Abstract: A stylus pen including a pen unit, a switch, and a control unit is provided. The pen unit includes a tip, and a shaft connected to the tip. The control unit is switchable between a default mode and an alternative mode in response to operation on the switch. In the default mode, the control unit emits a default-mode wave of a default-mode frequency to the shaft. In the alternative mode, the control unit emits an alternative-mode wave to the shaft in a pulsating pattern. The pulsating pattern has a wave period that includes a working period where the control unit emits the alternative-mode wave of the default-mode frequency, and a non-working period where the control unit stops emitting the alternative-mode wave.

Abstract: A stylus pen including a pen unit, a switch, and a control unit is provided. The pen unit includes a tip, and a shaft connected to the tip. The control unit is switchable between a default mode and an alternative mode in response to operation on the switch. In the default mode, the control unit emits a default-mode wave of a default-mode frequency to the shaft. In the alternative mode, the control unit emits an alternative-mode wave to the shaft in a pulsating pattern. The pulsating pattern has a wave period that includes a working period where the control unit emits the alternative-mode wave of the default-mode frequency, and a non-working period where the control unit stops emitting the alternative-mode wave.

Abstract: The present invention provides a self-power generating switch, a processing method therefor, and a processing system.

Abstract: To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.

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: The present disclosure relates to a diisocyanate stabilizer, use of the diisocyanate stabilizer for stabilizing diisocyanate, and a diisocyanate composition comprising the stabilizer. The diisocyanate stabilizer comprises a sterically hindered phenol other than butylated hydroxytoluene, a thioether, and a phosphite other than triphenyl phosphite. The present disclosure aims to provide a diisocyanate stabilizer which can make diisocyanates maintain stable during long-term storage and heating condition.

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 computer-implemented method for local arrangement of remote deployment is provided according to embodiments of the present disclosure. In this method, a starting request to connect with a remote virtualization entity proxy can be received. A network tunnel can be initiated between a local system and the remote virtualization entity proxy. Then, at least one component in the remote virtualization entity proxy can be arranged into a local virtualization entity in the local system via the network tunnel.

Abstract: A process manufacturing method, a method for adjusting a threshold voltage, a device, and a storage medium are provided.

Abstract: A computer-implemented method for local arrangement of remote deployment is provided according to embodiments of the present disclosure. In this method, a starting request to connect with a remote virtualization entity proxy can be received. A network tunnel can be initiated between a local system and the remote virtualization entity proxy. Then, at least one component in the remote virtualization entity proxy can be arranged into a local virtualization entity in the local system via the network tunnel.

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: To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.

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 for environment-specific training of a machine learning model, comprises receiving, for a local environment, a data stream including a plurality of sequential data snippets. Programmed labels are generated for each data snippet using a student version of a machine learning model. A portion of data snippets and associated programmed labels are selected and uploaded to a server-side computing device for evaluation by a teacher version of the machine learning model. An environment-specific training update is received from the server-side computing device. This training update is based on a comparison of the selected programmed labels and pseudolabels generated for the selected portion of data snippets by the teacher version. The environment-specific training update is applied to the student version to generate an updated student version. The updated student version of the machine learning model is then used to generate programmed labels for newly received data snippets.

Abstract: The present disclosure provides herein anti-CD4 antibodies or antigen-binding fragments thereof, isolated polynucleotides encoding the same, pharmaceutical compositions comprising the same, and the uses thereof.

Abstract: Disclosed are a porous aluminum macroscopic body, a fabrication system, and a method therefor, where the porous aluminum macroscopic body is a three-dimensional full-through-hole structure formed by connecting hollow aluminum wires, and the wall thickness of the hollow aluminum wires is 7-100 micrometers. The fabrication system comprises a magnetron sputtering subsystem, a high-temperature aluminum vapor subsystem, a low-temperature aluminum deposition subsystem, an aluminum vapor recovery subsystem, and a porous polymer film conveying subsystem. A preparation method therefor comprises first utilizing a magnetron sputtering method to rapidly sputter on a porous polymer film to form an aluminum layer that has a thickness of 1-500 nm, and then continuing to deposit the aluminum layer to a thickness of 7-100 micrometers while decomposing the polymer film in-situ so as to obtain the porous aluminum macroscopic body.

Abstract: The present disclosure provides herein anti-CD4 antibodies or antigen-binding fragments thereof, isolated polynucleotides encoding the same, pharmaceutical compositions comprising the same, and the uses thereof.

Abstract: A transparent and tint-free polyimide film, a preparation method thereof and an optical PI film are provided in the disclosure. The transparent and tint-free polyimide film not only has excellent light transmittance (?85% @500 nm), but also has high modulus (tensile modulus?3.8 GPa) and low coefficient of thermal expansion (?30 ppm/Celsius, 50-200 Celsius) and other characteristics, so as to the transparent and tint-free polyimide film can meet the needs of flexible optoelectronic display devices.

Abstract: A process manufacturing method, a method for adjusting a threshold voltage, a device, and a storage medium are provided.

Abstract: Described herein is a human pose prediction system and method. An image comprising at least a portion of a human body is received. A trained neural network is used to predict one or more human features (e.g., joints/aspects of a human body) within the received image, and, to predict one or more human poses in accordance with the predicted one or more human features. The trained neural network can be an end-to-end trained, single stage deep neural network. An action is performed based on the predicted one or more human poses. For example, the human pose(s) can be displayed as an overlay with received image.