Abstract: The present invention relates to a composition for the prevention, alleviation, or treatment of inflammatory diseases, comprising 1-alkyl-5-arylidene-2-selenoxoimidazolidin-4-one and derivatives thereof. Specifically, the novel compounds of the present invention, 1-alkyl-5-arylidene-2-selenoxoimidazolidin-4-one and derivatives thereof, can be used as excellent pharmaceutical compositions and food compositions for the prevention, alleviation, or treatment of inflammatory diseases.

Abstract: A camera module is provided. The camera module includes a first lens module including at least one lens and having light incident thereinto, a second lens module including at least one lens, and a reflection module having a reflective member configured to guide light that has passed through the first lens module to the second lens module, and perform an optical image stabilization operation. A light blocking member is disposed between the first lens module and the reflection module, and the first lens module is coupled to the reflection module and is configured to move together with the reflection module.

Abstract: The present invention relates to a plant-based COVID-19 variant recombinant spike protein expression vector, and a recombinant protein using the expression vector, wherein a plant expression system is used such that the recombinant protein does not have the disadvantages of animal-derived recombinant proteins and exhibits an excellent SARS coronavirus 2 prevention and treatment effect, and thus can be effectively utilized as a safe composition for preventing and treating COVID-19.

Abstract: Provided is a method and system for determining a magnetic bead concentration in a suspension according to the disclosure, including irradiating a plurality of different wavelengths of light to a suspension in which magnetic beads are suspended, measuring the intensities of a plurality of reflected light beams at the different wavelengths reflected from magnetic beads, and determining the concentration of the magnetic beads from the measured intensities of the reflected light beams at the different wavelengths.

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

Abstract: A nonvolatile memory device and method for fabricating the same are provided. The nonvolatile memory device comprising: a substrate; a mold structure including a first insulating pattern and a plurality of gate electrodes alternately stacked in a first direction on the substrate; and a word line cut region which extends in a second direction different from the first direction and cuts the mold structure, wherein the word line cut region includes a common source line, and the common source line includes a second insulating pattern extending in the second direction, and a conductive pattern extending in the second direction and being in contact with the second insulating pattern and a cross-section in the second direction.

Abstract: Disclosed is a method and apparatus for dynamic models to identify similar tasks when no task identifier is provided during the training phase in continual learning (CL). The method includes maintaining a memory comprising one or more previously learned tasks, determining, in response to receiving a new task, one of more similarities between at least one previously learned task and the new task, generating, based on the one or more similarities determined and a previously used task-specific encoder corresponding to the at least one previously learned task, a test error value for classifying the new task, and applying the previously used task-specific encoder to the new task based on the generated test error value.

Abstract: A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables; generating a variational posterior of the joint latent variable model; training the variational posterior; performing inference of the first variable from the second variable based on the variational posterior, wherein performing the inference comprises conditionally generating the first variable from the second variable; and extracting common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function.

Abstract: A multilayer capacitor includes a body including dielectric layers and internal electrodes, and external electrodes, wherein the body has first and second surfaces opposing each other in a first direction, third and fourth surfaces opposing each other in a second direction, and fifth and sixth surfaces opposing each other in a third direction perpendicular to the first and second directions. A length of a portion of the plurality of internal electrodes in the third direction in an intermediate region of the body in the first direction is greater than a length of the first surface or the second surface of the body in the third direction. The plurality of internal electrodes have a bottleneck structure between the intermediate region and at least one of the first and second surfaces, and wherein the bottleneck structure has a shape recessed into an inner portion of the body.

Abstract: A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

Abstract: A server and method thereof are provided for use in a federated network. A method includes receiving local updates from client devices; updating a global model based on the received local updates; quantizing the updated global model; reconstructing feature maps based on the received local updates; refining the quantized, updated global model based on the reconstructed feature maps; and transmitting the refined, quantized, updated global model to the client devices.

Abstract: According to the present disclosure, when drip coffee is extracted, it may learn drip coffee recipe information of a barista to be imitated and extract the drip coffee based on the learned reference acidity and reference concentration. The extracted drip coffee may be compared with the learned reference acidity and the reference concentration and evaluated, and when the acidity and concentration of the drip coffee are matched with the reference acidity and the reference concentration, the drip coffee having the same acidity and concentration as the drip coffee may be extracted. Alternatively, when the acidity and concentration of the drip coffee are not matched with the reference acidity and the reference concentration, drip coffee having the same or/and similar acidity and concentration as or/and to the reference acidity and the reference concentration may be extracted by changing extraction conditions of the drip coffee through reinforcement learning.

Abstract: A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables, generating a variational posterior of the joint latent variable model, training the variational posterior, and performing inference of the first variable from the second variable based on the variational posterior.

Abstract: A system and method for operating a neural network. In some embodiments, the neural network includes a variational autoencoder, and the training of the neural network includes training the variational autoencoder with a plurality of samples of a first random variable; and a plurality of samples of a second random variable, the plurality of samples of the first random variable and the plurality of samples of the second random variable being unpaired, the training of the neural network including updating weights in the neural network based on a first loss function, the first loss function being based on a measure of deviation from consistency between: a conditional generation path from the first random variable to the second random variable, and a conditional generation path from the second random variable to the first random variable.

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method includes training a conditional autoencoder using a Lagrange multiplier and training a neural network that includes the conditional autoencoder with mixed quantization bin sizes.

Abstract: A method and system for constructing a convolutional neural network (CNN) model are herein disclosed. The method includes regularizing spatial domain weights, providing quantization of the spatial domain weights, pruning small or zero weights in a spatial domain, fine-tuning a quantization codebook, compressing a quantization output from the quantization codebook, and decompressing the spatial domain weights and using either sparse spatial domain convolution and sparse Winograd convolution after pruning Winograd-domain weights.

Abstract: An electronic device and method for performing class-incremental learning are provided. The method includes designating a pre-trained first model for at least one past data class as a first teacher; training a second model; designating the trained second model as a second teacher; performing dual-teacher information distillation by maximizing mutual information at intermediate layers of the first teacher and second teacher; and transferring the information to a combined student model.

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, diagonals of a second-order partial derivative matrix (a Hessian matrix) of a loss function of network parameters of a neural network are determined and then used to weight (Hessian-weighting) the network parameters as part of quantizing the network parameters. In another aspect, the neural network is trained using first and second moment estimates of gradients of the network parameters and then the second moment estimates are used to weight the network parameters as part of quantizing the network parameters. In yet another aspect, network parameter quantization is performed by using an entropy-constrained scalar quantization (ECSQ) iterative algorithm. In yet another aspect, network parameter quantization is performed by quantizing the network parameters of all layers of a deep neural network together at once.