Hogun Park

Abstract With advances in artificial intelligence (AI)-based algorithms, gesture recognition accuracy from sEMG signals has continued to increase. Spatiotemporal multichannel-sEMG signals substantially increase the quantity and reliability of the data for any type of study. Here, we report an array of bipolar stretchable sEMG electrodes with a self-attention-based graph neural network to recognize gestures with high accuracy. The array is designed to spatially cover the skeletal muscles to acqui

Node classification is an important problem in relational machine learning. However, in scenarios where graph edges represent interactions among the entities (e.g., over time), the majority of current methods either summarize the interaction information into link weights or aggregate the links to produce a static graph. In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal-Static-Graph-Net (TSGNet). Our

Abstract The counterfeiting of products is a serious concern for any nation with the increasing activity of counterfeit markets. Anticounterfeiting tags demand low‐cost, unclonable, facile, and ultrafast manufacturing processes. In this study, a laser‐induced carbonization (LIC) technique is employed to produce discrete sizes of LIC spots distributed randomly in an array fashion, as a tag, preferably on a laser wavelength‐sensitive polyimide (PI) film. This technique enables the intrinsic creati

A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of) among sentences. To identify the relationships, we propose an end-to-e

Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed usin

This work was partially supported by the Korea Foundation for International Cooperation of Science & Technology (KICOS) through a grant provided by the Korean Ministry of Science & Technology (MOST) in K20711000007-07A0100-00710, and partially supported by 2nd phase of Brain Korea 21 project sponsored by Ministry of Education and Human Resources Development, Korea.

Learning with graph-structured data, such as social, biological, and financial networks, requires effective low-dimensional representations to handle their large and complex interactions. Recently, with the advances of neural networks and embedding algorithms, many unsupervised approaches have been proposed for many downstream tasks with promising results; however, there has been limited research on interpreting the unsupervised representations and, specifically, on understanding which parts of