Oceanographic Profiling Observations from the MOCE-3 Cruise: 27 October to 15 November 1994

This report contains results from the third cruise of the Marine Optical Characterization Experiment (Fig. 1). A variety of spectroradiometric observations of the upper water column and atmosphere were made by investigators from the University of Miami, NOAA, CHORS and MLML. Data presented here were obtained by oceanographic CTD profiler: salinity, temperatllre, dissolved oxygen, beam attenuation and chlorophyll-a fluorescence; and by water samplers: total suspended matter and suspended organic carbon and nitrogen, salinity, and dissolved oxygen

A Minimal Architecture for General Cognition

A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to transfer architectural inspiration from biology into artificial intelligence, MANIC seeks to minimize novelty and follow the most well-established constructs that have evolved within various sub-fields of data science. From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence. This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International Joint Conference on Neural Network

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods

Shipboard Techniques for Oceanographic Observations

This report gives the details of water sampling methods and chemical analyses used during MLML participation in the EOS MODIS investigations. It is intended to be used as a reference manual for those engaged in shipboard work. (PDF contains 50 pages