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Institution

University of North Carolina at Charlotte

EducationCharlotte, North Carolina, United States
About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.


Papers
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Journal ArticleDOI
TL;DR: The results show that the perception of norms mediate the relationship between SOVC and (a) observing and publicly exchanging support, (b) perceiving that others know one's identity, and using technical features to learn and create identity.

198 citations

Journal ArticleDOI
TL;DR: In this paper, the authors discuss the need for community-based organizations to provide evaluation information to government, foundations, and others, but the demand for this information may be increasing, although the field of evaluation may not be increasing.
Abstract: Increasingly, government, foundations, and others are asking community-based organizations for more evaluation information. Although the demand for this information may be increasing, the field kno...

197 citations

Journal ArticleDOI
TL;DR: The polymerase chain reaction was utilized to detect V. vulnificus DNA, thus eliminating the problem of nonculturability and possible reasons for the difficulties encountered in amplifying DNA from nonculturable cells, e.g., gene rearrangement or loss of the hemolysin gene are discussed.
Abstract: Vibrio vulnificus is a human pathogen associated with consumption of raw oysters. During the colder months the organism apparently enters a viable but nonculturable state and thus cannot be cultured by ordinary bacteriological methods. For this reason, another means of detecting this bacterium is necessary. In the present study we utilized the polymerase chain reaction (PCR) to detect V. vulnificus DNA, thus eliminating the problem of nonculturability. DNA from both culturable and nonculturable cells of V. vulnificus was amplified by PCR with primers flanking a 340-bp fragment of the cytotoxin-hemolysin gene. As little as 72 pg of DNA from culturable cells and 31 ng of DNA from nonculturable cells could be detected. Fifty cycles of a two-step reaction (30 s [each] at 94 and 65 degrees C) were found to be optimal as well as more time efficient than the three-step PCR. The total procedure from the point of DNA extraction to observation on a gel required less than 8 h. Possible reasons for the difficulties encountered in amplifying DNA from nonculturable cells, e.g., gene rearrangement or loss of the hemolysin gene, are discussed. Images

197 citations

Journal ArticleDOI
TL;DR: The value of an IT infrastructure depends on its use in an organizational context, and a relatively simple approach to understanding and assessing the value of IT infrastructure investments is presented, based on the asset valuation literature in finance.
Abstract: Information technology (IT) infrastructure investments are an extremely important part of e-business and constitute a major portion of IT investments in many organizations. IT infrastructure investments include investments in connectivity, systems integration, and data storage that may be used by multiple applications. Prior research has recognized the importance of a flexible IT infrastructure as a source of competitive advantage. Evidence regarding the value of IT infrastructures is anecdotal, and there is a realization that large investments in IT infrastructures are often difficult to justify. This paper expands on the idea that the value of an IT infrastructure depends on its use in an organizational context, and presents a relatively simple approach to understanding and assessing the value of IT infrastructure investments. This approach is based on the asset valuation literature in finance. An example is provided to illustrate the proposed approach, and managerial implications are discussed.

197 citations

Proceedings ArticleDOI
01 Jun 2016
TL;DR: The proposed multitask learning framework significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance and to model the multi-level relevance, label structures such as hierarchy or shared attributes are seamlessly embedded into the framework by generalizing the triplet loss.
Abstract: Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-grained image classification, which aims to differentiate subtle differences among subordinate classes. However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e.g., discovering cars from the same make or the same model, both of which require high precision. In this paper, we propose two main contributions to tackle this problem. 1) A multitask learning framework is designed to effectively learn fine-grained feature representations by jointly optimizing both classification and similarity constraints. 2) To model the multi-level relevance, label structures such as hierarchy or shared attributes are seamlessly embedded into the framework by generalizing the triplet loss. Extensive and thorough experiments have been conducted on three finegrained datasets, i.e., the Stanford car, the Car-333, and the food datasets, which contain either hierarchical labels or shared attributes. Our proposed method has achieved very competitive performance, i.e., among state-of-the-art classification accuracy when not using parts. More importantly, it significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance.

197 citations


Authors

Showing all 8936 results

NameH-indexPapersCitations
Chao Zhang127311984711
E. Magnus Ohman12462268976
Staffan Kjelleberg11442544414
Kenneth L. Davis11362261120
David Wilson10275749388
Michael Bauer100105256841
David A. B. Miller9670238717
Ashutosh Chilkoti9541432241
Chi-Wang Shu9352956205
Gang Li9348668181
Tiefu Zhao9059336856
Juan Carlos García-Pagán9034825573
Denise C. Park8826733158
Santosh Kumar80119629391
Chen Chen7685324974
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202361
2022231
20211,470
20201,561
20191,489
20181,318