Abstract: In one embodiment, there is provided a dynamic multi-source image reconstruction apparatus. The apparatus includes a first reconstruction stage, a second reconstruction stage, and a refinement stage. The first reconstruction stage is configured to receive an input data set including a group of data frames. Each data frame corresponds to a respective time step. Each data frame includes a number of projection data sets. Each projection data set corresponds to a respective source-detector pair of a stationary multi-source tomography system. The first reconstruction stage is further configured to reconstruct a first intermediate image based, at least in part, on the group of data frames. The second reconstruction stage is configured to receive a selected data frame and to reconstruct a second intermediate image with a constraint of the first intermediate image as prior. The refinement stage is configured to refine the second intermediate image to produce a three-dimensional output image.

Abstract: A decision network for moral decision-making includes a trained group artificial neural network (ANN), a trained individual ANN, and a fusion block. The trained group ANN is configured to receive a selected input vector and to produce an estimated en group output based, at least in part, on the selected input vector. The trained group ANN is trained based, at least in part, on group training data including a plurality of group input vectors and corresponding training group outputs. Each group input vector includes a plurality of scenario parameters. The trained individual ANN is configured to receive the selected input vector and to produce an estimated individual output based, at least in part, on the selected input vector. The trained individual ANN is trained after the trained group ANN is trained.

Abstract: A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

Abstract: The present disclosure relates to an apparatus for enhancing contrast sensitivity and resolution in a grating interferometer by machine learning, which can improve both image contrast sensitivity and spatial resolution in a grating interferometer by machine learning, the apparatus including: an image acquisition unit; a numerical phantom generation unit, a convolution layer generation unit to extract features from input data; an activation function application calculation unit that can apply a rectified linear activation function to an output value of the convolution calculation to perform smooth repetitive machine learning; a CNN repetitive machine learning unit that corrects a convolution calculation factor while repeatedly performing forward propagation and backward propagation processes; and an image matching output unit that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit.

Abstract: This application discloses a method of displaying information in a program interface of an application performed by a computer device. The method includes: displaying a virtual keyboard control and an extension bar control in the program interface; in response to an input operation in the virtual keyboard control, displaying at least one character string in the extension bar control, the at least one character string being determined according to the input operation in the virtual keyboard control; and in response to a select operation on a target string among the at least one character string in the extension bar control, displaying a function interface of applying a target function to the target string. This embodiment allows a user to quickly switch between function interfaces when using an application, thereby reducing operation steps of the user and improving human-computer interaction efficiency.

Abstract: The disclosed apparatus, systems and methods relate to a framelet-based iterative algorithm for polychromatic CT which can reconstruct two components using a single scan. The algorithm can have various steps including a scaled-gradient descent step of constant or variant step sizes; a non-negativity step; a soft thresholding step; and a color reconstruction step.

Abstract: In one embodiment, there is provided a magnetic resonance (MR) subsystem for magnetic resonance imaging (MRI). The MR subsystem includes a first magnet-coil assembly and a second magnet-coil assembly. The first magnet-coil assembly includes a first magnet structure and a first gradient coil. The second magnet-coil assembly includes a second magnet structure and a second gradient coil. The first magnet-coil assembly and the second magnet-coil assembly are separated by a gap. The gap is configured to facilitate transmission of an x-ray beam from an x-ray source to an x-ray detector. The x-ray source and the x-ray detector are included in a computed tomography (CT) subsystem.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes receiving a first CT image data; receiving a second CT image data; and generating, by an artificial neural network (ANN), CT output image data configured to include fewer artifacts compared to the first and second CT image data. The ANN includes at least two parallel CT image data streams and a CT output image data stream. A first of the at least two parallel CT image data streams is based, at least in part, on the first CT image data, a second of the at least two parallel CT image data stream is based, at least in part, on the second CT image data. The CT output image data stream is based, at least in part, on respective outputs of the at least two parallel CT image data streams.

Abstract: The present disclosure provides an OLED display panel, a preparation method thereof and an OLED display device. By providing an uneven surface on a side of a first encapsulation layer away from the organic light-emitting function layer in a non-display area, the uneven surface of the first encapsulation layer can block the flow of the second encapsulation layer to a certain extent, so as to reduce the fluidity and the climbing distance of the edge of the second encapsulation layer, and increase the edge stress and the slope angle. Thereby the narrow frame design of the product is achieved, the thickness uniformity of the edge of the second encapsulation layer is improved, the Mura defect in the non-display area is avoided, and the encapsulation result is guaranteed.

Abstract: A display substrate and a display device are provided. The display substrate includes: a plurality of sub-pixels; a first electrode layer including a plurality of anode structures; and a pixel definition layer located on a side of the first electrode layer away from the base substrate and including a plurality of openings. For at least some sub-pixels, an orthographic projection of the opening of each sub-pixel falls into that of the anode structure of the sub-pixel on the base substrate, a figure of the orthographic projection of the opening of each sub-pixel has a different shape from a figure of the orthographic projection of the anode structure of the sub-pixel on the base substrate. The figure of the orthographic projection of the anode structure of each sub-pixel has more symmetry axis than the figure of the orthographic projection of the opening of the sub-pixel on the base substrate.

Abstract: A machine-learning-based monochromatic CT image reconstruction method is described for quantitative CT imaging. The neural network is configured to learn a nonlinear mapping function from a training data set to map a CT image, which is reconstructed from a single spectral current-integrating projection data set, to monochromatic projections at a pre-specified energy level, realizing monochromatic CT imaging and overcoming beam hardening. An apparatus, method and/or system are configured to determine, by a trained artificial neural network (ANN), a monochromatic projection data set based, at least in part, on a measured CT image. The measured CT image may be reconstructed based, at least in part, on measured projection data. The measured projection data may be polychromatic. The apparatus, method and/or system may be further configured to reconstruct a monochromatic CT image based, at least in part, on the monochromatic projection data set.

Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.

Abstract: Techniques have been developed to facilitate (1) the capture and pitch correction of vocal performances on handheld or other portable computing devices and (2) the mixing of such pitch-corrected vocal performances with backing tracks for audible rendering on targets that include such portable computing devices and as well as desktops, workstations, gaming stations, even telephony targets. Implementations of the described techniques employ signal processing techniques and allocations of system functionality that are suitable given the generally limited capabilities of such handheld or portable computing devices and that facilitate efficient encoding and communication of the pitch-corrected vocal performances (or precursors or derivatives thereof) via wireless and/or wired bandwidth-limited networks for rendering on portable computing devices or other targets.

Abstract: One embodiment provides a method of training an artificial neural network (ANN) for denoising. The method includes generating, by a similarity module, a respective set of similar elements for each noisy input element of a number of noisy input elements included in a single noisy input data set. Each noisy input element includes information and noise. The method further includes generating, by a sample pair module, a plurality of training sample pairs. Each training sample pair includes a pair of selected similar elements corresponding to a respective noisy input element. The method further includes training, by a training module, an ANN using the plurality of training sample pairs. Each set of similar elements is generated prior to training the ANN. The plurality of training sample pairs is generated during training the ANN. The training is unsupervised.

Abstract: A memory system includes a first controller, a nonvolatile memory connected to the first controller, and a power supply circuit that applies a voltage to the first controller and the nonvolatile memory. The first controller writes first data to a first address in the nonvolatile memory and instructs the power supply circuit to apply the destruction voltage to the nonvolatile memory. The first controller determines whether the first data can be correctly read from the first address in the nonvolatile memory and checks destruction of the nonvolatile memory based on the determination.

Abstract: A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

Abstract: An offshore floating-type wind power combined semi-submersible platform foundation has at least three stand columns that form an enclosure having a polygonal structure. The adjacent stand columns are connected by an upper support and a lower support. Each stand column is composed of an upper column body and a lower column body that are arranged coaxially. An upper portion of the upper column body is provided with a support block, the upper support rests on the support block. A lower portion of the upper column body is provided with a lower support connecting portion that is connected to the lower support. With the structural form of the offshore floating-type wind power combined semi-submersible platform foundation, the requirements for a manufacturing site and a combination site are greatly reduced, providing the possibility of large-scale offsite construction.

Abstract: The present disclosure provides a cooling structure of heat pipe for superconducting magneto plasma dynamic thruster having a cylindrical structure and includes a cathode, an intermediate connector and an anode. The cathode is arranged inside the intermediate connector, the anode is arranged outside the intermediate connector; the cathode is provided with a cathode cooling mechanism, and the anode is provided with an anode cooling mechanism. The cathode cooling mechanism includes a cathode heat pipe and a cathode heat dissipation fin. The anode heat pipe cooling mechanism includes an anode heat pipe and an anode heat dissipation fin.

Abstract: Disclosed are a GLP-1-mimicking pharmaceutical composition for treating type 2 diabetes and a preparation method thereof, wherein the pharmaceutical composition contains polyethylene glycol loxenatide, a physiologically acceptable buffer with the pH of 3.0-7.0, and pharmaceutically acceptable excipients. The pharmaceutical composition has good drug stability.

Abstract: A display substrate, a method for manufacturing a display substrate, and a display device are provided. The display substrate includes an effective display area, a punch area, and a critical area between the effective display area and the punch area. The critical area of the display substrate includes: a substrate, and at least one first barrier wall on the substrate. The at least one first barrier wall is a convex structure.