Composite multi-vortex diffraction-free beams and van Hove singularities in honeycomb lattices

We find diffraction-free beams for graphene and MoS$_2$-type honeycomb optical lattices. The resulting composite solutions have the form of multi-vortices, with spinor topological charges ($n$, $n\pm1$). Exact solutions for the spinor components are obtained in the Dirac limit. The effects of the valley degree of freedom and the mass are analyzed. Passing through the van-Hove singularity the topological structure of the solutions is modified. Exactly at the singularity the diffraction-free beams take the form of strongly localized one-dimensional stripes.Comment: 4 pages, 6 figures, accepted for publication in Optics Letter

Statistical analysis of emotions and opinions at Digg website

We performed statistical analysis on data from the Digg.com website, which enables its users to express their opinion on news stories by taking part in forum-like discussions as well as directly evaluate previous posts and stories by assigning so called "diggs". Owing to fact that the content of each post has been annotated with its emotional value, apart from the strictly structural properties, the study also includes an analysis of the average emotional response of the posts commenting the main story. While analysing correlations at the story level, an interesting relationship between the number of diggs and the number of comments received by a story was found. The correlation between the two quantities is high for data where small threads dominate and consistently decreases for longer threads. However, while the correlation of the number of diggs and the average emotional response tends to grow for longer threads, correlations between numbers of comments and the average emotional response are almost zero. We also show that the initial set of comments given to a story has a substantial impact on the further "life" of the discussion: high negative average emotions in the first 10 comments lead to longer threads while the opposite situation results in shorter discussions. We also suggest presence of two different mechanisms governing the evolution of the discussion and, consequently, its length.Comment: 26 pages, 16 figures, 6 table

Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions

Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study emergence of the emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite network of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion-classifier developed for this type of texts. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore robustness of these critical states, we design a network automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states

Public self-consciousness, pre-loading and drinking harms among university students

Background: Social anxiety and self-consciousness are associated with alcohol-related problems in students. The practice of pre-loading is one avenue for exploration regarding this relationship. Individuals may pre-load to reduce social anxiety and feel more confident when socialising, which could lead to the increased harms experienced. The current study aimed to explore reasons for pre-loading, and whether public and private self-consciousness and social anxiety were related to pre-loading, increased drinking and harms. Method: Prospective study with four-week follow up of 325 UK students aged 18-30 years old. Participants completed measures of private and public self-consciousness, social anxiety, alcohol consumption, alcohol-related harms and pre-loading. Results: Financial motives and mood-related reasons, such as gaining confidence were reported as reasons for pre-loading. Pre-loading predicted hazardous alcohol consumption, but social anxiety, and public and private self-consciousness did not. However, pre-loading, public self-consciousness and social anxiety predicted alcohol-related harms. Furthermore, public self-consciousness mediated the relationship between pre-loading and harms in a positive direction and this appeared to be more relevant in high risk (AUDIT 8+) than low risk drinkers. Conclusion: Students who scored higher in public self-consciousness appeared to be at greater risk of harms from pre-loading. Further research should examine this relationship further with particular attention to high risk drinkers, and explore which aspects of a night out are related to heightened self-consciousness. Interventions could incorporate measures to reduce public self-consciousness, in order to reduce the negative impacts of pre-loading

Strain-induced interface reconstruction in epitaxial heterostructures

We investigate in the framework of Landau theory the distortion of the strain fields at the interface of two dissimilar ferroelastic oxides that undergo a structural cubic-to-tetragonal phase transition. Simple analytical solutions are derived for the dilatational and the order parameter strains that are globally valid over the whole of the heterostructure. The solutions reveal that the dilatational strain exhibits compression close to the interface which may in turn affect the electronic properties in that region.Comment: 7 pages, 5 figures, to be published in Physical Review

Using virtual reality to understand and treat depression

Depression is a prevalent mental health disorder with serious consequences for the patients’ lives. Virtual Reality (VR) is a promising technology that can immerse individuals in a virtual environment, and has increasingly been used to conduct experiments and deliver interventions with higher ecological validity. One of the studies discussed here showed that individuals with depression score lower in a novel Spatial Memory Navigation task than controls. Two other studies showed some promising results in using VR to deliver interventions for depression to reduce stress and increase self-compassion in depressed individuals. Longitudinal randomized controlled trials are needed to further validate these promising results