Abstract: A method for managing charging and discharging of a parallel-connected battery pack, including: obtaining a voltage value and a state of charge of a plurality of battery packs; comparing the voltage value and the state of charge of each of the battery packs with a reference voltage value and a reference state of charge; and managing charging and discharging of the plurality of battery packs based on a comparison result. The method can enhance automation of paralleling of battery packs, increase adaptability of a paralleling system, improve paralleling efficiency, further reduce operation and maintenance costs, and improve user experience.

Abstract: Bioconjugates and methods of making bioconjugates are provided, wherein the bioconjugates comprise glucose oxidase and nanoparticles that can kill tumor cells. For example, glucose oxidase-iron oxide bioconjugates can produce reactive oxygen species from blood glucose, causing cell death. The use of superparamagnetic iron oxide nanoparticles can provide magnetic resonance imaging guidance to facilitate imaging-guided drug delivery and combine diagnostics with therapy.

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.

Abstract: Disclosed herein is an anti-CD79b antibody or antigen-binding fragment thereof, a drug conjugate thereof and use thereof. The anti-CD79b antibody or antigen-binding fragment thereof herein comprises: HCDR1 comprising the amino acid sequence of SEQ ID NO: 1; HCDR2 comprising the amino acid sequence of SEQ ID NO: 2; HCDR3 comprising the amino acid sequence of SEQ ID NO: 3; LCDR1 comprising the amino acid sequence of SEQ ID NO: 4; LCDR2 comprising the amino acid sequence of SEQ ID NO: 5; and LCDR3 comprising the amino acid sequence of SEQ ID NO: 6.

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.