Abstract: One example method includes obtaining, by an artificial intelligence (AI) system, first model parameters of a first machine learning model; generating a second machine learning model based on the first model parameters, the second machine learning model being at a given hierarchical layer of a hierarchical model structure; refining the second machine learning model to obtain an updated second machine learning model comprising second model parameters, wherein the first model parameters and the second model parameters have a different value for at least one model parameter; and generating, by the AI system, a third machine learning model based on the second model parameters, wherein (i) the third machine learning model is at a lower hierarchical layer of the hierarchical model structure beneath the given hierarchical layer of the second machine learning model, and (ii) the second machine learning model and the third machine learning model have a first common attribute.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for efficiently providing digital components that are generated using artificial intelligence based on prompts are described. In one aspect, a method includes maintaining, by an AI system, a data structure that stores a set of pre-generated digital components and, for each pre-generated digital component, a stored embedding the represents a prompt used to generate the pre-generated digital component. The AI system receives, from a device, a first input prompt for generating one or more first target digital components using one or more trained machine learning models. The AI system generates a first input embedding that represents the multi-word text of the input prompt. The AI system determines, for each pre-generated digital component, a similarity metric that represents a measure of similarity between the stored embedding used to generate the digital component and the first input embedding.

Abstract: One example method includes obtaining, by an artificial intelligence (AI) system, first model parameters associated with a first machine learning model at a first hierarchical layer; generating, by the AI system, a second machine learning model including the first model parameters, the second machine learning model at a second hierarchical layer beneath the first hierarchical layer; and refining, by the AI system, the second machine learning model to obtain an updated second machine learning model including second model parameters, where the first model parameters and the second model parameters have different value for at least one model parameter, and the second model parameters are unshareable with the first machine learning model.

Abstract: A method and system for multi-organization joint variety yield prediction with data privacy protection are provided. The method includes: determining a local optimal partition feature with a minimum degree of difference in a local partition feature set as a global optimal partition feature; obtaining ciphertext sample sets of a left subtree and a right subtree transmitted by a target client corresponding to the global optimal partition feature; broadcasting the ciphertext sample sets of the left and right subtrees to other clients among the multiple local clients, such that the other clients generate tree nodes with the same structure as the target client; constructing multiple decision trees according to preset parameters until a federated isomorphic forest model is completed; performing prediction on a to-be-predicted test set among the multiple local clients based on the federated isomorphic forest model to obtain a yield prediction result of the target variety.

Abstract: A multifunctional insulator cleaning device includes: a cleaning component, a driving assembly, an adjustment component, and a triggering component, the cleaning component including the driving assembly, and two transmission assemblies symmetrically and slidably disposed at two ends of the driving assembly; abutment assemblies symmetrically fixed to the transmission assemblies; a cleaning assembly disposed at the abutment assemblies, the cleaning assembly being detachably insertable into the transmission assemblies, the driving assembly configured to drive the two transmission assemblies to operate, the two transmission assemblies configured to drive the cleaning assembly to rotate. When the two transmission assemblies move away from each other, the cleaning assembly is detachable from the transmission assemblies and the abutment assemblies.

Abstract: A method of generating high-performance images includes generating, by one or more processors, a first plurality of captions each corresponding to a different one of a first plurality of images. Generating the first plurality of captions includes inputting the first plurality of images into a first generative artificial intelligence (AI) model. The method also includes training or finetuning, by the one or more processors, a second generative AI model using the first plurality of images and the first plurality of captions, and generating, by the one or more processors, a second plurality of images. Generating the second plurality of images includes inputting a plurality of text prompts into the trained or finetuned second generative AI model.

Abstract: A protective apparatus includes a shell. The shell is provided with at least one first connecting member, the at least one first connecting member is over-molded with the shell, and a reinforcing structure is disposed between the first connecting member and the shell. The first connecting member and the shell are over-molded, the connection strength of the first connecting member and the shell is higher. The first connecting member may clamp and fix accessories such as an illuminating lamp and a warning device on the shell more firmly.

Abstract: Techniques for determining perceptual quality indicators of video content items are provided. In some embodiments, a system including one or more processors executes instructions to: receive a video content item comprising a plurality of frames; determine, using a first subnetwork of a deep neural network, a content quality indicator for each frame, wherein the content quality indicator corresponds to one or more semantic content indicators for each frame; determine, using a second subnetwork of the deep neural network, a video distortion indicator for each frame, wherein the video distortion indicator indicates a quality of the frame based on distortions contained within the frame; generate a quality level for each frame based on at least the content quality indicator and the video distortion indicator for the frame; and output an indication of quality for the video content item based on the quality level for each frame.

Abstract: An energy storage device and method based on carbon dioxide gas-liquid phase change.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for fine-tuning diffusion-based generative neural networks in compact parameter spaces for text-to-image generation. In one aspect, a method performed by one or more computers for fine-tuning a diffusion-based generative neural network to obtain a fine-tuned version of the diffusion-based generative neural network is described.

Abstract: The present disclosure includes a mobile charging device and a mobile charging method. The mobile charging device includes a mobile vehicle body defining an accommodating chamber; a charging port assembly, arranged in the accommodating chamber including a mounting base and a movable base, the movable base is configured to move back and forth relative to the mounting base in a first direction; a plug assembly, including a charging plug having pins adapted to the charging interface; and a driving mechanism connected with the charging plug, and configured to drive the charging plug to move in the plug hole. The mobile vehicle body provided by the present disclosure charges external devices (electric vehicles) through a charging head.

Abstract: Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.

Abstract: Embodiments of this disclosure provide a quality of service detection method and apparatus, a node device and a storage medium. The method includes: after obtaining a transmission message of a target service flow, sending the transmission message to a service function (SF) node, and receiving a processed message resulting from a service processing performed by the SF node on the transmission message; in a case that a first node device obtains an enablement indication for performing quality detection on the SF node, detecting quality of service of the target service flow according to the transmission message sent to the SF node and the received processed message, to obtain quality detection data.

Abstract: The present disclosure is directed to object and/or character recognition for use in applications such as computer vision. Advantages of the present disclosure include lightweight functionality that can be used on devices such as smart phones. Aspects of the present disclosure include a sequential architecture where a lightweight machine-learned model can receive an image, detect whether an object is present in one or more regions of the image, and generate an output based on the detection. This output can be applied as a filter to remove image data that can be neglected for more memory intensive machine-learned models applied downstream.

Abstract: A rotation shaft assembly includes a connector, a first rotation shaft, a second rotation shaft, and a lock mechanism. The first rotation shaft is rotatably connected to the connector. The second rotation shaft is rotatably connected to the connector. The lock mechanism is arranged between the first rotation shaft and the second rotation shaft and is configured to switch a rotation of the first rotation shaft and a rotation of the second rotation shaft. In response to being at a first predetermined position, the rotation of the first rotation shaft is unlocked, and the rotation of the second rotation shaft is locked. In response to being at a second predetermined position, the rotation of the first rotation shaft is locked, and the rotation of the second rotation shaft is unlocked.

Abstract: One example method includes receiving, by an artificial intelligence (AI) system, a query; generating, by the AI system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the AI system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the AI system, a candidate digital component of the plurality of candidate digital components having a highest acceptance level; generating, by the AI system and based on the candidate digital component, training data; and refining, by the AI system, the machine learning model using the training data.

Abstract: A communication system includes a network device and a communication device. The communication device can communicate with the network device. The communication device includes a transmitter module and a control circuit. The control circuit can control the transmitter module to selectively transmit an RF (Radio Frequency) signal to the network device.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, that relate to generating quantization tables that are used during digital image compression of a digital image. Multiple training images are obtained. A model can be trained using the training images to generate a quantization table that can be used during encoding of an input image. For each training image, a quantization table can be obtained using the model. Using the quantization table, an encoded digital image is obtained for the training image. Using the encoded digital image and the training image, an image quality loss and a compression loss can be determined. An overall loss of the model can be determined by combining the image quality loss and the compression loss for the training image. The model can be updated based on the overall loss.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generative neural network that has parameters. In one aspect, one of the methods include: obtaining a context input; processing, by the generative neural network, the context input to generate a plurality of training outputs; for each objective in a set of objectives and for each of the plurality of training outputs: determining a respective quality score of the training output relative to each other training input in the plurality of training outputs with respect to the objective; and determining a calibrated reward for the training output with respect to the objective based on the respective quality scores of the training output with respect to the objective; selecting a positive training output and a negative training output; and training the generative neural network on the positive training output and the negative training output.