Abstract: The present disclosure provides a display substrate and a display device, and belongs to the field of display technology. The display substrate includes: a base substrate having a peripheral region, and thin film transistors disposed in the peripheral region of the base substrate. A gate electrode of the thin film transistor includes a plurality of sub-electrodes arranged at intervals.

Abstract: An Android penetration method and device for implementing silent installation based on accessibility services. The method includes: acquiring a second target application by adding a load program to a first target application and adding penetration permissions using an Android decompilation technology; and implementing silent installation of the second target application using an accessibility service technology.

Abstract: A method for constructing an artificial intelligence (AI) integrated model is provided, including: obtaining a training dataset, an initial graph network model, and a plurality of base models; then iteratively training the initial graph network model by using training data in the training dataset and the plurality of base models, to obtain a graph network model; and then constructing the AI integrated model based on the graph network model and the plurality of base models, where an input of the graph network model is a graph structure consisting of outputs of the plurality of base models. Since the graph network model considers neighboring nodes of each node in the graph structure when processing the graph structure, the graph network model fully considers differences and correlations between the base models when fusing the outputs of the plurality of base models.

Abstract: Embodiments of the present disclosure disclose a display method, apparatus, device and storage medium. The method includes: displaying a media stream display interface, the media stream display interface including first media content, the media stream including first media content and second media content; in response to determining that predetermined media content switching for the media stream display interface is detected, switching the first media content of the media stream display interface to the second media content; where the second media content or the first media content includes information of a predetermined theme and a predetermined control associated with the predetermined theme, and the predetermined control is configured to post target content associated with the predetermined theme.

Abstract: The present disclosure provides for a wearable device having a wireless power receiving system that may inductively receive or transmit power. The wireless power receiving system includes a receiver coil having a profile that follows a contour of a bottom cover of the wearable device. A transmitter coil from a wireless charging device has a complementary profile that mates with the profile defined by the receiver coil. In one example, the wireless power receiving system includes a shielding, and a receiver coil attached to the shielding. The receiver coil further includes an inner wall and an outer wall connected by a top surface of a coil body. The inner wall defines a center opening in the receiver coil, wherein the receiver coil is conical in shape.

Abstract: A method, computer system, and a computer program product for data compression is provided. The present invention may include receiving operational data. The present invention may include profiling the operational data using one or more normalizing functions. The present invention may include extracting a plurality of patterns from the operational data. The present invention may include compressing the operational data based on the plurality of patterns extracted.

Abstract: Embodiments of this application disclose a training method for a semi-supervised learning model which can be applied to computer vision in the field of artificial intelligence. The method includes: first predicting classification categories of some unlabeled samples by using a trained first semi-supervised learning model, to obtain a prediction label; and determining whether each prediction label is correct in a one-bit labeling manner, and if prediction is correct, obtaining a correct label (a positive label) of the sample, or if prediction is incorrect, excluding an incorrect label (a negative label) of the sample. Then, in a next training phase, a training set (a first training set) is reconstructed based on the information, and an initial semi-supervised learning model is retrained based on the first training set, to improve prediction accuracy of the model. In one-bit labeling, an annotator only needs to answer “yes” or “no” for the prediction label.

Abstract: This application relates to Artificial intelligence and provides a method for training a classifier, one example method including: obtaining a first training sample, where the first training sample includes a corresponding semantic tag; obtaining a plurality of second training samples, where each of the second training samples includes a corresponding semantic tag; determining a target sample from the plurality of second training samples based on semantic similarities between the first training sample and the plurality of second training samples; and training the classifier based on the first training sample, the target sample, and a semantic similarity between the first training sample and the target sample.

Abstract: An image processing method and apparatus in the field of artificial intelligence, including: decomposing a first image to obtain a first structure sub-image and a first detail sub-image, where the first image is any frame of image in video data other than a first frame; fusing first hidden state information and the first structure sub-image to obtain a second structure sub-image, and splicing the first hidden state information and the first detail sub-image to obtain a second detail sub-image; performing feature extraction based on the second structure sub-image and the second detail sub-image to obtain a structure feature and a detail feature; and obtaining an output image based on the structure feature and the detail feature, where resolution of the output image is higher than resolution of the first image.

Abstract: This application relates to the field of image annotation and recognition in the field of artificial intelligence technologies, and in particular, to a data annotation method. The method includes: using at least two different classification models; pretraining one of the classification models as an initial classification model, and annotating a label for data in a to-be-annotated source dataset as initial data by using the pretrained classification model; and controlling the classification models to perform alternating training and data annotation a quantity of times. Operations of current training and current data annotation include: obtaining data that is re-annotated with a label by a previously trained classification model, selecting a first part of the data to train a current classification model, and re-annotating, by the trained current classification model, a label for a second part of data that is not selected.

Abstract: A model training method and a related apparatus are provided, which may be used in computer vision to perform image detection. The method includes: extracting feature information in a target image; further separately extracting features of a target object from the feature information by using a Gaussian mask to obtain a first local feature and a second local feature; determining a feature loss by using the first local feature and the second local feature; performing prediction by using the first network and the second network based on a same region proposal set to obtain a first classification predicted value and a second classification predicted value, and obtaining a classification loss based on the first classification predicted value and the second classification predicted value; and training the second network based on the classification loss and the feature loss to obtain a target network.

Abstract: A method for training an image processing model is provided. After an augmented image is obtained, a soft label of the augmented image is obtained, and the image processing model is trained based on guidance of the soft label, to improve performance of the image processing model. In addition, according to the method, the image processing model is trained based on guidance of a soft label, with a relatively high score, selected from soft labels of the augmented image, to further improve performance of the image processing model.

Abstract: A video clip location technology in the field of computer vision pertaining to artificial intelligence that provides a video processing method and apparatus. The method includes: obtaining a semantic feature of an input sentence; performing semantic enhancement on a video frame based on the semantic feature to obtain a video feature of the video frame, where the video feature includes the semantic feature; and determining, based on the semantic feature and the video feature, whether a video clip to which the video frame belongs is a target video clip corresponding to the input sentence. The method helps improve accuracy of recognizing a target video clip corresponding to an input sentence.

Abstract: This document describes techniques and apparatuses directed at reducing inductive-charging power loss. In aspects, a mobile device includes a multi-layer flexible printed circuit board (FPCB) coil that forms a receiver coil having a Litz-wire structure. The FPCB coil includes a multi-trace bundle, having traces systematically routed throughout the different layers of the FPCB coil to reduce eddy current losses.

Abstract: Methods, systems, and techniques for safely controlling wireless charging in the presence of a foreign object are presented. A method includes determining a power difference between a power transmitted by a wireless charger and a power received by an electronic device, determining a level of misalignment of the electronic device with respect to the wireless charger; estimating an amount of power difference due to the level of misalignment of the electronic device with respect to the wireless charger, adjusting a power difference threshold for the wireless charger based on the estimated amount of power difference, and controlling operation of the wireless charger based on the power difference and the adjusted power difference threshold.

Abstract: This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: performing feature extraction on an image to obtain a basic feature map of the image; determining a proposal of a region possibly including a pedestrian in the image; processing the basic feature map of the image to obtain an object visibility map in which a response to a pedestrian visible part is greater than a response to a pedestrian blocked part and a background part; performing weighted summation processing on the object visibility map and the basic feature map to obtain an enhanced feature map of the image; and determining, based on the proposal of the image and the enhanced feature map of the image, a bounding box including a pedestrian in the image and a confidence level of the bounding box including the pedestrian in the image.

Abstract: This document describes techniques and apparatuses directed at reducing inductive-charging power loss. In aspects, a mobile device includes a multi-layer flexible printed circuit board (FPCB) coil that forms a receiver coil having a Litz-wire structure. The FPCB coil includes a multi-trace bundle, having traces systematically routed throughout the different layers of the FPCB coil to reduce eddy current losses.

Abstract: Example neural architecture search methods and image processing methods and apparatuses in the field of computer vision in the field of artificial intelligence are provided. The example neural architecture search method includes determining search space and a plurality of construction units, superimposing the plurality of construction units to obtain a search network, adjusting, in the search space, network architectures of the construction units in the search network, to obtain optimized construction units, and establishing a target neural network based on the optimized construction units. In each construction unit, some channels of an output feature map of each node are processed by using a to-be-selected operation to obtain a processed feature map, and the processed feature map and a remaining feature map are stitched and then input to a next node.

Abstract: The present disclosure provides for a wearable device having a wireless power receiving system that may inductively receive or transmit power. The wireless power receiving system includes a receiver coil having a profile that follows a contour of a bottom cover of the wearable device. A transmitter coil from a wireless charging device has a complementary profile that mates with the profile defined by the receiver coil. In one example, the wireless power receiving system includes a shielding, and a receiver coil attached to the shielding. The receiver coil further includes an inner wall and an outer wall connected by a top surface of a coil body. The inner wall defines a center opening in the receiver coil, wherein the receiver coil is conical in shape.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for improving wireless charging. In some implementations, an electronic device determines a power demand of the electronic device. The electronic device includes a wireless power receiver including a wireless power receiving coil configured to receive power through inductive coupling with a wireless charge. The electronic device determines an operating voltage or operating frequency for the wireless charger based on the power demand of the electronic device. The electronic device sends, to the wireless charger, data indicating the operating voltage or operating frequency for the wireless charger.