1,261 research outputs found
Conquer the fine structure splitting of excitons in self-assembled InAs/GaAs quantum dots via combined stresses
Eliminating the fine structure splitting (FSS) of excitons in self-assembled
quantum dots (QDs) is essential to the generation of high quality entangled
photon pairs. It has been shown that the FSS has a lower bound under uniaxial
stress. In this letter, we show that the FSS of excitons in a general
self-assembled InGaAs/GaAs QD can be fully suppressed via combined stresses
along the [110] and [010] directions. The result is confirmed by atomic
empirical pseudopotential calculations. For all the QDs we studied, the FSS can
be tuned to be vanishingly small ( 0.1 eV), which is sufficient small
for high quality entangled photon emission.Comment: 4 pages, 3 figure, 1 tabl
Robust Probabilistic Prediction for Stochastic Dynamical Systems
It is critical and challenging to design robust predictors for stochastic
dynamical systems (SDSs) with uncertainty quantification (UQ) in the
prediction. Specifically, robustness guarantees the worst-case performance when
the predictor's information set of the system is inadequate, and UQ
characterizes how confident the predictor is about the predictions. However, it
is difficult for traditional robust predictors to provide robust UQ because
they were designed to robustify the performance of point predictions. In this
paper, we investigate how to robustify the probabilistic prediction for SDS,
which can inherently provide robust distributional UQ. To characterize the
performance of probabilistic predictors, we generalize the concept of
likelihood function to likelihood functional, and prove that this metric is a
proper scoring rule. Based on this metric, we propose a framework to quantify
when the predictor is robust and analyze how the information set affects the
robustness. Our framework makes it possible to design robust probabilistic
predictors by solving functional optimization problems concerning different
information sets. In particular, we design a class of moment-based optimal
robust probabilistic predictors and provide a practical Kalman-filter-based
algorithm for implementation. Extensive numerical simulations are provided to
elaborate on our results
Multi-period Optimal Control for Mobile Agents Considering State Unpredictability
The optimal control for mobile agents is an important and challenging issue.
Recent work shows that using randomized mechanism in agents' control can make
the state unpredictable, and thus improve the security of agents. However, the
unpredictable design is only considered in single period, which can lead to
intolerable control performance in long time horizon. This paper aims at the
trade-off between the control performance and state unpredictability of mobile
agents in long time horizon. Utilizing random perturbations consistent with
uniform distributions to maximize the attackers' prediction errors of future
states, we formulate the problem as a multi-period convex stochastic
optimization problem and solve it through dynamic programming. Specifically, we
design the optimal control strategy considering both unconstrained and input
constrained systems. The analytical iterative expressions of the control are
further provided. Simulation illustrates that the algorithm increases the
prediction errors under Kalman filter while achieving the control performance
requirements successfully
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