Questions of 'h' in Northern Ireland: Breathing New Life on the Aspiration Theory

This paper considers existing ideas concerning pronunciation of the letter name for (LNH) in Northern Irish English (NIE). Traditionally, the status of LNH realisation as an ethnic marker has gone unquestioned: Catholics are thought to say [het&Mac186;] while the Protestant norm is assumed to be [etS]. The phonetic difference between these realisations is consistently described as word-initial aspiration versus non-aspiration, with aspiration attributed exclusively to Irish language influence. Here, we show that an explanation based on aspiration alone is phonologically unsatisfying and question whether aspiration is, in fact, an Irish language or ethnically dictated phenomenon. It is further suggested here that the overwhelming stigmatisation of LNH realisation may be responsible for blocking a potential sound change in NIE. While this paper is not intended as a detailed account of ethnolinguistic differences in NI phonology, it engages critically with the over-simplistic and widespread notion that LNH realisation is a result of transfer from the Irish language to the English used by Catholics in Northern Ireland.

Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.

Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.

Stereotypes of Age Differences in Personality Traits: Universal and Accurate?

Age trajectories for personality traits are known to be similar across cultures. To address whether stereotypes of age groups reflect these age-related changes in personality, we asked participants in 26 countries (N = 3,323) to rate typical adolescents, adults, and old persons in their own country. Raters across nations tended to share similar beliefs about different age groups; adolescents were seen as impulsive, rebellious, undisciplined, preferring excitement and novelty, whereas old people were consistently considered lower on impulsivity, activity, antagonism, and Openness. These consensual age group stereotypes correlated strongly with published age differences on the five major dimensions of personality and most of 30 specific traits, using as criteria of accuracy both self-reports and observer ratings, different survey methodologies, and data from up to 50 nations. However, personal stereotypes were considerably less accurate, and consensual stereotypes tended to exaggerate differences across age groups.

Exclusion of linkage between schizophrenia and the D2 dopamine receptor gene region of chromosome 11q in 112 Irish multiplex families

A leading theory hypothesizes that schizophrenia arises from dysregulation of the dopamine system in certain brain regions. As this dysregulation could arise from abnormal expression of D2 dopamine receptors, the D2 receptor gene (DRD2) on chromosome 11q is a candidate locus for schizophrenia. We tested whether allelic variation at DRD2 and five surrounding loci cosegregated with schizophrenia in 112 small- to moderate-size Irish families containing two or more members affected with schizophrenia or schizoaffective disorder, defined by DSM-III-R. Evidence of linkage was assessed using varying definitions of illness and modes of transmission. Assuming genetic homogeneity, linkage between schizophrenia and large regions of 11q around DRD2 could be strongly excluded. Assuming genetic heterogeneity, variation at the DRD2 locus could be rejected as a major risk factor for schizophrenia in more than 50% of these families for all models tested and in as few as 25% of the families for certain models. The DRD2 linkage in fewer than 25% of these families could not be excluded under any of the models tested. Our results suggest that the major component of genetic susceptibility to schizophrenia is not due to allelic variation at the DRD2 locus or other genes in the surrounding chromosomal region.

Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

FE simulation and experimental tests of high-strength structural bolts under tension

Experimental tests have been completed for high-strength 8.8 bolts for studying their mechanical performance subjected to tensile loading. As observed from these tests, failure of structural bolts has been identified as in one of two ways: threads stripping and necking of the threaded portion of the bolt shank, which is possibly due to the degree of fit between internal and external threads. Following the experimental work, a numerical approach has been developed for demonstration of the tensile performance with proper consideration of tolerance class between bolts and nuts. The degree of fit between internal and external threads has been identified as a critical factor affecting failure mechanisms of high-strength structural bolts in tension, which is caused by the machining process. In addition, different constitutive material laws have been taken into account in the numerical simulation, demonstrating the entire failure mechanism for structural bolts with different tolerance classes in their threads. It is also observed that the bolt capacities are closely associated with their failure mechanisms.