Within a video frame, elements are identified. A graph is constructed for a portion of video content including the video frame. Using the graph and an excitement level corresponding to an element in the plurality of elements, the video frame is divided into an alterable region and an unalterable region. By solving an optimization problem, a compute resource and a rendering application are selected, the compute resource represented by a runtime feature vector encoding a plurality of features describing execution of the rendering application on the compute resource. Using the compute resource and the rendering application, a background image corresponding to the alterable region is rendered. The unalterable region and the background image are combined into a rendered video frame, the rendered video frame replacing the video frame within the portion of video content.

A method, including collecting overlapping video segments (36) that cover a warehouse (20) storing items (22) in respective bins (24), and stitching together the videos so as to generate a merged video (54). In the merged video, individuals (38) are identified performing picking actions (144) from different bins at respective coordinates (146), and based on the merged video, respective coordinates (84) of the bins from which the picking actions were performed are identified. A set of orders are retrieved from a warehouse management system (86), each of the first orders performed by a given individual and including one or more of the items. The picking actions, the coordinates of the bins, and the first orders are analyzed so as to establish a correspondence between the bins and the items, and the correspondence is applied to verify execution of second orders performed subsequent to performance of the set of first orders.

Systems, apparatuses, methods, and computer program products are provided herein. For example, a method may include receiving a plurality of datasets. In some embodiments, the method includes identifying a relationship between a first dataset, a second dataset, and a third dataset of the plurality of datasets. In some embodiments, the method includes causing display of a graph depicting the first dataset, the second dataset, and the third dataset via a graphical user interface. In some embodiments, the method includes receiving a user interaction with the graphical user interface indicating a first user-selected datapoint. In some embodiments, the method includes identifying a first datapoint within the first dataset and a second datapoint within the second dataset corresponding to the first user-selected datapoint. In some embodiments, the method includes causing display of an element indicating a relationship between the first datapoint and the second datapoint.

Systems, methods, and devices for performing geomagnetic field distribution monitoring and correction. One system includes a reference magnetic sensor configured to detect components of a magnetic field at a first location and generate reference sensor signals, non-transitory computer-readable storage media storing instructions, and at least one electronic processor. The at least one electronic processor is configured to execute the instructions to receive the reference sensor signals from the reference magnetic sensor, compute a local adjustment factor based on the reference sensor signals, receive operational sensor signals from an operational magnetic sensor configured to detect components of the magnetic field at a second location, compute an orientation representation based on the operational sensor signals, and apply the local adjustment factor to the orientation representation to generate a corrected orientation representation.

An example operation may include one or more of ingesting data from a plurality of systems, dividing the data into a plurality of bins based on binning configuration settings, identifying a bin of data among the plurality of bins which contains an anomaly at a point in time based on thresholds for the plurality of bins, identifying a different bin of data among the plurality of bins which does not contain the anomaly at the point in time based on the thresholds for the plurality of bins, generating a heat map comprising a plurality of display elements corresponding to the plurality of bins including a display element corresponding to the bin of data with the anomaly with a different visual appearance than a display element corresponding to the different bin which does not contain the anomaly, and rendering the heat map via a GUI.

Embodiments are disclosed for managing multiple data visualizations on a digital canvas. In some embodiments, a method of managing multiple data visualizations includes generating a first graphic object on a digital canvas. A first dataset is received and used to generate a first chart based on the first dataset and a visual property of the first graphic object. The first chart comprises a first plurality of graphic objects including the first graphic object. A second dataset is then received and used to generate a second chart on the digital canvas based on the second dataset. The second chart includes a second plurality of graphic objects. An axis of the first chart and an axis of the second chart are merged such that the axis the first chart and the axis of the second chart share a scale attribute.

For each item represented within log events that have a power law-oriented distribution, first and second metrics for the item are computed based on the log events which pertain to the item. The items are organized over bins according to the first metric. The bins correspond to different ranges of the first metric. For each bin, the items in the bin are ordered according to the second metric. A plot of the bins over which the items have been organized according to the first metric, is graphically displayed, which includes displaying, for each bin, the items in the bin as have been ordered according to the second metric.

In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map indicating a possibility of lesion of the lesion candidate in the endoscopic video by a color. The display control means displays a position of a lesion on a frame of the endoscopic video based on the lesion candidate and the heat map.

In part, the disclosure relates to an analysis system in communication with one or more sources of brain region or brain tissue-specific spectroscopy data; and computer-executable logic, encoded in memory of the analysis system, for interpreting brain region or brain tissue-specific spectroscopy data, where the computer-executable logic is configured for execution of: processing the brain region or brain tissue-specific spectroscopy data to obtain one or more spectroscopic graphical representations of the brain region or brain tissue-specific spectroscopy data; co-registering such representations with regions of interest in imaging data obtained relative thereto, where the imaging data is obtained simultaneously with the spectroscopy data; and displaying a first visual representation of co-registered brain region or brain tissue-specific spectroscopy data and imaging data. The visual representation may include one or more views of brain tissue and a spatially correlated map of changes in brain region or tissue-specific spectroscopy data.

A system for enabling testing a target data pipeline within a graphical user interface (GUI) is disclosed. The system is programmed to present a GUI. The GUI shows a data pipeline including data transforms that are related based on data dependencies as a graph. The GUI then accept user inputs to identify a target pipeline from the data pipeline and set up a test for the target pipeline. The user inputs include interacting with the graph and providing test inputs and expected outputs for the target data pipeline, including specifying in each expected output for a test output one or more assertions related to the test output. The GUI further accepts user inputs to execute the test and review test results. The GUI facilitates specifying rich assertions, determining the source of a test failure, and updating the target data pipeline or the test based on the test results.