Smoothness of scale functions for spectrally negative Lévy processes

Scale functions play a central role in the fluctuation theory of spectrally negative Lévy processes and often appear in the context of martingale relations. These relations are often require excursion theory rather than Itô calculus. The reason for the latter is that standard Itô calculus is only applicable to functions with a sufficient degree of smoothness and knowledge of the precise degree of smoothness of scale functions is seemingly incomplete. The aim of this article is to offer new results concerning properties of scale functions in relation to the smoothness of the underlying Lévy measure. We place particular emphasis on spectrally negative Lévy processes with a Gaussian component and processes of bounded variation. An additional motivation is the very intimate relation of scale functions to renewal functions of subordinators. The results obtained for scale functions have direct implications offering new results concerning the smoothness of such renewal functions for which there seems to be very little existing literature on this topic.

Isomap nonlinear dimensionality reduction and bimodality of Asian monsoon convection

It is known that the empirical orthogonal function method is unable to detect possible nonlinear structure in climate data. Here, isometric feature mapping (Isomap), as a tool for nonlinear dimensionality reduction, is applied to 1958–2001 ERA-40 sea-level pressure anomalies to study nonlinearity of the Asian summer monsoon intraseasonal variability. Using the leading two Isomap time series, the probability density function is shown to be bimodal. A two-dimensional bivariate Gaussian mixture model is then applied to identify the monsoon phases, the obtained regimes representing enhanced and suppressed phases, respectively. The relationship with the large-scale seasonal mean monsoon indicates that the frequency of monsoon regime occurrence is significantly perturbed in agreement with conceptual ideas, with preference for enhanced convection on intraseasonal time scales during large-scale strong monsoons. Trend analysis suggests a shift in concentration of monsoon convection, with less emphasis on South Asia and more on the East China Sea.

20th century intraseasonal Asian monsoon dynamics viewed from Isomap

The Asian summer monsoon is a high dimensional and highly nonlinear phenomenon involving considerable moisture transport towards land from the ocean, and is critical for the whole region. We have used daily ECMWF reanalysis (ERA-40) sea-level pressure (SLP) anomalies to the seasonal cycle, over the region 50-145°E, 20°S-35°N to study the nonlinearity of the Asian monsoon using Isomap. We have focused on the two-dimensional embedding of the SLP anomalies for ease of interpretation. Unlike the unimodality obtained from tests performed in empirical orthogonal function space, the probability density function, within the two-dimensional Isomap space, turns out to be bimodal. But a clustering procedure applied to the SLP data reveals support for three clusters, which are identified using a three-component bivariate Gaussian mixture model. The modes are found to appear similar to active and break phases of the monsoon over South Asia in addition to a third phase, which shows active conditions over the Western North Pacific. Using the low-level wind field anomalies the active phase over South Asia is found to be characterised by a strengthening and an eastward extension of the Somali jet whereas during the break phase the Somali jet is weakened near southern India, while the monsoon trough in northern India also weakens. Interpretation is aided using the APHRODITE gridded land precipitation product for monsoon Asia. The effect of large-scale seasonal mean monsoon and lower boundary forcing, in the form of ENSO, is also investigated and discussed. The outcome here is that ENSO is shown to perturb the intraseasonal regimes, in agreement with conceptual ideas.

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

The analysis step of the (ensemble) Kalman filter is optimal when (1) the distribution of the background is Gaussian, (2) state variables and observations are related via a linear operator, and (3) the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA) can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1)-(3) are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

Mixture of changing uniaxial micellar forms in lyotropic biaxial nematics

Lyotropic nematics consisting of surfactant-cosurfactant water solutions may present a biaxial phase or direct U(+) <-> U(-) transitions, in different regions of the temperature-relative concentration phase diagram, for different systems and compositions. We propose that these may be related to changes of uniaxial micellar form, which may occur either smoothly or abruptly. Smooth change of cylinder-like into disc-like shapes requires a distribution of Maier-Saupe interaction constants and we consider two limiting cases for the distribution of forms: a single Gaussian and a double Gaussian. Alternatively, an abrupt change of form is described by a discontinuous distribution of interaction constants. Our results show that the dispersive distributions yield a biaxial phase, while an abrupt change of shape leads to coexistence of uniaxial phases. Fitting the theory to the experiment for the ternary system KL/decanol/D2O leads to transition lines in very good agreement with experimental results. In order to rationalise the results of the comparison, we analyse temperature and concentration form dependence, which connects micellar and experimental macroscopic parameters. Physically consistent variations of micellar asymmetry, amphiphile partitioning and volume are obtained. To the best of the authors` knowledge, this is the first truly statistical microscopic approach that is able to model experimentally observed lyotropic biaxial nematic phases.

Evaluation via multivariate techniques of scale factor variability in the rietveld method applied to quantitative phase analysis with X ray powder diffraction

ABSTRACT: The present work uses multivariate statistical analysis as a form of establishing the main sources of error in the Quantitative Phase Analysis (QPA) using the Rietveld method. The quantitative determination of crystalline phases using x ray powder diffraction is a complex measurement process whose results are influenced by several factors. Ternary mixtures of Al2O3, MgO and NiO were prepared under controlled conditions and the diffractions were obtained using the Bragg-Brentano geometric arrangement. It was possible to establish four sources of critical variations: the experimental absorption and the scale factor of NiO, which is the phase with the greatest linear absorption coefficient of the ternary mixture; the instrumental characteristics represented by mechanical errors of the goniometer and sample displacement; the other two phases (Al2O3 and MgO); and the temperature and relative humidity of the air in the laboratory. The error sources excessively impair the QPA with the Rietveld method. Therefore it becomes necessary to control them during the measurement procedure.

A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.

Water-assisted Flow of Heavy Oil in a Vertical Pipe: Pilot-scale Experiments

The use of the core-annular flow pattern, where a thin fluid surrounds a very viscous one, has been suggested as an attractive artificial-lift method for heavy oils in the current Brazilian ultra-deepwater production scenario. This paper reports the pressure drop measurements and the core-annular flow observed in a 2 7/8-inch and 300 meter deep pilot-scale well conveying a mixture of heavy crude oil (2000 mPa.s and 950 kg/m3 at 35 C) and water at several combinations of the individual flow rates. The two-phase pressure drop data are compared with those of single-phase oil flow to assess the gains due to water injection. Another issue is the handling of the core-annular flow once it has been established. High-frequency pressure-gradient signals were collected and a treatment based on the Gabor transform together with neural networks is proposed as a promising solution for monitoring and control. The preliminary results are encouraging. The pilot-scale tests, including long-term experiments, were conducted in order to investigate the applicability of using water to transport heavy oils in actual wells. It represents an important step towards the full scale application of the proposed artificial-lift technology. The registered improvements in terms of oil production rate and pressure drop reductions are remarkable.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Full scale test for explosion water barriers in small cross-section galleries

Underground coal mines explosions generally arise from the inflammation of a methane/air mixture. This explosion can also generate a subsequent coal dust explosion. Traditionally such explosions have being fought eliminating one or several of the factors needed by the explosion to take place. Although several preventive measures are taken to prevent explosions, other measures should be considered to reduce the effects or even to extinguish the flame front. Unlike other protection methods that remove one or two of the explosion triangle elements, namely; the ignition source, the oxidizing agent and the fuel, explosion barriers removes all of them: reduces the quantity of coal in suspension, cools the flame front and the steam generated by vaporization removes the oxygen present in the flame. Passive water barriers are autonomous protection systems against explosions that reduce to a satisfactory safety level the effects of methane and/or flammable dust explosions. The barriers are activated by the pressure wave provoked in the explosion destroying the barrier troughs and producing a uniform dispersion of the extinguishing agent throughout the gallery section in quantity enough to extinguish the explosion flame. Full scale tests have been carried out in Polish Barbara experimental mine at GIG Central Mining Institute in order to determine the requirements and the optimal installation conditions of these devices for small sections galleries which are very frequent in the Spanish coal mines. Full scale tests results have been analyzed to understand the explosion timing and development, in order to assess on the use of water barriers in the typical small crosssection Spanish galleries. Several arrangements of water barriers have been designed and tested to verify the effectiveness of the explosion suppression in each case. The results obtained demonstrate the efficiency of the water barriers in stopping the flame front even with smaller amounts of water than those established by the European standard. According to the tests realized, water barriers activation times are between 0.52 s and 0.78 s and the flame propagation speed are between 75 m/s and 80 m/s. The maximum pressures (Pmax) obtained in the full scale tests have varied between 0.2 bar and 1.8 bar. Passive barriers protect effectively against the spread of the flame but cannot be used as a safeguard of the gallery between the ignition source and the first row of water troughs or bags, or even after them, as the pressure could remain high after them even if the flame front has been extinguished.

Repeatability and Two-Dimensionality of Model Scale Sloshing Impacts

Canonical test cases for sloshing wave impact problems are pre-sented and discussed. In these cases the experimental setup has been simpli?ed seeking the highest feasible repeatability; a rectangular tank subjected to harmonic roll motion has been the tested con?guration. Both lateral and roof impacts have been studied, since both cases are relevant in sloshing assessment and show speci?c dynamics. An analysis of the impact pressure of the ?rst four impact events is provided in all cases. It has been found that not in all cases a Gaussian ?tting of each individual peak is feasible. The tests have been conducted with both water and oil in order to obtain high and moderate Reynolds number data; the latter may be useful as simpler test cases to assess the capabilities of CFD codes in simulating sloshing impacts. The re-peatability of impact pressure values increases dramatically when using oil. In addition, a study of the two-dimensionality of the problem using a tank con?guration that can be adjusted to 4 di?erent thicknesses has been carried out. Though the kinemat-ics of the free surface does not change signi cantly in some of the cases, the impact pressure values of the ?rst impact events changes substantially from the small to the large aspect ratios thus meaning that attention has to be paid to this issue when reference data is used for validation of 2D and 3D CFD codes.

3D video quality of experience - influence of scale and crosstalk

This paper gives an overview of three recent studies by the authors on the topic of 3D video Quality of Experience (QoE). Two of studies [1,2] investigated different psychological dimension that may be needed for describing 3D video QoE and the third the visibility and annoyance of crosstalk[3]. The results shows that the video quality scale could be sufficient for evaluating S3D video experience for coding and spatial resolution reduction distortions. It was also confirmed that with a more complex mixture of degradations more than one scale should be used to capture the QoE in these cases. The study found a linear relationship between the perceived crosstalk and the amount of crosstalk.