Visual field and structural alterations in age-related macular degeneration

The thesis investigated progression of the central 10° visual field with structural changes at the macula in a cross-section of patients with varying degrees of agerelated macular degeneration (AMD). The relationships between structure and function were investigated for both standard and short-wavelength automated perimetry (SWAP). Factors known to influence the measure of visual field progression were considered, including the accuracy of the refractive correction on SWAP thresholds and the learning effect. Techniques of assessing the structure to function relationships between fundus images and the visual field were developed with computer programming and evaluated for repeatability. Drusen quantification of fundus photographs and retro-mode scanning laser ophthalmoscopic images was performed. Visual field progression was related to structural changes derived from both manual and automated methods. Principal Findings: • Visual field sensitivity declined with advancing stage of AMD. SWAP showed greater sensitivity to progressive changes than standard perimetry. • Defects were confined to the central 5°. SWAP defects occurred at similar locations but were deeper and wider than corresponding standard perimetry defects. • The central field became less uniform as severity of AMD increased. SWAP visual field indices of focal loss were of more importance when detecting early change in AMD, than indices of diffuse loss. • The decline in visual field sensitivity over stage of severity of AMD was not uniform, whereas a linear relationship was found between the automated measure of drusen area and visual field parameters. • Perimetry exhibited a stronger relationship with drusen area than other measures of visual function. • Overcorrection of the refraction for the working distance in SWAP should be avoided in subjects with insufficient accommodative facility. • The perimetric learning effect in the 10° field did not differ significantly between normal subjects and AMD patients. • Subretinal deposits appeared more numerous in retro-mode imaging than in fundus photography.

Learning and teaching through context: a data-driven approach

A word may have many potential meanings, but its actual meaning in any authentic written or spoken text is determined by its context: its collocations, structural patterns, and pragmatic functions. Large language corpora offer access to words in a wide range of natural contexts, which can improve and enrich both language learning and teaching.

Looking under the bonnet:factors affecting student adoption of e-learning systems in Jordan

The primary questions addressed in this paper are the following: what are the factors that affect students’ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students’ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students’ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students’ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Supervised learning of graph structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.