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Institution

Oklahoma State University–Stillwater

EducationStillwater, Oklahoma, United States
About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.


Papers
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Journal ArticleDOI
TL;DR: This paper examined political polarization over climate change within the American public by analyzing data from 10 nationally representative Gallup Polls between 2001 and 2010 and found that liberals and Democrats are more likely to report beliefs consistent with the scientific consensus and express personal concern about global warming than are conservatives and Republicans.
Abstract: We examine political polarization over climate change within the American public by analyzing data from 10 nationally representative Gallup Polls between 2001 and 2010. We find that liberals and Democrats are more likely to report beliefs consistent with the scientific consensus and express personal concern about global warming than are conservatives and Republicans. Further, the effects of educational attainment and self-reported understanding on global warming beliefs and concern are positive for liberals and Democrats, but are weaker or negative for conservatives and Republicans. Last, significant ideological and partisan polarization has occurred on the issue of climate change over the past decade.

1,681 citations

Journal ArticleDOI
26 Oct 2006-Nature
TL;DR: The genome sequence of the honeybee Apis mellifera is reported, suggesting a novel African origin for the species A. melliferA and insights into whether Africanized bees spread throughout the New World via hybridization or displacement.
Abstract: Here we report the genome sequence of the honeybee Apis mellifera, a key model for social behaviour and essential to global ecology through pollination. Compared with other sequenced insect genomes, the A. mellifera genome has high A+T and CpG contents, lacks major transposon families, evolves more slowly, and is more similar to vertebrates for circadian rhythm, RNA interference and DNA methylation genes, among others. Furthermore, A. mellifera has fewer genes for innate immunity, detoxification enzymes, cuticle-forming proteins and gustatory receptors, more genes for odorant receptors, and novel genes for nectar and pollen utilization, consistent with its ecology and social organization. Compared to Drosophila, genes in early developmental pathways differ in Apis, whereas similarities exist for functions that differ markedly, such as sex determination, brain function and behaviour. Population genetics suggests a novel African origin for the species A. mellifera and insights into whether Africanized bees spread throughout the New World via hybridization or displacement.

1,673 citations

Journal ArticleDOI
TL;DR: Fast algorithms for computing persistence and experimental evidence for their speed and utility are given for topological simplification within the framework of a filtration, which is the history of a growing complex.
Abstract: We formalize a notion of topological simplification within the framework of a filtration, which is the history of a growing complex. We classify a topological change that happens during growth as either a feature or noise depending on its lifetime or persistence within the filtration. We give fast algorithms for computing persistence and experimental evidence for their speed and utility.

1,671 citations

Journal ArticleDOI
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as mentioned in this paper provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.

1,656 citations

Journal ArticleDOI
TL;DR: A quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies, local luminance and contrast masking and changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images.
Abstract: The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.

1,651 citations


Authors

Showing all 18403 results

NameH-indexPapersCitations
Gerald I. Shulman164579109520
James M. Tiedje150688102287
Robert J. Sternberg149106689193
Josh Moss139101989255
Brad Abbott137156698604
Itsuo Nakano135153997905
Luis M. Liz-Marzán13261661684
Flera Rizatdinova130124289525
Bernd Stelzer129120981931
Alexander Khanov129121987089
Dugan O'Neil128100080700
Michel Vetterli12890176064
Josu Cantero12684673616
Nicholas A. Kotov12357455210
Wei Chen122194689460
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202336
2022254
20211,902
20201,780
20191,633
20181,529