Information-Theoretic Methods for Identifying Relationships among Climate Variables

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a set of computational techniques that allow one to accurately compute marginal and joint entropies. These algorithms are probabilistic in nature and thus provide information on the uncertainty in our estimates, which enable us to establish statistical significance of our findings. We demonstrate these methods by identifying relations between cloud data from the International Satellite Cloud Climatology Project (ISCCP) and data from other sources, such as equatorial pacific sea surface temperatures (SST).Comment: Presented at the Earth-Sun System Technology Conference (ESTC 2008), Adelphi, MD. http://esto.nasa.gov/conferences/estc2008/ 3 pages, 3 figures. Appears in the Proceedings of the Earth-Sun System Technology Conference (ESTC 2008), Adelphi, M

Sustainable investment in Turkey 2010

The main objectives of this report are as follows: 1 To understand and provide a review of the current state of the Sustainable Investment (SI) market in Turkey, 2 To identify the drivers and obstacles for sustainable investments, and assess the commercial feasibility of different approaches and initiatives that may stimulate the SI market in Turkey, 3 To analyze the institutional prerequisites and interventions that will fuel the development of investments, which would, in turn, encourage a betterallocation of local and international capital to sustainable enterprises and hence support sustainable development of the Turkish economy. This study forms part of a series of assessments of Sustainable Investment (SI) in Brazil (2009), India (2009) and China (2009), and draws upon earlier reports published by IFC jointly with the Economist Intelligence Unit: Sustainable Invest ing in Emerging Markets: Unscathed by the Financial Crises (2010) and with Mercer; Gaining Ground, Integrating Environmental, Social and Governance (ESG) Factors into Investment Processes in Emerging Markets (2009)

Engineering nonlinear response of nanomaterials using Fano resonances

We show that, nonlinear optical processes of nanoparticles can be controlled by the presence of interactions with a molecule or a quantum dot. By choosing the appropriate level spacing for the quantum emitter, one can either suppress or enhance the nonlinear frequency conversion. We reveal the underlying mechanism for this effect, which is already observed in recent experiments: (i) Suppression occurs simply because transparency induced by Fano resonance does not allow an excitation at the converted frequency. (ii) Enhancement emerges since nonlinear process can be brought to resonance. Path interference effect cancels the nonresonant frequency terms. We demonstrate the underlying physics using a simplified model, and we show that the predictions of the model are in good agreement with the 3-dimensional boundary element method (MNPBEM toolbox) simulations. Here, we consider the second harmonic generation in a plasmonic converter as an example to demonstrate the control mechanism. The phenomenon is the semi-classical analog of nonlinearity enhancement via electromagnetically induced transparency.Comment: 10 pages, 6 figure

Connectivity-Driven Coherence in Complex Networks

We study the emergence of coherence in complex networks of mutually coupled non-identical elements. We uncover the precise dependence of the dynamical coherence on the network connectivity, on the isolated dynamics of the elements and the coupling function. These findings predict that in random graphs, the enhancement of coherence is proportional to the mean degree. In locally connected networks, coherence is no longer controlled by the mean degree, but rather on how the mean degree scales with the network size. In these networks, even when the coherence is absent, adding a fraction s of random connections leads to an enhancement of coherence proportional to s. Our results provide a way to control the emergent properties by the manipulation of the dynamics of the elements and the network connectivity.Comment: 4 pages, 2 figure

Velocity Correlations in Dense Gravity Driven Granular Chute Flow

We report numerical results for velocity correlations in dense, gravity-driven granular flow down an inclined plane. For the grains on the surface layer, our results are consistent with experimental measurements reported by Pouliquen. We show that the correlation structure within planes parallel to the surface persists in the bulk. The two-point velocity correlation function exhibits exponential decay for small to intermediate values of the separation between spheres. The correlation lengths identified by exponential fits to the data show nontrivial dependence on the averaging time \dt used to determine grain velocities. We discuss the correlation length dependence on averaging time, incline angle, pile height, depth of the layer, system size and grain stiffness, and relate the results to other length scales associated with the rheology of the system. We find that correlation lengths are typically quite small, of the order of a particle diameter, and increase approximately logarithmically with a minimum pile height for which flow is possible, \hstop, contrary to the theoretical expectation of a proportional relationship between the two length scales.Comment: 21 pages, 16 figure

Determination of ontogenetic selection criteria for grain yield in spring barley (Hordeum vulgare) by pathcoefficient analysis

Path-coefficient analysis was performed to determine the interrelationships among grain yield, yield components (spike number per m-2, kernel number per spike, average kernel weight) and somephenological characteristics (duration and growing degree-days of vegetative and grain-filling periods) in spring barley genotypes in 2004-2005. Grain yield depended mainly on spike number per m-2 andkernel number per spike; average kernel weight had a negligible effect on grain yield in spring barley genotypes. Grain yield was significantly and positively associated with the spike number per m-2 andnegatively correlated with other characteristics studied. Spike number per m-2 had considerable negative effect on the average kernel weight. A lengthening of the grain-filling period induced an increase in the average kernel weight and a positive and significant correlation was found between the two characteristics. Spike number per m-2 and kernel number had positive direct effects on grain yield in spring barley genotypes. The growing degree-days (GDD) for vegetative period had significant positive direct effect on kernel number, and the GDD for grain-filling period had significant positive direct effect on kernel weight. The results indicated that spike number per m-2, kernel number per spike and the GDD for vegetative and grain filling period were the most reliable selection criteria for improving grain yield in spring barley in cool and short-season environments

Quantum Simulator Based on the Paraxial Wave Equation

We propose a paraxial quantum simulator that requires only widely available optical fibers or metamaterials. Such a simulator would facilitate cost-effective quantum simulation without specialized techniques. We show theoretically that the method accurately simulates quantum dynamics and quantum effects for an example system, which invites extension of the method to many-body systems using nonlinear optical elements and implementation of the paraxial quantum simulator to extend access to quantum computation and prototype quantum parity-time reversal ($\mathcal{PT}$) symmetric technologies

A drift-diffusion model for robotic obstacle avoidance

We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Peclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform. For more information: Kod*La