Magnetic Insulator-Induced Proximity Effects in Graphene: Spin Filtering and Exchange Splitting Gaps

We report on first-principles calculations of spin-dependent properties in graphene induced by its interaction with a nearby magnetic insulator (Europium oxide, EuO). The magnetic proximity effect results in spin polarization of graphene $\pi$ orbitals by up to 24 %, together with large exchange splitting bandgap of about 36 meV. The position of the Dirac cone is further shown to depend strongly on the graphene-EuO interlayer. These findings point towards the possible engineering of spin gating by proximity effect at relatively high temperature, which stands as a hallmark for future all-spin information processing technologies.Comment: 5 pages, 4 figure

Through a glass darkly: a case for the study of virtual space

This paper begins to examine the similarities and differences between virtual space and real space, as taken from anarchitectural (as opposed to a biological, psychological, geographic, philosophical or information theoretic)standpoint. It continues by introducing a number of criteria, suggested by the authors as being necessary for virtualspace to be used in a manner consistent with our experience of real space. Finally, it concludes by suggesting apedagogical framework for the benefits and associated learning outcomes of the study and examination of thisrelationship. This is accompanied by examples of recent student work, which set out to investigate this relationship

Actions for Vacuum Einstein's Equation with a Killing Symmetry

In a space-time $M$ with a Killing vector field $\xi^a$ which is either everywhere timelike or everywhere spacelike, the collection of all trajectories of $\xi^a$ gives a 3-dimension space $S$. Besides the symmetry-reduced action from that of Einstein-Hilbert, an alternative action of the fields on $S$ is also proposed, which gives the same fields equations as those reduced from the vacuum Einstein equation on $M$.Comment: 8 pages, the difference between the action we proposed and the symmetry-reduced action is clarifie

Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide