Abstract: A spatial indexing system receives a video that is a sequence of frames depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.

Abstract: Methods and systems are described that mark and/or correct margin lines and/or other features of dental sites. In one example a three-dimensional model of a dental site is generated from intraoral scan data of the dental site, the three-dimensional model comprising a representation of a preparation tooth. An image of the preparation tooth is received or generated. Data from the image is processed using a trained machine learning model that has been trained to identify margin lines of preparation teeth, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the image, a probability that the pixel depicts a margin line. The three-dimensional model of the dental site is then updated by marking the margin line on the representation of the preparation tooth based on the probability map.

Abstract: Systems and methods for depth estimation are provided. One aspect of the systems and methods includes obtaining an image. Another aspect of the systems and methods includes generating a depth map of the image using a monocular depth estimation (MDE) network, where the MDE network is trained using training data including edge data generated by a depth edge estimation (DEE) network.

Abstract: Methods for producing hydrocarbons from subterranean reservoirs utilizing a production well having a plurality of fluid-inlet components spaced apart to define a plurality of production-well fluid-inlet zones. An injection fluid comprising a solvent is injected into the reservoir, resulting in a drainage fluid with a liquid phase and a gas phase occupying one or more of the production-well fluid-inflow zones. The production fluid is produced at a production-flow rate via a pump, and the gas phase:liquid phase ratio of the production fluid is modulated by orchestrating variations in the pump speed and one or more of the plurality of fluid-inlet components to prioritize hydraulic communication with a subset of the plurality of production-well fluid-inlet zones.

Abstract: In an example, an apparatus comprises at least one execution platform; and logic, at least partially including hardware logic, to receive a trained neural network model in a model optimizer and convert the trained neural network model to an optimized model comprising parameters that are fit to the at least one execution platform. Other embodiments are also disclosed and claimed.

Abstract: A system including a radar unit, a processor unit and a communication module, the radar unit including a transmitter antenna connected to an oscillator and transmitting electromagnetic waves into a monitored region, and a receiver antenna receiving electromagnetic waves reflected by objects within the monitored region and generating raw data, the communication module communicating alerts to third parties, the processor unit including a moving body isolation processor, a tracker module receiving and processing data from the radar unit to identify and track location of moving targets over time thereby generating target data, an alert-threshold generator generating an alert-threshold, a neural network receiving from the tracker module target data inputs selected from height profiles, signal-to-noise ratio and radial distance to object and generating a fall likelihood score, and a fall identification module receiving the fall likelihood score and detecting a fall event if the likelihood score is above an alert-th

Abstract: Methods are provided for sequestering a pollutant gas of carbon dioxide (CO2) gas and/or hydrogen sulfide (H2S) gas in a subterranean reservoir. In one method, a carrier gas containing pollutant-sorbent particles (e.g., nanoparticles) is pumped into the subterranean reservoir, the pollutant-sorbent particles attach to the subterranean reservoir, the pollutant gas is pumped into the subterranean reservoir, and the pollutant-sorbent particles attached to the subterranean reservoir adsorb the pollutant gas. In another method, pollutant gas is introduced into a carrier liquid containing pollutant-sorbent particles to produce a pollutant-rich carrier liquid, the pollutant-rich carrier liquid is pumped into the subterranean reservoir, and the pollutant-rich carrier liquid is allowed to remain in the subterranean reservoir.

Abstract: A system aligns a 3D model of an environment with image frames of the environment and generates a visualization interface that displays a portion of the 3D model and a corresponding image frame. The system receives LIDAR data collected in the environment and generates a 3D model based on the LIDAR data. For each image frame, the system aligns the image frame with the 3D model. After aligning the image frames with the 3D model, when the system presents a portion of the 3D model in an interface, it also presents an image frame that corresponds to the portion of the 3D model.

Abstract: Examples of the present disclosure describe systems and methods for video genre classification. In one example implementation, video content is received. A plurality of sliding windows of the video content is sampled. The plurality of sliding windows comprises audio data and video data. The audio data is analyzed to identify a set of audio features. The video data is analyzed to identify a set of video features. The set of audio features and the set of video features is provided to a classifier. The classifier is configured to detect a genre for the video content using the set of audio features and the set of video features. The video content is indexed based on the genre.

Abstract: Examples of methods, systems, and computer program products relating to supervising data in a wireless network are disclosed. At least part of a system may be located between a packet data network and a base station, and/or may be at least logically separate from the base station. The system may be capable of evaluating the service provided by the base station, and may be capable of determining whether or not any action should consequently be performed. Examples of an action may include an action which may not necessarily affect en-route data packets such as outputting a report, and/or an action which may affect en-route data packets such as delaying packets, not delaying packets, and/or stopping the delaying of packets. An action which affects data packets may or may not affect data packets uniformly. An action may or may not result in an improvement in quality of user experience.

Abstract: A system automatically identifies objects in an environment based on a walkthrough video and an annotated floorplan of the environment. The annotated floorplan indicates locations and types of objects that are expected to be in the environment. The system receives the walkthrough video and generates a 3D model of the environment. The system applies a machine learning model to the walkthrough video to identify regions within frames where objects are captured. After identifying the regions within frames of the walkthrough video that include objects, the system modifies corresponding regions of the 3D model to include the identified objects. For each of the identified objects, the system determines a likelihood of the identified object being present at a location in the environment based on a comparison of the modified 3D model and the annotated floorplan.

Abstract: A spatial indexing system receives a set of walkthrough videos of an environment taken over a period of time and receives an image search query that includes an image of an object. The spatial indexing system searches the set of walkthrough videos for instances of the object. The spatial indexing system presents search results in a user interface, displaying in a first portion a 2D map associated with one walkthrough video with marked locations of instances of the object and a second portion with a histogram of instances of the object over time in the set of walkthrough videos.

Abstract: Examples of methods, systems, and computer program products relating to supervising data in a wireless network are disclosed. At least part of a system may be located between a packet data network and a base station, and/or may be at least logically separate from the base station. The system may be capable of evaluating the service provided by the base station, and may be capable of determining whether or not any action should consequently be performed. Examples of an action may include an action which may not necessarily affect en-route data packets such as outputting a report, and/or an action which may affect en-route data packets such as delaying packets, not delaying packets, and/or stopping the delaying of packets. An action which affects data packets may or may not affect data packets uniformly. An action may or may not result in an improvement in quality of user experience.

Abstract: A system automatically identifies objects in an environment based on a walkthrough video and an annotated floorplan of the environment. The annotated floorplan indicates locations and types of objects that are expected to be in the environment. The system receives the walkthrough video and generates a 3D model of the environment. The system applies a machine learning model to the walkthrough video to identify regions within frames where objects are captured. After identifying the regions within frames of the walkthrough video that include objects, the system modifies corresponding regions of the 3D model to include the identified objects. For each of the identified objects, the system determines a likelihood of the identified object being present at a location in the environment based on a comparison of the modified 3D model and the annotated floorplan.

Abstract: A spatial indexing system receives a set of walkthrough videos of an environment taken over a period of time and receives an image search query that includes an image of an object. The spatial indexing system searches the set of walkthrough videos for instances of the object. The spatial indexing system presents search results in a user interface, displaying in a first portion a 2D map associated with one walkthrough video with marked locations of instances of the object and a second portion with a histogram of instances of the object over time in the set of walkthrough videos.

Abstract: A spatial indexing system receives a video that is a sequence of frames depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.

Abstract: A system aligns a 3D model of an environment with image frames of the environment and generates a visualization interface that displays a portion of the 3D model and a corresponding image frame. The system receives LIDAR data collected in the environment and generates a 3D model based on the LIDAR data. For each image frame, the system aligns the image frame with the 3D model. After aligning the image frames with the 3D model, when the system presents a portion of the 3D model in an interface, it also presents an image frame that corresponds to the portion of the 3D model.

Abstract: A spatial indexing system receives a video that is a sequence of frames depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.

Abstract: A system aligns a 3D model of an environment with image frames of the environment and generates a visualization interface that displays a portion of the 3D model and a corresponding image frame. The system receives LIDAR data collected in the environment and generates a 3D model based on the LIDAR data. For each image frame, the system aligns the image frame with the 3D model. After aligning the image frames with the 3D model, when the system presents a portion of the 3D model in an interface, it also presents an image frame that corresponds to the portion of the 3D model.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e.g., as part of a ratings system.