An adaptive Metropolis-Hastings scheme: sampling and optimization

We propose an adaptive Metropolis-Hastings algorithm in which sampled data are used to update the proposal distribution. We use the samples found by the algorithm at a particular step to form the information-theoretically optimal mean-field approximation to the target distribution, and update the proposal distribution to be that approximatio. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm in sampling and in annealing optimization.Comment: To appear in Europhysics Letter

Collective Intelligence for Control of Distributed Dynamical Systems

We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, ``The American Economic Review'', 84(2): 406--411 (1994), D. Challet and Y.C. Zhang, ``Physica A'', 256:514 (1998)). We view it as an instance of the general problem of how to configure the nodal elements of a distributed dynamical system so that they do not ``work at cross purposes'', in that their collective dynamics avoids frustration and thereby achieves a provided global goal. We summarize a mathematical theory for such configuration applicable when (as in the bar problem) the global goal can be expressed as minimizing a global energy function and the nodes can be expressed as minimizers of local free energy functions. We show that a system designed with that theory performs nearly optimally for the bar problem.Comment: 8 page

Nonlinear Information Bottleneck

Information bottleneck (IB) is a technique for extracting information in one random variable $X$ that is relevant for predicting another random variable $Y$. IB works by encoding $X$ in a compressed "bottleneck" random variable $M$ from which $Y$ can be accurately decoded. However, finding the optimal bottleneck variable involves a difficult optimization problem, which until recently has been considered for only two limited cases: discrete $X$ and $Y$ with small state spaces, and continuous $X$ and $Y$ with a Gaussian joint distribution (in which case optimal encoding and decoding maps are linear). We propose a method for performing IB on arbitrarily-distributed discrete and/or continuous $X$ and $Y$, while allowing for nonlinear encoding and decoding maps. Our approach relies on a novel non-parametric upper bound for mutual information. We describe how to implement our method using neural networks. We then show that it achieves better performance than the recently-proposed "variational IB" method on several real-world datasets

Social scale and collective computation: Does information processing limit rate of growth in scale?

Collective computation is the process by which groups store and share information to arrive at decisions for collective behavior. How societies engage in effective collective computation depends partly on their scale. Social arrangements and technologies that work for small- and mid-scale societies are inadequate for dealing effectively with the much larger communication loads that societies face during the growth in scale that is a hallmark of the Holocene. An important bottleneck for growth may be the development of systems for persistent recording of information (writing), and perhaps also the abstraction of money for generalizing exchange mechanisms. Building on Shin et al., we identify a Scale Threshold to be crossed before societies can develop such systems, and an Information Threshold which, once crossed, allows more or less unlimited growth in scale. We introduce several additional articles in this special issue that elaborate or evaluate this Thresholds Model for particular types of societies or times and places in the world.1 Introduction 2 Seshat: The Global History Databank 2.1 Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization 2.2 Scale and information-processing thresholds in Holocene social evolution 2.3 Evolution of collective computational abilities of (pre)historic societies 3 Empirical Fluctuation, or Stochastic Law? 4 Opening the Discussion on Collective Computation: Historical Survey and Introduction to the Case Studies 4.1 Marcus Hamilton: Collective computation and the emergence of hunter-gatherer small-worlds 4.2 Laura Ellyson: Applying Gregory Johnson’s concepts of scalar stress to scale and Information Thresholds in Holocene social evolution 4.3 Johannes Müller et al.: Tripolye mega-sites: “Collective computational abilities” of prehistoric proto-urban societies? 4.4 Steven Wernke: Explosive expansion, sociotechnical diversity, and fragile sovereignty in the domain of the Inka 4.5 Gary Feinman and David Carballo: Communication, computation, and governance: A multiscalar vantage on the prehispanic Mesoamerican World 4.6 Ian Morris: Scale, information-processing, and complementarities in Old-World Axial-Age societies 5 Conclusion 6 Postscript: the Second Social Media Revolutio

Analytic Continuation for Asymptotically AdS 3D Gravity

We have previously proposed that asymptotically AdS 3D wormholes and black holes can be analytically continued to the Euclidean signature. The analytic continuation procedure was described for non-rotating spacetimes, for which a plane t=0 of time symmetry exists. The resulting Euclidean manifolds turned out to be handlebodies whose boundary is the Schottky double of the geometry of the t=0 plane. In the present paper we generalize this analytic continuation map to the case of rotating wormholes. The Euclidean manifolds we obtain are quotients of the hyperbolic space by a certain quasi-Fuchsian group. The group is the Fenchel-Nielsen deformation of the group of the non-rotating spacetime. The angular velocity of an asymptotic region is shown to be related to the Fenchel-Nielsen twist. This solves the problem of classification of rotating black holes and wormholes in 2+1 dimensions: the spacetimes are parametrized by the moduli of the boundary of the corresponding Euclidean spaces. We also comment on the thermodynamics of the wormhole spacetimes.Comment: 28 pages, 14 figure

Positional Information – a concept underpinning our understanding of developmental biology

© 2019 Wiley Periodicals, Inc.Peer reviewedPostprin

A low-energy solar cosmic ray experiment for OGO-F

Instrumentation data for low energy solar cosmic ray measurements using OGO-F satellit

Pattern Formation by Boundary Forcing in Convectively Unstable, Oscillatory Media With and Without Differential Transport

Motivated by recent experiments and models of biological segmentation, we analyze the exicitation of pattern-forming instabilities of convectively unstable reaction-diffusion-advection (RDA) systems, occuring by means of constant or periodic forcing at the upstream boundary. Such boundary-controlled pattern selection is a generalization of the flow-distributed oscillation (FDO) mechanism that can include Turing or differential flow instability (DIFI) modes. Our goal is to clarify the relationships among these mechanisms in the general case where there is differential flow as well as differential diffusion. We do so by analyzing the dispersion relation for linear perturbations and showing how its solutions are affected by differential transport. We find a close relationship between DIFI and FDO, while the Turing mechanism gives rise to a distinct set of unstable modes. Finally, we illustrate the relevance of the dispersion relations using nonlinear simulations and we discuss the experimental implications of our results.Comment: Revised version with added content (new section and figures added), changes to wording and organizatio