Rejoinder: Bayesian Checking of the Second Levels of Hierarchical Models

Rejoinder: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]Comment: Published in at http://dx.doi.org/10.1214/07-STS235REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Ideal-Modified Bosonic Gas Trapped in an Arbitrary Three Dimensional Power-Law Potential

We analyze the effects caused by an anomalous single-particle dispersion relation suggested in several quantum-gravity models, upon the thermodynamics of a Bose-Einstein condensate trapped in a generic 3-dimensional power-law potential. We prove that the shift in the condensation temperature, caused by a deformed dispersion relation, described as a non-trivial function of the number of particles and the shape associated to the corresponding trap, could provide bounds for the parameters associated to such deformation. Additionally, we calculate the fluctuations in the number of particles as a criterium of thermodynamic stability for these systems. We show that the apparent instability caused by the anomalous fluctuations in the thermodynamic limit can be suppressed considering the lowest energy associated to the system in question.Comment: 10 pages. arXiv admin note: text overlap with arXiv:1202.380

Ultra--cold gases and the detection of the Earth's rotation: Bogoliubov space and gravitomagnetism

The present work analyzes the consequences of the gravitomagnetic effect of the Earth upon a bosonic gas in which the corresponding atoms have a non--vanishing orbital angular momentum. Concerning the ground state of the Bogoliubov space of this system we deduce the consequences, on the pressure and on the speed of sound, of the gravitomagnetic effect. We prove that the effect on a single atom is very small, but we also show that for some thermodynamical properties the consequences scale as a non--trivial function of the number of particles.Comment: 4 page

A very fast inference algorithm for finite-dimensional spin glasses: Belief Propagation on the dual lattice

Starting from a Cluster Variational Method, and inspired by the correctness of the paramagnetic Ansatz (at high temperatures in general, and at any temperature in the 2D Edwards-Anderson model) we propose a novel message passing algorithm --- the Dual algorithm --- to estimate the marginal probabilities of spin glasses on finite dimensional lattices. We show that in a wide range of temperatures our algorithm compares very well with Monte Carlo simulations, with the Double Loop algorithm and with exact calculation of the ground state of 2D systems with bimodal and Gaussian interactions. Moreover it is usually 100 times faster than other provably convergent methods, as the Double Loop algorithm.Comment: 23 pages, 12 figures. v2: improved introductio

PNEPs, NEPs for context free parsing: Application to natural language processing

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02478-8_59Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain.This work tests the suitability of NEPs to parse languages. We propose PNEP, a simple extension to NEP, and a procedure to translate a grammar into a PNEP that recognizes the same language. These parsers based on NEPs do not impose any additional constrain to the structure of the grammar, which can contain all kinds of recursive, lambda or ambiguous rules. This flexibility makes this procedure specially suited for Natural Languge Processing (NLP). In a first proof with a simplified English grammar, we got a performance (a linear time complexity) similar to that of the most popular syntactic parsers in the NLP area (Early and its derivatives). All the possible derivations for ambiguous grammars were generatedThis work was partially supported by MEC, project TIN2008-02081/TIN and by DGUI CAM/UAM, project CCG08-UAM/TIC-4425

Finite Size Effects in Separable Recurrent Neural Networks

We perform a systematic analytical study of finite size effects in separable recurrent neural network models with sequential dynamics, away from saturation. We find two types of finite size effects: thermal fluctuations, and disorder-induced `frozen' corrections to the mean-field laws. The finite size effects are described by equations that correspond to a time-dependent Ornstein-Uhlenbeck process. We show how the theory can be used to understand and quantify various finite size phenomena in recurrent neural networks, with and without detailed balance.Comment: 24 pages LaTex, with 4 postscript figures include

Controlling the quality factor of a tuning-fork resonance between 9 K and 300 K for scanning-probe microscopy

We study the dynamic response of a mechanical quartz tuning fork in the temperature range from 9 K to 300 K. Since the quality factor Q of the resonance strongly depends on temperature, we implement a procedure to control the quality factor of the resonance. We show that we are able to dynamically change the quality factor and keep it constant over the whole temperature range. This procedure is suitable for applications in scanning probe microscopy.Comment: 5 pages, 6 figure