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 ε. (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 ($i$), a buckled bar reveals itself as either a boxy/peanut-shaped bulge (at high $i$) or as a barlens structure (at low $i$). 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 ($M_{*} < 10^{10.5}\, M_{\odot}$) and high gas mass ratio. In contrast, the buckled bar fraction increases to 80\% toward massive and early-type disks ($M_{*} > 10^{10.5}\, M_{\odot}$), 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 506$-$G004) 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 adde