Abstract: The present disclosure provides an image processing method, including: displaying a first task information entry and a user control of a second user associated with the first task information entry on a task interface of a first user; obtaining at least one second task information entry of the second user in response to a preset operation on the user control; displaying a task sub-interface of the second user on the task interface of the first user; and displaying the at least one second task information entry on the task sub-interface of the second user based on a correlation between the at least one second task information entry and the first task information entry.

Abstract: Integrated optical filter and photodetectors and methods of fabrication thereof are described herein according to the present disclosure. An example of an integrated optical filter and photodetector described herein includes a substrate, an insulator layer on the substrate, and a semiconductor layer on the insulator layer. An optical filter having a resonant cavity is formed in or on the semiconductor layer. The integrated optical filter and photodetector further includes two first metal fingers and a second metal finger interdigitated between the two first metal fingers on the semiconductor layer forming Schottky barriers. The first metal fingers are constructed from a different metal relative to the second metal finger.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a deposit transaction predictor model to facilitate downstream access to deposit transaction prediction data through a data pipeline. For instance, the disclosed systems can enable universal access to deposit transaction prediction data to various downstream services by utilizing a data pipeline that identifies data for a user account from various data sources, transforming the data into deposit transaction prediction data utilizing a deposit transaction predictor model, and updating a deposit transaction prediction data source with the deposit transaction prediction data. For instance, the disclosed systems can utilize the deposit transaction predictor model to analyze various user account data to determine patterns that indicate deposit transaction prediction data.

Abstract: A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

Abstract: According to an aspect of an embodiment, operations may comprise receiving an approximate geographic location of a vehicle, accessing a map of a region within which the approximate geographic location of the vehicle is located, identifying a first region on the map within a first threshold distance of the approximate geographic location of the vehicle, identifying a second region on the map associated with one or more roads on the map, determining a search space on the map within which the vehicle is likely to be present, the search space representing an intersection of the first region and the second region, and determining a more accurate geographic location of the vehicle by performing a search within the search space.

Abstract: According to an aspect of an embodiment, operations may comprise accessing a set of vehicle poses of one or more vehicles; for each of the set of vehicle poses, accessing a high definition (HD) map of a geographical region surrounding the vehicle pose, with the HD map comprising a three-dimensional (3D) representation of the geographical region, determining a measure of constrainedness for the vehicle pose, with the measure of constrainedness representing a confidence for performing localization for the vehicle pose based on 3D structures surrounding the vehicle pose, and storing the measure of constrainedness for the vehicle pose; and for each of the geographical regions surrounding each of the set of vehicle poses, determining a measure of constrainedness for the geographical region based on measures of constrainedness of vehicle poses within the geographical region, and storing the measure of constrainedness for the geographical region.

Abstract: Operations of the present disclosure include obtaining a first measure of velocity of a vehicle based on a plurality of locations determined for the vehicle. The operations also include obtaining, based on IMU measurements of an inertial measurement unit (IMU) of the vehicle, a second measure of velocity of the vehicle. In addition, the operations include performing calibration of the IMU based on the first measure of velocity and the second measure of velocity.

Abstract: According to an aspect of an embodiment, operations may comprise receiving, from a LIDAR mounted on a vehicle, a first 3D point cloud comprising points of a region around the vehicle as observed by the LIDAR. The operations may also comprise accessing an HD map comprising a second 3D point cloud comprising points of the region around the vehicle. The operations may also comprise segmenting LIDAR ground points from LIDAR non-ground points in the first 3D point cloud. The operations may also comprise segmenting map ground points from map non-ground points in the second 3D point cloud. The operations may also comprise determining a pose of the vehicle by matching the LIDAR ground points to the map ground points and by matching the LIDAR non-ground points to the map non-ground points.

Abstract: The present invention is directed to tetramaleimide linkers and use thereof, more specifically to the compounds represented by formula I and their use in the preparation of antibody-drug conjugates (ADCs). The ADCs obtained from the tetramaleimide linkers have high homogeneity and stability, and could be used effectively for the treatment of various diseases including tumors. The definition of the groups in formula I is the same as that in the description.

Abstract: According to an aspect of an embodiment, operations may comprise (a) accessing a portion of a high definition (HD) map comprising a point cloud of a region through which a vehicle is driving, (b) identifying a base LIDAR from a plurality of LIDARs mounted on the vehicle, (c) for each of the LIDARs: receiving a LIDAR scan comprising a point cloud of the region, and determining a pose for the LIDAR, (d) for each LIDAR other than the base LIDAR, determining a transform for the LIDAR with respect to the base LIDAR, (e) repeating (c) to generate a plurality of samples, (f) for each of the samples, repeating (d) to determine a plurality of transforms for each LIDAR with respect to the base LIDAR, and (g) calibrating each of the LIDARs other than the base LIDAR by determining an aggregate transform for the LIDAR.

Abstract: According to an aspect of an embodiment, operations may comprise receiving, from a LIDAR mounted on a vehicle, a first 3D point cloud comprising points of a region around the vehicle as observed by the LIDAR. The operations may also comprise accessing an HD map comprising a second 3D point cloud comprising points of the region around the vehicle. The operations may also comprise segmenting LIDAR ground points from LIDAR non-ground points in the first 3D point cloud. The operations may also comprise segmenting map ground points from map non-ground points in the second 3D point cloud. The operations may also comprise determining a pose of the vehicle by matching the LIDAR ground points to the map ground points and by matching the LIDAR non-ground points to the map non-ground points.

Abstract: According to an aspect of an embodiment, operations may comprise obtaining a first point cloud that includes a first point. The operations also comprises obtaining a second point cloud that is a copy of the first point cloud and that includes a second point that is a copy of the first point. The operations also comprises moving the second point cloud with respect to the first point cloud according to a first vector. The operations also comprises identifying a closest point of the first point cloud that is closest to the second point of the second point cloud. The operations also comprises determining a second vector between the closest point and the second point. The operations also comprises determining a measure of usefulness of the first point based on the first vector and the second vector. The operations also comprises indicating the measure of usefulness of the first point.

Abstract: A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

Abstract: Operations may comprise obtaining a plurality of light detection and ranging (LIDAR) scans of a region. The operations may also comprise identifying a plurality of LIDAR poses that correspond to the plurality of LIDAR scans. In addition, the operations may comprise identifying, as a plurality of keyframes, a plurality of images of the region that are captured during capturing of the plurality of LIDAR scans. The operations may also comprise determining, based on the plurality of LIDAR poses, a plurality of camera poses that correspond to the keyframes. Further, the operations may comprise identifying a plurality of two-dimensional (2D) keypoints in the keyframes. The operations also may comprise generating one or more three-dimensional (3D) keypoints based on the plurality of 2D keypoints and the respective camera poses of the plurality of keyframes.

Abstract: Disclosed is a portable multifunctional oral cavity cleaner which includes a tongue scraper, a material storage bottle, a material spraying device and a detachable housing; the tongue scraper can be changed into a contracted state from a stretched state via bending, rotating or folding, so as to reduce the volume of the oral cavity cleaner; an oral cavity cleaner body including the tongue scraper in the contracted state is housed in an inner cavity of the housing; and the housing can be reversely combined at the bottom of the oral cavity cleaner after being detached and used as a handle for prolonging the length of the oral cavity cleaner. The present application, featured with small size, light weight and portability, ensures hygiene in the carrying process and realizes multiple functions such as tongue coating removal, oral spray sterilization and interdental cleaning at one time.

Abstract: According to an aspect of an embodiment, operations may comprise accessing a set of vehicle poses of one or more vehicles; for each of the set of vehicle poses, accessing a high definition (HD) map of a geographical region surrounding the vehicle pose, with the HD map comprising a three-dimensional (3D) representation of the geographical region, determining a measure of constrainedness for the vehicle pose, with the measure of constrainedness representing a confidence for performing localization for the vehicle pose based on 3D structures surrounding the vehicle pose, and storing the measure of constrainedness for the vehicle pose; and for each of the geographical regions surrounding each of the set of vehicle poses, determining a measure of constrainedness for the geographical region based on measures of constrainedness of vehicle poses within the geographical region, and storing the measure of constrainedness for the geographical region.

Abstract: According to an aspect of an embodiment, operations may comprise accessing a set of vehicle poses of one or more vehicles; for each of the set of vehicle poses, accessing a high definition (HD) map of a geographical region surrounding the vehicle pose, with the HD map comprising a three-dimensional (3D) representation of the geographical region, determining a measure of constrainedness for the vehicle pose, with the measure of constrainedness representing a confidence for performing localization for the vehicle pose based on 3D structures surrounding the vehicle pose, and storing the measure of constrainedness for the vehicle pose; and for each of the geographical regions surrounding each of the set of vehicle poses, determining a measure of constrainedness for the geographical region based on measures of constrainedness of vehicle poses within the geographical region, and storing the measure of constrainedness for the geographical region.

Abstract: Operations may comprise obtaining a plurality of light detection and ranging (LIDAR) scans of a region. The operations may also comprise identifying a plurality of LIDAR poses that correspond to the plurality of LIDAR scans. In addition, the operations may comprise identifying, as a plurality of keyframes, a plurality of images of the region that are captured during capturing of the plurality of LIDAR scans. The operations may also comprise determining, based on the plurality of LIDAR poses, a plurality of camera poses that correspond to the keyframes. Further, the operations may comprise identifying a plurality of two-dimensional (2D) keypoints in the keyframes. The operations also may comprise generating one or more three-dimensional (3D) keypoints based on the plurality of 2D keypoints and the respective camera poses of the plurality of keyframes.

Abstract: A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

Abstract: The present invention is directed to oxadiazole linkers and use thereof, more specifically to the compounds represented by formula I, II, and III, and their use in the preparation of antibody-drug conjugates (ADCs). The ADCs obtained from said oxadiazole linkers have high homogeneity and stability, and could be used effectively for the treatment of various diseases including tumors. The definition of the groups in formula I, II, and III is the same as that in the description.