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