On Comparison and Analysis of Algorithms for Multiplication in GF(2m)

AbstractThe design of a good finite field multiplication algorithm that can be realized easily on VLSI chips is important in the implementation of Reed-Solomon encoders, decoders, and in some cryptographic algorithms. In this paper, a new algorithm to carry out fast multiplication in the finite field GF(2m) using modified standard basis is presented. The new algorithm will be proved to be more efficient than the usual normal basis algorithm. The implementation has been done in a SUN SPARC-2 station, using C-language

Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes

Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): A Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches

Edge-exposed MoS2 nano-assembled structures as efficient electrocatalysts for hydrogen evolution reaction

Edge-exposed MoS2 nano-assembled structures are designed for high hydrogen evolution reaction activity and long term stability. The number of sulfur edge sites of nano-assembled spheres and sheets is confirmed by Raman spectroscopy and EXAFS analysis. By controlling the MoS2 morphology with the formation of nano-assembled spheres with the assembly of small-size fragments of MoS2, the resulting assembled spheres have high electrocatalytic HER activity and high thermodynamic stability. © 2014 The Royal Society of Chemistry.11261261sciescopu