Keeping in Touch with Context: Non-verbal Behavior as a Manifestation of Communality and Dominance

This research investigated the influence of observed touch on the perceptions of communality and dominance in dyadic interactions. We manipulated four key situational features of haptic behavior in two experiments: the initiation, reciprocity, the degree of formality of touch (Studies 1 and 2), and the context of the interaction (Study 2). The results showed that the default perception of touch, irrespective of whether it is initiated or reciprocated, is the communal intention of the toucher. Furthermore, the initiation of touch was seen as an act of dominance, particularly, when the contact between the actors was primed as being hierarchical. Reciprocation neutralized the perceived asymmetry in dominance, but such inferences seemed to hinge on the fit of the touch with the context: reciprocation of formal touch reduced the asymmetry in the hierarchical context, whereas reciprocation of informal touch reduced the asymmetry in the non-hierarchical context.     

Bootstrap methods for multivariate hypothesis testing

The nonparametric and parametric bootstrap methods for multivariate hypothesis testing are developed. They are used to approximate the null distribution of the test statistics proposed by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), resulting in bootstrap testing procedures. In the problem of testing for the mean vector of a multivariate distribution, the asymptotic validity of the bootstrap methods is proved. The finite sample performance of the new solutions is demonstrated by means of Monte Carlo simulation studies. They indicate that for small-sample size, the bootstrap tests provide a better finite sample properties than the asymptotic tests considered by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).     

Two Means Together? Effects of Response Bias and Sensitivity on Communicative Action Detection

Numerous lines of research suggest that communicative dyadic actions elicit preferential processing and more accurate detection compared to similar but individual actions. However, it is unclear whether the presence of the second agent provides additional cues that allow for more accurate discriminability between communicative and individual intentions or whether it lowers the threshold for perceiving third-party encounters as interactive. We performed a series of studies comparing the recognition of communicative actions from single and dyadic displays in healthy individuals. A decreased response threshold for communicative actions was observed for dyadic vs. single-agent animations across all three studies, providing evidence for the dyadic communicative bias. Furthermore, consistent with the facilitated recognition hypothesis, congruent response to a communicative gesture increased the ability to accurately interpret the actions. In line with dual-process theory, we propose that both mechanisms may be perceived as complementary rather than competitive and affect different stages of stimuli processing.

     

2-Rainbow domination number of Cartesian products: C_{n}\square C_{3} and C_{n}\square C_{5}

A function \(f:V(G)\rightarrow \mathcal P (\{1,\ldots ,k\})\) is called a \(k\) -rainbow dominating function of \(G\) (for short \(kRDF\) of \(G)\) if \( \bigcup \nolimits _{u\in N(v)}f(u)=\{1,\ldots ,k\},\) for each vertex \( v\in V(G)\) with \(f(v)=\varnothing .\) By \(w(f)\) we mean \(\sum _{v\in V(G)}\left|f(v)\right|\) and we call it the weight of \(f\) in \(G.\) The minimum weight of a \( kRDF\) of \(G\) is called the \(k\) -rainbow domination number of \(G\) and it is denoted by \(\gamma _{rk}(G).\) We investigate the \(2\) -rainbow domination number of Cartesian products of cycles. We give the exact value of the \(2\) -rainbow domination number of \(C_{n}\square C_{3}\) and we give the estimation of this number with respect to \(C_{n}\square C_{5},\) \((n\ge 3).\) Additionally, for \(n=3,4,5,6,\) we show that \(\gamma _{r2}(C_{n}\square C_{5})=2n.\)      

First and Second Derivatives in Time Series Classification Using DTW

In our previous work, we developed a new distance function based on a derivative and showed that our algorithm is effective. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than standard distance of function (value) comparison. The new distance was used in classification with the nearest neighbor rule. Now we improve on our previous technique by adding the second derivative. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series datasets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined datasets.     

Winter habitat selection of Corvids in an urban ecosystem

Urban Ecosystems - Urban areas attract birds during the winter when cities provide a predictable source of food and relatively stable weather conditions. However, many other factors determine the...     

Subjective Welfare, Well-Being, and Self-Reported Food Hypersensitivity in Four European Countries: Implications for European Policy

This study estimates the effects of food hypersensitivity on individuals?? perceived welfare and well-being compared to non-food hypersensitive individuals. Study respondents were recruited in the Netherlands, Poland, Spain and UK. The difference in welfare between food hypersensitive respondents and those asymptomatic to foods was estimated using a subjective welfare approach, including income evaluation. Well-being was measured using the Cantril Ladder-of-Life Scale, and health status using the Self-Perceived Health Scale. The difference in well-being, welfare and health status between participant groups was explained further using a number of background variables. No significant within-country differences in welfare between food hypersensitive respondents and respondents asymptomatic to foods were found. In terms of well-being, adult food hypersensitive respondents and their spouses reported significantly less happiness than respondents and their spouses asymptomatic to foods in the Netherlands and Poland. In Spain, the spouses of the food hypersensitive respondents were significantly less happy than respondents aymptomatic to foods. The well-being of children did not significantly differ between groups. The degree of severity of food hypersensitivity was negatively related to overall health status. In Poland, food hypersensitive respondents reported worse health status compared to asymptomatic respondents. In Spain, the converse was true. Food hypersensitive respondents were generally less happy with their life as a whole than respondents asymptomatic to foods, presumably because they experienced more negative effects, which were not related to perceived health status.     

A note on variable selection in functional regression via random subspace method

Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors. The functional model can be represented by certain multiple linear regression model via basis expansions of functional variables. Based on this model and random subspace method of Mielniczuk and Teisseyre (Comput Stat Data Anal 71:725–742, 2014), two simple variable selection procedures for scalar response functional regression model are proposed. The final functional model is selected by using generalized information criteria. Monte Carlo simulation studies conducted and a real data example show very satisfactory performance of new variable selection methods under finite samples. Moreover, they suggest that considered procedures outperform solutions found in the literature in terms of correctly selected model, false discovery rate control and prediction error.