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Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 727 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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write CS(t) = CS(V t , r t , {X t }), where V is firm value, r is the spot rate, and {X t } represents all of the other "state variables" needed to specify the model. 6 Since credit spreads are uniquely determined given the current values of the state variables, it follows that credit spread
A steepest descent method for oscillatory Riemann–Hilbert problems: asymptotics for the MKdV equation
 Ann. of Math
, 1993
"... In this announcement we present a general and new approach to analyzing the asymptotics of oscillatory RiemannHilbert problems. Such problems arise, in particular, in evaluating the longtime behavior of nonlinear wave equations solvable by the inverse scattering method. We will restrict ourselves ..."
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Cited by 303 (27 self)
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for the MKdV equation leads to a RiemannHilbert factorization problem for a 2 × 2 matrix valued function m = m(·; x, t) analytic in C\R, (1) where m+(z) = m−(z)vx,t, z ∈ R, m(z) → I as z → ∞, m±(z) = lim ε↓0 m(z ± iε; x, t), vx,t(z) ≡ e −i(4tz3 +xz)σ3 v(z)e i(4tz 3 +xz)σ3, σ3 =
Performance persistence
 Journal of Finance
, 1995
"... Most optimizationbased decision support systems are used repeatedly with only modest changes to input data from scenario to scenario. Unfortunately, optimization (mathematical programming) has a welldeserved reputation for amplifying small input changes into drastically different solutions. A prev ..."
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Cited by 325 (12 self)
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of persistence with respect to previous values of variables, constraints, or even exogenous considerations. We use case studies to highlight how modeling with persistence has improved managerial acceptance and describe how to incorporate persistence as an intrinsic feature of any optimization model. T
Pyramidal implementation of the Lucas Kanade feature tracker
 Intel Corporation, Microprocessor Research Labs
, 2000
"... grayscale value of the two images are the location x = [x y] T, where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or ..."
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Cited by 308 (0 self)
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grayscale value of the two images are the location x = [x y] T, where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or
The Nature of Theory in Information Systems
 MIS Quarterly
, 2006
"... The aim of this research essay is to examine the structural nature of theory in information systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanatio ..."
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Cited by 289 (6 self)
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are distinguished: (i) theory for analysing; (ii) theory for explaining, (iii) theory for predicting; (iv) theory for explaining and predicting; and (v) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample
A bilinear estimate with applications to the KdV equation
 J. Amer. Math. Soc
, 1996
"... In this article we continue our study of the initial value problem (IVP) for the Kortewegde Vries (KdV) equation with data in the classical Sobolev space Hs(R). Thus we consider ..."
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Cited by 209 (10 self)
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In this article we continue our study of the initial value problem (IVP) for the Kortewegde Vries (KdV) equation with data in the classical Sobolev space Hs(R). Thus we consider
Optimal investment, growth options, and security returns
 Journal of Finance
, 1999
"... As a consequence of optimal investment choices, a firm’s assets and growth options change in predictable ways. Using a dynamic model, we show that this imparts predictability to changes in a firm’s systematic risk, and its expected return. Simulations show that the model simultaneously reproduces: ~ ..."
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Cited by 246 (10 self)
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: ~i! the timeseries relation between the booktomarket ratio and asset returns; ~ii! the crosssectional relation between booktomarket, market value, and return; ~iii! contrarian effects at short horizons; ~iv! momentum effects at longer horizons; and ~v! the inverse relation between interest
Generalized Arc Consistency for Global Cardinality Constraint
"... A global cardinality constraint (gcc) is specified in terms of a set of variables X = fx1 ; :::; xpg which take their values in a subset of V = fv1 ; :::; vdg. It constrains the number of times a value v i 2 V is assigned toavariable in X to be in an interval (l i ;c i ). Cardinality constraints hav ..."
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Cited by 204 (11 self)
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A global cardinality constraint (gcc) is specified in terms of a set of variables X = fx1 ; :::; xpg which take their values in a subset of V = fv1 ; :::; vdg. It constrains the number of times a value v i 2 V is assigned toavariable in X to be in an interval (l i ;c i ). Cardinality constraints
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