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.

Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.

Abstract: An artificial intelligence enabled system is disclosed. The system includes a core component for enabling AI-powered interactions between the system and its users and one or more agents that understand user intent and automatically interact with products and services on the web and/or in the physical world through imitation of a human user.

Abstract: Multiple-patterning techniques described herein enable forming fin structures of a semiconductor device in a manner that enables decreased fin-to-fin spacing of the fin structures while providing precise control over etching depth of the fin structures. In some implementations, an etch operation is performed to form a pattern in one or more mask layers that is used to etch a substrate to form the fin structures. The etch operation includes an advanced pulsing technique, in which a high-frequency radio frequency (RF) source and a low-frequency RF source are pulsed. Pulsing the high-frequency RF source and the low-frequency RF source in the etch operation reduces consumption of a thickness of the one or more mask layers which increases the aspect ratio of the pattern. This enables deeper etching of the substrate when forming the fin structures, which reduces the likelihood of under etching.

Abstract: Systems and methods for determining an order accepting mode for a target service provider are provided. A method includes receiving a request for determining an order accepting mode for the target service provider. The method also includes obtaining an order accepting evaluation parameter with respect to the target service provider in response to the request. The method also includes determining an order accepting mode for the target service providers based on the order accepting evaluation parameter. The order accepting mode includes at least one of an order designating mode, an order striving mode, or an order quick-accepting mode.

Abstract: Systems and methods for determining an order accepting mode for a target service provider are provided. A method includes receiving a request for determining an order accepting mode for the target service provider. The method also includes obtaining an order accepting evaluation parameter with respect to the target service provider in response to the request. The method also includes determining an order accepting mode for the target service providers based on the order accepting evaluation parameter. The order accepting mode includes at least one of an order designating mode, an order striving mode, or an order quick-accepting mode.