Two New Mutillidae From Colorado

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Explaining microbial population genomics through phage predation

The remarkable diversity of genes within the pool of prokaryotic genomes belonging to the same species or pan-genome is difficult to reconcile with the widely accepted paradigm which asserts that periodic selection within bacterial populations would regularly purge genomic diversity by clonal replacement. Recent evidence from metagenomics indicates that even within a single sample a large diversity of genomes can be present for a single species. We have found that much of the differential gene content affects regions that are potential phage recognition targets. We therefore propose the operation of Constant-Diversity dynamics in which the diversity of prokaryotic populations is preserved by phage predation. We provide supporting evidence for this model from metagenomics, mathematical analysis and computer simulations. Periodic selection and phage predation dynamics are not mutually exclusive; we compare their predictions to indicate under which ecological circumstances each dynamics could predominate

Least squares support vector machines for direction of arrival estimation

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented

Least squares support vector machines for direction of arrival estimation with error control and validation

The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm\u27s capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy

Machine learning based CDMA power control

This paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control systems

Ptychographic ultrafast pulse reconstruction

We demonstrate a new ultrafast pulse reconstruction modality which is somewhat reminiscent of frequency resolved optical gating but uses a modified setup and a conceptually different reconstruction algorithm that is derived from ptychography. Even though it is a second order correlation scheme it shows no time ambiguity. Moreover, the number of spectra to record is considerably smaller than in most other related schemes which, together with a robust algorithm, leads to extremely fast convergence of the reconstruction.Comment: 4 pages, 4 figures, 3 references added, new figure 2, matches published versio

Least squares support vector machines for fixed-step and fixed-set CDMA power control

This paper presents two machine learning based algorithms for CDMA power control. The least squares support vector machine (LS-SVM) algorithms classify eigenvalues estimates into sets of power control commands. A binary LS-SVM algorithm generates fixed step power control (FSPC) commands, while the one vs. one multiclass LS-SVM algorithm generates estimates for fixed set power control

Feather growth rate and mass in nearctic passerines with variablemigratory behavior and molt pattern

Bird species vary greatly in the duration of their annual complete feather molt. However, such variation is not well documented in birds from many biogeographic areas, which restricts our understanding of the diversification of molt strategies. Recent research has revealed that molt duration can be estimated in passerines from ptilochronology-based measurements of the growth rate of their tail feathers. We used this approach to explore how molt duration varied in 98 Nearctic species that have different migratory strategies and molt patterns. As previously documented for Palearctic species, migration was associated with a shortening of molt duration among species that molted during summer on their breeding range. However, molts of winter-molting migratory species were as long as those of summer-molting sedentary species, which suggests that winter molt also allows Nearctic migrants to avoid the temporal constraints experienced during summer. Our results also suggest that migratory species that undergo a stopover molt within the Mexican monsoon region have the shortest molt duration among all Nearctic passerines. Interestingly, and contrary to expectations from a potential tradeoff between molt duration and feather quality, observed variation in feather growth rate was positively correlated with differences in tail feather mass, which may be caused by differences among groups in the availability of resources for molting. We encourage the use of similar approaches to study the variation in molt duration in other geographic areas where knowledge of the evolution of molt is limited.

Improvements to the Indiana Geological Survey’s Petroleum Database Management System

This poster was presented at the 2011 Annual Meeting of the American Association of Petroleum Geologists, Eastern Section, in Arlington, Virginia, in September 2011.The Indiana Geological Survey’s Petroleum Database Management System (PDMS) is a web application that provides online access to petroleum-related geological information. Since its debut in 2004, the application has been widely used by the petroleum industry, academia, government agencies, and the general public. On June 6, 2011, a significantly enhanced version of the PDMS went online. New features include a robust search menu that permits elaborate queries of more than 74,000 petroleum wells, rapid and convenient online viewing and downloading of PDF-file well reports and both PDF- and TIFF-file geophysical and other well logs, and streamlined menus for easily accessing extensive well data. An interactive, context-driven web help explains every concept or term used. The PDMS is organized in three main sections. The Well Tables Section includes such information as well location descriptions, completion zones, logs, operators, lease names, tests, reports, hydrocarbon shows, samples, cores, geologic formations and tops, and directional survey data. The Map Viewer Section contains many user-selectable layer options for showing well locations, petroleum fields, producing formations, aerial photographs, and topographic maps. Wells shown in the Map Viewer are hyperlinked to the Well Tables for easy access to the well data. The Fields and Production Section summarizes oil, natural gas, and gas storage field data, including historical oil production volumes in both tables and charts

Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function. The so called selection probabilities can be computed along the way.Comment: This is the version published in Journal of Mathematical Imaging and Vision, online first, 1 august 201