Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

In an example, predictions/recommendations using machine learned models are made even more accurate by using three models instead of a single Generalized Linear Mixed (GLMix) model. Specifically, rather than having a single GLMix model with different coefficients for users and items, three separate models are used and then combined. Each of these models has different granularities and dimensions. A global model models the similarity between user attributes (e.g., from the member profile or activity history) and item attributes. A per-user model models user attributes and activity history. A per-item model models item attributes and activity history. Such a model may be termed a Generalized Additive Mixed Effect (GAME) model.

Iterations of a machine learned model training process are performed until a convergence occurs. A fixed effects machine learned model is trained using a first machine learning algorithm. Residuals of the training of the fixed effects machine learned model are determined by comparing results of the trained fixed effects machine learned model to a first set of target results. A first random effects machine learned model is trained using a second machine learning algorithm and the residuals of the training of the fixed effects machine learned model. Residuals of the training of the first random effect machine learned model are determined by comparing results of the trained first random effects machine learned model to a second set of target result, in each subsequent iteration the training of the fixed effects machine learned model uses residuals of the training of a last machine learned model trained in a previous iteration.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

A computer-implemented method for quadratic classification involves generating a data-driven likelihood ratio test based on a dual locus of likelihoods and principal eigenaxis components that contains Bayes' likelihood ratio and automatically generates the best quadratic decision boundary. A dual locus of likelihoods and principal eigenaxis components, formed by a locus of weighted reproducing kernels of extreme points, satisfies fundamental statistical laws for a quadratic classification system in statistical equilibrium and is the basis of an optimal quadratic system for which the eigenenergy and the Bayes' risk are minimized, so that the classification system achieves Bayes' error rate and exhibits optimal generalization performance. Quadratic classification systems can be linked with other such systems to perform multiclass quadratic classification and to fuse feature vectors from different data sources. Quadratic classification systems also provide a practical statistical gauge that measures data distribution overlap and Bayes' error rate.

Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.

A method includes obtaining from an online social media site a plurality of instances of images of objects associated with a person; analyzing with a data processor the plurality of instances of the images with a plurality of predetermined style classifiers to obtain a score for each image for each style classifier; and determining with the data processor, based on the obtained scores, a likely preference of the person for a particular style of object. The plurality of instances of images of objects associated with the person can be images that were posted, shared or pinned by person, and images that the person expressed a preference for. In a non-limiting embodiment the object is clothing, and the style can include a fashion style or fashion genre including color preferences. A system and a computer program product to perform the method are also disclosed.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.