Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?

The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, On-line Support Groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically verify the presence of therapeutic factors in social networking websites by using Natural Language Processing (NLP) techniques. The methodology is evaluated on On-line asynchronous multi-party conversations collected from an OSG and Twitter. The results of the analysis indicate that therapeutic factors occur more frequently in OSG conversations than in Twitter conversations. Moreover, the analysis of OSG conversations reveals that the users of that platform are supportive, and interactions are likely to lead to the improvement of their emotional state. We believe that our method provides a stepping stone towards automatic analysis of emotional states of users of online platforms. Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users' mental health, and/or support in the analysis of their impact on specific individuals

Optical absorption of BaF[2] crystals with different prehistory when irradiated by high-energy electrons

The spectra of stable optical absorption of BaF[2] crystals containing uncontrollable impurities after irradiation with 3 MeV electrons are studied at room temperature. The dependence of the efficiency of stable color accumulation in the region of emerging cross-luminescence on the absorption coefficients measured near the fundamental absorption edge in unirradiated crystals of various prehistory is traced

Mathematical modelling of energy conversion of pulsed electron beam in BaF[2] crystal

This paper presents the results of mathematical modeling of spatiotemporal distribution of energy in a BaF[2] crystal in an area of dissipation of energy of an pulsed electron beam. Fluence is varied from 0.188 to 0.626 J/cm{2}. Electron beam pulse duration was 24 ns, and the maximum electron energy of 280 KeV. Based on the obtained of energy distributions temperature change of the material during irradiation were calculated

the unitn discourse parser in conll 2015 shared task token level sequence labeling with argument specific models

Penn Discourse Treebank style discourse parsing is a composite task of identifying discourse relations (explicit or nonexplicit), their connective and argument spans, and assigning a sense to these relations from the hierarchy of senses. In this paper we describe University of Trento parser submitted to CoNLL 2015 Shared Task on Shallow Discourse Parsing. The span detection tasks for explicit relations are cast as token-level sequence labeling. The argument span decisions are conditioned on relations' being intra- or intersentential. Non-explicit relation detection and sense assignment tasks are cast as classification. In the end-to-end closedtrack evaluation, the parser ranked second with a global F-measure of 0.218