A model of a pumped continuous atom laser

We present a model of a cw atom laser based on a system of coupled GP equations. The model incorporates continuous Raman outcoupling, pumping and three-body recombination. The outcoupled field has minimal atomic density fluctuations and is locally monochromatic.Comment: 10 pages, 8 eps figures, typos fixe

Stability of continuously pumped atom lasers

A multimode model of a continuously pumped atom laser is shown to be unstable below a critical value of the scattering length. Above the critical scattering length, the atom laser reaches a steady state, the stability of which increases with pumping. Below this limit the laser does not reach a steady state. This instability results from the competition between gain and loss for the excited states of the lasing mode. It will determine a fundamental limit for the linewidth of an atom laser beam.Comment: 4 page

Remote Detection of Saline Intrusion in a Coastal Aquifer Using Borehole Measurements of Self-Potential

Funded by NERC CASE studentship . Grant Number: NE/I018417/1Peer reviewedPublisher PD

DNA and pacific commensal models : applications, construction, limitations, and future prospects

Components of the Pacific transported landscape have been used as proxies to trace the prehistoric movement of humans across the Pacific for almost two decades. Analyses of archaeological remains and DNA sequences of plants, animals, and microorganisms moved by or with humans have contributed to understanding prehistoric migration, trade, exchange, and sometimes revealed the geographic origins of particular plants and animals. This paper presents the basic elements of a DNA-based commensal model and discusses the phylogenetic and population genetic approaches these models employ. A clear delineation of the underlying assumptions of these models and the background information required to construct them have yet to appear in the literature. This not only provides a framework with which to construct a commensal model but also highlights gaps in current knowledge. The ways in which commensal models have enriched archaeological reconstructions will be highlighted, as will their current limitations. With these limitations in mind, options will be outlined for augmenting commensal models through the application of established techniques and new technologies in order to provide the best tools for reconstructing ancient human mobility and behavior in the Pacific and beyond

Sparse Nerves in Practice

Topological data analysis combines machine learning with methods from algebraic topology. Persistent homology, a method to characterize topological features occurring in data at multiple scales is of particular interest. A major obstacle to the wide-spread use of persistent homology is its computational complexity. In order to be able to calculate persistent homology of large datasets, a number of approximations can be applied in order to reduce its complexity. We propose algorithms for calculation of approximate sparse nerves for classes of Dowker dissimilarities including all finite Dowker dissimilarities and Dowker dissimilarities whose homology is Cech persistent homology. All other sparsification methods and software packages that we are aware of calculate persistent homology with either an additive or a multiplicative interleaving. In dowker_homology, we allow for any non-decreasing interleaving function $\alpha$. We analyze the computational complexity of the algorithms and present some benchmarks. For Euclidean data in dimensions larger than three, the sizes of simplicial complexes we create are in general smaller than the ones created by SimBa. Especially when calculating persistent homology in higher homology dimensions, the differences can become substantial

Bayesian Exponential Random Graph Models with Nodal Random Effects

We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.Comment: 23 pages, 9 figures, 3 table