Analysing Pedestrian Traffic Around Public Displays

This paper presents a powerful approach to evaluating public technologies by capturing and analysing pedestrian traffic using computer vision. This approach is highly flexible and scales better than traditional ethnographic techniques often used to evaluate technology in public spaces. This technique can be used to evaluate a wide variety of public installations and the data collected complements existing approaches. Our technique allows behavioural analysis of both interacting users and non-interacting passers-by. This gives us the tools to understand how technology changes public spaces, how passers-by approach or avoid public technologies, and how different interaction styles work in public spaces. In the paper, we apply this technique to two large public displays and a street performance. The results demonstrate how metrics such as walking speed and proximity can be used for analysis, and how this can be used to capture disruption to pedestrian traffic and passer-by approach patterns

Understanding Public Evaluation: Quantifying Experimenter Intervention

Public evaluations are popular because some research questions can only be answered by turning “to the wild.” Different approaches place experimenters in different roles during deployment, which has implications for the kinds of data that can be collected and the potential bias introduced by the experimenter. This paper expands our understanding of how experimenter roles impact public evaluations and provides an empirical basis to consider different evaluation approaches. We completed an evaluation of a playful gesture-controlled display – not to understand interaction at the display but to compare different evaluation approaches. The conditions placed the experimenter in three roles, steward observer, overt observer, and covert observer, to measure the effect of experimenter presence and analyse the strengths and weaknesses of each approach

Comparison of Gaussian ARTMAP and the EM Algorithm

Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a Gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known Expectation-Maximization (EM) approach to mixture-modeling. GAM outperforms an EM classification algorithm on a classification benchmark, thereby demonstrating the advantage of the ART match criterion for regulating learning, and the ARTMAP match tracking operation for incorporate environmental feedback in supervised learning situations.Office of Naval Research (N00014-95-1-0409

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Self-Organization of Topographic Mixture Networks Using Attentional Feedback

This paper proposes a biologically-motivated neural network model of supervised learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers. Possible relationships are discussed between the network's learning properties and those of biological neural networks. Possible future extensions of the network are also discussed.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409

Neural Network for Dynamic Binding with Graph Representation: Form, Linking, and Depth-From-Occlusion

A neural network is presented which explicity represents form attributes and relations between them, thus solving the binding problem without temporal coding. Rather, the network creates a graph representation by dynamically allocating nodes to code local form attributes and establishing ares to link. With this representation, the network selectivly groups and segments in depth objects based on line junction information, producing results consistent with those of several recent visual search eperiments. In addiction to depth-from-occlusion, the network provides a sufficient framework for local line-labelling processes to recover other 3-D variables, such as edge/surface contiguity, edge, slant, and edge convexity.Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI-90-24877, IRI-90-00530); Office of Naval Research (N0014-91-J-4100, N00014-92-J-4015

Gaussian Artmap: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps

A new neural network architecture for incremental supervised learning of analalog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an Adaptive Resonance Theory (ART) neural network, achieved by defining the ART choice function as the discriminant function of a Gaussian classifer with separable distributions, and the ART match function as the same, but with the a priori probabilities of the distributions discounted. While Gaussian ARTMAP retains the attractive parallel computing and fast learning properties of fuzzy ARTMAP, it learns a more efficient internal representation of a mapping while being more resistant to noise than fuzzy ARTMAP on a number of benchmark databases. Several simulations are presented which demonstrate that Gaussian ARTMAP consistently obtains a better trade-off of classification rate to number of categories than fuzzy ARTMAP. Results on a vowel classiflcation problem are also presented which demonstrate that Gaussian ARTMAP outperforms many other classifiers.National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-92-J-4015, 40014-91-J-4100

QCD Factorization and SCET at NLO

In this article we review recent advances in the area of QCD factorization. We begin with a brief outline of QCD factorization, as well as a description of some recent results regarding B-->VV decays. We examine what is necessary at NLO, look at recent advances towards that goal and consider what remains to be done.Comment: Invited talk at "Flavor Physics & CP Violation Conference," Vancouver, 2006, 5 pages, 2 figures. To be published in Electronic Conference Proceedings Archiv

Vehicle charging and potential on the STS-3 mission

An electron gun with fast pulse capability was used in the vehicle charging and potential experiment carried on the OSS-1 pallet to study dielectric charging, return current mechanisms, and the techniques required to manage the electrical charging of the orbiter. Return currents and charging of the dielectrics were measured during electron beam emission and plasma characteristics in the payload bay were determined in the absence of electron beam emission. The fast pulse electron generator, charge current probes, spherical retarding potential analyzer, and the digital control interface unit which comprise the experiment are described. Results show that the thrusters produce disturbances which are variable in character and magnitude. Strong ram/wake effects were seen in the ion densities in the bay. Vehicle potentials are variable with respect to the plasma and depend upon location on the vehicle relative to the main engine nozzles, the vehicle attitude, and the direction of the geomagnetic field