1,198 research outputs found
Decomposition Algorithms for Stochastic Programming on a Computational Grid
We describe algorithms for two-stage stochastic linear programming with
recourse and their implementation on a grid computing platform. In particular,
we examine serial and asynchronous versions of the L-shaped method and a
trust-region method. The parallel platform of choice is the dynamic,
heterogeneous, opportunistic platform provided by the Condor system. The
algorithms are of master-worker type (with the workers being used to solve
second-stage problems, and the MW runtime support library (which supports
master-worker computations) is key to the implementation. Computational results
are presented on large sample average approximations of problems from the
literature.Comment: 44 page
Thermal (Silicon Diode) Data Acquisition Systems
Marshall Space Flight Center s X-ray Cryogenic Facility (XRCF) has been performing cryogenic testing to 20 Kelvin since 1999. Two configurations for acquiring data from silicon diode temperature sensors have been implemented at the facility. The facility's environment is recorded via a data acquisition system capable of reading up to 60 silicon diodes. Test article temperature is recorded by a second data acquisition system capable of reading 150+ silicon diodes. The specifications and architecture of both systems will be presented
Validating Sample Average Approximation Solutions with Negatively Dependent Batches
Sample-average approximations (SAA) are a practical means of finding
approximate solutions of stochastic programming problems involving an extremely
large (or infinite) number of scenarios. SAA can also be used to find estimates
of a lower bound on the optimal objective value of the true problem which, when
coupled with an upper bound, provides confidence intervals for the true optimal
objective value and valuable information about the quality of the approximate
solutions. Specifically, the lower bound can be estimated by solving multiple
SAA problems (each obtained using a particular sampling method) and averaging
the obtained objective values. State-of-the-art methods for lower-bound
estimation generate batches of scenarios for the SAA problems independently. In
this paper, we describe sampling methods that produce negatively dependent
batches, thus reducing the variance of the sample-averaged lower bound
estimator and increasing its usefulness in defining a confidence interval for
the optimal objective value. We provide conditions under which the new sampling
methods can reduce the variance of the lower bound estimator, and present
computational results to verify that our scheme can reduce the variance
significantly, by comparison with the traditional Latin hypercube approach
Producing Textbook Sociology
The conservative role of the textbook in reproducing the dominant ideas of a disciplinary field is well known. The factors driving that content have remained almost entirely unexamined. Reviewing the universe of textbooks aimed at the American market between 1998 and 2004, we explore the persistence of the identification in American sociology textbooks of a paradigm in which structural functionalism, conflict theory, and symbolic interactionism are used to frame the theoretical core of the discipline. We examine how over time the textbook market produces both supply and demand pressures to reproduce content that is at odds with the mainstream of the profession. We draw upon in-depth interviews with recent textbook authors and their editors
Poster - Private Pete Fights Illiteracy at Fort Ontario: The Men in Charge
A poster created by history students from Morehead State University for display at Fort Ontario Historic Site in March of 2023.https://scholarworks.moreheadstate.edu/stu_1210th_fort_ontario/1097/thumbnail.jp
Poster - Private Pete Fights Illiteracy at Fort Ontario: The Men it Changed
A poster created by history students of Morehead State University for display at Fort Ontario Historic Site in March of 2023.https://scholarworks.moreheadstate.edu/stu_1210th_fort_ontario/1099/thumbnail.jp
Conflicted and confused? Health harming industries and research funding in leading UK universities
University researchers face growing expectations to engage with commercial sources of funding. This pressure is likely to increase in the context of the covid-19 squeeze1 and, in the UK, both Brexit and a research impact agenda promoting external collaboration.2 Alongside this, there are efforts to reduce conflicts of interest in research involving pharmaceutical and medical device companies,3 and policies rejecting tobacco industry funding.4 Yet limited attention has been paid to funding from other health damaging industries such as alcohol, gambling, and ultra-processed food and drink. How well are universities equipped to manage such conflicts of interest
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