Splitting of the Raman 2D2D band of graphene subjected to strain

The Raman $2D$-band -important for the analysis of graphene- shows a splitting for uniaxial strain. The splitting depends on the strength and direction of the applied strain and on the polarization of the incident and outgoing light. We expand the double-resonance Raman model in order to explain the strain direction dependence and the polarization dependence of the splitting. The analysis of this splitting gives new insight into the origin of the $2D$-band. Our prediction of the strain direction and polarization dependence agrees well with recent experiments

Non-Gaussianity and direction dependent systematics in HST key project data

Two new statistics, namely $\Delta_\chi^2$ and $\Delta_\chi$, based on extreme value theory, were derived in \cite{gupta08,gupta10}. We use these statistics to study direction dependence in the HST key project data which provides the most precise measurement of the Hubble constant. We also study the non-Gaussianity in this data set using these statistics. Our results for $\Delta_\chi^2$ show that the significance of direction dependent systematics is restricted to well below one $\sigma$ confidence limit, however, presence of non-Gaussian features is subtle. On the other hand $\Delta_\chi$ statistic, which is more sensitive to direction dependence, shows direction dependence systematics to be at slightly higher confidence level, and the presence of non-Gaussian features at a level similar to the $\Delta_\chi^2$ statistic.Comment: 6 pages, 4 figures; accepted for publication in MNRA

On direction of dependence

Under the assumption of the existence of linear relationship between two random variables, new formulas are introduced to express the coefficient of correlation. One of these formulas, the fourth power of the correlation coefficient is used to determine the direction of dependency between two random variables. Also an interpretation of the correlation coefficient as an asymmetric function of kurtosis coefficient and skewness coefficient of dependent variable and independent variable is provided. In the absent of the intercept in linear regression, the correlation coefficient is also expressed as a ratio of coefficients of variation between independent and dependent variable

Mesoscopic anisotropic magnetoconductance fluctuations in ferromagnets

The conductance of a ferromagnetic particle depends on the relative orientation of the magnetization with respect to the direction of current flow. This phenomenon is known as "anisotropic magnetoresistance". Quantum interference leads to an additional, random dependence of the conductance on the magnetization direction. These "anisotropic magnetoresistance fluctuations" are caused by spin-orbit scattering, which couples the electron motion to the exchange field in the ferromagnet. We report a calculation of the dependence of the conductance autocorrelation function on the rotation angle of the magnetization direction.Comment: 4 pages, 3 figures, revtex

Directional Spatial Dependence and Its Implications for Modeling Systemic Yield Risk

The objective of this study is to evaluate and model the spatial dependence of systemic yield risk. Various spatial autoregressive models are explored to account for county level dependence of crop yields. The results show that the time trend parameters of yields are correlated across spaces and the spatial correlations are changing with time. In addition, the spatial correlation of neighborhood in west/east direction is stronger than that of north/south direction. The information of the spatial dependence of yield risk will help the construction of better risk management programs for protecting producers from systemic yield risks.Spatial Autoregressive Model, Spatial Dependence, Risk and Uncertainty,