1,226 research outputs found
The dust emission of high-redshift quasars
The detection of powerful near-infrared emission in high redshift (z>5)
quasars demonstrates that very hot dust is present close to the active nucleus
also in the very early universe. A number of high-redshift objects even show
significant excess emission in the rest frame NIR over more local AGN spectral
energy distribution (SED) templates. In order to test if this is a result of
the very high luminosities and redshifts, we construct mean SEDs from the
latest SDSS quasar catalogue in combination with MIR data from the WISE
preliminary data release for several redshift and luminosity bins. Comparing
these mean SEDs with a large sample of z>5 quasars we could not identify any
significant trends of the NIR spectral slope with luminosity or redshift in the
regime 2.5 < z < 6 and 10^45 < nuL_nu(1350AA) < 10^47 erg/s. In addition to the
NIR regime, our combined Herschel and Spitzer photometry provides full infrared
SED coverage of the same sample of z>5 quasars. These observations reveal
strong FIR emission (L_FIR > 10^13 L_sun) in seven objects, possibly indicating
star-formation rates of several thousand solar masses per year. The FIR excess
emission has unusally high temperatures (T ~ 65 K) which is in contrast to the
temperature typically expected from studies at lower redshift (T ~ 45 K). These
objects are currently being investigated in more detail.Comment: 6 pages, 3 figures, to appear in the proceedings to "The Central
Kiloparsec in Galactic Nuclei (AHAR2011)", Journal of Physics: Conference
Series (JPCS), IOP Publishin
Neural Language Models for Nineteenth-Century English
We present four types of neural language models trained on a large historical dataset of books in English, published between 1760 and 1900, and comprised of ≈5.1 billion tokens. The language model architectures include word type embeddings (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the type embeddings, and four instances considering different time slices for BERT. Our models have already been used in various downstream tasks where they consistently improved performance. In this paper, we describe how the models have been created and outline their reuse potential
Detecting Controversies in Online News Media
This paper sets out to detect controversial news reports using online discussions as a source of information. We define controversy as a public discussion that divides society and demonstrate that a content and stylometric analysis of these debates yields useful signals for extracting disputed news items. Moreover, we argue that a debate-based approach could produce more generic models, since the discussion architectures we exploit to measure controversy occur on many different platforms
Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)
This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification
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