Abstract: A video generation method, a video playing method, a video generation device, a video playing device, an electronic apparatus and a computer-readable storage medium are provided. The video generation method includes: acquiring a first video; executing Gaussian blur special effect processing on the first video to obtain a second video; superimposing an additional dynamic effect on the second video to generate a third video, wherein the additional dynamic effect is a dynamic image that presents a related information corresponding to the first video in a dynamic effect; and splicing the first video with the third video to generate a fourth video, wherein an image area of the fourth video comprises a first image area configured to display an image of the first video and a second image area configured to display an image of the third video.

Abstract: Provided are an interaction method and apparatus, an electronic device, and a storage medium. The method includes displaying a target page, where target content containing a target object is displayed in the target page; receiving a shake trigger operation of a user; and in response to the shake trigger operation, displaying associated content of the target content in a layer above the target page and displaying the detail page of the target object after completing displaying the associated content.

Abstract: A federated computing method, an electronic device and a storage medium. Metadata to be processed is obtained from each participant based on a task identifier (ID) to be executed. n data slices corresponding to each participant are obtained by dividing the metadata, where n is a positive integer greater than 1. n data sets are generated based on the n data slices corresponding to each participant. n data processing services corresponding to the task ID are called to process each data set using each of the data processing services. A federated computing result corresponding to the task ID is determined based on a processing result of each of the data processing services.

Abstract: A display method, apparatus, device and storage medium, the method includes: displaying a user interface for playing a target video (S101), where the user interface includes an image display area, and the target video includes multiple target sub-videos; playing each target sub-video in the target video sequentially on the user interface while displaying multiple images in the image display area (S102), where the multiple target sub-videos respectively correspond to the multiple images displayed in the image display area; and in response to a first triggering operation acting on a target image in the image display area, jumping to and displaying target information associated with the target image (S103), where the target image includes any image displayed in the image display area. The method can provide users with convenient operations to realize multi-functionalization, thereby meeting their needs and enhancing their operational experience.

Abstract: Provided are an image occlusion method, a model training method, a device, and a storage medium, which relate to the technical field of artificial intelligence, in particular, to the field of computer vision technologies and deep learning, and may be applied to image recognition, model training and other scenarios. The specific implementation solution is as follows: generating a candidate occlusion region according to an occlusion parameter; according to the candidate occlusion region, occluding an image to be processed to obtain a candidate occlusion image; determining a target occlusion region from the candidate occlusion region according to visual security and data availability of the candidate occlusion image; and according to the target occlusion region, occluding the image to be processed to obtain a target occlusion image. In this manner, the image to be processed is desensitized while the accuracy of target recognition is ensured.

Abstract: The present disclosure provides a method for increasing a methionine content in grains by gene knockout. A maize ZmMETS2 gene is knocked out by a CRISPR/Cas9 technology, and it is found that the methionine content in maize grains is increased to a certain extent after the gene is knocked out. In the present disclosure, the CRISPR/Cas9 target site of the maize ZmMETS2 gene is designed and a function of the gene is found to significantly affect the methionine content. This provides a reference for CRISPR researches of the gene in maize and other crops, and provides a theoretical basis for methionine metabolism of the maize and other crops. Accordingly, the deficiency of human amino acid nutrition is alleviated by increasing the methionine content of transgenic plants.

Abstract: A video generation method, a video playing method, a video generation device, a video playing device, an electronic apparatus and a computer-readable storage medium are provided. The video generation method includes: acquiring a first video; executing Gaussian blur special effect processing on the first video to obtain a second video; superimposing an additional dynamic effect on the second video to generate a third video, wherein the additional dynamic effect is a dynamic image that presents a related information corresponding to the first video in a dynamic effect; and splicing the first video with the third video to generate a fourth video, wherein an image area of the fourth video comprises a first image area configured to display an image of the first video and a second image area configured to display an image of the third video.

Abstract: The invention provides a polymerizable composition and a hydrogel obtained or obtainable from the polymerizable composition. The polymerizable composition comprises cucurbituril and a first monomer having a guest for the cucurbituril, wherein the total monomer concentration, Cmon, within the polymerizable composition is at least 0.5 M. The composition may be an aqueous composition.

Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural n

Abstract: A technical solution relates to distributed machine learning, and relates to the field of artificial intelligence technologies, such as machine learning technologies, or the like. An implementation includes: acquiring, based on delay information, an optimal scheduling queue of a plurality of edge devices participating in training; and scheduling each edge device of the plurality of edge devices to train a machine learning model based on the optimal scheduling queue of the plurality of edge devices.

Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.

Abstract: The present disclosure relates to the technical field of video playback, and provides a multimedia information playback method and apparatus, an electronic device, and a computer readable storage medium. The method includes: during a process of an information flow being browsed, in response to that display time of a target information entry in the information flow on a display interface of the information flow reaches a first time threshold, displaying a preset prompt message on an upper layer of the target information entry, so as to prompt a user to input a corresponding gesture; and in response to that a preset gesture operation to the target information entry inputted by the user is detected, playing multimedia information corresponding to the target information entry.

Abstract: A method of training a model, a method of determining an asset valuation, a device, a storage medium, and a program product, which relate to a field of artificial intelligence, in particular to fields of deep learning and natural language understanding. A specific implementation can include: determining an event-level representation according to a first set of feature data; performing a multi-task learning for a first model according to the event-level representation, to obtain first price distribution data, and transmitting the first price distribution data to a central server; determining a first intra-region representation according to a second set of feature data; adding a noise signal to the first intra-region representation, and transmitting the noised intra-region representation to a client; and adjusting a parameter of the first model according to a noised parameter gradient in response to the noised parameter gradient being received from the central server.

Abstract: A federated learning method and system, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of computer vision and deep learning technologies. The method includes: performing a plurality of rounds of training until a training end condition is met, to obtain a trained global model; and publishing the trained global model to a plurality of devices. Each of the plurality of rounds of training includes: transmitting a current global model to at least some devices in the plurality of devices; receiving trained parameters for the current global model from the at least some devices; performing an aggregation on the received parameters to obtain a current aggregation model; and adjusting the current aggregation model based on a globally shared dataset, and updating the adjusted aggregation model as a new current global model for a next round of training.

Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.

Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.

Abstract: A method for training a longitudinal federated learning model is provided, and is applied to a first participant device. The first participant device includes label data. The longitudinal federated learning model includes a first bottom layer sub-model, an interaction layer sub-model, a top layer sub-model based on a Lipschitz neural network and a second bottom layer sub-model in a second participant device. First bottom layer output data of the first participant device and second bottom layer output data sent by the second participant device are obtained. The first bottom layer output data and the second bottom layer output data are input into an interaction layer sub-model to obtain interaction layer output data. Top layer output data is obtained based on the interaction layer output data and the top layer sub-model. The longitudinal federated learning model is trained according to the top layer output data and the label data.

Abstract: The present disclosure generally relates to printed circuit boards or printed circuit board assemblies for fiber optic communications. In one example, an optoelectronic assembly may include a printed circuit board including a laser-roughened area, at least one optoelectronic component coupled to a surface of the printed circuit board, and an optical component attached to the printed circuit board. The coupling area may be defined by the optical component contacting the printed circuit board, and the laser-roughened area may be positioned entirely within the coupling area defined by the optical component contacting the printed circuit board.

Abstract: The invention provides a polymerizable composition and a hydrogel obtained or obtainable from the polymerizable composition. The polymerizable composition comprises cucurbituril and a first monomer having a guest for the cucurbituril, wherein the total monomer concentration, Cmon, within the polymerizable composition is at least 0.5 M. The composition may be an aqueous composition.

Abstract: A technical solution relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process. The encoder performs a coding operation using region features of the sample regions to obtains a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region.