q-Deformation of the Krichever-Novikov Algebra

The recent focus on deformations of algebras called quantum algebras can be attributed to the fact that they appear to be the basic algebraic structures underlying an amazingly diverse set of physical situations. To date many interesting features of these algebras have been found and they are now known to belong to a class of algebras called Hopf algebras [1]. The remarkable aspect of these structures is that they can be regarded as deformations of the usual Lie algebras. Of late, there has been a considerable interest in the deformation of the Virasoro algebra and the underlying Heisenberg algebra [2-11]. In this letter we focus our attention on deforming generalizations of these algebras, namely the Krichever-Novikov (KN) algebra and its associated Heisenberg algebra.Comment: AmsTex. To appear in Letters in Mathematical Physic

Klein tunneling through an oblique barrier in graphene ribbons

We study a transmission coefficient of graphene nanoribbons with a top gate which acts as an oblique barrier. Using a Green function method based on the Dirac-like equation, scattering among transverse modes due to the oblique barrier is taken into account numerically. In contrast to the 2-dimensional graphene sheet, we find that the pattern of transmission in graphene ribbons depends strongly on the electronic structure in the region of the barrier. Consequently, irregular structures in the transmission coefficient are predicted while perfect transmission is still calculated in the case of metallic graphene independently of angle and length of the oblique barrier

A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion

We consider the problem of reconstructing a low-rank matrix from a small subset of its entries. In this paper, we describe the implementation of an efficient algorithm called OptSpace, based on singular value decomposition followed by local manifold optimization, for solving the low-rank matrix completion problem. It has been shown that if the number of revealed entries is large enough, the output of singular value decomposition gives a good estimate for the original matrix, so that local optimization reconstructs the correct matrix with high probability. We present numerical results which show that this algorithm can reconstruct the low rank matrix exactly from a very small subset of its entries. We further study the robustness of the algorithm with respect to noise, and its performance on actual collaborative filtering datasets.Comment: 26 pages, 15 figure