203,787 research outputs found

    Mutual independence of critical temperature and superfluid density under pressure in optimally electron-doped superconducting LaFeAsO1x_{1-x}Fx_{x}

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    The superconducting properties of LaFeAsO1x_{1-x}Fx_{x} in conditions of optimal electron-doping are investigated upon the application of external pressure up to 23\sim 23 kbar. Measurements of muon-spin spectroscopy and dc magnetometry evidence a clear mutual independence between the critical temperature TcT_{c} and the low-temperature saturation value for the ratio ns/mn_{s}/m^{*} (superfluid density over effective band mass of Cooper pairs). Remarkably, a dramatic increase of 30\sim 30 % is reported for ns/mn_{s}/m^{*} at the maximum pressure value while TcT_{c} is substantially unaffected in the whole accessed experimental window. We argue and demonstrate that the explanation for the observed results must take the effect of non-magnetic impurities on multi-band superconductivity into account. In particular, the unique possibility to modify the ratio between intra-band and inter-bands scattering rates by acting on structural parameters while keeping the amount of chemical disorder constant is a striking result of our proposed model.Comment: 8 pages (Main text: 5 pages. Paper merged with supplemental information), 5 figure

    Mutual Fund Performance with Learning Across Funds

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    This paper is based on the premise that knowledge about the alphas of one set of funds will influence an investor's beliefs about other funds. This will be true insofar as an investor's expectation about the performance of a fund is partly a belief about the abilities of mutual fund managers as a group and, more generally, a belief about the degree to which financial markets are efficient. We develop a simple framework for incorporating this prior dependence' and find that it can have a substantial impact on the cross-section of posterior beliefs about fund performance as well as asset allocation. Under independence, the maximum posterior mean alpha increases without bound as the number of funds increases and 'extremely large' estimates are randomly observed. This is true even when fund managers have no skill. In contrast, with prior dependence, investors aggregate information across funds to form a general belief about the potential for abnormal performance. Each fund's alpha estimate is shrunk toward the aggregate estimate, mitigating extreme views. An additional implication is that restricting the estimation to surviving funds, a common practice in this literature, imparts an upward bias to the average fund alpha.

    On the Optimal Transmission of Non-Gaussian Signals through a Noisy Channel with Feedback

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    This paper is concerned with the optimal transmission of a non-Gaussian signal (a non-Gaussian message) through a channel with Gaussian white noise by a cording which is linear in the signal. Under the assumptions of square integrability on the signal and the independence between the signal and the noise, it will be shown that the optimal cording which maximizes the mutual information between the signal (the non-Gaussian message) and the observation process (the channel output) is to generate the estimation error process multiplied by a deterministic coefficient so that the mean power of the encorded signal takes the maximum admissible value. The result shows that the optimal transmission is such that the channel output becomes the innovations process

    An information theory for preferences

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    Recent literature in the last Maximum Entropy workshop introduced an analogy between cumulative probability distributions and normalized utility functions. Based on this analogy, a utility density function can de defined as the derivative of a normalized utility function. A utility density function is non-negative and integrates to unity. These two properties form the basis of a correspondence between utility and probability. A natural application of this analogy is a maximum entropy principle to assign maximum entropy utility values. Maximum entropy utility interprets many of the common utility functions based on the preference information needed for their assignment, and helps assign utility values based on partial preference information. This paper reviews maximum entropy utility and introduces further results that stem from the duality between probability and utility

    A Class of Models for Uncorrelated Random Variables

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    We consider the class of multivariate distributions that gives the distribution of the sum of uncorrelated random variables by the product of their marginal distributions. This class is defined by a representation of the assumption of sub-independence, formulated previously in terms of the characteristic function and convolution, as a weaker assumption than independence for derivation of the distribution of the sum of random variables. The new representation is in terms of stochastic equivalence and the class of distributions is referred to as the summable uncorrelated marginals (SUM) distributions. The SUM distributions can be used as models for the joint distribution of uncorrelated random variables, irrespective of the strength of dependence between them. We provide a method for the construction of bivariate SUM distributions through linking any pair of identical symmetric probability density functions. We also give a formula for measuring the strength of dependence of the SUM models. A final result shows that under the condition of positive or negative orthant dependence, the SUM property implies independence

    Bayesian Network Structure Learning with Permutation Tests

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    In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this paper we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for Complex Problems', Padova, June 15, 201

    On a comparative study between dependence scales determined by linear and non-linear measures

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    In this manuscript we present a comparative study about the determination of the relaxation (\textit{i.e.}, independence) time scales obtained from the correlation function, the mutual information, and a criterion based on the evaluation of a nonextensive generalisation of mutual entropy. Our results show that, for systems with a small degree of complexity, standard mutual information and the criterion based on its nonextensive generalisation provide the same scale, whereas for systems with a higher complex dynamics the standard mutual information presents a time scale consistently smaller.Comment: 14 pages. To appear in Physica
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