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64
A Unified Approach to Spatial Outliers Detection
 GeoInformatica
, 2003
"... Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as loc ..."
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Cited by 16 (6 self)
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Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. In this paper, we first provide a general definition of Soutliers for spatial outliers. This definition subsumes the traditional definitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods and present scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we provide experimental evaluations of our algorithms using a MinneapolisSt. Paul(Twin Cities) traffic data set.
The Architecture of Cooperation: How Code Architecture Mitigates Free Riding in the Open Source Development Model
, 2003
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Identifying functional modules in proteinprotein interaction networks: an integrated exact approach
 Bioinformatics
, 2008
"... Motivation: With the exponential growth of expression and proteinprotein interaction (PPI) data, the frontier of research in system biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sha ..."
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Cited by 12 (1 self)
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Motivation: With the exponential growth of expression and proteinprotein interaction (PPI) data, the frontier of research in system biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature. Results: Here we present the first exact solution for this problem, which is based on integer linear programming and its connection to the wellknown prizecollecting Steiner tree problem from Operations
Modularity in the Design of Complex Engineering Systems
 2004 FUJIMOTO, TAKAHIRO AND AKIRA TAKEISHI (2001) MODULARIZATION IN THE AUTO INDUSTRY: INTERLINKED MULTIPLE HIERARCHIES OF PRODUCT, PRODUCTION AND SUPPLIER SYSTEMS, TOKYO UNIVERSITY DISCUSSION PAPER, CIRJEF107
, 2004
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Identifying Distributional Characteristics in Random Coefficients Panel Data Models
, 2009
"... We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity a ..."
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Cited by 9 (2 self)
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We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child’s birth weight.
Estimating APosteriori Probabilities Using Stochastic Network Models
 IN PROCEEDINGS OF THE SUMMER SCHOOL ON NEURAL NETWORKS
, 1994
"... In this paper we present a systematic approach to constructing neural network classifiers based on stochastic model theory. A two step process is described where the first problem is to model the stochastic relationship between sample patterns and their classes using a stochastic neural network. The ..."
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Cited by 8 (1 self)
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In this paper we present a systematic approach to constructing neural network classifiers based on stochastic model theory. A two step process is described where the first problem is to model the stochastic relationship between sample patterns and their classes using a stochastic neural network. Then we convert the stochastic network to a deterministic one, which calculates the aposteriori probabilities of the stochastic counterpart. That is, the outputs of the final network estimate aposteriori probabilities by construction. The wellknown method of normalizing network outputs by applying the softmax function in order to allow a probabilistic interpretation is shown to be more than a heuristic, since it is wellfounded in the context of stochastic networks. Simulation results show a performance of our networks superior to standard multilayer networks in the case of few training samples and a large number of classes.
An operational technique for relating the movement of existing tropical cyclones to past tracks
 Monthly Weather Review
, 1970
"... The HURRAN (hurricane analog) technique for selecting analogs for an existing tropical storm or hurricane is described. This fully computerized program examines tracks of all Atlantic tropical storms or hurricanes since 1886, and those that have designated characteristics similar to an existing stor ..."
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Cited by 8 (2 self)
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The HURRAN (hurricane analog) technique for selecting analogs for an existing tropical storm or hurricane is described. This fully computerized program examines tracks of all Atlantic tropical storms or hurricanes since 1886, and those that have designated characteristics similar to an existing storm are selected and identified. Positions of storms selected as analogs are determined at 12, 24, 36, 48, and 72 hr after the initial time. Probability ellipses are computed from the resulting arrays and plotted on an 2, y (CALCOMP) offline plotter. The program also has the option of computing the probability that the storm center will be located within a fixed distance of a given point at a specific time. Operational use of HURRAN during the 1969 hurricane season, including both its utility and limitations, is described. 1.
A deterministic annealing framework for unsupervised texture segmentation
 Tech. Rep. IAITR962
, 1996
"... We present a novel framework for unsupervised texture segmentation, which relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a pairwise data clustering problem with a sparse neighborhood structure. The pairwise dissimilarities of texture blocks are compute ..."
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Cited by 7 (1 self)
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We present a novel framework for unsupervised texture segmentation, which relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a pairwise data clustering problem with a sparse neighborhood structure. The pairwise dissimilarities of texture blocks are computed using a multiscale image representation based on Gabor filters, which are tuned to spatial frequencies at different scales and orientations. We derive and discuss a family of objective functions to pose the segmentation problem in a precise mathematical formulation. An efficient optimization method, known as deterministic annealing, is applied to solve the associated optimization problem. The general framework of deterministic annealing and meanfield approximation is introduced and the canonical way to derive efficient algorithms within this framework is described in detail. Moreover the combinatorial optimization problem is examined from the viewpoint of scale space theory. The novel algorithm has been extensively tested on Brodatzlike microtexture mixtures and on realword images. In addition, benchmark studies with alternative segmentation techniques are reported.
Flaw Detection In Woven Textiles By Neural Network
 Neural Computing: Research and Applications III, Proc. 5th Irish Neural Network Conference, St. Patrick's
, 1997
"... This paper describes a technique for detection of flaws in woven textile fabric, using Fourier transform spectral texture features. Two chief attributes make it a particularly suitable approach to this problem: because of the repetitive nature of the woven pattern, patterns can be expressed by very ..."
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Cited by 6 (3 self)
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This paper describes a technique for detection of flaws in woven textile fabric, using Fourier transform spectral texture features. Two chief attributes make it a particularly suitable approach to this problem: because of the repetitive nature of the woven pattern, patterns can be expressed by very few spectral components (features), and, the whole detection system  both the texture feature extractor and the subsequent decision mechanism  have potential for parallel implementation via a feedforward neural network structure. The technique also has potential for selfcalibration, and hence a capability for adaptive operation. The performance of the technique is evaluated on a set of samples of denim fabric, containing real flaws. Constraints and tradeoffs of practical implementation are discussed, including considerations of analog VLSI implementation of the neural network structure. 1 Introduction The cost of poor quality, i.e. the cost to repair and the damage to reputation, are...
Meade Use of coefficient of variation in assessing variability of quantitative assays. Clinical and Diagnostic Laboratory Immunology 9
, 2002
"... These include: This article cites 4 articles, 3 of which can be accessed free at: ..."
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Cited by 5 (0 self)
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These include: This article cites 4 articles, 3 of which can be accessed free at: