Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks

Copyright @ Springer-Verlag Berlin Heidelberg 2010.The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council(EPSRC) of UK under Grant EP/E060722/1

A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

Entangled Quantum Clocks for Measuring Proper-Time Difference

We report that entangled pairs of quantum clocks (non-degenerate quantum bits) can be used as a specialized detector for precisely measuring difference of proper-times that each constituent quantum clock experiences. We describe why the proposed scheme would be more precise in the measurement of proper-time difference than a scheme of two-separate-quantum-clocks. We consider possibilities that the proposed scheme can be used in precision test of the relativity theory.Comment: no correction, 4 pages, RevTe

Status of atmospheric neutrino(mu)<-->neutrino(tau) oscillations and decoherence after the first K2K spectral data

We review the status of nu_mu-->nu_tau flavor transitions of atmospheric neutrinos in the 92 kton-year data sample collected in the first phase of the Super-Kamiokande (SK) experiment, in combination with the recent spectral data from the KEK-to-Kamioka (K2K) accelerator experiment (including 29 single-ring muon events). We consider a theoretical framework which embeds flavor oscillations plus hypothetical decoherence effects, and where both standard oscillations and pure decoherence represent limiting cases. It is found that standard oscillations provide the best description of the SK+K2K data, and that the associated mass-mixing parameters are determined at 1 sigma (and d.o.f.=1) as: Delta m^2=(2.6 +- 0.4)x10^{-3} eV^2 and sin^2(2theta)=1.00+0.00-0.05. As compared with standard oscillations, the case of pure decoherence is disfavored, although it cannot be ruled out yet. In the general case, additional decoherence effects in the nu_mu-->nu_tau channel do not improve the fit to the SK and K2K data, and upper bounds can be placed on the associated decoherence parameter. Such indications, presently dominated by SK, could be strengthened by further K2K data, provided that the current spectral features are confirmed with higher statistics. A detailed description of the statistical analysis of SK and K2K data is also given, using the so-called ``pull'' approach to systematic uncertainties.Comment: 18 pages (RevTeX) + 12 figures (PostScript

Time and Amplitude of Afterpulse Measured with a Large Size Photomultiplier Tube

We have studied the afterpulse of a hemispherical photomultiplier tube for an upcoming reactor neutrino experiment. The timing, the amplitude, and the rate of the afterpulse for a 10 inch photomultiplier tube were measured with a 400 MHz FADC up to 16 \ms time window after the initial signal generated by an LED light pulse. The time and amplitude correlation of the afterpulse shows several distinctive groups. We describe the dependencies of the afterpulse on the applied high voltage and the amplitude of the main light pulse. The present data could shed light upon the general mechanism of the afterpulse.Comment: 11 figure

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Proceedings of: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011.The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Spectral Correlation in Incommensurate Multi-Walled Carbon Nanotubes

We investigate the energy spectra of clean incommensurate double-walled carbon nanotubes, and find that the overall spectral properties are described by the so-called critical statistics of Anderson metal-insulator transition. In the energy spectra, there exist three different regimes characterized by Wigner-Dyson, Poisson, and semi-Poisson distributions. This feature implies that the electron transport in incommensurate multi-walled nanotubes can be either diffusive, ballistic, or intermediate between them, depending on the position of the Fermi energy.Comment: final version to appear in Phys. Rev. Let

Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms

Proceedings of: 3rd European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM 2010) [associated to: EvoApplications 2010. European Conference on the Applications of Evolutionary Computation]. Istambul, Turkey, April 7-9, 2010In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model-building algorithms. Most current model-building schemes used so far off-the-shelf machine learning methods. These methods are mostly error-based learning algorithms. However, the model-building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertionsThis work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-0Publicad

Measurement of single pi0 production in neutral current neutrino interactions with water by a 1.3 GeV wide band muon neutrino beam

Neutral current single pi0 production induced by neutrinos with a mean energy of 1.3 GeV is measured at a 1000 ton water Cherenkov detector as a near detector of the K2K long baseline neutrino experiment. The cross section for this process relative to the total charged current cross section is measured to be 0.064 +- 0.001 (stat.) +- 0.007 (sys.). The momentum distribution of produced pi0s is measured and is found to be in good agreement with an expectation from the present knowledge of the neutrino cross sections.Comment: 6 pages, 4 figures, Submitted to Phys. Lett.