Abstract: The present invention provides methods and compositions comprising at least one neutral metalloprotease enzyme that has improved storage stability. In some embodiments, the neutral metalloprotease finds use in cleaning and other applications. In some particularly preferred embodiments, the present invention provides methods and compositions comprising neutral metalloprotease(s) obtained from Bacillus sp. In some more particularly preferred embodiments, the neutral metalloprotease is obtained from B. amyloliquefaciens. In still further preferred embodiments, the neutral metalloprotease is a variant of the B. amyloliquefaciens neutral metalloprotease. In yet additional embodiments, the neutral metalloprotease is a homolog of the the B. amyloliquefaciens neutral metalloprotease. The present invention finds particular use in applications including, but not limited to cleaning, bleaching and disinfecting.

Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

Abstract: A vehicle to everything (V2X) mesh network system supporting a mobility operation of a production factory includes a road side unit (RSU) which is disposed in plural in the production factory, and connects infra-to-infra (I2I) wireless communication with an infrastructure facility, and connects vehicle-to-infra (V2I) wireless communication with an on board unit (OBU) mounted on an autonomous driving vehicle to form a V2X mesh network; and a control server controlling operation states of the RSU and the vehicle through the V2X mesh network.

Abstract: In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

Abstract: Embodiments of the disclosure provided herein include an apparatus and method for additive manufacturing a component for a semiconductor processing apparatus. The apparatus for additive manufacturing includes a printhead configured to selectively deposit layers on build the component, wherein the printhead may selectively deposit a first set of layers onto the build platform, and selectively deposit a second set of layers on the first set of layers, each of the second set of layers comprising a gap that forms a cooling channel through the component. The cooling channel is filled a filler material. A third set of layers covers the cooling channel before the channel is exposed to a liquidizing agent, wherein the liquidizing agent reacts with the filler material to produce an effluent fluid which is then drained. In other embodiments, the cooling channel may be machined into the component before being filled with the filler material.

Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN.

Abstract: A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

Abstract: In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

Abstract: The present invention provides methods and compositions comprising at least one neutral metalloprotease enzyme that has improved storage stability. In some embodiments, the neutral metalloprotease finds use in cleaning and other applications. In some particularly preferred embodiments, the present invention provides methods and compositions comprising neutral metalloprotease(s) obtained from Bacillus sp. In some more particularly preferred embodiments, the neutral metalloprotease is obtained from B. amyloliquefaciens. In still further preferred embodiments, the neutral metalloprotease is a variant of the B. amyloliquefaciens neutral metalloprotease. In yet additional embodiments, the neutral metalloprotease is a homolog of the the B. amyloliquefaciens neutral metalloprotease. The present invention finds particular use in applications including, but not limited to cleaning, bleaching and disinfecting.

Abstract: A vehicle radar inspection system and method are provided for inspecting a mounting state of a radar sensor mounted to a vehicle. The vehicle radar inspection system includes a centering portion that aligns a position of the vehicle by driving rollers, displacement sensors that are respectively disposed at front and rear sides of the centering portion, an array antenna that measures propagation intensity of a radar signal transmitted from the radar sensor, and a server that connects wireless communication with a wireless terminal of the vehicle, calculates a mounting position of the radar sensor, and detects a mounting error of the radar sensor with reference to a normal reference mounting specification.

Abstract: A production factory unmanned transfer system includes: a vehicle, which connects Vehicle to Everything (V2X) communication with an infrastructure facility in a vehicle production factory and transfers a worker to a set destination in an unmanned manner through autonomous driving; and a road side unit, which is fixed around a road in the production factory to relay the V2X communication and which generates positioning error correction information based on high-precision Real Time Kinematic-Global Navigation Satellite System (RTK-GNSS) based on a fixed absolute coordinate and transmits the generated positioning error correction information to the vehicle. The vehicle includes a vehicle terminal, which controls autonomous driving according to a lane of a precise map based on high-precise positioning information obtained by correcting an error of satellite-based vehicle location information with the positioning error correction information.

Abstract: A V2X network handover system supporting autonomous driving of an unmanned transport vehicle may include, a plurality of RSUs disposed in a production plant, each being configured to broadcast a WSA message in a service area, and an OBU installed in the unmanned transport vehicle to transmit and receive V2X communication data through an integrated antenna and configured to obtain vehicle position information based on GNSS, where the OBU may be configured to execute a handover determination algorithm from the WSA message received in a service overlap area of a plurality of RSUs to select a handover target RSU based on an integrated condition combined with two or more among a distance-based condition based on a distance between each RSU and the unmanned transport vehicle, a weighted moving average condition of received signal strength indication (RSSI), and a data normal reception count condition.

Abstract: A vehicle to everything (V2X) mesh network system supporting a mobility operation of a production factory includes a road side unit (RSU) which is disposed in plural in the production factory, and connects infra-to-infra (I2I) wireless communication with an infrastructure facility, and connects vehicle-to-infra (V2I) wireless communication with an on board unit (OBU) mounted on an autonomous driving vehicle to form a V2X mesh network; and a control server controlling operation states of the RSU and the vehicle through the V2X mesh network.

Abstract: A vehicle radar inspection system and method are provided for inspecting a mounting state of a radar sensor mounted to a vehicle. The vehicle radar inspection system includes a centering portion that aligns a position of the vehicle by driving rollers, displacement sensors that are respectively disposed at front and rear sides of the centering portion, an array antenna that measures propagation intensity of a radar signal transmitted from the radar sensor, and a server that connects wireless communication with a wireless terminal of the vehicle, calculates a mounting position of the radar sensor, and detects a mounting error of the radar sensor with reference to a normal reference mounting specification.

Abstract: A production factory unmanned transfer system includes: a vehicle, which connects Vehicle to Everything (V2X) communication with an infrastructure facility in a vehicle production factory and transfers a worker to a set destination in an unmanned manner through autonomous driving; and a road side unit, which is fixed around a road in the production factory to relay the V2X communication and which generates positioning error correction information based on high-precision Real Time Kinematic-Global Navigation Satellite System (RTK-GNSS) based on a fixed absolute coordinate and transmits the generated positioning error correction information to the vehicle. The vehicle includes a vehicle terminal, which controls autonomous driving according to a lane of a precise map based on high-precise positioning information obtained by correcting an error of satellite-based vehicle location information with the positioning error correction information.

Abstract: Provided herein, inter alia, are compositions and methods for treating bladder cancer, including CD4+T cells that can be cultured ex vivo to generate a population of cytotoxic CD4+T cells capable of killing bladder cancer tumor cells. Pharmaceutical compositions containing such a cytotoxic CD4+T cell population, as well as methods for treating an individual having or suspected of having bladder cancer are also provided.

Abstract: A vehicle radar inspection system and method are provided for inspecting a mounting state of a radar sensor mounted to a vehicle. The vehicle radar inspection system includes a centering portion that aligns a position of the vehicle by driving rollers, displacement sensors that are respectively disposed at front and rear sides of the centering portion, an array antenna that measures propagation intensity of a radar signal transmitted from the radar sensor, and a server that connects wireless communication with a wireless terminal of the vehicle, calculates a mounting position of the radar sensor, and detects a mounting error of the radar sensor with reference to a normal reference mounting specification.

Abstract: A system for aiming a radar sensor angle which adjusts an angle of the radar sensor mounted on a vehicle entering an inspection line includes: a radar sensor mounted inside a front bumper of the vehicle, a wireless terminal connected to the radar sensor through an in-vehicle communication line and connected to the outside through a repeater, a centering unit for aligning a position of the vehicle by a driving roller based on a reference inspection position of the radar sensor, an array antenna for measuring an intensity of a radar signal transmitted from the radar sensor and detecting a radar center value, and a server detecting an angular error value of the radar sensor by comparing the radar center value with a reference center value of a set mounting specification and transmitting an angular correction value to the radar sensor.

Abstract: A system for aiming a radar sensor angle which adjusts an angle of the radar sensor mounted on a vehicle entering an inspection line includes: a radar sensor mounted inside a front bumper of the vehicle, a wireless terminal connected to the radar sensor through an in-vehicle communication line and connected to the outside through a repeater, a centering unit for aligning a position of the vehicle by a driving roller based on a reference inspection position of the radar sensor, an array antenna for measuring anintensity of a radar signal transmitted from the radar sensor and detecting a radar center value, and a server detecting an angular error value of the radar sensor by comparing the radar center value with a reference center value of a set mounting specification and transmitting an angular correction value to the radar sensor.