14,033 research outputs found
Which Constituent Quark Model Is Better?
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
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
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
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
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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