14,033 research outputs found

    Which Constituent Quark Model Is Better?

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    A comparative study has been done by calculating the effective baryon-baryon interactions of the 64 lowest channels consisting of octet and decuplet baryons with three constituent quark models: the extended quark gluon exchange model, the Goldstone boson exchange model and the quark gluon meson exchange hybrid model. We find that these three models give similar results for 44 channels. Further tests of these models are discussed.Comment: 6pp., 3 figs., Asia-Pacific Few-Body Conf. II (Shanghai, Aug.25-30 2002), to appear in MPLA; references adde

    Double-Layer Bose-Einstein Condensates with Large Number of Vortices

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    In this paper we systematically study the double layer vortex lattice model, which is proposed to illustrate the interplay between the physics of a fast rotating Bose-Einstein condensate and the macroscopic quantum tunnelling. The phase diagram of the system is obtained. We find that under certain conditions the system will exhibit one novel phase transition, which is consequence of competition between inter-layer coherent hopping and inter-layer density-density interaction. In one phase the vortices in one layer coincide with those in the other layer. And in another phase two sets of vortex lattices are staggered, and as a result the quantum tunnelling between two layers is suppressed. To obtain the phase diagram we use two kinds of mean field theories which are quantum Hall mean field and Thomas-Fermi mean field. Two different criteria for the transition taking place are obtained respectively, which reveals some fundamental differences between these two mean field states. The sliding mode excitation is also discussed.Comment: 12 pages, 8 figure

    Evolving small-world networks with geographical attachment preference

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    We introduce a minimal extended evolving model for small-world networks which is controlled by a parameter. In this model the network growth is determined by the attachment of new nodes to already existing nodes that are geographically close. We analyze several topological properties for our model both analytically and by numerical simulations. The resulting network shows some important characteristics of real-life networks such as the small-world effect and a high clustering.Comment: 11 pages, 4 figure

    Glycine/Glycolic acid based copolymers

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    Glycine/glycolic acid based biodegradable copolymers have been prepared by ring-opening homopolymerization of morpholine-2,5-dione, and ring-opening copolymerization of morpholine-2,5-dione and glycolide. The homopolymerization of morpholine-2,5-dione was carried out in the melt at 200°C for 3 min using stannous octoate as an initiator, and continued at lower reaction temperatures (100-160°C) for 2-48 h. The highest yields (60%) and intrinsic viscosities ([] = 0.50 dL/g; DMSO, 25°C) were obtained after 3 min reaction at 200°C and 17 h at 130°C using a molar ratio of monomer and initiator of 1000. The polymer prepared by homopolymerization of morpholine-2,5-dione was composed of alternating glycine and glycolic acid residues, and had a glass transition temperature of 67°C and a melting temperature of 199°C. Random copolymers of glycine and glycolic acid were synthesized by copolymerization of morpholine-2,5-dione and glycolide in the melt at 200°C, followed by 17 h reaction at 130°C using stannous octoate as an initiator. The morphology of the copolymers varied from semi-crystalline to amorphous, depending on the mole fraction of glycolic acid residues incorporated

    Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data

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    This paper investigates the theoretical foundations of the t-distributed stochastic neighbor embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data visualization method. A novel theoretical framework for the analysis of t-SNE based on the gradient descent approach is presented. For the early exaggeration stage of t-SNE, we show its asymptotic equivalence to power iterations based on the underlying graph Laplacian, characterize its limiting behavior, and uncover its deep connection to Laplacian spectral clustering, and fundamental principles including early stopping as implicit regularization. The results explain the intrinsic mechanism and the empirical benefits of such a computational strategy. For the embedding stage of t-SNE, we characterize the kinematics of the low-dimensional map throughout the iterations, and identify an amplification phase, featuring the intercluster repulsion and the expansive behavior of the low-dimensional map, and a stabilization phase. The general theory explains the fast convergence rate and the exceptional empirical performance of t-SNE for visualizing clustered data, brings forth interpretations of the t-SNE visualizations, and provides theoretical guidance for applying t-SNE and selecting its tuning parameters in various applications.Comment: Accepted by Journal of Machine Learning Researc
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