Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: Briefly, embodiments are directed to a system, method, and article for acquiring a symbol comprising a representation of input data. The symbol may be converted into an emergent language expression in an emergent language via processing of a first neural network. Transmission of the emergent language expression may be initiated over a communications network, where the emergent language comprises a language based on and specific to the input data. The emergent language expression may be translated back into the representation of the input data via processing of a second neural network.

Abstract: One or more location-based clients can be activated on a mobile device for providing location-based services. The location-based clients can be provided with information (e.g., presets, defaults) related to the current location and/or mode of the mobile device. The information can be obtained from one or more network resources. In some implementations, a location-based client can concurrently display map and vehicle information related to a location of the mobile device.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may access a scan plan that includes a sequence of poses for the UAV to assume to capture images of a scan target using one or more image sensors. The UAV may check a next pose of the scan plan for obstructions. Responsive to detection of an obstruction, the UAV may determine a backup pose based at least on a field of view of the next pose. The UAV may control a propulsion mechanism to cause the UAV to fly to assume the backup pose. The UAV may capture, based on the backup pose and using the one or more image sensors, one or more images of the scan target.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may identify a scan target. The UAV may navigate to two or more positions in relation to the scan target. The UAV may capture, using one or more image sensors of the UAV, two or more images of the scan target from different respective positions in relation to the scan target. For instance, the two or more respective positions may be selected by controlling a spacing between the two or more respective positions to enable determination of parallax disparity between a first image captured at a first position and a second image captured at a second position of the two or more positions. The UAV may determine a three-dimensional model corresponding to the scan target based in part on the determined parallax disparity of the two or more images including the first image and the second image.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine, based on a three-dimensional (3D) model including a plurality of points corresponding to a scan target, a scan plan for scanning at least a portion of the scan target. For instance, the scan plan may include a plurality of poses for the UAV to assume to capture images of the scan target. The UAV may capture with one or more image sensors, one or more images of the scan target from one or more poses of the plurality of poses. Further, the UAV may determine an update to the 3D model based at least in part on the one or more images. Additionally, the UAV may update the scan plan based at least in part on the update to the 3D model.

Abstract: One or more location-based clients can be activated on a mobile device for providing location-based services. The location-based clients can be provided with information (e.g., presets, defaults) related to the current location and/or mode of the mobile device. The information can be obtained from one or more network resources. In some implementations, a location-based client can concurrently display map and vehicle information related to a location of the mobile device.

Abstract: In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

Abstract: One or more location-based clients can be activated on a mobile device for providing location-based services. The location-based clients can be provided with information (e.g., presets, defaults) related to the current location and/or mode of the mobile device. The information can be obtained from one or more network resources. In some implementations, a location-based client can concurrently display map and vehicle information related to a location of the mobile device.

Abstract: Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

Abstract: An independently mountable sensor system for a wet-mateable subsea connector or a dry-mateable subsea connector having at least one of a receptacle connector body or a plug connector body with a pressure vessel; one or more sensors, a power source, a transmitter, a processor and a memory within the pressure vessel. The pressure inside the pressure vessel is maintained at a predetermined pressure and an antenna is provided in the sensor system. The pressure vessel includes an electromagnetically transparent section to permit electromagnetic waves to pass between the sensor system and an external receiver. The sensor system is configured to be mounted between a back end and a front end of the receptacle connector body, or between a back end and a front end of the plug connector body; or configured to be mounted to the connector body back end.

Abstract: Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

Abstract: Described herein are systems for roof scan using an unmanned aerial vehicle.

Abstract: Determination of a set of acoustic parameters for a headset is presented herein. The set of acoustic parameters can be determined based on a virtual model of physical locations stored at a mapping server. The virtual model describes a plurality of spaces and acoustic properties of those spaces, wherein the location in the virtual model corresponds to a physical location of the headset. A location in the virtual model for the headset is determined based on information describing at least a portion of the local area received from the headset. The set of acoustic parameters associated with the physical location of the headset is determined based in part on the determined location in the virtual model and any acoustic parameters associated with the determined location. The headset presents audio content using the set of acoustic parameters received from the mapping server.

Abstract: Provided herein are methods of treating diseases and disorder responsive to the inhibition of PDE4 comprising administering apremilast and a Tyk2 inhibitor to a subject. Also provided herein are pharmaceutical compositions comprising apremilast and a Tyk2 inhibitor.

Abstract: Embodiments are described for detecting optical discrepancies associated with image capture analyzing pixels in multiple images corresponding to common points of reference in a physical environment. In an embodiment, photometric error values are averaged over time to compute the mean error at each pixel. Once the estimate of the mean error has a sufficient number of updates above a specified value, the estimate is thresholded to provide a mask of any optical discrepancies occurring in the stereo pair of images. Applications include detecting optical discrepancies in images captured for use by a visual navigation system in guiding an autonomous vehicle (e.g., an unmanned aerial vehicle).

Abstract: Described herein are systems for roof scan using an unmanned aerial vehicle. For example, some methods include capturing, using an unmanned aerial vehicle, an overview image of a roof of a building from above the roof; presenting a suggested bounding polygon overlaid on the overview image to a user; determining a bounding polygon based on the suggested bounding polygon and user edits; based on the bounding polygon, determining a flight path including a sequence of poses of the unmanned aerial vehicle with respective fields of view at a fixed height that collectively cover the bounding polygon; fly the unmanned aerial vehicle to a sequence of scan poses with horizontal positions matching respective poses of the flight path and vertical positions determined to maintain a consistent distance above the roof; and scanning the roof from the sequence of scan poses to generate a three-dimensional map of the roof.

Abstract: Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

Abstract: One or more location-based clients can be activated on a mobile device for providing location-based services. The location-based clients can be provided with information (e.g., presets, defaults) related to the current location and/or mode of the mobile device. The information can be obtained from one or more network resources. In some implementations, a location-based client can concurrently display map and vehicle information related to a location of the mobile device.

Abstract: An analogy generating system includes one or more image databases that include a first set of images depicting a first symbolic class and a second set of images depicting a second symbolic class and an autoencoder that receive images from the first set of images and the second set of images; determines a first characteristic shared between the first symbolic class and the second symbolic class using a first node from multiple nodes on a neural network; determine a second characteristic shared between the first symbolic class and the second symbolic class using a second node from multiple nodes on the neural network; and exchange the first characteristic and the second characteristic between the first node and the second node to establish an analogy between the first symbolic class and the second symbolic class.