A deterministic algorithm for experimental design applied to tomographic and microseismic monitoring surveys

SUMMARY Most general experimental design algorithms are either: (i) stochastic and hence give different designs each time they are run with finite computing power, or (ii) deterministic but converge to results that depend on an initial or reference design, taking little or no account of the range of all other possible designs. In this paper we introduce an approximation to standard measures of experimental design quality that enables a new algorithm to be used. The algorithm is simple, deterministic and the resulting experimental design is influenced by the full range of possible designs, thus addressing problems (i) and (ii) above. Although the designs produced are not guaranteed to be globally optimal, they significantly increase the magnitude of small eigenvalues in the model–data relationship (without requiring that these eigenvalues be calculated). This reduces the model uncertainties expected post-experiment. We illustrate the method on simple tomographic and microseismic location examples with varying degrees of seismic attenuation

Patterns of Participation and Motivation in Folding@home: The Contribution of Hardware Enthusiasts and Overclockers

Folding@home is a distributed computing project in which participants run protein folding simulations on their computers. Participants complete work units and are awarded points for their contribution. An investigation into motivations to participate and patterns of participation revealed the significant contribution of a sub-community composed of individuals who custom-build computers to maximise their processing power. These individuals, known as “overclockers” or “hardware enthusiasts,” use distributed computing projects such as Folding@home to benchmark their modified computers and to compete with one another to see who can process the greatest number of project work units. Many are initially drawn to the project to learn about computer hardware from other overclockers and to compete for points. However, once they learn more about the scientific outputs of Folding@home, some participants become more motivated by the desire to contribute to scientific research. Overclockers form numerous online communities where members collaborate and help each other maximise their computing output. They invest heavily in their computers and process the majority of Folding@home’s simulations, thus providing an invaluable (and free) resource