RPC Gap Production and Performance for CMS RE4 Upgrade

CMS experiment constructed the fourth Resistive Plate Chamber (RPC) trigger station composed of 144 RPCs to enhance the high momentum muon trigger efficiency at both endcap regions. All new CMS endcap RPC gaps are produced in accordance with QA and QC at the Korea Detector Laboratory (KODEL) in Korea. All qualified gaps have been delivered to three assembly sites: CERN in Switzerland, BARC in India, and Ghent University in Belgium for the RPC detector assembly. In this paper, we present the detailed procedures used in the production of RPC gaps adopted for the CMS upgrade.Comment: RPC2014 conference contribution, 7 pages, 8 figure

Electrical Investigation of the Oblique Hanle Effect in Ferromagnet/Oxide/Semiconductor Contacts

We have investigated the electrical Hanle effect with magnetic fields applied at an oblique angle ({\theta}) to the spin direction (the oblique Hanle effect, OHE) in CoFe/MgO/semiconductor (SC) contacts by employing a three-terminal measurement scheme. The electrical oblique Hanle signals obtained in CoFe/MgO/Si and CoFe/MgO/Ge contacts show clearly different line shapes depending on the spin lifetime of the host SC. Notably, at moderate magnetic fields, the asymptotic values of the oblique Hanle signals (in both contacts) are consistently reduced by a factor of cos^2({\theta}) irrespective of the bias current and temperature. These results are in good agreement with predictions of the spin precession and relaxation model for the electrical oblique Hanle effect. At high magnetic fields where the magnetization of CoFe is significantly tilted from the film plane to the magnetic field direction, we find that the observed angular dependence of voltage signals in the CoFe/MgO/Si and CoFe/MgO/Ge contacts are well explained by the OHE, considering the misalignment angle between the external magnetic field and the magnetization of CoFe.Comment: 19 pages, 8 figure

Quantum Key Distribution with Blind Polarization Bases

We propose a new quantum key distribution scheme that uses the blind polarization basis. In our scheme the sender and the receiver share key information by exchanging qubits with arbitrary polarization angles without basis reconciliation. As only random polarizations are transmitted, our protocol is secure even when a key is embedded in a not-so-weak coherent-state pulse. We show its security against the photon number splitting attack and the impersonation attack.Comment: Security has been improved upon referee's comment. 4 pages and 2 figure

Phytosynthesis of Silver and Gold Nanoparticles Using the Hot Water Extract of Mixed Woodchip Powder and Their Antibacterial Efficacy

This study investigates the phytosynthesis, characterization, and antibacterial efficacy of silver and gold nanoparticles (NPs) produced using the hot water extract of mixed woodchip powder. The woodchip extract (WCE) was successfully used as both a reducing and stabilizing agent for the phytosynthesis of both crystalline metal NPs. The effects of different physicochemical factors affecting the formation of the metal NPs including reaction pH, concentration of the precursor metal salts, amount of WCE, and external energy input were evaluated. The characterization of the metal NPs was performed by transmission electron microscopy, selected area electron diffraction (SAED), energy dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD) pattern analysis. In addition, the antibacterial efficacy of the phytosynthesized NPs was measured. The AgNPs showed clear antibacterial activity against four representative bacterial strains. However, the AuNPs did not exhibit bactericidal activity, probably due to their surface modifications and relatively large size. These results suggest that the phytosynthesis of the metal NPs using WCE is highly efficient, and its convenience makes it suitable for use in large-scale production

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC score of 0.94 for the diagnosis task. Furthermore, we present a publicly available prototype web application that integrates our predictive model, with the goal of making effective glaucoma diagnosis available to a wide audience.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/010120