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Erickson D.,United Investments | Andrews N.,Low Income Investment Fund
Health Affairs | Year: 2011

Safe, vibrant neighborhoods are vital to health. The community development "industry"-a network of nonprofit service providers, real estate developers, financial institutions, foundations, and government-draws on public subsidies and other financing to transform impoverished neighborhoods into better-functioning communities. Although such activity positively affects the "upstream" causes of poor health, the community development industry rarely collaborates with the health sector or even considers health effects in its work. Examples of initiatives-such as the creation of affordable housing that avoids nursing home placement-suggest a strong potential for cross-sector collaborations to reduce health disparities and slow the growth of health care spending, while at the same time improving economic and social well-being in America's most disadvantaged communities. We propose a four-point plan to help ensure that these collaborations achieve positive outcomes and sustainable progress for residents and investors alike. Source


Frangioni A.,University of Pisa | Gentile C.,CNR Institute for System Analysis and Computer Science Antonio Ruberti | Lacalandra F.,United Investments
International Journal of Electrical Power and Energy Systems | Year: 2011

The short-term Unit Commitment (UC) problem in hydro-thermal power generation is a fundamental problem in short-term electrical generation scheduling. Historically, Lagrangian techniques have been used to tackle this large-scale, difficult Mixed-Integer NonLinear Program (MINLP); this requires being able to efficiently solve the Lagrangian subproblems, which has only recently become possible (efficiently enough) for units subject to significant ramp constraints. In the last years, alternative approaches have been devised where the nonlinearities in the problem are approximated by means of piecewise-linear functions, so that UC can be approximated by a Mixed-Integer Linear Program (MILP); in particular, using a recently developed class of valid inequalities for the problem, called "Perspective Cuts", significant improvements have been obtained in the efficiency and effectiveness of the solution algorithms. These two different approaches have complementary strengths; Lagrangian ones provide very good lower bounds quickly, but they require sophisticated heuristics - which may need to be changed every time that the mathematical model changes - for producing actual feasible solutions. MILP approaches have been shown to be able to provide very good feasible solutions quickly, but their lower bound is significantly worse. We present a sequential approach which combines the two methods, trying to exploit each one's strengths; we show, by means of extensive computational experiments on realistic instances, that the sequential approach may exhibit significantly better efficiency than either of the two basic ones, depending on the degree of accuracy requested to the feasible solutions. © 2010 Elsevier Ltd. All rights reserved. Source


Chatterjee S.,United Investments | Kishinevsky M.,Intel Corporation
Formal Methods in System Design | Year: 2012

Abstract microarchitectural models of communication fabrics present a challenge for verification. Due to the presence of deep pipelining, a large number of queues and distributed control, the state space of such models is usually too large for enumeration by protocol verification tools such as Murphi. On the other hand, we find that state-of-the-art RTL model checkers such as ABC have poor performance on these models since there is very little opportunity for localization and most of the recent capacity advances in RTL model checking have come from better ways of discarding the irrelevant parts of the model. In this work we explore a new approach for verifying these models where we capture a model at a high level of abstraction by requiring that it be described using a small set of well-defined microarchitectural primitives. We exploit the high level structure present in this description, to automatically strengthen some classes of properties, in order to make them 1-step inductive, and then use an RTL model checker to prove them. In some cases, even if we cannot make the property inductive, we can dramatically reduce the number and complexity of lemmas that are needed to make the property inductive. © Springer Science+Business Media, LLC 2011. Source


Choi M.J.,United Investments | Torralba A.,Massachusetts Institute of Technology | Willsky A.S.,Massachusetts Institute of Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2012

There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. A context model can rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit of context models has been limited because most of the previous methods were tested on data sets with only a few object categories, in which most images contain one or two object categories. In this paper, we introduce a new data set with images that contain many instances of different object categories, and propose an efficient model that captures the contextual information among more than a hundred object categories using a tree structure. Our model incorporates global image features, dependencies between object categories, and outputs of local detectors into one probabilistic framework. We demonstrate that our context model improves object recognition performance and provides a coherent interpretation of a scene, which enables a reliable image querying system by multiple object categories. In addition, our model can be applied to scene understanding tasks that local detectors alone cannot solve, such as detecting objects out of context or querying for the most typical and the least typical scenes in a data set. © 2012 IEEE. Source


Klingenberg B.,Marist College | Timberlake R.,Ultra-Scan Corporation | Geurts T.G.,George Washington University | Brown R.J.,United Investments
International Journal of Production Economics | Year: 2013

Operations management designs, schedules, and controls organizational processes to increase productivity by using methods such as Just-in-Time (JIT)/Lean Manufacturing, Total Quality Management (TQM) or Environmental Management Systems (EMS). Following implementation, managers generally want to determine the impact of such operational innovations on firm performance. Past studies analyzed financial ratios to prove the usefulness of the operational methods; however, findings are mixed. While some reported positive relationships between operational innovations and financial performance, others found no or inconsistent relationships. Motivated to uncover explanations for said inconsistencies, this paper takes a critical look at the appropriateness of the profitability ratios Return on Asset (ROA), Return on Equity (ROE) and Basic Earning Power (BEP) in determining the impact of a given operations strategy on firm performance. Focusing on JIT/Lean Manufacturing, the relationship between these ratios and inventory management ratios is analyzed. Fixed-effect regression shows that no consistent relationship between ROA, ROE, BEP and inventory management ratios exists. This result may be explained, as the profitability of a firm is affected by at least two factors: results from its operations, and how these are financed (e.g. usage of cheap debt, which enhances profitability). This paper suggests that the impact of an individual operations strategy is difficult to isolate from other firm activities, such as its financial management. Hence, profitability ratios such as ROA, ROE and BEP that aggregate all of a firm's activities may not be suitable metrics to determine the effect of JIT/Lean Manufacturing methods on financial firm performance. © 2012 Elsevier B.V. Source

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