Probing Low Energy Neutrino Backgrounds with Neutrino Capture on Beta Decaying Nuclei

We study the interaction of low energy neutrinos on nuclei that spontaneously undergo beta decay showing that the product of the cross section times neutrino velocity takes values as high as 10^{-42} cm^2 c for some specific nuclei that decay via allowed transitions. The absence of energy threshold and the value of the cross section single out these processes as a promising though very demanding approach for future experiments aimed at a direct detection of low energy neutrino backgrounds such as the cosmological relic neutrinos.Comment: Includes a discussion of local relic neutrino density effect on neutrino capture rate. Accepted for publication in JCA

Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case

The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base content of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, the elasticity parameters of the unzipped strands

Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random Satisfiability problem, and its application to stop-and-restart resolutions

A large deviation analysis of the solving complexity of random 3-Satisfiability instances slightly below threshold is presented. While finding a solution for such instances demands an exponential effort with high probability, we show that an exponentially small fraction of resolutions require a computation scaling linearly in the size of the instance only. This exponentially small probability of easy resolutions is analytically calculated, and the corresponding exponent shown to be smaller (in absolute value) than the growth exponent of the typical resolution time. Our study therefore gives some theoretical basis to heuristic stop-and-restart solving procedures, and suggests a natural cut-off (the size of the instance) for the restart.Comment: Revtex file, 4 figure

Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems

I consider the problem of deriving couplings of a statistical model from measured correlations, a task which generalizes the well-known inverse Ising problem. After reminding that such problem can be mapped on the one of expressing the entropy of a system as a function of its corresponding observables, I show the conditions under which this can be done without resorting to iterative algorithms. I find that inverse problems are local (the inverse Fisher information is sparse) whenever the corresponding models have a factorized form, and the entropy can be split in a sum of small cluster contributions. I illustrate these ideas through two examples (the Ising model on a tree and the one-dimensional periodic chain with arbitrary order interaction) and support the results with numerical simulations. The extension of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure

Trajectories in phase diagrams, growth processes and computational complexity: how search algorithms solve the 3-Satisfiability problem

Most decision and optimization problems encountered in practice fall into one of two categories with respect to any particular solving method or algorithm: either the problem is solved quickly (easy) or else demands an impractically long computational effort (hard). Recent investigations on model classes of problems have shown that some global parameters, such as the ratio between the constraints to be satisfied and the adjustable variables, are good predictors of problem hardness and, moreover, have an effect analogous to thermodynamical parameters, e.g. temperature, in predicting phases in condensed matter physics [Monasson et al., Nature 400 (1999) 133-137]. Here we show that changes in the values of such parameters can be tracked during a run of the algorithm defining a trajectory through the parameter space. Focusing on 3-Satisfiability, a recognized representative of hard problems, we analyze trajectories generated by search algorithms using growth processes statistical physics. These trajectories can cross well defined phases, corresponding to domains of easy or hard instances, and allow to successfully predict the times of resolution.Comment: Revtex file + 4 eps figure

High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that Maximum Lik elihood inference is deeply related to Principal Component Analysis when the amp litude of the pattern components, xi, is negligible compared to N^1/2. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in xi/N^1/2. We stress that it is important to generalize the Hopfield model and include both attractive and repulsive patterns, to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size, N and of the amplitude, xi. The inference approach is illustrated on synthetic and biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics (2011) to appea

Sport identification, moral perceptions and collective action: A study with young football players

We conducted a cross-sectional study investigating whether sport identification predicts different forms of collective action intentions aimed to redress the unfavourable condition faced by disadvantaged individuals. In doing so, moral perceptions (moral convictions, moral violation and moral obligation) were tested as mediators. Participants were young football players from the grassroots of a professional Italian club (N = 111). Results revealed that sport identification was indirectly associated with greater willingness to engage in both normative and non-normative solidarity-based collective action via stronger moral obligation perceptions; moral convictions mediated the relationship between sport identification and normative collective action, while no mediation effects emerged for moral violation. We discuss findings in relation to collective action and sport research

Electronic structure study by means of X-ray spectroscopy and theoretical calculations of the "ferric star" single molecule magnet

The electronic structure of the single molecule magnet system M[Fe(L)2]3*4CHCl3 (M=Fe,Cr; L=CH3N(CH2CH2O)2) has been studied using X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, soft X-ray emission spectroscopy, and density functional calculations. There is good agreement between theoretical calculations and experimental data. The valence band mainly consists of three bands between 2 eV and 30 eV. Both theory and experiments show that the top of the valence band is dominated by the hybridization between Fe 3d and O 2p bands. From the shape of the Fe 2p spectra it is argued that Fe in the molecule is most likely in the 2+ charge state. Its neighboring atoms (O,N) exhibit a magnetic polarisation yielding effective spin S=5/2 per iron atom, giving a high spin state molecule with a total S=5 effective spin for the case of M = Fe.Comment: Fig.2 replaced as it will appear in J. Chem. Phy

Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests

We present a procedure to solve the inverse Ising problem, that is to find the interactions between a set of binary variables from the measure of their equilibrium correlations. The method consists in constructing and selecting specific clusters of variables, based on their contributions to the cross-entropy of the Ising model. Small contributions are discarded to avoid overfitting and to make the computation tractable. The properties of the cluster expansion and its performances on synthetic data are studied. To make the implementation easier we give the pseudo-code of the algorithm.Comment: Paper submitted to Journal of Statistical Physic