The Gordian Knot: How the United States, the European Union, and Organization for Economic Cooperation and Development took action against corporate tax avoidance

In 2016, the United States had the highest corporate tax rate in the world. Perhaps, the high tax rate could be why American corporations are holding an estimated $2.5 trillion abroad (Cox 2016). According to a study by the Bureau of Economic Analysis, U.S. firms pay a measly 3% in tax to foreign governments on those profits, rather than the 35% U.S. corporate tax rate. How are these corporations able to legally avoid paying taxes on a large percentage of their profits? Many use various loopholes in the laws to shift profits into other countries or U.S. states referred to as “tax havens.” These tax havens promote low tax rates and favorable economic conditions to increase business activity. Lately, the phenomena of shifting profits to tax havens has been of topic of public interest with the release of the notorious Panama papers, the Luxi Leaks, the European Union suing Apple for supposedly unpaid taxes, and the Pfizer Ireland scandal. What the public does not realize is that the struggle against tax havens has been going on since the 1960s. Despite countless attempts at controlling or reducing the number of tax havens, U.S. corporations continue to successfully shift billions of dollars in tax revenue overseas each year. But, how? This study will address the following question, how successful have states’ actions been at combatting tax havens? This research question will be answered by analyzing three case studies: the United States (U.S.), the European Union (EU), and the Organization for Economic Co-operation and Development (OCED). The research shows that the U.S. methods have been unsuccessful. Conversely, The EU and the OECD have made significant progress in the last few years with their action

Perfumes and perfume-making in the Celestina

Celestina’s house, as Dorothy Sherman Severin notes, is at the same time ‘a bawdy house, a factory for perfumes and cosmetics, and a symbol of the misrule of a woman empowered by her illegal professions of sorceress, witch and bawd’ (Severin 1995: 45). In these pages I will endeavour to set Celestina’s skills in the context of making perfume and uses of it in the early 16th century

Circle Practice: Stories of Organizational Change, Relationships and Community

This project explores the impacts of an organizational development process in a non-profit domestic and sexual violence program. The organizational changes documented in this initiative illuminate a disconnect between the process for organizational development and the quality of relationships among staff and the larger community. This project also explores the practice of Circle as a sacred gift from Indigenous peoples that has the capacity for creating individual, organizational, and community transformation in ways that can bring healing, love, authenticity and belonging to other initiatives working to end violence. The experiences from this project have led to a further exploration of how organizations might embody their core values and align with their mission, while examining the implications and applications of these insights on non-profit social change organizations

Market Power and Technological Bias: The Case of Electricity Generation

It is difficult to elminated all market power in electricity markets and it is therefore frequently suggested that some market power should be tolerated: extra revenues contribute to fixed cost recovery,facilitate investment and increase security of supply. This suggestion implicitly assumes all generation technologiesbenefit equally from market power. We assess a mixture of conventional and intermittent generation, eg coal plants and wind power. If all output is sold in the spot market, then intermittent generation benefits less frommarket power than conventional generation. Forward contracts or option contracts reduce the levelof market power but bias against intermittent generators persists

The NASA/industry Design Analysis Methods for Vibrations (DAMVIBS) program: Sikorsky Aircraft: Advances toward interacting with the airframe design process

A short history is traced of the work done at Sikorsky Aircraft under the NASA/industry DAMVIBS program. This includes both work directly funded by the program as well as work which was internally funded but which received its initial impetus from DAMVIBS. The development of a finite element model of the UH-60A airframe having a marked improvement in vibration-predicting ability is described. A new program, PAREDYM, developed at Sikorsky, which automatically adjusts an FEM so that its modal characteristics match test values, is described, as well as the part this program played in the improvement of the UH-60A model. Effects of the bungee suspension system on the shake test data used for model verification are described. The impetus given by the modeling improvement, as well as the recent availability of PAREDYM, has brought for the first time the introduction of low-vibration design into the design cycle at Sikorsky

Characterization of the interactions between the resident microbial flora and chytridimycoses in Desmognathus fuscus communities of Central Virginia

Undergraduate Basi

Label Propagation for Learning with Label Proportions

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag.Comment: Accepted to MLSP 201

Neural ODEs with stochastic vector field mixtures

It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representations. This paper introduces two other fundamental tasks to the set that baseline methods cannot solve, and proposes mixtures of stochastic vector fields as a model class that is capable of solving these essential problems. Dynamic vector field selection is of critical importance for our model, and our approach is to propagate component uncertainty over the integration interval with a technique based on forward filtering. We also formalise several loss functions that encourage desirable properties on the trajectory paths, and of particular interest are those that directly encourage fewer expected function evaluations. Experimentally, we demonstrate that our model class is capable of capturing the natural dynamics of human behaviour; a notoriously volatile application area. Baseline approaches cannot adequately model this problem