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Rotation of Linear Polarization Plane and Circular
, 802
"... Abstract. We show that a time evolving pseudoscalar field coupled to the electromagnetic tensor generates circular polarization and does not only rotate the plane of linear polarization. We compute analytically and numerically the propagation of the Stokes parameters from the last scattering surfac ..."
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Abstract. We show that a time evolving pseudoscalar field coupled to the electromagnetic tensor generates circular polarization and does not only rotate the plane of linear polarization. We compute analytically and numerically the propagation of the Stokes parameters from the last scattering
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n ..."
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Cited by 549 (6 self)
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as well as a large number of features N, while each example has only s << N nonzero features. This paper presents a CuttingPlane Algorithm for training linear SVMs that provably has training time O(sn) for classification problems and O(sn log(n)) for ordinal regression problems. The algorithm
Impulses and Physiological States in Theoretical Models of Nerve Membrane
 Biophysical Journal
, 1961
"... ABSTRACT Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of nonlinear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model " has two variables of state, representing excitabi ..."
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Cited by 505 (0 self)
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ABSTRACT Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of nonlinear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model " has two variables of state, representing
Matching pursuits with timefrequency dictionaries
 IEEE Transactions on Signal Processing
, 1993
"... AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures t ..."
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Cited by 1671 (13 self)
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AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures
Robust wide baseline stereo from maximally stable extremal regions
 In Proc. BMVC
, 2002
"... The widebaseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions, is introduced. Extremal regions possess highly desir ..."
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Cited by 1016 (35 self)
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sirable properties: the set is closed under 1. continuous (and thus projective) transformation of image coordinates and 2. monotonic transformation of image intensities. An efficient (near linear complexity) and practically fast detection algorithm (near frame rate) is presented for an affinelyinvariant stable
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete
A path independent integral and the approximate analysis of strain concentration by notches and cracks
, 1967
"... An integral is exhibited which has the same value for all paths surrounding a class of notches in twodimensional deformation fields of linear or nonlinear elastic materials. The integral may be evaluated almost by inspection for a few notch configurations. Also, for materials of the elasticplasti ..."
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Cited by 419 (11 self)
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An integral is exhibited which has the same value for all paths surrounding a class of notches in twodimensional deformation fields of linear or nonlinear elastic materials. The integral may be evaluated almost by inspection for a few notch configurations. Also, for materials of the elastic
The curvelet transform for image denoising
 IEEE TRANS. IMAGE PROCESS
, 2002
"... We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform [2] and the curvelet transform [6], [5]. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A cen ..."
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Cited by 404 (40 self)
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central tool is Fourierdomain computation of an approximate digital Radon transform. We introduce a very simple interpolation in Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudopolar sampling set based on a concentric squares geometry. Despite
CuttingPlane Training of Structural SVMs
, 2007
"... Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or i ..."
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Cited by 321 (10 self)
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or intractable on large datasets. To overcome this bottleneck, this paper explores how cuttingplane methods can provide fast training not only for classification SVMs, but also for structural SVMs. In particular, we show that in an equivalent “1slack” reformulation of the linear SVM training problem, our
Robust Linear Programming Discrimination Of Two Linearly Inseparable Sets
, 1992
"... INTRODUCTION We consider the two pointsets A and B in the ndimensional real space R n represented by the m \Theta n matrix A and the k \Theta n matrix B respectively. Our principal objective here is to formulate a single linear program with the following properties: (i) If the convex hulls of A ..."
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Cited by 239 (32 self)
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of A and B are disjoint, a strictly separating plane is obtained. (ii) If the convex hulls of A and B intersect, a plane is obtained that minimizes some measure of misclassification points, for all possible cases. (iii) No extraneous constraints are imposed on the linear program that rule out any
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