Gaussian Bounds for Noise Correlation of Functions

In this paper we derive tight bounds on the expected value of products of {\em low influence} functions defined on correlated probability spaces. The proofs are based on extending Fourier theory to an arbitrary number of correlated probability spaces, on a generalization of an invariance principle recently obtained with O'Donnell and Oleszkiewicz for multilinear polynomials with low influences and bounded degree and on properties of multi-dimensional Gaussian distributions. The results derived here have a number of applications to the theory of social choice in economics, to hardness of approximation in computer science and to additive combinatorics problems.Comment: Typos and references correcte

Avalanche dynamics of radio pulsar glitches

We test statistically the hypothesis that radio pulsar glitches result from an avalanche process, in which angular momentum is transferred erratically from the flywheel-like superfluid in the star to the slowly decelerating, solid crust via spatially connected chains of local, impulsive, threshold-activated events, so that the system fluctuates around a self-organised critical state. Analysis of the glitch population (currently 285 events from 101 pulsars) demonstrates that the size distribution in individual pulsars is consistent with being scale invariant, as expected for an avalanche process. The waiting-time distribution is consistent with being exponential in seven out of nine pulsars where it can be measured reliably, after adjusting for observational limits on the minimum waiting time, as for a constant-rate Poisson process. PSR J0537$-$6910 and PSR J0835$-$4510 are the exceptions; their waiting-time distributions show evidence of quasiperiodicity. In each object, stationarity requires that the rate $\lambda$ equals $- \epsilon \dot{\nu} /$, where $\dot{\nu}$ is the angular acceleration of the crust, $$ is the mean glitch size, and $\epsilon\dot{\nu}$ is the relative angular acceleration of the crust and superfluid. There is no evidence that $\lambda$ changes monotonically with spin-down age. The rate distribution itself is fitted reasonably well by an exponential for $\lambda \geq 0.25 {\rm yr^{-1}}$. For $\lambda < 0.25 {\rm yr^{-1}}$, its exact form is unknown; the exponential overestimates the number of glitching pulsars observed at low $\lambda$, where the limited total observation time exercises a selection bias.Comment: Accepted for publication in the Astrophysical Journa

Stretched Exponential Relaxation in the Biased Random Voter Model

We study the relaxation properties of the voter model with i.i.d. random bias. We prove under mild condions that the disorder-averaged relaxation of this biased random voter model is faster than a stretched exponential with exponent $d/(d+\alpha)$, where $0<\alpha\le 2$ depends on the transition rates of the non-biased voter model. Under an additional assumption, we show that the above upper bound is optimal. The main ingredient of our proof is a result of Donsker and Varadhan (1979).Comment: 14 pages, AMS-LaTe

Fitting and goodness-of-fit test of non-truncated and truncated power-law distributions

Power-law distributions contain precious information about a large variety of processes in geoscience and elsewhere. Although there are sound theoretical grounds for these distributions, the empirical evidence in favor of power laws has been traditionally weak. Recently, Clauset et al. have proposed a systematic method to find over which range (if any) a certain distribution behaves as a power law. However, their method has been found to fail, in the sense that true (simulated) power-law tails are not recognized as such in some instances, and then the power-law hypothesis is rejected. Moreover, the method does not work well when extended to power-law distributions with an upper truncation. We explain in detail a similar but alternative procedure, valid for truncated as well as for non-truncated power-law distributions, based in maximum likelihood estimation, the Kolmogorov-Smirnov goodness-of-fit test, and Monte Carlo simulations. An overview of the main concepts as well as a recipe for their practical implementation is provided. The performance of our method is put to test on several empirical data which were previously analyzed with less systematic approaches. The databases presented here include the half-lives of the radionuclides, the seismic moment of earthquakes in the whole world and in Southern California, a proxy for the energy dissipated by tropical cyclones elsewhere, the area burned by forest fires in Italy, and the waiting times calculated over different spatial subdivisions of Southern California. We find the functioning of the method very satisfactory.Comment: 26 pages, 9 figure

General variational many-body theory with complete self-consistency for trapped bosonic systems

In this work we develop a complete variational many-body theory for a system of $N$ trapped bosons interacting via a general two-body potential. In this theory both the many-body basis functions {\em and} the respective expansion coefficients are treated as variational parameters. The optimal variational parameters are obtained {\em self-consistently} by solving a coupled system of non-eigenvalue -- generally integro-differential -- equations to get the one-particle functions and by diagonalizing the secular matrix problem to find the expansion coefficients. We call this theory multi-configurational Hartree for bosons or MCHB(M), where M specifies explicitly the number of one-particle functions used to construct the configurations. General rules for evaluating the matrix elements of one- and two-particle operators are derived and applied to construct the secular Hamiltonian matrix. We discuss properties of the derived equations. It is demonstrated that for any practical computation where the configurational space is restricted, the description of trapped bosonic systems strongly depends on the choice of the many-body basis set used, i.e., self-consistency is of great relevance. As illustrative examples we consider bosonic systems trapped in one- and two-dimensional symmetric and asymmetric double-well potentials. We demonstrate that self-consistency has great impact on the predicted physical properties of the ground and excited states and show that the lack of self-consistency may lead to physically wrong predictions. The convergence of the general MCHB(M) scheme with a growing number M is validated in a specific case of two bosons trapped in a symmetric double-well.Comment: 53 pages, 8 figure

Constraints on cosmic hemispherical power anomalies from quasars

Recent analyses of the cosmic microwave background (CMB) maps from the WMAP satellite have uncovered evidence for a hemispherical power anomaly, i.e. a dipole modulation of the CMB power spectrum at large angular scales with an amplitude of +/-14 percent. Erickcek et al have put forward an inflationary model to explain this anomaly. Their scenario is a variation on the curvaton scenario in which the curvaton possesses a large-scale spatial gradient that modulates the amplitude of CMB fluctuations. We show that this scenario would also lead to a spatial gradient in the amplitude of perturbations sigma_8, and hence to a dipole asymmetry in any highly biased tracer of the underlying density field. Using the high-redshift quasars from the Sloan Digital Sky Survey, we find an upper limit on such a gradient of |nabla sigma_8|/sigma_8<0.027/r_{lss} (99% posterior probability), where r_{lss} is the comoving distance to the last-scattering surface. This rules out the simplest version of the curvaton spatial gradient scenario.Comment: matches JCAP accepted version (minor revisions

On the Inability of Markov Models to Capture Criticality in Human Mobility

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy. Since its inception, this bound has been widely used and empirically validated using Markov chains. We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound. In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias. By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay. This is in contrast to the initial assumption that human mobility follows an exponential decay. This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound. We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability. We finally highlight that this approach tends to overlook long-range correlations in human mobility. This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability