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The Berkeley FrameNet Project

by Collin F. Baker , Charles J. Fillmore, John B. Lowe - IN PROCEEDINGS OF THE COLING-ACL , 1998
"... FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year #NSF IRI-9618838, #Tools for Lexicon Building"#. The project's key features are #a# a commitment to corpus evidence for semantic and syntactic generalizations, and #b# the repr ..."
Abstract - Cited by 624 (3 self) - Add to MetaCart
FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year #NSF IRI-9618838, #Tools for Lexicon Building"#. The project's key features are #a# a commitment to corpus evidence for semantic and syntactic generalizations, and #b

Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

by Emmanuel J. Candès , Terence Tao , 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
Abstract - Cited by 1513 (20 self) - Add to MetaCart
Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear

Projection Pursuit Regression

by Jerome H. Friedman, Werner Stuetzle - Journal of the American Statistical Association , 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general- smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
Abstract - Cited by 555 (6 self) - Add to MetaCart
A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general- smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, does not require the definition of a metric in the predictor space, and lends itself to graphi-cal interpretation.

The TSIMMIS Project: Integration of Heterogeneous Information Sources

by Sudarshan Chawathe, Hector Garcia-Molina, Joachim Hammer, Kelly Ireland, Yannis Papakonstantinou, Jeffrey Ullman, Jennifer Widom
"... The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects, ..."
Abstract - Cited by 534 (19 self) - Add to MetaCart
The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects

Distributed Computing in Practice: The Condor Experience

by Douglas Thain, Todd Tannenbaum, Miron Livny - Concurrency and Computation: Practice and Experience , 2005
"... Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history ..."
Abstract - Cited by 542 (7 self) - Add to MetaCart
Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide

The Digital Michelangelo Project: 3D Scanning of Large Statues

by Marc Levoy, Szymon Rusinkiewicz, Brian Curless, Matt Ginzton, Jeremy Ginsberg, Kari Pulli, David Koller, Sean Anderson, Jonathan Shade, Lucas Pereira, James Davis, Duane Fulk , 2000
"... We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merg ..."
Abstract - Cited by 488 (8 self) - Add to MetaCart
is of the David - 2 billion polygons and 7,000 color images. In this paper, we discuss the challenges we faced in building this system, the solutions we employed, and the lessons we learned. We focus in particular on the unusual design of our laser triangulation scanner and on the algorithms and software we

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 664 (14 self) - Add to MetaCart
the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques

Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes

by Jean-paul Ryckaert, Giovanni Ciccotti, Herman J. C. Berendsen - J. Comput. Phys , 1977
"... A numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated. The relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration. The method ..."
Abstract - Cited by 682 (6 self) - Add to MetaCart
(MD), which has been widely used in the past for studying simple liquids and solids, has more recently been applied to molecular systems with internal degrees of freedom such as N, [l], H,O [2] and even C,H,, [3]. In applying the MD method three problems arise: (a) the choice of a suitable mechanical

Limma: linear models for microarray data

by Gordon K. Smyth, Matthew Ritchie, Natalie Thorne, James Wettenhall, Wei Shi - Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005
"... This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
Abstract - Cited by 759 (13 self) - Add to MetaCart
This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents

Bid, ask and transaction prices in a specialist market with heterogeneously informed traders

by Lawrence R. Glosten, Paul R. Milgrom - Journal of Financial Economics , 1985
"... The presence of traders with superior information leads to a positive bid-ask spread even when the specialist is risk-neutral and makes zero expected profits. The resulting transaction prices convey information, and the expectation of the average spread squared times volume is bounded by a number th ..."
Abstract - Cited by 1217 (5 self) - Add to MetaCart
that is independent of insider activity. The serial correlation of transaction price dif-ferences is a function of the proportion of the spread due to adverse selection. A bid-ask spread implies a divergence between observed returns and realizable returns. Observed returns are approximately realizable returns plus
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