Korea University

About: Korea University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 39756 authors who have published 82424 publications receiving 1860927 citations. The organization is also known as: Bosung College & Bosung Professional College.

The CMS trigger system

Abstract: This paper describes the CMS trigger system and its performance during Run 1 of the LHC. The trigger system consists of two levels designed to select events of potential physics interest from a GHz (MHz) interaction rate of proton-proton (heavy ion) collisions. The first level of the trigger is implemented in hardware, and selects events containing detector signals consistent with an electron, photon, muon, tau lepton, jet, or missing transverse energy. A programmable menu of up to 128 object-based algorithms is used to select events for subsequent processing. The trigger thresholds are adjusted to the LHC instantaneous luminosity during data taking in order to restrict the output rate to 100 kHz, the upper limit imposed by the CMS readout electronics. The second level, implemented in software, further refines the purity of the output stream, selecting an average rate of 400 Hz for offline event storage. The objectives, strategy and performance of the trigger system during the LHC Run 1 are described.

Pneumococcal Capsules and Their Types: Past, Present, and Future.

Abstract: Streptococcus pneumoniae (the pneumococcus) is an important human pathogen. Its virulence is largely due to its polysaccharide capsule, which shields it from the host immune system, and because of this, the capsule has been extensively studied. Studies of the capsule led to the identification of DNA as the genetic material, identification of many different capsular serotypes, and identification of the serotype-specific nature of protection by adaptive immunity. Recent studies have led to the determination of capsular polysaccharide structures for many serotypes using advanced analytical technologies, complete elucidation of genetic basis for the capsular types, and the development of highly effective pneumococcal conjugate vaccines. Conjugate vaccine use has altered the serotype distribution by either serotype replacement or switching, and this has increased the need to serotype pneumococci. Due to great advances in molecular technologies and our understanding of the pneumococcal genome, molecular approaches have become powerful tools to predict pneumococcal serotypes. In addition, more-precise and -efficient serotyping methods that directly detect polysaccharide structures are emerging. These improvements in our capabilities will greatly enhance future investigations of pneumococcal epidemiology and diseases and the biology of colonization and innate immunity to pneumococcal capsules.

Bypassing the Kohn-Sham equations with machine learning

Abstract: Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.