Abstract: Programming data in memory is described herein. An example apparatus includes an array of memory cells having a plurality of access lines to which the cells are coupled, and a processing device that performs a program operation on the array, including programming data to be stored in one page of cells of the array to the cells of the array coupled to a first one of the access lines, programming additional data to be stored in that page to the cells of the array coupled to a second one of the access lines adjacent to the first one of the access lines, sensing the data programmed to the cells of the array coupled to the first one of the access lines, and programming data to be stored in two pages of cells of the array to the cells of the array coupled to the first one of the access lines.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: A method for training a face swap model includes concatenating an expression feature of a template image and an identity feature of a source face image to obtain a combined feature; performing encoding based on the source face image and the template image to obtain an encoding feature required for face swap; fusing the encoding feature and the combined feature to obtain a fused feature; performing decoding based on the fused feature to obtain a swapped face image; respectively predicting image attribute discrimination results of the swapped face image and a reference image by using a discriminator network of the face swap model; and calculating a difference between an expression feature of the swapped face image and the template image, calculating a difference between an identity feature of the swapped face image and the source face image, and updating the generator network and the discriminator network.

Abstract: A plasma apparatus of a plasma processing system is provided. The plasma apparatus defines a toroidal plasma channel and includes multiple end blocks defining respective portions of the toroidal plasma channel. Each end block includes an end-block tube constructed from a first electrically conductive material and a dielectric coating disposed on an interior surface of the end-block tube. The plasma apparatus also includes multiple mid-blocks defining respective portions of the toroidal plasma channel. Each mid-block includes at least one heat sink located adjacent to a substantially linear tube with a thermal interface disposed therebetween. The thermal interface is in physical communication with the tube and the at least one heat sink. The mid-block tube has a substantially uniform wall thickness and is constructed from a dielectric material. The at least one heat sink is constructed from a second electrically conductive material.

Abstract: An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

Abstract: An image synthesis method includes: obtaining a source image including source image identity information for synthesis and a template image including template image background information for synthesis; performing a target synthesis operation on the source image and the template image to obtain an initial synthesized image, the initial synthesized image including the source image identity information, the template image background information, and a partial region to be corrected; performing a target correction operation on the source image, the template image, and the initial synthesized image to obtain a target residual image; and synthesizing the initial synthesized image with the target residual image to generate a target synthesized image.

Abstract: A model training method includes obtaining a training sample set including a triplet training sample that includes a source image, a template image, and a true value image, performing face swapping on the source image and the template image through a first image processing model having a re-parameterization structure to obtain a face-swapped image, obtaining a second image processing model corresponding to the first image processing model and being a pre-trained image processing model, calculating a fusion loss function of the first image processing model according to the second image processing model, the first face-swapped image, and the true value image, training the first image processing model according to the fusion loss function, and determining a model parameter of the first image processing model in response to a training convergence condition of the first image processing model being reached.

Abstract: An image processing method, performed by a computer device, includes: obtaining an input image to be processed; performing face detection on the input image to obtain an input face image to be processed including at least one defect element relating to a skin element; and inputting the input face image into an image processing model to obtain a target face image corresponding to the input face image without a first defect element amongst the at least one defect element, wherein a training sample of the image processing model includes a first face image with a first face distortion degree less than a preset threshold and that is annotated with the first defect element.

Abstract: A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Abstract: Methods, systems, and devices for one or more acceleration engines for memory sub-system operations are described. An acceleration engine can perform one or more validation procedures on one or more codewords of a management unit. The acceleration engine can collect validation data for the management unit based on performing the validation procedures. The acceleration engine can aggregate the validation data into group validation data associated with a set of management units. The acceleration engine can transmit the group validation data to firmware of a memory sub-system or a host device.

Abstract: An in-situ current sensing apparatus for a plasma processing system has one or more sections defining a plasma channel, the plasma processing system configured to form a plasma in the plasma channel using a process gas. The in-situ current sensing apparatus includes an assembly including an electrically conductive housing coupled to a dielectric coating layer, wherein a surface of the dielectric coating layer is configured to physically contact at least one of the process gas or the plasma in the plasma channel. The in-situ current sensing apparatus also includes a current probe, inductively or electrically connected to the assembly, for sensing a capacitively-coupled current within the assembly caused by the formation of plasma in the plasma channel. The in-situ current-sensing apparatus further includes at least one dielectric break for electrically isolating the assembly from the one or more sections. The assembly may include an electrode embedded within the housing.

Abstract: A plasma apparatus of a plasma processing system is provided. The plasma apparatus defines a toroidal plasma channel and includes multiple end blocks defining respective portions of the toroidal plasma channel. Each end block includes an end-block tube constructed from a first electrically conductive material and a dielectric coating disposed on an interior surface of the end-block tube. The plasma apparatus also includes multiple mid-blocks defining respective portions of the toroidal plasma channel. Each mid-block includes at least one heat sink located adjacent to a substantially linear tube with a thermal interface disposed therebetween. The thermal interface is in physical communication with the tube and the at least one heat sink. The mid-block tube has a substantially uniform wall thickness and is constructed from a dielectric material. The at least one heat sink is constructed from a second electrically conductive material.

Abstract: An image generation method, performed by an electronic device includes obtaining an original face image frame, audio driving information, and emotion driving information, performing spatial feature extraction on the original face image frame to obtain an original face spatial feature corresponding to the original face image frame; performing feature interaction processing on the audio driving information and the emotion driving information to obtain a face local pose feature of the to-be-adjusted object issuing the voice content with the target emotion; and performing, based on the original face spatial feature and the face local pose feature, face reconstruction processing on the to-be-adjusted object to generate a target face image frame.

Abstract: Methods, systems, and devices for one or more acceleration engines for memory sub-system operations are described. An acceleration engine can perform one or more validation procedures on one or more codewords of a management unit. The acceleration engine can collect validation data for the management unit based on performing the validation procedures. The acceleration engine can aggregate the validation data into group validation data associated with a set of management units. The acceleration engine can transmit the group validation data to firmware of a memory sub-system or a host device.

Abstract: Provided is a method for producing at least one type of thermoplastic resin selected from polycarbonate, polyester and polyester carbonate. The method includes a step of subjecting a dihydroxy compound and diaryl carbonate and/or dicarboxylic acid ester to melt polycondensation in the presence of a transesterification catalyst selected from a compound represented by formula (1) and/or formula (2). In formula (1), R1 to R24 are each a hydrogen atom, an alkyl group having 1-10 carbon atoms or a cycloalkyl group. Some of the carbon atoms may be substituted by heteroatoms. Among R1 to R24, alkyl groups or the like substituted on the same N atom may bond to each other to form a ring. The values of a to d are each 0 or 1. X? is a monovalent anion. In formula (2), Ar1 to Ar12 are each a substituted or unsubstituted aryl group. M? is a monovalent anion.

Abstract: In an image optimization method, an image generation network, a to-be-optimized image, and a plurality of preset image features are obtained. A target feature is selected from the plurality of preset image features based on (i) the target feature and the to-be-optimized image and (ii) a preset similarity condition. The target feature and an initial offset parameter are input to the image generation network. The initial offset parameter is adjusted according to a difference between an output of the image generation network and the to-be-optimized image, to obtain a target offset parameter. The target feature and the target offset parameter are input to the image generation network, to generate an optimized image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: An image processing method including obtaining a fake template sample group comprising a first source image, a real labeled image, and a fake template image, inputting the fake template image into an identity swapping model to obtain a first identity swapping image of the fake template image, obtaining a fake labeled sample group comprising a second source image, a real template image, and a fake labeled image, the fake labeled image being based on identity swapping processing of the real template image, inputting the real template image into the identity swapping model to obtain a second identity swapping image of the real template image, and training the identity swapping model based on the fake template sample group, the first identity swapping image, the fake labeled sample group, and the second identity swapping image to generate a trained identity swapping model.

Abstract: This application discloses a method for generating an image processing model performed by a computer device. The method includes: performing training by using a first source image sample, a first template image sample, and a first standard synthesized image, to obtain a first parameter adjustment model, and combining the first parameter adjustment model and a first resolution update layer into a first update model; adjusting the first update model into a second parameter adjustment model using a second source image sample and a second template image sample and a second standard synthesized image; combining the second parameter adjustment model and a second resolution update layer into a second update model; and adjusting the second update model into a target image fusion model using a third source image sample, a third template image sample and a third standard synthesized image.

Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Abstract: A video processing method includes: obtaining a first video frame and a second video frame in a target video, the first video frame being a previous frame of the second video frame; inputting the first video frame and the second video frame to a target neural network, and obtaining a target intermediate video frame output by the target neural network, the target neural network being trained based on an optical flow distillation constraint and a feature consistency constraint; and interpolating the target intermediate video frame between the first video frame and the second video frame.