16,726 research outputs found
Anomalous spin Hall effects in Dresselhaus (110) quantum wells
Anomalous spin Hall effects that belong to the intrinsic type in Dresselhaus
(110) quantum wells are discussed. For the out-of-plane spin component,
antisymmetric current-induced spin polarization induces opposite spin Hall
accumulation, even though there is no spin-orbit force due to Dresselhaus (110)
coupling. A surprising feature of this spin Hall induction is that the spin
accumulation sign does not change upon bias reversal. Contribution to the spin
Hall accumulation from the spin Hall induction and the spin deviation due to
intrinsic spin-orbit force as well as extrinsic spin scattering, can be
straightforwardly distinguished simply by reversing the bias. For the inplane
component, inclusion of a weak Rashba coupling leads to a new type of
intrinsic spin Hall effect solely due to spin-orbit-force-driven spin
separation.Comment: 6 pages, 5 figure
Functional principal component analysis of spatially correlated data
This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data. Additionally, in the sparse observation framework, we propose a novel approach of spatial principal analysis by conditional expectation to explicitly estimate spatial correlations and reconstruct individual curves. Assuming spatial stationarity, empirical spatial correlations are calculated as the ratio of eigenvalues of the smoothed covariance surface Cov (Xi(s),Xi(t))(Xi(s),Xi(t)) and cross-covariance surface Cov (Xi(s),Xj(t))(Xi(s),Xj(t)) at locations indexed by i and j. Then a anisotropy Matérn spatial correlation model is fitted to empirical correlations. Finally, principal component scores are estimated to reconstruct the sparsely observed curves. This framework can naturally accommodate arbitrary covariance structures, but there is an enormous reduction in computation if one can assume the separability of temporal and spatial components. We demonstrate the consistency of our estimates and propose hypothesis tests to examine the separability as well as the isotropy effect of spatial correlation. Using simulation studies, we show that these methods have some clear advantages over existing methods of curve reconstruction and estimation of model parameters
Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
Distributed adaptive filtering has been considered as an effective approach
for data processing and estimation over distributed networks. Most existing
distributed adaptive filtering algorithms focus on designing different
information diffusion rules, regardless of the nature evolutionary
characteristic of a distributed network. In this paper, we study the adaptive
network from the game theoretic perspective and formulate the distributed
adaptive filtering problem as a graphical evolutionary game. With the proposed
formulation, the nodes in the network are regarded as players and the local
combiner of estimation information from different neighbors is regarded as
different strategies selection. We show that this graphical evolutionary game
framework is very general and can unify the existing adaptive network
algorithms. Based on this framework, as examples, we further propose two
error-aware adaptive filtering algorithms. Moreover, we use graphical
evolutionary game theory to analyze the information diffusion process over the
adaptive networks and evolutionarily stable strategy of the system. Finally,
simulation results are shown to verify the effectiveness of our analysis and
proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin
Functional factor analysis for periodic remote sensing data
We present a new approach to factor rotation for functional data. This is
achieved by rotating the functional principal components toward a predefined
space of periodic functions designed to decompose the total variation into
components that are nearly-periodic and nearly-aperiodic with a predefined
period. We show that the factor rotation can be obtained by calculation of
canonical correlations between appropriate spaces which make the methodology
computationally efficient. Moreover, we demonstrate that our proposed rotations
provide stable and interpretable results in the presence of highly complex
covariance. This work is motivated by the goal of finding interpretable sources
of variability in gridded time series of vegetation index measurements obtained
from remote sensing, and we demonstrate our methodology through an application
of factor rotation of this data.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS518 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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