2,660 research outputs found
Integrable discretizations of a two-dimensional Hamiltonian system with a quartic potential
In this paper, we propose integrable discretizations of a two-dimensional
Hamiltonian system with quartic potentials. Using either the method of
separation of variables or the method based on bilinear forms, we construct the
corresponding integrable mappings for the first three among four integrable
cases
Discontinuous Galerkin approximation of linear parabolic problems with dynamic boundary conditions
In this paper we propose and analyze a Discontinuous Galerkin method for a
linear parabolic problem with dynamic boundary conditions. We present the
formulation and prove stability and optimal a priori error estimates for the
fully discrete scheme. More precisely, using polynomials of degree on
meshes with granularity along with a backward Euler time-stepping scheme
with time-step , we prove that the fully-discrete solution is bounded
by the data and it converges, in a suitable (mesh-dependent) energy norm, to
the exact solution with optimal order . The sharpness of the
theoretical estimates are verified through several numerical experiments
Image sets of fractional Brownian sheets
Let be an -fractional Brownian
sheet with Hurst index . The main objective of
the present paper is to study the Hausdorff dimension of the image sets
, and , in the dimension
case . Following the seminal work of
Kaufman (1989), we establish uniform dimensional properties on , answering
questions raised by Khoshnevisan et al (2006) and Wu and Xiao (2009).
For the purpose of this work, we introduce a refinement of the sectorial
local-nondeterminism property which can be of independent interest to the study
of other fine properties of fractional Brownian sheets.Comment: 14 pages, 1 figur
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Matrix factorization techniques have been widely used as a method for
collaborative filtering for recommender systems. In recent times, different
variants of deep learning algorithms have been explored in this setting to
improve the task of making a personalized recommendation with user-item
interaction data. The idea that the mapping between the latent user or item
factors and the original features is highly nonlinear suggest that classical
matrix factorization techniques are no longer sufficient. In this paper, we
propose a multilayer nonlinear semi-nonnegative matrix factorization method,
with the motivation that user-item interactions can be modeled more accurately
using a linear combination of non-linear item features. Firstly, we learn
latent factors for representations of users and items from the designed
multilayer nonlinear Semi-NMF approach using explicit ratings. Secondly, the
architecture built is compared with deep-learning algorithms like Restricted
Boltzmann Machine and state-of-the-art Deep Matrix factorization techniques. By
using both supervised rate prediction task and unsupervised clustering in
latent item space, we demonstrate that our proposed approach achieves better
generalization ability in prediction as well as comparable representation
ability as deep matrix factorization in the clustering task.Comment: version
Wigner crystal of a two-dimensional electron gas with a strong spin-orbit interaction
The Wigner-crystal phase of two-dimensional electrons interacting via the
Coulomb repulsion and subject to a strong Rashba spin-orbit coupling is
investigated. For low enough electronic densities the spin-orbit band splitting
can be larger than the zero-point energy of the lattice vibrations. Then the
degeneracy of the lower subband results in a spontaneous symmetry breaking of
the vibrational ground state. The rotational symmetry of the
triangular (spin-orbit coupling free) structure is lost, and the unit cell of
the new lattice contains two electrons. Breaking the rotational symmetry also
leads to a (slight) squeezing of the underlying triangular lattice.Comment: 5 pages + appendix, 3 figures, minor improvements to the tex
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