ON A NEW CLASS OF SMARANDACHE PRIME NUMBERS

The purpose of this note is to report on the discovery of some new prime numbers that were built from factorials, the Smarandache Consecutive Sequence, and the Smarandache Reverse Sequenc

Caddo Pottery in Modern and Contemporary Art and Protection of Native American Cultures in Fine Arts by the IACB’s Indian Arts and Crafts Act

Hello, my name is Chase Kawinhut Earles. I was named by Julia Edge, daughter of Pauline Washington, who was the granddaughter of the Caddo chief, George Washington. I recently, well, not that very long ago started creating Caddo pottery with the much appreciated guidance from Jeri Redcorn. I have been an artist all my life, but mostly only a painter, not much clay, sculpture or pottery. I was inspired to create pottery though, but my experiences were with the Southwest and the Pueblo artists, as this is what I grew up around and what I learned. But I never started. I never found any inspiration. I realized one day it was because I am not a Pueblo Indian and creating Pueblo or Southwest pottery would, to me, feel hollow. I would feel as though I was just creating knock-offs or replications, and not truly inspired or authentic art. This beginning is what defines me and my ideas about Native American Art. Jeri Redcorn and I are two of only maybe a few active Caddo traditional potters. As we work to revive our long tradition and heritage of pottery we have started to unfold an ancient legacy that has proven to be very unique among other native cultures

North American liaisons

Not only are there strong cultural connections between Northern Ireland and North America, but much of the geology of Northern Ireland is related to its shared history with the eastern seaboard of Canada and the USA. Even the opening of the Atlantic Ocean and the parting of North America from Europe left the Giant’s Causeway as a legacy. Events like this over geological time have given Northern Ireland a greater geological diversity than any similar-sized area on Earth and have provided opportunities to explore for minerals, to understand how we can manage groundwater sustainably and to enthuse generations about the mysteries of our landscape

Combining Functional Data Registration and Factor Analysis

We extend the definition of functional data registration to encompass a larger class of registered functions. In contrast to traditional registration models, we allow for registered functions that have more than one primary direction of variation. The proposed Bayesian hierarchical model simultaneously registers the observed functions and estimates the two primary factors that characterize variation in the registered functions. Each registered function is assumed to be predominantly composed of a linear combination of these two primary factors, and the function-specific weights for each observation are estimated within the registration model. We show how these estimated weights can easily be used to classify functions after registration using both simulated data and a juggling data set.Comment: The paper was updated with a better real data exampl

Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure

Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e. duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naive approach, utilizing one random sample from the input distribution (i.e. disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real-time applications.Comment: 15 pages, 6 figure