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Pressure Gradient Effects on Hypersonic Cavity Flow Heating
"... The effect of a pressure gradient on the local heating disturbance of rectangular cavities tested at hypersonic freestream conditions has been globally assessed using the twocolor phosphor thermography method. These experiments were conducted in the Langley 31Inch Mach 10 Tunnel and were initiated ..."
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The effect of a pressure gradient on the local heating disturbance of rectangular cavities tested at hypersonic freestream conditions has been globally assessed using the twocolor phosphor thermography method. These experiments were conducted in the Langley 31Inch Mach 10 Tunnel and were
CHAPTER 34 WIND/WAVE RELATION AND THE PRESSURE GRADIENT EFFECT
"... The S.M.B. prediction model is used worldwide to hindcast sea wave characteristics using wind data. Even though wave parameters showed that qualitative similarities are present between ocean and small basin waves, major weather factors such as the wind speed and barometer pressure gradients in relat ..."
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The S.M.B. prediction model is used worldwide to hindcast sea wave characteristics using wind data. Even though wave parameters showed that qualitative similarities are present between ocean and small basin waves, major weather factors such as the wind speed and barometer pressure gradients
Pegasos: Primal Estimated subgradient solver for SVM
"... We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
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Cited by 542 (20 self)
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We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS
, 1993
"... A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradientdescent, RPROP performs a local adaptation of the weightupdates according to the behaviour of the errorfunction. In substantial difference to other adaptive tech ..."
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Cited by 938 (34 self)
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A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradientdescent, RPROP performs a local adaptation of the weightupdates according to the behaviour of the errorfunction. In substantial difference to other adaptive
Inside the black box: Raising standards through classroom assessment
 Phi Delta Kappan
, 1998
"... Raising the standards of learning that are achieved through school education is an important national priority. Governments have been vigorous in the last ten years in making changes in pursuit of this aim. National curriculum testing, the development of the GCSE, league tables of school performance ..."
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Cited by 564 (7 self)
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performance, initiatives to improve school planning and management, target setting, more frequent and thorough inspection; these are all means to the end. But the sum of all of these doesn’t add up to an effective policy because something is missing. Learning is driven by what teachers and pupils do
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
 Proc. Natl. Acad. Sci. USA
, 1990
"... ABSTRACT Paramagnetic deoxyhemoglobin in venous blood is a naturally occurring contrast agent for magnetic resonance imaging (MRI). By accentuating the effects of this agent through the use of gradientecho techniques in high fields, we demonstrate in vivo images of brain microvasculature with imag ..."
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Cited by 648 (1 self)
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ABSTRACT Paramagnetic deoxyhemoglobin in venous blood is a naturally occurring contrast agent for magnetic resonance imaging (MRI). By accentuating the effects of this agent through the use of gradientecho techniques in high fields, we demonstrate in vivo images of brain microvasculature
Reconstruction and Representation of 3D Objects with Radial Basis Functions
 Computer Graphics (SIGGRAPH ’01 Conf. Proc.), pages 67–76. ACM SIGGRAPH
, 2001
"... We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from pointcloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs al ..."
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Cited by 505 (1 self)
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noninterpolating approximation when the data is noisy. The functional representation is in effect a solid model, which means that gradients and surface normals can be determined analytically. This helps generate uniform meshes and we show that the RBF representation has advantages for mesh
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural
Results 1  10
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