352,388 research outputs found

    Neel Order and Electron Spectral Functions in the Two-Dimensional Hubbard Model: a Spin-Charge Rotating Frame Approach

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    Using recently developed quantum SU(2)xU(1) rotor approach, that provides a self-consistent treatment of the antiferromagnetic state we have performed electronic spectral function calculations for the Hubbard model on the square lattice. The collective variables for charge and spin are isolated in the form of the space-time fluctuating U(1) phase field and rotating spin quantization axis governed by the SU(2) symmetry, respectively. As a result interacting electrons appear as composite objects consisting of bare fermions with attached U(1) and SU(2) gauge fields. This allows us to write the fermion Green's function in the space-time domain as the product CP^1 propagator resulting from the SU(2) gauge fields, U(1) phase propagator and the pseudo-fermion correlation function. As a result the problem of calculating the spectral line shapes now becomes one of performing the convolution of spin, charge and pseudo-fermion Green's functions. The collective spin and charge fluctuations are governed by the effective actions that are derived from the Hubbard model for any value of the Coulomb interaction. The emergence of a sharp peak in the electron spectral function in the antiferromagnetic state indicates the decay of the electron into separate spin and charge carrying particle excitations.Comment: 16 pages, 5 figures, submitted to Phys. Rev.

    Geodesic boundary value problems with symmetry

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    This paper shows how left and right actions of Lie groups on a manifold may be used to complement one another in a variational reformulation of optimal control problems equivalently as geodesic boundary value problems with symmetry. We prove an equivalence theorem to this effect and illustrate it with several examples. In finite-dimensions, we discuss geodesic flows on the Lie groups SO(3) and SE(3) under the left and right actions of their respective Lie algebras. In an infinite-dimensional example, we discuss optimal large-deformation matching of one closed curve to another embedded in the same plane. In the curve-matching example, the manifold \Emb(S^1, \mathbb{R}^2) comprises the space of closed curves S1S^1 embedded in the plane R2\mathbb{R}^2. The diffeomorphic left action \Diff(\mathbb{R}^2) deforms the curve by a smooth invertible time-dependent transformation of the coordinate system in which it is embedded, while leaving the parameterisation of the curve invariant. The diffeomorphic right action \Diff(S^1) corresponds to a smooth invertible reparameterisation of the S1S^1 domain coordinates of the curve. As we show, this right action unlocks an important degree of freedom for geodesically matching the curve shapes using an equivalent fixed boundary value problem, without being constrained to match corresponding points along the template and target curves at the endpoint in time.Comment: First version -- comments welcome

    Human action recognition using local spatiotemporal discriminant embedding

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    Human action video sequences can be considered as nonlinear dynamic shape manifolds in the space of image frames. In this paper, we address learning and classifying human actions on embedded low-dimensional manifolds. We propose a novel manifold embedding method, called Local Spatio-Temporal Discriminant Embedding (LSTDE). The discriminating capabilities of the proposed method are two-fold: (1) for local spatial discrimination, LSTDE projects data points (silhouette-based image frames of human action sequences) in a local neighborhood into the embedding space where data points of the same action class are close while those of different classes are far apart; (2) in such a local neighborhood, each data point has an associated short video segment, which forms a local temporal subspace on the embedded manifold. LSTDE finds an optimal embedding which maximizes the principal angles between those temporal subspaces associated with data points of different classes. Benefiting from the joint spatio-temporal discriminant embedding, our method is potentially more powerful for classifying human actions with similar space-time shapes, and is able to perform recognition on a frame-byframe or short video segment basis. Experimental results demonstrate that our method can accurately recognize human actions, and can improve the recognition performance over some representative manifold embedding methods, especially on highly confusing human action types. 1

    Computer Aided Design of Side Actions for Injection Molding of Complex Parts

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    Often complex molded parts include undercuts, patches on the part boundaries that are not accessible along the main mold opening directions. Undercuts are molded by incorporating side actions in the molds. Side actions are mold pieces that are removed from the part using translation directions different than the main mold opening direction. However, side actions contribute to mold cost by resulting in an additional manufacturing and assembly cost as well as by increasing the molding cycle time. Therefore, generating shapes of side actions requires solving a complex geometric optimization problem. Different objective functions may be needed depending upon different molding scenarios (e.g., prototyping versus large production runs). Manually designing side actions is a challenging task and requires considerable expertise. Automated design of side actions will significantly reduce mold design lead times. This thesis describes algorithms for generating shapes of side actions to minimize a customizable molding cost function. Given a set of undercut facets on a polyhedral part and the main parting direction, the approach works in the following manner. First, candidate retraction space is computed for every undercut facet. This space represents the candidate set of translation vectors that can be used by the side action to completely disengage from the undercut facet. As the next step, a discrete set of feasible, non-dominated retractions is generated. Then the undercut facets are grouped into undercut regions by performing state space search over such retractions. This search step is performed by minimizing the customizable molding cost function. After identifying the undercut regions that can share a side action, the shapes of individual side actions are computed. The approach presented in this work leads to practically an optimal solution if every connected undercut region on the part requires three or fewer side actions. Results of computational experiments that have been conducted to assess the performance of the algorithms described in the thesis have also been presented. Computational results indicate that the algorithms have acceptable computational performance, are robust enough to handle complex part geometries, and are easy to implement. It is anticipated that the results shown here will provide the foundations for developing fully automated software for designing side actions in injection molding
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