Abstract: One aspect of the present disclosure is a pharmaceutical composition which includes (R)—N-[1-(3,5-difluoro-4-methansulfonylamino-phenyl)-ethyl]-3-(2-propyl-6-trifluoromethyl-pyridin-3-yl)-acrylamide as a first component and a cellulosic polymer as a second component, wherein the composition of one aspect of the present disclosure has a formulation characteristic in which crystal formation is delayed for a long time.

Abstract: A strain sensor may have a conductive elastic yarn including a first fiber having a predetermined length and a shape of a fiber yarn and a second fiber having electrical conductivity and a sheet shape. The strain sensor may have a pair of wiring members electrically connected to both ends of the conductive elastic yarn. The conductive elastic yarn, with the second fiber wrapped around the first fiber, is twisted in a coil shape.

Abstract: The present disclosure relates to an organic electroluminescent compound, a plurality of host materials, and an organic electroluminescent device comprising the same. By comprising the compound according to the present disclosure or by comprising a specific combination of compounds according to the present disclosure as a plurality of host materials, it is possible to produce an organic electroluminescent device having improved driving voltage, luminous efficiency, and/or lifetime properties compared to the conventional organic electroluminescent devices.

Abstract: The present disclosure relates to a plurality of host materials comprising a first host material comprising a compound represented by formula 1, and a second host material comprising a compound represented by formula 2, and an organic electroluminescent device comprising the same. By comprising a specific combination of compounds as host materials, it is possible to provide an organic electroluminescent device having lower driving voltage, higher luminous efficiency, higher power efficiency, and/or superior lifespan characteristics compared to conventional organic electroluminescent devices.

Abstract: A display device including a controller and a display panel. The controller receives original image data and output a display image signal. The display panel receives the display image signal and displays a display image corresponding to the display image signal. The controller includes an image shift controller and a memory. The image shift controller generates shifted image data by modulating the original image data to shift the display image sequentially along a preset shift path on the display panel. The memory stores a shift path value indicating a distance by which the display image has been shifted on the preset shift path. The image shift controller generates the display image signal by processing the shifted image data. When the display device is powered on, the image shift controller generates shifted image data corresponding to a shift path value stored in the memory.

Abstract: An organic light emitting diode (OLED) includes an emissive layer with at least one emitting part includes at least one emitting material layer, a first electron transport layer and a second electron transport layer disposed sequentially between two facing electrodes, wherein the first electron transport layer includes a first electron transporting material of a benzimidazole-based compound substituted with at least one spiro-structured fluorenyl group and the second electron transport layer includes a second electron transporting material of a benzimidazole-based compound substituted with at least one anthracenyl group. The first electron transport including the first electron transport material with excellent thermal stability is disposed adjacently to the emitting material layer so that the OLED can maintain good luminescent intensity in an environment of high temperature and implement beneficial luminous properties.

Abstract: In an embodiment of the present invention, a method for displaying an image of a display device includes moving the image displayed on an image display region along a movement path including a first position and a second position during a period of time, wherein, during the period of time, a total time for which the image is located at the first position is greater than a total time for which the image is located at the second position.

Abstract: A semiconductor chip may include: a body portion with a front surface and a rear surface; a pair of through electrodes penetrating the body portion; an insulating layer disposed over the rear surface of the body portion and the pair of through electrodes; and a rear connection electrode disposed over the insulating layer and connected simultaneously with the pair of through electrodes, wherein a distance between the pair of through electrodes is greater than twice a thickness of the insulating layer.

Abstract: A system (100) is extracting targeted medical information from clinical notes stored in memory (120). The system (100) includes a preprocessing module (120a) configured to retrieve from the memory (120) a sequence of clinical texts of electronic health records, and to tokenize the sequence of clinical texts to obtain a sequence of input tokens. The system (100) further includes a sequence to structure model module (120b) configured to transform, using a trained natural language based transformer, the sequence of input tokens into a sequence of structured output tokens. The system (100) further includes a post-processing unit (110) configured to obtain annotated text-label pairs of the clinical texts from the structure output tokens.

Abstract: A method for treating sleep disturbance according to an embodiment of the present disclosure includes administering to a subject in need thereof a composition including saponarin of Chemical Formula 1, a salt thereof acceptable for use in food, or a hydrate thereof as an effective component: In an animal model with lack of sleep caused by caffeine, saponarin of the present invention restores the travel distance, mobility time, mobility frequency, and immobility time to normal as well as increases the mRNA expression of neuropeptide Y, cholecystokinin, and GABA A ?1 receptor, thereby alleviating sleep disturbance. Therefore, the saponarin of the present invention can be advantageously used as a raw material for a functional health food or a pharmaceutical product for preventing, alleviating, or treating sleep disturbance.

Abstract: A system (1000) for automated question answering, including: semantic space (210) generated from a corpus of questions and answers; a user interface (1030) configured to receive a question; and a processor (1100) comprising: (i) a question decomposition engine (1050) configured to decompose the question into a domain, a keyword, and a focus word; (ii) a question similarity generator (1060) configured to identify one or more questions in a semantic space using the decomposed question; (iii) an answer extraction and ranking engine (1080) configured to: extract, from the semantic space, answers associated with the one or more identified questions; and identify one or more of the extracted answers as a best answer; and (iv) an answer tuning engine (1090) configured to fine-tune the identified best answer using one or more of the domain, keyword, and focus word; wherein the fine-tuned answer is provided to the user via the user interface.

Abstract: Disclosed herein is a method for outer-product-based matrix multiplication for a floating-point data type includes receiving first floating-point data and second floating-point data and performing matrix multiplication on the first floating-point data and the second floating-point data, and the result value of the matrix multiplication is calculated based on the suboperation result values of floating-point units.

Abstract: Provided through the present disclosure is a method for reducing nitrogen dioxide in exhaust gas of a stationary source by using selective catalytic reduction (SCR) without injection of a reducing agent, the method comprising the steps of: (a) providing exhaust gas generated in the stationary source wherein the exhaust gas includes at least one of CO, H2, and hydrocarbon; (b) contacting the exhaust gas with a catalyst to reduce nitrogen dioxide in the exhaust gas; and (c) discharging into air the exhaust gas that has undergone step (b).

Abstract: Techniques are described herein for training and applying memory neural networks, such as “condensed” memory neural networks (“C-MemNN”) and/or “average” memory neural networks (“A-MemNN”). In various embodiments, the memory neural networks may be iteratively trained using training data in the form of free form clinical notes and clinical reference documents. In various embodiments, during each iteration of the training, a so-called “condensed” memory state may be generated and used as part of the next iteration. Once trained, a free form clinical note associated with a patient may be applied as input across the memory neural network to predict one or more diagnoses or outcomes of the patient.

Abstract: The described embodiments relate to systems, methods, and apparatus for providing a multimodal deep memory network (200) capable of generating patient diagnoses (222). The multimodal deep memory network can employ different neural networks, such as a recurrent neural network and a convolution neural network, for creating embeddings (204, 214, 216) from medical images (212) and electronic health records (206). Connections between the input embeddings (204) and diagnoses embeddings (222) can be based on an amount of attention that was given to the images and electronic health records when creating a particular diagnosis. For instance, the amount of attention can be characterized by data (110) that is generated based on sensors that monitor eye movements of clinicians observing the medical images and electronic health records.

Abstract: Techniques are described herein for training and applying memory neural networks, such as “condensed” memory neural networks (“C-MemNN”) and/or “average” memory neural networks (“A-MemNN”). In various embodiments, the memory neural networks may be iteratively trained using training data in the form of free form clinical notes and clinical reference documents. In various embodiments, during each iteration of the training, a so-called “condensed” memory state may be generated and used as part of the next iteration. Once trained, a free form clinical note associated with a patient may be applied as input across the memory neural network to predict one or more diagnoses or outcomes of the patient.

Abstract: A system and method for visualizing and annotating temporal trends of an abnormal condition in patient data. A classification and visualization module detects one or more conditions in one or more images, e.g. X-ray images, and visualizes the condition on the image. A temporal disease state extraction module analyzes text, e.g. radiology reports, for indications of a change in the condition. A multimodal disease state comparison module fuses the extracted data into a compact representation of the condition changes over time.

Abstract: A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

Abstract: A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first po

Abstract: A method for treating a sleep disorder according to an embodiment of the present disclosure includes administering a composition comprising an extract of Hibiscus syriacus as an effective ingredient to a subject in need thereof. Because the Hibiscus syriacus extract of the present invention has an effect of improving sleep time deteriorated by stress, and an effect of reducing the content of corticosterone, which increases due to stress, and increasing the content of serotonin in blood. In addition, the composition has an effect of enhancing in a significant sense the expression of GABAR A ?1 in cerebral cortex, which decreases due to stress, in a group administered with Hibiscus syriacus extract. As such, the composition comprising Hibiscus syriacus extract as an effective ingredient as an effective ingredient can be advantageously used as a functional health food composition or a pharmaceutical composition for preventing, ameliorating or treating sleeping disorders.