Abstract: A method and apparatus for image compression using a latent variable are provided. The multiple components of the latent variable may be sorted in order of importance. Through sorting, when the feature information of only some of the multiple components is used, the quality of a reconstructed image may be improved. In order to generate a latent variable, the components of which are sorted in order of importance, learning may be performed in various manners. Also, less important information may be eliminated from the latent variable, and processing, such as quantization, may be applied to the latent variable. Through elimination and processing, the amount of data for the latent variable may be reduced.

Abstract: Provided is a method for preparing L-glufosinate from an L-homoserine derivative compound, the method including a step (step a) of preparing a compound of Chemical Formula 2 from a compound of Chemical Formula 1, and a step (step b) of preparing a compound of Chemical Formula 3 from the compound of Chemical Formula 2.

Abstract: Disclosed herein are a method and apparatus for image compression based on machine learning using a global context. The disclosed image compression network employs an existing image quality enhancement network for an end-to-end joint learning scheme. The image compression network may jointly optimize image compression enhancement and quality enhancement. The image compression networks and image quality enhancement networks may be easily combined within a unified architecture which minimizes total loss, and may be easily jointly optimized.

Abstract: Disclosed herein is a context-adaptive entropy model for end-to-end optimized image compression. The entropy model exploits two types of contexts. The two types of contexts are a bit-consuming context and a bit-free context, respectively, and these contexts are classified depending on the corresponding context requires the allocation of additional bits. Based on these contexts, the entropy model may more accurately estimate the distribution of each latent representation using a more generalized form of entropy models, thus improving compression performance.

Abstract: In a memory controller included in a memory system for processing a program operation fail, the memory controller controls a plurality of memory devices commonly coupled to a channel, the plurality of memory devices respectively performing preset program operations, and includes: a buffer memory for storing data to be stored in the plurality of memory devices, based on a buffer memory index; and a program error processor for acquiring fail data corresponding to a program operation fail from a fail memory device and acquiring reprogram data that is data to be stored together with the fail data, based on the buffer memory index.

Abstract: A memory system includes a memory device and a controller. The memory device includes a memory block configured to store target data. The controller is configured to maintain the target data in a write buffer until a program operation to the memory block succeeds, update a write cache tag regarding the target data when the program operation fails and a read only mode is entered, and read the target data from the write buffer based on the write cache tag when a read command regarding the target data is received.

Abstract: Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding using prediction. Multiple candidate prediction images are derived, and a final prediction image is generated using the multiple prediction images. The multiple candidate prediction images may be respectively generated using different methods. The multiple candidate prediction images may be generated using neural networks. Here, the multiple candidate prediction images may be respectively derived using different values for a specific coding parameter. Multiple neural networks may use different values for the specific coding parameter. Various coding parameters related to image encoding and/or decoding may be used for embodiments.

Abstract: Disclosed herein are a network environment synchronization apparatus and method. The network environment synchronization apparatus includes one or more processors, and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to collect data from a network environment and generate a management structure in which collected data is distributed into preset respective group units, generate data discriminators for respective group units using a preset hash function, determine whether data of the management structure has changed with reference to data newly collected from the network environment based on the data discriminators, and when it is determined whether data of the management structure has changed, update the data of the management structure with the newly collected data.

Abstract: A memory system includes: a memory device; a host interface suitable for receiving write commands and queueing the received write commands in an interface queue; a workload manager suitable for detecting, in a cache program mode, a mixed workload when a read count is greater than a first threshold value, the read count representing a number of read commands queued in the interface queue and the mixed workload representing receipt of a mix of read and write commands; a mode manager suitable for switching from the cache program mode to a normal program mode when the mixed workload is detected; and a processor suitable for processing write commands queued in a command queue in the cache program mode and processing write commands queued in the interface queue in the normal program mode when the mixed workload is detected.

Abstract: Disclosed herein are a method, an apparatus and a storage medium for image encoding/decoding using a binary mask. An encoding method includes generating a latent vector using an input image, generating a selected latent vector component set using a binary mask, and generating a main bitstream by performing entropy encoding on the selected latent vector component set. A decoding method includes generating a selected latent vector component set including one or more selected latent vector components by performing entropy decoding on a main bitstream and generating the latent vector in which the one or more selected latent vector components are relocated by relocating the selected latent vector component set in the latent vector.

Abstract: Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding using filtering. The image encoding method and the image decoding method perform classification on a classification unit of a target image, and perform filtering that uses a filter on the classification unit. A classification index of the classification unit is determined, and the classification index indicates a filter to be used for filtering on the classification unit, among multiple filters. The classification index is determined by various coding parameters, such as an encoding mode of the target block and an encoding mode of a neighboring block adjacent to the target block.

Abstract: Disclosed herein are a method, apparatus, system, and computer-readable recording medium for image compression. An encoding apparatus performs preprocessing of feature map information, frame packing, frame classification, and encoding. A decoding apparatus performs decoding, frame depacking, and postprocessing in order to reconstruct feature map information. By encoding the feature map information, inter-prediction and intra-block prediction for a frame are performed. The encoding apparatus provides the decoding apparatus with a feature map information bitstream for reconstructing the feature map information along with an image information bitstream.

Abstract: Provided herein may be a storage device and a method of operating the same. A memory controller for controlling a memory device including a plurality of memory blocks having improved read performance may include a random read workload control unit configured to control a state of a random read workload such that the random read workload is in any one of a set state and a clear state depending on a random read count obtained by counting a number of random read requests that are inputted from an external host; and a random read processing unit configured to retrieve a physical address corresponding to a logical address of the respective random read requests depending on the state of the random read workload.

Abstract: An imaging apparatus an image sensor which generates a reference image data of a reference area that includes a measuring area in which a display panel is positioned and an image processor which determines the measuring area by analyzing luminance of the reference image data and generates a crop image data that includes a measuring image data of the measuring area.

Abstract: There are provided a method, apparatus, system, and computer-readable recording medium for image compression. An encoding apparatus performs domain transformation and quantization on feature map information and image information. The encoding apparatus rearranges the result of domain transformation and quantization so as to have a form advantageous to the encoding procedure and encodes the result of rearrangement, thereby generating a bitstream. A decoding apparatus receives the bitstream, decodes the received bitstream, and performs inverse transformation, dequantization, and inverse rearrangement using information transmitted through the bitstream. The result of inverse transformation, dequantization, and inverse rearrangement is used for the machine-learning task of a neural network.

Abstract: Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding. An intra-prediction mode for the target block is derived, and intra-prediction for the target block that uses the derived intra-prediction mode is performed. The intra-prediction mode for the target block is derived using an artificial neural network, and an MPM list for the target block is derived using information about the target block, pieces of information about blocks adjacent to the target block, and the artificial neural network. The artificial neural network outputs one or more available intra-prediction modes. Further, the artificial neural network outputs match probabilities for one or more candidate intra-prediction modes, and each of the match probabilities for the candidate intra-prediction modes indicates a probability that the corresponding candidate intra-prediction mode matches the intra-prediction mode for the target block.

Abstract: There are provided an apparatus, method, system, and recording medium for performing selective encoding/decoding on feature information. An encoding apparatus generates residual feature information. The encoding apparatus transmits the residual feature information to a decoding apparatus through a residual feature map bitstream. The residual feature information is the difference between feature information extracted from an original image and feature information extracted from a reconstructed image. Feature information of the reconstructed image is generated using the reconstructed image. Reconstructed feature information is generated using the feature information of the reconstructed image and reconstructed residual feature information.

Abstract: A driving information display apparatus may include a processor configured to display driving information depending on a speed limit of a road in augmented reality; and a storage configured to store data and algorithms driven by the processor, wherein the processor is configured to adjust a display form of a display object depending on at least one of a remaining distance to an enforcement point depending on the speed limit of the road, a speeding degree compared to the speed limit, and a speeding degree compared to a user-set speed to display the display object, and the driving information display apparatus is disposed within a vehicle or outside the vehicle, and when disposed outside the vehicle, transmits the driving information to the vehicle or a mobile device.

Abstract: An information display apparatus may include a processor configured to display a display object in augmented reality; and a storage configured to store data and algorithms driven by the processor, wherein the processor determines a position of the display object by use of at least one of a total number of lanes or a number of lanes in a road in a driving direction of a host vehicle, possible traveling direction information for each lane, and driving direction information related to the host vehicle, and the information display apparatus is disposed within a vehicle or outside the vehicle, and when disposed outside the vehicle, is configured to transmit display information related to the display object to the vehicle or a mobile device.

Abstract: The present invention relates to a light extraction substrate for an organic light emitting element and, more specifically, to a light extraction substrate for an organic light emitting element that can enhance the light extraction efficiency of the organic light emitting element by optimizing a stack structure that can maximize scattering efficiency, and an organic light emitting element comprising the same.