Abstract: Systems and methods for data visualization in the metaverse with portability to multiple metaverse channels are disclosed. In one embodiment, a method for data visualization in the metaverse with portability to multiple metaverse channels may include: (1) ingesting, by a data rendering computer program, data from a plurality of data sources, each data source associated with an entity; (2) categorizing, by the data rendering computer program, the ingested data into a plurality of categories, wherein each category of data comprises data from a subset of two or more of the plurality of the data sources; and (3) streaming, by the data rendering computer program, one of the categories of data to an immersive input/output device associated with a user over one of a plurality of metaverse channels, wherein the category of data is consumed by the immersive input/output device.

Abstract: A motorized covering for a window, including a selectively positionable plurality of slats; a flexible material layer connected to the plurality of slats, the flexible material layer extending generally parallel to the window when the plurality of slats is in the extended position, a plurality of temporary air cells being formed between the plurality of slats, the flexible material layer, and the window when the slats are in the extended position; and a motor system operatively connected to the plurality of slats and the flexible material layer to selectively lower and raise the slats and the flexible material layer, the motor system being configured to lower and raise the plurality of slats and the flexible material layer in response to at least one control signal, the at least one control signal being based on a determination to change an insulation factor for the window.

Abstract: A health condition or change in health condition of a person may be determined by processing the person's speech with a neural network. Speech from more than one time period may be processed and, in some implementations, speech from a time period may be associated with a health condition label. For each time period, a feature vector may be computed from the speech and the feature vector may be processed with a neural network to obtain a speech embedding vector. In some implementations, feature vector may include word-piece encodings and the neural network may be a transformer neural network. The speech embedding vectors may be processed with a mathematical model to determine a change in a health condition between two time periods or to determine a health condition label for a specific time period. In some implementations, the mathematical model may be a regression model or a fully-connected neural network.

Abstract: An organometallic compound represented by Chemical Formula 1, Ir(LA)m(LB)n, and an organic light-emitting diode including the same. The organometallic compound may act as a dopant of a light-emitting layer of the organic light-emitting diode. An operation voltage of the device may be lowered, and luminous efficiency and a lifespan thereof may be improved.

Abstract: A health condition or change in health condition of a person may be determined by processing the person's speech with a neural network. Speech from more than one time period may be processed and, in some implementations, speech from a time period may be associated with a health condition label. For each time period, a feature vector may be computed from the speech and the feature vector may be processed with a neural network to obtain a speech embedding vector. In some implementations, feature vector may include word-piece encodings and the neural network may be a transformer neural network. The speech embedding vectors may be processed with a mathematical model to determine a change in a health condition between two time periods or to determine a health condition label for a specific time period. In some implementations, the mathematical model may be a regression model or a fully-connected neural network.

Abstract: The present disclosure relates to an organic electroluminescent compound represented by formula 1 and an organic electroluminescent device comprising the same. By comprising an organic electroluminescent compound according to the present disclosure, it is possible to produce an organic electroluminescent device having improved current efficiency and/or lifetime properties.

Abstract: Disclosed are an organometallic compound represented by Chemical Formula 1, and an organic light-emitting diode containing the same.

Abstract: Disclosed are an organometallic compound, more particularly, an organometallic compound having phosphorescent properties, and an organic light-emitting diode containing the same. The organometallic compound is represented by Ir(LA)n(LB)3-n, where n is an integer of 1 to 3, LA is a ligand represented by one of Chemical Formula 1-1, Chemical Formula 1-2, and Chemical Formula 1-3, and LB is a bidentate ligand.

Abstract: An organometallic compound represented by Chemical Formula 1, and an organic light-emitting diode containing the same. In the Chemical Formula 1, Z represents one of O (oxygen), S (sulfur) or Se (selenium), m is 1 or 2, n is an integer from 0 to 2, and X Y is a bidentate ligand.

Abstract: Disclosed are an organometallic compound used as a phosphorescent dopant material of a light-emitting layer of an organic light-emitting diode, and the organic light-emitting diode containing the organometallic compound. The organometallic compound is represented by Ir(LA)m(LB)n, where LA is a ligand having a structure represented by Chemical Formula 2, and LB is a bidentate ligand.

Abstract: Disclosed are an organometallic compound used as a phosphorescent dopant material of a light-emitting layer of an organic light-emitting diode, and an organic light-emitting diode containing the same. The organometallic compound is represented by Ir(LA)m(LB)n. An organic light-emitting diode includes a first electrode; a second electrode facing the first electrode; and an organic layer disposed between the first electrode and the second electrode. The organic layer includes a light-emitting layer that contains the organometallic compound disclosed herein.

Abstract: An apparatus for storing a first substance intended for mixing with a second substance. The apparatus includes a hollow body for storing the first substance, the hollow body includes a first opening at a first end, an outer wall of the first end includes one or more angled threads. The hollow body also includes a second opening at a second end, the second opening being sealed via a breakable seal. An inner wall of the second end includes one or more angled grooves for engaging one or more threads of a container. The apparatus also includes a top cap detachably coupled to the first end via one or more angled threads, the top cap sealing the first opening when coupled to the first end.

Abstract: A force/torque sensor includes an outer body structure, an inner body structure, and three sensor structure extending between the outer structure and the inner structure. Overload structures are positioned between the sensor structures. An overload structure includes an outer portion that has a plurality of outer facets, and an inner portion that has a plurality of corresponding opposing inner facets. The outer facets and the inner facets are configured to permit a limited predetermined amount of relative motion between the inner body structure and the outer body structure in at least six degrees of freedom from a neutral position, and to inhibit relative motion between the inner body structure and the outer body structure beyond the predetermined amount. An overload structure may be formed by separating a bridge using wire-cut electrical discharge machining, optionally performed in a single pass.

Abstract: A learning system deploys, to one or more client devices, modules to be deployed in a learning environment of a respective client node. The learning environment of a respective client node may include modules for the client device (or client node) to collaborate with the central learning system and other client nodes via a distributed learning (e.g., federated learning, split learning) framework. In one embodiment, the learning system deploys an interoperable distributed learning environment for training a neural network encoder which can be used in heterogenous datasets to transform the heterogenous datasets across different institutions or entities into a common latent feature space. After training, the learning system receives data instances including a set of features and labels from different client nodes and trains a task neural network model configured to receive features in the latent space and generate an estimated label from the received data instances.

Abstract: Protein complexes are profound in various biological processes, and their ability to self-associate offers exciting opportunities for innovative biomaterials. However, the weak physical interactions within protein complexes result in limited mechanical stability and fast erosion rates, posing a significant challenge. Thus, a SAv-based hydrogel where SAv tetramers serve as crosslinking junctions is described herein. These mechanically reinforced SAv tetramers significantly enhanced the shear elasticity and decelerated the bioerosion process of the overall material. This simple approach to modulate the mechanical strength of protein complexes as crosslinking junctions present an attractive prospect for a myriad of biotechnological applications, such as biomimetic scaffolds and soft robotics.

Abstract: A health technology system provides various applications for providing intelligent informatics on health and medical histories of subjects. In one embodiment, the health technology system includes a visualization system for providing an interactive user interface (UI) for displaying health and medical history for a subject. Specifically, the visualization system obtains health and medical information for a subject and generates various elements on the UI for visualizing the health and medical history of the subject.

Abstract: A learning system deploys a dynamic data conversion module (DDCM) that is customized to perform one or more extract, transform, and load (ETL) operations on the local client data that is used to train at least a portion of a master machine-learning model for the distributed learning framework. The DDCM envelopes a set of components including at least a pre-assessment toolkit and a ETL model. The pre-assessment toolkit is configured to collect statistics and abstract information from the client database of the client device and provide the statistics and abstract information to the learning system. Based on the statistics and abstract information of a respective client device, the learning system generates conversion logics and standardized vocabularies and provides them to the DDCM of the client device.

Abstract: A health condition or change in health condition of a person may be determined by processing the person's speech with a neural network. Speech from more than one time period may be processed and, in some implementations, speech from a time period may be associated with a health condition label. For each time period, a feature vector may be computed from the speech and the feature vector may be processed with a neural network to obtain a speech embedding vector. In some implementations, feature vector may include word-piece encodings and the neural network may be a transformer neural network. The speech embedding vectors may be processed with a mathematical model to determine a change in a health condition between two time periods or to determine a health condition label for a specific time period. In some implementations, the mathematical model may be a regression model or a fully-connected neural network.

Abstract: An integrated optical beam steering device includes a planar Luneburg lens that collimates beams from different inputs in different directions within the lens plane. It also includes a curved (e.g., semi-circular or arced) grating coupler that diffracts the collimated beams out of the lens plane. The beams can be steered in the plane by controlling the direction along which the lens is illuminated and out of the plane by varying the beam wavelength. Unlike other beam steering devices, this device can operate over an extremely wide field of view—up to 180°—without any aberrations off boresight. In other words, the beam quality is uniform in all directions, unlike with aplanatic lenses, thanks to the circular symmetry of the planar Luneburg lens, which may be composed of subwavelength features. The lens is also robust to misalignment and fabrication imperfections and can be made using standard CMOS processes.