A data processing array comprises a plurality of modules, each with a memory, positioned in an array of rows and columns interconnected by a pooling chain that carries data to and receives data from selected ones or groups of the modules. Each modules can also have light modulator elements for transmitting data as light signals and a light sensor for receiving data in the form of modulated light. Pooling switches in the pooling chain between modules open and close the pooling chain lines for selecting and grouping modules. Analog data lines separate from the pooling chain can also carry data to and from the modules. Pooling control lines connected to the switches turn the switches on and off for the selecting and grouping of modules. Module control lines, also separate from the pooling chain, connected to the modules enable various data input, output, and processing by the memory or other components in the module.

A system and method for utilizing model predictive control for optimal interactions that include receiving environment e data associated with a surrounding environment of an ego agent and dynamic data associated with an operation of the ego agent. The system and method also include inputting the environment data and the dynamic data to variational autoencoders. The system and method additionally include utilizing the model predictive control through functional approximation with the variational autoencoders and decoders to output probabilistic action estimates. The system and method further include outputting an estimated optimal control trajectory based on analysis of the probabilistic action estimates to control at least one system of the ego agent to operate within the surrounding environment of the ego agent.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Embodiments including a method and apparatus for correction of a global navigation satellite system (GNSS) are described. In one example, the apparatus includes a communication interface and a processor. The communication interface is configured to a plurality of GNSS signals. The GNSS signals may include at least one almanac value and at least one ephemeris value. The processor is configured to generate a spatio-temporal graph model based on the at least one almanac value, the at least one ephemeris value, and a predetermined offset value for a base location. The spatio-temporal graph model analyzes subsequent GNSS signals to determined a predicted offset or a corrected GNSS position.

Various embodiments are generally directed to techniques for prediction based machine learning (ML) models, such as to utilize a ML model to generate predictions based on the output of another ML model. Some embodiments are particularly directed to a secondary ML model that revises predictions generated by a primary ML model based on structured input data. In many embodiments, the secondary ML model may utilize predictions from the primary ML model to learn metadata regarding the structured input data. In many such embodiments, the metadata regarding the structured input data may be used to revise the predictions from the primary ML model. For example, the secondary ML model may utilize a structure of the input data combined with patterns in the predictions from the primary ML model to revise the predictions from the primary ML model.

Machine learning models used in medical diagnosis should be validated after being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model post-deployment. In post-deployment validation, the validation process monitoring can include: (1) monitoring to ensure a model performs as well as a reference member such as another machine learning model, and (2) monitoring to detect anomalies in data. This post-deployment validation helps identify low-performing models that are already deployed, so that relevant parties can quickly take action to improve either the machine learning model or the input data.

Systems, methods, and computer readable media for messaging system with augmented reality (AR) makeup are presented. Methods include processing a first image to extract a makeup portion of the first image, the makeup portion representing the makeup from the first image and training a neural network to process images of people to add AR makeup representing the makeup from the first image. The methods may further include receiving, via a messaging application implemented by one or more processors of a user device, input that indicates a selection to add the AR makeup to a second image of a second person. The methods may further include processing the second image with the neural network to add the AR makeup to the second image and causing the second image with the AR makeup to be displayed on a display device of the user device.