A computer-implemented method for calculating Scalable Attributed Graph Embedding for Large-Scale Graph Analytics that includes computing a node embedding for a first node-attributed graph in a node embedded space. One or more random attributed graphs is generated in the node embedded space. A graph embedding operation is performed using a dissimilarity measure between one or more raw graphs and the one or more generated random graphs, and an edge-attributed graph into a second node-attributed graph using an adjoint graph.

Embodiments of the invention are directed to automatically capturing user interface screenshots for use in documentation of a software product. Aspects include identifying a user interface window of the software product and creating a degree-of-completion graph for the user interface window. Aspects also include capturing a plurality of screenshots of the user interface window during use of the software product and calculating a degree-of-completion percentage for each of the plurality of screenshots. Aspects further include identifying a subset of the plurality of screenshots to be included in the software product documentation based on the degree-of-completion percentage.

Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

A computer-implemented method for processing a set of virtual fibers into a set of clusters of virtual fibers, usable for manipulation on a cluster basis in a computer graphics generation system, may include determining aspects for virtual fibers in the set of virtual fibers, determining similarity scores between the virtual fibers based on their aspects, and determining an initial cluster comprising the virtual fibers of the set of virtual fibers. The method may further include instantiating a cluster list in at least one memory, adding the initial cluster to the cluster list, partitioning the initial cluster into a first subsequent cluster and a second subsequent cluster based on similarity scores among fibers in the initial cluster, adding the first subsequent cluster and the second subsequent cluster to the cluster list, and testing whether a number of clusters in the cluster list is below a predetermined threshold.

Presently disclosed is a method for co-segmenting three-dimensional models represented by sparse and low-rank feature, comprising: pre-segmenting each three-dimensional model of a three-dimensional model class to obtain three-dimensional model patches for the each three-dimensional model; constructing a histogram for the three-dimensional model patches of the each three-dimensional model to obtain a patch feature vector for the each three-dimensional model; performing a sparse and low-rank representation to the patch feature vector for the each three-dimensional model to obtain a representation coefficient and a representation error of the each three-dimensional model; determining a confident representation coefficient for the each three-dimensional model according to the representation coefficient and the representation error of the each three-dimensional model; and clustering the confident representation coefficient of the each three-dimensional model to co-segment the each three-dimensional model respectively.

A system and method for detecting a potential match between a candidate facial image and a dataset of facial images is described. Some implementations of the invention determine whether a candidate facial image (or multiple facial images) of a person taken, for example, at point of entry corresponds to one or more facial images stored in a dataset of persons of interest (e.g., suspects, criminals, terrorists, employees, VIPs, “whales,” etc.). Some implementations of the invention detect potential fraud in a dataset of facial images. In a first form of potential fraud, a same facial image is associated with multiple identities. In a second form of potential fraud, different facial images are associated with a single identity, as in the case, for example, of identity theft. According to various implementations of the invention, spectral clustering techniques are used to determine a likelihood that pairs of facial images (or pairs of facial image sets) correspond to the person or different persons.