949 resultados para generalized manifold


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2000 Mathematics Subject Classification: 47H04, 65K10.

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2000 Mathematics Subject Classification: 65G99, 65K10, 47H04.

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2000 Mathematics Subject Classification: Primary: 47B47, 47B10; secondary 47A30.

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MSC 2010: 46F30, 46F10

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MSC 2010: 35J05, 33C10, 45D05

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2000 Mathematics Subject Classification: 53C42, 53C15.

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2000 Mathematics Subject Classification: 53C40, 53B25.

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2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.

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2000 Mathematics Subject Classification: 47A10, 47A12, 47A30, 47B10, 47B20, 47B37, 47B47, 47D50.

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2010 Mathematics Subject Classification: 47A10.

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Exposure to counter-stereotypic gender role models (e.g., a woman engineer) has been shown to successfully reduce the application of biased gender stereotypes. We tested the hypothesis that such efforts may more generally lessen the application of stereotypic knowledge in other (non-gendered) domains. Specifically, based on the notion that counter-stereotypes can stimulate a lesser reliance on heuristic thinking, we predicted that contesting gender stereotypes would eliminate a more general group prototypicality bias in the selection of leaders. Three studies supported this hypothesis. After exposing participants to a counter-stereotypic gender role model, group prototypicality no longer predicted leadership evaluation and selection. We discuss the implications of these findings for groups and organizations seeking to capitalize on the benefits of an increasingly diverse workforce.

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Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.

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OBJECTIVE: The objective of this study was to examine medical illness and anxiety, depressive, and somatic symptoms in older medical patients with generalized anxiety disorder (GAD). METHOD: A case-control study was designed and conducted in the University of California, San Diego (UCSD) Geriatrics Clinics. A total of fifty-four older medical patients with GAD and 54 matched controls participated. MEASUREMENTS: The measurements used for this study include: Brief Symptom Inventory-18, Mini International Neuropsychiatric Interview, and the Anxiety Disorders Interview Schedule. RESULTS: Older medical patients with GAD reported higher levels of somatic symptoms, anxiety, and depression than other older adults, as well as higher rates of diabetes and gastrointestinal conditions. In a multivariate model that included somatic symptoms, medical conditions, and depressive and anxiety symptoms, anxiety symptoms were the only significant predictors of GAD. CONCLUSION: These results suggest first, that older medical patients with GAD do not primarily express distress as somatic symptoms; second, that anxiety symptoms in geriatric patients should not be discounted as a byproduct of medical illness or depression; and third, that older adults with diabetes and gastrointestinal conditions may benefit from screening for anxiety.