524 research outputs found

    Exploiting Domain Knowledge in Making Delegation Decisions

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    @inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin

    Covariance forecasting in equity markets

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    We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility riskpremium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks

    AEGIS: Infrared Spectral Energy Distributions of MIPS 70micron selected sources

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    We present 0.5 -160 micron Spectral Energy Distributions (SEDs) of galaxies, detected at 70microns with the Multiband Imaging Photometer for Spitzer (MIPS), using broadband imaging data from Spitzer and ground-based telescopes. Spectroscopic redshifts, in the range 0.2<z<1.5, have been measured as part of the Deep Extragalactic Evolutionary Probe2 (DEEP2) project. Based on the SEDs we explore the nature and physical properties of the sources. Using the optical spectra we derive Hbeta and [OII]-based Star Formation Rates (SFR) which are 10-100 times lower than SFR estimates based on IR and radio. The median offset in SFR between optical and IR is reduced by a factor of ~3 when we apply a typical extinction corrections. We investigate mid-to-far infrared correlations for low redshift (>0.5) and high redshift (0.5<z<1.2) bins. Using this unique ``far-infrared'' selected sample we derive an empirical mid to far-infrared relationship that can be used to estimate the infrared energy budget of galaxies in the high-redshift universe. Our sample can be used as a template to translate far-infrared luminosities into bolometric luminosities for high redshift objects.Comment: 4 pages, 5 figures, accepted for publication in AEGIS ApJL Special Issu

    Algorithms for Visualizing Phylogenetic Networks

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    We study the problem of visualizing phylogenetic networks, which are extensions of the Tree of Life in biology. We use a space filling visualization method, called DAGmaps, in order to obtain clear visualizations using limited space. In this paper, we restrict our attention to galled trees and galled networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

    Panchromatic spectral energy distributions of Herschel sources

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    (abridged) Far-infrared Herschel photometry from the PEP and HerMES programs is combined with ancillary datasets in the GOODS-N, GOODS-S, and COSMOS fields. Based on this rich dataset, we reproduce the restframe UV to FIR ten-colors distribution of galaxies using a superposition of multi-variate Gaussian modes. The median SED of each mode is then fitted with a modified version of the MAGPHYS code that combines stellar light, emission from dust heated by stars and a possible warm dust contribution heated by an AGN. The defined Gaussian grouping is also used to identify rare sources. The zoology of outliers includes Herschel-detected ellipticals, very blue z~1 Ly-break galaxies, quiescent spirals, and torus-dominated AGN with star formation. Out of these groups and outliers, a new template library is assembled, consisting of 32 SEDs describing the intrinsic scatter in the restframe UV-to-submm colors of infrared galaxies. This library is tested against L(IR) estimates with and without Herschel data included, and compared to eight other popular methods often adopted in the literature. When implementing Herschel photometry, these approaches produce L(IR) values consistent with each other within a median absolute deviation of 10-20%, the scatter being dominated more by fine tuning of the codes, rather than by the choice of SED templates. Finally, the library is used to classify 24 micron detected sources in PEP GOODS fields. AGN appear to be distributed in the stellar mass (M*) vs. star formation rate (SFR) space along with all other galaxies, regardless of the amount of infrared luminosity they are powering, with the tendency to lie on the high SFR side of the "main sequence". The incidence of warmer star-forming sources grows for objects with higher specific star formation rates (sSFR), and they tend to populate the "off-sequence" region of the M*-SFR-z space.Comment: Accepted for publication in A&A. Some figures are presented in low resolution. The new galaxy templates are available for download at the address http://www.mpe.mpg.de/ir/Research/PEP/uvfir_temp
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