A Lazy Approach for Supporting Nested Transactions

Transactional memory (TM) is a compelling alternative to traditional synchronization, and implementing TM primitives directly in hardware offers a potential performance advantage over software-based methods. In this paper, we demonstrate that many of the actions associated with transaction abort and commit may be performed lazily -- that is, incrementally, and on demand. This technique is ideal for hardware, since it requires little space or work; in addition, it can improve performance by sparing accesses to committing or aborting locations from having to stall until the commit or abort completes. We further show that our lazy abort and commit technique supports open nesting and orElse, two language-level proposals which rely on transactional nesting. We also provide design notes for supporting lazy abort and commit on our own hardware TM system, based on VTM

Identifying Clusters in Bayesian Disease Mapping

Disease mapping is the field of spatial epidemiology interested in estimating the spatial pattern in disease risk across $n$ areal units. One aim is to identify units exhibiting elevated disease risks, so that public health interventions can be made. Bayesian hierarchical models with a spatially smooth conditional autoregressive prior are used for this purpose, but they cannot identify the spatial extent of high-risk clusters. Therefore we propose a two stage solution to this problem, with the first stage being a spatially adjusted hierarchical agglomerative clustering algorithm. This algorithm is applied to data prior to the study period, and produces $n$ potential cluster structures for the disease data. The second stage fits a separate Poisson log-linear model to the study data for each cluster structure, which allows for step-changes in risk where two clusters meet. The most appropriate cluster structure is chosen by model comparison techniques, specifically by minimising the Deviance Information Criterion. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Bayesian cluster detection via adjacency modelling

Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed solution to this problem is a two-stage approach, which produces a set of potential cluster structures for the data and then chooses the optimal structure via a Bayesian hierarchical model. The first stage uses a spatially adjusted hierarchical agglomerative clustering algorithm. The second stage fits a Poisson log-linear model to the data to estimate the optimal cluster structure and the spatial pattern in disease risk. The methodology was applied to a study of chronic obstructive pulmonary disease (COPD) in local authorities in England, where a number of high risk clusters were identified

Spatial clustering of average risks and risk trends in Bayesian disease mapping

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Evidence of global-scale aeolian dispersal and endemism in isolated geothermal microbial communities of Antarctica

New evidence in aerobiology challenges the assumption that geographical isolation is an effective barrier to microbial transport. However, given the uncertainty with which aerobiological organisms are recruited into existing communities, the ultimate impact of microbial dispersal is difficult to assess. To evaluate the ecological significance of global-scale microbial dispersal, molecular genetic approaches were used to examine microbial communities inhabiting fumarolic soils on Mt. Erebus, the southernmost geothermal site on Earth. There, hot, fumarolic soils provide an effective environmental filter to test the viability of organisms that have been distributed via aeolian transport over geological time. We find that cosmopolitan thermophiles dominate the surface, whereas endemic Archaea and members of poorly understood Bacterial candidate divisions dominate the immediate subsurface. These results imply that aeolian processes readily disperse viable organisms globally, where they are incorporated into pre-existing complex communities of endemic and cosmopolitan taxa

AJAE Appendix: Nonlinear Dynamics and Structural Change in the U.S. Hog-Corn Ratio: A Time-Varying Star Approach

The material contained herein is supplementary to the article named in the title and published in the American Journal of Agricultural Economics, Volume 88, Number 1, February 2006.Agribusiness,

Convergence (and Divergence) in the Biological Standard of Living in the United States, 1820-1900

Standard economic indicators suggest that the United States experienced long-run economic growth throughout the nineteenth century. However, biological indicators, including human stature, offer a different picture, rising early in the century, falling (on average) mid-century, and rising again at the end of the century. This pattern varied across geographical regions. Using a unique data set, consisting of mean adult stature by state, we test for convergence in stature among states in the nineteenth century. We find that during the period of declining mean stature, heights actually diverged. Later in the century we find a type of “negative” convergence indicating that stature among states tended to converge to a new, lower steady state. Only towards the end of the century do we find classic convergence behavior. We argue that the diversity of economic experiences across regions, e.g. urbanization, industrialization, and transportation improvements, explain this pattern of divergence and then convergence.