Provided herein is a computer system for creating driving challenges for drivers. The computer system may include a processor in communication with a memory device, and the processor may be programmed to: (i) receive driving data associated with a driver, (ii) generate a first model that models the driving data associated with the driver, (iii) calculate a predicted driving score for the driver based at least in part upon the first model, (iv) generate a second model that predicts a confidence of the predicted driving score, (v) calculate a confidence value of the predicted driving score, wherein the confidence value is a squared error of the predicted driving score, and (vi) generate at least one driving challenge for the driver based at least in part upon the predicted driving score and the confidence value for that predicted driving score.

Certain aspects of the present disclosure provide techniques for performing machine learning computations in a compute in memory (CIM) array comprising a plurality of bit cells, including: determining that a sparsity of input data to a machine learning model exceeds an input data sparsity threshold; disabling one or more bit cells in the CIM array based on the sparsity of the input data prior to processing the input data; processing the input data with bit cells not disabled in the CIM array to generate an output value; applying a compensation to the output value based on the sparsity to generate a compensated output value; and outputting the compensated output value.

Certain aspects of the present disclosure provide techniques for performing machine learning computations in a compute in memory (CIM) array comprising a plurality of bit cells, including: determining that a sparsity of input data to a machine learning model exceeds an input data sparsity threshold; disabling one or more bit cells in the CIM array based on the sparsity of the input data prior to processing the input data; processing the input data with bit cells not disabled in the CIM array to generate an output value; applying a compensation to the output value based on the sparsity to generate a compensated output value; and outputting the compensated output value.

The presently disclosed subject matter includes a computerized method and system that provide the ability to train and execute a unique machine learning (ML) model specifically configured to enhance classifier (e.g., RegEx) output by identifying and removing false positive results from the classifiers output. Classifier output, comprising a collection of data-subsets (e.g., columns in a relational database) of one or more structured or semi-structured data sources (e.g., tables of a relational database), are transformed to be represented by a plurality of numerical vectors. The numerical vectors are used during a training phase (as well as the execution phase) for training a machine learning model to enhance the classifier output and reduce false positives.

Examples described herein provide a computer-implemented method that includes calculating, by a processing device, a motor acceleration error based at least in part on a motor torque and a motor speed. The method further includes calculating, by the processing device, a regression fit line based at least in part on the motor acceleration error. The method further includes identifying, by the processing device, a zero point using the regression fit line. The method further includes comparing, by the processing device, the zero point to a datum reference to determine a difference. The method further includes integrating, by the processing device, the difference to determine the lash angle. The method further includes controlling, by the processing device, the motor based at least in part on the lash angle.

Systems and computer-implemented methods of performing image registration. One method includes receiving a first image and a second image acquired from a patient at different times and, in each of the first image and the second image, detecting an upper boundary of an imaged object in an image coordinate system and detecting a lower boundary of the imaged object in the image coordinate system. The method further includes, based on the upper boundary and the lower boundary of each of the first image and the second image, cropping and padding at least one of the first image and the second image to create an aligned first image and an aligned second image and executing a registration model on the aligned first image and the aligned second image to compute a deformation field between the aligned first image and the aligned second image.

Various embodiments disclosed relate to automated docketing of incoming electronic communications and documents. The present disclosure includes methods and systems for identifying omnibus documents containing more than one event, and splitting those omnibus documents into the individual events or documents.

Various embodiments disclosed relate to automated docketing of incoming electronic communications and documents. The present disclosure includes methods and systems for identifying omnibus documents containing more than one event, and splitting those omnibus documents into the individual events or documents.

Apparatuses and methods are provided for training a feature extraction model determining a loss function for use in unsupervised image segmentation. A method includes determining a clustering loss from an image; determining a weakly supervised contrastive loss of the image using cluster pseudo labels based on the clustering loss; and determining the loss function based on the clustering loss and the weakly supervised contrastive loss.

Apparatuses and methods are provided for training a feature extraction model determining a loss function for use in unsupervised image segmentation. A method includes determining a clustering loss from an image; determining a weakly supervised contrastive loss of the image using cluster pseudo labels based on the clustering loss; and determining the loss function based on the clustering loss and the weakly supervised contrastive loss.