1,524 research outputs found
Analysis of hidden-bottom bb\bar{b}\bar{b} states
Motivated by the searching for states at LHC recently, we
calculate the ground-state energies of states with quantum
numbers in a nonrelativistic chiral quark model using the
Gaussian expansion method. In our calculations, two structures, meson-meson and
diquark-antidiquark, and their coupling, along with all possible color
configurations are considered. It is expected that the studies shall be helpful
for the experimental searching of fully-heavy exotic tetraquark states.Comment: 7 pages, 1 figur
Is the exotic a bound state?
Stimulated by the recent observation of the exotic state by D0
Collaboration, we study the four-quark system with quantum
numbers in the framework of chiral quark model. Two structures,
diquark-antidiquark and meson-meson, with all possible color configurations are
investigated by using Gaussian expansion method. The results show that energies
of the tetraquark states with diquark-antiquark structure are too high to the
candidate of , and no molecular structure can be formed in our
calculations. The calculation is also extended to the four-quark system
and the same results as that of are
obtained.Comment: 5 pages, 1 figur
Star-factors of tournaments
Let S_m denote the m-vertex simple digraph formed by m-1 edges with a common
tail. Let f(m) denote the minimum n such that every n-vertex tournament has a
spanning subgraph consisting of n/m disjoint copies of S_m. We prove that m lg
m - m lg lg m <= f(m) <= 4m^2 - 6m for sufficiently large m.Comment: 5 pages, 1 figur
MST-Based Semi-Supervised Clustering Using M-Labeled Objects
Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches
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