A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow. The system includes a scorecard interface operable to select a target and an indication of the metric control to apply for the target, and to apply the metric control to generate and display a historical performance visualization and a performance feed of the metric for the target.

Aspects if the disclosure are directed towards multi-step forecasting via temporal aggregation. An example embodiment includes a method the includes receiving a time series including a first time step value and a second time step value. The method can further include generating a temporally aggregated time series by summing the first time step value and the second time step value to create a third time step value. The method can further include calculating a first set of input values and a second set of input values from the temporally aggregated time series. The method can further include forecasting a fourth time step value using the first set of input values and the second set of input values, and a fifth time step using the second set of input values from the temporally aggregated time series.

Apparatus, method, computer program product and computer readable medium are disclosed for distance estimation between samples. The method includes: modeling the distribution of each of two feature vector sets by a non-parametric model; and calculating the distance of the two distributions, wherein a kernel function is used in the non-parametric model, the kernel function is optimized based on labeled training data, the first feature vector set includes a plurality of feature vectors extracted from a sample, and the second feature vector set includes a plurality of feature vectors extracted from another sample.

An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, cells may be classified according to at least one predefined membrane pattern. For each classified cell, a class label that identifies a class to which the classified cell belongs may be outputted.

Described is a system for controlling a device based on streaming data analysis using blind source separation. The system updates a set of parallel processing pipelines for two-dimensional (2D) tensor slices of streaming tensor data in different orientations, where the streaming tensor data includes incomplete sensor data. In updating the parallel processing pipelines, the system replaces a first tensor slice with a new tensor slice resulting in an updated set of tensor slices in different orientations. At each time step, a cycle of demixing, transitive matching, and tensor factor weight calculations is performed on the updated set of tensor slices. The tensor factor weight calculations are used for sensor data reconstruction, and based on the sensor data reconstruction, hidden sensor data is extracted. Upon recognition of an object in the extracted hidden sensor data, the device is caused to perform a maneuver to avoid a collision with the object.

Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

A system and methods for unsupervised land use and land cover detection using a classifier that produces a plurality of class image layers which are filtered to remove misclassified same-label pixel groupings, a class resolution module that reduces multiple pixel labels to a single one if applicable and a reconstruction module that generates the output land use and land cover image.

A system and methods for radiometric anomaly detection using non-target clustering, wherein a hierarchy generator organizes the image information content into a hierarchical data representation structure, and a non-target clustering engine processes the hierarchical model to identify large homogeneous regions and significantly dissimilar smaller regions within them based on search criteria.

A method for acoustic source separation comprises inputting acoustic data from a plurality of acoustic sensors, combined from a plurality of acoustic sources, converting the acoustic data to time-frequency domain data comprising time-frequency data frames, and constructing a multichannel filter for the time-frequency data frames to separate signals from the acoustic sources. The constructing comprises determining a set of de-mixing matrices (W.sub.f) to apply to each time-frequency data frame to determine a vector of separated outputs (y.sub.ft) by modifying each of the de-mixing matrices by a respective gradient value (G;G) for a frequency dependent upon a gradient of a cost function measuring a separation of the sources by the respective de-mixing matrix. The respective gradient values for each frequency are each calculated from a stochastic selection of the time-frequency data frames.

Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.