A posteriori agreement as a quality measure for readability prediction systems

All readability research is ultimately concerned with the research question whether it is possible for a prediction system to automatically determine the level of readability of an unseen text. A significant problem for such a system is that readability might depend in part on the reader. If different readers assess the readability of texts in fundamentally different ways, there is insufficient a priori agreement to justify the correctness of a readability prediction system based on the texts assessed by those readers. We built a data set of readability assessments by expert readers. We clustered the experts into groups with greater a priori agreement and then measured for each group whether classifiers trained only on data from this group exhibited a classification bias. As this was found to be the case, the classification mechanism cannot be unproblematically generalized to a different user group

Cosmological hydrogen recombination: Lyn line feedback and continuum escape

We compute the corrections to the cosmological hydrogen recombination history due to delayed feedback of Lyman-series photons and the escape in the Lyman-continuum. The former process is expected to slightly delay recombination, while the latter should allow the medium to recombine a bit faster. It is shown that the subsequent feedback of released Lyman-n photons on the lower lying Lyman-(n-1) transitions yields a maximal correction of DN_e/N_e 0.22% at z~ 1050. Including only Lyman-\beta feedback onto the Lyman-\alpha transition, accounts for most of the effect. We find corrections to the cosmic microwave background TT and EE power spectra \change{with typical peak to peak amplitude |DC^{TT}_l/C^{TT}_l|~0.15% and |\Delta C^{EE}_l/C^{EE}_l|~0.36% at l<~3000. The escape in the Lyman-continuum and feedback of Lyman-\alpha photons on the photoionization rate of the second shell lead to modifications of the ionization history which are very small (less than |DN_e/N_e|~few x 10^{-6}).Comment: 5+epsilon pages, 7 figures, accepted versio

The DEPOSIT computer code: calculations of electron-loss cross sections for complex ions colliding with neutral atoms

A description of the DEPOSIT computer code is presented. The code is intended to calculate total and m-fold electron-loss cross sections (m is the number of ionized electrons) and the energy T(b) deposited to the projectile (positive or negative ion) during a collision with a neutral atom at low and intermediate collision energies as a function of the impact parameter b. The deposited energy is calculated as a 3D-integral over the projectile coordinate space in the classical energy-deposition model. Examples of the calculated deposited energies, ionization probabilities and electron-loss cross sections are given as well as the description of the input and output data.Comment: 11 pages, 3 figure

Optical and near-infrared recombination lines of oxygen ions from Cassiopeia A knots

Context. Fast-moving knots (FMK) in the Galactic supernova remnant Cassiopeia A consist mainly of metals and allow to study element production in supernovae and shock physics in great detail. Aims. We work out theoretically and suggest to observe previously unexplored class of spectral lines -- metal recombination lines in optical and near-infrared bands -- emitted by the cold ionized and cooling plasma in the fast-moving knots. Methods. By tracing ion radiative and dielectronic recombination, collisional $l$-redistribution and radiative cascade processes, we compute resulting oxygen, silicon and sulphur recombination line emissivities. It allows us to determine the oxygen recombination line fluxes, based on the fast-moving knot model of Sutherland and Dopita (1995b), that predicts existence of highly-ionized ions from moderate to very low plasma temperatures. Results. The calculations predict oxygen ion recombination line fluxes detectable on modern optical telescopes in the wavelength range from 0.5 to 3 microns. Line ratios to collisionally-excited lines will allow to probe in detail the process of rapid cloud cooling after passage of a shock front, to test high abundances of O V and O VI ions at low temperatures and measure them, to test existing theoretical models of a FMK and to build more precise ones.Comment: 18 pages, 22 figures, version accepted by A&A. Electronic supplement available at http://www.mpa-garching.mpg.de/~dima/CasA_ORL/e-sup

Ionization of highly charged relativistic ions by neutral atoms and ions

Ionization of highly charged relativistic ions by neutral atoms and ions is considered. Numerical results of recently developed computer codes based on the relativistic Born and the equivalent-photon approximations are presented. The ionization of the outer shells dominate. For the outer projectile electron shells, which give the main contribution to the process, the non-relativistic Schr\"odinger wave functions can be used. The formulae for the non-relativistic reduction of the Dirac matrix-elements are obtained for ionization of electrons with arbitrary quantum numbers $n$ and $\ell$.Comment: 7 pages, 3 figure

Cosmological recombination: feedback of helium photons and its effect on the recombination spectrum

In this paper we consider the re-processing of high frequency photons emitted by HeII and HeI during the epoch of cosmological recombination by HeI and HI. We demonstrate that, in comparison to computations which neglect all feedback processes, the number of cosmological recombination photons that are related to the presence of helium in the early Universe could be increased by ~40%-70%. Our computations imply that per helium nucleus ~3-6 additional photons could be produced. Therefore, a total of ~12-14 helium-related photons are emitted during cosmological recombination. This is an important addition to cosmological recombination spectrum which in the future may render it slightly easier to determine the primordial abundance of helium using differential measurements of the CMB energy spectrum. Also, since these photons are the only witnesses of the feedback process at high redshift, observing them in principle offers a way to check our understanding of the recombination physics. Here most interestingly, the feedback of HeII photons on HeI leads to the appearance of several additional, rather narrow spectral features in the HeI recombination spectrum at low frequencies. Consequently, the signatures of helium-related features in the CMB spectral distortion due to cosmological recombination at some given frequency can exceed the average level of ~17% several times. We find that in particular the bands around nu ~10GHz, ~35GHz, ~80GHz, and ~200GHz seem to be affected strongly. In addition, we computed the changes in the cosmological ionization history, finding that only the feedback of primary HeI photons on the dynamics of HeII-->HeI recombination has an effect, producing a change of DN_e/N_e ~+ 0.17% at z~2300. This result seems to be ~2-3 times smaller than the one obtained in earlier computations for this process (abridged).Comment: 28 pages, 24 figures, submitted to MNRA

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112