Creation and characterization of vortex clusters in atomic Bose-Einstein condensates

We show that a moving obstacle, in the form of an elongated paddle, can create vortices that are dispersed, or induce clusters of like-signed vortices in 2D Bose-Einstein condensates. We propose new statistical measures of clustering based on Ripley's K-function which are suitable to the small size and small number of vortices in atomic condensates, which lack the huge number of length scales excited in larger classical and quantum turbulent fluid systems. The evolution and decay of clustering is analyzed using these measures. Experimentally it should prove possible to create such an obstacle by a laser beam and a moving optical mask. The theoretical techniques we present are accessible to experimentalists and extend the current methods available to induce 2D quantum turbulence in Bose-Einstein condensates.Comment: 9 pages, 9 figure

Two-dimensional supersolidity in a planar dipolar Bose gas

We investigate the crystalline stationary states of a dipolar Bose-Einstein condensate in a planar trapping geometry. Our focus is on the ground state phase diagram in the thermodynamic limit, where triangular, honeycomb and stripe phases occur. We quantify the superfluid fraction by calculating the non-classical translational inertia, which allows us to identify favorable parameter regimes for observing supersolid ground states. We develop two simplified theories to approximately describe the ground states, and consider the relationship to roton softening in the uniform ground state. This also allows us to extend the phase diagram to the low density regime. While the triangular and honeycomb states have an isotropic superfluid response tensor, the stripe state exhibits anisotropic superfluidity.Comment: 10 pages, 8 figure

Dynamic quantum clustering: a method for visual exploration of structures in data

A given set of data-points in some feature space may be associated with a Schrodinger equation whose potential is determined by the data. This is known to lead to good clustering solutions. Here we extend this approach into a full-fledged dynamical scheme using a time-dependent Schrodinger equation. Moreover, we approximate this Hamiltonian formalism by a truncated calculation within a set of Gaussian wave functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition or feature filtering.Comment: 15 pages, 9 figure

Spatially embedded random networks

Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples

A Network of Community Partners Representing Multiple Communities: Developing a Tool for Matching Community- Engaged Scholars with Community Partners

The Community Partnership for Ethical Research (CPER) was a multi-faceted research project designed to test a model of community engagement using a network of community partners called Community Advocates for Research (CARs). The goals of the project included developing systems to sustain and expand the CARs network. This article presents one facet of this project—a method of effectively and efficiently managing data about the CARs. User-friendly surveys and a database were designed for the management of these data. The web-based survey allows data capture in the community. Moreover, the web-based database tools facilitate centralized data collection and management that will contribute to the sustainability of the network of CARs beyond the initial grant that provided the funding for its development. This article describes the surveys and database and their utility for other institutions desiring to establish similar networks of community partners

Non-equilibrium dynamics of stochastic point processes with refractoriness

Stochastic point processes with refractoriness appear frequently in the quantitative analysis of physical and biological systems, such as the generation of action potentials by nerve cells, the release and reuptake of vesicles at a synapse, and the counting of particles by detector devices. Here we present an extension of renewal theory to describe ensembles of point processes with time varying input. This is made possible by a representation in terms of occupation numbers of two states: Active and refractory. The dynamics of these occupation numbers follows a distributed delay differential equation. In particular, our theory enables us to uncover the effect of refractoriness on the time-dependent rate of an ensemble of encoding point processes in response to modulation of the input. We present exact solutions that demonstrate generic features, such as stochastic transients and oscillations in the step response as well as resonances, phase jumps and frequency doubling in the transfer of periodic signals. We show that a large class of renewal processes can indeed be regarded as special cases of the model we analyze. Hence our approach represents a widely applicable framework to define and analyze non-stationary renewal processes.Comment: 8 pages, 4 figure

The Geography of Scientific Productivity: Scaling in U.S. Computer Science

Here we extract the geographical addresses of authors in the Citeseer database of computer science papers. We show that the productivity of research centres in the United States follows a power-law regime, apart from the most productive centres for which we do not have enough data to reach definite conclusions. To investigate the spatial distribution of computer science research centres in the United States, we compute the two-point correlation function of the spatial point process and show that the observed power-laws do not disappear even when we change the physical representation from geographical space to cartogram space. Our work suggests that the effect of physical location poses a challenge to ongoing efforts to develop realistic models of scientific productivity. We propose that the introduction of a fine scale geography may lead to more sophisticated indicators of scientific output.Comment: 6 pages, 3 figures; minor change

A Bayesian reassessment of nearest-neighbour classification

The k-nearest-neighbour procedure is a well-known deterministic method used in supervised classification. This paper proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment made in a previous analysis of this method undertaken by Holmes and Adams (2002,2003), and evaluated by Manocha and Girolami (2007), where the underlying probabilistic model is not completely well-defined. Once a clear probabilistic basis for the k-nearest-neighbour procedure is established, we derive computational tools for conducting Bayesian inference on the parameters of the corresponding model. In particular, we assess the difficulties inherent to pseudo-likelihood and to path sampling approximations of an intractable normalising constant, and propose a perfect sampling strategy to implement a correct MCMC sampler associated with our model. If perfect sampling is not available, we suggest using a Gibbs sampling approximation. Illustrations of the performance of the corresponding Bayesian classifier are provided for several benchmark datasets, demonstrating in particular the limitations of the pseudo-likelihood approximation in this set-up

Frequency Dependent Rheology of Vesicular Rhyolite

Frequency dependent rheology of magmas may result from the presence of inclusions (bubbles, crystals) in the melt and/or from viscoelastic behavior of the melt itself. With the addition of deformable inclusions to a melt possessing viscoelastic properties one might expect changes in the relaxation spectrum of the shear stresses of the material (e.g., broadening of the relaxation spectrum) resulting from the viscously deformable geometry of the second phase. We have begun to investigate the effect of bubbles on the frequency dependent rheology of rhyolite melt. The present study deals with the rheology of bubble-free and vesicular rhyolite melts containing spherical voids of 10 and 30 vol %. We used a sinusoidal torsion deformation device. Vesicular rhyolite melts were generated by the melting (at 1 bar) of an Armenian obsidian (Dry Fountain, Erevan, Armenia) and Little Glass Mountain obsidian (California). The real and imaginary parts of shear viscosity and shear modulus have been determined in a frequency range of 0.005–10 Hz and temperature range of 600°–900°C. The relaxed shear viscosities of samples obtained at low frequencies and high temperatures compare well with data previously obtained by parallel plate viscometry. The relaxed shear viscosity of vesicular rhyolites decreases progressively with increasing bubble content. The relaxation spectrum for rhyolite melt without bubbles has an asymmetric form and fits an extended exponent relaxation. The presence of deformable bubbles results in an imaginary component of the shear modulus that becomes more symmetrical and extends into the low-frequency/high-temperature range. The internal friction Q −1 is unaffected in the high-frequency/low-temperature range by the presence of bubbles and depends on the bubble content in the high-temperature/low-frequency range. The present work, in combination with the previous study of Stein and Spera (1992), illustrates that magma viscosity can either increase or decrease with bubble content, depending upon the rate of style of strain during magmatic flow

Generalized index for spatial data sets as a measure of complete spatial randomness

Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations—the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.Emily J. Hackett-Jones, Kale J. Davies, Benjamin J. Binder, and Kerry A. Landma