Abstract: Techniques are described to configure a reconfigurable intelligent surface (RIS) device or a repeater device. An example wireless communication method includes receiving, by a communication device (e.g., RIS device or a repeater device) from a network device, control information; and configuring the communication device according to the control information.

Abstract: A static voltage stability margin evaluation method and system, and a terminal device are related to the field of integrated energy system operation. The method includes the following steps: establishing a thermal dynamic model of a heating system; establishing a thermoelectric coupling device model; establishing a static voltage stability margin model of an electric power system that considers thermal dynamics of the heating system; and solving the model to obtain a voltage stability margin. In the present invention, a static voltage stability margin that considers thermal dynamics of a heating system can be obtained, and a Pareto boundary of the static voltage stability margin that considers the thermal dynamics can be obtained through a dual-objective nonlinear optimization method, so that an impact of thermoelectric coupling on voltage stability and an impact of thermal inertia of the heating system on a voltage stability margin can be revealed.

Abstract: A multiphase flow cylindrical model test system includes a loading structure, a multiphase flow displacement model bucket, a data acquisition and analysis system, a flexible seepage model bucket and a dynamic control system. A multi-parameter test method in the displacement process of pollutants in three-dimensional multi-field and multiphase media is reasonably configured based on the sequential and superimposed application requirements of heat and steam in the thermal enhanced soil vapor extraction process.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize a text-image alignment loss to train a diffusion model to generate digital images from input text. In particular, in some embodiments, the disclosed systems generate a prompt noise representation form a text prompt with a first text concept and a second text concept using a denoising step of a diffusion neural network. Further, in some embodiments, the disclosed systems generate a first concept noise representation from the first text concept and a second concept noise representation from the second text concept. Moreover, the disclosed systems combine the first and second concept noise representation to generate a combined concept noise representation. Accordingly, in some embodiments, by comparing the combined concept noise representation and the prompt noise representation, the disclosed systems modify parameters of the diffusion neural network.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

Abstract: Disclosed are an information transmission method, a first node, a second node, and a storage medium. The information transmission method may include: sending a first beam control message to a second node, the first beam control message comprises at least beam behavior information, and the beam behavior information is used for indicating a beam control behavior to be executed by the second node.

Abstract: A method of determining interaction information, an electronic device and a storage medium are provided, which relates to a field of artificial intelligence technology, in particular to a large model, a generative model, an NLP, an intelligent search and other fields. An implementation is to determine a plurality of questioning dimensions according to query information of a subject and historical query information, where each questioning dimension includes a dimension name and a plurality of options; determine a target questioning dimension from the plurality of questioning dimensions according to evaluation values of the plurality of questioning dimensions and whether semantic information of the plurality of questioning dimensions are consistent with semantic information of a query result associated with the query information; and determine the interaction information according to the dimension name and the plurality of options in the target questioning dimension.

Abstract: An image generation system enables user input during the process of training a generative model to influence the model's ability to generate new images with desired visual features. A source generative model for a source domain is fine-tuned using training images in a target domain to provide an adapted generative model for the target domain. Interpretable factors are determined for the source generative model and the adapted generative model. A user interface is provided that enables a user to select one or more interpretable factors. The user-selected interpretable factor(s) are used to generate a user-adapted generative model, for instance, by using a loss function based on the user-selected interpretable factor(s). The user-adapted generative model can be used to create new images in the target domain.

Abstract: The present disclosure provides a method for configuring a learning model for music generation and the corresponding learning model. The method includes training a masked autoencoder with training data comprising a combination of a reconstruction loss over time and frequency domains and a patch-based adversarial objective operating at different resolutions. An omnidirectional latent diffusion model is trained based on music data represented in a latent space to obtain a pretrained diffusion model. The pretrained diffusion model is fine-tuned based on text-guided music generation, bidirectional music in-painting, and unidirectional music continuation. The method enables high-fidelity music generation conditioned on text or music representations while maintaining computational efficiency.

Abstract: Systems and methods for training a Generative Adversarial Network (GAN) using feature regularization are described herein. Embodiments are configured to generate a candidate image using a generator network of a GAN, classify the candidate image as real or generated using a discriminator network of the GAN, and train the GAN to generate realistic images based on the classifying of the candidate image. The training process includes regularizing a gradient with respect to features extracted using a discriminator network of the GAN.

Abstract: A data management method includes network management service producer network elements that send, to a network management service consumer network element, moments at which measurement data of network objects is obtained for the first time after measurement jobs are activated. The network management service consumer network element creates digital twins based on measurement data of the network objects at a latest moment among the moments at which the network management service producer network elements obtain the measurement data of the network objects for the first time and after the latest moment.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

Abstract: Methods, systems, apparatus, and articles of manufacture to control a vehicle based on signal blending are disclosed. An example apparatus disclosed herein includes programmable circuitry at least determine a first yaw rate signal based on first signal data output by a yaw rate sensor of a vehicle, determine a second yaw rate signal based on second signal data output by a steering wheel angle sensor of the vehicle, determine a blended yaw rate signal based on the first yaw rate signal and the second yaw rate signal, and adjust a torque to be applied by a motor of the vehicle based on the blended yaw rate signal.

Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: A rotating extrusion rheometer includes a control monitoring mechanism, a melt extrusion mechanism, a rotating extrusion rheology machine head, a sensor, a drive chain wheel, a coupler and an electric motor. The control monitoring mechanism, the melt extrusion mechanism, the rotating extrusion rheology machine head are sequentially connected. The rotating extrusion rheology machine head is formed by a connecting pipe (1), a flow dividing support (3), a lower machine neck (12), a machine head piece (15), an opening mold (17), an opening-mold driving chain wheel (20), a core bar (21) and a core-bar driving mechanism. The rheology measurement method comprises the steps where some parameter values of the rheometer are collected first, and then the rheological behaviors of the polymer melt in the rotating extrusion process are obtained by performing calculation by means of using the derived formula.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.