We studied a graviton and massive symmetric rank-two tensor in string\ntheory, both of which carry spin two. A graviton is a massless spin-two\nparticle in closed string theory while a symmetric rank-two tensor is a massive\nparticle with spin two in open string theory. Using Polyakov's...
Statistical inference of the eigenspace components of a two-dimensional, symmetric rank-two ran- dom tensor, J. Geod., 78(7-8), 425-436.J.Q. Cai, E.W. Grafarend and B. Schaffrin, Statistical inference of the eigenspace components of a two-dimensional, symmetric rank-two random ten- ...
(3.41) A convenient parameterization for this symmetric, rank-two tensor is H(x + φ, y + ξ) = xi φi Gij − BikGklBlj BikGkj −Gik Bkj Gij yj . ξj (3.42) Plugging it into the action (3.40) and furthermore assuming that it just depends on the physical coordinates m but ...
Tensor[SymmetricProductsOfKillingTensors] - form all possible symmetric tensors of a given rank (linearly independent over the real numbers) from a list of symmetric tensors Calling Sequences SymmetricProducstOfKillingTensors( K , p , ptlist ) Parameters
Woo, Symmetric tensors: rank, Strassen's conjecture and e-computability, arXiv preprint:1506.03176, 2015.E. Carlini, M.V. Catalisano, L. Chiantini, A.V. Geramita, Y. Woo: Symmetric tensors: rank, Strassen's conjecture and e-computability, arXiv:1506.03176....
A symmetric traceless tensor of rank ℓℓ is (ℓ,ℓ)(ℓ,ℓ)(ℓ/2,ℓ/2)(ℓ/2,ℓ/2). If you want the reducible representation corresponding to a symmetric tensor, you have to add back the traces, which are lower rank tensors. So the representation is ⨁k=0ℓ(...
We show that each iteration of the Shifted Symmetric Higher Order Power Method (SS-HOPM), when applied to a rank-one symmetric tensor, moves towards the principal eigenvector for any input and shift parameter, under mild conditions. Finally, we explore the best choice of shift parameter for ...
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This paper characterizes the symmetric rank-2 stress–energy–momentum tensor associated with fields whose Lagrangian densities are expressed as the dot product of two multivector fields, e.g., scalar or gauge fields, in flat space–time. The tensor is derived by a direct application of exterior...
🚀 Feature Currently ReflectionPadNd does not repeat the boundary pixels, as demonstrated by the docs: >>> m = nn.ReflectionPad2d(2) >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3) >>> input tensor([[[0., 1., 2.], [3.,...