Methods and apparatus for training neural networks and detecting correlated objects are provided. In one aspect, a method of training a neural network includes: detecting a first-class object and second-class objects in an image; generating at least one candidate object group based on the detected first-class object and second-class objects, each candidate object group including at least one first-class object and at least two second-class objects; for each candidate object group, determining a matching degree between the first-class object and each second-class object in the candidate object group based on a neural network; determining a group correlation loss of the candidate object group based on the determined matching degree, the group correlation loss being positively correlated with a matching degree between the first-class object and a non-correlated second-class object; and adjusting network parameters of the neural network based on the group correlation loss.

Systems and methods for training a model for identifying an imaging modality. The systems and methods can be performed by a computer system having one or more processors and memory. A plurality of image vectors can be generated from first image data using a convolutional neural network. A loss function can be applied to each of the plurality of image vectors to produce an intermediate dataset. The intermediate dataset can be projected in a space having lower dimensional space that the intermediate dataset. A plurality of clusters can be identified from the intermediate dataset in the space using a clustering technique. Each of the plurality of clusters can be classified into one of a plurality of imaging modalities.

Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

A hypothesis generation system may determine sets of link types that are respectively associated with a plurality of nodes included in an incomplete knowledge graph to determine a plurality of intersection-over-union scores. The hypothesis generation system may determine, based on a plurality of vectors of an embedding space representation associated with the incomplete knowledge graph, a plurality of similarity scores and may determine, based on the plurality of intersection-over-union scores and the plurality of similarity scores, a plurality of affinity scores. The hypothesis generation system may determine, based on the plurality of affinity scores and the plurality of nodes, one or more node pairs; may generate, for a node pair, of the one or more node pairs, one or more triplet hypothesis candidate templates; and may generate, for a triplet hypothesis candidate template, of the one or more triplet hypothesis candidate templates, a plurality of triplet hypothesis candidates.

The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m≥3, and m>n≥2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

A device may receive activity data identifying activities of a process performed by users via user devices. The device may receive baseline data identifying baselines associated with the process and variant data identifying variants from the baselines. The device may apply a sequence alignment model, to the activity data and the baseline data, to determine first similar sequences associated with the activities and the baselines and may apply the sequence alignment model, to the activity data and the variant data, to determine second similar sequences associated with the activities and the variants. The device may determine, based on the first similar sequences, first label data identifying first labels for the activities and may determine, based on the second similar sequences, second label data identifying second labels for the activities. The device may generate a process diagram based on the activity data, the first label data, and the second label data.

A method accurately evaluates a corresponding operation capacity for an operating characteristic of a to-be-evaluated object in a to-be-evaluated time period in combination with already-operated historical data of the to-be-evaluated object, which specifically includes: for a capacity influence factor in an operating process of an airspace unit, constructing a capacity similarity characteristic model to form a capacity similarity characteristic index set; acquiring historical data of an evaluation object, on the basis of the capacity similarity characteristic index set, classifying historical data samples of different time periods by a clustering algorithm, and generating a capacity similarity time period sample set to which an evaluation time period of the current evaluation object belongs; and classifying historical capacity values of the capacity similarity time period sample set by a density clustering algorithm, and calculating a capacity reference value on the basis of a maximum class cluster.

An abnormality detection device, method, or a storage medium acquires learning target data and monitoring target data, generates a state observer by using a variable in an input variable configuration, generates a threshold, calculates an abnormality degree by combining a second state observation value and the monitoring target data and inputting a combined result to the competitive neural network, and calculates a determination result.

The plane-geometry consistency detection method, the computer device, and the storage medium of the present disclosure are achieved by obtaining first normal vector set, second normal vector set, first distance set, and second distance set; obtaining pairs of the first normal vectors and an angle-matched second normal vector pair to compute a first rotation matrix and a first projection matrix; clustering elements in a rotation vector set obtained from the first projection matrix to generate a first normal vector sequence and a second normal vector sequence according to a first target classification; computing a second rotation matrix, using a second projection matrix as the target rotation matrix; obtaining a distance difference set, clustering to obtain a second target classification and using the element thereof and the corresponding second normal vector to obtain the target translation matrix. The present disclosure achieves a high-consistency and fast plane mapping in different coordinate systems.

Predictive analytics techniques are provided to produce leading indicators of economic activity based on agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.