Energetic beams of negative and neutral hydrogen from intense laser plasma interaction

We present observations of intense beams of energetic negative hydrogen ions and fast neutral hydrogen atoms in intense (5 × 10 W/cm) laser plasma interaction experiments, which were quantified in numerical calculations. Generation of negative ions and neutral atoms is ascribed to the processes of electron capture and loss by a laser accelerated positive ion in the collisions with a cloud of droplets. A comparison with a numerical model of charge exchange processes provides information on the cross section of the electron capture in the high energy domain.

Effect of interparticle interaction in a free-oscillation atomic interferometer

We investigate the dynamics of two interacting bosons repeatedly scattering off a beam-splitter in a free oscillation atom interferometer. Using the interparticle scattering length and the beam-splitter probabilites as our control parameters, we show that even in a simple setup like this a wide range of strongly correlated quantum states can be created. This in particular includes the NOON state, which maximizes the quantum Fisher information and is a foremost state in quantum metrology. DOI: 10.1103/PhysRevA.87.043630

Whose statistical reasoning is improved by information about causal structure?

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430–450, 2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.

Attention during social interaction in children with autism: Comparison to specific language impairment, typical development, and links to cognition

Eye-tracking studies have shown how people with autism spend significantly less time looking at socially relevant information on-screen compared to those developing typically. This has been suggested to impact on the development of socio-cognitive skills in autism. We present novel evidence of how attention atypicalities in children with autism extend to real-life interaction, in comparison to typically developing (TD) children and children with specific language impairment (SLI). We explored the allocation of attention during social interaction with an interlocutor, and how aspects of attention (awareness checking) related to traditional measures of social cognition (false belief attribution). We found divergent attention allocation patterns across the groups in relation to social cognition ability. Even though children with autism and SLI performed similarly on the socio- cognitive tasks, there were syndrome-specific atypicalities of their attention patterns. Children with SLI were most similar to TD children in terms of prioritising attention to socially pertinent information (eyes, face, awareness checking). Children with autism showed reduced attention to the eyes and face, and slower awareness checking. This study provides unique and timely insight into real-world social gaze (a)typicality in autism, SLI and typical development, its relationship to socio-cognitive ability, and raises important issues for intervention.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.