Abstract: Provided is an electrolyte solution for a lithium secondary battery, the electrolyte solution comprising a non-aqueous organic solvent, a lithium salt, and an additive, wherein the additive includes a compound represented by Chemical Formula 1. The contents of Chemical Formula 1 are the same as described in the specification.

Abstract: A user interface comprising at least a set of reaction icons configured to initiate a search of content of a communication platform is described. In an example, at least the set of reaction icons can be presented via the user interface of the communication platform, wherein individual reaction icons of the set of reaction icons are selectable as search parameters in the user interface. In response to receiving a selection of at least one reaction icon, a search for content, associated with the at least one reaction icon, stored in a database associated with the communication platform can be initiated.

Abstract: The present disclosure relates to an organic electroluminescent compound represented by formula 1 and an organic electroluminescent device comprising the same. By comprising the organic electroluminescent compound of the present disclosure, an organic electroluminescent device having improved driving voltage and/or luminous efficiency characteristics can be provided.

Abstract: Provided is an encoding apparatus for integrally encoding and decoding a speech signal and a audio signal, and may include: an input signal analyzer to analyze a characteristic of an input signal; a stereo encoder to down mix the input signal to a mono signal when the input signal is a stereo signal, and to extract stereo sound image information; a frequency band expander to expand a frequency band of the input signal; a sampling rate converter to convert a sampling rate; a speech signal encoder to encode the input signal using a speech encoding module when the input signal is a speech characteristics signal; a audio signal encoder to encode the input signal using a audio encoding module when the input signal is a audio characteristic signal; and a bitstream generator to generate a bitstream.

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: A method and apparatus for encoding/decoding a neural network-based personalized speech are provided. The method includes outputting a first bit stream in which an input speech signal is encrypted, based on the input speech signal, and outputting a second bit stream in which speaker information of the input speech signal is encrypted, based on the input speech signal.

Abstract: A method and device for encoding/decoding an audio signal based on dequantization through potential diffusion are provided. The method of decoding an audio signal includes obtaining a discrete latent vector in which a speech signal is quantized and based on the discrete latent vector, outputting a continuous latent vector in which the discrete latent vector is dequantized.

Abstract: Disclosed are a device and method for audio signal processing. The audio signal processing device according to an embodiment includes a receiver configured to receive a bitstream corresponding to a compressed audio signal and a processor. The processor may be configured to generate a real restoration signal or a complex restoration signal by performing inverse quantization on real data of the bitstream or complex data of the bitstream, generate a result of real Frequency Domain Noise Shaping (FDNS) synthesis or a result of complex FDNS synthesis by performing FDNS synthesis on the real restoration signal or the complex restoration signal, and generate a restored audio signal by performing frequency-to-time transform on the result of the real FDNS synthesis or the result of the complex FDNS synthesis.

Abstract: An electrolyte additive for a rechargeable lithium battery includes a polymer obtained by polymerizing first compound represented by Chemical Formula 1, Chemical Formula 2, or Chemical Formula 3 as a monomer; and second compound represented by Chemical Formula 4.

Abstract: An encoding apparatus and a decoding apparatus in a transform between a Modified Discrete Cosine Transform (MDCT)-based coder and a different coder are provided. The encoding apparatus may encode additional information to restore an input signal encoded according to the MIDCT-based coding scheme, when switching occurs between the MDCT-based coder and the different coder. Accordingly, an unnecessary bitstream may be prevented from being generated, and minimum additional information may be encoded.

Abstract: Provided is random access method, performed by terminal for uplink data transmission during random access process between cellular based machine communication terminal and base station. The method includes performing random access process between cellular based machine communication terminal and base station, and the random access process includes selecting random access resource by considering coverage level and whether to support multi-tone transmission or not.

Abstract: Provided is a method and apparatus for designing and testing an audio codec using quantization based on white noise modeling. A neural network-based audio encoder design method includes generating a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, computing a total loss for training a neural network-based audio codec, based on the input signal, the reconstruction signal, and the quantized latent vector, training the neural network-based audio codec by using the total loss, and validating the trained neural network-based audio codec to select the best neural network-based audio codec.

Abstract: An encoding method, a decoding method, an encoder for performing the encoding method, and a decoder for performing the decoding method are provided. The encoding method includes outputting LP coefficients bitstream and a residual signal by performing an LP analysis on an input signal, outputting a first latent signal obtained by encoding a periodic component of the residual signal, a second latent signal obtained by encoding a non-periodic component of the residual signal, and a weight vector for each of the first latent signal and the second latent signal, using a first neural network module, and outputting a first bitstream obtained by quantizing the first latent signal, a second bitstream obtained by quantizing the second latent signal, and a weight bitstream obtained by quantizing the weight vector, using a quantization module.

Abstract: Disclosed are a method and apparatus for random access using random beam-switching in a wireless network environment. The random access method using random beam-switching in a wireless network environment includes selecting a given preamble when receiving a synchronization signal block from a base station, transmitting, to the base station, a first message including the selected preamble by using a sequential beam-sweeping method, transmitting a third message for a connection request for uplink data transmission when receiving, from the base station, a second message responding to the transmitted first message, and completing a random access channel operation when receiving a fourth message responding to the transmitted third message.

Abstract: An additive, an electrolyte for a rechargeable lithium battery, and a rechargeable lithium battery including the same, the additive being represented by Chemical Formula 1 or Chemical Formula 2:

Abstract: A positive electrode and a rechargeable lithium battery including the same, and the positive electrode includes a current collector, a positive electrode including a positive active material, and a heat suppression layer between the current collector and the positive active material layer, wherein a thickness ratio of the heat suppression layer and the positive active material layer is about 1:5 to about 1:20.

Abstract: An audio signal encoding and decoding method using a neural network model, and an encoder and decoder for performing the same are disclosed. A method of encoding an audio signal using a neural network model, the method may include identifying an input signal, generating a quantized latent vector by inputting the input signal into a neural network model encoding the input signal, and generating a bitstream corresponding to the quantized latent vector, wherein the neural network model may include i) a feature extraction layer generating a latent vector by extracting a feature of the input signal, ii) a plurality of downsampling blocks downsampling the latent vector, and iii) a plurality of quantization blocks performing quantization of a downsampled latent vector.

Abstract: Provided is an encoding apparatus for integrally encoding and decoding a speech signal and a audio signal, and may include: an input signal analyzer to analyze a characteristic of an input signal; a stereo encoder to down mix the input signal to a mono signal when the input signal is a stereo signal, and to extract stereo sound image information; a frequency band expander to expand a frequency band of the input signal; a sampling rate converter to convert a sampling rate; a speech signal encoder to encode the input signal using a speech encoding module when the input signal is a speech characteristics signal; a audio signal encoder to encode the input signal using a audio encoding module when the input signal is a audio characteristic signal; and a bitstream generator to generate a bitstream.

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.