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38
A Concept Exploration Method for Product Family Design
 in Mechanical Engineering. Atlanta, GA: Georgia Institute of Technology
, 1998
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McLaren’s improved snub cube and other new spherical designs in three dimensions
, 2002
"... Evidence is presented to suggest that, in three dimensions, spherical 6designs with N ..."
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Evidence is presented to suggest that, in three dimensions, spherical 6designs with N
Adaptive Tuning of Numerical Weather Prediction Models: Simultaneous Estimation of Weighting, Smoothing and Physical Parameters
, 1996
"... In Wahba et al (1995) it was shown how the randomized trace method could be used to adaptively tune numerical weather prediction models via generalized cross validation (GCV ) and related methods. In this paper a `toy' four dimensional data assimilation model is developed (actually one space an ..."
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Cited by 33 (10 self)
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In Wahba et al (1995) it was shown how the randomized trace method could be used to adaptively tune numerical weather prediction models via generalized cross validation (GCV ) and related methods. In this paper a `toy' four dimensional data assimilation model is developed (actually one space and one time variable), consisting of an equivalent barotropic vorticity equation on a latitude circle, and used to demonstrate how this technique may be used to simultaneously tune weighting, smoothing and physical parameters. Analyses both with the model as a strong constraint (corresponding to the usual 4DVar approach) and as a weak constraint (corresponding theoretically to a fixedinterval Kalman smoother) are carried out. The conclusions are limited to the particular toy problem considered but it can be seen how more elaborate experiments could be carried out, as well as how the method might be applied in practice. We have considered five adjustable parameters, two related to a distributed c...
Robust Experimental Design for Multivariate Generalized Linear Models,”
 Technometrics,
, 2006
"... Abstract: A simple heuristic is proposed for the construction of robust experimental designs for multivariate generalized linear models. The method is based on clustering a set of local optimal designs, and a method for …nding local Doptimal designs using available resources is also introduced. Cl ..."
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Abstract: A simple heuristic is proposed for the construction of robust experimental designs for multivariate generalized linear models. The method is based on clustering a set of local optimal designs, and a method for …nding local Doptimal designs using available resources is also introduced. Clustering, with its simplicity and minimal computation needs, is demonstrated to outperform more complex and sophisticated methods.
Packing planes in four dimensions and other mysteries
 in Proceedings of the Conference on Algebraic Combinatorics and Related Topics
, 1997
"... How should you choose a good set of (say) 48 planes in four dimensions? More generally, how do you find packings in Grassmannian spaces? In this article I give a brief introduction to the work that I have been doing on this problem in collaboration with A. R. Calderbank, J. H. Conway, R. H. Hardin, ..."
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How should you choose a good set of (say) 48 planes in four dimensions? More generally, how do you find packings in Grassmannian spaces? In this article I give a brief introduction to the work that I have been doing on this problem in collaboration with A. R. Calderbank, J. H. Conway, R. H. Hardin, E. M. Rains and P. W. Shor. We have found many nice examples of specific packings (70 4spaces in 8space, for instance), several general constructions, and an embedding theorem which shows that a packing in Grassmannian space G(m,n) is a subset of a sphere in R D, D = (m + 2)(m − 1)/2, and leads to a proof that many of our packings are optimal. There are a number of interesting unsolved problems. 1.
Active Learning Based on Locally Linear Reconstruction
, 2011
"... We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the parameter estimates or ..."
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We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the parameter estimates or predicted value. However, these methods see only global euclidean structure, while the local manifold structure is ignored. For example, Ioptimal design selects those data points such that other data points can be best approximated by linear combinations of all the selected points. In this paper, we propose a novel active learning algorithm which takes into account the local structure of the data space. That is, each data point should be approximated by the linear combination of only its neighbors. Given the local reconstruction coefficients for every data point and the coordinates of the selected points, a transductive learning algorithm called Locally Linear Reconstruction (LLR) is proposed to reconstruct every other point. The most representative points are thus defined as those whose coordinates can be used to best reconstruct the whole data set. The sequential and convex optimization schemes are also introduced to solve the optimization problem. The experimental results have demonstrated the effectiveness of our proposed method.
Operating manual for Gosset: A general purpose program for constructing experimental designs
 AT&T Bell Laboratories
, 1994
"... This is the second edition of the operating manual for gosset, a flexible and powerful computer program for constructing experimental designs. Variables may be discrete or continuous (or both), discrete variables may be numeric or symbolic (or both), and continuous variables may range over a cube or ..."
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This is the second edition of the operating manual for gosset, a flexible and powerful computer program for constructing experimental designs. Variables may be discrete or continuous (or both), discrete variables may be numeric or symbolic (or both), and continuous variables may range over a cube or a ball (or both). The variables may be required to satisfy linear equalities or inequalities, and the model to be fitted may be any low degree polynomial (e.g. a quadratic). The number of observations is specified by the user. The design may be required to include a specified set of points (so a sequence of designs can be found, each of which is optimal given that the earlier measurements have been made). The region where the model is to be fitted need not be the same as the region where measurements are to be made (so the designs can be used for interpolation or extrapolation). The following types of designs can be requested: I, A, D or Eoptimal; the same but with protection against the loss of one run; or packings (when no model is available). Block designs, and designs with correlated errors can also be constructed. The algorithm is powerful enough to routinely minimize functions of 1000 variables (e.g. can find optimal or nearly optimal designs for a quadratic model involving 12 variables). An extensive library of precomputed optimal designs is included for linear and quadratic designs in the cube, ball and simplex. The user does not have to specify starting points for the search. The user also has control over how much effort is expended by the algorithm, and can monitor the progress of the search. Applications so far include VLSI production, conductivity of diamond films, growth of protein crystals, flow through a catalytic converter, laser welding, etc.
How Much Internalization of Nuclear Risk Through Liability Insurance? Working Paper No 0211
, 2002
"... An important source of conflict surrounding nuclear energy is that with a very small probability, a largescale nuclear accident may occur. One way to internalize the associated financial risks is through mandating nuclear operators to have liability insurance. This paper presents estimates of consu ..."
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An important source of conflict surrounding nuclear energy is that with a very small probability, a largescale nuclear accident may occur. One way to internalize the associated financial risks is through mandating nuclear operators to have liability insurance. This paper presents estimates of consumers ’ willingness to pay for increased financial security provided by an extension of coverage, based on the ‘stated choice ’ approach. A Swiss citizen with median characteristics may be willing to pay 0.14 US cents per kwh to increase coverage beyond the current CHF 0.7 billion (bn.) (US $ 0.47 bn.). Marginal willingness to pay declines with higher coverage but exceeds marginal cost at least up to CHF 4 bn. (US $ 2.7 bn.). An extension of nuclear liability insurance coverage therefore may be efficiencyenhancing.