Abstract: A method for etching features in a carbon containing layer below a mask is provided. A simultaneous etch and passivation step is provided comprising flowing an etch gas comprising a boron containing passivant gas and an oxygen containing gas. A plasma is created from the etch gas, wherein the plasma etches features in the carbon containing layer.

Abstract: Embodiments of this application provide a method and an apparatus for resolving an intent conflict that includes: receiving conflict resolution policy information indicating a threshold and a first operation, the first operation includes: when a first preset condition is met, using an alternative solution for achieving a first intent, where the first preset condition includes that a first impact value of a value that is of a first KPI and that corresponds to the solution for a value that is of the first KPI and that corresponds to a second intent target is less than or equal to the threshold; and performing conflict resolution on the solution based on the conflict resolution policy information.

Abstract: This application provides an intent management method. The method includes: A first network element receives first information from a second network element, where the first information indicates that a first intent is not achieved; the first network element receives second information from the second network element, where the second information indicates that an intent operation corresponding to the first intent is not performed; and the first network element determines, based on the first information and the second information, not to adjust the first intent.

Abstract: Exemplary semiconductor processing methods may include providing a silicon-containing precursor to a processing region of a semiconductor processing chamber. A substrate may be disposed within the processing region of the semiconductor processing chamber. The methods may include forming a plasma of the silicon-containing precursor in the processing region. The plasma may be at least partially formed by an RF power operating at between about 50 W and 1,000 W, at a pulsing frequency below about 100,000 Hz, and at a duty cycle between about 5% and 95%. The methods may include forming a layer of material on the substrate. The layer of material may include a silicon-containing material.

Abstract: Disclosed are a method and control device for adjusting an opening size of a sampling window of a biopsy surgical device. The biopsy surgical device includes a motor, an outer cutter tube and an inner cutter tube. The outer cutter tube includes a sampling window on a side of the front end. The motor drives the inner cutter tube to move axially through a transmission mechanism, changing the axial relative position of the inner cutter tube and the sampling window. The method includes: obtaining an input instruction that includes a set value of an sampling window opening length; determining an axial movement distance of the inner cutter tube according to the current position of the inner cutter tube and the set value; calculating the number of rotations of the motor corresponding to the axial movement distance; and controlling the motor to rotate until the sampling window reaches the opening length.

Abstract: The method involves configuring a pretrained text to music AI model that includes a neural network implementing a diffusion model. The process includes receiving audio sample data corresponding to a specific audio concept, generating a concept identifier token based on the audio sample data, adapting a loss function of the diffusion model based on the concept identifier token, selecting pivotal parameters in weight matrices in a self-attention layer of the neural network of the AI model based on the audio sample data, and further training the pivotal parameters of the AI model, to optimize the AI model for the specific audio concept.

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 method and system for positioning a target in a brain region are provided. The method includes: obtaining datasets of N persons at a first time point and a second time point after stroke; constructing a first lesion mapping functional network based on each resting-state functional magnetic resonance imaging image in a first stroke dataset; constructing an acute phase cognitive-lesion mapping functional network; constructing a chronic phase cognitive-lesion mapping functional network; comparing the acute phase cognitive-lesion mapping functional network with the chronic phase cognitive-lesion mapping functional network to obtain a key improvement network; calculating a whole-brain functional connectivity network with each voxel as a seed point, and performing spatial correlation calculation on the whole-brain functional connectivity network and the key improvement network to obtain a spatial correlation network; and determining a therapeutic target of the functional image to be positioned.

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.

Abstract: Avatar generation from an image is performed using semi-supervised machine learning. An image space model undergoes unsupervised training from images to generate latent image vectors responsive to image inputs. An avatar parameter space model undergoes unsupervised training from avatar parameter values for avatar parameters to generate latent avatar parameter vectors responsive to avatar parameter value inputs. A cross-modal mapping model undergoes supervised training on image-avatar parameter pair inputs corresponding to the latent image vectors and the latent avatar parameter vectors. The trained image space model generates a latent image vector from an image input. The trained cross-modal mapping model translates the latent image vector to a latent avatar parameter vector. The trained avatar parameter space model generates avatar parameter values from the latent avatar parameter vector. The latent avatar parameter vector can be used to render an avatar having features corresponding to the input image.

Abstract: The present disclosure relates to an information obtaining method and apparatus, a device, and a medium. The method includes setting a label table corresponding to each sample sentence in a sample set, wherein row characters and column characters in the label table are set identically in accordance with an order of characters of the corresponding sample sentence; and marking cells composed of the row characters and the column characters in the label table with corresponding information category labels; taking each sample sentence in the sample set as input information to a model to be trained and the label table corresponding to each sample sentence as output information of the model to be trained, and performing model training according to a preset target function; and generating an information extraction model based on parameters of the trained model to extract target sentence information by the information extraction model.

Abstract: Embodiments of this application provide an intent pre-evaluation method and apparatus. The method includes: A first network element receives an intent target and a pre-evaluation type from a second network element; receives first information from a third network element, where the first information includes one or more of the following: operation impact information or first KPI impact information, the operation impact information indicates mutual exclusion information of the first operation, and the first KPI impact information indicates impact of the first operation on a KPI associated with the intent target or impact of the first operation on the network KPI; and determines, based on the first information, result information corresponding to the pre-evaluation type, and sends the result information to the second network element, where the result information includes pre-evaluation information of an intent achievement result or pre-evaluation information of impact on a KPI.

Abstract: Disclosed in embodiments of the present disclosure are an image display method and apparatus, a device, and a storage medium. The method comprises: obtaining current page information corresponding to a target page; determining, according to the current page information, a target display mode corresponding to a target image comprised in the target page, a corresponding target image resource comprising a file having dynamically changed image content, and the target display mode being a dynamic display mode or a static display mode; and finally, displaying, according to the target display mode, a target image resource corresponding to a corresponding target image on the target page.

Abstract: Metallic, electrically conductive, structures on smart glasses, which can be utilized to provide structural integrity and/or thermal dissipation capability, can be leveraged to provide antenna capability as well. Metallic structures on smart glasses are utilized as antenna grounds, with corresponding antenna elements being electrically coupled thereto, and located on the glasses temple. Such antenna elements implement folded antennas having an antenna length selected in accordance with desired communicational frequencies. A shorting pin establishes the electrical connection to the antenna ground. Metallic structures on smart glasses are also utilized as antenna elements, with different metallic structures acting as the antenna ground. Such antenna elements implement monopole antennas having a length selected in accordance with desired communicational frequencies, and a width that can maintain structural integrity and/or thermal dissipation capability.

Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure identify an original image depicting a face, identify a scribble image including a mask that indicates a portion of the original image for adding makeup to the face, and generate a target image depicting the face using a machine learning model based on the original image and the scribble image, where the target image includes the makeup in the portion indicated by the scribble image.

Abstract: Systems and methods for synthesizing images with increased high-frequency detail are described. Embodiments are configured to identify an input image including a noise level and encode the input image to obtain image features. A diffusion model reduces a resolution of the image features at an intermediate stage of the model using a wavelet transform to obtain reduced image features at a reduced resolution, and generates an output image based on the reduced image features using the diffusion model. In some cases, the output image comprises a version of the input image that has a reduced noise level compared to the noise level of the input image.

Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.