Knowledge Syntheses Search Strategy Repositories: Canadian Case Studies

Knowledge synthesis research is central to evidence-based medicine. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension (PRISMA-S) outlines full reporting of the search strategy component including uploading documentation of all search strategies into a data repository to increase accessibility, transparency, and reproducibility. In response to the PRISMA-S recommendations, Canadian universities and health care institutions have been increasingly offering local services for librarians to support depositing and sharing search strategies in a digital data repository on the Borealis platform. Borealis, the Canadian Dataverse Repository, is a bilingual, multidisciplinary, secure, Canadian research data repository which supports open discovery, management, sharing, and preservation of Canadian research data. We argue that knowledge synthesis searches are data, and therefore, deserve a place in data repositories. Three case studies of knowledge synthesis repositories from three institutions will be presented: McGill University, Université de Montréal teaching hospitals, and the Health Sciences Information Consortium (HSIC) which includes the University of Toronto and affiliated hospitals. This talk will discuss the reasons for choosing a data repository, decisions made, challenges encountered, and lessons learned

Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans

Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques

IEA SHC Task 61 / EBC Annex 77: Integrated Solutions for Daylighting and Electric Lighting - Subtask A: User perspective and requirements. A.3 Personas

The consumption of energy for lighting in buildings depends very much on the way people interact with the build environment. In this study the following building types were studied, office, school, university, commercial and industry buildings. For each building type typical user groups were identified. Then, Personas have been created for each group. As opposed to describing users with numbers and statistics, a single Persona reflects a group and is presented with a narrative. The Persona has a name, a family and living conditions that are representative for the group, also her/his values and interests are not uncommon. The Personas “typical day” includes a time schedule typical for the group. Visual conditions are common for the group, but some specific challenges connected to the visual conditions that may occur in the group are also mentioned. Personas were created using one of the three methods: interview at the working or learning place, workshop at the working place, and internet survey which was used in the time of pandemic lock-down. Those methods are described in the chapter 2 Methodology and evaluated based on the authors experience, chapter 9 Conclusions. Chapters 3-7 presents Personas for the offices, schools, university buildings, commercial buildings, and industry buildings. In the chapter 8 Home office personas, student and professional, are presented. The report ends with the description of energy consumption calculation that applies Lumen method which is based on the use of a Coefficient of Utilization (CU). CU computes the fraction of lamp lumens that directly reach the workplane and the fraction from interreflections. The boundary conditions of use of the respective buildings, based on the results from literature review and interviews with users, are also presented, table 2

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements