scispace - formally typeset
Search or ask a question
Institution

North Carolina State University

EducationRaleigh, North Carolina, United States
About: North Carolina State University is a education organization based out in Raleigh, North Carolina, United States. It is known for research contribution in the topics: Population & Thin film. The organization has 44161 authors who have published 101744 publications receiving 3456774 citations. The organization is also known as: NCSU & North Carolina State University at Raleigh.


Papers
More filters
Journal ArticleDOI
TL;DR: The theory-guided data science (TGDS) paradigm as mentioned in this paper is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery.
Abstract: Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

532 citations

Proceedings ArticleDOI
24 Apr 2000
TL;DR: The notion of a trust negotiation strategy is introduced and examined with respect to an abstract model of trust negotiation, and a language of credential expressions is presented.
Abstract: Distributed software subjects face the problem of determining one another's trustworthiness. The problem considered is managing the exchange of credentials between strangers for the purpose of property-based authentication and authorization when credentials are sensitive. An architecture for trust negotiation between client and server is presented. The notion of a trust negotiation strategy is introduced and examined with respect to an abstract model of trust negotiation. Two strategies with very different properties are defined and analyzed. A language of credential expressions is presented, with two example negotiations illustrating the two negotiation strategies. Ongoing work on policies governing credential disclosure and trust negotiation is summarized.

531 citations

Proceedings ArticleDOI
TL;DR: A new class of multidimensional representation systems, called shearlets, obtained by applying the actions of dilation, shear transformation and translation to a fixed function, and exhibit the geometric and mathematical properties, e.g., directionality, elongated shapes, scales, oscillations are described.
Abstract: In this paper we describe a new class of multidimensional representation systems, called shearlets. They are obtained by applying the actions of dilation, shear transformation and translation to a fixed function, and exhibit the geometric and mathematical properties, e.g., directionality, elongated shapes, scales, oscillations, recently advocated by many authors for sparse image processing applications. These systems can be studied within the framework of a generalized multiresolution analysis. This approach leads to a recursive algorithm for the implementation of these systems, that generalizes the classical cascade algorithm.

530 citations

Journal ArticleDOI
TL;DR: In this paper, the authors evaluate daily price linkages among four corn and four soybean markets in North Carolina and find strong support for market integration, though adjustments following shocks may take many days to be complete.
Abstract: A large body of research has evaluated price linkages in spatially separate markets. Much recent research has applied models appropriate for nonstationary data. Such analyses have been criticized for their ignorance of transactions costs, which may inhibit price adjustments and thus affect tests of integration. This analysis utilizes threshold autoregression and cointegration models to account for a neutral band representing transactions costs. We evaluate daily price linkages among four corn and four soybean markets in North Carolina. Nonlinear impulse response functions are used to investigate dynamic patterns of adjustments to shocks. Our results confirm the presence of thresholds and indicate strong support for market integration, though adjustments following shocks may take many days to be complete. In every case, the threshold models suggest much faster adjustments in response to deviations from equilibrium than is the case when threshold behavior is ignored. A large amount of empirical research has evaluated the extent to which spatially separate markets are integrated. Though the term is used loosely in the literature, tests of “market integration” usually consider the extent to which shocks are transmitted among spatially separate markets. The integration of markets can have important implications for price discovery and the operation of the market since persistent deviations from integration may imply riskless profit opportunities for spatial traders.

529 citations


Authors

Showing all 44525 results

NameH-indexPapersCitations
Yi Cui2201015199725
Jing Wang1844046202769
Rodney S. Ruoff164666194902
Carlos Bustamante161770106053
David W. Johnson1602714140778
Joseph Wang158128298799
David Tilman158340149473
Jay Hauser1552145132683
James M. Tour14385991364
Joseph T. Hupp14173182647
Bin Liu138218187085
Rudolph E. Tanzi13563885376
Richard C. Boucher12949054509
David B. Allison12983669697
Robert W. Heath128104973171
Network Information
Related Institutions (5)
University of Illinois at Urbana–Champaign
225.1K papers, 10.1M citations

96% related

Pennsylvania State University
196.8K papers, 8.3M citations

95% related

University of Maryland, College Park
155.9K papers, 7.2M citations

94% related

University of California, Davis
180K papers, 8M citations

94% related

Cornell University
235.5K papers, 12.2M citations

94% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023160
2022652
20215,262
20205,459
20194,888
20184,522