A combined spectrophotometric and morphometric study of the lunar mare dome fields near Cauchy, Arago, Hortensius, and Milichius

In this Study we examine the spectral and morphometric properties of the four important lunar mare dome fields near Cauchy, Arago, Hortensius. and Milichius. We utilize Clementine UV vis mulfispectral data to examine the soil composition of the mare domes while employing telescopic CCD imagery to compute digital elevation maps in order to determine their morphometric properties, especially flank slope, height, and edifice Volume. After reviewing previous attempts to determine topographic data for lunar domes, we propose an image-based 3D reconstruction approach which is based on a combination of photoclinometry and shape from shading. Accordingly, we devise a classification scheme for lunar Marc domes which is based on a principal component analysis of the determined spectral and morphometric features. For the effusive mare domes of the examined fields we establish four Classes, two of which are further divided into two subclasses, respectively, where each class represents distinct combinations of spectral and morphometric dome properties. As a general trend, shallow and steep domes formed out of low-TiO2 basalts are observed in the Hortensius and Milichius dome fields, while the domes near Cauchy and Arago that consist of high-TiO2 basalts are all very shallow. The intrusive domes of our data set cover a wide continuous range of spectral and morphometric quantities, generally characterized by larger diameters and shallower flank slopes than effusive domes. A comparison to effusive and intrusive mare domes in other lunar regions, highland domes, and lunar cones has shown that the examined four mare dome fields display Such a richness in spectral properties and 3D dome shape that the established representation remains valid in a more global context. Furthermore, we estimate the physical parameters of dome formation for the examined domes based on a rheologic model. Each class of effusive domes defined in terms of spectral and morphometric properties is characterized by its specific range of values for lava viscosity, effusion rate, and duration of the effusion process. For our data set we report lava viscosities between about 10(2) and 10(8) Pas, effusion rates between 25 and 600 m(3) s(-1), and durations of the effusion process between three weeks and 18 years. Lava viscosity decreases with increasing R-415/R-750 spectral ratio and thus TiO2 content; however, the correlation is not strong, implying an important influence of further parameters like effusion temperature on lava viscosity.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Composite materials impact damage detection using neural networks

This thesis considers two basic aspects of impact damage in composite materials, namely damage severity discrimination and impact damage location by using Acoustic Emissions (AE) and Artificial Neural Networks (ANNs). The experimental work embodies a study of such factors as the application of AE as Non-destructive Damage Testing (NDT), and the evaluation of ANNs modelling. ANNs, however, played an important role in modelling implementation. In the first aspect of the study, different impact energies were used to produce different level of damage in two composite materials (T300/914 and T800/5245). The impacts were detected by their acoustic emissions (AE). The AE waveform signals were analysed and modelled using a Back Propagation (BP) neural network model. The Mean Square Error (MSE) from the output was then used as a damage indicator in the damage severity discrimination study. To evaluate the ANN model, a comparison was made of the correlation coefficients of different parameters, such as MSE, AE energy, AE counts, etc. MSE produced an outstanding result based on the best performance of correlation. In the second aspect, a new artificial neural network model was developed to provide impact damage location on a quasi-isotropic composite panel. It was successfully trained to locate impact sites by correlating the relationship between arriving time differences of AE signals at transducers located on the panel and the impact site coordinates. The performance of the ANN model, which was evaluated by calculating the distance deviation between model output and real location coordinates, supports the application of ANN as an impact damage location identifier. In the study, the accuracy of location prediction decreased when approaching the central area of the panel. Further investigation indicated that this is due to the small arrival time differences, which defect the performance of ANN prediction. This research suggested increasing the number of processing neurons in the ANNs as a practical solution.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

Optical fibre limits:an approach using ASE channel estimation

In this talk we investigate the usage of spectrally shaped amplified spontaneous emission (ASE) in order to emulate highly dispersed wavelength division multiplexed (WDM) signals in an optical transmission system. Such a technique offers various simplifications to large scale WDM experiments. Not only does it offer a reduction in transmitter complexity, removing the need for multiple source lasers, it potentially reduces the test and measurement complexity by requiring only the centre channel of a WDM system to be measured in order to estimate WDM worst case performance. The use of ASE as a test and measurement tool is well established in optical communication systems and several measurement techniques will be discussed [1, 2]. One of the most prevalent uses of ASE is in the measurement of receiver sensitivity where ASE is introduced in order to degrade the optical signal to noise ratio (OSNR) and measure the resulting bit error rate (BER) at the receiver. From an analytical point of view noise has been used to emulate system performance, the Gaussian Noise model is used as an estimate of highly dispersed signals and has had consider- able interest [3]. The work to be presented here extends the use of ASE by using it as a metric to emulate highly dispersed WDM signals and in the process reduce WDM transmitter complexity and receiver measurement time in a lab environment. Results thus far have indicated [2] that such a transmitter configuration is consistent with an AWGN model for transmission, with modulation format complexity and nonlinearities playing a key role in estimating the performance of systems utilising the ASE channel emulation technique. We conclude this work by investigating techniques capable of characterising the nonlinear and damage limits of optical fibres and the resultant information capacity limits. REFERENCES McCarthy, M. E., N. Mac Suibhne, S. T. Le, P. Harper, and A. D. Ellis, “High spectral efficiency transmission emulation for non-linear transmission performance estimation for high order modulation formats," 2014 European Conference on IEEE Optical Communication (ECOC), 2014. 2. Ellis, A., N. Mac Suibhne, F. Gunning, and S. Sygletos, “Expressions for the nonlinear trans- mission performance of multi-mode optical fiber," Opt. Express, Vol. 21, 22834{22846, 2013. Vacondio, F., O. Rival, C. Simonneau, E. Grellier, A. Bononi, L. Lorcy, J. Antona, and S. Bigo, “On nonlinear distortions of highly dispersive optical coherent systems," Opt. Express, Vol. 20, 1022-1032, 2012.

Physical properties and sedimentology of 4 profiles fom the South Atlantic

Ultrasonic P wavc transmission seismograms recorded on sediment cores have been analyzed to study the acoustic and estimate the clastic properties of marine sediments from different provinces dominated by terrigenous, calcareous, amI diatomaceous sedimentation. Instantaneous frequencies computed from the transmission seismograms are displayed as gray-shaded images to give an acoustic overview of the lithology of each core. Ccntirneter-scale variations in the ultrasonic waveforms associated with lithological changes are illustrated by wiggle traces in detail. Cross-correlation, multiple-filter, and spectral ratio techniques are applied to derive P wave velocities and attenuation coefficients. S wave velocities and attenuation coefficients, elastic moduli, and permeabilities are calculated by an inversion scheme based on the Biot-Stoll viscoelastic model. Together wilh porosity measurements, P and S wave scatter diagrams are constructed to characterize different sediment types by their velocity- and attenuation-porosity relationships. They demonstrate that terrigenous, calcareous, and diatomaceous sediments cover different velocity- and attenuation-porosity ranges. In terrigcnous sediments, P wave vclocities and attenuation coefficients decrease rapidly with increasing porosity, whereas S wave velocities and shear moduli are very low. Calcareous sediments behave similarly at relatively higher porosities. Foraminifera skeletons in compositions of terrigenous mud and calcareous ooze cause a stiffening of the frame accompanied by higher shear moduli, P wave velocities, and attenuation coefficients. In diatomaceous ooze the contribution of the shear modulus becomes increasingly important and is controlled by the opal content, whereas attenuation is very low. This leads to the opportunity to predict the opal content from nondestructive P wave velocity measurements at centimeter-scale resolution.

Efficient numerical methods for the random-field Ising model: Finite-size scaling, reweighting extrapolation, and computation of response functions

It was recently shown [Phys. Rev. Lett. 110, 227201 (2013)] that the critical behavior of the random-field Ising model in three dimensions is ruled by a single universality class. This conclusion was reached only after a proper taming of the large scaling corrections of the model by applying a combined approach of various techniques, coming from the zero-and positive-temperature toolboxes of statistical physics. In the present contribution we provide a detailed description of this combined scheme, explaining in detail the zero-temperature numerical scheme and developing the generalized fluctuation-dissipation formula that allowed us to compute connected and disconnected correlation functions of the model. We discuss the error evolution of our method and we illustrate the infinite limit-size extrapolation of several observables within phenomenological renormalization. We present an extension of the quotients method that allows us to obtain estimates of the critical exponent a of the specific heat of the model via the scaling of the bond energy and we discuss the self-averaging properties of the system and the algorithmic aspects of the maximum-flow algorithm used.

Role of Hydrological Process Representation on Erosion, Deposition, and Sediment Yield Estimate

Soil erosion by water is a major driven force causing land degradation. Laboratory experiments, on-site field study, and suspended sediments measurements were major fundamental approaches to study the mechanisms of soil water erosion and to quantify the erosive losses during rain events. The experimental research faces the challenge to extent the result to a wider spatial scale. Soil water erosion modeling provides possible solutions for scaling problems in erosion research, and is of principal importance to better understanding the governing processes of water erosion. However, soil water erosion models were considered to have limited value in practice. Uncertainties in hydrological simulations are among the reasons that hindering the development of water erosion model. Hydrological models gained substantial improvement recently and several water erosion models took advantages of the improvement of hydrological models. It is crucial to know the impact of changes in hydrological processes modeling on soil erosion simulation.

This dissertation work first created an erosion modeling tool (GEOtopSed) that takes advantage of the comprehensive hydrological model (GEOtop). The newly created tool was then tested and evaluated at an experimental watershed. The GEOtopSed model showed its ability to estimate multi-year soil erosion rate with varied hydrological conditions. To investigate the impact of different hydrological representations on soil erosion simulation, a 11-year simulation experiment was conducted for six models with varied configurations. The results were compared at varied temporal and spatial scales to highlight the roles of hydrological feedbacks on erosion. Models with simplified hydrological representations showed agreement with GEOtopSed model on long temporal scale (longer than annual). This result led to an investigation for erosion simulation at different rainfall regimes to check whether models with different hydrological representations have agreement on the soil water erosion responses to the changing climate. Multi-year ensemble simulations with different extreme precipitation scenarios were conducted at seven climate regions. The differences in erosion simulation results showed the influences of hydrological feedbacks which cannot be seen by purely rainfall erosivity method.

Delta 13C measured on alkenones of surface sediment samples GeoB1008-6 to GeoB3603-1 from the South Atlantic

We have analyzed the stable carbon isotopic composition of the diunsaturated C37 alkenone in 29 surface sediments from the equatorial and South Atlantic Ocean. Our study area covers different oceanographic settings, including sediments from the major upwelling regions off South Africa, the equatorial upwelling, and the oligotrophic western South Atlantic. In order to examine the environmental influences on the sedimentary record the alkenone-based carbon isotopic fractionation (Ep) values were correlated with the overlying surface water concentrations of aqueous CO2 ([CO2(aq)]), phosphate, and nitrate. We found Ep positively correlated with 1/[CO2(aq)] and negatively correlated with [PO43-] and [NO3-]. However, the relationship between Ep and 1/[CO2(aq)] is opposite of what is expected from a [CO2(aq)] controlled, diffusive uptake model. Instead, our findings support the theory of Bidigare et al. (1997, doi:10.1029/96GB03939) that the isotopic fractionation in haptophytes is related to nutrient-limited growth rates. The relatively high variability of the Ep-[PO4] relationship in regions with low surface water nutrient concentrations indicates that here other environmental factors also affect the isotopic signal. These factors might be variations in other growth-limiting resources such as light intensity or micronutrient concentrations.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

INFLACIÓN E INCERTIDUMBRE INFLACIONARIA: EVIDENCIA PARA COSTA RICA

En este documento se estima una medida de la incertidumbre inflacionaria. Un modelo de inflación señala incertidumbre cuando los errores de pronóstico son heteroscedásticos. Por medio de la especificación de una ecuación GARCH (Generalized Autoregressive Conditional Heteroscedasticity), para la varianza del término de error de un modelo de inflación, es posible estimar una proxy de incertidumbre inflacionaria. La estimación simultánea del modelo de inflación y de la ecuación GARCH, produce un nuevo modelo de inflación en el cual los errores de pronóstico son homocedásticos. Existe consenso en la literatura económica en que hay una correlación positiva entre incertidumbre inflacionaria y la magnitud de la tasa de inflación, lo cual, como lo señaló Friedman (1977), representa uno de los costos asociados con la persistencia inflacionaria. Esto es porque tal incertidumbre dificulta la toma de decisiones óptimas por parte de los agentes económicos.La evidencia empírica, para el periodo 1954:01-2002:08, apoya la hipótesis de que para el caso de Costa Rica mientras mayor es la inflación mayor es la incertidumbre respecto a esta variable. En los últimos siete años (1997-2002) la incertidumbre presenta la variación media más baja de todo el periodo. Además, se identifica un efecto asimétrico de la inflación sobre la incertidumbre inflacionaria, es decir, la incertidumbre inflacionaria tiende a incrementarse más para el siguiente periodo cuando la inflación pronosticada está por debajo de la inflación actual, que cuando la inflación pronosticada está por arriba de la tasa observada de inflación. Estos resultados tienen una clara implicación para la política monetaria. Para minimizar la dificultad que la inflación causa en la toma óptima de decisiones de los agentes económicos es necesario perseguir no solamente un nivel bajo de inflación sino que también sea estable.AbstractThis paper estimates a measure of inflationary uncertainty. An inflation model signals uncertainty when the forecast errors are heteroskedastic. By the specification of a GARCH (Generalized Autoregressive Conditional Heteroscedasticity) equation, for the variance of the error term of the inflation model, it is possible to estimate a proxy for inflationary uncertainty. By the simultaneous estimation of the inflation model and the GARCH equation, a new inflation model is obtained in which the forecast errors are homocedastic. Most economists agree that there is a positive correlation between inflationary uncertainty and the magnitude of the inflation rate, which, as was pointed out by Friedman (1977), represents one of costs associated with the persistence of inflation. This is because such uncertainty clouds the decision-making process of consumers and investors.The empirical evidence for the period 1954:01-2002:08 confirms that in the case of Costa Rica inflationary uncertainty increases as inflation rises. In the last seven years(1997-2002) the uncertainty present the mean variation most small of the period. In addition, inflation has an asymmetric effect on inflationary uncertainty. That is, when the inflation forecast is below the actual inflation, inflationary uncertainty increases for the next period. The opposite happens when the inflation forecast is above the observed rate of inflation. Besides, the absolute value of the change on uncertainty is greater in the first case than the second. These results have a clear implication for monetary policy. To minimize the disruptions that inflation causes to the economic decision-making process, it is necessary to pursue, not only a low level of inflation, but a stable one as well.

CHARACTERIZATION OF THERMAL AND MECHANICAL PROPERTIES OF POLYPROPYLENE–BASED COMPOSITES FOR FUEL CELL BIPOLAR PLATES AND DEVELOPMENT OF EDUCATIONAL TOOLS IN HYDROGEN AND FUEL CELL TECHNOLOGIES

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi–walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbons’ Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International’s FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through–plane and in–plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in–plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through–plane and in–plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single–filler formulations. For thermal conductivity, Nielsen’s model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen’s model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

On the Wick product and Wong-Zakai approximations.

This thesis is a compilation of 6 papers that the author has written together with Alberto Lanconelli (chapters 3, 5 and 8) and Hyun-Jung Kim (ch 7). The logic thread that link all these chapters together is the interest to analyze and approximate the solutions of certain stochastic differential equations using the so called Wick product as the basic tool. In the first chapter we present arguably the most important achievement of this thesis; namely the generalization to multiple dimensions of a Wick-Wong-Zakai approximation theorem proposed by Hu and Oksendal. By exploiting the relationship between the Wick product and the Malliavin derivative we propose an original reduction method which allows us to approximate semi-linear systems of stochastic differential equations of the Itô type. Furthermore in chapter 4 we present a non-trivial extension of the aforementioned results to the case in which the system of stochastic differential equations are driven by a multi-dimensional fraction Brownian motion with Hurst parameter bigger than 1/2. In chapter 5 we employ our approach and present a “short time” approximation for the solution of the Zakai equation from non-linear filtering theory and provide an estimation of the speed of convergence. In chapters 6 and 7 we study some properties of the unique mild solution for the Stochastic Heat Equation driven by spatial white noise of the Wick-Skorohod type. In particular by means of our reduction method we obtain an alternative derivation of the Feynman-Kac representation for the solution, we find its optimal Hölder regularity in time and space and present a Feynman-Kac-type closed form for its spatial derivative. Chapter 8 treats a somewhat different topic; in particular we investigate some probabilistic aspects of the unique global strong solution of a two dimensional system of semi-linear stochastic differential equations describing a predator-prey model perturbed by Gaussian noise.