Abstract: Degradation compounds include a cyclic cell penetrating peptide (cCPP) and a degradation construct. The degradation construct includes a degradation moiety and a targeting moiety. The targeting moiety binds a target protein. When the targeting moiety is bound to the target protein, the degradation moiety mediates degradation of the target protein. The cCPP facilitates transfer of the degradation construct into a cell. The degradation compound may further include an exocyclic peptide to enhance endosomal escape of the compound or degradation construct once inside the cell.

Abstract: Provided is a method of decoding an image, the method including: determining at least one prediction unit included in a current frame that is one of at least one frame forming the image; determining a reference region to be referred to by a current prediction unit that is one of the at least one prediction unit; changing a sample value included in at least one of the current prediction unit and the reference region, based on an analyzing result of a sample value of the reference region; determining a sample value included in the current prediction unit, based on a result of changing the sample value; and decoding the image based on the determined sample value of the current prediction unit.

Abstract: Provided is a method of decoding an image, the method including: determining at least one prediction unit included in a current frame that is one of at least one frame forming the image; determining a reference region to be referred to by a current prediction unit that is one of the at least one prediction unit; changing a sample value included in at least one of the current prediction unit and the reference region, based on an analyzing result of a sample value of the reference region; determining a sample value included in the current prediction unit, based on a result of changing the sample value; and decoding the image based on the determined sample value of the current prediction unit.

Abstract: Provided is a method of decoding an image, the method including: determining at least one prediction unit included in a current frame that is one of at least one frame forming the image; determining a reference region to be referred to by a current prediction unit that is one of the at least one prediction unit; changing a sample value included in at least one of the current prediction unit and the reference region, based on an analyzing result of a sample value of the reference region; determining a sample value included in the current prediction unit, based on a result of changing the sample value; and decoding the image based on the determined sample value of the current prediction unit.

Abstract: Provided is a method of decoding an image, the method including: determining at least one prediction unit included in a current frame that is one of at least one frame forming the image; determining a reference region to be referred to by a current prediction unit that is one of the at least one prediction unit; changing a sample value included in at least one of the current prediction unit and the reference region, based on an analyzing result of a sample value of the reference region; determining a sample value included in the current prediction unit, based on a result of changing the sample value; and decoding the image based on the determined sample value of the current prediction unit.

Abstract: A method of manufacturing an insole may include providing an electronic element on a support layer; connecting a connector to the support layer; providing the support layer to a lower mold frame; covering the lower mold frame with an upper mold frame; and applying a foam material between the lower mold frame and the upper mold frame.

Abstract: Provided is a method of decoding a video according to an embodiment, the method including determining at least one processing block for splitting the video; determining an order of determining at least one largest coding unit in the at least one processing block; determining at least one largest coding unit on the basis of the determined order; and decoding the determined at least one largest coding unit, wherein the order is one of a plurality of orders for determining a largest coding unit.

Abstract: Disclosed is a medical equipment learning system which includes: a data extraction module configured to collect and then extract text-type data from medical data; a visualization module configured to generate image-type data as visualization data by using the text-type data extracted by the data extraction module; a pre-processing module configured to generate an input data set to execute equipment learning based on the visualization data; a learning module configured to execute equipment learning in the input data set generated by the pre-processing module; a prediction module configured to predict a disease when new image-type data is input based on the result learned in the learning module; and a storage module provided to store and check data of each module.

Abstract: A motion assistance apparatus may include a first fixing member to be fixed to a portion of a user, a second fixing member to be fixed to another portion of the user, a driving source provided in the first fixing member, a power transmitting member connected between the driving source and the second fixing member, a sensing portion configured to sense a fixing state of the first fixing member or the second fixing member, and a controller configured to control the driving source based on information received from the sensing portion.

Abstract: Disclosed is a health record system which includes a reception module configured to receive medical data including medical information generated in a medical institution; a data conversion module configured to extract text-type data from the medical data; a visualization module configured to generate image-type data by using the text-type data extracted by the data conversion module; a display module configured to display the image-type data to a user; and a storage-transmission module configured to store and transmit the data generated by the reception module, the data conversion module, and the visualization module.

Abstract: Provided is a method of decoding a video according to an embodiment, the method including determining at least one processing block for splitting the video; determining an order of determining at least one largest coding unit in the at least one processing block; determining at least one largest coding unit on the basis of the determined order; and decoding the determined at least one largest coding unit, wherein the order is one of a plurality of orders for determining a largest coding unit.

Abstract: Provided is a method of decoding a video according to an embodiment, the method including determining at least one processing block for splitting the video; determining an order of determining at least one largest coding unit in the at least one processing block; determining at least one largest coding unit on the basis of the determined order; and decoding the determined at least one largest coding unit, wherein the order is one of a plurality of orders for determining a largest coding unit.

Abstract: An apparatus and method for processing an image are disclosed. The method includes preprocessing an image, recognizing an object in the preprocessed image, determining whether to use a recognition result of the object based on a quality of the recognition result, selecting, in response to a determination that the recognition result is not to be used, one of a first process for postprocessing the preprocessed image and a second process for predicting the object based on a set operation mode, and operating according to the selected process.

Abstract: A motion assistance apparatus may include a first fixing member to be fixed to a portion of a user, a second fixing member to be fixed to another portion of the user, a driving source provided in the first fixing member, a power transmitting member connected between the driving source and the second fixing member, a sensing portion configured to sense a fixing state of the first fixing member or the second fixing member, and a controller configured to control the driving source based on information received from the sensing portion.

Abstract: Provided is an image predicting method including: obtaining a plurality of adjacent samples located adjacent to a current block; determining an adjacent sample as a reference sample to be referred to by a current sample, the adjacent sample being from among the plurality of adjacent samples and being indicated by a direction of an intra mode of the current block; and adjusting a sample value of the reference sample according to a reference distance indicating a distance between the reference sample and the current sample, and determining a prediction value of the current sample based on the adjusted sample value of the reference sample.

Abstract: An insole may include an insole body in a shape receivable in a shoe; an electronic element provided in the insole body; a connection line configured to electrically connect to the electronic element and including a contact terminal exposed to the outside of the insole body; and a connector including a detachable member configured to at least partially protrude outward from the insole body and configured to support the contact terminal.

Abstract: The present disclosure relates to an artificial intelligence (AI) system for simulating functions of a human brain such as cognition and decision-making by using machine learning algorithms such as deep learning, and applications thereof. In particular, the present disclosure provides a method of recognizing an object by using an AI system and its application, including: extracting pieces of first feature information respectively regarding a plurality of images, each image including an object; generating at least one piece of second feature information representing a correlation between the plurality of images by combining together the extracted pieces of first feature information respectively regarding the plurality of images; and recognizing, based on the at least one piece of second feature information, the object included in each of the plurality of images by using a pre-generated learning network model.

Abstract: A method and system for controlling motor driven power steering are provided. The system includes a magnetic field generation detection module that detects generation of a magnetic field upon operating an electric part of a vehicle and a torque offset determination unit that determines whether a temporary torque offset has occurred in a torque to be applied to a motor based on a detection signal of the magnetic field generation detection module. A torque offset correction unit then corrects the torque offset to a normal torque range based on a torque offset correction logic in response to determining that the torque offset has occurred.

Abstract: Provided are an artificial intelligence (AI) system that simulates functions of a human brain such as recognition and judgment by utilizing a machine learning algorithm such as deep learning, etc., and an application of the AI system. Provided are an AI system and a method of recognizing an object according to the application of the AI system, the method including: obtaining a plurality of pieces of sensor data about the object from a plurality of different types of sensors; converting at least some of the plurality of pieces of sensor data into two-dimensional (2D) sensor data; and recognizing the object by using a previously generated learning network model based on 2D image data obtained from an image sensor, which is one of the plurality of sensors, and the 2D sensor data.

Abstract: Provided is a method of decoding an image, the method including: determining at least one prediction unit included in a current frame that is one of at least one frame forming the image; determining a reference region to be referred to by a current prediction unit that is one of the at least one prediction unit; changing a sample value included in at least one of the current prediction unit and the reference region, based on an analyzing result of a sample value of the reference region; determining a sample value included in the current prediction unit, based on a result of changing the sample value; and decoding the image based on the determined sample value of the current prediction unit.