98,415 research outputs found
Quantum phase transition in an atomic Bose gas near a Feshbach resonance
We study the quantum phase transition in an atomic Bose gas near a Feshbach
resonance in terms of the renormalization group. This quantum phase transition
is characterized by an Ising order parameter. We show that in the low
temperature regime where the quantum fluctuations dominate the low-energy
physics this phase transition is of first order because of the coupling between
the Ising order parameter and the Goldstone mode existing in the bosonic
superfluid. However, when the thermal fluctuations become important, the phase
transition turns into the second order one, which belongs to the
three-dimensional Ising universality class. We also calculate the damping rate
of the collective mode in the phase with only a molecular Bose-Einstein
condensate near the second-order transition line, which can serve as an
experimental signature of the second-order transition.Comment: 8 pages, 2 figures, published version in Phys. Rev.
Parameter Sensitivity Analysis of Social Spider Algorithm
Social Spider Algorithm (SSA) is a recently proposed general-purpose
real-parameter metaheuristic designed to solve global numerical optimization
problems. This work systematically benchmarks SSA on a suite of 11 functions
with different control parameters. We conduct parameter sensitivity analysis of
SSA using advanced non-parametric statistical tests to generate statistically
significant conclusion on the best performing parameter settings. The
conclusion can be adopted in future work to reduce the effort in parameter
tuning. In addition, we perform a success rate test to reveal the impact of the
control parameters on the convergence speed of the algorithm
Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm
With the recent explosion in mobile data, the energy consumption and carbon
footprint of the mobile communications industry is rapidly increasing. It is
critical to develop more energy-efficient systems in order to reduce the
potential harmful effects to the environment. One potential strategy is to
switch off some of the under-utilized base stations during off-peak hours. In
this paper, we propose a binary Social Spider Algorithm to give guidelines for
selecting base stations to switch off. In our implementation, we use a penalty
function to formulate the problem and manage to bypass the large number of
constraints in the original optimization problem. We adopt several randomly
generated cellular networks for simulation and the results indicate that our
algorithm can generate superior performance
ASAP : towards accurate, stable and accelerative penetrating-rank estimation on large graphs
Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy Ξ΅. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches
The Carnegie-Irvine Galaxy Survey. V. Statistical study of bars and buckled bars
Simulations have shown that bars are subject to a vertical buckling
instability that transforms thin bars into boxy or peanut-shaped structures,
but the physical conditions necessary for buckling to occur are not fully
understood. We use the large sample of local disk galaxies in the
Carnegie-Irvine Galaxy Survey to examine the incidence of bars and buckled bars
across the Hubble sequence. Depending on the disk inclination angle (), a
buckled bar reveals itself as either a boxy/peanut-shaped bulge (at high )
or as a barlens structure (at low ). We visually identify bars,
boxy/peanut-shaped bulges, and barlenses, and examine the dependence of bar and
buckled bar fractions on host galaxy properties, including Hubble type, stellar
mass, color, and gas mass fraction. We find that the barred and unbarred disks
show similar distributions in these physical parameters. The bar fraction is
higher (70\%--80\%) in late-type disks with low stellar mass () and high gas mass ratio. In contrast, the buckled bar
fraction increases to 80\% toward massive and early-type disks (), and decreases with higher gas mass ratio. These
results suggest that bars are more difficult to grow in massive disks that are
dynamically hotter than low-mass disks. However, once a bar forms, it can
easily buckle in the massive disks, where a deeper potential can sustain the
vertical resonant orbits. We also find a probable buckling bar candidate (ESO
506G004) that could provide further clues to understand the timescale of the
buckling process.Comment: 9 pages, 7 figures, 2 tables. Accepted for publication in The
Astrophysical Journa
Localizable invariants of combinatorial manifolds and Euler characteristic
It is shown that if a real value PL-invariant of closed combinatorial
manifolds admits a local formula that depends only on the f-vector of the link
of each vertex, then the invariant must be a constant times the Euler
characteristic.Comment: 14 pages, 5 figures. Some arguments are improved and one picture is
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