A terahertz metamaterial with unnaturally high refractive index

Controlling the electromagnetic properties of materials, going beyond the limit that is attainable with naturally existing substances, has become a reality with the advent of metamaterials. The range of various structured artificial 'atoms' has promised a vast variety of otherwise unexpected physical phenomena, among which the experimental realization of a negative refractive index has been one of the main foci thus far. Expanding the refractive index into a high positive regime will complete the spectrum of achievable refractive index and provide more design flexibility for transformation optics. Naturally existing transparent materials possess small positive indices of refraction, except for a few semiconductors and insulators, such as lead sulphide or strontium titanate, that exhibit a rather high peak refractive index at mid- and far-infrared frequencies. Previous approaches using metamaterials were not successful in realizing broadband high refractive indices. A broadband high-refractive-index metamaterial structure was theoretically investigated only recently, but the proposed structure does not lend itself to easy implementation. Here we demonstrate that a broadband, extremely high index of refraction can be realized from large-area, free-standing, flexible terahertz metamaterials composed of strongly coupled unit cells. By drastically increasing the effective permittivity through strong capacitive coupling and decreasing the diamagnetic response with a thin metallic structure in the unit cell, a peak refractive index of 38.6 along with a low-frequency quasi-static value of over 20 were experimentally realized for a single-layer terahertz metamaterial, while maintaining low losses. As a natural extension of these single-layer metamaterials, we fabricated quasi-three-dimensional high-refractive-index metamaterials, and obtained a maximum bulk refractive index of 33.2 along with a value of around 8 at the quasi-static limit.     

Tissue-specific and reversible RNA interference in transgenic mice

Genetically engineered mice provide powerful tools for understanding mammalian gene function. These models traditionally rely on gene overexpression from transgenes or targeted, irreversible gene mutation. By adapting the tetracycline (tet)-responsive system previously used for gene overexpression, we have developed a simple transgenic system to reversibly control endogenous gene expression using RNA interference (RNAi) in mice. Transgenic mice harboring a tet-responsive RNA polymerase II promoter driving a microRNA-based short hairpin RNA targeting the tumor suppressor Trp53 reversibly express short hairpin RNA when crossed with existing mouse strains expressing general or tissue-specific 'tet-on' or 'tet-off' transactivators. Reversible Trp53 knockdown can be achieved in several tissues, and restoring Trp53 expression in lymphomas whose development is promoted by Trp53 knockdown leads to tumor regression. By leaving the target gene unaltered, this approach permits tissue-specific, reversible regulation of endogenous gene expression in vivo, with potential broad application in basic biology and drug target validation.     

Recombination and linkage disequilibrium in Arabidopsis thaliana

Linkage disequilibrium (LD) is a major aspect of the organization of genetic variation in natural populations. Here we describe the genome-wide pattern of LD in a sample of 19 Arabidopsis thaliana accessions using 341,602 non-singleton SNPs. LD decays within 10 kb on average, considerably faster than previously estimated. Tag SNP selection algorithms and 'hide-the-SNP' simulations suggest that genome-wide association mapping will require only 40%-50% of the observed SNPs, a reduction similar to estimates in a sample of African Americans. An Affymetrix genotyping array containing 250,000 SNPs has been designed based on these results; we demonstrate that it should have more than adequate coverage for genome-wide association mapping. The extent of LD is highly variable, and we find clear evidence of recombination hotspots, which seem to occur preferentially in intergenic regions. LD also reflects the action of selection, and it is more extensive between nonsynonymous polymorphisms than between synonymous polymorphisms.     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.