Gaussian process convolutions for Bayesian spatial classification

Master's Project (M.S.) University of Alaska Fairbanks, 2016We compare three models for their ability to perform binary spatial classification. A geospatial data set consisting of observations that are either permafrost or not is used for this comparison. All three use an underlying Gaussian process. The first model considers this process to represent the log-odds of a positive classification (i.e. as permafrost). The second model uses a cutoff. Any locations where the process is positive are classified positively, while those that are negative are classified negatively. A probability of misclassification then gives the likelihood. The third model depends on two separate processes. The first represents a positive classification, while the second a negative classification. Of these two, the process with greater value at a location provides the classification. A probability of misclassification is also used to formulate the likelihood for this model. In all three cases, realizations of the underlying Gaussian processes were generated using a process convolution. A grid of knots (whose values were sampled using Markov Chain Monte Carlo) were convolved using an anisotropic Gaussian kernel. All three models provided adequate classifications, but the single and two-process models showed much tighter bounds on the border between the two states

Holistic Leadership: A Model for Leader-Member Engagement and Development

Dr. Candis Best explores the theory of holistic leadership and further provides the model and framework for it to be empirically tested. At present, Best opines that holistic leadership produces leadership which supports the development of self-leadership capacity while preparing participating members for the exercise of increasing levels of self-determination and participatory decision-making

Right here, right now: situated interventions to change consumer habits

Consumer behavior-change interventions have traditionally encouraged consumers to form conscious intentions, but in the past decade it has been shown that while these interventions have a medium-to-large effect in changing intentions, they have a much smaller effect in changing behavior. Consumers often do not act in accordance with their conscious intentions because situational cues in the immediate environment automatically elicit learned, habitual behaviors. It has therefore been suggested that researchers refocus their efforts on developing interventions that target unconscious, unintentional influences on behavior, such as cue-behavior (“habit”) associations. To develop effective consumer behavior-change interventions, however, we argue that it is first important to understand how consumer experiences are represented in memory, in order to successfully target the situational cues that most strongly predict engagement in habitual behavior. In this article, we present a situated cognition perspective of habits and discuss how the situated cognition perspective extends our understanding of how consumer experiences are represented in memory, and the processes through which these situational representations can be retrieved in order to elicit habitual consumer behaviors. Based on the principles of situated cognition, we then discuss five ways that interventions could change consumer habits by targeting situational cues in the consumer environment and suggest how existing interventions utilizing these behavior-change strategies could be improved by integrating the principles of the situated cognition approach

Best rank-k approximations for tensors: generalizing Eckart-Young

Joint work with Jan Draisma and Giorgio Ottaviani. Given a tensor f in a Euclidean tensor space, we are interested in the critical points of the distance function from f to the set of tensors of rank at most k, which we call the critical rank-at-most-k tensors for f. When f is a matrix, the critical rank-one matrices for f correspond to the singular pairs of f . The critical rank-one tensors for f lie in a linear subspace H_f, the critical space of f. Our main result is that, for any k, the critical rank-at-most-k tensors for a sufficiently general f also lie in the critical space H_f. This is the part of Eckart-Young Theorem that generalizes from matrices to tensors. Moreover, we show that when the tensor format satisfies the triangle inequalities, the critical space H_f is spanned by the complex critical rank-one tensors. Since f itself belongs to H_f, we deduce that also f itself is a linear combination of its critical rank-one tensors. For simplicity, we will focus on binary forms during the talk.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech