Abstract: Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

Abstract: A display apparatus and a method of powering the display apparatus are provided. The display apparatus includes: a main control circuit, configured to provide a first control signal; a touch display driving circuit, configured to provide a second control signal; a power supply circuit, coupled to the touch display driving circuit, and configured to provide a power supply voltage to the touch display driving circuit; and a logic circuit coupled to the main control circuit, the touch display driving circuit, and the power supply circuit, and configured to continuously enable the power supply circuit in a first mode and intermittently enable the power supply circuit in a second mode, under a control of the first control signal and the second control signal.

Abstract: A microfluidic chip and a driving method thereof are provided. The microfluidic chip includes a base substrate, a driving circuit array, a first decoding circuit, and a second decoding circuit, the driving circuit array, the first decoding circuit, and the second decoding circuit are all integrated on the base substrate; the first decoding circuit is configured to generate and output a target scan driving signal to the driving circuit array; the second decoding circuit is configured to generate and output a target driving voltage signal to the driving circuit array; and the driving circuit array is configured to control an operation of a liquid droplet over the driving circuit array based on the target scan driving signal and the target driving voltage signal.

Abstract: Disclosed is a method for converting image data. The method includes: acquiring image data of a target image, wherein the image data includes first pixel values of m pixels in the target image, each of the first pixel values includes a first color value of at least one color channel, the first color value being within a target color value interval of the at least one color channel; dividing the target color value interval into n color value partitions; determining a color value partition where the first color value falls from the n color value partitions; and converting the first color value into a second color value according to a position of the first color value in the color value partition, the number of bits occupied by the second color value being less than the number of bits occupied by the first color value.

Abstract: A graph data processing method includes acquiring a directed graph, where a directed edge in the directed graph is represented as pointing to a destination vertex from a start vertex; representing the directed edge in a unified form according to a specified order between a vertex identifier of the start vertex and a vertex identifier of the destination vertex; generating a tagged edge for recording an original edge direction of the directed edge, to obtain a tagged directed graph; and identifying a category of a triangle constituted by a vertex in the tagged directed graph, a neighbor vertex of the vertex, and a common vertex commonly adjacent to the vertex and the neighbor vertex, based on tagged edges between two of the vertex, the neighbor vertex, and the common vertex.

Abstract: This application belongs to the technical field of artificial intelligence, and particularly relates to a data processing method, a data processing apparatus, a computer-readable medium, and an electronic device.

Abstract: This disclosure relates to a method for vertical federated learning. In multiple participation nodes deployed in a multi-way tree topology, an upper-layer participation node corresponds to k lower-layer participation nodes. After the upper-layer participation node and the k lower-layer participation nodes exchange public keys with each other, the upper-layer participation node performs secure two-party joint computation with the lower-layer participation nodes with a first public key and second public keys as encryption parameters to obtain k two-party joint outputs of a federated model. Further, the upper-layer participation node aggregates the k two-party joint outputs to obtain a first joint model output corresponding to the federated model. As such, a multi-way tree topology deployment-based vertical federated learning architecture is provided, improving the equality of each participation node in a vertical federated learning process.

Abstract: Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

Abstract: A display apparatus and a method of powering the display apparatus are provided. The display apparatus includes: a main control circuit, configured to provide a first control signal; a touch display driving circuit, configured to provide a second control signal; a power supply circuit, coupled to the touch display driving circuit, and configured to provide a power supply voltage to the touch display driving circuit; and a logic circuit coupled to the main control circuit, the touch display driving circuit, and the power supply circuit, and configured to continuously enable the power supply circuit in a first mode and intermittently enable the power supply circuit in a second mode, under a control of the first control signal and the second control signal.

Abstract: A method and system for distributed machine learning and model training are disclosed. In particular, a finite asynchronous parallel training scheme is described. The finite asynchronous parallel training takes advantage of the benefits of both asynchronous parallel training and synchronous parallel training. The computation delays in various distributed computation nodes are further considered when training parameter are updated during each round of iterative training. The disclosed method and system facilities increase of model training speed and efficiency.

Abstract: The present disclosure provides a sub-pixel unit, a display panel, a display apparatus, and a driving method of the display apparatus, which belongs to the field of display technology. The sub-pixel unit includes a plurality of sub-pixels; any of the sub-pixels includes a display module, a control module, and a driving module; wherein the control module is connected to a second gate line, a data line, a first voltage end and a first node, and configured to receive a data signal on the data line under control of a signal on the second gate line, and control one of the data line and the first voltage end to be connected to the first node according to the received data signal; and the driving module is connected to a first gate line, the first node and the display module, and configured to drive the display module according to a signal on the first node under control of a signal on the first gate line.

Abstract: Disclosed is a method for converting image data. The method includes: acquiring image data of a target image, wherein the image data includes first pixel values of m pixels in the target image, each of the first pixel values includes a first color value of at least one color channel, the first color value being within a target color value interval of the at least one color channel; dividing the target color value interval into n color value partitions; determining a color value partition where the first color value falls from the n color value partitions; and converting the first color value into a second color value according to a position of the first color value in the color value partition, the number of bits occupied by the second color value being less than the number of bits occupied by the first color value.

Abstract: The present disclosure provides a sub-pixel unit, a display panel, a display apparatus, and a driving method of the display apparatus, which belongs to the field of display technology. The sub-pixel unit includes a plurality of sub-pixels; any of the sub-pixels includes a display module, a control module, and a driving module; wherein the control module is connected to a second gate line, a data line, a first voltage end and a first node, and configured to receive a data signal on the data line under control of a signal on the second gate line, and control one of the data line and the first voltage end to be connected to the first node according to the received data signal; and the driving module is connected to a first gate line, the first node and the display module, and configured to drive the display module according to a signal on the first node under control of a signal on the first gate line.

Abstract: A method and system for distributed machine learning and model training are disclosed. In particular, a finite asynchronous parallel training scheme is described. The finite asynchronous parallel training takes advantage of the benefits of both asynchronous parallel training and synchronous parallel training. The computation delays in various distributed computation nodes are further considered when training parameter are updated during each round of iterative training. The disclosed method and system facilities increase of model training speed and efficiency.

Abstract: A method for accessing metadata includes: receiving an authentication information query request from a query engine, and sending the authentication information query request to a global database server; receiving authentication information from the global database server; sending the authentication information to the query engine so that the query engine determines, according to privilege authentication result of the authentication information, whether to access table information. A corresponding server and system are also disclosed.

Abstract: A method for accessing metadata includes: receiving an authentication information query request from a query engine, and sending the authentication information query request to a global database server; receiving authentication information from the global database server; sending the authentication information to the query engine so that the query engine determines, according to privilege authentication result of the authentication information, whether to access table information. A corresponding server and system are also disclosed.

Abstract: The present disclosure provides a method and system for implementing an analytic function based on MapReduce. The method includes: a table scan operator acquiring a data row from a file block, and sending the data row to a reduce sink operator; upon receipt of the data row, the reduce sink operator determining a reduce key, a partition key, and a sort key of the analytic function, and sending the data row to an analysis operator by means of a MapReduce framework; and upon receipt of the data row, the analysis operator analyzing the data row to obtain an analytic result, and forwarding the data row and the analytic result to a subsequent operator. The present disclosure can implement an analytic function in a distributed data warehouse of the MapReduce framework, thereby solving a problem that the analytic function cannot be used in the distributed data warehouse based on the MapReduce framework to perform data analytical processing.