An integration of attachment theory and reinforcement sensitivity theory

This thesis examined how relationship experiences shape people's sensitivity to detect threat and reward in romantic relationships and substance use scenarios. Findings indicated that anxious individuals experienced difficulty in distinguishing between threat and reward. In contrast, avoidant individuals were quick to detect threat either fleeing or confronting the problem aggressively.

Forces measured between latex spheres in aqueous electrolyte : Non-DLVO behavior and sensitivity to dissolved gas

An atomic force microscope was used to measure the forces acting between two polystyrene latex spheres in aqueous media. The results show an electrostatic repulsion at large separations which is overtaken by an attractive “hook” that pulls the two spheres into contact from a considerable range (20−400 nm), much larger than could be expected for a van der Waals attraction. The range of operation of this attraction varies from one experiment to another and is not correlated with electrolyte concentration. However, the range is found to decrease significantly when the level of dissolved gas in the water is reduced.

Software reliability growth models based on local polynomial modeling with kernel smoothing

Software reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.

Climate change sensitivity of comfort and energy performance criteria for offices

The climate change scenarios of the Intergovernmental Panel on Climate Change (IPCC) predict a significant increase in temperatures over the next decades. Architecture and building occupants have to respond to this change, but little information is currently available in how far the predicted changes are likely to affect comfort and energy performance in buildings. This study therefore investigates the climate change sensitivity of the following parameters: adaptive thermal comfort according to Ashrae Standard 55 and EN 15251, energy consumption, heating and cooling loads, and length of heating and cooling periods. The study is based on parametric simulations of typical office room configurations in the context of Athens, Greece. They refer to different building design priorities and account for different occupant behaviour by using an ideal and worst case scenario. To evaluate the impact of the climate change, simulations are compared based on a common standard weather data set for Athens, and a generated climate change data set for the IPCC A2 scenario. The results show a significant impact of the climate change on all investigated parameters. They also indicate that in this context the optimisation of comfort and energy performance is likely to be related to finding the best possible balance between building (design) and occupant behaviour and other contextual influences, rather than a straightforward optimisation of separated single parameters.

Sensitivity of deformation twinning to grain size in titanium and magnesium

The impact of grain size on deformation twinning in commercial purity titanium and magnesium alloy Mg–3Al–1Zn (AZ31) is investigated. Tensile tests were carried out for the titanium samples; compression testing was employed for the magnesium specimens. Average values of the true twin length, true twin thickness and the number density of twins were determined using stereology. A key difference between these two materials is that twinning contributes little to the plastic strain in the titanium while it accounts for nearly all of the early plastic strain in the magnesium. In some respects (e.g. volume fraction and number density) the phenomenology of twinning differed between the two materials, while in others (e.g. twin shape and size) both materials showed a similar response. It is found that in both materials, twins span the entirety of their parent grains only for grain sizes less than ∼30 μm. Both the nucleation density per unit of nucleating interface (i.e. grain and twin boundaries) and the aspect ratio of twins scale with applied stress. The impact of grain size on twin volume fraction is modelled analytically.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

A fast kernel dimension reduction algorithm with applications to face recognition

This paper presents a novel dimensionality reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-class distance and the sum of the within-class variances of the training samples for a given reduced dimension. This algorithm has lower complexity than the recently reported kernel dimension reduction(KDR) for supervised learning. We conducted several simulations with large training datasets, which demonstrate that the proposed algorithm has similar performance or is marginally better compared with KDR whilst having the advantage of computational efficiency. Further, we applied the proposed dimension reduction algorithm to face recognition in which the number of training samples is very small. This proposed face recognition approach based on the new algorithm outperforms the eigenface approach based on the principle component analysis (PCA), when the training data is complete, that is, representative of the whole dataset.