Abstract: The invention provides a roadside computing system (RCS), or an edge computing system, for an autonomous vehicle. The RCS comprises a hierarchy of roadside unit (RSU) and an onboard unit (OBU) in an individual vehicle. The RSU comprises a data processing module and a communication module, and is capable of generating guidance information and targeted instructions for individual vehicle. The data processing module of the RSU comprises two processors: an External Object Calculating Module (EOCM) and an AI processing unit. Thus, the RCS utilizes roadside edge computing power and AI models to support autonomous driving for the vehicle. The OBU comprises a data processing module, a communication module, and a vehicle control module, and is capable of generating vehicle-specific targeted instruction for the vehicle based on guidance information and targeted instructions received from RSUs, and controlling the vehicle based on vehicle-specific targeted instruction.

Abstract: Provided herein is an artificial intelligence-based mobile roadside intelligent unit (MRIU) for providing, supplementing, and/or enhancing the control and operation of autonomous vehicles in normal and long-tail scenarios. The MRIU comprises a computing module configured to provide supplemental computation capability for autonomous driving. The MRIU comprises a communication module to communicate and exchange data with a vehicle or a cloud. The MRIU provides prediction, decision-making, and/or control functions for autonomous driving. The MRIU provides edge computing capability for autonomous vehicles to train and operate artificial intelligence-based intelligent driving models in a distributed fashion. Specifically, an edge computing unit conducts data fusion and data feature extraction, provides prediction, formulates control strategies, generates vehicle control instructions, and/or distributes vehicle control information and/or instructions for an autonomous vehicle.

Abstract: This invention presents a function allocation system for an autonomous vehicle (AV). During the operations of the AV, some or all of its automated driving capabilities or functions could be downgraded due to long-tail events or malfunctioning. The roadside intelligent infrastructure, or the cloud platform, could supplement some or all of AV's automated driving functions, including sensing, prediction and decision-making, and/or control functions. The function allocation system dynamically allocates these functions between AV and intelligent infrastructure, achieving a higher system intelligence level S than the downgraded vehicle intelligence level V. In addition, a function allocation system could dynamically allocate sensing, prediction and decision-making, and/or control functions between AV and a cloud platform. This invention also presents a function integration system or a fusion system for an AV.

Abstract: New antibodies and methods of use are described.

Abstract: The invention provides a vehicle AI computing system (VACS) that supports autonomous driving through an Onboard Unit (OBU) for vehicle-based computing and distributed computing based on vehicle road-cloud. The vehicle-based computing can effectively complete various computational tasks by using onboard computing resources. The distributed computing allows the vehicle to work in collaboration with roadside units (RSUs) and/or the cloud to effectively complete various computational tasks. The VACS features an OBU with a sensing module, a communication module, and a data processing module that integrates data from vehicle sensors, RSUs, and the cloud. The OBU also includes a vehicle control module that helps control the vehicle based on the data of RSU and cloud. The VACS leverages high performance computation resources to implement end to end driving tasks including sensing, prediction, planning and decision making, and control.

Abstract: Provided herein is technology relating to automated driving and particularly, but not exclusively, to a mobile intelligent road infrastructure technology configured to serve automated driving systems by providing, supplementing, and/or enhancing autonomous driving functions for connected automated vehicles under common and unusual driving scenarios.

Abstract: A touch system is provided. The touch system includes a touch tool and a touch panel. The touch tool provides a downlink signal. The touch panel obtains a first sensing area sensed with the touch tool, and obtains a second sensing area other than the first sensing area according to the downlink signal. The touch panel provides a first uplink signal to the first sensing area, and provides a second uplink signal to the second sensing area. The first uplink signal is different from the second uplink signal. The touch tool generates a calculated uplink signal according to the first uplink signal and the second uplink signal, and provides the downlink signal according to the calculated uplink signal.

Abstract: The technology described herein provides Automated Driving System (ADS) methods and systems for coordinating and/or fusing intelligence and functions between Connected Automated Vehicles (CAV) and ADS infrastructure to provide target levels of automated driving. The technology provides systems and methods for function allocation comprising sensing allocation, prediction and decision-making allocation, and control allocation. The ADS operates across various intelligence levels, identified during vehicle operation. The function allocation system dynamically allocates essential functions based on the intelligence levels of both vehicles and infrastructures, ensuring that ADS achieves a system intelligence that surpasses that of individual components. This methodical function distribution enables ADS to manage both vehicles and infrastructures in a manner that enhances vehicular operations and control.

Abstract: New antibodies and methods of use are described.

Abstract: A method for positioning tea-bud picking points based on fused thermal images and RGB images is provided and includes: firstly, image pairs of several tea buds are acquired by using an image acquisition device, and are each labeled to obtain a tea-bud object detection database and a tea-bud keypoint detection database; secondly, in order to obtain a trained object detection model and a trained keypoint detection model, the tea-bud object detection database and the tea-bud keypoint detection database are inputted into an object detection model and a keypoint detection model for training, respectively; finally, the trained object detection model and keypoint detection model are used to sequentially process the tea-bud image pairs to obtain tea-bud keypoint positions, and then tea-bud picking point positions are obtained in combination with the tea-bud growth characteristics. The positioning accuracy and efficiency of the tea-bud picking points can be improved.

Abstract: Antibodies that bind to ?v?8 are provided.

Abstract: The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

Abstract: Antibodies that bind to ???8 are provided.

Abstract: New antibodies and methods of use are described.

Abstract: Provided herein are compositions including lipids and copolymers in the form of a nanodisc assembly. The subject copolymers include monomer units of styrene and monomer units selected from acrylic acid and an acrylic acid derivative. In certain cases, the copolymer is a copolymer of styrene and acrylic acid. Also provided herein, is an aqueous solution comprising the subject composition. Also provided herein, are methods for producing a nanodisc assembly, including incubation of a lipid and a subject copolymer. Further provided herein, are methods for solubilizing a membrane protein in an aqueous solution, wherein the method includes forming a nanodisc assembly of a lipid bilayer having one or more membrane proteins embedded therein, and a subject copolymer. Also provided are methods of solubilizing a hydrophobic constituent in an aqueous solution, including forming a nanodisc assembly of a lipid, a hydrophobic constituent, and a subject copolymer.

Abstract: Herein are innovations that enable facile cryo-EM analysis of diverse samples. Methods of functionalizing sample grids for cryo-EM are described, including methods of creating high quality graphene oxide films on cryo-EM substrates. The cryo-EM sample substrates are functionalized with affinity molecules that efficiently concentrate sample molecules and other specimen types on the grid, away from the air-water interface. Affinity groups include amines and proteins such as tagging system proteins and peptides that can be used to capture diverse sample types with high affinity. Optionally, spacers such as PEG chains are used to place sample particles away from the substrate surface, reducing substrate-induced artifacts.

Abstract: Antibodies that bind to ?v?8 are provided.

Abstract: New antibodies and methods of use are described.

Abstract: Provided is an antibody that specifically binds human ?v?? and blocks binding of TGFp peptide to ?v?8, wherein the antibody binds to the specificity determining loop (SDL) of human ?8. In some embodiments, the antibody further binds to one, two, or all three of the human av-head domain, the al helix of human ?8, or the al helix of human ?8. In some embodiments, the antibody is humanized or chimeric. In some embodiments, the antibody is linked to a detectable label. Also provided is a method of enhancing an immune response in a human individual, comprising administering a sufficient amount of the antibody to the individual, thereby enhancing an immune response. Also provided are pharmaceutical compositions comprising the anti-?v?? antibodies or antigen-binding molecules thereof.

Abstract: Provided is an antibody that specifically binds human ?v?? and blocks binding of TGFp peptide to ?v?8, wherein the antibody binds to the specificity determining loop (SDL) of human ?8. In some embodiments, the antibody further binds to one, two, or all three of the human av-head domain, the al helix of human ?8, or the al helix of human ?8. In some embodiments, the antibody is humanized or chimeric. In some embodiments, the antibody is linked to a detectable label. Also provided is a method of enhancing an immune response in a human individual, comprising administering a sufficient amount of the antibody to the individual, thereby enhancing an immune response. Also provided are pharmaceutical compositions comprising the anti-?v?? antibodies or antigen-binding molecules thereof.