نتایج جستجو برای: g manifold

تعداد نتایج: 468192  

Manifold learning is a dimension reduction method for extracting nonlinear structures of high-dimensional data. Many methods have been introduced for this purpose. Most of these methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds...

2009
HYUN HO LEE

We consider the Yang-Mills problem for a quantum Heisenberg manifold, which is a C∗-algebra defined by the (strict) deformation quantization of the ordinary Heisenberg manifold, in the setting of non-commutative differential geometry following Connes and Rieffel [Co] [Co1]. 1. Preliminaries Classical Yang-Mills theory is concerned with the set of connections (i.e. gauge potentials) on a vector ...

2008
EDWARD WITTEN

Geometric Langlands duality can be understood from statements of mirror symmetry that can be formulated in purely topological terms for an oriented two-manifold C. But understanding these statements is extremely difficult without picking a complex structure on C and using Hitchin’s equations. We sketch the essential statements both for the “unramified” case that C is a compact oriented two-mani...

2013
KEI NAKAMURA

If a closed smooth n-manifold M admits a finite cover M̂ whose Z/2Z-cohomology has the maximal cup-length, then for any riemannian metric g on M , we show that the systole Sys(M, g) and the volume Vol(M, g) of the riemannian manifold (M, g) are related by the following isosystolic inequality: Sys(M, g) n ≤ n!Vol(M, g). The inequality can be regarded as a generalization of Burago and Hebda’s ineq...

2010
Zhiqin Lu

1. Basic gradient estimate; different variations of the gradient estimates; 2. The theorem of Brascamp-Lieb, Barkey-Émery Riemannian geometry, relation of eigenvalue gap with respect to the first Neumann eigenvalue; the Friedlander-Solomayak theorem, 3. The definition of the Laplacian on L space, theorem of Sturm, 4. Theorem of Wang and its possible generalizations. 1 Gradient estimate of the f...

2000
Jerrold E. Marsden Matthew Perlmutter

Cotangent bundle reduction theory is a basic and well developed subject in which one performs symplectic reduction on cotangent bundles. One starts with a (free and proper) action of a Lie group G on a configuration manifold Q, considers its natural cotangent lift to T ∗Q and then one seeks realizations of the corresponding symplectic or Poisson reduced space. We further develop this theory by ...

2012
Sharief Deshmukh Falleh R. Al-Solamy

In this paper first it is proved that if ξ is a nontrivial closed conformal vector field on an n-dimensional compact Riemannian manifold (M, g) with constant scalar curvature S satisfying S ≤ λ1(n − 1), λ1 being first nonzero eigenvalue of the Laplacian operator ∆ on M and Ricci curvature in direction of a certain vector field is non-negative, then M is isometric to the n-sphere S(c), where S =...

2007
DANIEL C COHEN ALEXANDER I SUCIU Alexander I Suciu

We study the topology of the boundary manifold of a line arrangement in CP , with emphasis on the fundamental group G and associated invariants. We determine the Alexander polynomial ∆(G), and more generally, the twisted Alexander polynomial associated to the abelianization of G and an arbitrary complex representation. We give an explicit description of the unit ball in the Alexander norm, and ...

ژورنال: تحقیقات موتور 2019
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Design of exhaust manifold is important due to its effects on performance of catalyst, thermal and thermo- mechanical loads. In this paper, first transient analysis of fluid was carried out with FLUENT software, then time-average temperature and heat transfer coefficient contour for three cycle were mapped on inner surface of exhaust manifold with MATLAB software for thermomechanical analysis. ...

Journal: :CoRR 2011
Christopher R. Genovese Marco Perone-Pacifico Isabella Verdinelli Larry A. Wasserman

We find lower and upper bounds for the risk of estimating a man-ifold in Hausdorff distance under several models. We also show that there are close connections between manifold estimation and the problem of deconvolving a singular measure. 1. Introduction. Manifold learning is an area of intense research activity in machine learning and statistics. Yet a very basic question about manifold learn...

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