System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

In some implementations, there is provided a method, which includes transforming, by the recommendation system, a first data set into the principal component analysis domain; rotating, by the recommendation system, the transformed first data first data set into a common axis system; comparing, by the recommendation system, the rotated, transformed first data set to at least one of a plurality of reference data sets having been rotated into the common axis system; and identifying, by the recommendation system, at least one reference data set, the identifying based on the comparing. Related systems, methods, and articles of manufacture are also disclosed.

A system and method for estimating a sequence of human poses in an unconstrained video. In the present invention, a unified two stage, tree-based, optimization problem is solved for which an efficient and exact solution exists. While the proposed method finds an exact solution, it does not sacrifice the ability to model the spatial and temporal constraints between body parts in the video frames on the unconstrained video.

A method for analyzing one or more conditions of a transportation pathway includes obtaining, using an imaging device of an inspection system, image data reproducible as a plurality of images of the transportation pathway, each of the plurality of images being reproducible as an image of a portion of the transportation pathway, each portion of the transportation pathway having an associated location along a length of the transportation pathway, analyzing, using one or more processors of the inspection system, the image data to determine a first plurality of metrics indicative of a condition of the transportation pathway at each of the associated locations, and generating a first graph, using the determined first plurality of metrics, that is indicative of the condition of the transportation pathway at each of the associated locations.

A method for analyzing one or more conditions of a transportation pathway includes obtaining, using an imaging device of an inspection system, image data reproducible as a plurality of images of the transportation pathway, each of the plurality of images being reproducible as an image of a portion of the transportation pathway, each portion of the transportation pathway having an associated location along a length of the transportation pathway, analyzing, using one or more processors of the inspection system, the image data to determine a first plurality of metrics indicative of a condition of the transportation pathway at each of the associated locations, and generating a first graph, using the determined first plurality of metrics, that is indicative of the condition of the transportation pathway at each of the associated locations.

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks, and provide intra-operative surgical guidance; and a display configured to provide visual guidance to a user.

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks, and provide intra-operative surgical guidance; and a display configured to provide visual guidance to a user.

Described is a solution for an unlabeled target domain dataset challenge using a domain adaptation technique to train a neural network using an iterative 3D model fitting algorithm to generate refined target domain labels. The neural network supports the convergence of the 3D model fitting algorithm and the 3D model fitting algorithm provides refined labels that are used for training of the neural network. During real-time inference, only the trained neural network is required. A convolutional neural network (CNN) is trained using labeled synthetic frames (source domain) with unlabeled real depth frames (target domain). The CNN initializes an offline iterative 3D model fitting algorithm capable of accurately labeling the hand pose in real depth frames. The labeled real depth frames are used to continue training the CNN thereby improving accuracy beyond that achievable by using only unlabeled real depth frames for domain adaptation.