Observation of polarization domain wall solitons in weakly birefringent cavity fiber lasers

We report on the experimental observation of two types of phase-locked vector soliton in weakly birefringent cavity erbium-doped fiber lasers. While a phase-locked dark-dark vector soliton was only observed in fiber lasers of positive dispersion, a phase-locked dark-bright vector soliton was obtained in fiber lasers of either positive or negative dispersion. Numerical simulations confirmed the experimental observations, and further showed that the observed vector solitons are the two types of phase-locked polarization domain-wall solitons theoretically predicted.Comment: 14 pages, 4 Figure

Tests of Bayesian Model Selection Techniques for Gravitational Wave Astronomy

The analysis of gravitational wave data involves many model selection problems. The most important example is the detection problem of selecting between the data being consistent with instrument noise alone, or instrument noise and a gravitational wave signal. The analysis of data from ground based gravitational wave detectors is mostly conducted using classical statistics, and methods such as the Neyman-Pearson criteria are used for model selection. Future space based detectors, such as the \emph{Laser Interferometer Space Antenna} (LISA), are expected to produced rich data streams containing the signals from many millions of sources. Determining the number of sources that are resolvable, and the most appropriate description of each source poses a challenging model selection problem that may best be addressed in a Bayesian framework. An important class of LISA sources are the millions of low-mass binary systems within our own galaxy, tens of thousands of which will be detectable. Not only are the number of sources unknown, but so are the number of parameters required to model the waveforms. For example, a significant subset of the resolvable galactic binaries will exhibit orbital frequency evolution, while a smaller number will have measurable eccentricity. In the Bayesian approach to model selection one needs to compute the Bayes factor between competing models. Here we explore various methods for computing Bayes factors in the context of determining which galactic binaries have measurable frequency evolution. The methods explored include a Reverse Jump Markov Chain Monte Carlo (RJMCMC) algorithm, Savage-Dickie density ratios, the Schwarz-Bayes Information Criterion (BIC), and the Laplace approximation to the model evidence. We find good agreement between all of the approaches.Comment: 11 pages, 6 figure

Detection of OH absorption against PSR B1849+00

We have searched for OH absorption against seven pulsars using the Arecibo telescope. In both OH mainlines (at 1665 and 1667 MHz), deep and narrow absorption features were detected toward PSR B1849+00. In addition, we have detected several absorption and emission features against B33.6+0.1, a nearby supernova remnant (SNR). The most interesting result of this study is that a pencil-sharp absorption sample against the PSR differs greatly from the large-angle absorption sample observed against the SNR. If both the PSR and the SNR probe the same molecular cloud then this finding has important implications for absorption studies of the molecular medium, as it shows that the statistics of absorbing OH depends on the size of the background source. We also show that the OH absorption against the PSR most likely originates from a small (<30 arcsec) and dense (>10^5 cm^-3) molecular clump.Comment: 12 pages, 8 figures. Accepted for publication in Ap

Dressing Technique for Intermediate Hierarchies

A generalized AKNS systems introduced and discussed recently in \cite{dGHM} are considered. It was shown that the dressing technique both in matrix pseudo-differential operators and formal series with respect to the spectral parameter can be developed for these hierarchies.Comment: 16 pages, LaTeX Report/no: DFTUZ/94/2

Satellite Evidence of Hurricane-Induced Phytoplankton Blooms in an Oceanic Desert

The physical effects of hurricanes include deepening of the mixed layer and decreasing of the sea surface temperature in response to entrainment, curl-induced upwelling, and increased upper ocean cooling. However, the biological effects of hurricanes remain relatively unexplored. In this paper, we examine the passages of 13 hurricanes through the Sargasso Sea region of the North Atlantic during the years 1998 through 2001. Remotely sensed ocean color shows increased concentrations of surface chlorophyll within the cool wakes of the hurricanes, apparently in response to the injection of nutrients and/or biogenic pigments into the oligotrophic surface waters. This increase in post-storm surface chlorophyll concentration usually lasted 2-3 weeks before it returned to its nominal pre-hurricane level

“Doctor my eyes” : A natural experiment on the demand for eye care services

This paper is dedicated to our friend Divine Ikenwilo, who passed away on the 27th November 2015. Divine was a gifted researcher who was taken from us too early and will be sorely missed by everyone in the team. Our thoughts are with his family. This research was funded by a research grant (CGZ/2/533) from the Chief Scientist Office of the Scottish Government. The Health Economics Research Unit is funded by the Scottish Government Health and Social Care Directorate. The usual disclaimer applies.Peer reviewedPostprin

Dispersionful analogues of Benney's equations and NN-wave systems

We recall Krichever's construction of additional flows to Benney's hierarchy, attached to poles at finite distance of the Lax operator. Then we construct a ``dispersionful'' analogue of this hierarchy, in which the role of poles at finite distance is played by Miura fields. We connect this hierarchy with $N$-wave systems, and prove several facts about the latter (Lax representation, Chern-Simons-type Lagrangian, connection with Liouville equation, $\tau$-functions).Comment: 12 pages, latex, no figure

Neural Networks for Time Series Forecasting: Practical Implications of Theoretical Results

Research on the performance of neural networks in modeling nonlinear time series has produced mixed results. While neural networks have great potential because of their status as universal approximators When Faraway and Chatfield (1998) used an autoregressive neural network to forecast airline data, they found that the neural networks they specified frequently would not converge. When they did converge, they failed to find the global minimum of the objective function. In some cases, neural networks that fit the in-sample data well performed poorly on holdout samples. In conducting the NN3 competition, a time series forecasting competition designed to showcase autoregressive neural networks and other computationally-intensive methods of forecasting, standard methods such as ARIMA models still out-performed autoregressive neural networks (Crone et