Preprocessing Solar Images while Preserving their Latent Structure

Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics Observatory, a NASA satellite, collect massive streams of high resolution images of the Sun through multiple wavelength filters. Reconstructing pixel-by-pixel thermal properties based on these images can be framed as an ill-posed inverse problem with Poisson noise, but this reconstruction is computationally expensive and there is disagreement among researchers about what regularization or prior assumptions are most appropriate. This article presents an image segmentation framework for preprocessing such images in order to reduce the data volume while preserving as much thermal information as possible for later downstream analyses. The resulting segmented images reflect thermal properties but do not depend on solving the ill-posed inverse problem. This allows users to avoid the Poisson inverse problem altogether or to tackle it on each of $\sim$10 segments rather than on each of $\sim$10$^7$ pixels, reducing computing time by a factor of $\sim$10$^6$. We employ a parametric class of dissimilarities that can be expressed as cosine dissimilarity functions or Hellinger distances between nonlinearly transformed vectors of multi-passband observations in each pixel. We develop a decision theoretic framework for choosing the dissimilarity that minimizes the expected loss that arises when estimating identifiable thermal properties based on segmented images rather than on a pixel-by-pixel basis. We also examine the efficacy of different dissimilarities for recovering clusters in the underlying thermal properties. The expected losses are computed under scientifically motivated prior distributions. Two simulation studies guide our choices of dissimilarity function. We illustrate our method by segmenting images of a coronal hole observed on 26 February 2015

Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data

We examine the retail prices and wholesale prices of a large supermarket chain in Chicago over seven and one-half years. We show that prices tend to fall during the seasonal demand peak for a product and that changes in retail margins account for most of those price changes; thus we add to the growing body of evidence that markups are counter-cyclical. The pattern of margin changes that we observe is consistent with loss leader' models such as the Lal and Matutes (1994) model of retailer pricing and advertising competition. Other models of imperfect competition are less consistent with retailer behavior. Manufacturer behavior plays a more limited role in the counter-cyclicality of prices.

A view of PKS 2155-304 with XMM-Newton Reflection Grating Spectrometers

We present the high resolution X-ray spectrum of the BL Lac object PKS 2155-304 taken with the RGS units onboard XMM-Newton in November 2000. We detect a OVII Kalpha resonant absorption line from warm/hot local gas at 21.59A (~4.5 sigma detection). The line profile is possibly double peaked. We do not confirm the strong 20.02 A absorption line seen with Chandra and interpreted as z~0.05 OVIII Kalpha. A 3sigma upper limit of 14 mA on the equivalent width is set. We also detect the ~23.5 A interstellar OI 1s-->2p line and derive a factor <=1.5 subsolar O/H ratio in the ISM along PKS 2155-304 line of sight.Comment: 13 pages, 5 figures, 3 tables, emulateapj style. Accepted by Ap

Detecting Unspecified Structure in Low-Count Images

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet

On behavior strategy solutions in finite extended decision processes

Techniques for finding best behavior strategies on arbitrary information collection scheme

Survey on Classification of Brain Tumor using Wavelet Transform and PNN

This paper presents, a new method for Brain Tumor Classification using Probabilistic Neural Network with Discrete Wavelet Transformation is proposed. Human inspection was the conventional method available for computerized tomography, magnetic resonance brain images classification and tumor detection. The classification methods that are operator assisted are impractical incase of large amount of data that are also non reproducible. Operator performance leads to serious inaccuracies in classification by producing noise in Computerized Tomography and Magnetic Resonance images. Neural Network techniques has shown great potential in the field of medical diagnosis. Hence, in this paper the Probabilistic Neural Network with Discrete Wavelet Transform was applied for classification of brain tumors. Classification was performed in two steps, i) Dimensionality reduction and Feature extraction using the Discrete Wavelet Transform and ii) classification using Probabilistic Neural Network (PNN). Evaluation was performed on image data base of Brain Tumor images. The proposed method gives better accuracy when compared to previous methods of classification

X-raying the coronae of HD~155555

We present an analysis of the high-resolution Chandra observation of the multiple system, HD 155555 (an RS CVn type binary system, HD 155555 AB, and its spatially resolved low-mass companion HD 155555 C). This is an intriguing system which shows properties of both an active pre-main sequence star and a synchronised (main sequence) binary. We obtain the emission measure distribution, temperature structures, plasma densities, and abundances of this system and compare them with the coronal properties of other young/active stars. HD 155555 AB and HD 155555 C produce copious X-ray emission with log Lx of 30.54 and 29.30, respectively, in the 0.3-6.0 keV energy band. The light curves of individual stars show variability on timescales of few minutes to hours. We analyse the dispersed spectra and reconstruct the emission measure distribution using spectral line analysis. The resulting elemental abundances exhibit inverse first ionisation potential effect in both cases. An analysis of He-like triplets yields a range of coronal electron densities ~10^10-10^13 cm-3. Since HD 155555 AB is classified both as an RS CVn and a PMS star, we compare our results with those of other slightly older active main-sequence stars and T Tauri stars, which indicates that the coronal properties of HD 155555 AB closely resemble that of an older RS CVn binary rather than a younger PMS star. Our results also suggests that the properties of HD 155555 C is very similar to those of other active M dwarfs.Comment: 17 pages, 23 figues, Accepted in Ap