97 research outputs found

    Polarization fine-structure and enhanced single-photon emission of self-assembled lateral InGaAs quantum dot molecules embedded in a planar micro-cavity

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    Single lateral InGaAs quantum dot molecules have been embedded in a planar micro-cavity in order to increase the luminescence extraction efficiency. Using a combination of metal-organic vapor phase and molecular beam epitaxy samples could be produced that exhibit a 30 times enhanced single-photon emission rate. We also show that the single-photon emission is fully switchable between two different molecular excitonic recombination energies by applying a lateral electric field. Furthermore, the presence of a polarization fine-structure splitting of the molecular neutral excitonic states is reported which leads to two polarization-split classically correlated biexciton exciton cascades. The fine-structure splitting is found to be on the order of 10 micro-eV.Comment: 14 pages, 4 figures; the following article has been submitted to Journal of Applied Physics (29th ICPS - invited paper); after it is published, it will be found at http://jap.aip.org

    A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems

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    In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example

    Unifying local-global type properties in vector optimization.

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    It is well-known that all local minimum points of a semistrictly quasiconvex real-valued function are global minimum points. Also, any local maximum point of an explicitly quasiconvex real-valued function is a global minimum point, provided that it belongs to the intrinsic core of the function’s domain. The aim of this paper is to show that these “local min - global min” and “local max - global min” type properties can be extended and unified by a single general localglobal extremality principle for certain generalized convex vector-valued functions with respect to two proper subsets of the outcome space. For particular choices of these two sets, we recover and refine several local-global properties known in the literature, concerning unified vector optimization (where optimality is defined with respect to an arbitrary set, not necessarily a convex cone) and, in particular, classical vector/multicriteria optimization.Nicolae Popovici’s research was supported by a grant of the Romanian Ministry of Research and Innovation, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE- 2016-0190, within PNCDI III

    Capital Gains Taxes and Asset Prices: The Impact of Tax Awareness and Procrastination

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    We argue that the impact of capital gains taxation on asset pricing depends on the tax awareness of market participants. While institutional investors should be generally well-informed about tax regulations, private investors have only limited tax knowledge and resources. As a result, market reactions on tax law changes may be delayed if a considerable fraction of market participants is not fully tax-aware. In line with our argument, we find evidence that the introduction of a previously announced German flat tax on private capital gains in 2009 resulted in a temporarily strong and significant increase of trading volumes, daily returns and asset prices. Our research implies that tax law changes provide an opportunity for well-informed investors to generate arbitrage benefits. Corresponding to our estimate, the capital gains tax resulted in an increase demand for shares of 160 % as well as in an price surplus of about 7.4 % within the last two trading days 2008

    Inflationary differential evolution for Constrained Multi-Objective Optimisation Problem

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    In this paper we review several parameter-based scalarisation approaches used within Multi-Objective Optimisation. We propose then a proof-of-concept for a new memetic algorithm designed to solve the Constrained Multi-Objective Optimisation Problem. The algorithm is finally tested on a benchmark with a series of difficulties
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