Treatment of geophysical data as a non-stationary process

ABSTRACT: The Kalman-Bucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic non-stationary. The derivation of the estimator is based on the relationship between the Kalman-Bucy and Wiener approaches for linear systems. In the present work we emphasize the criterion used, the model with apriori information, the algorithm, and the quality as related to the results. The examples are for the ideal well-log response, and the results indicate that this method can be used on a variety of geophysical data treatments, and its study clearly offers a proper insight into modeling and processing of geophysical problems.

Non-normal modal logics, quantification, and deontic dilemmas. A study in multi-relational semantics

This dissertation is devoted to the study of non-normal (modal) systems for deontic logics, both on the propositional level, and on the first order one. In particular we developed our study the Multi-relational setting that generalises standard Kripke Semantics. We present new completeness results concerning the semantic setting of several systems which are able to handle normative dilemmas and conflicts. Although primarily driven by issues related to the legal and moral field, these results are also relevant for the more theoretical field of Modal Logic itself, as we propose a syntactical, and semantic study of intermediate systems between the classical propositional calculus CPC and the minimal normal modal logic K.

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. ^

Process capability estimation for leukocyte filtering process in blood service: a comparison study

This article considers a comparison study between different non-normal process capability estimation methods and utilizing themin the leukocyte filtering process in blood service sectors. Since the amount of leukocyte in a unit of the blood is a critical issue inthe blood transfusion process and patient safety, estimating and monitoring the capability of the leukocyte filtering process to meetthe target window is very important for blood service sectors. However, observed data from the leukocyte filtering process showthat the leukocyte levels after filtering demonstrate a right skewed distribution and applying conventional methods with a normalityassumption fails to provide trustful results. Hence, we first conduct a simulation study to compare different methods in estimating theprocess capability index of non-normal processes and then we apply these techniques to obtain the process capability of the leukocytefiltering process. The study reveals that the Box-Cox transformation method provides reliable estimation of the process capability ofthe leukocyte filtering process.

A lower bound to the survival probability and an approximate first passage time distribution for Markovian and non-Markovian dynamics in phase space

We derive a very general expression of the survival probability and the first passage time distribution for a particle executing Brownian motion in full phase space with an absorbing boundary condition at a point in the position space, which is valid irrespective of the statistical nature of the dynamics. The expression, together with the Jensen's inequality, naturally leads to a lower bound to the actual survival probability and an approximate first passage time distribution. These are expressed in terms of the position-position, velocity-velocity, and position-velocity variances. Knowledge of these variances enables one to compute a lower bound to the survival probability and consequently the first passage distribution function. As examples, we compute these for a Gaussian Markovian process and, in the case of non-Markovian process, with an exponentially decaying friction kernel and also with a power law friction kernel. Our analysis shows that the survival probability decays exponentially at the long time irrespective of the nature of the dynamics with an exponent equal to the transition state rate constant.

Microscopic origin of non-Gaussian distributions of financial returns

In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumption, akin to the Born-Oppenheimer approximation, in which the volatility has already relaxed to its equilibrium distribution and acts as a background to the evolution of the price process. In this approximation, we show that all models of stochastic volatility should exhibit a scaling relation in the time lag of zero-drift modified log-returns. We verify that the Dow-Jones Industrial Average index indeed follows this scaling. We then focus on two popular stochastic volatility models, the Heston and Hull-White models. In particular, we show that in the Hull-White model the resulting probability distribution of log-returns in this approximation corresponds to the Tsallis (t-Student) distribution. The Tsallis parameters are given in terms of the microscopic stochastic volatility model. Finally, we show that the log-returns for 30 years Dow Jones index data is well fitted by a Tsallis distribution, obtaining the relevant parameters. (c) 2007 Elsevier B.V. All rights reserved.

Unveiling hidden unstructured regions in process models

Process models define allowed process execution scenarios. The models are usually depicted as directed graphs, with gateway nodes regulating the control flow routing logic and with edges specifying the execution order constraints between tasks. While arbitrarily structured control flow patterns in process models complicate model analysis, they also permit creativity and full expressiveness when capturing non-trivial process scenarios. This paper gives a classification of arbitrarily structured process models based on the hierarchical process model decomposition technique. We identify a structural class of models consisting of block structured patterns which, when combined, define complex execution scenarios spanning across the individual patterns. We show that complex behavior can be localized by examining structural relations of loops in hidden unstructured regions of control flow. The correctness of the behavior of process models within these regions can be validated in linear time. These observations allow us to suggest techniques for transforming hidden unstructured regions into block-structured ones.

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Non-modal instabilities of two-dimensional disturbances in plane Couette flow of a power-law fluid

Instabilities of fluid flows have traditionally been investigated by normal mode analysis, i.e. by linearizing the equations of flow and testing for unstable eigenvalues of the linearized problem. However, the results of eigenvalue analysis agree poorly in many cases with experiments, especially for shear flows. In this paper we study the instabilities of two-dimensional Couette flow of a polymeric fluid in the framework of non-modal stability theory rather than normal mode analysis. A power-law model is used to describe the polymeric liquid. We focus on the response to external excitations and initial conditions by examining the pseudospectra structures and the transient energy growths. For both Newtonian and non-Newtonian flows, the results show that there can be a rather large transient growth even though the linear operator of Couette flow has no unstable eigenvalue. The effects of non-Newtonian viscosity on the transient behaviors are examined in this study. The results show that the "shear-thinning/shear-thickening" effect increases/decreases the amplitude of responses to external excitations and initial conditions. (C) 2010 Elsevier B.V. All rights reserved.

Linear Optics Simulation of Quantum Non-Markovian Dynamics

The simulation of open quantum dynamics has recently allowed the direct investigation of the features of system-environment interaction and of their consequences on the evolution of a quantum system. Such interaction threatens the quantum properties of the system, spoiling them and causing the phenomenon of decoherence. Sometimes however a coherent exchange of information takes place between system and environment, memory effects arise and the dynamics of the system becomes non-Markovian. Here we report the experimental realisation of a non-Markovian process where system and environment are coupled through a simulated transverse Ising model. By engineering the evolution in a photonic quantum simulator, we demonstrate the role played by system-environment correlations in the emergence of memory effects.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Concept de non-violence chez le théologien processuel David Ray Griffin entre la guerre du Vietnam et celle d'Iraq (1968-2008)

La non-violence fait référence à une idéologie et un ensemble de pratiques qui ont pour caractéristique commune de rejeter la violence sous toutes ses formes dans l’actualisation quotidienne. La non-violence est cependant devenue également un outil auquel certains recourrent dans des objectifs qui ne servent pas nécessairement le bien commun. En d’autres termes, la non-violence n’est pas systématiquement un outil de paix. Elle est un moyen d’obtenir ce que l’on veut, sans recourir à la violence. Cette thèse propose une vision de la non-violence au service du bien commun. Elle puise dans l’historicité de grands événements et acteurs qui ont utilisé la non-violence pour libérer une collectivité de formes d’oppression qui amenuisaient la dignité humaine. Elle fait référence à des auteurs et acteurs qui ont influencé le théologien processuel David Ray Griffin dans sa propre démarche d’enseignement et de recherche théologiques sur une quarantaine d’années, soient de la guerre du Vietnam à celle d’Iraq. Les dates survolées vont de 1968 à 2008. Une première démarche entreprise par la recherche est de comprendre le plus précisément possible quelles sont les avenues les plus récentes concernant la non-violence et d’explorer ses influences sur la vie et la carrière du théologien processuel États-Unien David Ray Griffin. En second lieu, une rétrospective historique des événements marquants aux États-Unis permet de cerner le contexte au sein duquel Griffin a évolué et comment son discours a laissé transparaître ces influences historiques, sociales et académiques. Une analyse plus centrée sur la politique extérieure des États-Unis en matière d’économie et de militarisme aiguille vers l’identification de signes que Griffin qualifie lui-même d’anti-théologiques, ce qui l’incite à élaborer une vision paradigmatique globalisante, équilibrée selon lui, où les ressources planétaires sont redistribuées dans un souci d’équité et de justice. Pour ce faire, un tribunal international, une religion globale, à l’image de ce que propose la pensée processuelle whiteheadienne-hartshornienne sont proposés. Griffin en brosse les grands traits dans un discours où l’exhortation s’assortit d’une méthodologie et d’une pédagogie éprouvés depuis 40 ans. Une grille d’analyse des textes griffiniens est par la suite élaborée, structurant les différentes composantes fondamentales de sa pensée. Un modèle d’intégration des valeurs de la non-violence est dégagé des lectures, applicable à d’autres disciplines. Appuyé sur une tradition authentique d’auteurs non-violents, David Ray Griffin présente les caractéristiques d’un homme de paix, duquel les idéaux débordent le cadre national pour rejoindre le planétaire, dans une visée résolument sotériologique. Cette visée devient urgente alors que les événements des attentats terroristes du World Trade Center du 11 septembre 2001 font dire à Griffin que non seulement les États-Unis sont engagés dans une démarche impérialiste démoniaque, mais qu’ils contribuent de manière accélérée à la destruction de la planète. Il faut absolument, croit-il, renverser le courant et devenir, pour le monde, un leader de la réparation des écosystèmes, des économies et des sociétés. S’adjoignant des auteurs d’autres disciplines, et toujours dans un cadre processuel, Griffin entreprend le long périple pédagogique qu’est celui de convaincre le plus grand nombre d’individus possible que le temps est venu d’agir.

The binding of synthetic triiodo L-thyronine analogs to human transthyretin: Molecular basis of cooperative and non-cooperative ligand recognition

Transthyretin (TTR) is a tetrameric beta-sheet-rich transporter protein directly involved in human amyloid diseases. Several classes of small molecules can bind to TTR delaying its amyloid fibril formation, thus being promising drug candidates to treat TTR amyloidoses. In the present study, we characterized the interactions of the synthetic triiodo L-thyronine analogs and thyroid hormone nuclear receptor TR beta-selecfive agonists GC-1 and GC-24 with the wild type and V30M variant of human transthyretin (TTR). To achieve this aim, we conducted in vitro TTR acid-mediated aggregation and isothermal titration calorimetry experiments and determined the TTR:GC-1 and TTR:GC-24 crystal structures. Our data indicate that both GC-1 and GC-24 bind to TTR in a non-cooperative manner and are good inhibitors of TTR aggregation, with dissociation constants for both hormone binding sites (HBS) in the low micromolar range. Analysis of the crystal structures of TTRwt:GC-1(24) complexes and their comparison with the TTRwt X-ray structure bound to its natural ligand thyroxine (T4) suggests, at the molecular level, the basis for the cooperative process displayed by T4 and the non-cooperative process provoked by both GC-1 and GC-24 during binding to TTR. (C) 2010 Elsevier Inc. All rights reserved.

Univariate and multivariate process yield indices based on location-scale family of distributions

Several measures of process yield, defined on univariate and multivariate normal process characteristics, have been introduced and studied by several authors. These measures supplement several well-known Process Capacity Indices (PCI) used widely in assessing the quality of products before being released into the marketplace. In this paper, we generalise these yield indices to the location-scale family of distributions which includes the normal distribution as one of its member. One of the key contributions of this paper is to demonstrate that under appropriate conditions, these indices converge in distribution to a normal distribution. Several numerical examples will be used to illustrate our procedures and show how they can be applied to perform statistical inferences on process capability.