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This study is a cross-sectional investigation of caregiving practice patterns among Asian, Hispanic and non-Hispanic White American family caregivers of older adults across three immigrant generations. The 2009 California Health Interview Survey (CHIS) dataset was used, and 591 Asian, 989 Hispanic and 6537 non-Hispanic White American caregivers of older adults were selected. First, descriptive analyses of caregivers' characteristics, caregiving situations and practice patterns were examined by racial/ethnic groups and immigrant generations. Practice patterns measured were respite care use, hours and length of caregiving. Three hypotheses on caregiving patterns based on assimilation theory were tested and analyzed using logistic regression and generalized linear models by racial/ethnic groups and generations. Caregiving patterns of non-Hispanic White caregivers supported all three hypotheses regarding respite care use, caregiving hours and caregiving duration, showing less caregiving involvement in later generations. However, Asian and Hispanic counterparts showed mixed results. Third generation Asian and Hispanic caregivers used respite care the least and spent the most caregiving hours per week and had the longest caregiving duration compared to earlier generations. These caregiving patterns revealed underlying cultural values related to filial responsibility, even among later generations of caregivers of color. Findings suggest the importance of considering the cultural values of each racial/ethnic group regardless of generation when working with racially and ethnically diverse populations of family caregivers of older adults. [ABSTRACT FROM AUTHOR]

Why do the equally spaced dots in figure 1 appear regularly spaced? The answer 'because they are' is naive and ignores the existence of sensory noise, which is known to limit the accuracy of positional localization. Actually, all the dots in figure 1 have been physically perturbed, but in the case of the apparently regular patterns to an extent that is below threshold for reliable detection. Only when retinal pathology causes severe distortions do regular grids appear perturbed. Here, we present evidence that low-level sensory noise does indeed corrupt the encoding of relative spatial position, and limits the accuracy with which observers can detect real distortions. The noise is equivalent to a Gaussian random variable with a standard deviation of approximately 5 per cent of the inter-element spacing. The just-noticeable difference in positional distortion between two patterns is smallest when neither of them is perfectly regular. The computation of variance is statistically inefficient, typically using only five or six of the available dots. [ABSTRACT FROM AUTHOR]

Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single-command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information-theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near-infrared spectrometer. We develop an approach to 'science on the fly'' that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross-sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Extraction of flower regions from complex background is a difficult task and it is an important part of a flower image retrieval and recognition. In this article, we propose an Ant Colony Optimization (ACO) algorithm as a general color clustering method, and test it on flower images as a case study of object boundary extraction. The segmentation methodology on flower images consists of six steps: color space conversion, generation of candidate color cluster centers, ant colony optimization method to select optimum color cluster centers, merging of cluster centers which are close to each other, image segmentation by clustering, and extraction of flower region from the image. To evince that ACO algorithm can be a general segmentation method, some results of natural images in Berkeley segmentation benchmark have been presented. The method as a case study on flower region extraction has also been tested on the images of Oxford-17 Flowers dataset, and the results have confronted with other well established flower region extraction approaches. [Copyright &y& Elsevier]

Motivated by a recent resurgence in earthquake predictability research, we present a method for testing alarm-based earthquake predictions. The testing method is based on the Molchan diagram—a plot of miss rate and fraction of space–time occupied by alarm—and is applicable to a wide class of predictions, including probabilistic earthquake forecasts varying in space, time, and magnitude. A single alarm can be simply tested using the cumulative binomial distribution. Here we consider the more interesting case of a function from which a continuum of well-ordered alarms can be derived. For such an ‘alarm function’ we construct a cumulative performance measure, the area skill score, based on the normalized area above a trajectory on the Molchan diagram. A score of unity indicates perfect skill, a score of zero indicates perfect non-skill, and the expected score for a random alarm function is 1/2. The area skill score quantifies the performance of an arbitrary alarm function relative to a reference model. To illustrate the testing method, we consider the 10-yr experiment by J. Rundle and others to predict earthquakes in California. We test forecasts from three models: relative intensity (RI), a simple spatial clustering model constructed using only smoothed historical seismicity; pattern informatics (PI), a model that aims to capture seismicity dynamics by pattern recognition; and the U. S. Geological Survey National Seismic Hazard Map (NSHM), a model that comprises smoothed historical seismicity, zones of ‘background’ seismicity, and explicit fault information. Results show that neither PI nor NSHM provide significant performance gain relative to the RI reference model. We suggest that our testing method can be used to evaluate future experiments in the Collaboratory for the Study of Earthquake Predictability and to iteratively improve reference models for earthquake prediction hypothesis testing. [ABSTRACT FROM AUTHOR]

The article presents several conference papers on object perception, attention, and memory presented at a meeting in Long Beach, California. They include "Context Affects Figure-ground Organization Via Perceptual Grouping," by Joseph L. Brooks and Jon Driver, "Factors Governing Inhibition of Occluded Regions in Superimposed Objects," by Destinee Chambers and Kyle Cave, and "Learning to Form New Perceptual Groups," by Timothy J. Vickery, Joshua K. Hartshorne and Yuhong V. Jiang.