Abstract: The disclosed technology involves autonomously identifying goals and sub-goals from a user utterance and generating responses to the user based on the goals and sub-goals.

Abstract: Compounds, particles, and cathode active materials that can be used in lithium ion batteries are described herein. Methods of making such compounds, powders, and cathode active materials are described. The particles have a particle size distribution with a D50 ranging from 10 ?m to 20 ?m.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive configuration information that specifies one or more parameters associated with generating, by the UE, a time domain covariance matrix specific to the UE. The UE may receive a channel state information (CSI) reference signal (CSI-RS). The UE may transmit, based at least in part on the CSI-RS and the one or more parameters, a CSI report, the CSI report including data representing the time domain covariance matrix. Numerous other aspects are described.

Abstract: A method for predicting traffic flow or a travel time, includes: obtaining navigation route data; obtaining, according to the navigation route data, navigation flow of a road within a prediction time period; obtaining real-time traffic flow, a real-time travel time, past traffic flow, and a past travel time of the road; and training a neural network with the real-time travel time, the past travel time, the real-time traffic flow, the past traffic flow, and the navigation flow of the road to obtain predicted traffic flow or a predicted travel time of the road within the prediction time period.

Abstract: The embodiments of the present application provide a method and apparatus for modulation recognition of signals based on cyclic residual network, the method comprises: obtaining a signal matrix of a to-be-recognized signal, and extracting real part information and imaginary part information of the signal matrix; generating, according to extracted real part information and imaginary part information, a real-and-imaginary-part feature matrix of the to-be-recognized signal; converting, according to a preset matrix conversion method, the real-and-imaginary-part feature matrix into an amplitude-and-phase feature matrix; and inputting the amplitude-and-phase feature matrix into a pre-trained cyclic residual network to obtain a modulation mode corresponding to the to-be-recognized signal.

Abstract: The present disclosure provides a time calibration method, an electronic device, and a storage medium, and relates to the technical field of automatic driving. The method can include: determining a first observation error between first reference time point and first system time point; determining a second observation error between second reference time point and second system time point; determining a unit drift per second of the system to be calibrated based on the first system time point, the second system time point, the first observation error, and the second observation error; and calibrating the time of the system to be calibrated based on the unit drift per second.

Abstract: A device and a method for recognizing person activity in a sequence of frames (100) comprising: obtaining a set of consecutives 3D poses (103), obtaining a feature map (102), obtaining a vector of spatiotemporal features, obtaining a matrix of spatial attention weights, obtaining a matrix of temporal attention weights (110), modulating (106) the feature map using the matrix of spatial attention weights to obtain a spatially modulated feature map, modulating (111) the feature map using the vector of temporal attention weights to obtain a temporally modulated feature map, performing a convolution (114) of the spatially modulated feature map and of the temporally modulated feature map to obtain a convoluted feature map, performing a classification (115) using the convoluted feature map so as to determine the activity of the person in the video.

Abstract: The embodiments of the present application provide a method and apparatus for modulation recognition of signals based on cyclic residual network, the method comprises: obtaining a signal matrix of a to-be-recognized signal, and extracting real part information and imaginary part information of the signal matrix; generating, according to extracted real part information and imaginary part information, a real-and-imaginary-part feature matrix of the to-be-recognized signal; converting, according to a preset matrix conversion method, the real-and-imaginary-part feature matrix into an amplitude-and-phase feature matrix; and inputting the amplitude-and-phase feature matrix into a pre-trained cyclic residual network to obtain a modulation mode corresponding to the to-be-recognized signal.

Abstract: A method for predicting traffic flow or a travel time, includes: obtaining navigation route data; obtaining, according to the navigation route data, navigation flow of a road within a prediction time period; obtaining real-time traffic flow, a real-time travel time, past traffic flow, and a past travel time of the road; and training a neural network with the real-time travel time, the past travel time, the real-time traffic flow, the past traffic flow, and the navigation flow of the road to obtain predicted traffic flow or a predicted travel time of the road within the prediction time period.