ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

Estimates of fault strength from the Variscan foreland of the northern UK

We provide new insights into the long-standing debate regarding fault strength, by studying structures active in the late Carboniferous in the foreland of the Variscan Mountain range in the northern UK. We describe a method to estimate the seismogenic thickness for ancient deformation zones, at the time they were active, based upon the geometry of fault-bounded extensional basins. We then perform calculations to estimate the forces exerted between mountain ranges and their adjacent lowlands in the presence of thermal and compositional effects on the density. We combine these methods to calculate an upper bound on the stresses that could be supported by faults in the Variscan foreland before they began to slip. We find the faults had a low effective coefficient of friction (i.e. 0.02–0.24), and that the reactivated pre-existing faults were at least 30% weaker than unfaulted rock. These results show structural inheritance to be important, and suggest that the faults had a low intrinsic coefficient of friction, high pore-fluid pressures, or both.This work forms part of the NERC- and ESRC-funded project 'Earthquakes without Frontiers', and was partially supported by the NERC grant 'Looking Inside the Continents from Space'

Leadership for Educational Equity: Seek Understanding beyond the Words and Beneath the Practices

Current literature has identified multiple underlying causes for disproportionate and widespread underachievement between student groups: the history of inequities in American education (Noguera, 2012; Singleton, 2015), a prevailing White racial-frame worldview and systemic racism producing implicitly-biased educational policies and practices (Lawrence & Keleher, 2004; Feagin, 2014), and opportunity gaps perpetuating lower educational achievement and attainment by students of color and students from impoverished backgrounds (Jordan, Brown & Gutiérrez, 2010; Noguera, 2012). Educators must critically reflect on the obstacles to achieving educational equity and the lack of access to quality instructional opportunities for students from diverse backgrounds and the impact these barriers have on students’ lives (Nieto & Bode, 2012). The study focuses on developing a comprehensive understanding of the factors needed to achieve educational equity for all learners (GLEC, 2012, 2016a). Specifically, the study focuses on the actions of the education staff to pursue the key constructs of educational equity through organizational, curricular, and policy practices and by access to rigorous, challenging courses, meaningful participation and engagement, cultural representation and voice, and positive academic and social results and outcomes for all learners, especially those from diverse racial, ethnic, and low socioeconomic backgrounds (Fraser, 2008; GLEC, 2012, 2016a). The results of the study point out school leaders must enact systemic changes which move “beyond the words” and “beneath the practices” to create equitable learning environments

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

How to Beat the Boss: Game Workers Unite in the UK

This article provides an overview of the growth of game worker organising in Britain. These workers have not previously been organised in a trade union, but over the last 2 years, they have developed a campaign to unionise their sector and launched a legal trade union branch. This is a powerful example of so-called ‘greenfield’ organising, beyond the reach of existing trade unions and with workers who have not previously been members. The article provides an outline of the industry, the launch of the Game Workers Unite international network, the growth of the division in Britain as well as their formation as a branch of the Independent Workers’ Union of Great Britain. The aim is to draw out lessons for both the videogames industry, as well as other non-unionised industries, showing how the traditions of trade unionism can be translated and developed in new contexts

University of Maine Athletic Facilities Master Plan

The Harold Alfond Foundation’s historic investment in Maine and its people includes a $240 million commitment to the University of Maine System to bring transformative change to the state’s largest educational, research, innovation and talent development asset. Of that,$90 million will be used for athletic facilities at the University of Maine and the well-being of Maine people, providing support to maintain excellence in the state’s only Division I athletics program, strengthen gender equity, and provide a preferred destination for high school sports championships, large academic fairs and competitions, and community events. All of the university’s students and people from throughout Maine will be able to use the state-of-the-art athletic and convening venues at the state’s flagship university in Orono

Game Workers and the Empire: Unionisation in the UK Video Game Industry

This article investigates some of the key debates that have emerged within the nascent union organising project Game Workers Unite, with a specific focus on its UK branch (GWU UK). The analysis is based on a period of participatory observation and a series of interviews with board members of GWU UK. This article evaluates Game Workers Unite (GWU) in relation to other recent attempts at unionising the game industry. It concludes that the strategies adopted to counter the hyper-visibility and individualisation of the game worker are key contributions of GWU in contemporary video game labour. This article draws on the work of Dyer-Witheford and de Peuter (2009) Games of empire: Global capitalism and video games to evaluate the historical specificity of GWU and the importance of the organisation for the contemporary video game industry

Mortality, greenhouse gas emissions and consumer cost impacts of combined diet and physical activity scenarios: a health impact assessment study

$\textbf{Objective:}$ To quantify changes in mortality, greenhouse gas (GHG) emissions and consumer costs for physical activity and diet scenarios. $\textbf{Design:}$ For the physical activity scenarios, all car trips from <1 to <8 miles long were progressively replaced with cycling. For the diet scenarios, the study population was assumed to increase fruit and vegetable (F&V) consumption by 1–5 portions of F&V per day, or to eat at least 5 portions per day. Health effects were modelled with the comparative risk assessment method. Consumer costs were based on fuel cost savings and average costs of F&V, and GHG emissions to fuel usage and F&V production. $\textbf{Setting:}$ Working age population for England. $\textbf{Participants:}$ Data from the Health Survey for England, National Travel Survey and National Diet and Nutrition Survey. $\textbf{Primary outcomes measured:}$ Changes in premature deaths, consumer costs and GHG emissions stratified by age, gender and socioeconomic status (SES). $\textbf{Results:}$ Premature deaths were reduced by between 75 and 7648 cases per year for the physical activity scenarios, and 3255 and 6187 cases per year for the diet scenarios. Mortality reductions were greater among people of medium and high SES in the physical activity scenarios, whereas people with lower SES benefited more in the diet scenarios. Similarly, transport fuel costs fell more for people of high SES, whereas diet costs increased most for the lowest SES group. Net GHG emissions decreased by between 0.2 and 10.6 million tons of carbon dioxide equivalent (MtCO$_2$e) per year for the physical activity scenarios and increased by between 1.3 and 6.3 MtCO$_2$e/year for the diet scenarios. $\textbf{Conclusions:}$ Increasing F&V consumption offers the potential for large health benefits and reduces health inequalities. Replacing short car trips with cycling offers the potential for net benefits for health, GHG emissions and consumer costs.MT, PM, NJ and JW were supported by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. JW is also supported by an MRC Population Health Scientist fellowship (grant number: MR/K021796/1). CB is supported by the UK Research Councils (grant number: EPSRC EP/L024756/1) as part of the Decision Making Theme of the UK Energy Research Centre Phase 3