Central Bank Communication and Correlation between Financial Markets: Canada and the United States

We study the correlation between pairs of bond and stock markets in Canada and the United States between January 1998 and December 2009 in the framework of diagonal-BEKK models. Our research question is whether monetary policy actions and communications by the Bank of Canada and the Federal Reserve significantly affect the conditional co-movement of financial markets (i) within Canada and (ii) between Canada and the United States. We find that central bank communication significantly increases the correlation of financial markets within and across the two countries and is particularly important for the correlation of Canadian and US long-term interest rates.Bank of Canada, Central Bank Communication, Diagonal-BEKK Models, Dynamic Correlations, Federal Reserve, Financial Markets

Central Bank Communication and Correlation between Financial Markets: Canada and the United States

We study the correlation between pairs of bond and stock markets in Canada and the United States between January 1998 and December 2009 in the framework of diagonal-BEKK models. Our research question is whether monetary policy actions and communications by the Bank of Canada and the Federal Reserve significantly affect the conditional co-movement of financial markets (i) within Canada and (ii) between Canada and the United States. We find that central bank communication significantly increases the correlation of financial markets within and across the two countries and is particularly important for the correlation of Canadian and US long-term interest rates

Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse

LSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.Comment: 5 pages, 1 figure. To appear in Proceedings of the International Astronomical Unio

Evaluating the quality and efficacy of care provided by extended care permit dental hygienists in a school based dental home

Title from PDF of title page, viewed on December 2, 2013Dissertation advisor: Bonnie BransonVitaIncludes bibliographic references (pages 63-70)Thesis (Ph,D.)--School of Dentistry and Henry W. Bloch School of Management. University of Missouri--Kansas City, 2013Lack of access to oral health care is a growing problem for low income children in the United States. One proposed solution to this problem is utilizing dental hygienists to the full extent of their education and training. In 2003, Kansas altered the dental hygiene scope of practice and supervision regulations through the creation of the extended care permit (ECP) dental hygienist. In 2008, a school-based oral health intervention, that utilized ECP dental hygienists to provide preventive oral health care and referrals, was established in a Midwestern city suburb. The purpose of this case study was to assess the quality of oral health care provided by ECP dental hygienists. The central hypothesis of this investigation was ECP dental hygienists provide quality oral health care that improves the oral health of underserved children in a school-based setting. Using a case-study design, electronic medical records of children (n=986) who participated in the intervention were mined for data. Numerators and denominators from the Dental Quality Alliance Concept Set provided the framework for measurement. Patient-oriented outcomes were examined in a multi-encounter cohort (n=295) using MANOVA and Kruskal Wallace. Results revealed 26.3% of the children eligible to participate in the intervention chose to do so. On average 96.6% of the children were provided a minimum of one topical fluoride application and 34.0% had at least one sealant placed. Nearly half (48.7%) of the program participants had two or more topical fluoride applications. On average 52.8% of the children had sealants placed. The number of encounters with ECP dental hygienists had a statistically significant effect on changes in decay (p=0.014), changes in restorations (p=0.002) and changes in treatment urgency (p=0.022). A statistically significant effect of the number of fluoride applications on changes in restorations (p.0.031) was also present. These results suggests ECP dental hygienists can provide access to and the provision of timely and appropriate quality oral health care for low income children in a school-based setting and oral health care provided by ECP dental hygienists can improve the oral health status of low income children who lack access to oral health care.Introduction -- Methods -- Results -- Discussion -- Conclusion -- Literature cited -- Appendix 1. Glossary of terms -- Appendix 2. Dental quality alliance starter set of measure

Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

University of Minnesota Ph.D. dissertation.January 2018. Major: Astrophysics. Advisor: Claudia Scarlata. 1 computer file (PDF); xiii, 158 pages.The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology – challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for ∼ 5 × 10^7 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of ∼80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the ∼1 million SDSS galaxies an average of 40 times. Galaxy Zoo thus solved both the visual classification problem of time efficiency and improved accuracy by producing a distribution of independent classifications for each galaxy. While crowd-sourced galaxy classifications have proven their worth, challenges remain before establishing this method as a critical and standard component of the data processing pipelines for the next generation of surveys. In particular, though innovative, crowd-sourcing techniques do not have the capacity to handle the data volume and rates expected in the next generation of surveys. These algorithms will be delegated to handle the majority of the classification tasks, freeing citizen scientists to contribute their efforts on subtler and more complex assignments. This thesis presents a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides a factor of 11.4 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys

Working from home, health and wellbeing consequences of a pandemic

Drawing from a survey of 1,165 Sydney (Australia) workers conducted in late 2020, when restrictions from the first COVID-19 wave were easing across Australia, we explore the impact of the pandemic on perceived changes to working from home (WfH) and other travel behaviours. Based on this analysis, we identify three distinct segments of the population with differing physical activity (PA) and quality of life (QoL) outcomes: (1) ‘Active but Anxious’ (22%) – younger, higher income, largest increase in WfH, sitting most of the day, sufficient PA; (2) ‘Less Change, Less Worries’ (38%) – older and male, least change in WfH, sitting relatively less, largely sufficient PA; (3) ‘Stressed and Sedentary’ (40%) – average age, lower income, largest loss of paid work, highest levels of sedentary behaviour, lowest PA and QoL. In a probable future of greater opportunities for WfH, understanding these heterogenous outcomes has implications for individuals, employers and policy-makers

Pennsylvania Occupational Therapy Fieldwork Educator Practices and Preferences in Clinical Education

The shortage of clinical education fieldwork sites coupled with a concern over the quality of the required fieldwork experience poses an unintended outcome for the recent changes in the health care system and an increasing number of occupational therapy students. While the Accreditation Council for Occupational Therapy Education (ACOTE) issues standards for fieldwork education, the quality of the experience is known to vary. The present study employed a mixed methods concurrent nested design with a quantitative online survey alongside qualitative individual semi-structured online interviews to examine the practices and preferences of fieldwork educators in Pennsylvania ACOTE accredited programs. From the 49 quantitative online survey participants, 10 practices and preferences considered important when supervising fieldwork students emerged. Another five themes related to a quality fieldwork experience were garnered from the six qualitative semi-structured interviews. The results suggest that fieldwork educators understand the value of clinical education and intend to continue to supervise students in the future. However, while fieldwork educators value their role as an educator, they often lack the time and resources necessary to feel effective. Therefore, future research into resource use and ways in which academic programs and professional associations can support fieldwork educators is necessary