Self-Organizing File Cabinet

This thesis presents a self-organized file cabinet. This file cabinet uses electronic information to augment the physical world. By using a scanner to transform documents into electronic files, the self-organized file cabinet can index the documents on visual and textual information. The self-organized file cabinet helps the user find the documents at a later date. The focus of this thesis is on the design and evaluation of the self-organized file cabinet. User studies show that this tool is natural to use

Overview of the TREC 2022 NeuCLIR Track

This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.Comment: 22 pages, 13 figures, 10 tables. Part of the Thirty-First Text REtrieval Conference (TREC 2022) Proceedings. Replace the misplaced Russian result tabl

Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study:A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments

Grant information: STRADL is supported by the Wellcome Trust through a Strategic Award (104036/Z/14/Z). GS:SFHS received core support from the CSO of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). ADM is supported by Innovate UK, the European Commission, the Scottish Funding Council via the Scottish Imaging Network SINAPSE, and the CSO. HCW is supported by a JMAS SIM Fellowship from the Royal College of Physicians of Edinburgh, by an ESAT College Fellowship from the University of Edinburgh, and has received previous funding from the Sackler Trust. LR has previously received financial support from Pfizer (formerly Wyeth) in relation to imaging studies of people with schizophrenia and bipolar disorder. JDH is supported by the MRC. DJM is an NRS Clinician, funded by the CSO. RMR is supported by the British Heart Foundation. ISP-V and MRM are supported by the NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health; and MRM is also supported by the MRC MC_UU_12013/6). JMW is supported by MRC UK Dementia Research Institute and MRC Centre and project grants, EPSRC, Fondation Leducq, Stroke Association, British Heart Foundation, Alzheimer Society, and the European Union H2020 PHC-03-15 SVDs@Target grant agreement (666881). DJP is supported by Wellcome Trust Longitudinal Population Study funding (216767/Z/19/Z) the Eva Lester bequest to the University of Edinburgh. AMM is additionally supported by the MRC (MC_PC_17209, MC_PC_MR/R01910X/1, MR/S035818/1), The Wellcome Trust (216767/Z/19/Z ), The Sackler Trust, and has previously received research funding from Pfizer, Eli Lilly, and Janssen. Both AMM and IJD are members of The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the BBSRC and MRC is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer reviewedPublisher PD

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

LANGUAGE MODELS FOR HIERARCHICAL SUMMARIZATION (PROPOSAL FOR DISSERTATION)

Hierarchies have long been used for organization, summarization, and access to information. In this proposal we define summarization in terms of a probabilistic language model and use the definition to explore new techniques for automatically generating topic hierarchies. One technique applies a graph-theoretic algorithm, which is an approximation of the Dominating Set Problem. Another technique uses an entropy-based approach to choose topic terms. Both techniques efficiently select terms according to a language model. We compare the new techniques to previous methods proposed for constructing topic hierarchies including subsumption and lexical hierarchies, as well as words found using TF.IDF. Our preliminary results show that the new techniques perform as well as or better than these other techniques. We plan to evaluate the two techniques further through user studies as well as computer simulations. We will also develop a demo for better interaction with users