Toward improved identifiability of hydrologic model parameters: The information content of experimental data

We have developed a sequential optimization methodology, entitled the parameter identification method based on the localization of information (PIMLI) that increases information retrieval from the data by inferring the location and type of measurements that are most informative for the model parameters. The PIMLI approach merges the strengths of the generalized sensitivity analysis (GSA) method [Spear and Hornberger, 1980], the Bayesian recursive estimation (BARE) algorithm [Thiemann et al., 2001], and the Metropolis algorithm [Metropolis et al., 1953]. Three case studies with increasing complexity are used to illustrate the usefulness and applicability of the PIMLI methodology. The first two case studies consider the identification of soil hydraulic parameters using soil water retention data and a transient multistep outflow experiment (MSO), whereas the third study involves the calibration of a conceptual rainfall-runoff model

Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity

A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers

Deep Projective 3D Semantic Segmentation

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets. In this paper, we propose an alternative framework that avoids the limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first project the point cloud onto a set of synthetic 2D-images. These images are then used as input to a 2D-CNN, designed for semantic segmentation. Finally, the obtained prediction scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets a new state-of-the-art by achieving a relative gain of 7.9 %, compared to the previous best approach.Comment: Submitted to CAIP 201

In situ spectroscopic monitoring of CO2 reduction at copper oxide electrode

Copper oxide modified electrodes were investigated as a function of applied electrode potential using in situ infrared spectroscopy and ex situ Raman and X-ray photoelectron spectroscopy. In deoxygenated KHCO3 electrolyte bicarbonate and carbonate species were found to adsorb to the electrode during reduction and the CuO was reduced to Cu(I) or Cu(0) species. Carbonate was incorporated into the structure and the CuO starting material was not regenerated on cycling to positive potentials. In contrast, in CO2 saturated KHCO3 solution, surface adsorption of bicarbonate and carbonate was not observed and adsorption of a carbonato-species was observed with in situ infrared spectroscopy. This species is believed to be activated, bent CO2. On cycling to negative potentials, larger reduction currents were observed in the presence of CO2; however, less of the charge could be attributed to the reduction of CuO. In the presence of CO2 CuO underwent reduction to Cu2O and potentially Cu, with no incorporation of carbonate. Under these conditions the CuO starting material could be regenerated by cycling to positive potentials

Joint Blind Motion Deblurring and Depth Estimation of Light Field

Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods

In vitro efficacy of tavaborole topical solution, 5% after penetration through nail polish on ex vivo human fingernails

This document is the Accepted Manuscript of the following article: Aditya K. Gupta, et al, 'In vitro efficacy of tavaborole topical solution, 5% after penetration through nail polish on ex vivo human fingernails', Journal of Dermatological Treatment, Jan 2018. Under embargo until 10 January 2019. The final, published version is available online at doi: https://doi.org/10.1080/09546634.2017.1422078.Background: Topical antifungal treatments for onychomycosis are applied to clean, unpolished nails for 48 weeks or longer. Patients often wish to mask their infection with nail polish yet there is no evidence to suggest antifungal efficacy in the presence of nail polish. Objective: To determine if tavaborole retains the ability to penetrate the nail plate and inhibit fungal growth in the presence of nail polish. Method: Tavaborole was applied to human fingernails painted with 2 or 4 coats of nail polish, and unpainted nails in an ex vivo model. Nails were mounted on TurChub ® chambers seeded with Trichophyton rubrum and allowed to incubate for 7 days. Antifungal activity was assessed by measuring zones of inhibition. Results and conclusion: Tavaborole exhibited antifungal activity in all experimental groups. The zones of inhibition of T. rubrum for all experimental groups (2 or 4 coats of polish, unpolished) were greater than infected controls (polished and unpolished), p s <.001. Tavaborole penetrates polished nails and kills T. rubrum in this ex vivo model.Peer reviewe

Mexico City and the biogeochemistry of global urbanization

Mexico City is far advanced in its urban evolution, and cities in currently developing nations may soon follow a similar course. This paper investigates the strengths and weaknesses of infrastructures for the emerging megacities. The major driving force for infrastructure change in Mexico City is concern over air quality. Air chemistry data from recent field campaigns have been used to calculate fluxes in the atmosphere of the Valley of Mexico, for compounds that are important to biogeochemistry including methane (CH4), carbon monoxide (CO), nonmethane hydrocarbons (NMHCs), ammonia (NH3), sulfur dioxide (SO2), nitrogen oxides (NOx and NOy), soot, and dust. Leakage of liquified petroleum gas approached 10% during sampling periods, and automotive pollutant sources in Mexico City were found to match those in developed cities, despite a lower vehicle-to-person ratio of 0.1. Ammonia is released primarily from residential areas, at levels sufficient to titrate pollutant acids into particles across the entire basin. Enhancements of reduced nitrogen and hydrocarbons in the vapor phase skew the distribution of NOy species towards lower average deposition velocities. Partly as a result, downwind nutrient deposition occurs on a similar scale as nitrogen fixation across Central America, and augments marine nitrate upwelling. Dust suspension from unpaved roads and from the bed of Lake Texcoco was found to be comparable to that occurring on the periphery of the Sahara, Arabian, and Gobi deserts. In addition, sodium chloride (NaCl) in the dust may support heterogeneous chlorine oxide (ClOx) chemistry. The insights from our Mexico City analysis have been tentatively applied to the upcoming urbanization of Asia