Abstract: Disclosed in the present invention are a dynamic maximal clique enumeration device and method based on an FPGA with an HBM, the method including: the HBM stores a dynamic edge flow, a complete graph adjacency matrix, and candidate cliques; a matrix computing unit updates the complete graph adjacency matrix based on the dynamic edge flow, transmits the updated complete graph adjacency matrix to the HBM for storage, and determines header nodes, of which the corresponding candidate clique needs to be updated; a sequence computing unit constructs, according to the updated complete graph adjacency matrix and each header node to be updated, the sorted data set for reconstructing candidate cliques by data block sequencing; and an update computing unit executes, in parallel, an update task of the candidate clique corresponding to each header node to be updated based on the sorted data set, transmits the updated candidate cliques to the HBM for storage, and transmits the updated candidate cliques to the PC host to ext

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: A component of a computing system, including: processor circuitry; and a non-transitory computer-readable storage medium including instructions that, when executed by the processor circuitry, cause the processor circuitry to: dynamically monitor runtime metrics across processor cores of the computing system, wherein the runtime metrics comprise a measure of system-critical task residency and a measure of user-critical foreground application utilization; and initiate a power optimization action configured to transition the computing system into a power efficiency mode when the system-critical task residency is below a system-critical task residency threshold and the user-critical foreground application utilization is below a user-critical foreground application utilization threshold.

Abstract: A data classification method and apparatus, a device and a storage medium. A structural feature of the respective node in graph data may be determined according to a neighbor node of the respective node in the graph data through a deviation between the decoded feature obtained by decoding the embedded coding feature of the respective node in the graph data and the initial feature of the respective node, and then the embedded coding feature corresponding to the respective node is adjusted according to the decoded feature of the respective node and the structural feature of the respective node in the graph data to obtain the adjusted feature corresponding to the respective node, so that accuracy of an obtained feature of the respective node is improved, and thus accuracy of data classification may be improved.

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: A data classification method and apparatus, a device and a storage medium. A structural feature of the respective node in graph data may be determined according to a neighbor node of the respective node in the graph data through a deviation between the decoded feature obtained by decoding the embedded coding feature of the respective node in the graph data and the initial feature of the respective node, and then the embedded coding feature corresponding to the respective node is adjusted according to the decoded feature of the respective node and the structural feature of the respective node in the graph data to obtain the adjusted feature corresponding to the respective node, so that accuracy of an obtained feature of the respective node is improved, and thus accuracy of data classification may be improved.

Abstract: Methods, apparatuses, and computer readable media for clear-to-send duration field adjustments are disclosed. Apparatuses of a station (STA) are disclosed, where the apparatuses comprise processing circuitry configured to: decode a physical layer protocol data unit (PPDU), the PPDU including a request to send (RTS) frame, the RTS frame comprising an address of the STA and a first duration field, the first duration field indicating a first duration. The processing circuitry is further configured to: setting a second duration to a duration that ends before a transmission and encoding for transmission a clear-to-send (CTS) frame, the CTS frame comprising a second duration field, the second duration field indicating the reduced second duration.

Abstract: Biological chemicals, potentially found in blood are measured by collecting sweat and determining the concentration or meaning of the selected chemical in sweat. The sweat can be collected using a time based, interval collector and analyzed using an external device. It can also be collected on a one time basis, using a flexible, chemical capacitor, or on a continuous basis using a chemical, field effect transducer.

Abstract: Biological chemicals, potentially found in blood are measured by collecting sweat and determining the concentration or meaning of the selected chemical in sweat. The sweat can be collected using a time based, interval collector and analyzed using an external device. It can also be collected on a one time basis, using a flexible, chemical capacitor, or on a continuous basis using a chemical, field effect transducer.

Abstract: Biological chemicals, potentially found in blood are measured by collecting sweat and determining the concentration or meaning of the selected chemical in sweat. The sweat can be collected using a time based, interval collector 10 and analyzed using an external device. It can also be collected on a one time basis, using a flexible, chemical capacitor 50, or on a continuous basis using a chemical, field effect transducer 98.

Abstract: Biological chemicals, potentially found in blood are measured by collecting sweat and determining the concentration or meaning of the selected chemical in sweat. The sweat can be collected using a time based, interval collector and analyzed using an external device. It can also be collected on a one time basis, using a flexible, chemical capacitor, or on a continuous basis using a chemical, field effect transducer.