61,820 research outputs found

    Optical properties of Si/Si0.87Ge0.13 multiple quantum well wires

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    Nanometer-scale wires cut into a Si/Si0.87Ge0.13 multiple quantum well structure were fabricated and characterized by using photoluminescence and photoreflectance at temperatures between 4 and 20 K. It was found that, in addition to a low-energy broadband emission at around 0.8 eV and other features normally observable in photoluminescence measurements, fabrication process induced strain relaxation and enhanced electron-hole droplets emission together with a new feature at 1.131 eV at 4 K were observed. The latter was further identified as a transition related to impurities located at the Si/Si0.87Ge0.13 heterointerfaces

    To synchronize or not to synchronize, that is the question: finite-size scaling and fluctuation effects in the Kuramoto model

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    The entrainment transition of coupled random frequency oscillators presents a long-standing problem in nonlinear physics. The onset of entrainment in populations of large but finite size exhibits strong sensitivity to fluctuations in the oscillator density at the synchronizing frequency. This is the source for the unusual values assumed by the correlation size exponent ν′\nu'. Locally coupled oscillators on a dd-dimensional lattice exhibit two types of frequency entrainment: symmetry-breaking at d>4d > 4, and aggregation of compact synchronized domains in three and four dimensions. Various critical properties of the transition are well captured by finite-size scaling relations with simple yet unconventional exponent values.Comment: 9 pages, 1 figure, to appear in a special issue of JSTAT dedicated to Statphys2

    On controllability of neuronal networks with constraints on the average of control gains

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    Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently
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