Sungkyunkwan University

About: Sungkyunkwan University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Graphene & Thin film. The organization has 28229 authors who have published 56428 publications receiving 1352733 citations. The organization is also known as: 성균관대학교.

Observation of high-energy astrophysical neutrinos in three years of icecube data

Abstract: A search for high-energy neutrinos interacting within the IceCube detector between 2010 and 2012 provided the first evidence for a high-energy neutrino flux of extraterrestrial origin. Results from an analysis using the same methods with a third year (2012-2013) of data from the complete IceCube detector are consistent with the previously reported astrophysical flux in the 100 TeV-PeV range at the level of 10(-8) GeV cm(-2) s(-1) sr(-1) per flavor and reject a purely atmospheric explanation for the combined three-year data at 5.7 sigma. The data are consistent with expectations for equal fluxes of all three neutrino flavors and with isotropic arrival directions, suggesting either numerous or spatially extended sources. The three-year data set, with a live time of 988 days, contains a total of 37 neutrino candidate events with deposited energies ranging from 30 to 2000 TeV. The 2000-TeV event is the highest-energy neutrino interaction ever observed.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Wafer-Scale Synthesis and Transfer of Graphene Films

Abstract: We developed means to produce wafer scale, high-quality graphene films as large as 3 in. wafer size on Ni and Cu films under ambient pressure and transfer them onto arbitrary substrates through instantaneous etching of metal layers. We also demonstrated the applications of the large-area graphene films for the batch fabrication of field-effect transistor (FET) arrays and stretchable strain gauges showing extraordinary performances. Transistors showed the hole and electron mobilities of the device of 1100 ± 70 and 550 ± 50 cm2/(V s) at drain bias of −0.75 V, respectively. The piezo-resistance gauge factor of strain sensor was ∼6.1. These methods represent a significant step toward the realization of graphene devices in wafer scale as well as application in optoelectronics, flexible and stretchable electronics.

Breast cancer cell lines contain functional cancer stem cells with metastatic capacity and a distinct molecular signature.

Abstract: Tumors may be initiated and maintained by a cellular subcomponent that displays stem cell properties. We have used the expression of aldehyde dehydrogenase as assessed by the ALDEFLUOR assay to isolate and characterize cancer stem cell (CSC) populations in 33 cell lines derived from normal and malignant mammary tissue. Twenty-three of the 33 cell lines contained an ALDEFLUOR-positive population that displayed stem cell properties in vitro and in NOD/SCID xenografts. Gene expression profiling identified a 413-gene CSC profile that included genes known to play a role in stem cell function, as well as genes such as CXCR1/IL-8RA not previously known to play such a role. Recombinant interleukin-8 (IL-8) increased mammosphere formation and the ALDEFLUOR-positive population in breast cancer cell lines. Finally, we show that ALDEFLUOR-positive cells are responsible for mediating metastasis. These studies confirm the hierarchical organization of immortalized cell lines, establish techniques that can facilitate the characterization of regulatory pathways of CSCs, and identify potential stem cell markers and therapeutic targets.