Broadband X-ray spectrum of the newly discovered broad line radio galaxy IGR J21247+5058

In this paper we present radio and high energy observations of the INTEGRAL source IGR J21247+5058, a broad line emitting galaxy obscured by the Galactic plane. Archival VLA radio data indicate that IGR J21247+5058 can be classified as an FRII Broad Line Radio Galaxy. The spectrum between 610 MHz and 15 GHz is typical of synchrotron self-absorbed radiation with a peak at 8 GHz and a low energy turnover; the core fraction is 0.1 suggestive of a moderate Doppler boosting of the base of the jet. The high energy broad-band spectrum was obtained by combining XMM-Newton and Swift/XRT observation with INTEGRAL/IBIS data. The 0.4-100 keV spectrum is well described by a power law, with slope $\Gamma$=1.5, characterised by complex absorption due to two layers of material partially covering the source and a high energy cut-off around 70-80 keV. Features such as a narrow iron line and a Compton reflection component, if present, are weak, suggesting that reprocessing of the power law photons in the accretion disk plays a negligible role in the source.Comment: 7 pages, 7 figures, 3 tables, accepted for pubblication on MNRA

Resonant line transfer in a fog: Using Lyman-alpha to probe tiny structures in atomic gas

Motivated by observational and theoretical work which both suggest very small scale ($\lesssim 1\,$pc) structure in the circum-galactic medium of galaxies and in other environments, we study Lyman-$\alpha$ (Ly$\alpha$) radiative transfer in an extremely clumpy medium with many "clouds" of neutral gas along the line of sight. While previous studies have typically considered radiative transfer through sightlines intercepting $\lesssim 10$ clumps, we explore the limit of a very large number of clumps per sightline (up to $f_{\mathrm{c}} \sim 1000$). Our main finding is that, for covering factors greater than some critical threshold, a multiphase medium behaves similar to a homogeneous medium in terms of the emergent Ly$\alpha$ spectrum. The value of this threshold depends on both the clump column density and on the movement of the clumps. We estimate this threshold analytically and compare our findings to radiative transfer simulations with a range of covering factors, clump column densities, radii, and motions. Our results suggest that (i) the success in fitting observed Ly$\alpha$ spectra using homogeneous "shell models" (and the corresponding failure of multiphase models) hints towards the presence of very small-scale structure in neutral gas, in agreement within a number of other observations; and (ii) the recurrent problems of reproducing realistic line profiles from hydrodynamical simulations may be due to their inability to resolve small-scale structure, which causes simulations to underestimate the effective covering factor of neutral gas clouds.Comment: 18 pages, 21 figures; submitted to A&A; animations available at http://bit.ly/a-in-a-fo

ROSAT monitoring of persistent giant and rapid variability in the narrow-line Seyfert 1 galaxy IRAS 13224-3809

We report evidence for persistent giant and rapid X-ray variability in the radio-quiet, ultrasoft, strong Fe II, narrow-line Seyfert 1 galaxy IRAS 13224-3809. Within a 30 day ROSAT High Resolution Imager (HRI) monitoring observation at least five giant amplitude count rate variations are visible, with the maximum observed amplitude of variability being about a factor of 60. We detect a rise by a factor of about 57 in just two days. IRAS 13224-3809 appears to be the most X-ray variable Seyfert known, and its variability is probably nonlinear. We carefully check the identification of the highly variable X-ray source with the distant galaxy, and it appears to be secure. We examine possible explanations for the giant variability. Unusually strong relativistic effects and partial covering by occulting structures on an accretion disc can provide plausible explanations of the X-ray data, and we explore these two scenarios. Relativistic boosting effects may be relevant to understanding the strong X-ray variability of some steep spectrum Seyferts more generally.Comment: 14 pages, submitted to MNRA

Forecasting with many predictors - Is boosting a viable alternative?

This paper evaluates the forecast performance of boosting, a variable selection device, and compares it with the forecast combination schemes and dynamic factor models presented in Stock and Watson (2006). Using the same data set and comparison methodology, we find that boosting is a serious competitor for forecasting US industrial production growth in the short run and that it performs best in the longer run

Random Relational Rules

Exhaustive search in relational learning is generally infeasible, therefore some form of heuristic search is usually employed, such as in FOIL[1]. On the other hand, so-called stochastic discrimination provides a framework for combining arbitrary numbers of weak classifiers (in this case randomly generated relational rules) in a way where accuracy improves with additional rules, even after maximal accuracy on the training data has been reached. [2] The weak classifiers must have a slightly higher probability of covering instances of their target class than of other classes. As the rules are also independent and identically distributed, the Central Limit theorem applies and as the number of weak classifiers/rules grows, coverages for different classes resemble well-separated normal distributions. Stochastic discrimination is closely related to other ensemble methods like Bagging, Boosting, or Random forests, all of which have been tried in relational learning [3, 4, 5]

Efficient Diverse Ensemble for Discriminative Co-Tracking

Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the collective knowledge of the committee. Committee members could vary in their features, memory update schemes, or training data, however, it is inevitable to have committee members that excessively agree because of large overlaps in their version space. To remove this redundancy and have an effective ensemble learning, it is critical for the committee to include consistent hypotheses that differ from one-another, covering the version space with minimum overlaps. In this study, we propose an online ensemble tracker that directly generates a diverse committee by generating an efficient set of artificial training. The artificial data is sampled from the empirical distribution of the samples taken from both target and background, whereas the process is governed by query-by-committee to shrink the overlap between classifiers. The experimental results demonstrate that the proposed scheme outperforms conventional ensemble trackers on public benchmarks.Comment: CVPR 2018 Submissio

Efficient Version-Space Reduction for Visual Tracking

Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background. Sample selection, labeling and updating the classifier is prone to various sources of errors that drift the tracker. We introduce the use of an efficient version space shrinking strategy to reduce the labeling errors and enhance its sampling strategy by measuring the uncertainty of the tracker about the samples. The proposed tracker, utilize an ensemble of classifiers that represents different hypotheses about the target, diversify them using boosting to provide a larger and more consistent coverage of the version-space and tune the classifiers' weights in voting. The proposed system adjusts the model update rate by promoting the co-training of the short-memory ensemble with a long-memory oracle. The proposed tracker outperformed state-of-the-art trackers on different sequences bearing various tracking challenges.Comment: CRV'17 Conferenc