Miscellaneous studies

MISCELLANEOUS STUDIES, which includes the following papers: "Geology of the Area in and Around the Jim Woodruff Reservoir" by Charles W. Hendry, Jr. and J. William Yon, Jr.; "Phosphate Concentrations near Bird Rookeries in South Florida" by Dr. Ernest H. Lund, Department of Geology, Florida State University; and "An Analysis of Ochlockonee River Channel Sediments" by Dr. Ernest H. Lund, Associate Professor and Patrick C. Haley, Graduate Assistant, Department of Geology, Florida State University. (PDF contains 81 pages

Ernest Gellner: an intellectual biography

Catherine Hezser finds that John A. Hall’s biography of one of the most prominent social anthropologists of our time provides fascinating reading on issues and debates which are still of utmost importance. Ernest Gellner: An Intellectual Biography. John A. Hall. Verso. 2011. Paperback edition

Eleventh annual report of the New England Female Medical College

Nègre Ernest. Les noms de lieux Combres, Combret, Combraille en France. In: Nouvelle revue d'onomastique, n°3-4, 1984. L'Auvergne. pp. 72-73

Stephanie Sadownik, mezzo-soprano

Ernest ChaussonEdward Elga

Scotogenic Cobimaximal Dirac Neutrino Mixing from Δ(27)\Delta(27) and U(1)χU(1)_\chi

In the context of $SU(3)_C \times SU(2)_L \times U(1)_Y \times U(1)_\chi$, where $U(1)_\chi$ comes from $SO(10) \to SU(5) \times U(1)_\chi$, supplemented by the non-Abelian discrete $\Delta(27)$ symmetry for three lepton families, Dirac neutrino masses and their mixing are radiatively generated through dark matter. The gauge $U(1)_\chi$ symmetry is broken spontaneously. The discrete $\Delta(27)$ symmetry is broken softly and spontaneously. Together, they result in two residual symmetries, a global $U(1)_L$ lepton number and a dark symmetry, which may be $Z_2$, $Z_3$, or $U(1)_D$ depending on what scalar breaks $U(1)_\chi$. Cobimaximal neutrino mixing, i.e. $\theta_{13} \neq 0$, $\theta_{23} = \pi/4$, and $\delta_{CP} = \pm \pi/2$, may also be obtained.Comment: 11 pages, 1 figure, ref. adde

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham's razor non plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.Comment: 18 pages, 2 figures 3 table