Graded Signal Functions for ARTMAP Neural Networks

This study presents an analysis of a modified ARTMAP neural network in which a graded signal function replaces the standard choice-by-difference function. The modifications are introduced mathematically and the performance of the system is studied on two benchmark examples. It is shown that the modified ARTMAP system achieves classification accuracy superior to that of standard ARTMAP, while retaining comparable complexity of the internal code.Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657

Artificial neural networks in geospatial analysis

Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms

Impact of School-Based Sex Education on College Students’ Rape Myth Acceptance: An Exploratory Analysis

Research indicates nearly one-fourth of college women fall victim to sexual assault (Cantor et. al., 2015). Two predictors of high proclivity to rape are endorsement of rape myths and adherence to traditional gender norms (King & Roberts, 2011). Additionally, research shows school-based sex education in the United States presents gender and sexual norms in troubling ways that disproportionately harm women (Kendall, 2013). However, research on sexual assault and rape myths have not examined the impact school-based sex education has on rape supportive attitudes. This study aimed to bridge that gap by using original survey data from undergraduate students at a large public university. Analyses indicate sex education has an inconsistent impact on rape myth acceptance; additionally, seeking sexual health information online was found to significantly lower endorsement of rape myths. Study outcomes suggest that further research is needed to explore the relationship between sex education curricula and rape supportive attitudes

Legal Education Unbundled (and Rebundled)

This essay calls for an unbundling of legal education, much like the kind of unbundling we have seen in the cable, music, and print news media. It suggests that the standard legal education bundle -the generalized JD-is just one of many forms of legal education that can be packaged appropriately for today\u27s legal education market needs

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Analogous: Digital / Analogue Metaphors.

When discussing our understanding of the world, the term ‘analogue’ has become shorthand for anything not digital, and has become an analogy of its own. ‘Digital’ has also become an analogy for anything requiring a computer. This essay starts to investigate some of the analogies of analogue and digital media to reveal the complexity of thinking about animation

Shaggy Modernism

A short paper situating James Hutchinson's artwork 'Shaggy Modernism' within the history of craft and computing

Neural Network Models of Learning and Memory: Leading Questions and an Emerging Framework

Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (NIH 20-316-4304-5

Adaptive Resonance: An Emerging Neural Theory of Cognition

Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, neural, computational, and technological domains. Minimal models provide a conceptual framework, for formulating questions about the nature of cognition; an architectural framework, for mapping cognitive functions to cortical regions; a semantic framework, for precisely defining terms; and a computational framework, for testing hypotheses. These systems are here exemplified by the distributed ART (dART) model, which generalizes localist ART systems to allow arbitrarily distributed code representations, while retaining basic capabilities such as stable fast learning and scalability. Since each component is placed in the context of a unified real-time system, analysis can move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness. Local design is driven by global functional constraints, with each network synthesizing a dynamic balance of opposing tendencies. The self-contained working ART and dART models can also be transferred to technology, in areas that include remote sensing, sensor fusion, and content-addressable information retrieval from large databases.Office of Naval Research and the defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (20-316-4304-5