Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis-Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler's exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the \emph{exact} posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this paper: when the likelihood has some \emph{local} regularity, the number of model evaluations per MCMC step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ODE and PDE inference problems, with both synthetic and real data.Comment: A major update of the theory and example

Apparatus and method using a holographic optical element for converting a spectral distribution to image points

A holographic optical element transforms a spectral distribution of light to image points. The element comprises areas, each of which acts as a separate lens to image the light incident in its area to an image point. Each area contains the recorded hologram of a point source object. The image points can be made to lie in a line in the same focal plane so as to align with a linear array detector. A version of the element has been developed that has concentric equal areas to match the circular fringe pattern of a Fabry-Perot interferometer. The element has high transmission efficiency, and when coupled with high quantum efficiency solid state detectors, provides an efficient photon-collecting detection system. The element may be used as part of the detection system in a direct detection Doppler lidar system or multiple field of view lidar system

Inhibitory Activity of Leaves Extracts of Citrullus colocynthis Schrad. on HT29 Human Colon Cancer Cells

Aims: Citrullus colocynthis is a plant endemic in Asia, Africa and in the Mediterranean basin. It is used in folk medicine against infections, inflammations and cardiovascular and immune-related diseases. There are further evidences of the use of Citrullus colocynthis Schrad in the treatment of cancer in traditional practices. The present study aimed to determine the potential antiproliferative effects of different Citrullus colocynthis leaf extracts on human cancer cells. Methodology: Antiproliferative and antioxidant effects on HT-29 human colon cancer cells were detected by MTS assay and a modified protocol of the alkaline Comet assay. In vitro antioxidant activities of different leaf extracts were evaluated through DPPH, \u3b2-carotene/linoleic acid and reducing power assays. Results: The leaf chloroform extract exhibited the higher cell growth inhibitory activity without induction of DNA damage; it showed to be able to significantly decrease DNA damage induced by H2O2 (100 M). This antioxidant activity seems to be comparable to that of vitamin C (1 mM). Ethyl acetate, acetone and methanol leaf extracts showed to be the most effective in reducing the stable free DPPH radical (IC50 =113 g/ml), in transforming the Fe3+ to Fe2+ (IC50 = 134 \ub5g/ml) and in inducing linoleic acid oxidation with an inhibition of 31.9 %. Conclusion: Our results confirm the antiproliferative potential of Citrullus colocynthis Schrad. on human cancer cells

Psychosocial health among immigrants in central and southern Europe.

Migration exposes people to a number of risks that threaten their health, including those related to psychosocial health. Self-perceived health is usually the main indicator used to assess psychosocial health. Electronic databases were used to examine the literature on the psychosocial health of immigrants in Europe and of North Africans living in their own countries. Immigrants of various ethnic groups show a similar risk of psychosocial disorders but generally present a higher risk than the local population. This risk is related to gender (being higher in women), poor socio-economic status and acculturation, discrimination, time elapsed since migration and age on arrival in the new country. Although the stressors and situations the different ethnic groups experience in the host country may be shared, the way they deal with them may differ according to cultural factors. There is a need to collect detailed data on psychosocial health among the various immigrant groups in Europe, as well as to monitor this aspect in North African residents who lack access to specific services

Bayesian reconstruction of binary media with unresolved fine-scale spatial structures

We present a Bayesian technique to estimate the fine-scale properties of a binary medium from multiscale observations. The binary medium of interest consists of spatially varying proportions of low and high permeability material with an isotropic structure. Inclusions of one material within the other are far smaller than the domain sizes of interest, and thus are never explicitly resolved. We consider the problem of estimating the spatial distribution of the inclusion proportion, F(x), and a characteristic length-scale of the inclusions, δ, from sparse multiscale measurements. The observations consist of coarse-scale (of the order of the domain size) measurements of the effective permeability of the medium (i.e., static data) and tracer breakthrough times (i.e., dynamic data), which interrogate the fine scale, at a sparsely distributed set of locations. This ill-posed problem is regularized by specifying a Gaussian process model for the unknown field F(x) and expressing it as a superposition of Karhunen–Loève modes. The effect of the fine-scale structures on the coarse-scale effective permeability i.e., upscaling, is performed using a subgrid-model which includes δ as one of its parameters. A statistical inverse problem is posed to infer the weights of the Karhunen–Loève modes and δ, which is then solved using an adaptive Markov Chain Monte Carlo method. The solution yields non-parametric distributions for the objects of interest, thus providing most probable estimates and uncertainty bounds on latent structures at coarse and fine scales. The technique is tested using synthetic data. The individual contributions of the static and dynamic data to the inference are also analyzed.United States. Dept. of Energy. National Nuclear Security Administration (Contract DE-AC04_94AL85000

Diffeomorphic random sampling using optimal information transport

In this article we explore an algorithm for diffeomorphic random sampling of nonuniform probability distributions on Riemannian manifolds. The algorithm is based on optimal information transport (OIT)---an analogue of optimal mass transport (OMT). Our framework uses the deep geometric connections between the Fisher-Rao metric on the space of probability densities and the right-invariant information metric on the group of diffeomorphisms. The resulting sampling algorithm is a promising alternative to OMT, in particular as our formulation is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when a large number of samples from a low dimensional nonuniform distribution is needed.Comment: 8 pages, 3 figure

Discovery of 6.035GHz Hydroxyl Maser Flares in IRAS18566+0408

We report the discovery of 6.035GHz hydroxyl (OH) maser flares toward the massive star forming region IRAS18566+0408 (G37.55+0.20), which is the only region known to show periodic formaldehyde (4.8 GHz H2CO) and methanol (6.7 GHz CH3OH) maser flares. The observations were conducted between October 2008 and January 2010 with the 305m Arecibo Telescope in Puerto Rico. We detected two flare events, one in March 2009, and one in September to November 2009. The OH maser flares are not simultaneous with the H2CO flares, but may be correlated with CH3OH flares from a component at corresponding velocities. A possible correlated variability of OH and CH3OH masers in IRAS18566+0408 is consistent with a common excitation mechanism (IR pumping) as predicted by theory.Comment: Accepted for publication in the Astrophysical Journa

Parallel Local Approximation MCMC for Expensive Models

Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of PDEs. In recent work [J. Amer. Statist. Assoc., 111 (2016), pp. 1591-1607] we described a framework for constructing and refining local approximations of such models during an MCMC simulation. These posterior-adapted approximations harness regularity of the model to reduce the computational cost of inference while preserving asymptotic exactness of the Markov chain. Here we describe two extensions of that work. First, we prove that samplers running in parallel can collaboratively construct a shared posterior approximation while ensuring ergodicity of each associated chain, providing a novel opportunity for exploiting parallel computation in MCMC. Second, focusing on the Metropolis-adjusted Langevin algorithm, we describe how a proposal distribution can successfully employ gradients and other relevant information extracted from the approximation. We investigate the practical performance of our approach using two challenging inference problems, the first in subsurface hydrology and the second in glaciology. Using local approximations constructed via parallel chains, we successfully reduce the run time needed to characterize the posterior distributions in these problems from days to hours and from months to days, respectively, dramatically improving the tractability of Bayesian inference.United States. Department of Energy. Office of Science. Scientific Discovery through Advanced Computing (SciDAC) Program (award DE-SC0007099)Natural Sciences and Engineering Research Council of CanadaUnited States. Office of Naval Researc

Robust Online Hamiltonian Learning

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic

Fabrication and characterization of dual function nanoscale pH-scanning ion conductance microscopy (SICM) probes for high resolution pH mapping

The easy fabrication and use of nanoscale dual function pH-scanning ion conductance microscopy (SICM) probes is reported. These probes incorporate an iridium oxide coated carbon electrode for pH measurement and an SICM barrel for distance control, enabling simultaneous pH and topography mapping. These pH-SICM probes were fabricated rapidly from laser pulled theta quartz pipets, with the pH electrode prepared by in situ carbon filling of one of the barrels by the pyrolytic decomposition of butane, followed by electrodeposition of a thin layer of hydrous iridium oxide. The other barrel was filled with an electrolyte solution and Ag/AgCl electrode as part of a conductance cell for SICM. The fabricated probes, with pH and SICM sensing elements typically on the 100 nm scale, were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and various electrochemical measurements. They showed a linear super-Nernstian pH response over a range of pH (pH 2–10). The capability of the pH-SICM probe was demonstrated by detecting both pH and topographical changes during the dissolution of a calcite microcrystal in aqueous solution. This system illustrates the quantitative nature of pH-SICM imaging, because the dissolution process changes the crystal height and interfacial pH (compared to bulk), and each is sensitive to the rate. Both measurements reveal similar dissolution rates, which are in agreement with previously reported literature values measured by classical bulk methods