Abstract: Certain aspects relate to systems and techniques for surgical robotic arm setup. In one aspect, there is provided a system including a first robotic arm configured to manipulate a medical instrument, a processor, and a memory. The processor may be configured to: determine a minimum stroke length of the first robotic arm that allows advancing of the medical instrument by the first robotic arm to reach a target region from an access point via a path, determine a boundary for an initial pose of the first robotic arm based on the minimum stroke length and a mapping stored in the memory, and during an arm setup phase prior to performing a procedure, provide an indication of the boundary during movement of the first robotic arm.

Abstract: Disclosed are performance metrics for evaluating the accuracy of a dynamic model in predicting the trajectory of ADV when simulating the behavior of the ADV under the control commands. The performance metrics may indicate the degree of similarity between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle when applied with identical control commands. The performance metrics measure deviations of the predicted trajectory of the dynamic model from the actual trajectory based on the ground truths. The performance metrics may include cumulative or mean absolute trajectory error, end-pose difference (ED), two-sigma defect rate (?2?), the Hausdirff Distance (HAU), the longest common sub-sequence error (LCSS), or dynamic time warping (DTW). The two-sigma defect rate represents the ratio of the number of points with true location error falling out of the 2? range of the predicted location error over the total number of points in the trajectory.

Abstract: Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Abstract: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Abstract: The present disclosure provides a method for preparing a superhydrophobic surface of an aluminum alloy through laser peening, including the following steps: coating a surface of the aluminum alloy as an absorption layer with an organic component-containing confinement layer to obtain a coated aluminum alloy, where the organic component-containing confinement layer is a mixed organic solution including 5 mL to 10 mL of perfluorooctyltriethoxysilane (FOTS), 100 mL to 200 mL of absolute ethanol, and 30 mL to 50 mL of distilled water; and subjecting a surface of the coated aluminum alloy to the laser peening to form the superhydrophobic surface.

Abstract: The present application provides modified immune cells that express TLR receptors. In some embodiments, the modified immune cell further comprises an engineered receptor such as a chimeric antigen receptor (CAR). The present application also provides methods and pharmaceutical compositions for cancer treatment using the modified immune cells described herein.

Abstract: According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

Abstract: According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

Abstract: Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S1, selecting a shale reservoir of a target interval of a place; and collecting data of parameters of cores of each of a known gas-bearing shale reservoir A and a known water-bearing shale reservoir B; S2, calculating average values of the parameters of each of the reservoirs A and B respectively; S3, calculating average differences of the parameters of each of the reservoirs; S4, calculating covariance values of the parameters of each of the reservoirs; S5, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S6, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S7, obtaining values of parameters of cores of a sample of the unknown shale reservoir, calculating a discriminant value, and determining gas-bearing situation of the unknown shale reservoir.

Abstract: A semiconductor device includes a gate structure on a substrate, a first spacer on a sidewall of the gate structure, a second spacer on a sidewall of the first spacer, a third spacer on a sidewall of the second spacer, and first and second stacks of an epitaxial layer and a cap layer respectively disposed at first and second sides of the gate structure. Preferably, a part of the second spacer comprises an I-shape, the cap layer includes a planar top surface and an inclined sidewall, the cap layer contacts the second spacer and the third spacer directly, and the cap layer includes a vertical sidewall connected to the inclined sidewall.

Abstract: Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S1, selecting a shale reservoir of a target interval of a place; and collecting data of parameters of cores of each of a known gas-bearing shale reservoir A and a known water-bearing shale reservoir B; S2, calculating average values of the parameters of each of the reservoirs A and B respectively; S3, calculating average differences of the parameters of each of the reservoirs; S4, calculating covariance values of the parameters of each of the reservoirs; S5, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S6, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S7, obtaining values of parameters of cores of a sample of the unknown shale reservoir, calculating a discriminant value, and determining gas-bearing situation of the unknown shale reservoir.

Abstract: In one example, a system for executing applications can include a main processor to initialize a virtual machine to execute an application. The main processor can also determine a main utilization indicator of the main processor is above a threshold and an auxiliary utilization indicator of an auxiliary processor is below a threshold, wherein the auxiliary processor is based on an auxiliary instruction set architecture. Additionally, the main processor can transmit an instruction from the application to the auxiliary processor for execution and update context data for the application in response to receiving an execution result from the auxiliary processor.

Abstract: In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

Abstract: A computing device may obtain one or more raw images captured from sensors of an autonomous driving vehicle (ADV). The computing device may apply one or more signal processing parameters to one or more raw images, resulting in one or more processed images. The computing device may apply an object detection algorithm of the ADV to the one or more processed images. The computing device may determine a score of the object detection algorithm as applied to the one or more processed images. One or more optimized signal processing parameters may be determined based on the score of the object detection algorithm and the signal processing parameter. The one or more optimized signal processing parameters may be used to configure the ADV for use during autonomous driving.

Abstract: According to some embodiments, systems, methods, and media for operating an autonomous driving vehicle (ADV) encountered with small objects are described. According to a method, the ADV, when detecting an object in a lane in which the ADV is travelling, can determine whether the ADV can safely drive over the object based on attributes of the object and attributes of the ADV, and if so, can generate one or more planned trajectories that each enable the ADV to drive over the object. From the one or more planned trajectories, the ADV can select a planned trajectory that enables the ADV to drive over the object along the centerline of the ADV without causing the ADV to drive out of the lane. If no such planned trajectory exists, the ADV can bypass the object, or stop within a predetermined distance of the object.

Abstract: In one embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

Abstract: The disclosure describes an autotuning framework for tuning an image signal processing (ISP) module of an autonomous driving vehicle (ADV). The autotuning framework can generate different sets of parameter values using a variety of optimization algorithms to configure the ISP module. Each processed image generated by the ISP module configured with the different sets of parameter values is compared by an ISP module testing device with a reference image stored therein to generate an objective core measuring one or more differences between each of the processed images and the reference image. The objective scores and the corresponding sets of parameter values are stored in a database. Different sets of optimal parameter values for different environments can be selected from the database, and uploaded to a cloud database for use by an ADV, which can select a different set of ISP parameter values based on an environment that the ADV is travelling in.

Abstract: In one embodiment, a system generates an image using either a first or a second image signal processing (ISP) algorithm, where the first or second ISP algorithm is applied to raw image data of a camera of an autonomous driving vehicle (ADV) to generate the image. The system applies a machine learning model to the image to identify a representation of an obstacle, where the machine learning model is generated by a few shots learning algorithm that contrasts labeled data of a positive training sample from images corresponding to the first and second ISP algorithms to labeled data of a negative training sample from images corresponding to the first and second ISP algorithm. The system determines a classification and a location of the obstacle based on the representation of the obstacle. The system plans a motion control of the ADV based on the classification and location of the detected object.