Abstract: A mode conversion apparatus includes a first freeform-surface mirror to reflect incident light provided from a front-end optical system and output the reflected light in a first direction, and a second freeform-surface mirror to reflect the reflected light and output mode-converted light in a second direction where surface shapes of the first and second mirrors are determined based on a freeform-surface coefficient determined by at least five optical parameters of a distance from the first mirror to a confocal point of the first mirror and the second mirror, a distance from the confocal point to the second mirror, a distance from a waist of the incident light to the first mirror, a distance from the second mirror to a waist of the mode-converted light, and an incident angle of the incident light for the first mirror and an incident angle of the reflected light for the second mirror.

Abstract: A supervised learning method and system for explicit spatial filtering of speech are disclosed. According to an embodiment, the supervised learning method for spatial filtering of speech, performed by a beamformer learning system, includes: receiving, as input into a neural network-based beamformer model, a multi-channel speech signal incident on a microarray in a reverberant environment and a beam condition representing the direction of interest (DOI); and outputting a desired signal corresponding to the beam condition from the multi-channel speech signal by using the neural network-based beamformer model, wherein the neural network-based beamformer model is trained to extract a speech signal with azimuth and elevation angles that are set for the beam condition, by using training data.

Abstract: An apparatus for estimating bio-information is disclosed. The apparatus may include: a pulse wave sensor configured to measure a pulse wave signal from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the force sensor; and a processor configured to obtain a first input value, a second input value, and a third input value based on the pulse wave signal and the force signal, to extract a feature vector by inputting the first input value, the second input value, and the third input value into a first neural network model, and to obtain the bio-information by inputting the feature vector into a second neural network model.

Abstract: Disclosed herein are compounds of Formula (I), or a pharmaceutically acceptable salt thereof, that are inhibitors of inhibitors of epidermal growth factor receptor (EGFR), including EGFR C797S mutants. Also disclosed herein are pharmaceutical compositions comprising the compounds of Formula (I), or pharmaceutically acceptable salts thereof, and one or more pharmaceutically acceptable excipients. Further disclosed herein are methods of treating cancer in a subject in need thereof, comprising administering to the subject an amount of a compound of Formula (I), or a pharmaceutically acceptable salt thereof.

Abstract: An apparatus for and a method for estimating blood pressure are provided. The apparatus for estimating blood pressure includes: a sensor configured to measure a pulse wave signal from an object; and a processor configured to obtain a mean arterial pressure (MAP) based on the pulse wave signal, configured to classify a phase of the obtained MAP according to at least one classification criterion, and to obtain a systolic blood pressure (SBP) by using an estimation model corresponding to the classified phase of the MAP among estimation models corresponding to respective phases of the MAP.

Abstract: Disclosed are a multiparty participatory MIDI-based content generation system and method configured such that a plurality of users communicates with a server via respective user terminals and each of the users designates his or her singing attribute for a song selected over a network at different times and in different spaces and transmits voice and video data according to singing to the server, the server generates and provides complete choral song content including a solo part and a chorus part of each of the plurality of users by matching the MIDI music with the voice data and the video data of each user according to the singing attribute designated by each of the plurality of users, and a new user participates in the content over the network to generate another version of the choral song content, thereby enabling the plurality of users to participate and generate complete choral song content.

Abstract: A ceiling storage system includes a first support rail and a second support rail that extend in a first direction; a first driving rail that extends in the first direction and is parallel to the first support rail and the second support rail; a second driving rail movably coupled to the first support rail, the second support rail, and the first driving rail, where the second driving rail is configured to move along the first support rail, the second support rail, and the first driving rail; a transfer vehicle movably coupled to the second driving rail and configured to move along the second driving rail; and a connection device that connects the first driving rail and the second driving rail to each other, where the connection device and the second driving rail are rotatable around a first axis that is parallel to the first direction.

Abstract: An article transfer system includes a plurality of stage modules respectively provided at a plurality of layers, an interlayer transfer apparatus configured to transfer an article to each of the plurality of stage modules, and a plurality of loading and unloading apparatus respectively provided on each stage module of the plurality of stage modules, the plurality of loading and unloading apparatus configured to load an article onto respective stage modules of the plurality of stage modules. The interlayer transfer apparatus includes a mast frame extending so as to intersect each stage module of the plurality of stage modules, and a plurality of carriage units configured to move along a length direction of the mast frame, transfer articles, and move in a parallel manner with each other.

Abstract: Presented are a combined learning method and device using a transformed loss function and feature enhancement based on a deep neural network for speaker recognition that is robust in a noisy environment. A combined learning method using a transformed loss function and feature enhancement based on a deep neural network, according to one embodiment, can comprise the steps of: learning a feature enhancement model based on a deep neural network; learning a speaker feature vector extraction model based on the deep neural network; connecting an output layer of the feature enhancement model with an input layer of the speaker feature vector extraction model; and considering the connected feature enhancement model and speaker feature vector extraction model as one mode and performing combined learning for additional learning.

Abstract: Disclosed herein are compounds of Formula (I), or pharmaceutically acceptable salts thereof, that are inhibitors of Polo Like Kinase 4 (PLK4). Also disclosed herein are pharmaceutical compositions comprising the compounds of Formula (I), or pharmaceutically acceptable salts thereof, and one or more pharmaceutically acceptable excipients. Further disclosed herein are methods of treating cancer in a subject in need thereof, comprising administering to the subject an amount of a compound of Formula (I), or a pharmaceutically acceptable salt thereof.

Abstract: A mask case includes a lower case including a first side surface and a second side surface opposite each other, a first rail on the first side surface, and a second rail on the second side surface, an upper case separable from the lower case, and includes third and fourth side surfaces corresponding to the first and second side surfaces, a third rail on the third side, and a fourth rail on the fourth side, a first sliding lock member slidable along the first and third rails, and a second sliding lock member slidable along the second and fourth rails, first and second holes for coupling first and second pins of first and second locking open and close devices are in the first and second sliding lock members, and the first and second sliding lock members are slid by the first and second locking open and close devices.

Abstract: A swash plate compressor including: a housing; a rotating shaft rotatably mounted to the housing; a swash plate accommodated in a crank chamber of the housing and rotating with the rotating shaft; a piston forming a compression chamber with the housing and interlocking with the swash plate to reciprocate; a discharge flow path guiding a refrigerant of the crank chamber to a suction chamber of the housing wherein an inclination angle of the swash plate is adjusted; and a discharge flow path control valve having a valve chamber provided in the discharge flow path and a valve core reciprocating inside the valve chamber, and the valve core includes: a first communication path constantly communicating the discharge flow path; and a second communication path communicating the discharge flow path when differential pressure between pressure of the crank chamber and pressure of the suction chamber is within a certain pressure range.

Abstract: In one example, an electronic device includes a lower substrate comprising a lower substrate upper side and a lower substrate lower side, and an upper substrate comprising an upper substrate upper side and an upper substrate lower side. The electronic device also includes a first electronic component and a second electronic component coupled to the upper substrate upper side. A first device interconnect and a second device interconnect couple the lower substrate upper side to the upper substrate lower side. The electronic device also includes a connect die coupled to the lower substrate upper side that electrically couples the first electronic component to the second electronic component. Other examples and related methods are also disclosed herein.

Abstract: Proposed are a deep neural network-based non-autoregressive voice synthesizing method and a system therefor. A deep neural network-based non-autoregressive voice synthesizing system according to an embodiment may comprise: a voice feature vector column synthesizing unit which constitutes a non-recursive deep neural network based on multiple decoders, and gradually produces a voice feature vector column through the multiple decoders from a template including temporal information of a voice; and a voice reconstituting unit which transforms the voice feature vector column into voice data, wherein the voice feature vector column synthesizing unit produces a template input, and produces a voice feature vector column by adding, to the template input, sentence data refined through an attention mechanism.

Abstract: An exemplary embodiment of the present disclosure is a speech synthesis method based on multi-speaker training dataset of a speech synthesis apparatus including pre-training a speech synthesis model using a previously stored neural network with a single speaker training dataset with most spoken sentences, among training datasets of a plurality of speakers, fine-tuning the pre-trained speech synthesis model with a training dataset of a plurality of speakers, and applying a target speech dataset to the fine-tuned speech synthesis model to be converted into a mel spectrogram.

Abstract: The present disclosure provides an operating method of a speech synthesis system, which includes, inputting a first text and a first speech for the first text, and a second text and a second speech for the second text; generating a speech synthesis model trained by applying the first and second texts and the first and second speeches to curriculum learning; and outputting a target synthesis speech corresponding to a target text based on the speech synthesis model when inputting the target text for speech output, and the generating of the speech synthesis model includes generating a concatenation text in which the first and second texts are concatenated and a concatenation speech in which the first and second speeches are concatenated, and adding the concatenation text and the concatenation speech to the speech synthesis model when an error rate is smaller than a set reference rate when learning-concatenating the concatenation text and the concatenation speech.

Abstract: Disclosed are a method and an apparatus for detecting a voice end point by using acoustic and language modeling information to accomplish strong voice recognition. A voice end point detection method according to an embodiment may comprise the steps of: inputting an acoustic feature vector sequence extracted from a microphone input signal into an acoustic embedding extraction unit, a phonemic embedding extraction unit, and a decoder embedding extraction unit, which are based on a recurrent neural network (RNN); combining acoustic embedding, phonemic embedding, and decoder embedding to configure a feature vector by the acoustic embedding extraction unit, the phonemic embedding extraction unit, and the decoder embedding extraction unit; and inputting the combined feature vector into a deep neural network (DNN)-based classifier to detect a voice end point.

Abstract: A voice signal estimation apparatus using attention mechanism according to an embodiment may comprise a microphone encoder that receives a microphone input signal including an echo signal, and a user's voice signal, converts the microphone input signal into first input information, and outputs the converted first input information, a far-end signal encoder that receives a far-end signal, converts the far-end signal into second input information, and outputs the converted second input information, an attention unit outputting weight information by applying an attention mechanism to the first input information and the second input information, a pre-learned first artificial neural network with third input information, which is the sum information of the weight information and the second input information, as input information, and with first output information including mask information for estimating the voice signal from the second input information as output information and a voice signal estimator outputtin

Abstract: Disclosed herein are compounds of Formula (I), or pharmaceutically acceptable salts thereof, that are inhibitors of Polo Like Kinase 4 (PLK4). Also disclosed herein are pharmaceutical compositions comprising the compounds of Formula (I), or pharmaceutically acceptable salts thereof, and one or more pharmaceutically acceptable excipients. Further disclosed herein are methods of treating cancer in a subject in need thereof, comprising administering to the subject an amount of a compound of Formula (I), or a pharmaceutically acceptable salt thereof.

Abstract: [SUMMARY] An integrated noise and echo signal removal device using parallel deep neural network according to an embodiment comprises a microphone encoder that receives a microphone input signal including an echo signal, and a speaker's voice signal, converts the microphone input signal into first input information, and outputs the converted first input information, a far-end signal encoder that receives a far-end signal, converts the far-end signal into second input information, and outputs the converted second input information, a pre-learned second artificial neural network having a third input information, which is the sum of the first input information and the second input information, as input information, and having an estimated echo signal obtained by estimating the echo signal from the second input information as output information, a pre-learned third artificial neural network having the third input information as input information and having an estimated noise signal obtained by estimating the noise