9,384 research outputs found
Photon-assisted electron transmission resonance through a quantum well with spin-orbit coupling
Using the effective-mass approximation and Floquet theory, we study the
electron transmission over a quantum well in semiconductor heterostructures
with Dresselhaus spin-orbit coupling and an applied oscillation field. It is
demonstrated by the numerical evaluations that Dresselhaus spin-orbit coupling
eliminates the spin degeneracy and leads to the splitting of asymmetric
Fano-type resonance peaks in the conductivity. In turn, the splitting of
Fano-type resonance induces the spin- polarization-dependent electron-current.
The location and line shape of Fano-type resonance can be controlled by
adjusting the oscillation frequency and the amplitude of external field as
well. These interesting features may be a very useful basis for devising
tunable spin filters.Comment: 10pages,4figure
Dynamics of Vibrated Granular Monolayers
We study statistical properties of vibrated granular monolayers using
molecular dynamics simulations. We show that at high excitation strengths, the
system is in a gas state, particle motion is isotropic, and the velocity
distributions are Gaussian. As the vibration strength is lowered the system's
dimensionality is reduced from three to two. Below a critical excitation
strength, a gas-cluster phase occurs, and the velocity distribution becomes
bimodal. In this phase, the system consists of clusters of immobile particles
arranged in close-packed hexagonal arrays, and gas particles whose energy
equals the first excited state of an isolated particle on a vibrated plate.Comment: 4 pages, 6 figs, revte
Robust Control, Informational Frictions, and International Consumption Correlations
In this paper we examine the effects of model misspecification (robustness or RB) on international consumption correlations in an otherwise standard small open economy model with endogenous capital accumulation. We show that in the presence of capital mobility in financial markets, RB lowers the international consumption correlations by generating heterogeneous responses of consumption to productivity shocks across countries facing different macroeconomic uncertainty. In addition, we show that RB can also improve the model's predictions in three other moments of consumption dynamics: the relative volatility of consumption to income, the persistence of consumption, and the correlation between consumption and output. After calibrating the RB parameter using the detection error probabilities, we show that the model can explain the observed international consumption correlations as well as the other consumption moments quantitatively. Finally, we show that the main conclusions of our benchmark model do not change in an extension in which the agent cannot observe the state perfectly due to finite information-processing capacity.postprin
Unsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic structures are unreliable/inaccurate when the redundant and
noisy features are not removed. Therefore, we face a dilemma here: one need the
true structures of data to identify the informative features, and one need the
informative features to accurately estimate the true structures of data. To
address this, we propose a unified learning framework which performs structure
learning and feature selection simultaneously. The structures are adaptively
learned from the results of feature selection, and the informative features are
reselected to preserve the refined structures of data. By leveraging the
interactions between these two essential tasks, we are able to capture accurate
structures and select more informative features. Experimental results on many
benchmark data sets demonstrate that the proposed method outperforms many state
of the art unsupervised feature selection methods
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