A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

A method comprising: receiving an input tensor having a shape defined by [n.sub.1, ...,n.sub.k], where k is equal to a number of dimensions that characterize the input tensor; receiving tile tensor metadata comprising: a tile tensor shape defined by [t.sub.1, ..., t.sub.k], and information indicative of an interleaving stride to be applied with respect to each dimension of the tile tensor; constructing an output tensor comprising a plurality of the tile tensors, by applying a packing algorithm which maps each element of the input tensor to at least one slot location of one of the plurality of tile tensors, based on the tile tensor shape and the interleaving stride, wherein the interleaving stride results in non-contiguous mapping of the elements of the input tensor, such that each of the tile tensors includes a subset of the elements of the input tensor which are spaced within the input tensor according to the interleaving stride.

In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

System and methods enable vehicles to make ethical/empathetic driving decisions by using deep learning aided location intelligence. The systems and methods identify moral islands/complex driving scenarios where a complex ethical decision is required. A Generative Adversarial Network (GAN) is used to generate synthetic training data to capture varied ethically complex driving situations. Embodiments train a deep learning model (ETHNET) that is configured to output one or more driving decisions to be taken when a vehicle comes across an ethically complex driving situations in the real world.

A method and system for evaluating performance of operation resources using Artificial Intelligence (AI) is disclosed. In some embodiments, the method includes receiving, each of a plurality of performance parameters associated with a set of operation resources. The method further includes determining a set of features for each of the plurality of performance parameters. The method further includes creating one or more feature vectors corresponding to each of the plurality of performance parameters. The one or more feature vectors are created based on a first pre-trained machine learning model. The method further includes assessing the one or more feature vectors, based on the first pre-trained machine learning model and classifying the set of operation resources into one of a set of performance categories based on the assessing of the one or more feature vectors. The method further includes evaluating performance of at least one of the set of operation resources.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and unsupervised anomalous table row detection machine learning models.

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.

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.

Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.