13,435 research outputs found

    Margarita Night

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    First Run II Measurement of the W Boson Mass with CDF

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    The CDF collaboration has analyzed ~200/pb of Tevatron Run II data taken between February 2002 and September 2003 to measure the W boson mass. With a sample of 63964 W->e nu decays and 51128 W->mu nu decays, we measure M_W = 80413+-34(stat)+-34(syst) MeV. The total measurement uncertainty of 48 MeV makes this result the most precise single measurement of the W boson mass to date.Comment: Conference Proceedings for Rencontres de Moriond EW 200

    Supplying Compliance: Why and When the United States Complies with WTO Rulings

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    In studies of compliance with international law, the focus is usually on the “demand side” – that is, how to increase the pressure on the state to comply. Less attention has been paid, however, to the consequences of the “supply side” – who within the state is responsible for the compliance. This Article is the first study to systematically address the issue of how different actors within the United States government alter national policy in response to the violations of international law. The Article does so by examining cases initiated under the World Trade Organization (WTO) Dispute Settlement Understanding (DSU). This Article presents empirical evidence that who within the government must supply compliance is the most important factor in explaining both whether and when the United States government complies with WTO rulings, even after controlling for important characteristics of the state filing the request and the political importance of the affected industry. These results demonstrate that understanding the domestic supply of compliance is a critical, if neglected, aspect of international law theory. The results also highlight how the dominant “unitary actor” model (adopted by international law scholars to explain compliance) obscures important causal pathways in the compliance process. This Article opens up a new and rich field of study into what makes international law effective or ineffective

    Ending Hunger in Montgomery County

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    Though Montgomery County is listed as the 20th wealthiest county in the United States and has been ranked the 9th Best Place to Raise a Family by Forbes Magazine, it has seen an extraordinary increase in eligibility for food stamps. Such an increase suggests that families are struggling to pay for food and other basic needs. Food insecurity, known as the lack of access to enough food for an active and healthy life, is associated with an increase in developmental risk, risk of poor health, and poor school performance. Food insecurity is also associated with increased rates of maternal depressive symptoms, exposure to childhood violence, and stress disorders. This report provides a preliminary needs assessment regarding food insecurity and hunger for Montgomery County by utilizing multiple data sources, connecting with key stakeholders, and understanding the immediate and long-term needs of low?income families. It describes a variety of measures for food insecurity and food hardship, showing that approximately 16% of children were food insecure in Montgomery County in 2011. For potentially more severe forms of food insecurity, where people cut the size of their meal due to lack of money, the overall rate rose from 5.0% in 2004 to 8.6% in 2010. Increases in this rate were more pronounced in Pottstown and Norristown compared to the North Penn area. Clearly, efforts at protecting vulnerable citizens in the North Penn area have helped to limit the negative effects of the recession

    Compositional Verification for Autonomous Systems with Deep Learning Components

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    As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verification of autonomous systems that contain Deep Neural Network components. The approach uses assume-guarantee reasoning whereby {\em contracts}, encoding the input-output behavior of individual components, allow the designer to model and incorporate the behavior of the learning-enabled components working side-by-side with the other components. We illustrate the approach on an example taken from the autonomous vehicles domain

    Environmental aspects of tensile membrane enclosed spaces

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    Buildings enclosed by fabric membranes are very sensitive to changes in environmental conditions as a result of their low mass and low thermal insulation values. Development in material technology and the understanding of the structural behaviour of tensile membrane structures along with the vast progress in computer formfinding software, has made it possible for structural design of tensile membrane structures to be approached with almost total confidence. On the contrary, understanding of the environmental behaviour in the spaces enclosed by fabric membrane and their thermal performance is still in its infancy, which to some extent has hindered their wide acceptance by the building industry. The environmental behaviour of tensile membrane structures is outlined and the possible use of the fabric’s topology and geometry particularly to enhance ventilation rates and airflow velocities within the enclosed space is discussed. A need for further research in this area is identified in order to fully realise the potential benefits offered by these structures
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