Evasive maneuver subsequent to CSM/LM ejection from the S-4B in earth orbit - Project Apollo

Apollo 9 evasive maneuver after command service module/lunar module ejection from Saturn S-4B stage in earth orbi

Inferring meta-covariates in classification

This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results

The influence of social network size on speech perception

Infants and adults learn new phonological varieties better when exposed to multiple rather than a single speaker. This article tests whether having a larger social network similarly facilitates phonological performance. Experiment 1 shows that people with larger social networks are better at vowel perception in noise, indicating that the benefit of laboratory exposure to multiple speakers extends to real life experience and to adults tested in their native language. Furthermore, the experiment shows that this association is not due to differences in amount of input or to cognitive differences between people with different social network sizes. Follow-up computational simulations reveal that the benefit of larger social networks is mostly due to increased input variability. Additionally, the simulations show that the boost that larger social networks provide is independent of the amount of input received but is larger if the population is more heterogeneous. Finally, a comparison of “adult” and “child” simulations reconciles previous conflicting findings by suggesting that input variability along the relevant dimension might be less useful at the earliest stages of learning. Together, this article shows when and how the size of our social network influences our speech perception. It thus shows how aspects of our lifestyle can influence our linguistic performance

Detection of elliptical shapes via cross-entropy clustering

The problem of finding elliptical shapes in an image will be considered. We discuss the solution which uses cross-entropy clustering. The proposed method allows the search for ellipses with predefined sizes and position in the space. Moreover, it works well for search of ellipsoids in higher dimensions

Adult attachment style across individuals and role-relationships: Avoidance is relationship-specific, but anxiety shows greater generalizability

A generalisability study examined the hypotheses that avoidant attachment, reflecting the representation of others, should be more relationship-specific (vary across relationships more than across individuals), while attachment anxiety, reflecting self-representation, should be more generalisable across a person’s relationships. College students responded to 6-item questionnaire measures of these variables for 5 relationships (mother, father, best same-gender friend, romantic partner or best opposite-gender friend, other close person), on 3 (N = 120) or 2 (N = 77) occasions separated by a few weeks. Results supported the hypotheses, with the person variance component being larger than the relationship-specific component for anxiety, and the opposite happening for avoidance. Anxiety therefore seems not to be as relationship-specific as previous research suggested. Possible reasons for discrepancies between the current and previous studies are discussed

Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Model selection in High-Dimensions: A Quadratic-risk based approach

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC.Comment: Updated with reviewer suggestion

Model-based clustering via linear cluster-weighted models

A novel family of twelve mixture models with random covariates, nested in the linear $t$ cluster-weighted model (CWM), is introduced for model-based clustering. The linear $t$ CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Maximum likelihood parameter estimation is carried out within the EM framework, and both the BIC and the ICL are used for model selection. A simple and effective hierarchical random initialization is also proposed for the EM algorithm. The novel model-based clustering technique is illustrated in some applications to real data. Finally, a simulation study for evaluating the performance of the BIC and the ICL is presented

Conscientiousness and fruit and vegetable consumption: exploring behavioural intention as a mediator

Clear associations have emerged between conscientiousness and health behaviours, such that higher levels of conscientiousness are predictive of beneficial health behaviours. This study investigated the conscientiousness-fruit and vegetable consumption relationship and whether behavioural intention mediated this relationship. A large sample of adults (N = 2136) completed an online battery of questionnaires measuring conscientiousness, behavioural intentions to consume fruit and vegetables, together with self-reported behaviour. Correlation analysis revealed that conscientiousness and each of its facets were positively associated with behavioural intention and self-reported behaviour. Hierarchical multiple regression analyses revealed that after controlling for age, gender and education, total conscientiousness, and the facets of responsibility, industriousness, order and virtue predicted self-reported behaviour. Further analysis revealed that in line with the Theory of Planned Behaviour, behavioural intention fully mediated the conscientiousness-fruit and vegetable behaviour relationship. In conclusion, low levels of conscientiousness were found to be associated with lower fruit and vegetable intentions, with the latter also associated with fruit and vegetable consumption

Adult attachment styles and the psychological response to infant bereavement

Background: Based on Bowlby's attachment theory, Bartholomew proposed a four-category attachment typology by which individuals judged themselves and adult relationships. This explanatory model has since been used to help explain the risk of psychiatric comorbidity. Objective: The current study aimed to identify attachment typologies based on Bartholomew's attachment styles in a sample of bereaved parents on dimensions of closeness/dependency and anxiety. In addition, it sought to assess the relationship between the resultant attachment typology with a range of psychological trauma variables. Method: The current study was based on a sample of 445 bereaved parents who had experienced either peri- or post-natal death of an infant. Adult attachment was assessed using the Revised Adult Attachment Scale (RAAS) while reaction to trauma was assessed using the Trauma Symptom Checklist (TSC). A latent profile analysis was conducted on scores from the RAAS closeness/dependency and anxiety subscales to ascertain if there were underlying homogeneous attachment classes. Emergent classes were used to determine if these were significantly different in terms of mean scores on TSC scales. Results: A four-class solution was considered the optimal based on fit statistics and interpretability of the results. Classes were labelled “Fearful,” “Preoccupied,” “Dismissing,” and “Secure.” Females were almost eight times more likely than males to be members of the fearful attachment class. This class evidenced the highest scores across all TSC scales while the secure class showed the lowest scores. Conclusions: The results are consistent with Bartholomew's four-category attachment styles with classes representing secure, fearful, preoccupied, and dismissing types. While the loss of an infant is a devastating experience for any parent, securely attached individuals showed the lowest levels of psychopathology compared to fearful, preoccupied, or dismissing attachment styles. This may suggest that a secure attachment style is protective against trauma-related psychological distress