Institution
Indiana University
Education•Bloomington, Indiana, United States•
About: Indiana University is a education organization based out in Bloomington, Indiana, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 64480 authors who have published 150058 publications receiving 6392902 citations. The organization is also known as: Indiana University system & indiana.edu.
Topics: Population, Poison control, Health care, Transplantation, Cancer
Papers published on a yearly basis
Papers
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2,785 citations
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TL;DR: In this paper, the economics of small business finance in private equity and debt markets are examined. But the authors focus on the macroeconomic environment and do not consider the impact of the macro economic environment on small business.
Abstract: This article examines the economics of financing small business in private equity and debt markets. Firms are viewed through a financial growth cycle paradigm in which different capital structures are optimal at different points in the cycle. We show the sources of small business finance, and how capital structure varies with firm size and age. The interconnectedness of small firm finance is discussed along with the impact of the macroeconomic environment. We also analyze a number of research and policy issues, review the literature, and suggest topics for future research.
2,778 citations
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TL;DR: Although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
Abstract: In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks—including their spatial statistics and their persistence across time—can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
2,771 citations
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TL;DR: In this paper, the most important experimental results from the first three years of nucleus-nucleus collision studies at RHIC were reviewed, with emphasis on results of the STAR experiment.
2,750 citations
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TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.
2,741 citations
Authors
Showing all 64884 results
Name | H-index | Papers | Citations |
---|---|---|---|
Frank B. Hu | 250 | 1675 | 253464 |
Stuart H. Orkin | 186 | 715 | 112182 |
Bruce M. Spiegelman | 179 | 434 | 158009 |
David R. Williams | 178 | 2034 | 138789 |
D. M. Strom | 176 | 3167 | 194314 |
Markus Antonietti | 176 | 1068 | 127235 |
Lei Jiang | 170 | 2244 | 135205 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Nahum Sonenberg | 167 | 647 | 104053 |
Carl W. Cotman | 165 | 809 | 105323 |
Yang Yang | 164 | 2704 | 144071 |
Jaakko Kaprio | 163 | 1532 | 126320 |
Ralph A. DeFronzo | 160 | 759 | 132993 |
Gavin Davies | 159 | 2036 | 149835 |
Tyler Jacks | 158 | 463 | 115172 |