Abstract: In an alloy metal plate according to an embodiment, diffraction intensity of a (111) plane of the alloy metal plate is defined as I (111), diffraction intensity of a (200) plane of the alloy metal plate is defined as I (200), diffraction intensity of a (220) plane of the alloy metal plate is defined as I (220), a diffraction intensity ratio of I (200) is defined by the following Equation 1, and a diffraction intensity ratio of I (220) is defined by the following Equation 2. At this time, the A is 0.5 to 0.6, the B is 0.3 to 0.5, and the value A may be larger than a value B. A = I ? ( 200 ) / { I ? ( 200 ) + I ? ( 220 ) + I ? ( 111 ) } [ Equation ? 1 ] The diffraction intensity ratio of I (220) is defined by the following Equation 2.

Abstract: A display substrate according to the embodiment includes a base material containing chromium and iron, wherein the base material includes a surface portion and a central portion, the surface portion is defined as a depth region from a surface of the base material to a depth of 5 nm in a thickness direction of the base material, and a ratio of chromium atoms to iron atoms (Cr/Fe) of the surface portion is greater than that of the central portion.

Abstract: Various disclosed embodiments are directed to deriving an albedo output image from an input image based on deriving an inverse shading map. For example, an input image can be a photograph of a human face (i.e., the geometric features) with RGB values representing the color values of the face as well as pixels representing shadows (i.e., the shadow features) underneath the chin of the human face. The inverse shading map may be a black and white pixel value image that contains pixels representing the same human face without the RGB values and the shadows underneath the chin. The inverse shading map thus relies on the geometric space, rather than RGB space. Geometric space, for example, allows embodiments to capture the geometric features of a face, as opposed to those geometric features' RGB or shadow details.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a multi-branch harmonization neural network architecture to harmonize composite images. For example, in one or more implementations, the semantic-guided transformer-based harmonization system uses a convolutional branch, a transformer branch, and a semantic branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. The semantic branch includes a visual neural network that generates semantic features that inform the harmonization of the composite images.

Abstract: A display substrate according to the embodiment includes a base material containing chromium and iron, wherein the base material includes a surface portion and a central portion, the surface portion is defined as a depth region from a surface of the base material to a depth of 5 nm in a thickness direction of the base material, and a ratio of chromium atoms to iron atoms (Cr/Fe) of the surface portion is greater than that of the central portion.

Abstract: One or more aspects of a method, apparatus, and non-transitory computer readable medium include obtaining an input description and an input image depicting a subject, encoding the input description using a text encoder of an image generation model to obtain a text embedding, and encoding the input image using a subject encoder of the image generation model to obtain a subject embedding. A guidance embedding is generated by combining the subject embedding and the text embedding, and then an output image is generated based on the guidance embedding using a diffusion model of the image generation model. The output image depicts aspects of the subject and the input description.

Abstract: In an alloy metal plate according to an embodiment, diffraction intensity of a (111) plane of the alloy metal plate is defined as I (111), diffraction intensity of a (200) plane of the alloy metal plate is defined as I (200), diffraction intensity of a (220) plane of the alloy metal plate is defined as I (220), a diffraction intensity ratio of I (200) is defined by the following Equation 1, and a diffraction intensity ratio of I (220) is defined by the following Equation 2. At this time, the A is 0.5 to 0.6, the B is 0.3 to 0.5, and the value A may be larger than a value B. A = I ? ( 200 ) / { I ? ( 200 ) + I ? ( 220 ) + I ? ( 111 ) } [ Equation ? 1 ] The diffraction intensity ratio of I (220) is defined by the following Equation 2.

Abstract: In an alloy metal plate according to an embodiment, diffraction intensity of a (111) plane of the alloy metal plate is defined as I (111), diffraction intensity of a (200) plane of the alloy metal plate is defined as I (200), diffraction intensity of a (220) plane of the alloy metal plate is defined as I (220), a diffraction intensity ratio of I (200) is defined by the following Equation 1, and a diffraction intensity ratio of I (220) is defined by the following Equation 2. At this time, the A is 0.5 to 0.6, the B is 0.3 to 0.5, and the value A may be larger than a value B. A=I(200)/{I(200)+I(220)+I(111)}??[Equation 1] The diffraction intensity ratio of I (220) is defined by the following Equation 2.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a multi-branch harmonization neural network architecture to harmonize composite images. For example, in one or more implementations, the semantic-guided transformer-based harmonization system uses a convolutional branch, a transformer branch, and a semantic branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. The semantic branch includes a visual neural network that generates semantic features that inform the harmonization of the composite images.

Abstract: Embodiments of the present disclosure relate systems and methods for detecting cognitive decline of a subject using passively obtained data from at least one mobile device. In an exemplary embodiment, a computer-implemented method comprises receiving passively obtained data from at least one mobile device. The method further comprises generating digital biomarker data from the passively obtained data. The method further comprises analyzing the digital biomarker data to determine whether the subject is exhibiting signs of cognitive decline.

Abstract: A display substrate according to the embodiment includes a base material containing chromium and iron, wherein the base material includes a surface portion and a central portion, the surface portion is defined as a depth region from a surface of the base material to a depth of 5 nm in a thickness direction of the base material, and a ratio of chromium atoms to iron atoms (Cr/Fe) of the surface portion is greater than that of the central portion.

Abstract: In an alloy metal plate according to an embodiment, diffraction intensity of a (111) plane of the alloy metal plate is defined as I (111), diffraction intensity of a (200) plane of the alloy metal plate is defined as I (200), diffraction intensity of a (220) plane of the alloy metal plate is defined as I (220), a diffraction intensity ratio of I (200) is defined by the following Equation 1, and a diffraction intensity ratio of I (220) is defined by the following Equation 2. At this time, the A is 0.5 to 0.6, the B is 0.3 to 0.5, and the value A may be larger than a value B. A=I(200)/{1(200)+I(220)+I(111)}??[Equation 1] The diffraction intensity ratio of I (220) is defined by the following Equation 2.

Abstract: Techniques for evaluating the quality of results obtained by a search engine. In an aspect, an evaluation platform utilizes task-level formulation to increase the accuracy of search result quality evaluation. Furthermore, initial queries may be reformulated until search results are deemed to satisfy the task description. Side-by-side comparison of results from multiple search engines is further provided to enhance the sensitivity of evaluation. Alternative aspects provide for collection of behavioral signals for training a classifier to classify the quality of an evaluator's feedback, as may be applied in, e.g., a crowd-sourcing context.