Recording brain activity can function as an implied social presence and alter neural connectivity.

People often behave differently when they know they are being watched. Here, we report the first investigation of whether such social presence effects also include brain monitoring technology, and also their impacts on the measured neural activity. We demonstrate that merely informing participants that fMRI has the potential to observe (thought-related) brain activity is sufficient to trigger changes in functional connectivity within and between relevant brain networks that have been previously associated selectively with executive and attentional control as well as self-relevant processing, social cognition, and theory of mind. These results demonstrate that an implied social presence, mediated here by recording brain activity with fMRI, can alter brain functional connectivity. These data provide a new manipulation of social attention, as well as shining light on a methodological hazard for researchers using equipment to monitor brain activity

Elemental Abundances in NGC 3516

We present RGS data from an XMM-Newton observation of the Seyfert 1 galaxy NGC 3516, taken while the continuum source was in an extreme low state. The spectrum shows numerous emission lines including the H-like lines of C, N and O and the He-like lines of N, O and Ne. These data show that the N lines are far stronger than would be expected from gas of solar abundances. Based on our photoionization models, we find that N is overabundant compared to C, O and Ne by at least a factor of 2.5. We suggest this is the result of secondary production of N in intermediate mass stars, and indicative of the history of star formation in NGC 3516.Comment: 19 pages, 3 color figures. ApJ in pres

The effect of normative social influence and cultural diversity on group interactions

Motivated by concerns regarding the impact of cultural diversity on group interaction processes and a desire to extend the Social Influence Model of Technology Use, this paper discusses the impact of normative social influence on enhancing group media use and group decision making performance over time in different cultural group compositions. This paper proposes that the strength of attraction to the group influences the similarity in media perception and use of group members. The similarity of group media perception and use is proposed to influence group performance. Concurrently, group cohesion, similarity of media perception and use, and group performance are positively correlated over time. Since culture affects individuals' values, beliefs and behavior, this paper proposes that the degree of similarity in media perception and media use may differ when group composition varies by culture. Several propositions for empirical examination are highlighted. Finally, the paper concludes with a discussion of the importance and implications of understanding cultural diversity and social influence on technology use and group performance. © 2006 IEEE

Messaging media perceptions and preferences: a pilot study in two distinct cultures

This study empirically examines university students' perceptions and their views of when they adopt Instant Messaging (IM) and Short Messaging Service (SMS) and how they perceive and prefer these two media, in conjunction with other media (face-to-face, telephone, and email), in their university learning activities across two different cultural contexts: Australian university and Chinese university. The overall results of this study support some aspects of media richness theory. Although IM is perceived to be richer than email, it is not perceived to be the most popular medium for any situation. Data also demonstrate cultural differences in media perceptions of and preferences for new media. Specifically, Australian students have higher preference for email than their Chinese counterparts and Australian students also perceive SMS as leaner in terms of medium richness and have less preference for SMS than their Chinese counterparts. © 2007 IEEE

X-Ray Spectral Study of AGN Sources Content in Some Deep Extragalactic XMM-Newton Fields

We undertake a spectral study of a sample of bright X-ray sources taken from six XMM-Newton fields at high galactic latitudes, where AGN are the most populous class. These six fields were chosen such that the observation had an exposure time more than 60 ksec, had data from the EPIC-pn detector in the full-Frame mode and lying at high galactic latitude $|b| > 25^o$ . The analysis started by fitting the spectra of all sources with an absorbed power-law model, and then we fitted all the spectra with an absorbed power-law with a low energy black-body component model.The sources for which we added a black body gave an F-test probability of 0.01 or less (i.e. at 99% confidence level), were recognized as sources that display soft excess. We perform a comparative analysis of soft excess spectral parameters with respect to the underlying power-law one for sources that satisfy this criterion. Those sources, that do not show evidence for a soft excess, based on the F-test probability at a 99% confidence level, were also fitted with the absorbed power-law with a low energy black-body component model with the black-body temperature fixed at 0.1 and 0.2 keV. We establish upper limits on the soft excess flux for those sources at these two temperatures. Finally we have made use of Aladdin interactive sky atlas and matching with NASA/IPAC Extragalactic Database (NED) to identify the X-ray sources in our sample. For those sources which are identified in the NED catalogue, we make a comparative study of the soft excess phenomenon for different types of systems

Statistical Algorithms for Ontology-based Annotation of Scientific Literature

Background: Ontologies encode relationships within a domain in robust data structures that can be used to annotate data objects, including scientific papers, in ways that ease tasks such as search and meta-analysis. However, the annotation process requires significant time and effort when performed by humans. Text mining algorithms can facilitate this process, but they render an analysis mainly based upon keyword, synonym and semantic matching. They do not leverage information embedded in an ontology’s structure. Methods: We present a probabilistic framework that facilitates the automatic annotation of literature by indirectly modeling the restrictions among the different classes in the ontology. Our research focuses on annotating human functional neuroimaging literature within the Cognitive Paradigm Ontology (CogPO). We use an approach that combines the stochastic simplicity of naïve Bayes with the formal transparency of decision trees. Our data structure is easily modifiable to reflect changing domain knowledge. Results: We compare our results across naïve Bayes, Bayesian Decision Trees, and Constrained Decision Tree classifiers that keep a human expert in the loop, in terms of the quality measure of the F1-mirco score. Conclusions: Unlike traditional text mining algorithms, our framework can model the knowledge encoded by the dependencies in an ontology, albeit indirectly. We successfully exploit the fact that CogPO has explicitly stated restrictions, and implicit dependencies in the form of patterns in the expert curated annotations

Automated Annotation of Functional Imaging Experiments via Multi-Label Classification

Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert’s annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k -nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text

Quantifying the radiation belt seed population in the 17 March 2013 electron acceleration event

Abstract We present phase space density (PSD) observations using data from the Magnetic Electron Ion Spectrometer instrument on the Van Allen Probes for the 17 March 2013 electron acceleration event. We confirm previous results and quantify how PSD gradients depend on the first adiabatic invariant. We find a systematic difference between the lower-energy electrons (1-MeV with a source region within the radiation belts. Our observations show that the source process begins with enhancements to the 10s-100s-keV energy seed population, followed by enhancements to the \u3e1-MeV population and eventually leading to enhancements in the multi-MeV electron population these observations provide the clearest evidence to date of the timing and nature of the radial transport of a 100s keV electron seed population into the heart of the outer belt and subsequent local acceleration of those electrons to higher radiation belt energies. Key Points Quantification of phase space density gradients inside geostationary orbit Clear differences between the source of low energy and relativistic electrons Clear observations of how the acceleration process evolves in energy