Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

Midge: Generating Image Descriptions From Computer Vision Detections

Abstract: This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees. Results show that the generation system outperforms state-of-the-art systems, automatically generating some of the most natural image descriptions to date.

Comparing self-reported and physician-reported medical history.

Abstract: The authors compared self-reported medical history and medication use in a cataract case-control study of 1,380 persons (1985-1989) in Boston, Massachusetts, with information from the participants' physicians. Under- and overreporting varied by condition and type of medication. A self-reported history of hypertension had the highest sensitivity (91%), and diabetes history had the highest specificity (97%). Among different medications investigated, self-reported antihypertensive medication use was the most sensitive (88%), while self-reported use of insulin was the most specific (99%). Differences between patient- and physician-reported frequencies were very small, except for arthritis (15%) and regular aspirin use (21%). Results suggest an accurate recall of medical and drug usage history in well-defined chronic conditions.

Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data

Abstract: This paper aims to provide guidance to ecologists with limited experience in spatial analysis to help in their choice of techniques. It uses examples to compare methods of spatial analysis for ecological field data. A taxonomy of different data types is presented, including point- and area-referenced data, with and without attributes. Spatially and non-spatially explicit data are distinguished. The effects of sampling and other transformations that convert one data type to another are discussed; the possible loss of spatial information is considered. Techniques for analyzing spatial pattern, developed in plant ecology, animal ecology, landscape ecology, geostatistics and applied statistics are reviewed briefly and their overlap in methodology and philosophy noted. The techniques are categorized according to their output and the inferences that may be drawn from them, in a discursive style without formulae. Methods are compared for four case studies with field data covering a range of types. These are: 1) percentage cover of three shrubs along a line transect; 2) locations and volume of a desert plant in a 1 ha area; 3) a remotely-sensed spectral index and elevation from 105 km2 of a mountainous region; and 4) land cover from three rangeland types within 800 km2 of a coastal region. Initial approaches utilize mapping, frequency distributions and variance-mean indices. Analysis techniques we compare include: local quadrat variance, block quadrat variance, correlograms, variograms, angular correlation, directional variograms, wavelets, SADIE, nearest neighbour methods, Ripley's (t), and various landscape ecology metrics. Our advice to ecologists is to use simple visualization techniques for initial analysis, and subsequently to select methods that are appropriate for the data type and that answer their specific questions of interest. It is usually prudent to employ several different techniques.

Integrating GIS-based environmental data into evolutionary biology.

Abstract: Many evolutionary processes are influenced by environmental variation over space and time, including genetic divergence among populations, speciation and evolutionary change in morphology, physiology and behaviour. Yet, evolutionary biologists have generally not taken advantage of the extensive environmental data available from geographic information systems (GIS). For example, studies of phylogeography, speciation and character evolution often ignore or use only crude proxies for environmental variation (e.g. latitude and distance between populations). Here, we describe how the integration of GIS-based environmental data, along with new spatial tools, can transform evolutionary studies and reveal new insights into the ecological causes of evolutionary patterns.

The Automaticity of Affect for Political Leaders, Groups, and Issues: An Experimental Test of the Hot Cognition Hypothesis

Abstract: We report the results of three experimental tests of the “hot cognition” hypothesis, which posits that all sociopolitical concepts that have been evaluated in the past are affectively charged and that this affective charge is automatically activated within milliseconds on mere exposure to the concept, appreciably faster than conscious appraisal of the object. We find support for the automaticity of affect toward political leaders, groups, and issues; specifically: • Most Ss show significantly faster reaction times to affectively congruent political concepts and significantly slower response times to affectively incongruent concepts; • These facilitation and inhibition effects, which hold for a cross-section of political leaders, groups, and issues, are strongest for those with the strongest prior attitudes, with sophisticates showing the strongest effect on “harder” political issues. • Even semantically unrelated affective concepts (e.g., “sunshine,”“cancer”) have a strong effect on the evaluation of political leaders, groups, and issues. We conclude with a discussion of the “so what?” question—the conceptual, substantive, and normative implications of hot cognition for political judgments, evaluations, and choice. One clear expectation, given that affect appears to be activated automatically on mere exposure to sociopolitical concepts, is that most citizens, but especially those sophisticates with strong political attitudes, will be biased information processors.