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: Methods for determining an arm aneuploidy score in a tumor sample genome include selectively amplifying nucleic acid sequences at specific locations in the tumor genome using a targeted panel to generate sequence reads. Next, divide the genome locations into segments with homogeneous copy numbers based on log odds of heterozygous SNPs and CNV log ratios of the sequence reads. Identify gain and loss segments relative to a reference copy number and intersecting respective chromosome arms. Compare the cellularities of these segments to a minimum threshold. Retain the longest segment for the arm that meets the minimum cellularity and sum its total bases. Divide this total by the number of bases in the arm to yield a fraction. If this fraction meets a minimum threshold, filter the segment based on fold changes and determine gains or losses. Count the arms with called gains or losses to generate the arm aneuploidy score.

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

Abstract: A system uses a single vision model to combine lower resolution images of a body and higher resolution images of a targeted body part to more efficiently identify a human action with respect to an object. The system receives images of a scene that include a body. For instance, the images may be sequential frames in a video captured by a camera. The system generates a body image by extracting a region from an image that includes a body. The system generates a target image by extracting a region from the image that includes a targeted body part interacting with an object. The system is configured to perform similar operations on the body image and the target image to ensure that a single vision model can process the target image at a more granular level compared to the body image.

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 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 relates to anti-CD3 and anti-CD38 antibodies or antigen-binding fragments thereof. The present disclosure also relates to bispecific antibodies targeting both CD3 and CD8. To expand the therapeutic index, the bispecific antibodies may contain masking domains to minimize systemic toxicity. The unmasking of the shielded bispecific antibodies occurs predominantly by proteases and enzymes in the tumor microenvironment or in the disease tissues. The present disclosure also provides a unique design that employs a human VHO single domain molecule linked to the hinge region of an antibody, which may allow better tissue penetration than conventional antibodies.

Abstract: The disclosure provides antibodies and fragments targeting EGFR, VEGF, PD-L1, or cMET. The disclosure also provides multispecific antibodies that comprise a first variable domain that can bind the epidermal growth factor receptor (EGFR), a second variable domain that can bind cMET, and a third variable domain that can bind PD-L1 or VEGF. The multispecific antibodies are effective in treating cancers and/or other diseases, disorders, and conditions where pathogenesis is mediated by EGFR, VEGF or PD-L1, and cMET.

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

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.