An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

A system for event prediction using schema networks includes a first antecedent entity state that represents a first entity at a first time; a first consequent entity state that represents the first entity at a second time; a second antecedent entity state that represents a second entity at the first time; and a first schema factor that couples the first and second antecedent entity states to the first consequent entity state; wherein the first schema factor is configured to predict the first consequent entity state from the first and second antecedent entity states.

Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

A system and method for generating data visualizations. The method includes generating an enriched data layer based on a plurality of knowledge graphs, the plurality of knowledge graphs including a plurality of first nodes, the enriched data layer including a plurality of second nodes, wherein each of the plurality of second nodes is connected via an edge to at least one of the plurality of first nodes; and generating a data visualization based on the enriched data layer and a request for data, wherein the request for data indicates a type of data corresponding to at least one of the plurality of second nodes, wherein the data visualization is generated using data represented by at least one of the plurality of first nodes connected to the at least one of the plurality of second nodes.

Techniques are described for optimizing and assigning video encoding ladders.

Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives image data showing a portion of a delivery location. The computer system determines an artificial intelligence (AI) model associated with the delivery location. The computer system inputs the image data to the AI model. The computer system receives an indication of whether the portion corresponds to a correct drop-off location and causes a presentation about the indication to be provided at a device.

This application provides a method for managing a resource in a computer system and a terminal device. The method includes: obtaining data, where the data includes application sequence feature data related to a current foreground application, and the data further includes at least one of the following real-time data: a system time of the computer system, current status data of the computer system, and current location data of the computer system; selecting, from a plurality of machine learning models based on at least one of the real-time data, a target machine learning model that matches the real-time data; inputting the obtained data into the target machine learning model to rank importance of a plurality of applications installed in the computer system; and performing resource management based on a result of the importance ranking.

Systems and methods for mapping neuronal circuitry in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating a neuronal shape graph, including obtaining functional brain imaging data from an imaging device, where the functional brain imaging data includes a time-series of voxels describing neuronal activation over time in a patient's brain, lowering the dimensionality of the functional brain imaging data to a set of points, where each point represents the brain state at a particular time in the timeseries, binning the points into a plurality of bins, clustering the binned points, and generating a shape graph from the clustered points, where nodes in the shape graph represent a brain state and edges between the nodes represent transitions between brain states.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.