Abstract: A susceptor assembly for supporting a crucible during a crystal growth process includes a susceptor base, a tubular sidewall connected to the susceptor base, and a removable sacrifice ring interposed between the susceptor base and the sidewall. Each of the susceptor base and the sidewall is formed of a carbon-containing material. The susceptor base has an annular wall and a shoulder extending radially outward from an outer surface of the annular wall. The sidewall has a first end that receives the annular wall to connect the sidewall to the susceptor base. The sacrifice ring has a first surface that faces the outer surface of the annular wall, a second surface that faces an interior surface of the sidewall, and a ledge extending outward from the second surface that engages the first end of the sidewall.

Abstract: A material mounting apparatus may include a body having a front side and a back side. One or more body attachment elements may be secured to the back side of the body. One or more connectors may be secured to and extend from the one or more body attachment elements. A material may be placed over the front side of the body. An outer perimeter of the material may extend beyond a perimeter of the body. The outer perimeter of the material may be folded across the perimeter of the body into an adjacent relationship with the back side of the body. The one or more connectors may be secured to the material to retain and/or stretch the material over the material mounting apparatus.

Abstract: A method of detecting defects on a semiconductor wafer includes directing diffuse light to the semiconductor wafer and reflecting the diffuse light off of the semiconductor wafer. The method further includes detecting the diffuse light with a camera to generate an image of the semiconductor wafer and analyzing the image to detect defects on the semiconductor wafer.

Abstract: Disclosed is a system for controlling a robot via a foot-operable controller. The foot-operable controller includes a grid of large pressure-sensitive tiles that are responsive to being stepped on. Signals from the pressure-sensitive tiles are provided to a controller interface that converts the signals to control messages compatible with existing robot control interfaces, such as a universal serial bus. The foot-operable controller may be used to control various robots, including for example a robot equipped with a claw arm or a robot equipped with a ring launcher.

Abstract: The technologies described herein relate to computing a point cloud based upon data output by several radar sensors in a distributed radar system. The radar sensors generate tensors based upon echo signals detected by the radar sensors. Values are extracted from the tensors and a sequence of tokens is created, where the sequence of tokens includes the values extracted from the tensors. The sequence of tokens is provided as input to a transformer model, which outputs a point cloud based upon the sequence of tokens.

Abstract: Autonomous vehicles (AVs) utilize perception and understanding of objects on the road to predict behaviors of the objects, and to plan a trajectory for the vehicle. In some situations, an object may be occluded and undetected by an AV. However, a different AV viewing the same scene may detect the object. With information from multiple views of the same scene, it is possible to determine occlusion attributes for the object, such as relational occlusion information and extent of occlusion. For the AV that is driving on the road, having knowledge of the occluded object and the occlusion attributes can improve the performance of perception, understanding, tracking, prediction, and/or planning algorithms. For the algorithms, occlusion attributes can be generated from the multi-view data and included as part of labeled data for machine learning training. The models in the algorithms can learn to better handle occluded objects.

Abstract: Autonomous vehicles utilize perception to identify objects in a vehicle's surrounding environment and plan a trajectory. In perceiving an AV's surroundings, an object detection model uses sensor data from AV sensors to detect objects in the AV surroundings. An object detection model can be trained to detect objects using training datasets having sensor data and ground truth objects. In training, the object detection model uses the sensor data to identify objects in the locations of the ground truth objects. However, sometimes there is little or no sensor data corresponding to the ground truth objects, and the model is not able to learn patterns in sensor data to correctly detect the objects. Systems and methods are provided herein for removing ground truth objects that do not have sufficient corresponding sensor data from the training dataset, thereby preventing these objects from dominating the loss and interfering with the model learning useful patterns.

Abstract: Systems and methods for automatic removal for selected training data from a dataset are provided. Systems and methods are provided for identifying portions of radar data that contain information that is not present in camera and/or lidar data. The identified portions of the radar data can be processed by a neural network to provide the additional information. The identified parts of the radar data can then be processed while the remaining parts of the radar data remain unprocessed. In various examples, features can be extracted from identified regions of the radar data using a neural network and fused with camera and/or lidar features. In some examples, the fused features can be used for object detection.

Abstract: Autonomous vehicles (AVs) utilize perception and understanding of objects on the road to predict behaviors of the objects, and to plan a trajectory for the vehicle. In some scenarios, an AV may benefit from having information about an object that is not within a perceivable area of the sensors of the AV. Another AV in the area that has the object within a perceivable area of the sensors of the other AV may share information with the AV. Rich information about the object determined by the other AV can be compressed using an encoder and transferred efficiently to the AV for inclusion in a temporal map that combines locally determined object information and transferred object information determined by other AV(s). The temporal map may be used in one or more parts of the software stack of the AV.

Abstract: A computer vision module facilitates multi-sensor vision of AVs based on privileged information learning. The module may input first sensor data captured by a first sensor into an already-trained privileged model, input second sensor data captured by a second sensor into a first backbone of a target model, and input third sensor data captured by a third sensor into a second backbone of the target model. The three sensors may be of different types. To train the target model, internal parameters of the target model may be modified to minimize a loss, which may include a privilege loss, which indicates a difference between a latent representation of the first sensor data and a latent representation of the second sensor data, and another privileged loss, which indicates a difference between the latent representation of the first sensor data and a latent representation of the third sensor data.

Abstract: A method includes dividing an image into a plurality of patches; generating for the patches image tokens each including a halting score associated with the image token; generating a contextual features token representative of at least one contextual feature in connection with the image; processing the image tokens and the contextual features token using a vision transformer block; performing adaptive halting on the image tokens output from the transformer block, the adaptive halting comprising updating the halting scores associated with the image tokens and discarding ones of the image tokens having halting scores greater than or equal to a predetermined threshold score, wherein the non-discarded image tokens comprise remaining image tokens; and forwarding the remaining image tokens to a next processing block.

Abstract: The present invention relates to methods and apparatuses (radio base station) and UE for enabling a UE to perform a random access. According to embodiments of the present invention, an identification number of a dedicated random access preamble is, in a message, transmitted together with information indicating in which channel or channels the preamble is valid for the UE. The UE can then use the received information and based on that information perform a random access.

Abstract: A lumbar interbody fusion device includes a first wing, a second wing, and a bridge. The bridge has an arcuate resting shape and include a first end connected to the first wing, a second end connected to the second wing, and at least one aperture extending through the bridge in a radial direction relative to the arcuate resting shape of the bridge. The bridge is elastically deformable such that a distance between the first wing and the second wing may vary according to elastic deformation of the bridge.

Abstract: An apparatus comprises a vacuum chamber configured to produce a substantially atmospheric vacuum environment for plasma processing; a radio frequency (RF) power supply located outside of the vacuum chamber; an RF matching network operatively coupled to the RF power supply; and a plurality of electrodes mounted within the vacuum chamber, the plurality of electrodes configured to receive RF power signals from the RF power supply through the RF matching network. The RF power signals are simultaneously delivered to the plurality of electrodes during a sputtering operation. The plurality of electrodes and a set of electrical components are operative to manage the inductive and capacitive reactance for coupling the RF power signals to provide more desirable plasma coupling during the sputtering operation.

Abstract: Technologies described herein relate to a distributed aperture radar (DAR) system that includes multiple radar sensors. A graph neural network (GNN) is employed to cause radar data output by the multiple radar sensors to correspond to a same coordinate system.