No / In normal vision, detecting a kink (a change in orientation) in a line is scale invariant: it depends solely on the length/width ratio of the line (D. Whitaker, D. M. Levi, & G. J. Kennedy, 2008). Here we measure detection of a change in the orientation of lines of different length and blur and show that strabismic amblyopia is qualitatively different from normal foveal vision, in that: 1) stimulus blur has little effect on performance in the amblyopic eye, and 2) integration of orientation information follows a different rule. In normal foveal vision, performance improves in proportion to the square root of the ratio of line length to blur (L: B). In strabismic amblyopia improvement is proportional to line length. Our results are consistent with a substantial degree of internal neural blur in first-order cortical filters. This internal blur results in a loss of scale invariance in the amblyopic visual system. Peripheral vision also shows much less effect of stimulus blur and a failure of scale invariance, similar to the central vision of strabismic amblyopes. Our results suggest that both peripheral vision and strabismic amblyopia share a common bottleneck in having a truncated range of spatial mechanisms-a range that becomes more restricted with increasing eccentricity and depth of amblyopia. / Leverhulme Trust, Wellcome Trust, NIH

This thesis focuses on mining user access patterns from netflow database collected from the core router of a regional network center. We use the attributed relational graph representation to formulate user access patterns on the Internet, and then propose a procedure to generalize common connection patterns and detect deviation patterns with such methods as large graph generalization, error correcting graph matching, frontier identification and pattern base recognition. The major contributions of this thesis are on represeting the network connection with attributed relational graph and developing data mining tehcniques for identifying access paterns and detecting deviation. The results can be used for better managing regional network in order to improve user satification in using regional netwrok netwrok services.

The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method.