Enhanced photoluminescence emission from two-dimensional silicon photonic crystal nanocavities

We present a temperature dependent photoluminescence study of silicon optical nanocavities formed by introducing point defects into two-dimensional photonic crystals. In addition to the prominent TO phonon assisted transition from crystalline silicon at ~1.10 eV we observe a broad defect band luminescence from ~1.05-1.09 eV. Spatially resolved spectroscopy demonstrates that this defect band is present only in the region where air-holes have been etched during the fabrication process. Detectable emission from the cavity mode persists up to room-temperature, in strong contrast the background emission vanishes for T > 150 K. An Ahrrenius type analysis of the temperature dependence of the luminescence signal recorded either in-resonance with the cavity mode, or weakly detuned, suggests that the higher temperature stability may arise from an enhanced internal quantum efficiency due to the Purcell-effect

Dephasing of quantum dot exciton polaritons in electrically tunable nanocavities

We experimentally and theoretically investigate dephasing of zero dimensional microcavity polaritons in electrically tunable single dot photonic crystal nanocavities. Such devices allow us to alter the dot-cavity detuning in-situ and to directly probe the influence on the emission spectrum of varying the incoherent excitation level and the lattice temperature. By comparing our results with theory we obtain the polariton dephasing rate and clarify its dependence on optical excitation power and lattice temperature. For low excitation levels we observe a linear temperature dependence, indicative of phonon mediated polariton dephasing. At higher excitation levels, excitation induced dephasing is observed due to coupling to the solid-state environment. The results provide new information on coherence properties of quantum dot microcavity polaritons.Comment: Figure 2, panel (b) changed to logarithmic + linear scal

A Correlation between the Emission Intensity of Self-Assembled Germanium Islands and the Quality Factor of Silicon Photonic Crystal Nanocavities

We present a comparative micro-photoluminescence study of the emission intensity of self-assembled germanium islands coupled to the resonator mode of two-dimensional silicon photonic crystal defect nanocavities. The emission intensity is investigated for cavity modes of L3 and Hexapole cavities with different cavity quality factors. For each of these cavities many nominally identical samples are probed to obtain reliable statistics. As the quality factor increases we observe a clear decrease in the average mode emission intensity recorded under comparable optical pumping conditions. This clear experimentally observed trend is compared with simulations based on a dissipative master equation approach that describes a cavity weakly coupled to an ensemble of emitters. We obtain evidence that reabsorption of photons emitted into the cavity mode is responsible for the observed trend. In combination with the observation of cavity linewidth broadening in power dependent measurements, we conclude that free carrier absorption is the limiting effect for the cavity mediated light enhancement under conditions of strong pumping.Comment: 8 pages, 5 figure

The aging investor: Insights from neuroeconomics

Individuals in most industrialized countries have to make investment decisions throughout their adult life span to save for their retirement. These decisions substantially affect their living standards in old age. Research on cognitive aging has already demonstrated several changes in cognitive functions (e.g., processing speed) that likely influence investment decisions. This review brings together research on behavioral and neural aspects of financial decision making and aging to advance knowledge on age-related changes in financial decision making. The dopaminergic system plays a key role in financial decision making, both in financial decisions from description and financial decisions from experience. Importantly, both dopaminergic neuromodulation and financial decision making change during healthy aging. Especially when the parameters of the return distribution have to be learned from experience, older adults show a different and suboptimal choice behavior compared to younger adults. Based on these observations we suggest ways to circumvent the age-related bias in financial decision making to improve older adults' wealth

Attraction effect in risky choice can be explained by subjective distance between choice alternatives

Individuals make decisions under risk throughout daily life. Standard models of economic decision making typically assume that people evaluate choice options independently. There is, however, substantial evidence showing that this independence assumption is frequently violated in decision making without risk. The present study extends these findings to the domain of decision making under risk. To explain the independence violations, we adapted a sequential sampling model, namely Multialternative Decision Field Theory (MDFT), to decision making under risk and showed how this model can account for the observed preference shifts. MDFT not only better predicts choices compared with the standard Expected Utility Theory, but it also explains individual differences in the size of the observed context effect. Evidence in favor of the chosen option, as predicted by MDFT, was positively correlated with brain activity in the medial orbitofrontal cortex (mOFC) and negatively correlated with brain activity in the anterior insula (aINS). From a neuroscience perspective, the results of the present study show that specific brain regions, such as the mOFC and aINS, not only code the value or risk of a single choice option but also code the evidence in favor of the best option compared with other available choice options

In Silico Approaches and the Role of Ontologies in Aging Research

The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin

Risk patterns and correlated brain activities: Multidimensional statistical analysis of fMRI data with application to risk patterns

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specific brain regions of all subjects in this high-dimensional time series data, by a flexible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior. Keywords: risk, risk attitude, fMRI, decision making, medial orbifrontal cortex, semiparametric model, factor structure, SVMDecision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specific brain regions of all subjects in this high-dimensional time series data, by a exible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior

Ultracold Dipolar Gases in Optical Lattices

This tutorial is a theoretical work, in which we study the physics of ultra-cold dipolar bosonic gases in optical lattices. Such gases consist of bosonic atoms or molecules that interact via dipolar forces, and that are cooled below the quantum degeneracy temperature, typically in the nK range. When such a degenerate quantum gas is loaded into an optical lattice produced by standing waves of laser light, new kinds of physical phenomena occur. These systems realize then extended Hubbard-type models, and can be brought to a strongly correlated regime. The physical properties of such gases, dominated by the long-range, anisotropic dipole-dipole interactions, are discussed using the mean-field approximations, and exact Quantum Monte Carlo techniques (the Worm algorithm).Comment: 56 pages, 26 figure