On the influence of spatial information for hyper-spectral satellite imaging characterization

Land-use classification for hyper-spectral satellite images requires a previous step of pixel characterization. In the easiest case, each pixel is characterized by its spectral curve. The improvementof the spectral and spatial resolution in hyper-spectral sensors has led to very large data sets. Some researches have focused on better classifiers that can handle big amounts of data. Others have faced the problem of band selection to reduce the dimensionality of the feature space. However, thanks to the improvement in the spatial resolution of the sensors, spatial information may also provide new featuresfor hyper-spectral satellite data. Here, an study on the influence of spectral-spatial features combined with an unsupervised band selection method is presented. The results show that it is possible to reduce very significantly the number of spectral bands required while having an adequate description of the spectral-spatial characteristics of the image for pixel classification tasksThis work has been partly supported by grant FPI PREDOC/2007/20 from Fundació Caixa Castelló-Bancaixa and projects CSD2007-00018 (Consolider Ingenio 2010) and AYA2008-05965-C04-04 from the Spanish Ministry of Science and Innovatio

NGC 5548 in a Low-Luminosity State: Implications for the Broad-Line Region

We describe results from a new ground-based monitoring campaign on NGC 5548, the best studied reverberation-mapped AGN. We find that it was in the lowest luminosity state yet recorded during a monitoring program, namely L(5100) = 4.7 x 10^42 ergs s^-1. We determine a rest-frame time lag between flux variations in the continuum and the Hbeta line of 6.3 (+2.6/-2.3) days. Combining our measurements with those of previous campaigns, we determine a weighted black hole mass of M_BH = 6.54 (+0.26/-0.25) x 10^7 M_sun based on all broad emission lines with suitable variability data. We confirm the previously-discovered virial relationship between the time lag of emission lines relative to the continuum and the width of the emission lines in NGC 5548, which is the expected signature of a gravity-dominated broad-line region. Using this lowest luminosity state, we extend the range of the relationship between the luminosity and the time lag in NGC 5548 and measure a slope that is consistent with alpha = 0.5, the naive expectation for the broad line region for an assumed form of r ~ L^alpha. This value is also consistent with the slope recently determined by Bentz et al. for the population of reverberation-mapped AGNs as a whole.Comment: 24 pages, 3 tables, 7 figures, accepted for publication in Ap

The Mass of the Black Hole in the Seyfert 1 Galaxy NGC 4593 from Reverberation Mapping

We present new observations leading to an improved black hole mass estimate for the Seyfert 1 galaxy NGC 4593 as part of a reverberation-mapping campaign conducted at the MDM Observatory. Cross-correlation analysis of the H_beta emission-line light curve with the optical continuum light curve reveals an emission-line time delay of 3.73 (+-0.75) days. By combining this time delay with the H_beta line width, we derive a central black hole mass of M_BH = 9.8(+-2.1)x10^6 M_sun, an improvement in precision of a factor of several over past results.Comment: 22 pages, 3 tables, 5 figures, accepted for publication in Ap

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor