Testable models of aquatic systems: A NERC special topic for integrating experiments and modelling

A major part of the support for fundamental research on aquatic ecosystems continues to be provided by the Natural Environment Research Council (NERC). Funds are released for ”thematic” studies in a selected special topic or programme. ”Testable Models of Aquatic Ecosystems” was a Special Topic of the NERC, initiated in 1995, the aim of which was to promote ecological modelling by making new links between experimental aquatic biologists and state-of-the-art modellers. The Topic covered both marine and freshwater systems. This paper summarises projects on aspects of the responses of individual organisms to the effects of environmental variability, on the assembly, permanence and resilience of communities, and on aspects of spatial models. The authors conclude that the NERC Special Topic has been highly successful in promoting the development and application of models, most particularly through the interplay between experimental ecologists and formal modellers

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.British Petroleum (98-A-1204); Defense Advanced Research Projects Agency (90-0083, 90-0175, 90-0128); National Science Foundation (IRI-90-00539); Army Research Office (DAAL-03-88-K0088

Spatially Varying X-ray Synchrotron Emission in SN 1006

A growing number of both galactic and extragalactic supernova remnants show non-thermal (non-plerionic) emission in the X-ray band. New synchrotron models, realized as SRESC and SRCUT in XSPEC 11, which use the radio spectral index and flux as inputs and include the full single-particle emissivity, have demonstrated that synchrotron emission is capable of producing the spectra of dominantly non-thermal supernova remnants with interesting consequences for residual thermal abundances and acceleration of particles. In addition, these models deliver a much better-constrained separation between the thermal and non-thermal components, whereas combining an unconstrained powerlaw with modern thermal models can produce a range of acceptable fits. While synchrotron emission can be approximated by a powerlaw over small ranges of energy, the synchrotron spectrum is in fact steepening over the X-ray band. Having demonstrated that the integrated spectrum of SN 1006, a remnant dominated by non-thermal emission, is well described by synchrotron models I now turn to spatially resolved observations of this well studied remnant. The synchrotron models make both spectral and spatial predictions, describing how the non-thermal emission varies across the remnant. Armed with spatially resolved non-thermal models and new thermal models such as VPSHOCK we can now dissect the inner workings of SN 1006.Comment: 4 pages, 4 figures. To appear in "Young Supernova Remnants" the 11th Annual October Maryland Astrophysics Conference AIP eds. Steve Holt and Una Hwan

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-1075