Projection approach for STAP

2003 ◽  
Author(s):  
S.U. Pillai ◽  
S.R. Pillai
Keyword(s):  
Projection Approach

A new model for shallow water in the low-Rossby-number limit

2002 ◽  
Vol 450 ◽  
pp. 287-296
Author(s):  
RUPERT FORD ◽  
SIMON J. A. MALHAM ◽  
MARCEL OLIVER
Keyword(s):  
Asymptotic Expansion ◽  
Shallow Water ◽  
Energy Functional ◽  
Positive Definite ◽  
Rossby Number ◽  
Computational Domain ◽  
Dirac Bracket ◽  
New Model ◽  
Projection Approach

We revisit Salmon's ‘Dirac bracket projection’ approach to constructing generalized semi-geostrophic equations. One of the obstacles to the method's applicability is that it leads to a sign-indefinite energy functional in the computational domain. In some instances this can cause severe failure of the model. We demonstrate in the simple context of shallow-water semi-geostrophy that the Hamiltonian remains positive definite when the asymptotic expansion at the heart of this method is carried to the next order. The resulting new model can be interpreted in the framework of regularization by Lagrangian averaging, which is currently receiving much attention.

scholarly journals A matrix projection approach for matching shapes under affine distortions

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhaoyang Zhang ◽  
Zheng Tian ◽  
Weiwei Cui
Keyword(s):  
Projection Approach

Use of the Orthogonal Projection Approach (OPA) to Monitor Batch Processes

2003 ◽  
Vol 57 (1) ◽  
pp. 80-87 ◽  
Author(s):  
S. Gourvénec ◽  
C. Lamotte ◽  
P. Pestiaux ◽  
D. L. Massart
Keyword(s):  
Orthogonal Projection ◽  
Spectroscopic Data ◽  
Batch Processes ◽  
Multivariate Curve Resolution ◽  
Concentration Profiles ◽  
Curve Resolution ◽  
On Line ◽  
Real Concentration ◽  
Projection Approach

The orthogonal projection approach (OPA) and multivariate curve resolution (MCR) are presented as a way to monitor batch processes using spectroscopic data. Curve resolution allows one to look within a batch and predict on-line real concentration profiles of the different species appearing during reactions. Taking into account the variations of the process by using an augmented matrix of complete batches, the procedure explained here calculates some prediction coefficients that can afterwards be applied for a new batch.

Orthogonal Projection Approach Applied to Peak Purity Assessment

1996 ◽  
Vol 68 (1) ◽  
pp. 79-85 ◽  
Author(s):  
F. Cuesta Sánchez ◽  
J. Toft ◽  
B. van den Bogaert ◽  
D. L. Massart
Keyword(s):  
Orthogonal Projection ◽  
Purity Assessment ◽  
Projection Approach ◽  
Peak Purity

scholarly journals Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications

2015 ◽  
Vol 41 (4) ◽  
pp. 625-664 ◽  
Author(s):  
Michael Roth ◽  
Anette Frank
Keyword(s):  
Computational Models ◽  
Current Model ◽  
Implicit Argument ◽  
Implicit Arguments ◽  
Argument Realization ◽  
Projection Approach ◽  
Implicit And Explicit ◽  
Graph Based Model ◽  
A Current ◽  
Sentential Meaning

In this article, we investigate aspects of sentential meaning that are not expressed in local predicate–argument structures. In particular, we examine instances of semantic arguments that are only inferable from discourse context. The goal of this work is to automatically acquire and process such instances, which we also refer to as implicit arguments, to improve computational models of language. As contributions towards this goal, we establish an effective framework for the difficult task of inducing implicit arguments and their antecedents in discourse and empirically demonstrate the importance of modeling this phenomenon in discourse-level tasks. Our framework builds upon a novel projection approach that allows for the accurate detection of implicit arguments by aligning and comparing predicate–argument structures across pairs of comparable texts. As part of this framework, we develop a graph-based model for predicate alignment that significantly outperforms previous approaches. Based on such alignments, we show that implicit argument instances can be automatically induced and applied to improve a current model of linking implicit arguments in discourse. We further validate that decisions on argument realization, although being a subtle phenomenon most of the time, can considerably affect the perceived coherence of a text. Our experiments reveal that previous models of coherence are not able to predict this impact. Consequently, we develop a novel coherence model, which learns to accurately predict argument realization based on automatically aligned pairs of implicit and explicit arguments.

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