Cloud identification using genetic algorithms and massively parallel computation

As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced

Combining the Performance Strengths of the Logistic Regression and Neural Network Models: A Medical Outcomes Approach

The assessment of medical outcomes is important in the effort to contain costs, streamline patient management, and codify medical practices. As such, it is necessary to develop predictive models that will make accurate predictions of these outcomes. The neural network methodology has often been shown to perform as well, if not better, than the logistic regression methodology in terms of sample predictive performance. However, the logistic regression method is capable of providing an explanation regarding the relationship(s) between variables. This explanation is often crucial to understanding the clinical underpinnings of the disease process. Given the respective strengths of the methodologies in question, the combined use of a statistical (i.e., logistic regression) and machine learning (i.e., neural network) technology in the classification of medical outcomes is warranted under appropriate conditions. The study discusses these conditions and describes an approach for combining the strengths of the models

An Intelligence Representation in Agent Systems: An Extended π-Calculus

Intelligent mobile agent technology is one of the most promising of the newer software paradigms for providing solutions to complex, distributed computing problems. Agent properties of autonomy, intelligence and mobility provide a powerful platform for implementations that can utilize techniques involving collaborative problem solving and adaptive behavior. Although the technological tools and capabilities have advanced to this point, research into formal models and extensions to support representations of this new computing paradigm has not been kept pace. Specifically, we find that current formal processing models are lacking in representation abilities for: (1) intelligence capabilities, (2) team-based problem-solving approaches, and (3) mobility. In this paper, we present an extension of π-calculus that addresses the first of these deficiencies, the representation of intelligence

An Inexact Inferencing Strategy for Spatial Objects with Determined and Indeterminate Boundaries

For many years, spatial querying has been of interest for the researchers in the GIS community. Any successful implementation and long-term viability of the GIS technology depends on the issue of accuracy of spatial queries. In order to improve the accuracy and quality of spatial querying, the problems associated with the areas of fuzziness and uncertainty need to be addressed. There has been a strong demand to provide approaches that deal with inaccuracy and uncertainty in GIS. In this paper, we develop an approach that can perform fuzzy spatial querying under uncertainty. An inexact inferencing strategy for objects with determined and indeterminate boundaries is investigated, using type-2 fuzzy set theory

Fuzzy Spatial Querying with Inexact Inference

The issue of spatial querying accuracy has been viewed as critical to the successful implementation and long-term viability of the GIS technology. In order to improve the spatial querying accuracy and quality, the problems associated with the areas of fuzziness and uncertainty are of great concern in the spatial database community. There has been a strong demand to provide approaches that deal with inaccuracy and uncertainty in GIS. In this paper, we are dedicated to develop an approach that can perform fuzzy spatial querying under uncertainty. An inexact inferring strategy is investigated. The study shows that the fuzzy set and the certainty factor can work together to deal with spatial querying. Querying examples implemented by FuzzyClips are also provided

Sharing vocabularies: towards horizontal alignment of values-driven business functions

This paper highlights the emergence of different ‘vocabularies’ that describe various values-driven business functions within large organisations and argues for improved horizontal alignment between them. We investigate two established functions that have long-standing organisational histories: Ethics and Compliance (E&C) and Corporate Social Responsibility (CSR). By drawing upon research on organisational alignment, we explain both the need for and the potential benefit of greater alignment between these values-driven functions. We then examine the structural and socio-cultural dimensions of organisational systems through which E&C and CSR horizontal alignment can be coordinated to improve synergies, address tensions, and generate insight to inform future research and practice in the field of Business and Society. The paper concludes with research questions that can inform future scholarly research and a practical model to guide organizations’ efforts towards inter-functional, horizontal alignment of values-driven organizational practice

Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation

There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity