Abstract: An electronic device includes a supporting film, a flexible substrate, an integrated circuit unit, and a sensing structure. A least a portion of the flexible substrate and the integrated circuit unit respectively correspond to opposite sides of the supporting film. The sensing structure is electrically connected to the integrated circuit unit.

Abstract: The disclosure provides a display device. The display device includes a display panel and a vibration generating module. The vibration generating module is attached to the display panel and includes a substrate, a circuit layer, and a plurality of vibrators. The circuit layer and the plurality of vibrators are disposed on the substrate, and the plurality of vibrators are electrically connected to the circuit layer.

Abstract: The present disclosure provides a semiconductor wafer. The semiconductor wafer includes: a scribe line between a first row of dies and a second row of dies; and a benchmark circuit disposed adjacent to the scribe line and electrically coupled to a first conductive contact and a second conductive contact. The benchmark circuit includes a first device-under-test (DUT); a second DUT; a first switching circuit configured to selectively couple the first DUT and the second DUT to the first conductive contact; and a second switching circuit configured to selectively couple the first DUT and the second DUT to the second conductive contact.

Abstract: Embodiments are directed to deploying a workload on the best/highest performance node. Nodes configured to accommodate a request for a workload are selected. Information is collected on each of the selected nodes and the workload. Predicted response times expected for the workload running on each of the selected nodes are determined. The workload is deployed on a node of the selected nodes, the node having a corresponding predicted response time for the workload, the workload being deployed on the node based at least in part on the corresponding predicted response time.

Abstract: An electronic system using a USB type-C port and an abnormal elimination method thereof are provided. After sending a hard reset request to a power adapter, a host device determines whether to enter an error recovery state according to whether an initialization signal is at a first preset level for a first preset time, so that the initialization signal is changed to a second preset level, or whether to force the initialization signal to change to the second preset level according to whether the initialization signal is at the first preset level for a second preset time.

Abstract: The present disclosure provides an electronic device including a covering layer and a flexible substrate structure. The covering layer includes a first region and a second region. The flexible substrate structure is disposed under the covering layer and includes a first portion corresponding to the first region and a second portion corresponding to the second region. A Gaussian curvature of the first region of the covering layer is different from a Gaussian curvature of the second region of the covering layer, and a Poisson's ratio of the first portion of the flexible substrate structure is different from a Poisson's ratio of the second portion of the flexible substrate structure.

Abstract: The freshness of one or more terms in a documentation, indicative of a currency of the one or more terms is computed. Each term includes one or more constituent words, and the terms are visually marked as current or out-of-date based on the computed freshness. Upon marking a term as out-of-date, a latest term for the out-of-date term is retrieved or a most possible latest term for the out-of-date term is predicted.

Abstract: The present disclosure provides a transmission feedback method and a UE. The transmission feedback method includes: transmitting a time-frequency transmission resource for transmitting feedback information for unicasting or multicasting transmission to at least one target UE which has established a connection with a source UE; acquiring the feedback information transmitted on the time-frequency transmission resource from the target UE; and determining a unicasting or multicasting transmission state in accordance with the feedback information.

Abstract: A display device includes a patterned substrate, a plurality of light emitting unit, a first insulating layer and a sensing layer. The patterned substrate has a plurality of island structures and a plurality of bridge structures, and at least one bridge structure connects two adjacent island structures. At least one light emitting unit is disposed on one of the two adjacent island structures. The first insulating layer is disposed on the light emitting units. The sensing layer is disposed on the first insulating layer and has a mesh unit, the mesh unit has a mesh frame and an opening, and the at least one light emitting unit is disposed in the opening.

Abstract: User data security is provided. Encrypted user data are identified in a virtual machine. A private key of a public/private cryptographic key pair corresponding to a user is retrieved. The encrypted user data is decrypted within the virtual machine utilizing the private key corresponding to the user to form decrypted user data. The encrypted user data are replaced in the virtual machine with the decrypted user data. The decrypted user data is processed in the virtual machine to perform a service in a cloud environment.

Abstract: Provided is an image classification method, an electronic device and a storage medium, relating to a field of artificial intelligence technology, and specifically, to the technical fields of deep learning, image processing and computer vision, which may be applied to scenes such as image classification. The image classification method includes: extracting a first image feature of a target image by using a first network model, where the first network model includes a convolutional neural network module; extracting a second image feature of the target image by using a second network model, where the second network model includes a deep self-attention transformer network (Transformer) module; fusing the first image feature and the second image feature to obtain a target feature to be recognized; and classifying the target image based on the target feature to be recognized.

Abstract: A semiconductor device package comprises a semiconductor device, a first encapsulant surrounding the semiconductor device, a second encapsulant covering the semiconductor device and the first encapsulant, and a redistribution layer extending through the second encapsulant and electrically connected to the semiconductor device.

Abstract: An electronic device package and a method for manufacturing an electronic device package are provided. The electronic device package includes electronic device structure which includes a first electronic device and a first encapsulant, a second electronic device, and a second encapsulant. The first encapsulant encapsulates the first electronic device. The second electronic device is adjacent to the electronic device structure. The second encapsulant encapsulates the electronic device structure and the second electronic device. A first extension line along a lateral surface of the first electronic device and a second extension line along a lateral surface of the first encapsulant define a first angle, the second extension line along the lateral surface of the first encapsulant and a third extension line along a lateral surface of the second electronic device define a second angle, and the first angle is different from the second angle.

Abstract: A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.

Abstract: An approach is provided in which the approach captures a first user activity log of a first user accessing multiple systems and captures a set of second user activity logs of a set of second users accessing the multiple systems. The approach determines a set of system monitoring preferences based the first user activity log and the set of second user activity logs, and scores the multiple systems based on the set of system monitoring preferences. The approach generates a recommended system monitoring list based on the scored multiple systems, and transmits the recommended system monitoring list to the first user.

Abstract: A system including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving user session activity information and campaign impression information; determining a sample of the user session activity information and the campaign impression information based on a sampling criterion; analyzing the sample using (i) a first logistic regression model and (ii) a second linear regression model; determining a weighting value for the campaign impression information based on a first output of the first logistic regression model and a second output of the second linear regression model; and determining a sub cut lift measurement for the campaign impression information based on a first lift measurement for the campaign impression information and the weighting value for the campaign impression information. Other embodiments are described.

Abstract: An embodiment includes detecting an interface element and an element attribute of the interface element in a series of views of a user interface, and then after an update of the user interface, detecting a candidate element and a candidate element attribute in a series of views of the updated user interface. The embodiment then determines that the updated user interface lacks any errors using a decision tree that includes comparisons of all interface elements of the user interface to corresponding candidate elements of the updated user interface. The embodiment then generates an optimized decision tree based at least in part on an analysis of the comparisons of the user interface to the updated user interface resulting in a condition that allows for the determining of a lack of errors based on comparisons of a subset of the interface elements to corresponding candidate elements.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A method for training a multi-target image-text matching model and an image-text retrieval method are provided. The method for training the multi-target image-text matching model includes: obtaining a plurality of training samples that include sample pairs each including a sample image and a sample text, the sample image including a plurality of targets; obtaining, for each of the plurality of training samples, a heat map corresponding to the sample text in the training sample, the heat map representing a region of the target in the sample image that corresponds to the sample text; and training an image-text matching model based on a plurality of the sample texts and corresponding heat maps to obtain the multi-target image-text matching model.

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.