenna current distribution: establishing and solving N current integral equations satisfied by the array antennas to obtain the current distribution of each antenna in the array; calculation of the radiation fields of the array antennas: calculating and summing the radiation fields of all antenna elements to obtain the radiation field of the array antennas; calculation of an array factor: analyzing a propagation phase difference between the ith antenna and the first antenna, and calculating the array factor of the array antennas accordingly, further simplifying the radiation field of the array antennas. The present disclosure has advantages of less calculation time consumption and high accuracy.

Abstract: The present application provides an integrated antenna and an antenna apparatus. The integrated antenna includes an antenna substrate, a photodiode and an antenna radiating body. The photodiode is disposed on the antenna substrate and includes two electrodes. The antenna radiating body is disposed on the antenna substrate and connected with the two electrodes. The antenna radiating body is disposed on an upper surface of the photodiode.

Abstract: The present invention relates to quality assurance methods for use in amplification techniques. such as Next Generation Sequencing (NGS).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

Abstract: This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output that characterizes a scene at a current time step. In one aspect, one of the systems include: a voxel neural network that generates a current early-stage feature representation of the current point cloud, a fusion subsystem that generates a current fused feature representation at the current time step; a backbone neural network that generates a current late-stage feature representation at the current time step, and an output neural network that generate an output that characterizes a scene at the current time step.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

Abstract: Techniques for performing visual clustering with a hierarchical graph neural network framework including a joint linkage prediction and density estimation graph model are described. Embodiments herein recurrently run the joint linkage prediction and density estimation graph model to generate intermediate clusters in multiple iterations (e.g., until convergence) to obtain a final clustering result. In certain embodiments, for each iteration, the input graph contains nodes that are merged from nodes assigned to intermediate clusters from the previous iteration. By using a small and fixed bandwidth k in each iteration, embodiments herein alleviate the sensitivity to the k selection for different clustering applications. Certain embodiments herein remove the tuning of a different k (e.g., k-bandwidth) for k-nearest neighbor graph construction over different clustering applications.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels.

Abstract: This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels.

Abstract: A line isolating power connector can include a housing for connecting with a power cable and a power source, and wires positioned through the housing that include at least a line wire and a neutral wire. The housing is separated by an aperture into a line conduit and a neutral conduit, and the aperture is sized to receive a portion of a contactless power quality probe such that the power quality probe can be connected around the first line conduit or neutral conduit in order to measure power quality in either the line wire or the neutral wire.

Abstract: The present invention relates to quality assurance methods for use in amplification techniques, such as Next Generation Sequencing (NGS).

Abstract: The present invention relates to quality assurance methods for use in amplification techniques, such as Next Generation Sequencing (NGS).

Abstract: The present invention relates to quality assurance methods for use in amplification techniques, such as Next Generation Sequencing (NGS).

Abstract: The present disclosure discloses methods, IM clients, and IM group servers of transmitting Instant Messenger (IM) group communication information. Based on the technical scheme of the embodiments of the present disclosure, upon identifying that group communication information inputted by a user includes a predefined name identifier, an IM client on a sending end sends the group communication information to group users that are listed after the name identifier. In response to identifying that the received group communication information includes the name identifier, an IM client at a receiving end prominently displays the group communication information that includes the name identifier, and sends a user response of the group communication information to users (other than the user himself/herself) that are listed after the name identifier and a user who sends the group communication information.