320,226 research outputs found
Optimal solution of the nearest correlation matrix problem by minimization of the maximum norm
No full textThe nearest correlation matrix problem is to find a valid (positive semidefinite) correlation matrix, R(m,m), that is nearest to a given invalid (negative semidefinite) or pseudo-correlation matrix, Q(m,m); m larger than 2. In the literature on this problem, 'nearest' is invariably defined in the sense of the least Frobenius norm. Research works of Rebonato and Jaeckel (1999), Higham (2002), Anjos et al. (2003), Grubisic and Pietersz (2004), Pietersz, and Groenen (2004), etc. use Frobenius norm explicitly or implicitly. However, it is not necessary to define 'nearest' in this conventional sense. The thrust of this paper is to define 'nearest' in the sense of the least maximum norm (LMN) of the deviation matrix (R-Q), and to obtain R nearest to Q. The LMN provides the overall minimum range of deviation of the elements of R from those of Q. We also append a computer program (source codes in FORTRAN) to find the LMN R from a given Q. Presently we use the random walk search method for optimization. However, we suggest that more efficient methods based on the Genetic algorithms may replace the random walk algorithm of optimization.Nearest correlation matrix problem; Frobenius norm; maximum norm; LMN correlation matrix; positive semidefinite; negative semidefinite; positive definite; random walk algorithm; Genetic algorithm; computer program; source codes; FORTRAN; simulation
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Energy Crop Supply in France: A Min-Max Regret Approach
No full textThis paper attempts to estimate energy crop supply using an LP model comprising hundreds of representative farms of the arable cropping sector in France. In order to enhance the predictive ability of such a model and to provide an analytical tool useful to policy makers, interval linear programming (ILP) is used to formalise bounded rationality conditions. In the presence of uncertainty related to yields and prices it is assumed that the farmer minimises the distance from optimality once uncertainty resolves introducing an alternative criterion to the classic profit maximisation rationale. Model validation based on observed activity levels suggests that about 40% of the farms adopt the min-max regret criterion. Then energy crop supply curves, generated by the min-max regret model, are proved to be upward sloped alike classic LP supply curves.interval linear programming, min-max regret, energy crops, France, Crop Production/Industries, Resource /Energy Economics and Policy, C61, D81, Q18,
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Min-Max Dom-Saturation Number of a Tree
No full textIn this paper we present a dynamic programming algorithm for determining the min-max domsaturation number of a tree
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
General fuzzy min-max neural network for clustering and classification
No full textThis paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given
An extension of min/max flow framework
No full textIn this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the fol-
24 lowing three parts. The first is to analyze the reason of the speckle generation and then to modify the
25 original scheme. The second is to point out that the continued application of this scheme cannot result
26 in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme
27 through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as
28 to control the smoothing effect. Our experimental results with image restoration show that the proposed
29 schemes can reach a steady state solution while preserving the essential structures of objects. The third is
30 to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an
31 intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework pro-
32 vides us with an effective way to approximate the optimal solution. From an implementation point of
33 view, this extended scheme makes the speed function simpler and more flexible. The experimental
34 results of segmentation and region tracking show that the boundary leaking problem can be effectively
35 suppressed
Letter from R. R. Zellick, Assistant Trust Officer, Anglo California National Bank of San Francisco, to Joseph R. Goodman, October 2, 1942
No full textLetter from R. R. Zellick, Assistant Trust Officer at The Anglo California National Bank of San Francisco, to Joseph R. Goodman, regarding property owned by Dave Tatsuno. Zellick mentions a dispute between current tenants and Tatsuno, and that Tatsuno has asked Goodman to help locate trustworthy tenants.Personal correspondence, organizational records, government documents, publications, and other papers created or collected by Joseph R. Goodman documenting the forced removal and incarceration of Japanese Americans during World War II, as well as organized resistance to incarceration. Included in the collection are records of the Japanese Young Men's Christian Association and the Japanese American Citizens' League in San Francisco, including papers of the Japanese YMCA's executive secretary Lincoln Kanai; Sakai family papers; Goodman's correspondence to and from Japanese American incarcerees, organizations opposing forced removal and incarceration of Japanese Americans, the War Relocation Authority, and others; publications, photographs, and ephemera from the Topaz Relocation Center, where Goodman taught high school; War Relocation Authority records and publications; and newspaper clippings, pamphlets, and reports about forced removal and incarceration created by various government, religious, and civic organizations, in California and nationwide
Heterogeneity of R&D in family firms
No full textDrawing on the loss aversion framework, this research posits that the risk behaviors of family business group (FBG) affiliates are more positive than those of family standalones. Empirical results, using the case of Taiwan, confirm that the use of R&D by these affiliates is greater than that by family standalones. Further analysis, however, indicates that this greater positive effect of FBG affiliates than of family standalones is attenuated if the managerial power exercised by controlling shareholders is greater than the power driven by legal ownership. By demonstrating the heterogeneity of risk behaviors within family firms, our research adds value to the existing literature by focusing on the differences between family and nonfamily firms.Full Tex
- …
