A RAPID YES-NO COMPUTER-AIDED COMMUNICATOR

Abstract

When attacking the German Enigma cipher machine during the 1930s, the Polish mathematician Marian Rejewski developed a catalog of disjoint cycles of permutations generated by Enigma indicators. By comparing patterns that resulted from message indicators with his catalog, Rejewski was able to determine the ground settings. Well, not quite—the mapping from the disjoint cycles to the ground settings is not one-to-one. Rejewski's catalog no longer exists. This article reports on the output of a program that “recreates” the catalog and answers the question “How far from being one-to-one is the mapping?”     

Statistical analysis of manual segmentations of structures in medical images

The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.     

MANAGING DISTRIBUTED COMPUTING Five Practices for Success

Abstract

Management of distributed environments requires corporate IS managers to shift from being operators of a central utility to facilitators who can recognize the synergy among loosely related groups of users. Success depends on the development of new ways of visualizing and measuring the service delivery process.     

Aesthetic 3D model evolution

A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.     

Structural failure determination with fuzzy sets

This paper has four objectives. Firstly, modifications are recommended and presented to a well known set of US Army test data representing eleven tests that involve destruction of buried concrete box structures by ground transmitted shock waves. The resultant database is available for failure mode analysis. Secondly, a new approach forfailure mode analysis based on pattern recognition techniques to the structural engineering community is introduced. Thirdly, a comparison is presented of the results of preprocessing, feature extraction, and cluster analysis for failure modes to several previous studies. The objective here is to inform the structural engineering community about data interpretations that can be inferred using this approach; the authors do not claim to have a conclusive or definitive classification of failure modes in these data. Finally, the importance of a priori decisions about data analysis during the planning stages of physical experiments is emphasized by pointing out the many statistical and other uncertainities that are associated with the data being discussed.     

On the formalization and reuse of scientific research

The reuse of scientific knowledge obtained from one investigation in another investigation is basic to the advance of science. Scientific investigations should therefore be recorded in ways that promote the reuse of the knowledge they generate. The use of logical formalisms to describe scientific knowledge has potential advantages in facilitating such reuse. Here, we propose a formal framework for using logical formalisms to promote reuse. We demonstrate the utility of this framework by using it in a worked example from biology: demonstrating cycles of investigation formalization [F] and reuse [R] to generate new knowledge. We first used logic to formally describe a Robot scientist investigation into yeast (Saccharomyces cerevisiae) functional genomics [f₁]. With Robot scientists, unlike human scientists, the production of comprehensive metadata about their investigations is a natural by-product of the way they work. We then demonstrated how this formalism enabled the reuse of the research in investigating yeast phenotypes [r₁ = R(f₁)]. This investigation found that the removal of non-essential enzymes generally resulted in enhanced growth. The phenotype investigation was then formally described using the same logical formalism as the functional genomics investigation [f₂ = F(r₁)]. We then demonstrated how this formalism enabled the reuse of the phenotype investigation to investigate yeast systems-biology modelling [r₂ = R(f₂)]. This investigation found that yeast flux-balance analysis models fail to predict the observed changes in growth. Finally, the systems biology investigation was formalized for reuse in future investigations [f₃ = F(r₂)]. These cycles of reuse are a model for the general reuse of scientific knowledge.