Sejong Open Cluster Survey (SOS). 0. Target Selection and Data Analysis

Star clusters are superb astrophysical laboratories containing cospatial and coeval samples of stars with similar chemical composition. We have initiated the Sejong Open cluster Survey (SOS) - a project dedicated to providing homogeneous photometry of a large number of open clusters in the SAAO Johnson-Cousins' $UBVI$ system. To achieve our main goal, we have paid much attention to the observation of standard stars in order to reproduce the SAAO standard system. Many of our targets are relatively small, sparse clusters that escaped previous observations. As clusters are considered building blocks of the Galactic disk, their physical properties such as the initial mass function, the pattern of mass segregation, etc. give valuable information on the formation and evolution of the Galactic disk. The spatial distribution of young open clusters will be used to revise the local spiral arm structure of the Galaxy. In addition, the homogeneous data can also be used to test stellar evolutionary theory, especially concerning rare massive stars. In this paper we present the target selection criteria, the observational strategy for accurate photometry, and the adopted calibrations for data analysis such as color-color relations, zero-age main sequence relations, Sp - Mv relations, Sp - Teff relations, Sp - color relations, and Teff - BC relations. Finally we provide some data analysis such as the determination of the reddening law, the membership selection criteria, and distance determination.Comment: 21 pages, 16 figures, accepted for publication in J. of Korean Astronomical Society (JKAS

Improving the Expressiveness of Deep Learning Frameworks with Recursion

Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of support for recursion. In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions. Unlike iterative implementations, which can only understand the topological index of each node in recursive data structures, our recursive implementation is able to exploit the recursive relationships between nodes for efficient execution based on parallel computation. We present an implementation on TensorFlow and evaluation results with various recursive neural network models, showing that our recursive implementation not only conveys the recursive nature of recursive neural networks better than other implementations, but also uses given resources more effectively to reduce training and inference time.Comment: Appeared in EuroSys 2018. 13 pages, 11 figure

Anti-Inflammatory Activity of the Methanol Extract of Moutan Cortex in LPS-Activated Raw264.7 Cells

Moutan Cortex (MCE) has been used in traditional medicine to remove heat from the blood, promote blood circulation and alleviate blood stasis. This study was conducted to evaluate the effects of MCE on regulatory mechanisms of cytokines and nitric oxide (NO) involved in immunological activity of Raw264.7 cells. Cells were pretreated with methanolic extracts of MCE, and further cultured for an appropriate time after lipopolyssacharide (LPS) addition. During the entire experimental period, 0.1 and 0.3 mg ml−1 of MCE had no cytotoxicity. In these concentrations, MCE inhibited the production of NO and prostaglandin E2 (PGE2), the expression of inducible NO synthase (iNOS), cyclooxygenase-2 (COX-2) and phosphorylated inhibitor of κBα (p-IκBα), and the activation of nuclear factor κB (NF-κB). MCE also reduced the concentration of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) in the Raw264.7 cells that were activated by LPS. These results demonstrate that MCE has anti-inflammatory effects through the inhibition of iNOS and COX-2 expression by suppressing the phosphorylation of I-κBα and the activation of NF-κB

Health monitoring in composite structures using piezoceramic sensors and fiber optic sensors

Abstract: Health monitoring is a major concern not only in the design and manufacturing but also in service stages for composite laminated structures. Excessive loads or low velocity impact can cause matrix cracks and delaminations that may severely degrade the load carrying capability of the composite laminated structures. To develop the health monitoring techniques providing on-line diagnostics of smart composite structures can be helpful in keeping the composite structures sound during their service. In this presentation, we discuss the signal processing techniques and some applications for health monitoring of composite structures using piezoceramic sensors and fiber optic sensors