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Network Centric Warfare: Developing and Leveraging Information Superiority
 Command and Control Research Program (CCRP), US DoD
, 2000
"... the mission of improving DoD’s understanding of the national security implications of the Information Age. Focusing upon improving both the state of the art and the state of the practice of command and control, the CCRP helps DoD take full advantage of the opportunities afforded by emerging technolo ..."
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Cited by 308 (5 self)
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the mission of improving DoD’s understanding of the national security implications of the Information Age. Focusing upon improving both the state of the art and the state of the practice of command and control, the CCRP helps DoD take full advantage of the opportunities afforded by emerging technologies. The CCRP pursues a broad program of research and analysis in information superiority, information operations, command and control theory, and associated operational concepts that enable us to leverage shared awareness to improve the effectiveness and efficiency of assigned missions. An important aspect of the CCRP program is its ability to serve as a bridge between the operational, technical, analytical, and educational communities. The CCRP provides leadership for the command and control research community by: n n
Lowrank matrix recovery via efficient schatten pnorm minimization
 In AAAI
, 2012
"... As an emerging machine learning and information retrieval technique, the matrix completion has been successfully applied to solve many scientific applications, such as collaborative prediction in information retrieval, video completion in computer vision, etc. The matrix completion is to recover a ..."
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Cited by 9 (4 self)
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a lowrank matrix with a fraction of its entries arbitrarily corrupted. Instead of solving the popularly used trace norm or nuclear norm based objective, we directly minimize the original formulations of trace norm and rank norm. We propose a novel Schatten pNorm optimization framework
Interpolation and approximation in Taylor spaces
, 2009
"... Abstract: The univariate Taylor formula without remainder allows to reproduce a function completely from certain derivative values. Thus one can look for Hilbert spaces in which the Taylor formula acts as a reproduction formula. It turns out that there are many Hilbert spaces which allow this, and ..."
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Cited by 2 (1 self)
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Abstract: The univariate Taylor formula without remainder allows to reproduce a function completely from certain derivative values. Thus one can look for Hilbert spaces in which the Taylor formula acts as a reproduction formula. It turns out that there are many Hilbert spaces which allow this
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 231 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
Approximating Matrix pnorms
"... We consider the problem of computing the q ↦ → p norm of a matrix A, which is defined for p, q ≥ 1, as ‖Ax‖p ‖A‖q↦→p = max. x̸=⃗0 ‖x‖q This is in general a nonconvex optimization problem, and is a natural generalization of the wellstudied question of computing singular values (this corresponds to ..."
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Cited by 6 (0 self)
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We consider the problem of computing the q ↦ → p norm of a matrix A, which is defined for p, q ≥ 1, as ‖Ax‖p ‖A‖q↦→p = max. x̸=⃗0 ‖x‖q This is in general a nonconvex optimization problem, and is a natural generalization of the wellstudied question of computing singular values (this corresponds
Robust Matrix Completion via Joint Schatten
"... Abstract—The lowrank matrix completion problem is a fundamental machine learning problem with many important applications. The standard lowrank matrix completion methods relax the rank minimization problem by the trace norm minimization. However, this relaxation may make the solution seriously de ..."
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Cited by 3 (1 self)
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Abstract—The lowrank matrix completion problem is a fundamental machine learning problem with many important applications. The standard lowrank matrix completion methods relax the rank minimization problem by the trace norm minimization. However, this relaxation may make the solution seriously
NUMERICAL TAYLOR EXPANSIONS FOR INVARIANT MANIFOLDS
"... Abstract: We consider numerical computation of Taylor expansions of invariant manifolds around equilibria of maps and °ows. These are obtained by writing the corresponding functional equation in a number of points, setting up a nonlinear system of equations and solving that using a simpli¯ed Newton& ..."
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Cited by 1 (0 self)
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Abstract: We consider numerical computation of Taylor expansions of invariant manifolds around equilibria of maps and °ows. These are obtained by writing the corresponding functional equation in a number of points, setting up a nonlinear system of equations and solving that using a simpli¯ed Newton
Taylor expansion for an operator function
, 2004
"... A simple version for the extension of the Taylor theorem to the operator functions was found. The expansion was done with respect to a value given by a diagonal matrix for the noncommutative case, and the coefficients are given both by recurrence relations and Cauchy integrals. In Quantum Physics, ..."
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A simple version for the extension of the Taylor theorem to the operator functions was found. The expansion was done with respect to a value given by a diagonal matrix for the noncommutative case, and the coefficients are given both by recurrence relations and Cauchy integrals. In Quantum Physics
POWERS OF GENERATORS AND TAYLOR EXPANSIONS OF INTEGRATED SEMIGROUPS OF OPERATORS
"... Abstract. Let A be the generator of an ntimes integrated semigroup T (·) and let r ∈ N. We first prove the equivalence of Riemann, Peano, and Taylor operators, which are three different expressions of the rth power of A1, the part of A in the closure of the domain D(A) of A. Then we discuss optima ..."
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optimal and nonoptimal rates of approximation of T (·)x for x ∈ D(A r−1 1), via the (n + r)th Taylor expansion of T (·) in terms of Ak 1, k =0,...,r − 1. 1.
Stochastic Taylor expansions . . .
, 2009
"... These notes focus on the applications of the stochastic Taylor expansion of solutions of stochastic differential equations to the study of heat kernels in small times. As an illustration of these methods we provide a new heat kernel proof of the ChernGaussBonnet theorem. ..."
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These notes focus on the applications of the stochastic Taylor expansion of solutions of stochastic differential equations to the study of heat kernels in small times. As an illustration of these methods we provide a new heat kernel proof of the ChernGaussBonnet theorem.
Results 1  10
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