Graduate School and University Center Archives Finding Aid - Record Group IV: Committee Records

This is part of the finding aid to the Graduate School and University Center (GSUC) Archives. Record Group t IV is records from various GSUC committees.

State Political Action Committee Financing and the 2008 Presidential Election: A Detailed Look at Mitt Romney and the Commonwealth PACs

This study looks at the historical context in which PACs developed, as well as the current legal environment in which they operate. It will also briefly discuss the legal and procedural challenges that candidates face and the ways in which PACs alleviate some of these pressures in ways that presidential committees cannot. An understanding of the strategic dilemmas which cause candidates to seek extraneous structures through which to establish campaign networks is essential to extrapolating the potential future of campaign finance strategy. Furthermore, this study provides an in-depth analysis of the state Commonwealth PACs both in terms of fundraising and spending, and discusses the central issues this state PAC strategy raises with respect to campaign finance law. The study will conclude with a look into the future of campaign financing and the role these state-level PACs may play if current rules are not revised.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Towards a new international financial architecture: report of the Task Force of the Executive Committee on Economic and Social Affairs of the United Nations, 21 January 1999 = Hacia una nueva arquitectura financiera internacional: informe del Grupo de Trabajo del Comité Ejecutivo de Asuntos Económicos y Sociales de las Naciones Unidas, 21 de enero de 1999

Publicación bilingüe

Finding solutions to the debt problems of developing countries: report of the Executive Committee on Economic and Social Affairs of the United Nations (New York, 20 May 1999)

Includes bibliography