Observation of a (2X8) surface reconstruction on Si_(1-x)Ge_x alloys grown on (100) Si by molecular beam epitaxy

We present evidence supporting the formation of a new, (2×8) surface reconstruction on Si_(1−x)Ge_x alloys grown on (100) Si substrates by molecular‐beam epitaxy. Surfaces of Si_(1−x)Ge_x alloys were studied using reflection high‐energy electron diffraction (RHEED) and low‐energy electron diffraction (LEED) techniques. RHEED patterns from samples with Ge concentrations, x, falling within the range 0.10–0.30 and grown at temperatures between 350 and 550 °C, exhibit n/8 fractional‐order diffraction streaks in addition to the normal (2×1) pattern seen on (100) Si. The presence of fractional‐order diffracted beams is indicative of an eight‐fold‐periodic modulation in electron scattering factor across the alloy surface. LEED patterns from surfaces of samples grown under similar conditions are entirely consistent with these results. In addition, the LEED patterns support the conclusion that the modulation is occurring in the direction of the dimer chains of a (2×1) reconstruction. We have examined the thermal stability of the (2×8) reconstruction and have found that it reverts to (2×1) after annealing to 700 °C and reappears after the sample temperature is allowed to cool below 600 °C. Such behavior suggests that the reconstruction is a stable, ordered phase for which the pair‐correlation function of surface Ge atoms exhibits an eightfold periodicity in the "1" direction of a Si‐like (2×1) reconstruction. We also present a simulation in the kinematic approximation, confirming the validity of our interpretation of these finding

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

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

On fine differentiability properties of horizons and applications to Riemannian geometry

We study fine differentiability properties of horizons. We show that the set of end points of generators of a n-dimensional horizon H (which is included in a (n+1)-dimensional space-time M) has vanishing n-dimensional Hausdorff measure. This is proved by showing that the set of end points of generators at which the horizon is differentiable has the same property. For 1\le k\le n+1 we show (using deep results of Alberti) that the set of points where the convex hull of the set of generators leaving the horizon has dimension k is ``almost a C^2 manifold of dimension n+1-k'': it can be covered, up to a set of vanishing (n+1-k)-dimensional Hausdorff measure, by a countable number of C^2 manifolds. We use our Lorentzian geometry results to derive information about the fine differentiability properties of the distance function and the structure of cut loci in Riemannian geometry.Comment: Latex2e, 13 pages in A4 forma

Dielectric spectroscopy study of thermally-aged extruded model power cables

“Model” extruded power cables, having a much reduced geometry but using the same extrusion techniques and materials as full-sized cables, have been examined using dielectric spectroscopy techniques to study their thermal ageing effects. Cables insulated with homo-polymer XLPE and co-polymer of XLPE with micron-sized ethylene-butyl-acrylate (EBA) islands were studied by both frequency-domain and time-domain dielectric spectroscopy techniques after accelerated thermal ageing under 135°C for 60 days. In the frequency domain, a frequency response analyzer (FRA) was used to measure the frequency range from 10-4Hz to 1Hz at temperatures from 20°C to 80°C. In the time domain, a special charging/discharging current measurement system was developed to measure the frequencies from 10-1Hz to 102Hz. These techniques were chosen to cope with the extremely low dielectric losses of the model cables. The results are compared with those from new model power cables that were degassed at 80°C for 5 days. Thermal ageing was found to increase the low-frequency conductivity, permittivity and the discharging current. Both homo- and co-polymer cables have substantial increase of dielectric loss after ageing

Topological meaning of Z2_2 numbers in time reversal invariant systems

We show that the Z$_2$ invariant, which classifies the topological properties of time reversal invariant insulators, has deep relationship with the global anomaly. Although the second Chern number is the basic topological invariant characterizing time reversal systems, we show that the relative phase between the Kramers doublet reduces the topological quantum number Z to Z$_2$.Comment: 4 pages, typos correcte

77Se NMR Investigation of the K(x)Fe(2-y)Se(2) High Tc Superconductor (Tc=33K)

We report a comprehensive 77Se NMR study of the structural, magnetic, and superconducting properties of a single crystalline sample of the newly discovered FeSe-based high temperature superconductor K(x)Fe(2-y)Se(2) (Tc=33K) in a broad temperature range up to 290 K. We will compare our results with those reported for FeSe (Tc=9K) and FeAs-based high Tc systems.Comment: Final versio

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)