Abstract: Proposed are a fast multidimensional partial Fourier transform method and apparatus. According to an aspect, there is provided a fast multidimensional partial Fourier transform method, the fast multidimensional partial Fourier transform method being performed by a fast multidimensional partial Fourier transform apparatus, the fast multidimensional partial Fourier transform method including: setting a plurality of hyperparameters used in partial Fourier transform based on constraints on the tolerance and degree of the polynomial for polynomial approximation; and approximating and computing the multidimensional Fourier coefficients of a partial Fourier transform for multidimensional data based on the plurality of hyperparameters.

Abstract: A memory test device is provided including a command feature vector extractor and an address feature vector extractor. The command feature vector extractor extracts a command feature vector, based commands executed on memory cells among a plurality of memory cells. The address feature vector extractor extracts an address feature vector, based on address-related information indicating locations of the memory cells executing the commands.

Abstract: A memory test device is provided including a command feature vector extractor and an address feature vector extractor. The command feature vector extractor extracts a command feature vector, based commands executed on memory cells among a plurality of memory cells. The address feature vector extractor extracts an address feature vector, based on address-related information indicating locations of the memory cells executing the commands.

Abstract: Proposed herein are a link prediction method and apparatus. The link prediction method that is performed by the link prediction apparatus includes predicting one or more edges having a probability of being connected in the structure of an edge-incomplete graph by entering the edge-incomplete graph into a link prediction model. The link prediction model is a model that performs binary classification by processing at least one edge observed in the structure of the edge-incomplete graph as positive data and processing at least one node pair unconnected in the structure of the edge-incomplete graph as unlabeled data.

Abstract: An electronic device is provided. The electronic device includes an interface, a memory, and a processor and configured to provide a control recommendation of an external electronic device using a learning model. The learning model is configured to generate a first output vector by encoding sequential control information about a user using a transformer and summarizing the encoded sequential control information using a query vector, and output a second output vector by encoding the first output vector using a transformer and summarizing the encoded first output vector using time information.

Abstract: A memory test device is provided including a command feature vector extractor and an address feature vector extractor. The command feature vector extractor extracts a command feature vector, based commands executed on memory cells among a plurality of memory cells. The address feature vector extractor extracts an address feature vector, based on address-related information indicating locations of the memory cells executing the commands.

Abstract: A method for determining a similarity between a plurality of graphs includes inferring a low-rank representation of a first graph, inferring a low-rank representation of a second graph, wherein the low-rank representations of the first and second graphs are stored in memory, estimating a left interaction between the first and second graphs, estimating a middle interaction between the first and second graphs, estimating a right interaction between the first and second graphs, wherein the estimations are based on the low-rank representations of the first and second graphs stored in memory, and aggregating the left interaction, the middle interaction and the right interaction into a kernel, wherein the kernel is indicative of the similarity between the first and second graphs.

Abstract: A method for determining a correspondence between a first node set of a first graph and a second node set of a second graph includes building a feature representation for each of the first graph and the second graph, and inferring the correspondence between the first node set and the second node set based on the feature representations.

Abstract: Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.

Abstract: A method, system and computer program product for managing and querying a graph. The method includes the steps of: receiving a graph; partitioning the graph into homogeneous blocks; compressing the homogeneous blocks; and storing the compressed homogeneous blocks in files where at least one of the steps is carried out using a computer device.

Abstract: A method, system and computer program product for managing and querying a graph. The method includes the steps of: receiving a graph; partitioning the graph into homogeneous blocks; compressing the homogeneous blocks; and storing the compressed homogeneous blocks in files where at least one of the steps is carried out using a computer device.

Abstract: Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.