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

Birth of a Learning Law

Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-92-J-1309

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

MIXED-USE SAFETY ON RURAL FACILITIES IN THE PACIFIC NORTHWEST: Consideration of Vehicular, Non-Traditional, and Non-Motorized Users

In the United States, one in 12 households do not own a personal automobile and approximately 13% of those who are old enough to drive do not. Trips by these individuals are being made in one of many other possible modes, creating the need to “share space” between many forms of travel. The goal of this project is to: improve safety and minimize the dangers for all transportation mode types while traveling in mixed-use environments on rural facilities through the development and use of engineering and education safety measures. To that end, this report documents three specific efforts by the project team. First, a comprehensive literature review of mixed-use safety issues with consideration of non-motorized and non-traditional forms of transportation. Second, a novel analysis of trauma registry data. Third, development, execution and analysis of the Pacific Northwest Transportation Survey geared toward understanding safety perceptions of mixed-use users. Most notably, findings indicate that ATVs (and similar non-traditional-type vehicles) are used on or near roads 24% of the time and snowmachines are used on or near roads 23% of the time. There are significantly more (twice as many) ATV-related on-road traumas in connected places than isolated places in Alaska and three times more traumas in highway connected places than in secondary road connected places. Comparably, bicycles had 449 on-road traumas between 2004 and 2011 whereas ATVs had 352 on-road traumas. Users of all modes who received formalized training felt safer in mixed-use environments than those who reported having no training at all

The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

Speaking for Themselves: Advocates' Perspectives on Evaluation

"Speaking for Themselves: Advocates' Perspectives on Evaluation" will give you a better understanding of advocates' views on evaluation, the advocacy strategies and capacities they find effective, and current evaluation practices. Based on Innovation's Network's research, the report includes recommendations for advocates, funders, and evaluators. Both the research and publication were made possible by the Annie E. Casey Foundation and The Atlantic Philanthropies

Visitor profiling for cable car mountain destinations as a basis for protected area management : a case study of the summer season in the Tatra Mountains at Kasprowy Wierch (Poland) and Skalnaté Pleso (Slovakia)

Protected areas play a crucial role in the conservation of vulnerable mountain ecosystems, but at the same time they may serve as tourist destinations and attract large numbers of visitors. Areas located in close proximity to cable cars belong to some of the most challenging sites for mountain protected area management. This study focuses on two cable car areas: Kasprowy Wierch (Tatra National Park, Poland) and Skalnaté Pleso (Tatra National Park, Slovakia). Both sites belong to the most heavily used leisure destinations in the Tatra Mountains. The study focused on the summer, snow-free tourist peak-season, for which there is an ongoing discussion concerning the development of cable car services. In 2014 and 2015, on-site interviews were conducted in the two study areas (n = 3 304). In order to better understand visitors’ needs and goals, visitor profiling using K-means clustering was performed. Four distinct segments based on visitor motivations were identified: nature oriented (32 %), family / friends & well-being oriented (23 %), sports oriented (14 %), and a mixed segment with multiple motivations (31 %). The results show that two tourist segments were not particularly interested in nature experience, although they visited protected areas. A significant relationship between motivational segments and trip characteristics was identified. The visitor segments defined can be used practically in the management of cable car destinations located within protected areas