Abstract: A device includes one or more processors configured to access a raster image representing geospatial data of a geographical region. The one or more processors are also configured to process the raster image based on application of at least one machine learning model to generate output data corresponding to a vector image that corresponds to at least a portion of the geographical region.

Abstract: A method includes obtaining first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data. The method also includes performing a plurality of deep image prior operations, using an image prior based on the first image data, to generate filter data. The method further includes modifying the first image data based on the filter data to generate second image data. The method also includes performing an artifact reduction process based on the second image data to generate third image data.

Abstract: A method includes using a machine-learning model to determine multiple sets of image data, each representing an estimated solution to an inverse problem associated with multiple waveform return measurements. First image data are based on a first set of waveform return measurements and first model parameters of the machine-learning model, and second image data are based on a second set of waveform return measurements and a second model parameters of the machine-learning model. The method also includes determining, based on the multiple sets of image data, a representative image. The method further includes generating output data that identifies a first area of the representative image as less reliable than a second area of the representative image based on a statistical evaluation of two or more sets of image data of the multiple sets of image data.

Abstract: A method includes determining, using a physics-based model and based on a plurality of observations, first solution data. The first solution data is descriptive of a first estimated solution to an inverse problem associated with the plurality of observations, and the first solution data includes artifacts due, at least in part, to a count of observations of the plurality of observations. The method also includes performing a plurality of iterations of a gradient descent artifact reduction process to generate second solution data. The artifacts are reduced in the second solution data relative to the first solution data. A particular iteration of the gradient descent artifact reduction process includes determining, using a machine-learning model, a value of a gradient metric associated with particular solution data and adjusting the particular solution data based on the value of the gradient metric to generate updated solution data.

Abstract: A method includes obtaining waveform return data including waveform return records for multiple sampling events associated with an observed area and determining a relevance score for the waveform return records of the waveform return data. The relevance score for a particular waveform return record is based, at least partially, on estimated information gain associated with the particular waveform return record. The method also includes, based on the relevance scores, selecting a first subset of waveform return records, where one or more waveform return records are excluded from the first subset of waveform return records. The method also includes generating image data based on the first subset of waveform return records.

Abstract: Anomaly detection and filtering of time-series data, including: identifying, for a multivariate time-series signal, one or more previously observed multivariate time-series signals that are similar within a predetermined threshold to the multivariate time-series signal; and labelling the multivariate time-series signal based on the labels associated with the one or more previously observed multivariate time-series signals.

Abstract: A method includes obtaining historical data including sensor data from one or more sensors associated with a device and contextual data indicative of one or more conditions external to the device and independent of operation of the device. The method also includes providing at least a portion of the historical data as input to one or more machine-learning-based projection models to generate projection data associated with a future condition of the device. The method further includes providing input data to one or more machine-learning-based optimization models to determine one or more operational parameters that are expected to improve an operational metric associated with one or more devices. The one or more devices include the device, and the input data is based, at least in part, on the historical data and the projection data.

Abstract: Calibration of offline combustion engines using simulations, including: simulating, based on one or more simulator models operating on simulator input, operation of an engine being simulated; training, based on simulating the operation of the engine being simulated, one or more trained models; and generating, for offline engine calibration and based at least on the one or more trained models, calibration data corresponding to one or more electronically controllable components of an engine being calibrated.

Abstract: Calibration of online combustion engines using simulations, including: simulating, on a processor coupled to an engine and based on operation data generated during operation of the engine, operation of the engine; training, based on simulating the operation of the engine, one or more trained models; and generating, based at least on the one or more trained models, calibration data corresponding to one or more electronically controllable components of the engine.

Abstract: Calibrating combustion engines using simulation learning, including: receiving simulator input for one or more simulator models corresponding to one or more aspects of an engine; simulating, based at least on one or more simulator models operating on the simulator input, operation of the engine; and generating, based at least on simulator output from simulating operation of the engine, calibration data corresponding to one or more electronically controllable components of the engine.

Abstract: Autonomous behaviors in a multiagent adversarial scene, including: assigning, by a scene manager, to each friendly agent of plurality of friendly agents, a role comprising an engagement to an adversarial agent of one or more adversarial agents; assigning, by the scene manager, to each friendly agent of the plurality of friendly agents, a policy; and wherein each friendly agent of the plurality of friendly agents is configured to determine, based on a tactical model corresponding to the assigned policy, one or more actions.

Abstract: A method includes determining a feature importance ranking for each pair of clusters of a plurality of clusters to generate a first plurality of feature importance rankings. The method further includes determining a feature importance ranking between a particular data element and each cluster to generate a second plurality of feature importance rankings. A distance value associated with each pair of clusters of the plurality of clusters is determined to generate a plurality of distance values, and a probability value associated with each data element is determined to generate a plurality of probability values. The method further includes weighting the first plurality of feature importance rankings based on the plurality of distance values to determine a first plurality of weighted feature importance rankings and weighting the second plurality of feature importance rankings based on the plurality of probability values to determine a second plurality of weighted feature importance rankings.