Post-correlation radio frequency interference classification methods

We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency interference mitigation is pressing. We investigated several classification methods and find that, for the data sets we used, the most accurate among them is the SumThreshold method. This is a new method formed from a combination of existing techniques, including a new way of thresholding. This iterative method estimates the astronomical signal by carrying out a surface fit in the time-frequency plane. With a theoretical accuracy of 95% recognition and an approximately 0.1% false probability rate in simple simulated cases, the method is in practice as good as the human eye in finding RFI. In addition it is fast, robust, does not need a data model before it can be executed and works in almost all configurations with its default parameters. The method has been compared using simulated data with several other mitigation techniques, including one based upon the singular value decomposition of the time-frequency matrix, and has shown better results than the rest.Comment: 14 pages, 12 figures (11 in colour). The software that was used in the article can be downloaded from http://www.astro.rug.nl/rfi-software

Iron Oxide Genesis and Its Influence on the Spectral Reflectance Properties of Gossans

Gossans in the Mt. Bross area of the Alma mining district, Colorado, were characterized by use of iron oxide mineralogy and spectral reflectance as a possible aid to mineral exploration using remotely sensed data. Mine site gossan samples produced by the weathering of lead-zinc replacement deposits were found to be composed primarily of goethite, and nonmine samples produced by the weathering of pyrite in the local country rock were found to be composed primarily of jarosite. A three stage genetic model was proposed to explain the observed iron oxide assemblages, whereby goethite precipitated early and was followed by jarosite and hematite. Bidirectional reflectance factor was measured on undisturbed sample surfaces with an Exotech 20C spectroradiometer from 0.5 µm to 2.35 µm. A pressed barium sulfate powder reference was used for calibration. The samples were organized into two groups. The first contained goethite as the major oxide. The spectra showed a 0.65 µm shoulder, broad 0.94 µm absorption, and low reflectance factor in the visible and near infrared (13 percent at 0.75 µm). The other group was mainly hematite; the spectra showed a weak 0.65 µm shoulder, sharp 0.85 um absorption, and high reflectance factor (37 percent at 0.75 µm.) It appeared that hematite, although a minor constituent in the nonmine gossans when compared to jarosite, was spectrally dominant. Exploration programs, using spectral reflectance studies to characterize gossan types, should be undertaken with care because iron oxide genesis is influenced by a number of physio-chemical factors that can produce similar mineralogies and spectral characteristics from different parent sulfide assemblages. In the Alma district, the observed spectral differences between gossans appeared to be indirectly a function of wall rock chemistry and not parent sulfide assemblage

Online Learning with Ensembles

Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no improvement. This is explained by showing that for any learning rule $f$ a transform $\tilde{f}$ exists, such that a single student using $\tilde{f}$ has the same generalization behaviour as an ensemble of $f$-students.Comment: 8 pages, 1 figure. Submitted to J.Phys.

A two step algorithm for learning from unspecific reinforcement

We study a simple learning model based on the Hebb rule to cope with "delayed", unspecific reinforcement. In spite of the unspecific nature of the information-feedback, convergence to asymptotically perfect generalization is observed, with a rate depending, however, in a non- universal way on learning parameters. Asymptotic convergence can be as fast as that of Hebbian learning, but may be slower. Moreover, for a certain range of parameter settings, it depends on initial conditions whether the system can reach the regime of asymptotically perfect generalization, or rather approaches a stationary state of poor generalization.Comment: 13 pages LaTeX, 4 figures, note on biologically motivated stochastic variant of the algorithm adde

On-line learning of non-monotonic rules by simple perceptron

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.Comment: LaTeX 20 pages using IOP LaTeX preprint style file, 14 figure

Machine Learning and Data Analysis in Astroinformatics

Astroinformatics is a new discipline at the cross-road of astronomy, advanced statistics and computer science. With next generation sky surveys, space missions and modern instrumentation astronomy will enter the Petascale regime raising the demand for advanced computer science techniques with hard- and software solutions for data management, analysis, efficient automation and knowledge discovery. This tutorial reviews important developments in astroinformatics over the past years and discusses some relevant research questions and concrete problems. The contribution ends with a short review of the special session papers in these proceedings, as well as perspectives and challenges for the near future

Effect of tool coatings on surface grain refinement in orthogonal cutting of AISI 4140 steel

Recrystallization mechanisms leading to the generation of ultrafine grains (UFG) by surface severe plastic deformation (S2PD) at low temperatures (< 0.5Tm (melting temperature)) have been investigated over the last years. Material removal processes like broaching impose large plastic strains along the shear plane during chip formation, leading in many cases to changes in the workpiece subsurface microstructure. In this work the influence of the cutting material on surface grain recrystallization were studied on broaching of AISI 4140q&t steel. Orthogonal cutting tests were carried out in dry conditions on a broaching machine using tools with different coatings. Uncoated cemented carbide inserts were geometrically prepared using fixed abrasive grinding processes and then coated by physical vapor deposition (PVD) with Al2O3 and CrVN thin films. Workpiece subsurface layers were analyzed after machining by Focused Ion Beam (FIB-SEM) and X-ray diffraction (XRD). The presented results show the influence of the cutting material on the final microstructure of the machined workpieces through the determination of the final grain sizes and dislocation densities

Generalizing with perceptrons in case of structured phase- and pattern-spaces

We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are included. As learning algorithms we discuss Hebbian learning, Gibbs learning, and Bayesian learning with different priors, using methods from statistics and the replica formalism. We find that the Hebb rule is quite sensitive to the structure of the actual learning problem, failing asymptotically in most cases. Contrarily, the behaviour of the more sophisticated methods of Gibbs and Bayes learning is influenced by the spatial correlations only in an intermediate regime of $\alpha$, where $\alpha$ specifies the size of the training set. Concerning the Bayesian case we show, how enhanced prior knowledge improves the performance.Comment: LaTeX, 32 pages with eps-figs, accepted by J Phys