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

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

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

Asymmetric Autocorrelation Function To Resolve Directional Ambiguity In PIV Images

ABSTRACT Autocorrelation of a double-exposed image, unlike cross-correlation between two images, produces a correlation function that i

Design and Implementation of a Compact Automated Spirulina Cultivation System for Households

Spirulina is considered to be the most nutritious whole food source in nature. It is promoted as a dietary supplement and an active ingredient in functional foods. Factors such as conflicts, supply chain disruptions, and economic fallout are driving food prices to unprecedented levels. Low- and middle-income populations are affected by these rising costs. The design and implementation of a compact Spirulina cultivation system to be used in the household is presented in this paper. The system contains light, temperature, pH, and turbidity sensors. All sensors are connected to microcontrollers which activate a heater, air pump, mixing pump, pool fall pump, and two LEDs according to the readings received to ensure proper and continuous growth of Spirulina. The proposed system is user-friendly, economical, and can be easily stored and operated at homes to stimulate and monitor the growth of Spirulina. The primary objective of the proposed compact cultivation system is to furnish the necessary tools for generating a nutritionally valuable food source on a smaller scale, specifically within households, at a relatively affordable cost

Wide Field Infrared Survey Telescope (WFIRST) Observatory Overview

NASA's Wide Field Infrared Survey Telescope (WFIRST) is being designed to deliver unprecedented capability in dark energy and exoplanet science, and to host a technology demonstration coronagraph for exoplanet imaging and spectroscopy. The observatory design has matured since 2013; we present a comprehensive description of the observatory configuration as refined during the WFIRST Phase-A study. The observatory is based on an existing, repurposed 2.4 meter space telescope coupled with a 288 megapixel near-infrared (0.6 to 2 microns) HgCdTe focal plane array with multiple imaging and spectrographic modes. Together they deliver a 0.28 square degree field of view, which is approximately 100 times larger than the Hubble Space Telescope, and a sensitivity that enables rapid science surveys. In addition, the coronagraph technology demonstration will prove the feasibility of new techniques for exoplanet discovery, imaging, and spectral analysis. A composite truss structure meters both instruments to the telescope assembly, and the instruments and the spacecraft are flight serviceable. We present configuration changes since 2013 that improved interfaces, improved testability, and reduced technical risk. We provide an overview of our Integrated Modeling results, performed at an unprecedented level for a phase-A study, to illustrate performance margins with respect to static wavefront error, jitter, and thermal drift

Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals

In this article we develop a new sequential Monte Carlo method for multilevel Monte Carlo estimation. In particular, the method can be used to estimate expectations with respect to a target probability distribution over an infinite-dimensional and noncompact space—as produced, for example, by a Bayesian inverse problem with a Gaussian random field prior. Under suitable assumptions the MLSMC method has the optimal O(ε −2 ) bound on the cost to obtain a mean-square error of O(ε 2 ). The algorithm is accelerated by dimension-independent likelihood-informed proposals [T. Cui, K. J. Law, and Y. M. Marzouk, (2016), J. Comput. Phys., 304, pp. 109–137] designed for Gaussian priors, leveraging a novel variation which uses empirical covariance information in lieu of Hessian information, hence eliminating the requirement for gradient evaluations. The efficiency of the algorithm is illustrated on two examples: (i) inversion of noisy pressure measurements in a PDE model of Darcy flow to recover the posterior distribution of the permeability field and (ii) inversion of noisy measurements of the solution of an SDE to recover the posterior path measure