Forms of retreat and return in the novels of Don Delillo

This thesis explores themes of retreat and return within the novels of the contemporary American author Don DeLillo. While several of these forms of retreat and return are well-documented in the established body of academic work devoted to DeLillo’s major novels of the 1980s and 1990s, there has been less attention paid to their prominence within his earlier writing. This thesis moves away from existing academic readings of the author’s work as either being defined by a three-part structure, or being categorized into two canonical and precanonical phases, to assess DeLillo’s work as one body. Within this more holistic view of DeLillo’s writing, ideas of retreat and return can be seen as a uniting authorial concern that runs throughout his novels, from the earliest to the most recent. Throughout the decades in which DeLillo has been recognised as one of the ‘great’ figures in American fiction, his work has featured many expressions of a fixation on forms of personal retreat. Characters in his work repeatedly withdraw from society through physical exile, self-sabotage, fasts, and periods of silence, in retreats influenced by secularised, and often vague, spiritual and religious antecedents. These forms of retreat are often followed by some form of elective or passive return, either physical or spiritual, so that a kind of ebb and flow of retreat and return becomes visible when DeLillo’s novels are viewed as a complete body of work. Alongside instances of characters enacting this ebb and flow, DeLillo’s work has dramatized an engagement with society and history within a context of diminished objectivity. His historiographic work explores a retreat from historical certainty following the assassination of President John F. Kennedy. This exploration is achieved in part through the fictional rendering of historical figures and events. In his most recent work, DeLillo appears to have enacted his own retreat from the title of ‘great American author’, while returning to some of the figures and fixations of his early publications. Images of ghosts and haunting become increasingly important within this simultaneous retreat and return, as DeLillo’s novels continue to be haunted by events from American history and figures and ideas from his own work. Finally, DeLillo imagines a near-future in which notions of objectivity appear to have retreated altogether, and in which individual characters retreat into a haunted state of contingency and uncertainty, which nevertheless implies the lasting possibility of return

Inferring Actual Treatment Pathways from Patient Records

Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. This study aims to infer the actual treatment steps for a particular patient group from administrative health records (AHR) - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. We introduce Defrag, a method for examining AHRs to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability and compare against baselines. We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag method, and to compare Defrag to several baselines where it significantly outperforms non-NN-based methods. Defrag significantly outperforms several existing pathway-inference methods and offers an innovative and effective approach for inferring treatment pathways from AHRs. Open-source code is provided to encourage further research in this area

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Summary of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1)

Challenges related to development, deployment, and maintenance of reusable software for science are becoming a growing concern. Many scientists’ research increasingly depends on the quality and availability of software upon which their works are built. To highlight some of these issues and share experiences, the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1) was held in November 2013 in conjunction with the SC13 Conference. The workshop featured keynote presentations and a large number (54) of solicited extended abstracts that were grouped into three themes and presented via panels. A set of collaborative notes of the presentations and discussion was taken during the workshop. Unique perspectives were captured about issues such as comprehensive documentation, development and deployment practices, software licenses and career paths for developers. Attribution systems that account for evidence of software contribution and impact were also discussed. These include mechanisms such as Digital Object Identifiers, publication of “software papers”, and the use of online systems, for example source code repositories like GitHub. This paper summarizes the issues and shared experiences that were discussed, including cross-cutting issues and use cases. It joins a nascent literature seeking to understand what drives software work in science, and how it is impacted by the reward systems of science. These incentives can determine the extent to which developers are motivated to build software for the long-term, for the use of others, and whether to work collaboratively or separately. It also explores community building, leadership, and dynamics in relation to successful scientific software

The SDSS and e-science archiving at the University of Chicago Library

The Sloan Digital Sky Survey (SDSS) is a co-operative scientific project involving over 25 institutions worldwide and managed by the Astrophysical Research Consortium (ARC) to map one- quarter of the entire sky in detail, determining the positions and absolute brightness of hundreds of millions of celestial objects. The project was completed in October 2008 and produced over 100 terabytes of data comprised of object catalogs, images, and spectra. While the project remained active, SDSS data was housed at Fermilab. As the project neared completion the SDSS project director (and University of Chicago faculty member) Richard Kron considered options for long term storage and preservation of the data turning to the University of Chicago Library for assistance. In 2007-2008 the University of Chicago Library undertook a pilot project to investigate the feasibility of long term storage and archiving of the project data and providing ongoing access by scientists and educators to the data through the SkyServer user interface. In late 2008 the University of Chicago Library entered into a formal agreement with ARC agreeing to assume responsibility for: • Archiving of the survey data (long-term scientific data archiving) • Serving up survey data to the public • Managing the HelpDesk • Preserving the SDSS Administrative Record This paper outlines the various aspects of the project as well as implementation

Probing ∼L* Lyman-break galaxies at z ≈ 7 in GOODS-South with WFC3 on Hubble Space Telescope

We analyse recently acquired near-infrared Hubble Space Telescope imaging of the Great Observatories Origins Deep Survey (GOODS)-South field to search for star-forming galaxies at z ≈ 7.0. By comparing Wide Field Camera 3 (WFC3) 0.98 μm Y-band images with Advanced Camera for Surveys (ACS) z-band (0.85 μm) images, we identify objects with colours consistent with Lyman-break galaxies at z ≃ 6.4–7.4. This new data cover an area five times larger than that previously reported in the WFC3 imaging of the Hubble Ultra Deep Field and affords a valuable constraint on the bright end of the luminosity function. Using additional imaging of the region in the ACS B, V and i bands from GOODS v2.0 and the WFC3J band, we attempt to remove any low-redshift interlopers. Our selection criteria yields six candidates brighter than Y_(AB) = 27.0, of which all except one are detected in the ACS z-band imaging and are thus unlikely to be transients. Assuming all six candidates are at z ≈ 7, this implies a surface density of objects brighter than Y_(AB) = 27.0 of 0.30 ± 0.12 arcmin⁻², a value significantly smaller than the prediction from z≈ 6 luminosity function. This suggests continued evolution of the bright end of the luminosity function between z= 6 and 7, with number densities lower at higher redshift

Discovery of soft and hard X-ray time lags in low-mass AGNs

The scaling relations between the black hole (BH) mass and soft lag properties for both active galactic nuclei (AGNs) and BH X-ray binaries (BHXRBs) suggest the same underlying physical mechanism at work in accreting BH systems spanning a broad range of mass. However, the low-mass end of AGNs has never been explored in detail. In this work, we extend the existing scaling relations to lower-mass AGNs, which serve as anchors between the normal-mass AGNs and BHXRBs. For this purpose, we construct a sample of low-mass AGNs ($M_{\rm BH}<3\times 10^{6} M_{\rm \odot}$) from the XMM-Newton archive and measure frequency-resolved time delays between the soft (0.3-1 keV) and hard (1-4 keV) X-ray emissions. We report that the soft band lags behind the hard band emission at high frequencies $\sim[1.3-2.6]\times 10^{-3}$ Hz, which is interpreted as a sign of reverberation from the inner accretion disc in response to the direct coronal emission. At low frequencies ($\sim[3-8]\times 10^{-4}$ Hz), the hard band lags behind the soft band variations, which we explain in the context of the inward propagation of luminosity fluctuations through the corona. Assuming a lamppost geometry for the corona, we find that the X-ray source of the sample extends at an average height and radius of $\sim 10r_{\rm g}$ and $\sim 6r_{\rm g}$, respectively. Our results confirm that the scaling relations between the BH mass and soft lag amplitude/frequency derived for higher-mass AGNs can safely extrapolate to lower-mass AGNs, and the accretion process is indeed independent of the BH mass.Comment: 11 pages, 5 figures, 4 tables, Published in MNRA