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.
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.
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.
Wehr J.,University of Arizona |
Wasielak A.,United Investments
Journal of Statistical Physics | Year: 2016
We study ground states of Ising models with random ferromagnetic couplings, proving the triviality of all zero-temperature metastates. This result sheds a new light on the properties of these systems, putting strong restrictions on their possible ground state structure. Open problems related to existence of interface-supporting ground states are stated and an interpretation of the main result in terms of first-passage and random surface models in a random environment is presented. © 2015, Springer Science+Business Media New York.
Coggin T.D.,United Investments
International Journal of Climatology | Year: 2012
In this note I present and illustrate recently developed econometric trend tests using the HadCRUT3 global and hemispheric surface temperature data updated through 2009, specifically allowing statistical complications of structural change, serial correlation, and unit roots. My results confirm the general finding of earlier studies: the HadCRUT3 data present a consistent pattern of warming in recent years (post-1975). © 2010 Royal Meteorological Society.
Djamasbi S.,Worcester Polytechnic Institute |
Siegel M.,United Investments |
Tullis T.,United Investments
International Journal of Human Computer Studies | Year: 2010
Generation Y (age 18-31) is a very large and economically powerful generation, containing eighty-two million people and spending $200 billion annually. It is not surprising that companies are interested in gaining the patronage of this group, particularly via the web. Surprisingly, very little research into making web pages appealing to this important demographic has been done. This paper addresses this need through two separate studies. The first, an online survey, provides evidence that our proposed score for predicting the visual appeal of web pages reflects the self report measure of what pages Generation Y likes. To refine these findings, an eye tracking study is conducted using the pages that were most and least liked in Study I. Participants' eye movement is tracked while browsing these pages, providing evidence of what attracts their attention. The results of these two studies suggest that Generation Y may prefer pages that include a main large image, images of celebrities, little text, and a search feature. This research has important implications. © 2010 Elsevier Ltd. All rights reserved.
United Investments | Date: 2016-03-29
United Investments | Date: 2016-03-29
United Investments | Date: 2016-04-01