Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.

This proceedings volume examines the state-of-the art of productivity and efficiency analysis and adds to the existing research by bringing together a selection of the best papers from the 8th North American Productivity Workshop (NAPW). It also aims to analyze world-wide perspectives on challenges that local economies and institutions may face when changes in productivity are observed. The volume comprises of seventeen papers that deal with productivity measurement, productivity growth, dynamics of productivity change, measures of labor productivity, measures of technical efficiency in different sectors, frontier analysis, measures of performance, industry instability and spillover effects. These papers are relevant to academia, but also to public and private sectors in terms of the challenges firms, financial institutions, governments and individuals may face when dealing with economic and education related activities that lead to increase or decrease of productivity. The North American Productivity Workshop brings together academic scholars and practitioners in the field of productivity and efficiency analysis from all over the world. It is a four day conference exploring topics related to productivity, production theory and efficiency measurement in economics, management science, operations research, public administration, and related fields. The papers in this volume also address general topics as health, energy, finance, agriculture, utilities, and economic dev elopment, among others. The editors are comprised of the 2014 local organizers, program committee members, and celebrated guest conference speakers.

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

The six papers in this vohune represent state-of-the-art empirical and conceptual research on various aspects of the taxation of multinational corporations. They were commissioned for and presented at a conference organized by Price Waterhouse LLP on behalf of the International Tax Policy Forum, held in Washington, DC in March, 1994. The ftrst four papers were originally published in the May, 1995 issue of International Tax and Public Finance. The Slemrod paper appeared in the Policy Watch Section of the November, 1995 issue of that journal. The foregoing papers were subject to the normal refereeing procedures of the journal, and the summaries that follow are drawn from there. The Leamer paper has not been previously published. Altshuler and Mintz examine one aspect of the 1986 u. s. Tax Reform Act --the change in the rules for the allocation of interest expense between domestic-(U. S. ) and foreign-source income. In the absence of rules, a parent with excess credits could reduce U. S. tax liability by allocating interest expense toward itself; thus reducing its taxable domestic income without any compensating increase in either the U. S. tax due on foreign-source income or the foreign tax due (which is independent of U. S. rules).

The first comprehensive overview charting the accountability of higher education. As the price tag of higher education continues to rise, colleges and universities across the country are under increasing pressure to demonstrate their value. Graded on numerous metrics, including cost and ability to prepare students for the job market, colleges must satisfy requirements from multiple stakeholders. State and federal governments demand greater accountability. Foundations and private donors, as well as today's parents and students, approach education with a consumer sensibility. How can colleges navigate these pressures while trying to stay true to their missions and values? In Higher Education Accountability, Robert Kelchen delivers the first comprehensive overview of how colleges in the United States came to face such overwhelming scrutiny. Beginning with the earliest efforts to regulate schools, Kelchen reveals the rationale behind accountability and outlines the historical development of how federal and state policies, accreditation practices, private-sector interests, and internal requirements have become so important to institutional success and survival. With so many diverse and conflicting entities holding colleges responsible for their performance, the variety of accountability systems in play can have both intended and unintended consequences. Immersed as they are in current debates about how best to respond to these pressures, faculty and administrators will welcome this up-to-date and timely account, which offers not only a look at current practices but also an examination of the future of accountability in American higher education.

In June of 1990, a conference was held on Probablity Models and Statisti cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians. The conference took place at the University of Massachusetts, Amherst, and was attended by 36 participants, including statisticians, mathematicians, psychologists and sociologists from the United States, Canada, Israel, Italy, and The Nether lands. There were 18 presentations on a wide variety of topics involving ranking data. This volume is a collection of 14 of these presentations, as well as 5 miscellaneous papers that were contributed by conference participants. We would like to thank Carole Kohanski, summer program coordinator for the American Mathematical Society, for her assistance in arranging the conference; M. Steigerwald for preparing the manuscripts for publication; Martin Gilchrist at Springer-Verlag for editorial advice; and Persi Diaconis for contributing the Foreword. Special thanks go to the anonymous referees for their careful readings and constructive comments. Finally, we thank the National Science Foundation for their sponsorship of the AMS-IMS-SIAM Joint Summer Programs. Contents Preface vii Conference Participants xiii Foreword xvii 1 Ranking Models with Item Covariates 1 D. E. Critchlow and M. A. Fligner 1. 1 Introduction. . . . . . . . . . . . . . . 1 1. 2 Basic Ranking Models and Their Parameters 2 1. 3 Ranking Models with Covariates 8 1. 4 Estimation 9 1. 5 Example. 11 1. 6 Discussion. 14 1. 7 Appendix . 15 1. 8 References.