Final Report of the AFIT Quality Initiative Internal Discovery Committee

This document contains results of a study designed to document the key elements for student success at AFIT in our continuing education and graduate programs and discover to what degree they exist at AFIT. The effort represents an attempt to guide improvement of our graduate and continuing education programs through experience available from our faculty, staff and students. The process outlined herein was designed to achieve success by allowing the participants to define what it means to succeed and then self-assess the presence of these factors at AFIT. It’s therefore a true internal discovery process since its output reflects the state of our internal understanding of teaching and learning excellence. This inclusive approach, which garnered participation from 400 people across AFIT’s schools, will be used in conjunction with the external committee\u27s recommendations to determine a course of action to invest into AFIT\u27s instructional capabilities

Ursa: A Neural Network for Unordered Point Clouds Using Constellations

This paper describes a neural network layer, named Ursa, that uses a constellation of points to learn classification information from point cloud data. Unlike other machine learning classification problems where the task is to classify an individual high-dimensional observation, in a point-cloud classification problem the goal is to classify a set of d-dimensional observations. Because a point cloud is a set, there is no ordering to the collection of points in a point-cloud classification problem. Thus, the challenge of classifying point clouds inputs is in building a classifier which is agnostic to the ordering of the observations, yet preserves the d-dimensional information of each point in the set. This research presents Ursa, a new layer type for an artificial neural network which achieves these two properties. Similar to new methods for this task, this architecture works directly on d-dimensional points rather than first converting the points to a d-dimensional volume. The Ursa layer is followed by a series of dense layers to classify 2D and 3D objects from point clouds. Experiments on ModelNet40 and MNIST data show classification results comparable with current methods, while reducing the training parameters by over 50 percent