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

Thermodynamics of deterministic finite automata operating locally and periodically

Real-world digital computers have operational constraints that cause nonzero entropy production (EP). In particular, almost all real-world computers are "periodic" in that they iteratively undergo the same physical process, and are "local" in that not all physical variables that are statistically coupled are also directly coupled physically. These constraints are so universal because the ability to decompose a complex computation into small, iterative logical updates is what makes digital computers so powerful. Here we first derive expressions for the nonzero EP caused by these two particular constraints in physical implementations of deterministic finite automata (DFA), a foundational system of computer science theory. We then relate this minimal EP to the computational characteristics of the DFA. Specifically, we show that DFA divide into two classes: those with an invertible local update map, which have zero local and periodic EP, and those with a non-invertible local update map, which have high minimal EP. We also demonstrate the thermodynamic advantages of implementing a DFA with a physical process that is agnostic about the inputs that it processes. \end{abstract

The effect of disorder in multi-component covalent organic frameworks

We examined the effect of two different types of linker distribution—random or correlated distribution—on the pore size and shape within single-layers of three multi-component COFs. We reveal a relationship between linker distribution and the porosity of COF solid solutions. The methods presented in this paper are generalisable and could be used in further studies to examine the properties of disordered framework materials

Child- and school-level predictors of children's bullying behavior: A multilevel analysis in 648 primary schools

© 2017 American Psychological Association. A great deal of bullying behavior takes place at school, however, existing literature has predominantly focused on individual characteristics of children associated with bullying with less attention on schoollevel factors. The current study, comprising 23,215 children (51% boys) recruited from Year 4 or Year 5 (M = 9.06 years, SD = .56 years) from 648 primary schools in England, aimed to examine the independent and combined influence of child- and school-level predictors on bullying behavior in primary school. Children provided information on bullying behavior and school climate. Demographic characteristics of children were obtained from the National Pupil Database, and demographic characteristics of schools were drawn from EduBase. Multilevel logistic regression models showed that individual child gender, ethnicity, deprivation and special educational needs status all predicted bullying behavior. Of the school-level predictors, only overall school deprivation and school climate were predictive of bullying behavior once child-level predictors were taken into account. There was a significant interaction between child- and school-level deprivation; high-deprivation schools were a risk factor for bullying only for children that came from nondeprived backgrounds, whereas deprived children reported engaging in bullying behavior irrespective of school-level deprivation. Given the independent and combined role of child- and school-level factors for bullying behavior, the current study has implications for targeted school interventions to tackle bullying behavior, both in terms of identifying high-risk children and identifying high-risk schools

Bayesian optimization with a finite budget: An approximate dynamic programming approach

We consider the problem of optimizing an expensive objective function when a finite budget of total evaluations is prescribed. In that context, the optimal solution strategy for Bayesian optimization can be formulated as a dynamic programming instance. This results in a complex problem with uncountable, dimension-increasing state space and an uncountable control space. We show how to approximate the solution of this dynamic programming problem using rollout, and propose rollout heuristics specifically designed for the Bayesian optimization setting. We present numerical experiments showing that the resulting algorithm for optimization with a finite budget outperforms several popular Bayesian optimization algorithms

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials

Bagging ensemble selection for regression

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classification, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non negative least squares algorithm is a viable approach for pruning an ensemble of ensembles

Normalized entropy aggregation for inhomogeneous large-scale data

It was already in the fifties of the last century that the relationship between information theory, statistics, and maximum entropy was established, following the works of Kullback, Leibler, Lindley and Jaynes. However, the applications were restricted to very specific domains and it was not until recently that the convergence between information processing, data analysis and inference demanded the foundation of a new scientific area, commonly referred to as Info-Metrics. As huge amount of information and large-scale data have become available, the term "big data" has been used to refer to the many kinds of challenges presented in its analysis: many observations, many variables (or both), limited computational resources, different time regimes or multiple sources. In this work, we consider one particular aspect of big data analysis which is the presence of inhomogeneities, compromising the use of the classical framework in regression modelling. A new approach is proposed, based on the introduction of the concepts of info-metrics to the analysis of inhomogeneous large-scale data. The framework of information-theoretic estimation methods is presented, along with some information measures. In particular, the normalized entropy is tested in aggregation procedures and some simulation results are presented.publishe

Quantitative analysis of cell types during growth and morphogenesis in Hydra

Tissue maceration was used to determine the absolute number and the distribution of cell types in Hydra. It was shown that the total number of cells per animal as well as the distribution of cells vary depending on temperature, feeding conditions, and state of growth. During head and foot regeneration and during budding the first detectable change in the cell distribution is an increase in the number of nerve cells at the site of morphogenesis. These results and the finding that nerve cells are most concentrated in the head region, diminishing in density down the body column, are discussed in relation to tissue polarity

Quantum Liouville theory in the background field formalism I. Compact Riemann surfaces

Using Polyakov's functional integral approach with the Liouville action functional defined in \cite{ZT2} and \cite{LTT}, we formulate quantum Liouville theory on a compact Riemann surface X of genus g > 1. For the partition function and for the correlation functions with the stress-energy tensor components $$, we describe Feynman rules in the background field formalism by expanding corresponding functional integrals around a classical solution - the hyperbolic metric on X. Extending analysis in \cite{LT1,LT2,LT-Varenna,LT3}, we define the regularization scheme for any choice of global coordinate on X, and for Schottky and quasi-Fuchsian global coordinates we rigorously prove that one- and two-point correlation functions satisfy conformal Ward identities in all orders of the perturbation theory. Obtained results are interpreted in terms of complex geometry of the projective line bundle \cE_{c}=\lambda_{H}^{c/2} over the moduli space $\mathfrak{M}_{g}$, where c is the central charge and $\lambda_{H}$ is the Hodge line bundle, and provide Friedan-Shenker \cite{FS} complex geometry approach to CFT with the first non-trivial example besides rational models.Comment: 67 pages, 4 figures (Typos corrected as in the published version