Pseudo-High-Order Symplectic Integrators

Symplectic N-body integrators are widely used to study problems in celestial mechanics. The most popular algorithms are of 2nd and 4th order, requiring 2 and 6 substeps per timestep, respectively. The number of substeps increases rapidly with order in timestep, rendering higher-order methods impractical. However, symplectic integrators are often applied to systems in which perturbations between bodies are a small factor of the force due to a dominant central mass. In this case, it is possible to create optimized symplectic algorithms that require fewer substeps per timestep. This is achieved by only considering error terms of order epsilon, and neglecting those of order epsilon^2, epsilon^3 etc. Here we devise symplectic algorithms with 4 and 6 substeps per step which effectively behave as 4th and 6th-order integrators when epsilon is small. These algorithms are more efficient than the usual 2nd and 4th-order methods when applied to planetary systems.Comment: 14 pages, 5 figures. Accepted for publication in the Astronomical Journa

Geolinguistic Patterns in a Vast Speech Community

The Dialect Topography of Canada has reached a kind of plateau. After ten years of data-gathering, from 1992 to 2002, we have assembled large databases on language variants in regions across Canada. The databases are accessible at dialect. topography. chass. utoronto. ca. The website, constructed by Dr. Tony Pi, is free of charge and user-friendly, with tutorials and analytic aids. We are not presently engaged in Dialect Topography surveys in other regions. In years to come, there will undoubtedly be more regional surveys and new surveys of the original regions, but the time gap between the existing ones and the ones that will follow entails that they will relate to one another not as additional contemporaneous surveys but as real-time comparisons. In this article, I illustrate the breadth of coverage by investigating three geolinguistic patterns that have emerged from our research. I begin with a brief introduction to the methods and goals of Dialect Topography. In so doing, I cannot avoid noting a salubrious coincidence. The first public presentation on Dialect Topography took place at Universite de Moncton, at a meeting of the Atlantic Provinces Linguistic Association in 1992. The presentation on which this article is based, which represents a kind of stock-taking on what we have accomplished with Dialect Topography at this juncture, also took place at Universite de Moncton. That first presentation, fourteen years ago, resulted in an article that provided an introduction to Dialect Topography (Chambers 1994). That article is fuller and more discursive than space allows here, and I am pleased to refer readers to it to fill in any gaps I leave here. The "distance" between that first presentation and this one symbolically represents a huge investment of time and effort by a team of dedicated scholars.  Our bond comes not only from the many hours we spent working together but also in the shared belief that we have left behind a resource that has almost limitless potential

A ‘work in progress’?: UK researchers and participation in public engagement

The funders of UK research seek to embed public engagement by researchers within the culture of UK research. Within this context, this paper provides a snapshot of the UK public engagement landscape by reporting on new quantitative research that examines the experiences and perspectives of UK researchers (n = 2,454) and public engagement support staff (n = 260). The research suggests that ambitions to embed public engagement by researchers within institutional cultures can be understood as a 'work in progress'. There are indications that public engagement is part of the UK research landscape. At the same time, the research suggests that researchers' public engagement efforts are currently constrained; there is evidence of a disconnect between researchers themselves and broader institutional contexts of public engagement, and the sector is overwhelmingly driven by funding and rewards for research, teaching and other activities. In conclusion, these results indicate that, while current strategies have been helpful, longer term effort is required, perhaps targeting particular domains and, more fundamentally, perhaps featuring greater support and reward for public engagement

On data skewness, stragglers, and MapReduce progress indicators

We tackle the problem of predicting the performance of MapReduce applications, designing accurate progress indicators that keep programmers informed on the percentage of completed computation time during the execution of a job. Through extensive experiments, we show that state-of-the-art progress indicators (including the one provided by Hadoop) can be seriously harmed by data skewness, load unbalancing, and straggling tasks. This is mainly due to their implicit assumption that the running time depends linearly on the input size. We thus design a novel profile-guided progress indicator, called NearestFit, that operates without the linear hypothesis assumption and exploits a careful combination of nearest neighbor regression and statistical curve fitting techniques. Our theoretical progress model requires fine-grained profile data, that can be very difficult to manage in practice. To overcome this issue, we resort to computing accurate approximations for some of the quantities used in our model through space- and time-efficient data streaming algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive empirical assessment over the Amazon EC2 platform on a variety of real-world benchmarks shows that NearestFit is practical w.r.t. space and time overheads and that its accuracy is generally very good, even in scenarios where competitors incur non-negligible errors and wide prediction fluctuations. Overall, NearestFit significantly improves the current state-of-art on progress analysis for MapReduce

Cloning of the rDNA repeat unit: An EcoRI fragment spanning the entire nontranscribed spacer region of Neurospora crassa wild type strain 74A

Cloning of the rDNA repeat unit: An EcoRI fragment spanning the entire nontranscribed spacer region of Neurospora crassa wild type strain 74