Abstract: Disclosed is an anti-adhesion agent composition which uses a high molecular weight compound having biocompatibility and an oil derived from animals, plants, and minerals so as to improve low viscosity and low strength, which are problems of existing anti-adhesion agents, and prevent adhesion between living tissues. The anti-adhesion composition according to the present disclosure performs a physical barrier function through simple mixing of a polymer material, serving as a physical barrier in its anti-adhesion effect, and a solvent, and remains in the body for a certain period of time, then decomposes, absorbs, and is excreted. The anti-adhesion composition does not interfere with the healing of the wound after surgery, and exhibits an excellent effect of convenient application to the surgical site.

Abstract: The present invention relates to a context-based QA generation architecture, and an object of the present invention is to generate diverse QA pairs from a single context. To achieve the object, the present invention includes a latent variable generating network including at least one encoder and an artificial neural network (Multi-Layer Perceptron: MLP) and configured to train the artificial neural network using a first context, a first question, and a first answer, and generate a second question latent variable and a second answer latent variable by applying the trained artificial neural network to a second context, an answer generating network configured to generate a second answer by decoding the second answer latent variable, and a question generating network configured to generate a second question based on a second context and the second answer.

Abstract: Provided is a memory-based reinforcement learning method and system capable of storing optional information in streaming data. A question-answering (QA) method using memory-based reinforcement learning method includes receiving, in an episodic memory reader (EMR), streaming data about an input context that is input from a user; analyzing, in the EMR, the received streaming data and storing preset semantic information used for QA in an external memory; and, in response to an input of a question front the user, determining, in a pretrained QA model, an answer to the input question based on semantic information stored in the external memory.

Abstract: Disclosed herein are an apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning. The apparatus for task-adaptive neural network retrieval based on meta-contrastive learning includes: memory configured to store a database including a learning model pool consisting of a plurality of datasets and neural networks pre-trained on the datasets and also store a program for task-adaptive neural network retrieval based on meta-contrastive learning; and a controller configured to perform task-adaptive neural network retrieval based on meta-contrastive learning by executing the program. In this case, the controller learns a cross-modal latent space for datasets and neural networks trained on the datasets by calculating the similarity between each dataset and a neural network trained on the dataset while considering constraints included in any one task previously selected from the database, thereby retrieving an optimal neural network.

Abstract: Disclosed are a neural network-based training method, inference method and apparatus. The neural network-based inference method includes receiving a quantization level for quantizing a weight of a neural network and an activation value that is processed by the neural network, receiving a weight quantized based on the quantization level, generating a quantized activation value by quantizing the activation value based on the quantization level, and performing inference based on the quantized weight and the quantized activation value.

Abstract: Provided is a memory-based reinforcement learning method and system capable of storing optional information in streaming data. A question-answering (QA) method using memory-based reinforcement learning method includes receiving, in an episodic memory reader (EMR), streaming data about an input context that is input from a user; analyzing, in the EMR, the received streaming data and storing preset semantic information used for QA in an external memory; and, in response to an input of a question from the user, determining, in a pretrained QA model, an answer to the input question based on sematic information stored in the external memory.

Abstract: A processor-implemented neural network method includes: determining an adaptive parameter and an adaptive mask of a current task to be learned among a plurality of tasks of a neural network; determining a model parameter of the current task based on the adaptive parameter, the adaptive mask, and a shared parameter of the plurality of tasks; and training the model parameter and an adaptive parameter of a previous task with respect to the current task, wherein the adaptive parameter of the previous task and the shared parameter are trained with respect to the previous task.

Abstract: A processor-implemented neural network method includes: extracting, by a feature extractor of a neural network, a plurality of training feature vectors corresponding to a plurality of training class data of each of a plurality of classes including a first class and a second class; determining, by a feature sample generator of the neural network, an additional feature vector of the second class based on a mean vector and a variation vector of the plurality of training feature vectors of each of the first class and the second class; and training a class vector of the second class included in a classifier of the neural network based on the additional feature vector and the plurality of training feature vectors of the second class.

Abstract: Disclosed are a method and system for transfer learning to a random target dataset and model structure based on meta learning. A transfer learning method may include determining the form and amount of information to be transferred, used by a pre-trained model, using a meta model based on similarity between a source dataset and a new target dataset and performing transfer-learning on a target model using the form and amount of information of the pre-trained model determined by the meta model.

Abstract: Disclosed herein are a lamination apparatus which adheres substrates to a cover window having a curved surface and a lamination method using the same. The lamination apparatus includes a first jig on which a cover window is mounted, wherein a curved surface portion is formed in the cover window and a curvature center thereof is positioned behind the curved surface portion, a second jig on which a guide member is seated, wherein a substrate is mounted on the guide member and the guide member has a width greater than that of the substrate, and an interference member provided to interfere with both facing front surfaces of the guide member, wherein the interference member interferes with the guide member and the guide member is bent when the second jig approaches the first jig.

Abstract: Techniques disclose an incrementally expanding object detection model. An object detection tool identifies, based on an object detection model, one or more objects in a sequence of video frames. The object detection model provides an object space including a plurality of object classes. Each object class includes one or more prototypes. Each object is classified as being an instance of one of the object classes. Each identified object is tracked across at least one of the frames. The object detection tool generates a measure of confidence for that object based on the tracking. Upon determining that the measure of confidence exceeds a threshold, the object detection tool adds a prototype of the instance to the object detection model.

Abstract: There are provided systems and methods for performing object classification through semantic mapping. Such an object classification system includes a system processor, a system memory, and an object categorizing unit stored in the system memory. The system processor is configured to execute the object categorizing unit to receive image data corresponding to an object, and to transform the image data into a directed quantity expressed at least in part in terms of semantic parameters. The system processor is further configured to determine a projection of the directed quantity onto an object representation map including multiple object categories, and to associate the object with a category from among the multiple object categories based on the projection.

Abstract: Techniques disclose an incrementally expanding object detection model. An object detection tool identifies, based on an object detection model, one or more objects in a sequence of video frames. The object detection model provides an object space including a plurality of object classes. Each object class includes one or more prototypes. Each object is classified as being an instance of one of the object classes. Each identified object is tracked across at least one of the frames. The object detection tool generates a measure of confidence for that object based on the tracking. Upon determining that the measure of confidence exceeds a threshold, the object detection tool adds a prototype of the instance to the object detection model.

Abstract: Disclosed herein are a lamination apparatus which adheres substrates to a cover window having a curved surface and a lamination method using the same. The lamination apparatus includes a first jig on which a cover window is mounted, wherein a curved surface portion is formed in the cover window and a curvature center thereof is positioned behind the curved surface portion, a second jig on which a guide member is seated, wherein a substrate is mounted on the guide member and the guide member has a width greater than that of the substrate, and an interference member provided to interfere with both facing front surfaces of the guide member, wherein the interference member interferes with the guide member and the guide member is bent when the second jig approaches the first jig.

Abstract: There are provided systems and methods for performing object classification through semantic mapping. Such an object classification system includes a system processor, a system memory, and an object categorizing unit stored in the system memory. The system processor is configured to execute the object categorizing unit to receive image data corresponding to an object, and to transform the image data into a directed quantity expressed at least in part in terms of semantic parameters. The system processor is further configured to determine a projection of the directed quantity onto an object representation map including multiple object categories, and to associate the object with a category from among the multiple object categories based on the projection.

Abstract: There are provided systems and methods of incremental category embedding for categorization. One method including selecting one or more input categories from a plurality of input categories to be added to learned categories, determining at least one representative category from the learned categories for each input category from the one or more input categories, the at least one representative category representing the input category, and approximating the input category using the at least one representative category.

Abstract: There are provided systems and methods of incremental category embedding for categorization. One method including selecting one or more input categories from a plurality of input categories to be added to learned categories, determining at least one representative category from the learned categories for each input category from the one or more input categories, the at least one representative category representing the input category, and approximating the input category using the at least one representative category.