Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Contingency contractor optimization. Phase 2, use cases and workflows strategic contractor planning tool prototype.

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An Introduction to Wavelet Theory and Analysis

This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides information about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, defme common wavelet terminology and describe wavelet analysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary. Contents

Using wavelets to synthesize stochastic-based sounds for immersive virtual environments

Presented at the 4th International Conference on Auditory Display (ICAD), Palo Alto, California, November 2-5, 1997.Stochastic, or non-pitched, sounds fill our real world environment. Humans almost continuously hear stochastic sounds such as wind, rain, motor sounds, and different types of impact sounds. Because of their prevalence in real-world environments, it is important to include these types of sounds for realistic virtual environment simulations. This paper describes a synthesis approach that uses wavelets for modeling stochastic-based sounds. Parameterizations of the wavelet models yield a variety of related sounds from a small set of models. The result is dynamic sound models that can change according to changes in the virtual environment. This paper contains a description of the sound synthesis process, several developed models, and the on-going perceptual experiments for validating the sound synthesis veracity. The developed models and results demonstrate proof of the concept and illustrate the potential of this approach

Data collection and analysis techniques for evaluating the perceptual qualities of auditory stimuli

Presented at 5th International Conference on Auditory Display (ICAD), Glasgow, UK, November 1-4, 1998.This paper describes a general methodological framework for evaluating the perceptual properties of auditory stimuli. The framework provides analysis techniques that can ensure the effective use of sound for a variety of applications including virtual reality and data sonification systems. Specifically, we discuss data collection techniques for the perceptual qualities of single auditory stimuli including identification tasks, context-based ratings, and attribute ratings. In addition, we present methods for comparing auditory stimuli, such as discrimination tasks, similarity ratings, and sorting tasks. Finally, we discuss statistical techniques that focus on the perceptual relations among stimuli, such as Multidimensional Scaling (MDS) and Pathfinder Analysis. These methods are presented as a starting point for an organized and systematic approach for non-experts in perceptual experimental methods, rather than as a complete manual for performing the statistical techniques and data collection methods. It is our hope that this paper will help foster further interdisciplinary collaboration among perceptual researchers, designers, engineers, and others in the development of effective auditory displays

Contingency contractor optimization. Phase 2, architecture document.

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Contingency contractor optimization. Phase 2, model description and formulation.

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