A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.Comment: Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin, German

Magnetic domain walls in constrained geometries

Magnetic domain walls have been studied in micrometer-sized Fe20Ni80 elements containing geometrical constrictions by spin-polarized scanning electron microscopy and numerical simulations. By controlling the constriction dimensions, the wall width can be tailored and the wall type modified. In particular, the width of a 180 degree Neel wall can be strongly reduced or increased by the constriction geometry compared with the wall in unconstrained systems.Comment: 4 pages, 6 figure

Dynamics of Neural Networks with Continuous Attractors

We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.Comment: 6 pages, 7 figures with 4 caption

Ion-acoustic solitary waves and shocks in a collisional dusty negative ion plasma

We study the effects of ion-dust collisions and ion kinematic viscosities on the linear ion-acoustic instability as well as the nonlinear propagation of small amplitude solitary waves and shocks (SWS) in a negative ion plasma with immobile charged dusts. {The existence of two linear ion modes, namely the `fast' and `slow' waves is shown, and their properties are analyzed in the collisional negative ion plasma.} {Using the standard reductive perturbation technique, we derive a modified Korteweg-de Vries-Burger (KdVB) equation which describes the evolution of small amplitude SWS.} {The profiles of the latter are numerically examined with parameters relevant for laboratory and space plasmas where charged dusts may be positively or negatively charged.} It is found that negative ion plasmas containing positively charged dusts support the propagation of SWS with negative potential. However, the perturbations with both positive and negative potentials may exist when dusts are negatively charged. The results may be useful for the excitation of SWS in laboratory negative ion plasmas as well as for observation in space plasmas where charged dusts may be positively or negatively charged.Comment: 13 pages, 9 figures; To appear in Physical Review

Informed Scheduling by Stochastic Residual Belief Propagation in Distributed Wireless Networks

This letter devises a novel algorithm for cooperative spectrum sensing based on belief propagation (BP) for distributed wireless networks. The algorithm, called stochastic residual belief propagation (SR-BP), extends the use of residual belief propagation (R-BP) to distributed networks, improving the accuracy, convergence rate, and communication cost for cooperative spectrum sensing. We demonstrate that SR-BP converges to a unique fixed point under conditions similar to those ensuring convergence of asynchronous BP. Then, we develop a way to derive a probability distribution from the residual of each message. Finally, we provide numerical results to showcase the improvements in convergence speed, message overhead and detection accuracy of SR-BP

Pion Interferometry for Hydrodynamical Expanding Source with a Finite Baryon Density

We calculate the two-pion correlation function for an expanding hadron source with a finite baryon density. The space-time evolution of the source is described by relativistic hydrodynamics and the Hanbury-Brown-Twiss (HBT) radius is extracted after effects of collective expansion and multiple scattering on the HBT interferometry have been taken into account, using quantum probability amplitudes in a path-integral formalism. We find that this radius is substantially smaller than the HBT radius extracted from the freeze-out configuration.Comment: 4 pages, 2 figure

Phason modes in spin-density wave in the presence of long-range Coulomb interaction

We study the effect of long-range Coulomb interaction on the phason in spin-density wave (SDW) within mean field theory. In the longitudinal limit and in the absence of SDW pinning the phason is completely absorbed by the plasmon due to the Anderson-Higgs mechanism. In the presence of SDW pinning or when the wave vector {\bf q} is tilted from the chain direction, though the plasmon still almost exhausts the optical sum rule, another optical mode appears at $\omega < 2\Delta(T)$, with small optical weight. This low frequency mode below the SDW gap may be accessible to electron energy loss spectroscopy (EELS).Comment: 7 pages, Revtex 2.1, SZFKI 102/9