Abstract: The present invention relates to a novel use of NCKAP1 gene in neurodegenerative diseases. More specifically, the present invention relates to a marker composition for predicting the prognosis of a neurodegenerative disease, comprising a NCKAP1 protein or a gene encoding same, a composition and a kit for predicting the prognosis of a neurodegenerative disease, which comprises a formulation for measuring the level of the protein or an mRNA of the gene encoding same, and a pharmaceutical composition for preventing or treating neurodegenerative disease, comprising the protein or the gene encoding same as an active ingredient.

Abstract: A color conversion panel includes a first color filter and a second color filter that are disposed on a substrate, a low refractive index layer disposed on the substrate, the first color filter, and the second color filter, the low refractive index layer including at least one of a first blue pigment and a first blue dye, a first color conversion layer overlapping the first color filter and including a semiconductor nanocrystal, a second color conversion layer overlapping the second color filter and including a semiconductor nanocrystal, and a transmissive layer that overlaps the low refractive index layer.

Abstract: A cleaning apparatus including a vacuum cleaner and a docking station is provided. The cleaning apparatus includes a vacuum cleaner including a dust collecting chamber in which foreign substances are collected, and a docking station configured to be connected to the dust collecting chamber to remove the foreign substances collected in the dust collecting chamber. The dust collecting chamber is configured to collect foreign substances through centrifugation, and configured to be docked to the docking station, and the docking station includes a suction device configured to suction the foreign substances and air in the dust collecting chamber docked to the docking station.

Abstract: An electronic device includes one or more processors and a memory storing instructions configured to, when executed by the one or more processors, cause the one or more processors to: implement a machine learning-based conditional generative model configured to reconstruct target data from latent vectors, the conditional generative model trained based on an existing data set for a target task; determine an extrapolation weight; generate an augmented latent vector and augmented condition data by extrapolating, based on the extrapolation weight, from a latent vector corresponding to the existing dataset and from existing condition data corresponding to the existing dataset; and generate a new dataset comprising augmented target data generated by the conditional generative model based on the augmented condition data and based on the augmented latent vector.

Abstract: A display device including: a first substrate; a transflective layer disposed on a surface of the first substrate; a wavelength conversion layer disposed on the transflective layer; a capping layer disposed on the wavelength conversion layer, a first polarizing layer disposed on the capping layer; and a second polarizing layer disposed on the other surface of the first substrate. The first polarizing layer and the second polarizing layer have different polarization directions.

Abstract: Disclosed is a first cleaning apparatus including a first cleaning bath, a cover provided at the upper part of the first cleaning bath, a drainage portion provided at the lower part of the first cleaning bath, a first cleaning unit and a second cleaning unit provided respectively at a first side surface and a second side surface in the first cleaning bath, and first and second moving units configured to move the first and second cleaning units, respectively, wherein each of the first and second cleaning units includes a plurality of cleaning solution supply pipes provided at different heights and a plurality of nozzles provided at each of the cleaning solution supply pipes, the nozzles provided at one cleaning solution supply pipe have identical cleaning solution spray angles, and the nozzles provided at the other cleaning solution supply pipes have different cleaning solution spray angles.

Abstract: A processor-implemented speech recognition method includes: applying, to an input layer of a neural network, a frame of a speech sequence; obtaining an output of a hidden layer of the neural network corresponding to the frame; calculating a statistical value of at least one previous output of the hidden layer corresponding to at least one previous frame of the speech sequence; normalizing the output based on the statistical value; applying the normalized output to a subsequent layer of the neural network; and recognizing the speech sequence based on the applying of the normalized output.

Abstract: An LED lighting apparatus includes a first LED light source and a second LED light source included in a first light source group; a third LED light source and a fourth LED light source included in a second light source group; a mode detection circuit configured to determine a mode; a current control unit for serial and parallel connection of the first light source group and the second light source group; balancing circuits for serial and parallel connection of the respective light source groups; and a current path providing circuit for providing current paths. The LED lighting apparatus performs light emission in various states depending on the change of a rectified voltage and the change of a mode.

Abstract: A processor-implemented method of personalizing a speech recognition model includes: obtaining statistical information of first scaling vectors combined with a base model for speech recognition; obtaining utterance data of a user; and generating a personalized speech recognition model by modifying a second scaling vector combined with the base model based on the utterance data of the user and the statistical information.

Abstract: A method and apparatus for personalizing a speech recognition model is disclosed. The apparatus may obtain feedback data that is a result of recognizing a first speech input of a user using a trained speech recognition model, determine whether to update the speech recognition model based on the obtained feedback data, and selectively update, dependent on the determining, the speech recognition model based on the feedback data.

Abstract: An LED lighting apparatus includes a first LED light source and a second LED light source included in a first light source group; a third LED light source and a fourth LED light source included in a second light source group; a mode detection circuit configured to determine a mode; a current control unit for serial and parallel connection of the first light source group and the second light source group; balancing circuits for serial and parallel connection of the respective light source groups; and a current path providing circuit for providing current paths. The LED lighting apparatus performs light emission in various states depending on the change of a rectified voltage and the change of a mode.

Abstract: Disclosed is a first cleaning apparatus including a first cleaning bath, a cover provided at the upper part of the first cleaning bath, a drainage portion provided at the lower part of the first cleaning bath, a first cleaning unit and a second cleaning unit provided respectively at a first side surface and a second side surface in the first cleaning bath, and first and second moving units configured to move the first and second cleaning units, respectively, wherein each of the first and second cleaning units includes a plurality of cleaning solution supply pipes provided at different heights and a plurality of nozzles provided at each of the cleaning solution supply pipes, the nozzles provided at one cleaning solution supply pipe have identical cleaning solution spray angles, and the nozzles provided at the other cleaning solution supply pipes have different cleaning solution spray angles.

Abstract: A method and apparatus for personalizing a speech recognition model is disclosed. The apparatus may obtain feedback data that is a result of recognizing a first speech input of a user using a trained speech recognition model, determine whether to update the speech recognition model based on the obtained feedback data, and selectively update, dependent on the determining, the speech recognition model based on the feedback data.

Abstract: A processor-implemented model training method and apparatus are provided. The method calculates an entropy of each of a plurality of previously trained models based on training data, selects a previously trained model from the plurality of previously trained models based on the calculated entropy, and trains a target model, distinguished from the plurality of previously trained models, based on the training data and the selected previously trained model.

Abstract: Provided is a method and device to personalize a speech recognition model, the device that personalizes a speech recognition model by identifying a language group corresponding to a user, and generating a personalized speech recognition model by applying a group scale matrix corresponding to the identified language group to at least a layer of a speech recognition model.

Abstract: Disclosed is an LED lighting apparatus. The LED lighting apparatus may include a leakage current control unit configured to provide a leakage current path when a rectified voltage is retained at a predetermined voltage or less for a preset time or more, and a leakage current and retention current may be discharged through a leakage current path.

Abstract: A processor-implemented speech recognition method includes: applying, to an input layer of a neural network, a frame of a speech sequence; obtaining an output of a hidden layer of the neural network corresponding to the frame; calculating a statistical value of at least one previous output of the hidden layer corresponding to at least one previous frame of the speech sequence; normalizing the output based on the statistical value; applying the normalized output to a subsequent layer of the neural network; and recognizing the speech sequence based on the applying of the normalized output.

Abstract: A speech signal recognition method, apparatus, and system. The speech signal recognition method may include obtaining by or from a terminal an output of a personalization layer, with respect to a speech signal provided by a user of the terminal, having been implemented by input of the speech signal to the personalization layer, the personalization layer being previously trained based on speech features of the user, implementing a global model by providing the obtained output of the personalization layer to the global model, the global model being configured to output a phonemic signal indicating a phoneme included in the speech signal through the global model being previously trained based on speech features common to a plurality of users, and re-training the personalization layer based on the phonemic signal output from the global model, where the personalization layer and the global model collectively represent an acoustic model.

Abstract: A processor-implemented method of personalizing a speech recognition model includes: obtaining statistical information of first scaling vectors combined with a base model for speech recognition; obtaining utterance data of a user; and generating a personalized speech recognition model by modifying a second scaling vector combined with the base model based on the utterance data of the user and the statistical information.

Abstract: Provided is a method and device to personalize a speech recognition model, the device that personalizes a speech recognition model by identifying a language group corresponding to a user, and generating a personalized speech recognition model by applying a group scale matrix corresponding to the identified language group to at least a layer of a speech recognition model.