Lines of Flight: Everyday Resistance along England’s Backbone

The visual and the cultural impact of ‘social industry’ has made a permanent impression on the landscape and on individual minds, whether for ill or for good, particularly in the Peak and Pennines region of northern England. In the current research we examine this impact and consider how both its visible and less apparent effects took hold and how they set in motion an ongoing process of productive/consumptive estrangement from life’s primordial forces, which continue to be alien and obscure, or else appear arcane and overly nostalgic to present-day life. Drawing on the methodology of a short film (incorporating narrative and verse) and using rock climbing as an illustration, we will invoke several, radically dynamic ‘lines of flight’ to open up and articulate an aesthetic appreciation of concrete experience in the fight against coding and to engender a call for action and passion so that we might come to a renewed belief in free activity, which can prompt us, in turn, to think about how we live and work and how we might change things.

Effect of Temperature and Charged Particle Fluence on the Resistivity of Polycrystalline CVD Diamond Sensors

The resistivity of polycrystalline chemical vapor deposition diamond sensors is studied in samples exposed to fluences relevant to the environment of the High Luminosity Large Hadron Collider. We measure the leakage current for a range of bias voltages on samples irradiated with 800 MeV protons up to 1.6\times 10^{16} p/cm^2. The proton beam at LANSCE, Los Alamos National Laboratory, was applied to irradiate the samples. The devices' resistivity is extracted for temperatures in the -10^\circC to +20^\circC range.Comment: 13 pages, 8 figure

The legacy of nineteenth-century replicas for object cultural biographies : lessons in duplication from 1830s Fife

Peer reviewedPostprin

ATLAS Pixel Radiation Monitoring with HVPP4 System

In this talk we present the basis for the protocol for radiation monitoring of the ATLAS Pixel Sensors. The monitoring is based on a current measurement system, HVPP4. The status on the ATLAS HVPP4 system development is also presented.Comment: Talk given at DPF-2009, Detroit, MI, July 2009; 8 pages, 4 figures, 6 tables; To be published in the proceedings of DPF-2009, Detroit, MI, July 2009, eConf C09072

Building an Academic Partnership Through a Strategic Plan: University of Kentucky Auxiliary Services

The Auxiliary Services department at the University of Kentucky (UK) is a far-reaching department on campus that contains everything from housing to dining to events. If there is an office or activity on campus that provides a service to a student, staff or faculty member, it is most likely within Auxiliary Service’s jurisdiction. Some of the departments and/or offices that fall under this umbrella include all residence halls, dining locations, the Gatton Student Center, University Events Management, and the UK Bookstore. Auxiliary Services also manages the partnerships with external companies that provide services to the campus like housing (Greystar), dining (Aramark), and the bookstore (Barnes & Noble). Through a reorganization effort and rebranding launch, the department looks to create and implement an Academic Partnership. This will include both paid student employment internships and collaborations with classes on projects benefiting the department and campus community. Research Design/Results: Interviews were held with key stakeholders in the beginning stages of this partnership to gauge their current position and determine what strategic tools would be most helpful to them moving forward. A preliminary SWOT/C Analysis, Stakeholder Analysis, Goal-Setting, and Performance Metrics were developed based on results. These show the potential for an Academic Partnership and the impact it would have on the UK’s campus should it be successful. A key element of the results was an emphasis on performance metrics and a schedule to measure success. Because the purpose of this partnership is to ensure sustainability, tracking progress will be important to maintain for all stakeholders involved

Locally Adaptive Nonparametric Binary Regression

A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing parameter and with the mixture weights depending on the covariates. The estimator is compared to a single spline estimator and to a recently proposed locally adaptive estimator. The methodology is illustrated by applying it to both simulated and real examples.Comment: 31 pages, 10 figure

Do Students Benefit From Supplemental Instruction? Evidence From a First-Year Statistics Subject in Economics and Business

Peer assisted study sessions (PASS) are a type of supplemental instruction (SI) that provide students with out-of-class study review sessions with a group of peers. A student, who has successfully completed the subject and acts as a mentor, facilitates the voluntary sessions. Results of the PASS program at the University of Wollongong have been quite positive in that students, on average, who attend more PASS, achieve higher marks. However, a simple comparison does not control for self-selection bias. We control for self-selection in two ways. Firstly, we use Heckman’s two-stage correction technique to analyze the 2002 cohort. Secondly, students in the 2003 cohort were randomly allocated into three groups of equal size: 1. A control group that was allocated to normal tutorials with standard class sizes and ineligible to attend PASS; 2. A group that was eligible to attend PASS and had normal tutorials of standard sizes; 3. A group that was ineligible to attend PASS but allocated to normal tutorials with smaller class sizes. The results of both methods are consistent and indicate the PASS program has a positive impact on the academic performance of students after correcting for selection bias.Economics Education; Teaching of Economics; Design of Experiments

Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions

This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account, especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%. In addition, we note that the performance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PIs. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation