Abstract: An intelligent driving method causes a vehicle to perform roaming based on a high-definition map, where the roaming can include lane switching, driving direction changing, or partial navigation strategy changing. The vehicle may perform the lane switching, the driving direction changing, or the partial navigation strategy changing based on an operation of a driver.

Abstract: This disclosure provides methods and apparatuses for training a neural network model. One example method performed by a terminal device includes: obtaining annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model, training a second neural network model by using the annotation data of the service to obtain a trained second neural network model, and updating a first neural network model based on the trained second neural network model.

Abstract: An electronic device displays a first user interface; receives a first operation of a user; obtains first content of the first user interface in response to the first operation; displays a second user interface, where content displayed in the second user interface includes a text in the first content, and the second user interface covers a part of a display area of the first user interface; reads a first sentence in the content of the second user interface; and displays marking information of a text that is in the second user interface and that corresponds to the first sentence that is being read. Embodiments of this application are used for text reading.

Abstract: A method includes acquiring timing analysis data associated with a cell and activity data of one or more inputs of the cell, determining a glitch toggle rate for an output of the cell based on the activity data of the one or more inputs of the cell and the timing analysis data, and estimating a glitch power based on at least the glitch toggle rate.

Abstract: This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.

Abstract: A model file management method includes that a terminal device receives a storage address of a target model file package from a server and the terminal device obtains the target model file package based on the storage address of the target model file package, where the target model file package is based on a parameter of a model file package locally stored in the terminal device and a parameter of a model file package managed by the server. In an artificial intelligence (AI) field, an application may implement a specific function by using an AI model file. An application is decoupled from an AI model file such that the terminal device performs centralized management on a general model file.

Abstract: A vehicle control method and apparatus 10, and a vehicle 100 are provided. The vehicle 100 has a cross-solid-line lane change permission switch 16. When the cross-solid-line lane change permission switch 16 is in an on state, and a driver switches a turn signal switch to a state that a left turn signal or a right turn signal is on, the vehicle 100 can cross a solid line to change a lane.

Abstract: An isolated CD19-binding chimeric antigen receptor, a CRISPR-Cas9 system-based method for knocking out TRAC, B2M and PD-1 genes in T cells in vitro, crRNA used in the method, gene knockout T cells obtained according to the method, and a use thereof.

Abstract: Glitch source identification and ranking is provided by: identifying a plurality of glitch sources in a circuit layout; back referencing the plurality of glitch sources to corresponding lines in a Resistor Transistor Logic (RTL) file defining the plurality of glitch sources; identifying, in the circuit layout, a plurality of glitch terminuses associated with the plurality of glitch sources; determining a plurality of glitch power consumption values associated with the plurality of glitch sources based on fanouts in the circuit layout extending from the plurality of glitch sources to the plurality of glitch terminuses; ranking, by a processor, the plurality of glitch sources based on corresponding glitch power consumption values of the plurality of glitch power consumption values corresponding to individual glitch sources of the plurality of glitch sources; and reporting the corresponding lines in the RTL file associated with the ranked plurality of glitch sources.

Abstract: A method and reactor are disclosed for separating and removing heavy metals from wastewater using a sulfhydryl-modified nano-magnetized activated carbon. The method includes the steps of preparing a sulfhydryl-modified nano-magnetized activated carbon first; introducing heavy-metal-containing wastewater into a reactor which is equipped with a stirrer and keeping stirring, and then adding the sulfhydryl-modified nano-magnetized activated carbon, continuously stirring for a reaction; after reacting for a period, precipitating under a magnetic field generated by a magnet separator, discharging the resulting supernate, and then discharging the precipitated sludge.

Abstract: This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.

Abstract: The present disclosure provides an electroplating method, comprising providing an electroplating solution, wherein the electroplating solution includes an effective microorganisms aqueous solution and metal chloride; disposing a workpiece, wherein at least a part of the workpiece is in contact with the electroplating solution; and performing an electroplating process to electroplate metal of the metal chloride onto the workpiece.

Abstract: A system and method for performing operating state analysis of an integrated circuit (IC) design is disclosed. The method includes simulating a switching operation from a first operating state to a second operating state for one or more cells of the IC design using a plurality of vectors corresponding to one or more user-specified constraints. The method include generating a time-based waveform for each cell of the one or more cells changing an operating state from the first operating state to the second operating state, and based on the generated time-based waveform, identifying one or more operating state changes corresponding to the operating state analysis and associated timing window and cell information. The method includes verifying the one or more operating state changes by the each cell of the one or more cells of the IC design meet the one or more user-specified constraints for generating an analysis report.

Abstract: This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.

Abstract: A method and reactor are disclosed for separating and removing heavy metals from wastewater using a sulfhydryl-modified nano-magnetized activated carbon. The method includes the steps of preparing a sulfhydryl-modified nano-magnetized activated carbon first; introducing heavy-metal-containing wastewater into a reactor which is equipped with a stirrer and keeping stirring, and then adding the sulfhydryl-modified nano-magnetized activated carbon, continuously stirring for a reaction; after reacting for a period, precipitating under a magnetic field generated by a magnet separator, discharging the resulting supernate, and then discharging the precipitated sludge.

Abstract: Glitch source identification and ranking is provided by: identifying a plurality of glitch sources in a circuit layout; back referencing the plurality of glitch sources to corresponding lines in a Resistor Transistor Logic (RTL) file defining the plurality of glitch sources; identifying, in the circuit layout, a plurality of glitch terminuses associated with the plurality of glitch sources; determining a plurality of glitch power consumption values associated with the plurality of glitch sources based on fanouts in the circuit layout extending from the plurality of glitch sources to the plurality of glitch terminuses; ranking, by a processor, the plurality of glitch sources based on corresponding glitch power consumption values of the plurality of glitch power consumption values corresponding to individual glitch sources of the plurality of glitch sources; and reporting the corresponding lines in the RTL file associated with the ranked plurality of glitch sources.

Abstract: A model file management method includes that a terminal device receives a storage address of a target model file package from a server and the terminal device obtains the target model file package based on the storage address of the target model file package, where the target model file package is based on a parameter of a model file package locally stored in the terminal device and a parameter of a model file package managed by the server. In an artificial intelligence (AI) field, an application may implement a specific function by using an AI model file. An application is decoupled from an AI model file such that the terminal device performs centralized management on a general model file.

Abstract: A tree topology based computing system and method, where the system may include a plurality of node clusters, where the plurality of node clusters constitute a multi-layer network structure in a tree topology manner, any minimum tree in the network structure includes a second node cluster and at least one first node cluster. The first node cluster is configured to obtain a first computing result based on a first computing input, and send the first computing result to the second node cluster, and the second node cluster is configured to receive at least one first computing result sent by the at least one first node cluster, and aggregate the at least one first computing result and a second computing result to obtain a third computing result.

Abstract: This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.

Abstract: Technical effects of a method, an apparatus, and a system for operating a shared resource in an asynchronous multiprocessing system that are provided in the present invention are as follows: A processor in an asynchronous multiprocessing system implements an operation on a shared resource by locking a hardware resource lock, and the hardware resource lock is implemented by a register; in this way, a bus in the asynchronous multiprocessing system does not need to support a synchronization operation, and the processor also does not need to have a feature of supporting a synchronization operation, and is capable of implementing the operation on the shared resource only in a manner of accessing the register, which simplifies the operation on the shared resource by the processor in the asynchronous multiprocessing system, enlarges a selection range of the processor in the asynchronous multiprocessing system, and further improves flexibility of the asynchronous multiprocessing system.