Abstract: The present disclosure relates to a novel peptide derivative, etc., the peptide derivative being a partially modified adiponectin receptor peptide which, compared to conventional adiponectin receptor peptides, has higher stability, superior p-AMPK activity, and improved physical properties and activity and thus offers advantages when formulated into a drug. Therefore, the peptide derivative of the present invention can be used to prevent or treat inflammatory skin diseases, wounds, hair loss, fibrosis, metabolic diseases, or cancer, to prevent or alleviate aging, sensitive skin, or wrinkles, or to enhance moisturization.

Abstract: The present invention relates to a novel peptoid derivative and the like. The peptoid derivative is obtained by partially modifying an adiponectin receptor peptide, and has high stability and excellent p-AMPK activity, compared to existing adiponectin receptor peptides and has improved physical properties and activity, thus having the advantage of being suitable for drug formulation. Therefore, the peptoid derivative of the present invention can be used for the prevention or treatment of skin inflammatory diseases, wounds, hair loss, fibrosis, metabolic diseases, or cancer, or the prevention or amelioration of aging, sensitive skin, wrinkles, or moisturization.

Abstract: An image decoding method comprises the steps of: obtaining a current coding tree unit (CTU) of a first size configuring a current picture; determining whether to implicitly divide the current coding tree unit; dividing the current coding tree unit into one or more coding units (CUs) based on the determination; obtaining, from a bitstream, division information for a current coding unit among the one or more coding units; and dividing and decoding the current coding unit..

Abstract: An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Abstract: Disclosed herein are an apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning. The apparatus for task-adaptive neural network retrieval based on meta-contrastive learning includes: memory configured to store a database including a learning model pool consisting of a plurality of datasets and neural networks pre-trained on the datasets and also store a program for task-adaptive neural network retrieval based on meta-contrastive learning; and a controller configured to perform task-adaptive neural network retrieval based on meta-contrastive learning by executing the program. In this case, the controller learns a cross-modal latent space for datasets and neural networks trained on the datasets by calculating the similarity between each dataset and a neural network trained on the dataset while considering constraints included in any one task previously selected from the database, thereby retrieving an optimal neural network.

Abstract: An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Abstract: Provided are a positive electrode active material, a method of preparing the same, and a lithium secondary battery using the positive electrode active material, and more particularly, a positive electrode active material in which a surface of layer-structured lithium transition metal composite oxide is coated with one or more indium-based compounds selected from the group consisting of indium oxides and alloys including indium, a method of preparing the positive electrode active material, and a lithium secondary battery using the positive electrode active material. According to the present disclosure, degradation of cycle characteristics according to repetitive discharge of a battery may be prevented and thermal stability and rate characteristics may be improved.

Abstract: Provided is a positive electrode active material for a lithium ion secondary battery including layer-structured lithium metal oxide expressed by the following Chemical Formula 1 and at least one selected from the group consisting of spinel-structured lithium metal oxides and olivine-structured lithium metal oxides. xLiMO2·(1?x)Li2MnO3??(1) (where M is one or more selected from the group consisting of nickel (Ni), cobalt (Co), and manganese (Mn), and 0<x<1.