Forward-Mode Automatic Differentiation in Julia

We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support for higher-order differentiation and differentiation using custom number types (including complex numbers). For gradient and Jacobian calculations, ForwardDiff provides a variant of vector-forward mode that avoids expensive heap allocation and makes better use of memory bandwidth than traditional vector mode. In our numerical experiments, we demonstrate that for nontrivially large dimensions, ForwardDiff's gradient computations can be faster than a reverse-mode implementation from the Python-based autograd package. We also illustrate how ForwardDiff is used effectively within JuMP, a modeling language for optimization. According to our usage statistics, 41 unique repositories on GitHub depend on ForwardDiff, with users from diverse fields such as astronomy, optimization, finite element analysis, and statistics. This document is an extended abstract that has been accepted for presentation at the AD2016 7th International Conference on Algorithmic Differentiation.Comment: 4 page

Lolita in the Contemporary American Classroom: Pedagogical and Learning Approaches

The purpose of this study is to discover effective collegiate-level teaching and learning strategies for Vladimir Nabokov’s 1958 novel Lolita in the midst of the current American political and social climate. Some of the factors of the current political and social climate in the United States thought to have an effect on the teaching of Lolita, and were thus considered for further inquiry, were cancel culture, the Me Too Movement, and trigger warnings. Primary research was collected from college students and English college professors. To obtain this research and the opinions of respondents regarding this topic, a combination of both surveys and interviews were distributed and conducted; surveys were distributed to both students and professors and interviews were limited to professors only. The results found in this study were that both students and professors favor the inclusion of trigger warnings, the Me Too Movement has impacted professors’ decisions to currently teach Lolita, and cancel culture does not have a significant effect on teaching and learning Lolita. The ten strategies included in this study are based on current trends in higher education and collegiate pedagogy and the responses from students and professors; they are recommended for effective teaching and learning of Lolita at the collegiate level

The Effect of 100 Percent Population Testing on the Perception of CPA Firms with Limited Liability Exposure

The present study investigates whether retail investors believe that a CPA firm’s liability exposure or the type of evidence collected by the firm impacts their ability to remain unbiased during the audit. This study is inspired by calls for further research pertaining to the benefits and effectiveness that big data can provide to the audit industry or whether it could lead to more regulation. After receiving approval for the study by the Institutional Review Board, retail investors responded to an experiment in order to gather information for the study. The results indicate that retail investors believe that a CPA firm’s decision will not be biased by neither the firm’s liability exposure nor by the type of evidence the firm collects. These results are useful to accounting lawmakers who previously expressed concern that a reduction of liability would impair auditors’ judgment during the audit. Similarly, these results may assist accounting lawmakers in deciding whether or how to change auditing standards to reflect the benefits of big data in auditing. Keywords: Audit Evidence, Limited Liability Agreement, Sampling, Full Population Testing, Big Data

Grander in Her Daughters: Florida\u27s Women During The Civil War

The citizens of the Flowery are determined to maintain their just rights at all hazards; and the fair daughters of Florida are prompt to encourage and cheer their bold defenders,” the Philadelphia Inquirer reported on February 2, 1861.1 The newspaper not only relayed the latest happenings in the secession movement but prophetically established the trend for the historical view of women’s lives during the Civil War

The Effects of Computer Mediated Communication on Computer-Based Training

Student performance using computer-based training (CBT) may be related to the degree of interaction that occurs between students and the instructor, or between students and each other. This is significant in that the individualized nature of CBT (and perhaps Web-based training) is contrary to the social interaction needs of students. Using relevant pedagogical and social communication theories as a basis, this study employed empirical research methods on undergraduate participants to achieve the following objectives: to explore the use of computer mediated communication (CMC) as a surrogate for face-to-face interaction with CBT students, and to provide research-based recommendations for human resource development (HRD) managers charged with deployment of these and related technologies. The research results showed that undergraduate CBT participants who were sent personalized email once per week performed significantly better than participants who were sent no email

A Machine Learning Method for Predicting Liver Transplant Survival Outcomes

For years, doctors have utilized the Model for End-stage Liver Disease (MELD) score to aid in the allocation of organs for liver transplants (LT). A major issue with using the MELD score to allocate organs for transplantation is that the MELD score does not accurately predict post-transplant survival. This research project aims to investigate the use of machine learning (ML) methods to predict LT survival using the newer Scientific Registry of Transplant Recipients (SRTR) dataset. For this project, death and nonfatal graft failure were treated equally as both cases result in a loss of a donated organ. The ML algorithms used in this project were provided by both the Weka and Orange software packages. Initial trials investigated a binary classification of patients based on whether they survived for three years post-transplant and primarily utilized a random forest algorithm. Later trials moved to a multi-class classification using both random forest and other classifier algorithms. Initial results from the three-year binary classification seemed promising but performance metrics failed to improve with continued work. All multi-class trials performed similarly using various classifier algorithms. Unexpectedly, the class for 12-year survival showed a promising increase in its area under the receiver operating characteristic curve. The results of this project help to create a baseline for future ML studies utilizing the SRTR dataset and will hopefully spur further research into liver transplant survival prediction

Innovations that Address Socioeconomic, Cultural, and Geographic Barriers to Preventive Oral Health Care

This report focuses on nine oral health innovations that integrate service delivery and workforce models in order to reduce or eliminate socioeconomic, geographic, and cultural barriers to care. Two additional reports in this series describe the remaining programs that provide care in non-dental settings and care to young children. Although the programs are diverse in their approaches as well as in the specific characteristics of the communities they serve, a common factor among them is the implementation of multiple strategies to increase the number of children from low-income families who access preventive care, and also to engage families and communities in investing in and prioritizing oral health. For low-income children and their families, the barriers that must be addressed to increase access to preventive oral health care are numerous. For example, even children covered by public insurance programs face a shortage of dentists that accept Medicaid and who specialize in pediatric dentistry.(Guay, 2004).The effects of poverty intersect with other barriers such as living in remote geographic areas and community-wide history of poor access to dental care in populations such as recent immigrants . Overcoming these barriers requires creative strategies that address transportation barriers; establish welcoming environments for oral health care; and are linguistically and culturally relevant. Each of these nine programs is based on such strategies, including:-Expanding the dental workforce through training new types of providers or adding new providers to their workforce to increase reach and community presence;-Implementing new strategies to increase the cost-effectiveness of care so that more oral health care services are available and accessible;-Providing training and technical assistance that increase opportunities for and competence in delivering oral health education and care to children;-Developing creative service delivery models that address transportation and cultural barriers as well as the fear and stigma associated with dental care that may arise in communities with historically poor access.The findings from the EAs of these programs are synthesized to highlight diverse and innovative strategies for overcoming barriers to access that have potential for rigorous evaluation that could emerge as best practices. If proven effective, these innovative program elements could then be disseminated and replicated to increase access for populations in need of preventive oral health care

Investigating Drug-Related Violence in Indian Country: The Lumbee Tribe of North Carolina

Drug-related violence (DRV) impacts the over-all wellbeing of communities, with disparate health outcomes observable in many poor, minority communities. The purpose of this study was to better understand the multi-level social and environmental factors influencing elevated rates and prevention of DRV within the Lumbee, a southeastern American Indian Tribe. This was accomplished via in-depth, one-on-one interviews with 37 Lumbee Tribal Members and Key Leaders. The findings align with existing research, revealing the influence of such factors as poor socioeconomic conditions, coping strategies, broken family structures, and the influence of federal policy. Of interest was the substantial influence of the local Christian church on the beliefs, attitudes, and practices of the Lumbee community surrounding DRV. The findings of this study indicate that social-environmental factors, seemingly independent of prevention and treatment, play an integral role in the Lumbee community’s ability to recover from the long-term consequences of DRV. Identifying these unique barriers to and facilitators of prevention and treatment will be critical to improving the welfare of tribal communities

Chemical Leaching Methods and Measurements of Marine Labile Particulate [Fe] and Δ56Fe

Iron (Fe) is an essential nutrient for life. However its low solubility in the seawater limits primary productivity in many regions of the world\u27s oceans. Dissolved phase Fe (\u3c0.4μm) has traditionally been considered the most biologically accessible form, but, recent studies suggest the particulate phase (\u3e0.4μm) may contain an important, labile reservoir of Fe that may also be available to phytoplankton. Although valuable, Fe concentrations of particles alone do not provide complete evidence about the sources of particulate Fe to the ocean, or the extent particulate iron may be accessed by phytoplankton. Fe isotopes may help to reveal the biogeochemical cycling of particulate Fe. This study aims to develop a chemical leaching technique that accesses the labile reservoir of particulate Fe and the isotope signatures associated with this reservoir. A comparative study of thirty-six different chemical leaches was performed on a marine sediment reference material, MESS-3 and sediment trap samples from the Cariaco Basin, Venezuela. The combinations included three different acids (25% acetic acid, 0.01M HCl, and 0.5M HCl), various redox conditions (0.02M hydroxylamine hydrochloride or untreated), three temperatures (25°C, 60°C, 90°C), and three time points (10 minutes, 2 hours, 24 hours). Leached Fe concentrations were determined by high-resolution inductively coupled plasma-mass spectrometery (ICP-MS) and varied from 1mg/g to 35mg/g, with longer treatment times, stronger acids, and hotter temperatures generally associated with an increase in leached Fe. δ56Fe in these leachates were determined by Neptune, mulit-collector ICP-MS and varied from -1.3 / to +0.2 /. Regardless of acidic leaching method used, there was a very similar relationship between the amount of Fe leached from the particles and the δ56Fe of this iron. Isotopically lighter δ56Fe values were associated with smaller amounts of leached Fe whereas isotopically heavier δ56Fe values were associated with larger amounts of leached Fe. However, a leach of 0.1M oxalate-0.05M EDTA at pH 8 did not show this relationship. Therefore the oxalate-EDTA leach was determined to be the leach best suited to extract the labile fraction of particulate Fe and the iron isotope signatures associated with this fraction. This leach was applied to three stations (the coast of Bermuda, over the TAG hydrothermal plume, and the coast of Maurtiania) along the US GEOTRACES North Atlantic transect. Labile iron in these profiles was found to be isotopically light in comparison to crustal values and remained relatively light throughout the water column. Labile particulate iron concentrations increased near the coast of Mauritania and even lighter δ56Fe values of these particles are associated with reductive dissolution from the continental shelf and at depth from the nephloid layer off Mauritani