Perception of Emotions in the Hand Movement Quality of Finnish Sign Language

This study investigated whether people can decode emotion (happiness, neutrality, and anger) communicated via hand movements in Finnish sign language when these emotions are expressed in semantically neutral sentences. Twenty volunteer participants without any knowledge of sign language took part in the experiment. The results indicated that the subjects were able to reliably decode anger and neutrality from the quality of hand movements. For happy hand expressions, the responses of happiness and neutrality were confused. Thus, the study showed that emotion-related information can be encoded in the quality of hand movements during signing and that this information can be decoded without previous experience with this particular mode of communication.     

A Probabilistic Transmission Model of Salmonella in the Primary Broiler Production Chain

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%-17.4%] (no intervention), and [0.9%-5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%-43.1%] and [1.0%-5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.     

How Do Efforts to Enhance Career Preparation Affect Peer Groups?

The present study investigated how efforts to enhance career preparation affect peer groups. The participants were 710 9th graders who were randomized into control and intervention groups and assessed 3 times during 1 academic year: at baseline (T1, Fall), immediately after the career preparation intervention (T2, 3 months after baseline), and 5 months after the intervention (T3, Spring). The results showed, first, that maintenance of a stable clique was more likely when most clique members participated in the intervention. Second, it was found that the members of adolescents' cliques resembled each other in respect of the strength of the intervention effect. Finally, the results showed that the similarity of the clique members in career choice preparedness first decreased as a result of the short‐term impact of the intervention but then increased after the intervention was over, suggesting that peer groups were partly responsible for weakening the effect of the intervention. These results have important implications for understanding how peer groups moderate external influences on their members and how the stability of peer groups is affected by these influences.     

Optimal Viterbi Bayesian predictive classification for data from finite alphabets

A family of Viterbi Bayesian predictive classifiers has been recently popularized for speech recognition applications with continuous acoustic signals modeled by finite mixture densities embedded in a hidden Markov framework. Here we generalize such classifiers to sequentially observed data from multiple finite alphabets and derive the optimal predictive classifier under exchangeability of the emitted symbols. We demonstrate that the optimal predictive classifier which learns from unlabelled test items improves considerably upon marginal maximum a posteriori rule in the presence of sparse training data. It is shown that the learning process saturates when the amount of test data tends to infinity, such that no further gain in classification accuracy is possible upon arrival of new test items in the long run.     

Have I seen you before? Principles of Bayesian predictive classification revisited

A general inductive Bayesian classification framework is considered using a simultaneous predictive distribution for test items. We introduce a principle of generative supervised and semi-supervised classification based on marginalizing the joint posterior distribution of labels for all test items. The simultaneous and marginalized classifiers arise under different loss functions, while both acknowledge jointly all uncertainty about the labels of test items and the generating probability measures of the classes. We illustrate for data from multiple finite alphabets that such classifiers achieve higher correct classification rates than a standard marginal predictive classifier which labels all test items independently, when training data are sparse. In the supervised case for multiple finite alphabets the simultaneous and the marginal classifiers are proven to become equal under generalized exchangeability when the amount of training data increases. Hence, the marginal classifier can be interpreted as an asymptotic approximation to the simultaneous classifier for finite sets of training data. It is also shown that such convergence is not guaranteed in the semi-supervised setting, where the marginal classifier does not provide a consistent approximation.     

Stratified Gaussian graphical models

Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivariate normal distribution, use of this model family may hide certain forms of context-specific independence that are natural to consider from an applied perspective. Such independencies have been earlier introduced to generalize discrete graphical models and Bayesian networks into more flexible model families. Here, we adapt the idea of context-specific independence to Gaussian graphical models by introducing a stratification of the Euclidean space such that a conditional independence may hold in certain segments but be absent elsewhere. It is shown that the stratified models define a curved exponential family, which retains considerable tractability for parameter estimation and model selection.     

Child development in foster family care – what really counts?

Previous research has demonstrated that children who grow up in foster family care – along with other child welfare recipients – manage less well in adulthood compared to those children who do not. Given this challenge, this integrative literature review locates the critical factors that either positively or negatively affect a child’s development in foster family care. The articles were analysed using theory-driven content classification in relation to Bronfenbrenner’s bioecological framework on child development. The results of the review suggest that there is a wide range of factors that could impact on a child’s development in foster family care. Child­related factors such as the child’s age, gender, behavioural or mental health problems, etc. were mentioned most often in the data. Micro­environmental factors are also essential to a child’s development. Linkages taking place between two or more of the child’s circumstances, such as the relationship between the child’s birth and foster families and between the foster family and the social worker, were also identified as being influential. Service usage and political and legislative factors, as well as attitudes towards children in care, were also indicated to be strongly influential. It is suggested that the factors identified in this review should be carefully considered as important aspects of care for fostered children and care documentation.     

Categorical Representation of Facial Expressions in the Infant Brain

Categorical perception, demonstrated as reduced discrimination of within‐category relative to between‐category differences in stimuli, has been found in a variety of perceptual domains in adults. To examine the development of categorical perception in the domain of facial expression processing, we used behavioral and event‐related potential (ERP) methods to assess discrimination of within‐category (happy‐happy) and between‐category (happy‐sad) differences in facial expressions in 7‐month‐old infants. Data from a visual paired‐comparison test and recordings of attention‐sensitive ERPs showed no discrimination of facial expressions in the within‐category condition, whereas reliable discrimination was observed in the between‐category condition. The results also showed that face‐sensitive ERPs over occipital‐temporal scalp (P400) were attenuated in the within‐category condition relative to the between‐category condition, suggesting a potential neural basis for the reduced within‐category sensitivity. Together, these results suggest that the neural systems underlying categorical representation of facial expressions emerge during the early stages of postnatal development, before acquisition of language.     

Computing exact clustering posteriors with subset convolution

ABSTRACT

An exponential-time exact algorithm is provided for the task of clustering n items of data into k clusters. Instead of seeking one partition, posterior probabilities are computed for summary statistics: the number of clusters and pairwise co-occurrence. The method is based on subset convolution and yields the posterior distribution for the number of clusters in O(n3ⁿ) operations or O(n³2ⁿ) using fast subset convolution. Pairwise co-occurrence probabilities are then obtained in O(n³2ⁿ) operations. This is considerably faster than exhaustive enumeration of all partitions.     

Mixed estimation of old‐age mortality

The estimation of the mortality of the “oldest old”; is subject to considerable random error, but important prior information exists that can be used to make the estimates more robust. Mixed estimation is a method of incorporating auxiliary information into the statistical estimation of linear models. We extend the method to cover general maximum likelihood estimation, and show that the mixed estimator can be represented approximately as a weighted average of the purely data based estimator and the auxiliary estimator. The methods can be applied to the analysis of the old‐age mortality via logistic and Poisson regression. A major advantage of the mixed estimator is the simplicity with which it can incorporate partial prior information. Moreover, no special software is needed in the fitting. We show how the targeting methods of Coale and Kisker can be represented as mixed estimation in a natural way that is more flexible than the original proposal. We also derive empirical estimates of the target information based on pooled data from several countries with high quality data. We consider the mortality of Finland at ages 80 +, study the reliability of the evidence of mortality crossover, and derive estimates of life expectancy at age 100.