A method of converting a vector corresponding to an input image includes receiving input vector data associated with an input image including an object; and converting the received input vector data into feature data based on a projection matrix having a fixed rank, wherein a first dimension of the input vector data is higher than a second dimension of the feature data.

A method and system are provided for performing causal inferencing in a computationally efficient manner. In one embodiment, a computer-implemented method includes collecting user interaction data for a plurality of users, within a specified observation window. The collected data comprises a treatment observation for at least one user and an outcome observation for at least one user. Memory for a feature table is allocated, wherein a size the allocated memory is proportional to a number of features in the collected data. Feature-related values are stored in the feature table based on respective pre-treatment observation periods for each of the plurality of users. A selected number of confounders are identified from the feature table. An effect of the treatment is computed on the outcome using the selected confounders.

Systems and methods for discriminating between malignant and benign pigmented skin lesions based on optical analysis using spatial distribution maps, morphological parameters, and additional diagnostic parameters derived from images of tissue lesions. A handheld optical transfer diagnosis device is disclosed capable of capturing a series of reflectance images of a skin lesion at a variety of angles of illumination and observation.

Systems and computer-implemented methods of modeling card member data to classify a card member into one of a plurality of classifications based on interchange fees derived from the use of a card issued to the card member. The modeling may handle data distribution from one time period to another time period to address unavailability and/or variability of historical data, implement a neural network architecture based on transformers and discriminators for accurate data scaling, perform data filling for missing data, and fine-tuning for card types that have less card member data, which may result in enhanced performance and faster convergence resulting in reduced computational time. Such fine-tuning may leverage uniform standardization in the neural network to handle multiple card types, which is facilitated through the use of the transformers and discriminators for data scaling.

A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

An image processing device includes: a determination unit configured to determine, based on a feature amount of input image data, a category of the input image data and a score representing confidence of the category, with a classification model; a display unit configured to display an image representing the input image data and the determined category of the input image data; an acceptance unit configured to accept a correction of the displayed category from a user; and an updating unit configured to update the classification model, based on the correction of the category.

Systems and methods are described for user classification with semi-supervised machine learning. The systems and methods may include receiving user information for a first set of users, receiving survey data for a second set of users wherein the second set of users is a proper subset of the first set of users, training a first neural network and a second neural network based on the second set of users, mapping the user information for the first set of users to the embedding space using the first neural network, predicting category membership propensities for the first set of users using a low-density separation algorithm on the user information for the first set of users mapped to the embedding space, updating the first neural network and the second neural network based on the prediction, and reclassifying the first set of users based on the updated first neural network and the updated second neural network.

Systems and methods for performing face recognition and image searching are provided. A system for face recognition and image searching includes an ingestion system, a search system, a user device, and a database of galley files that include feature vectors. The ingestion system crawls the internet starting with a seed URL to scrape image files and generate feature vectors. Feature vectors of images input by a user may be compared by the search system to feature vectors in the gallery files. A method for generating feature vectors includes landmark detection, component aligning, texture mapping, vector computation, comparing cluster centers defined by vectors stored in a database with vectors generated based on an input image, linear discriminant analysis, and principal component analysis.

Fisher discriminant analysis is performed on data sets of typically developing (TD) individuals and data sets of autism spectrum disorder (ASD) individuals to produce a model that classifies TD individuals from ASD individuals. The ASD data sets include pre-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data and post-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data for patients receiving one or more ASD treatments. Changes in adaptive behavior are predicted by utilizing regression of changes in adaptive behavior and changes in biochemical measurements observed in the data sets. Thus, the system can be used to predict the effectiveness of a given course of treatment for an ASD patient based on measured metabolite data of that patient, or to predict the overall effectiveness of a clinical trial based on metabolite data for the trial participants.

The present disclosure provides a method and an apparatus for face recognition and a computer readable storage medium. The method includes: inputting a to-be-recognized blurry face image into a generator of a trained generative adversarial network to obtain a to-be-recognized clear face image; inputting the to-be-recognized clear face image to the feature extraction network to obtain a facial feature of the to-be-recognized clear face image; matching the facial feature of the to-be-recognized clear face image with each user facial feature in a preset facial feature database to determine the user facial feature best matching the to-be-recognized clear face image as a target user facial feature; and determining a user associated with the target user facial feature as a recognition result. Through this solution, the accuracy of the recognition of blurry faces can be improved.