Structural graph matching using the EM algorithm and singular value decomposition

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method

Critical Relaxation and Critical Exponents

Dynamic relaxation of the XY model and fully frustrated XY model quenched from an initial ordered state to the critical temperature or below is investigated with Monte Carlo methods. Universal power law scaling behaviour is observed. The dynamic critical exponent $z$ and the static exponent $\eta$ are extracted from the time-dependent Binder cumulant and magnetization. The results are competitive to those measured with traditional methods

Probing the QCD Critical Point with Higher Moments of Net-proton Multiplicity Distributions

Higher moments of event-by-event net-proton multiplicity distributions are applied to search for the QCD critical point in the heavy ion collisions. It has been demonstrated that higher moments as well as moment products are sensitive to the correlation length and directly connected to the thermodynamic susceptibilities computed in the Lattice QCD and Hadron Resonance Gas (HRG) model. In this paper, we will present measurements for kurtosis ($\kappa$), skewness ($S$) and variance ($\sigma^{2}$) of net-proton multiplicity distributions at the mid-rapidity ($|y|<0.5$) and $0.4<p_{T}<0.8$ GeV/$c$ for Au+Au collisions at $\sqrt{s_{NN}}$=19.6, 39, 62.4, 130 and 200 GeV, Cu+Cu collisions at $\sqrt{s_{NN}}$=22.4, 62.4 and 200 GeV, d+Au collisions at $\sqrt{s_{NN}}$=200 GeV and p+p collisions at $\sqrt{s_{NN}}$=62.4 and 200 GeV. The moment products $\kappa \sigma^{2}$ and $S \sigma$ of net-proton distributions, which are related to volume independent baryon number susceptibility ratio, are compared to the Lattice QCD and HRG model calculations. The $\kappa \sigma^{2}$ and $S \sigma$ of net-proton distributions are consistent with Lattice QCD and HRG model calculations at high energy, which support the thermalization of the colliding system. Deviations of $\kappa \sigma^{2}$ and $S \sigma$ for the Au+Au collisions at low energies from HRG model calculations are also observed.Comment: 10 pages, 8 figures, Proceedings of 27th Winter Workshon on Nuclear Dynamics. Feb. 6-13 (2011

Training Optimization for Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as an effective way to solve the lifetime challenge of wireless sensor networks, as it can continuously harvest energy from the environment. Unfortunately, it is challenging to guarantee a satisfactory short-term performance in EH communication systems because the harvested energy is sporadic. In this paper, we consider the channel training optimization problem in EH communication systems, i.e., how to obtain accurate channel state information to improve the communication performance. In contrast to conventional communication systems, the optimization of the training power and training period in EH communication systems is a coupled problem, which makes such optimization very challenging. We shall formulate the optimal training design problem for EH communication systems, and propose two solutions that adaptively adjust the training period and power based on either the instantaneous energy profile or the average energy harvesting rate. Numerical and simulation results will show that training optimization is important in EH communication systems. In particular, it will be shown that for short block lengths, training optimization is critical. In contrast, for long block lengths, the optimal training period is not too sensitive to the value of the block length nor to the energy profile. Therefore, a properly selected fixed training period value can be used.Comment: 6 pages, 5 figures, Globecom 201

Optimal Scheduling and Power Allocation for Two-Hop Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider two different design objectives: 1) short-term throughput maximization; and 2) transmission completion time minimization. Both problems are joint scheduling and power allocation problems, rendered quite challenging by the half-duplex constraint at the relay. A key finding is that directional water-filling (DWF), which is the optimal power allocation algorithm for the single-hop EH system, can serve as guideline for the design of two-hop communication systems, as it not only determines the value of the optimal performance, but also forms the basis to derive optimal solutions for both design problems. Based on a relaxed energy profile along with the DWF algorithm, we derive key properties of the optimal solutions for both problems and thereafter propose efficient algorithms. Simulation results will show that both scheduling and power allocation optimizations are necessary in two-hop EH communication systems.Comment: Submitted to IEEE Transaction on Wireless Communicatio