A Neural Network Model of 3-D Lightness Perception

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions. Simulations of the model address data on lightness perception, including the coplanar ratio hypothesis, the Benary cross and VVhite's illusion.Air Force Office of Scientific Research (F49620-92-J-0334); Office of Naval Research (N00014-91-J-4100); HNC SC-94-00

A Contrast/Filling-In Model of 3-D Lightness Perception

Wallach's ratio hypothesis states that local luminance ratios clr!termine lightness perception under variable illumination. While local luminance ratios successfully discount gradual variations in illumination (illumination constancy or Type I constancy), they fail to explain lightness constancy in general. Some examples of failures of the ratio hypothesis include effects suggesting the coplanar ratio hypothesis (Gilchrist 1977), "assimilation" effects, and configural effects such as the Benary cross, and White's illusion. The present article extends the Boundary Contour System/Feature Contour System (BCS/FCS) approach to provide an explanation of these effects in terms of a neural model of 3-D lightness perception. Lightness constancy of objects in front of different backgrounds (background constancy or Type II constancy) is used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness. Simulations of the model applied to several stimuli including Benary cross and White's illusion show that contrast negation mechanisms modulate illumination constancy mechanisms to extend the explanatory power of the model. The model is also used to devise new stimuli that test theoretical predictions

Africa's growth tragedy : a retrospective, 1960-89

Africa's economic history since 1960 fits the classical definition of tragedy: potential unfulfilled with disastrous consequences. The authors use one mehthodology - cross-country regressions - to account for sub-Saharan Africa's growth performance over the past 30 years and to suggest policies to promote growth over the next 30 years. They statistically quantify the relationship between long-run growth and a wider array of factors than any previous study. They consider such standard variables as initial income to capture convergence effects, schooling, political stability and indicators of monetary, fiscal, trade, exchange rate, and financial sector policies. They also consider such new measures as infrastructure development, cultural diversity, and economic spillovers from neighbors'growth. Their analysis: 1) improves substantially on past attempts to account for the growth experience of sub-Saharan African countries; 2) shows that low school attainment, political instability, poorly developed financial systems, large black-market exchange-rate premia, large government deficits, and inadequate infrastructure are associated with slow growth; 3) finds that Africa's ethnic diversity tends to slow growth and reduce the likelihood of adopting good policies; 4) identifies spillovers of growth performance between neighboring countries. The spillover effects of growth have implications for policy strategy. Improving policies alone boosts growth substantially, but if neighboring countries act together, the effects on growth are much greater. Specifically, the results suggest that the effects of neighbor's adopting a policy change is 2.2 times greater than if a single country acted alone.Economic Conditions and Volatility,Public Health Promotion,Health Monitoring&Evaluation,Economic Theory&Research,Environmental Economics&Policies,Achieving Shared Growth,Governance Indicators,Economic Growth,Economic Conditions and Volatility,Inequality

It's Not Factor Accumulation: Stylized Facts and Growth Models

We document five stylized facts of economic growth. (1) The “residual” rather than factor accumulation accounts for most of the income and growth differences across nations. (2) Income diverges over the long run. (3) Factor accumulation is persistent while growth is not persistent and the growth path of countries exhibits remarkable variation across countries. (4) Economic activity is highly concentrated, with all factors of production flowing to the richest areas. (5) National policies closely associated with long-run economic growth rates. We argue that these facts do not support models with diminishing returns, constant returns to scale, some fixed factor of production, and that highlight the role of factor accumulation. Empirical work, however, does not yet decisively distinguish among the different theoretical conceptions of “total factor productivity growth.” Economists should devote more effort towards modeling and quantifying total factor productivity.

Furniture manufacturing and wood use in the north central region.

"Sponsored by the agricultural experiment stations of Illinois [and others]""Agricultural experiment stations of Alaska, Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin, and the U.S. Department of Agriculture cooperating."Bibliography: p. 66

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.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 (90-0083

Incremental Art: A Neural Network System for Recognition by Incremental Feature Extraction

Abstract Incremental ART extends adaptive resonance theory (ART) by incorporating mechanisms for efficient recognition through incremental feature extraction. The system achieves efficient confident prediction through the controlled acquisition of only those features necessary to discriminate an input pattern. These capabilities are achieved through three modifications to the fuzzy ART system: (1) A partial feature vector complement coding rule extends fuzzy ART logic to allow recognition based on partial feature vectors. (2) The addition of a F2 decision criterion to measure ART predictive confidence. (3) An incremental feature extraction layer computes the next feature to extract based on a measure of predictive value. Our system is demonstrated on a face recognition problem but has general applicability as a machine vision solution and as model for studying scanning patterns.Office of Naval Research (N00014-92-J-4015, N00014-92-J-1309, N00014-91-4100); Air Force Office of Scientific Research (90-0083); National Science Foundation (IRI 90-00530