Abstract: Systems and methods for deep neural network (DNN)-based cross component prediction are provided. A method includes inputting a reconstructed luma block of an image or video into a DNN; and predicting, by the DNN, a reconstructed chroma block of the image or video based on the reconstructed luma block that is input. Luma and chroma reference information and side information may also be input into the DNN to predict the reconstructed chroma block. The various inputs may also be generated using processes such as downsampling and transformation.

Abstract: An on-chip integrated circuit, a data processing device and a method are provided. The on-chip integrated circuit includes: a processor circuit and an accelerator circuit. The processor circuit includes a processor and a data storage area, the processor is connected to the data storage area through a first bus in the processor circuit. The accelerator circuit includes an accelerator and a second bus, the accelerator is connected to the second bus, and the second bus is bridged with the first bus corresponding to the data storage area, to perform data interaction between the accelerator and the data storage area, which can reduce the congestion on a bus of the processor and improve the quality of service of the application.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to receive an image or video comprising one or more blocks. The processing circuitry can decode a first post-filtering parameter in the image or video corresponding to the one or more blocks to be reconstructed. The first post-filtering parameter applies to at least one of the one or more blocks and has been updated by a post-filtering module in a post-filtering neural network (NN) that is trained based on a training dataset. The processing circuitry can determine the post-filtering NN in a video decoder corresponding to the one or more blocks based on the first post-filtering parameter. The processing circuitry can decode the one or more blocks based on the determined post-filtering NN corresponding to the one or more blocks.

Abstract: The present disclosure relates to a complex probiotic composition and a method for improving exercise performance of a subject with low intrinsic aerobic exercise capacity. The complex probiotic composition, which includes Lactobacillus rhamnosus GKLC1, Bifidobacterium lactis GKK24 and Clostridium butyricum GKB7, administered to the subject with the low intrinsic aerobic exercise capacity in a continuation period, can effectively reduce serum lactic acid and serum urea nitrogen after aerobic exercise, reduce proportion of offal fat and/or increase liver and muscle glycogen contents, thereby being as an effective ingredient for preparation of various compositions.

Abstract: A fingerprint spectrum construction method for a new compound aloe capsule and a fingerprint spectrum includes: (1) taking powder of a new compound aloe capsule, carrying out reflux extraction, adding methanol, carrying out ultrasonic treatment, filtering, and taking a subsequent filtrate as a test solution; dissolving barbaloin, aloe-emodin, indirubin, tryptanthrin, aloesin, and ?-sitosterol in methanol to obtain reference solutions; (2) injecting the test solution and the reference solutions into a high performance liquid chromatograph for gradient elution to obtain a chromatogram of the new compound aloe capsule and chromatograms of the reference solutions, respectively; and (3) labeling chemical components of peaks on the chromatogram of the new compound aloe capsule according to the chromatogram of the new compound aloe capsule and the chromatograms of the reference solutions to obtain a fingerprint spectrum of the new compound aloe capsule.

Abstract: This application relates to the field of lighting, and discloses an LED filament. The LED filament includes an LED chip unit, a light conversion layer, and an electrode. The light conversion layer covers the LED chip unit and part of the electrode, and a color of a light emitted by the LED filament after lighting is different from a color of the light conversion layer. This application has the characteristics of uniform light emission and good heat dissipation effect.

Abstract: Provided are transaminase mutants and uses thereof. The transaminase mutant is obtained by one or more amino acid mutations occurring in SEQ ID NO: 2 or is a mutant with a conserved amino acid mutation obtained by taking the sequence SEQ ID NO: 1 of a wild-type CvTA transaminase as a reference. Compared with wild-type transaminases, the catalytic activity of the mutant is improved to different degrees, so that the production efficiency of chiral amine compound synthesis may be improved.

Abstract: A method of manufacturing a light-emitting device, including: providing a substrate structure including a top surface; forming a precursor layer on the top surface; removing a portion of the precursor layer and a portion of the substrate from the top surface to form a base portion and a plurality of protrusions regularly arranged on the base portion; forming a buffer layer on the base portion and the plurality protrusions; and forming a III-V compound cap layer on the buffer layer; wherein one of the plurality of protrusions comprises a first portion and a second portion formed on the first portion; wherein the first portion is integrated with the base portion and has a first material which is the same as that of the base portion; and wherein the buffer layer contacts side surfaces of the plurality of protrusions and a surface of the base portion.

Abstract: This invention relates to the fields of mRNA vaccines, mRNA therapy, and gene therapy and specifically to the use of gene expression vectors or PCR amplicons containing various 5?UTR sequences followed by coding sequences for in vitro and in vivo production of mRNA or proteins of interest.

Abstract: A method and apparatus for neural network based cross component prediction with scaling factors during encoding or decoding of an image frame or a video sequence, which may include training a deep neural network (DNN) cross component prediction (CCP) model with at least one or more scaling factors, wherein the at least one or more scaling factors are learned by optimizing a rate-distortion loss based on an input video sequence comprising a luma component, and reconstructing a chroma component based on the luma component using the trained DNN CCP model with the at least one or more scaling factors for chroma prediction. The trained DNN CCP may be updated for chroma prediction of the input video sequence using the one or more scaling factors, and performing chroma prediction of the input video sequence using the updated DNN CCP model with the one or more scaling factors.

Abstract: The present application belongs to the technical field of solar cells, and relates to a p-type bifacial solar cell with partial rear surface field passivation and a preparation method therefor. The solar cell includes a p-type silicon substrate. At the bottom portion of the p-type silicon substrate are arranged, from top to bottom, a silicon oxide passivation layer, an aluminum oxide passivation layer and a rear side silicon nitride anti-reflection layer. A plurality of boron source-doped layers are embedded in the bottom portion of the p-type silicon substrate. Connected to the bottom of each of the boron source-doped layers is a rear side metal electrode layer, which penetrates each of the silicon oxide passivation layer, the aluminum oxide passivation layer and the rear side silicon nitride anti-reflection layer.

Abstract: In the present disclosure, a method for compressing a feature map is provided, where the feature map is generated by passing a first input through a deep neural network (DNN). A respective optimal index order and a respective optimal unifying method are determined for each of super-blocks that are partitioned from the feature map. A selective structured unification (SSU) layer is subsequently determined based on the respective optimal index order and the respective optimal unifying method for each of the super-blocks. The SSU layer is added to the DNN to form an updated DNN, and is configured to perform unification operations on the feature map. Further, a first estimated output is determined, where the first estimated output is generated by passing the first input through the updated DNN.

Abstract: A method and apparatus for neural network based cross component prediction with low-bit precision during encoding or decoding of an image frame or a video sequence, which may include reconstructing a chroma component based on a received luma component using a pre-trained deep neural network (DNN) cross component prediction (CCP) model for chroma prediction, and updating a set of parameters of the pre-trained DNN CCP model with low-bit precision. The method may also include generating an updated DNN CCP model for chroma prediction with low-bit precision based on at least one video sequence, and using the updated DNN CCP model for cross component prediction of the at least one video sequence at reduced processing time.

Abstract: A method of making a semiconductor device includes forming a first transistor on a substrate, wherein forming the first transistor comprises forming a first source/drain electrode in the substrate. The method further includes forming a second transistor on the substrate, wherein forming the second transistor comprises forming a second source/drain electrode. The method further includes forming an insulating layer extending into the substrate, wherein the insulating layer directly contacts the first source/drain electrode, and the insulating layer extends above a top-most surface of the substrate.

Abstract: A front light module includes a reflective display device, a front light guide, and a light emitting unit plate. The front light guide plate includes a micro-structure. The micro-structure has a first angle between a surface thereof close to the light emitting unit and an upper surface of the front light guide plate. The micro-structure has a second angle between a surface thereof away from the light emitting unit and the upper surface of the front light guide plate. The micro-structure has a third angle between the surface thereof close to the light emitting unit and the surface thereof away from the light emitting unit. The first angle is within a range between 30 degrees and 60 degrees, the second angle is within a range between 30 degrees and 59 degrees, and the third angle is greater than 90 degrees.

Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: An optical system affixed to an electronic apparatus is provided, including a first optical module, a second optical module, and a third optical module. The first optical module is configured to adjust the moving direction of a first light from a first moving direction to a second moving direction, wherein the first moving direction is not parallel to the second moving direction. The second optical module is configured to receive the first light moving in the second moving direction. The first light reaches the third optical module via the first optical module and the second optical module in sequence. The third optical module includes a first photoelectric converter configured to transform the first light into a first image signal.

Abstract: A method, computer program, or computer system is provided for compressing a neural network model. One or more blocks are identified from among a superblock corresponding to a multi-dimensional tensor associated with a neural network. A set of weight coefficients associated with the superblock is unified. A model of the neural network is compressed based on the unified set of weight coefficients.

Abstract: A semiconductor device includes: first and second active regions extending in a first direction and separated by a gap relative to a second direction substantially perpendicular to the first direction; and gate structures correspondingly over the first and second active regions, the gate structures extending in the second direction; and each of the gate structures extending at least unilaterally substantially beyond a first side of the corresponding first or second active region that is proximal to the gap or a second side of the corresponding first or second active region that is distal to the gap; and some but not all of the gate structures also extending bilaterally substantially beyond each of the first and second sides of the corresponding first or second active region.

Abstract: Aspects of the disclosure provide a method and an apparatus for video encoding. The apparatus includes processing circuitry configured to generate an initial feature representation from an input image to be encoded and perform an iterative update of values of a plurality of elements in the initial feature representation. The iterative update includes generate a coded representation corresponding to a final feature representation based on the final feature representation that has been updated from the initial feature representation by a number of iterations of the iterative update. A reconstructed image corresponding to the final feature representation is generated based on the coded representation. An encoded image corresponding to the final feature representation having updated values of the plurality of elements is generated. One of (i) a rate-distortion loss corresponding to the final feature representation or (ii) the number of iterations of the iterative update satisfies a pre-determined condition.