The Trauma Towers: Dimensions of Trauma in 9/11 Literature

The topic of this dissertation is the aspects of trauma and reaction to the traumatic experience that can be found in 9/11 literature. The research engages in a comparative analysis of five books that can be categorised as 9/11 literature, which means that the events of 9/11 are central in the novels and are a recurrent theme. The books have been written by authors of different nationalities: "Extremely Loud & Incredibily Close" by J. S. Foer, "Falling Man" by D. DeLillo, "Windows on the World" by F. Beigbeder, "Saturday" by I. McEwan and "The Reluctant Fundamentalist" by M. Hamid. The characters have either experienced the attacks personally or their lives have been largely influenced by the event. In either case, the protagonist has been traumatised by the tragedy. Therefore, in this study two different fields are fused together – the field of comparative literature and that of trauma studies.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.