Abstract: An infectious disease infection prediction method, an apparatus, and a storage medium based on macro-micrograph fusion are provided. The method includes: acquiring macrographs of a plurality of first regions and micrographs of second regions within a set period; inputting the macroscopic graphs and the microscopic graphs into two graph convolutional neural networks to obtain two hidden layer vectors respectively, and fusing the two hidden layer vectors to obtain fusion hidden layer information of the first regions; performing a time sequence calculation of the fusion hidden layer information to obtain time sequence hidden layer information of the first regions; inputting the time series hidden layer information into two prediction networks to obtain two prediction results, respectively, and performing fusion calculation of the two prediction results to obtain a final prediction result of infectious diseases in the first regions.

Abstract: Disclosed are a digital currency transaction method, apparatus and electronic device, and a computer-readable medium.

Abstract: This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Abstract: The present application discloses systems, methods, and computer-readable media that can utilize a handheld-movement-detection model to detect whether a computing device is moved by hand or otherwise by a person within a vehicle. For instance, the disclosed systems can receive movement data from a computing device and generate filtered signals. Subsequently, the disclosed systems can utilize the handheld-movement-detection model to convert the filtered signals into a binary movement-classification signal (based on a signal threshold) to indicate the presence of handheld movement of a device. Furthermore, the disclosed systems can also utilize movement data from a computing device to detect whether the computing device is mounted and/or to detect vehicular movements.

Abstract: The present disclosure provides a data storage system, including data cache module, data processing module, and a persistent memory. The data cache module includes an on-chip mapping data cache and an on-chip counter cache, where the mapping data cache is configured to cache mapping data, and when the free space of the mapping data cache is less than a preset threshold, the least recently used mapping data cache line will be evicted from the cache and written back to the persistent memory. The data processing module encrypts/decrypts persistent memory data by using their counters, and accesses the persistent memory blocks indicated by their corresponding mapping data. The persistent memory comprises the first and second storage regions for the latest checkpoint data and modified working data in the current checkpoint interval respectively.

Abstract: Embodiments of the present disclosure provide methods and apparatuses for RIS node in a communication network. A method performed by a reconfigurable intelligent surface, RIS, node, comprising: communicating with a base station, BS, in a communication network, to attach to the BS, via an access procedure; further communicating with the BS according to scheduling in the communication network, to receive a configuration from the BS; and reflecting wireless signals in the communication network, at least based on the configuration. According to embodiments of the present disclosure, the RIS node can be configured dynamically by a base station. Therefore, embodiments of the present disclosure will provide a communication network with control capability on RIS nodes/devices and make the RIS nodes/devices to have manageable behaviors.

Abstract: This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Abstract: A flow field identification method of artificial intelligence fish simulation system uses cluster server parallel sampling to obtain continuous flow field time series information data. After preprocessing the data, the method uses the lateral line perceptron of recurrent neural network or convolutional neural network based on long time series to continuously acquire data, train and iterate. Finally, the method can identify the flow field sequence data signal with time series property, wherein, the perceived flow field signals include but are not limited to velocity, pressure and vorticity, and the experimental results are continuously filled into the experimental database, which will have the effect of memory transplantation on the artificial fish and reduce the occurrence of repetitive errors. The method identifies the flow field of an artificial intelligence fish simulation system, which can fully reflect the characteristics of the flow field from the aspects of amplitude, frequency, and wavelength.

Abstract: The present application discloses systems, methods, and computer-readable media that can utilize a handheld-movement-detection model to detect whether a computing device is moved by hand or otherwise by a person within a vehicle. For instance, the disclosed systems can receive movement data from a computing device and generate filtered signals. Subsequently, the disclosed systems can utilize the handheld-movement-detection model to convert the filtered signals into a binary movement-classification signal (based on a signal threshold) to indicate the presence of handheld movement of a device. Furthermore, the disclosed systems can also utilize movement data from a computing device to detect whether the computing device is mounted and/or to detect vehicular movements.

Abstract: Methods and apparatuses for adaptation of communication parameter are disclosed. According to an embodiment, a transmitter performs a first transmission to a receiver. The transmitter receives, from the receiver, first information related to one or more candidate constellation maps adapted to one or more current operating conditions, or second information usable for deriving the one or more candidate constellation maps. The transmitter determines a target constellation map based at least on the first information or the second information. The transmitter performs a second transmission to the receiver based on the determined target constellation map.

Abstract: The present disclosure provides a data storage system, including data cache module, data processing module, and a persistent memory. The data cache module includes an on-chip mapping data cache and an on-chip counter cache, where the mapping data cache is configured to cache mapping data, and when the free space of the mapping data cache is less than a preset threshold, the least recently used mapping data cache line will be evicted from the cache and written back to the persistent memory. The data processing module encrypts/decrypts persistent memory data by using their counters, and accesses the persistent memory blocks indicated by their corresponding mapping data. The persistent memory comprises the first and second storage regions for the latest checkpoint data and modified working data in the current checkpoint interval respectively.

Abstract: Systems, methods, devices and storage media for graph data processing are provided. In one aspect, a graph data processing system includes a memory and a plurality of processing units, and each processing unit is provided with a decision module. Each processing unit is configured to determine set operations required for extracting one or more subgraphs matching a specified graph pattern from target graph data according to a preset graph pattern matching algorithm. Then, for each set operation, the decision module is configured to determine a cost value corresponding to a performance of the processing unit occupied to execute the set operation in accordance with different execution policies, and further select a target execution policy with a smallest cost value to execute the set operation.

Abstract: Provided is a method in a network device for radio beam determination. The method includes: obtaining current information data related to a terminal device; and estimating a candidate radio beam configuration for communication with the terminal device based on the current information data by using a probabilistic mechanism, algorithm or policy that is obtained by analyzing historical radio beam configurations for communication with the terminal device and history information data related to the terminal.

Abstract: A method of predicting infectious disease infections and a system thereof, a device, and a storage medium are provided. An increment module controls an input module to obtain new incremental data in the current cycle in response to a preset instruction. A graph engine iteratively trains a first graph model based on new incremental data until the first graph model meets a convergence condition, so as to obtain a second graph model and perform dynamic updating of the graph model. Each region acts as a node in the graph model, each node feature is obtained based on the regional disease information, edges are defined by nodes connected to each other according to a geographic location relationship among regions, and each edge is assigned an edge weight according to regional population information. The updated graph model predicts infections based on data to be predicted that is selected by the interaction module.

Abstract: The present disclosure provides a method (100) in a network device. The method (100) includes: determining (110) a number, N, of sets of beam vectors by: creating a first set of beam vectors orthogonal to each other; and determining an n-th set of beam vectors each obtained by linear combination of two or more beam vectors from the (n?1)-th set of beam vectors, where N and n are integers and N?n>1. The method (100) further includes: selecting (120), from one or more of the N sets, a plurality of beam vectors for beamforming of a radio signal.

Abstract: A lens antenna and a lens antenna array are disclosed. A radio device and a method performed by the radio device are also disclosed. The lens antenna may include a lens, and two or more sets of antenna elements which are respectively disposed on two or more imaginary surfaces on a lateral side of the lens. The two or more imaginary surfaces may have different distances to a center point of the lens. The lens antenna array may include a plurality of lens antennas described above. The antenna elements in the plurality of lens antennas may be disposed on a same lateral side of the lenses in the plurality of lens antennas. A method for selecting antenna elements is performed by the radio device controlling the lens antenna or lens antenna array, when the lens antenna or lens antenna array operates as transmission or reception radio antennas.

Abstract: Embodiments of the present disclosure provide method and apparatus for controlling training data. A method performed by a network device includes determining control information for training data. The method further includes transmitting the control information for training data to a wireless device. According to the control information, the transmitting and receiving of the training signals is carried out. The training data is used for air-interface machine learning.

Abstract: The present disclosure, a method performed by a Radio Unit, RU, is provided. The method includes: receiving, from a Digital Unit, DU, an instruction to receive a reference signal; receiving, from a terminal device, the reference signal in accordance with the instruction; and transmitting, to the DU, a positioning signal based on the received reference signal.

Abstract: Embodiments of the present disclosure provide methods and apparatus for terminal device behavior classification. A method performed by a first network function comprises obtaining communication measurement data related to at least one terminal device. The method further comprises determining terminal implementation characteristics for a group of terminal devices based on the communication measurement data. The method further comprises sending the terminal implementation characteristics and/or a policy for the group of terminal devices to at least one second network function. The policy for the group of terminal devices is determined based on the terminal implementation characteristics.

Abstract: A method in a terminal device for transmission to at least a first network device and a second network device over bundled Transmission Time Intervals (TTIs). The method comprises determining that a first TTI for transmission to the first network device overlaps a second TTI for transmission to the second network device based on a first Timing Advance (TA) value associated with the first network device and a second TA value associated with the second network device; and blanking a portion of the first TTI, such that the rest of the first TTI does not overlap the second TTI.