Effects of initial height on the steady-state persistence probability of linear growth models

The effects of the initial height on the temporal persistence probability of steady-state height fluctuations in up-down symmetric linear models of surface growth are investigated. We study the (1 + 1)-dimensional Family model and the (1 + 1)-and (2 + 1)-dimensional larger curvature (LC) model. Both the Family and LC models have up-down symmetry, so the positive and negative persistence probabilities in the steady state, averaged over all values of the initial height h(0), are equal to each other. However, these two probabilities are not equal if one considers a fixed nonzero value of h(0). Plots of the positive persistence probability for negative initial height versus time exhibit power-law behavior if the magnitude of the initial height is larger than the interface width at saturation. By symmetry, the negative persistence probability for positive initial height also exhibits the same behavior. The persistence exponent that describes this power-law decay decreases as the magnitude of the initial height is increased. The dependence of the persistence probability on the initial height, the system size, and the discrete sampling time is found to exhibit scaling behavior.

Surface Degradation of Silicone Rubber Nanocomposites Due to DC Corona Discharge

Corona discharge is recognized as one of the mechanisms that can influence the surface hydrophobicity of Silicone Rubber (SR) because of the chemical changes that occur on its surface. In this study SR samples were exposed to positive and negative DC corona for 25 and 50 hours using a needle-plane electrode system. Hydrophobicity changes were monitored using a sessile drop contact angle measurement facility. The physical changes on the surface were studied using Scanning Electron Microscopy (SEM) and surface roughness measurements. The effect of positive dc corona was found to be different from that of negative dc corona. Significant surface degradation and loss of hydrophobicity was found in the case of negative dc corona exposed samples. Significant improvement in the above mentioned properties were obtained by adding small quantities of nSIL into the SR matrix.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

The role of buoyancy in polarity reversals of the geodynamo

We investigate polarity reversals in the geodynamo using a rotating, convection-driven dynamo model. As the flow in rapidly rotating convection is dominated by columns aligned with the axis of rotation, the focus is on the dynamics of columnar vortices. By studying the growth of a seed magnetic field to a stable axial dipole field, we show that the magnetic field acts in ways that significantly enhance the relative helicity between cyclonic and anticyclonic vortices. This flow asymmetry is the hallmark of a dipolar dynamo. Strong buoyancy, on the other hand, offsets the effect of the magnetic field, establishing parity between positive and negative vortices. As the dipole field is deprived of the helicity required to support itself, the dynamo is pushed into a reversing state. This is a likely regime for polarity reversals in the Earth's core. The integral lengthscale at which buoyancy injects energy is not significantly different from the convective flow lengthscale, which implies that buoyancy does not feed vortices at the small scales where non-linear inertia is present. The lengthscale at which the Lorentz force acts in the reversing dynamo is small, which may allow the passive presence of non-linear inertia in the small scales.