A method and a system are described for context based clustering of one or more objects. The method comprises receiving, by the object clustering system, receiving, by an object clustering system, an object clustering request for one or more objects associated with a plurality of contextual parameters, where the plurality of contextual parameters comprises one or more physical attributes and one or more non-physical attributes. It further includes tagging the one or more non-physical attributes respectively to the one or more physical attributes. It further includes identifying a common context from the one or more physical attributes associated with the one or more objects based on the tagging. It further includes mapping the one or more physical attributes to the one or more objects based on the common context. It then includes clustering the one or more objects based on the mapping.

Embodiments of the present disclosure relate to a method, a device and a computer-readable storage medium for data processing. The method for data processing comprises: obtaining a set of observation samples regarding a plurality of factors, one of the set of observation samples comprising respective observed values of the plurality of factors. The method further comprises: estimating, for each of the plurality of factors and based on the set of observation samples, a distribution that differences between observed values of the factor and estimated values of the factor follow. The method further comprises determining, based at least on the estimated distribution, a causal structure representing a causal relationship among the plurality of factors. Embodiments of the present disclosure further provide a device and a computer-readable storage medium for implementing the above method. The embodiments of the present disclosure can accurately and robustly discover the causal relationship among a plurality of factors without making any assumptions about the relationship between the data distribution and the factors, and affect the observed value of the target factor based on the causal relationship.

Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.

A game management system identifies gaming data associated with online game players and determines, based at least in part on the gaming data, other players to recommend as friends for playing an online game. Gaming data from online games other than the online game for which friend recommendations are to be made may be used to provide the friend recommendations. A subset of categories of the gaming data may be used to initially bin the plurality of players into separate bins. Similarity of players, with respect to their gaming data, or a subset thereof, within each bin may be determined. This similarity analysis may result in a similarity score corresponding to each pair of players within a bin. The similarity scores may be used to determine if two players are compatible from an online gaming standpoint and provide friend recommendations to players.

Disclosed are various embodiments of systems and methods of deep learning for colorectal polyp screening and providing a prediction of neoplasticity of a polyp. A video of a colonoscopy procedure can be captured. Frames from the video or images associated with the colonoscopy procedure can be extracted. A model for classifying objects that appear in the frames or the images can be obtained. A classification can be determined for a polyp that appears in at least one of the frames or images based on applying the frames or images to an input layer of the model.

There is provided a system and method of time series (TS) prediction. The method includes providing a machine learning (ML) network trained to perform TS prediction with respect to one or more components, the ML network configured with a set of hyperparameters including one or more hyperparameters associated with each component, the ML network comprising one or more ML modules operatively connected to an output layer, each ML module configured to represent a respective component in accordance with a given model characterized by the one or more hyperparameters associated therewith, where values of the hyperparameters associated with each component are automatically optimized during training of the ML network; and in response to a user's request for TS prediction, using the trained ML network to perform TS prediction, giving rise to a prediction result comprising an overall predicted TS, and one or more decomposed TS corresponding to respective components.

The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.

There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion. The defect map also comprises non-clustered defects. Defects of interest (DOI) are identified in each cluster by performing respective defect filtrations for each cluster and non-clustered defects.

A navigation system for a vehicle comprises onboard sensors including a vision sensor, and an onboard map database of terrain maps. An onboard processer, coupled to the sensors and map database, includes a position PDF filter, which performs a method comprising: receiving image data from the vision sensor corresponding to terrain images captured by the vision sensor of a given area; receiving map data from the map database corresponding to a terrain map of the area; generating a first PDF of image features in the image data; generating a second PDF of map features in the map data; generating a measurement vector PDF by a convolution of the first PDF and second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF that include real-time measurement errors of position and angular orientation of the vehicle.

Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently generating groups of images portraying semantically similar objects for utilization in building machine learning models. In particular, the disclosed system utilizes metadata and spatial statistics to extract semantically similar objects from a repository of digital images. In some embodiments, the disclosed system generates color embeddings and content embeddings for the identified objects. The disclosed system can further group similar objects together within a query space by utilizing a clustering algorithm to create object clusters and then refining and combining the object clusters within the query space. In some embodiments, the disclosed system utilizes one or more of the object clusters to build a machine learning model.