Electrical spin protection and manipulation via gate-locked spin-orbit fields

The spin-orbit (SO) interaction couples electron spin and momentum via a relativistic, effective magnetic field. While conveniently facilitating coherent spin manipulation in semiconductors, the SO interaction also inherently causes spin relaxation. A unique situation arises when the Rashba and Dresselhaus SO fields are matched, strongly protecting spins from relaxation, as recently demonstrated. Quantum computation and spintronics devices such as the paradigmatic spin transistor could vastly benefit if such spin protection could be expanded from a single point into a broad range accessible with in-situ gate-control, making possible tunable SO rotations under protection from relaxation. Here, we demonstrate broad, independent control of all relevant SO fields in GaAs quantum wells, allowing us to tune the Rashba and Dresselhaus SO fields while keeping both locked to each other using gate voltages. Thus, we can electrically control and simultaneously protect the spin. Our experiments employ quantum interference corrections to electrical conductivity as a sensitive probe of SO coupling. Finally, we combine transport data with numerical SO simulations to precisely quantify all SO terms.Comment: 5 pages, 4 figures (color), plus supplementary information 18 pages, 8 figures (color) as ancillary arXiv pd

Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high- vs low-yield pathways

Formation of SOA from the aromatic species toluene, xylene, and, for the first time, benzene, is added to a global chemical transport model. A simple mechanism is presented that accounts for competition between low and high-yield pathways of SOA formation, wherein secondary gas-phase products react further with either nitrogen oxide (NO) or hydroperoxy radical (HO2) to yield semi- or non-volatile products, respectively. Aromatic species yield more SOA when they react with OH in regions where the [NO]/[HO2] ratios are lower. The SOA yield thus depends upon the distribution of aromatic emissions, with biomass burning emissions being in areas with lower [NO]/[HO2] ratios, and the reactivity of the aromatic with respect to OH, as a lower initial reactivity allows transport away from industrial source regions, where [NO]/[HO2] ratios are higher, to more remote regions, where this ratio is lower and, hence, the ultimate yield of SOA is higher. As a result, benzene is estimated to be the most important aromatic species with regards to formation of SOA, with a total production nearly equal that of toluene and xylene combined. In total, while only 39% percent of the aromatic species react via the low-NOx pathway, 72% of the aromatic SOA is formed via this mechanism. Predicted SOA concentrations from aromatics in the Eastern United States and Eastern Europe are actually largest during the summer, when the [NO]/[HO2] ratio is lower. Global production of SOA from aromatic sources is estimated at 3.5 Tg/yr, resulting in a global burden of 0.08 Tg, twice as large as previous estimates. The contribution of these largely anthropogenic sources to global SOA is still small relative to biogenic sources, which are estimated to comprise 90% of the global SOA burden, about half of which comes from isoprene. Compared to recent observations, it would appear there are additional pathways beyond those accounted for here for production of anthropogenic SOA. However, owing to differences in spatial distributions of sources and seasons of peak production, there are still regions in which aromatic SOA produced via the mechanisms identified here are predicted to contribute substantially to, and even dominate, the local SOA concentrations, such as outflow regions from North America and South East Asia during the wintertime, though total SOA concentrations there are small (~0.1 μg/m^³)

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Topological Insulators from Spontaneous Symmetry Breaking Induced by Electron Correlation on Pyrochlore Lattices

We study an extended Hubbard model with the nearest-neighbor Coulomb interaction on the pyrochlore lattice at half filling. An interaction-driven insulating phase with nontrivial Z_2 invariants emerges at the Hartree-Fock mean-field level in the phase diagram. This topological insulator phase competes with other ordered states and survives in a parameter region surrounded by a semimetal, antiferromagnetic and charge ordered insulators. The symmetries of these phases are group-theoretically analyzed. We also show that the ferromagnetic interaction enhances the stability of the topological phase.Comment: 8 pages, 5 figures, accepted for publication in J. Phys. Soc. Jp

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

X-ray Properties of Radio-Selected Dual Active Galactic Nuclei

Merger simulations predict that tidally induced gas inflows can trigger kpc-scale dual active galactic nuclei (dAGN) in heavily obscured environments. Previously with the Very Large Array, we have confirmed four dAGN with redshifts between $0.04 < z < 0.22$ and projected separations between 4.3 and 9.2 kpc in the SDSS Stripe 82 field. Here, we present $Chandra$ X-ray observations that spatially resolve these dAGN and compare their multi-wavelength properties to those of single AGN from the literature. We detect X-ray emission from six of the individual merger components and obtain upper limits for the remaining two. Combined with previous radio and optical observations, we find that our dAGN have properties similar to nearby low-luminosity AGN, and they agree well with the black hole fundamental plane relation. There are three AGN-dominated X-ray sources, whose X-ray hardness-ratio derived column densities show that two are unobscured and one is obscured. The low obscured fraction suggests these dAGN are no more obscured than single AGN, in contrast to the predictions from simulations. These three sources show an apparent X-ray deficit compared to their mid-infrared continuum and optical [OIII] line luminosities, suggesting higher levels of obscuration, in tension with the hardness-ratio derived column densities. Enhanced mid-infrared and [OIII] luminosities from star formation may explain this deficit. There is ambiguity in the level of obscuration for the remaining five components since their hardness ratios may be affected by non-nuclear X-ray emissions, or are undetected altogether. They require further observations to be fully characterized.Comment: 11 pages, 5 figures, Accepted for publication in the Astrophysical Journa