Mode-locking of incommensurate phase by quantum zero point energy in the Frenkel-Kontorova model

In this paper, it is shown that a configuration modulated system described by the Frenkel-Kontorova model can be locked at an incommensurate phase when the quantum zero point energy is taken into account. It is also found that the specific heat for an incommensurate phase shows different parameter-dependence in sliding phase and pinning phase. These findings provide a possible way for experimentalists to verify the phase transition by breaking of analyticity.Comment: 6 pages in Europhys style, 3 eps figure

Generalized Warped Disk Equations

The manner in which warps in accretion disks evolve depends on the magnitude of the viscosity. ... See full text for complete abstract

Renormalization and Quantum Scaling of Frenkel-Kontorova Models

We generalise the classical Transition by Breaking of Analyticity for the class of Frenkel-Kontorova models studied by Aubry and others to non-zero Planck's constant and temperature. This analysis is based on the study of a renormalization operator for the case of irrational mean spacing using Feynman's functional integral approach. We show how existing classical results extend to the quantum regime. In particular we extend MacKay's renormalization approach for the classical statistical mechanics to deduce scaling of low frequency effects and quantum effects. Our approach extends the phenomenon of hierarchical melting studied by Vallet, Schilling and Aubry to the quantum regime.Comment: 14 pages, 1 figure, submitted to J.Stat.Phy

Training a perceptron in a discrete weight space

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as $exp(-K \alpha^2)$/$exp(-e^{|\lambda| \alpha})$ in the case of on-line learning with binary/continuous activation functions, respectively, where $\alpha$ is the number of examples divided by N, the size of the input vector and $K$ is a positive constant that decays linearly with 1/L. For finite $N$ and $L$, a perfect agreement between the discrete student and the teacher is obtained for $\alpha \propto \sqrt{L \ln(NL)}$. A crossover to the generalization error $\propto 1/\alpha$, characterized continuous weights with binary output, is obtained for synaptic depth $L > O(\sqrt{N})$.Comment: 10 pages, 5 figs., submitted to PR

Controlled Longitudinal Emittance Blow-Up in the CERN PS

The longitudinal emittance of the bunches in the CERN PS must be increased before transition crossing to avoid beam loss due to a fast vertical instability. This controlled blow-up is essential for all high-intensity beams in the PS, including those for transfer to the LHC. The higher harmonic 200MHz RF system (six cavities) used for this blow-up has to generate a total RF voltage which, for the most demanding blow-up, is comparable to the voltage of the main RF cavities. The system is presently subject to a major upgrade and a possible reduction in the number of higher harmonic RF cavities installed is under consideration. To determine the minimum required, detailed simulations and machine development studies to optimize the longitudinal blow-up have been performed. Further options to produce the required longitudinal emittance using other RF systems are also analyzed. The results obtained for the different scenarios for the longitudinal blow-up are presented and compared in this paper

A two step algorithm for learning from unspecific reinforcement

We study a simple learning model based on the Hebb rule to cope with "delayed", unspecific reinforcement. In spite of the unspecific nature of the information-feedback, convergence to asymptotically perfect generalization is observed, with a rate depending, however, in a non- universal way on learning parameters. Asymptotic convergence can be as fast as that of Hebbian learning, but may be slower. Moreover, for a certain range of parameter settings, it depends on initial conditions whether the system can reach the regime of asymptotically perfect generalization, or rather approaches a stationary state of poor generalization.Comment: 13 pages LaTeX, 4 figures, note on biologically motivated stochastic variant of the algorithm adde

On-line learning of non-monotonic rules by simple perceptron

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.Comment: LaTeX 20 pages using IOP LaTeX preprint style file, 14 figure

Fractal Spin Glass Properties of Low Energy Configurations in the Frenkel-Kontorova chain

We study numerically and analytically the classical one-dimensional Frenkel-Kontorova chain in the regime of pinned phase characterized by phonon gap. Our results show the existence of exponentially many static equilibrium configurations which are exponentially close to the energy of the ground state. The energies of these configurations form a fractal quasi-degenerate band structure which is described on the basis of elementary excitations. Contrary to the ground state, the configurations inside these bands are disordered.Comment: revtex, 9 pages, 9 figure