On the Nature of MeV-blazars

Broad-band spectra of the FSRQ (flat-spectrum-radio quasars) detected in the high energy gamma-ray band imply that there may be two types of such objects: those with steep gamma-ray spectra, hereafter called MeV-blazars, and those with flat gamma-ray spectra, GeV-blazars. We demonstrate that this difference can be explained in the context of the ERC (external-radiation-Compton) model using the same electron injection function. A satisfactory unification is reachable, provided that: (a) spectra of GeV-blazars are produced by internal shocks formed at the distances where cooling of relativistic electrons in a jet is dominated by Comptonization of broad emission lines, whereas spectra of MeV-blazars are produced at the distances where cooling of relativistic electrons is dominated by Comptonization of near-IR radiation from hot dust; (b) electrons are accelerated via a two step process and their injection function takes the form of a double power-law, with the break corresponding to the threshold energy for the diffusive shock acceleration. Direct predictions of our model are that, on average, variability time scales of the MeV-blazars should be longer than variability time scales of the GeV-blazars, and that both types of the blazar phenomenon can appear in the same object.Comment: Accepted for publication in the Astrophysical Journa

First Person Perspective of Seated Participants Over a Walking Virtual Body Leads to Illusory Agency Over the Walking

Agency, the attribution of authorship to an action of our body, requires the intention to carry out the action, and subsequently a match between its predicted and actual sensory consequences. However, illusory agency can be generated through priming of the action together with perception of bodily action, even when there has been no actual corresponding action. Here we show that participants can have the illusion of agency over the walking of a virtual body even though in reality they are seated and only allowed head movements. The experiment (n = 28) had two factors: Perspective (1PP or 3PP) and Head Sway (Sway or NoSway). Participants in 1PP saw a life-sized virtual body spatially coincident with their own from a first person perspective, or the virtual body from third person perspective (3PP). In the Sway condition the viewpoint included a walking animation, but not in NoSway. The results show strong illusions of body ownership, agency and walking, in the 1PP compared to the 3PP condition, and an enhanced level of arousal while the walking was up a virtual hill. Sway reduced the level of agency. We conclude with a discussion of the results in the light of current theories of agency

Modeling group-specific interviewer effects on survey participation using separate coding for random slopes in multilevel models

Despite its importance in terms of survey participation, the literature is sparse on how face-to-face interviewers differentially affect specific groups of sample units. In this paper, we demonstrate how an alternative parametrization of the random components in multilevel models, so-called separate coding, delivers valuable insights into differential interviewer effects for specific groups of sample members. At the example of a face-to-face recruitment interview for a probability-based online panel, we detect small interviewer effects regarding survey participation for non-Internet households, whereas we find sizable interviewer effects for Internet households. Based on the proposed variance decomposition, we derive practical guidance for survey practitioners to address such differential interviewer effects

Modelling Group-Specific Interviewer Effects on Nonresponse Using Separate Coding for Random Slopes in Multilevel Models

To enhance response among underrepresented groups and hence, to increase response rates and to decrease potential nonresponse bias survey practitioners often use interviewers in population surveys (Heerwegh, 2009). While interviewers tend to increase overall response rates in surveys (see Heerwegh, 2009), research on the determinants of nonresponse have also identified human interviewers as one reason for variations in response rates (see for examples Couper & Groves, 1992; Durrant, Groves, Staetsky, & Steele, 2010; Durrant & Steele, 2009; Hox & de Leeuw, 2002; Loosveldt & Beullens, 2014; West & Blom, 2016). In addition, research on interviewer effects indicates that interviewers introduce nonresponse bias, if interviewers systematically differ in their success in obtaining response from specific respondent groups (see West, Kreuter, & Jaenichen, 2013; West & Olson, 2010). Therefore, interviewers might be a source of selective nonresponse in surveys. Interviewers might also differentially contribute to selective nonresponse in surveys and hence, potential nonresponse bias, when interviewer effects are correlated with characteristics of the approached sample units (for an example see Loosveldt & Beullens, 2014). Multilevel models including dummies in the random part of the model to distinguish between respondent groups are commonly used to investigate whether interviewer effects on nonresponse differ across specific respondent groups (see Loosveldt & Beullens, 2014). When dummy coding, which is also referred to as contrast coding (Jones, 2013), are included as random components in multilevel models for interviewers effects, the obtained variance estimates indicate to what extent the contrast between respondent groups varies across interviewers. Yet, such parameterization does not directly yield insight on the size of interviewer effects for specific respondent groups. Surveys with large imbalances among respondent groups gain from an investigation of the variation of interviewer effect sizes on nonresponse, as one gains insights on whether the interviewer effect size is the same for specific respondent groups. The importance of the interviewer effect size for specific groups of respondents lies in its prediction of the effectiveness of interviewer-related fieldwork strategies (for examples on liking, matching, or prioritizing respondents with interviewers see Durrant et al., 2010; Peytchev, Riley, Rosen, Murphy, & Lindblad, 2010; Pickery & Loosveldt, 2002, 2004) and thus, a effective mitigation of potential nonresponse bias. Consequently, understanding group-specific interviewer effect sizes can aide the efficiency of respondent recruitment, because we then understand why some interviewer-related fieldwork strategies have great impact on some respondent group’s participation while other strategies have little effect. To obtain information on differences in interviewer effect size, we propose to use an alternative coding strategy, so-called separate coding in multilevel models with random slopes (for examples see Jones, 2013; Verbeke & Molenberghs, 2000, ch. 12.1). In case of separate coding, every variable represents a direct estimate of the interviewer effects for specific respondent groups (rather than the contrast with a reference category). Investigating nonresponse during the recruitment of a probability-based online panel separately for persons with and without prior internet access (data used from the German Internet Panel, see Blom et al., 2017), we detect that the size of the interviewer effect differs between the two respondent groups. While we discover no interviewer effects on nonresponse for persons without internet access (offliners), we find sizable interviewer effects for persons with internet access (onliners). In addition, we identify interviewer characteristics that explain this group-specific nonresponse. Our results demonstrate that the implementation of interviewer-related fieldwork strategies might help to increase response rates among onliners, as for onliners the interviewer effect size was relatively large compared to the interviewer effect size for offliners

MOTIFATOR: detection and characterization of regulatory motifs using prokaryote transcriptome data

Summary: Unraveling regulatory mechanisms (e.g. identification of motifs in cis-regulatory regions) remains a major challenge in the analysis of transcriptome experiments. Existing applications identify putative motifs from gene lists obtained at rather arbitrary cutoff and require additional manual processing steps. Our standalone application MOTIFATOR identifies the most optimal parameters for motif discovery and creates an interactive visualization of the results. Discovered putative motifs are functionally characterized, thereby providing valuable insight in the biological processes that could be controlled by the motif.

Exact solution of the Zeeman effect in single-electron systems

Contrary to popular belief, the Zeeman effect can be treated exactly in single-electron systems, for arbitrary magnetic field strengths, as long as the term quadratic in the magnetic field can be ignored. These formulas were actually derived already around 1927 by Darwin, using the classical picture of angular momentum, and presented in their proper quantum-mechanical form in 1933 by Bethe, although without any proof. The expressions have since been more or less lost from the literature; instead, the conventional treatment nowadays is to present only the approximations for weak and strong fields, respectively. However, in fusion research and other plasma physics applications, the magnetic fields applied to control the shape and position of the plasma span the entire region from weak to strong fields, and there is a need for a unified treatment. In this paper we present the detailed quantum-mechanical derivation of the exact eigenenergies and eigenstates of hydrogen-like atoms and ions in a static magnetic field. Notably, these formulas are not much more complicated than the better-known approximations. Moreover, the derivation allows the value of the electron spin gyromagnetic ratio $g_s$ to be different from 2. For completeness, we then review the details of dipole transitions between two hydrogenic levels, and calculate the corresponding Zeeman spectrum. The various approximations made in the derivation are also discussed in details.Comment: 18 pages, 4 figures. Submitted to Physica Script