Analysis of the semantic network of post-traumatic stress disorder using Korean social big data

Introduction: In this study, we wanted to examine how post-traumatic stress disorder was discussed in Korean newspaper articles with semantic network analysis suitable for unstructured big data analysis. Methods: This study analyzed 11,304 articles related to post-traumatic stress reported by four major Korean newspapers for three years from July 30, 2017, to July 30, 2020. R 3.6.2 program was used to calculate TF and TF-IDF values, and UCINET 6.0 and interlocked NetDraw was used for DC, EC, and CONCOR values. Results: As a result of deriving 50 major keywords with high TF-IDF values ​​in newspaper articles related to a post-traumatic stress disorder, TF-IDF values were high in the order of 'sick leave', 'solitary confinement', 'detention center', 'standing order', and 'prisoner'. As a result of conducting a CONCOR analysis to determine which sub-clusters keywords are classified into, the researcher derived each cluster based on words included: 'PTSD by crops' (cluster 1), 'PTSD by broadcasting accidents' (clusters), 'PTSD by farm livestock accidents' (cluster 3), and 'PTSD by various accidents' (cluster 4). Conclusions Based on the research results, post-traumatic stress disorder needs to be managed nationally. As such, we intend to provide basic data for policy development and intervention programs

GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation

While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods. We have released GaIA at https://github.com/Karel911/GaIA.Comment: WACV 2023 accepted pape

Interfacial chemical bonding-mediated ionic resistive switching.

In this paper, we present a unique resistive switching (RS) mechanism study of Pt/TiO2/Pt cell, one of the most widely studied RS system, by focusing on the role of interfacial bonding at the active TiO2-Pt interface, as opposed to a physico-chemical change within the RS film. This study was enabled by the use of a non-conventional scanning probe-based setup. The nanoscale cell is formed by bringing a Pt/TiO2-coated atomic force microscope tip into contact with a flat substrate coated with Pt. The study reveals that electrical resistance and interfacial bonding status are highly coupled together. An oxygen-mediated chemical bonding at the active interface between TiO2 and Pt is a necessary condition for a non-polar low-resistance state, and a reset switching process disconnects the chemical bonding. Bipolar switching mode did not involve the chemical bonding. The nature of chemical bonding at the TiO2-metal interface is further studied by density functional theory calculations

Mobility-Induced Graph Learning for WiFi Positioning

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user's relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user's stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE's effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say root mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and semi-supervised learning cases, respectively.Comment: submitted to a possible IEEE journa

RF-sputtered HfO 2 Gate Insulator in High-Performance AlGaN/GaN MOS-HEMTs

We have proposed and fabricated AlGaN/GaN metal-oxidesemiconductor-high-electron-mobility transistors (MOS-HEMTs) on Si substrate employing RF-sputtered HfO2 gate insulator for a high breakdown voltage. The HfO2 sputtering conditions such as a sputtering power and working pressure have been optimized in order to improve reverse blocking characteristics. We obtained the high breakdown voltage of 1524 V, the low drain leakage current of 67 pA/mm when VDS= 100 V and VGS= -10 V, and on/off current ratio of 2.37×10 10 at sputtering power of 50 W and working pressure of 3 mTorr. In addition, we also discussed the mechanism of breakdown voltage improvement and investigated HfO2/GaN interface in the proposed devices by measuring the leakage current, capacitance-voltage characteristics, and X-ray diffraction (XRD)

High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “[Image: see text]” (“MERS (in Korean)”), “[Image: see text]” (“MERS symptoms (in Korean)”), and “[Image: see text]” (“MERS hospital (in Korean)”). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS