Numerical test of the Cardy-Jacobsen conjecture in the site-diluted Potts model in three dimensions

We present a microcanonical Monte Carlo simulation of the site-diluted Potts model in three dimensions with eight internal states, partly carried out on the citizen supercomputer Ibercivis. Upon dilution, the pure model’s first-order transition becomes of the second order at a tricritical point. We compute accurately the critical exponents at the tricritical point. As expected from the Cardy-Jacobsen conjecture, they are compatible with their random field Ising model counterpart. The conclusion is further reinforced by comparison with older data for the Potts model with four states.

Spin glass phase in the four-state three-dimensional Potts model

We perform numerical simulations, including parallel tempering, a four-state Potts glass model with binary random quenched couplings using the JANUS application-oriented computer. We find and characterize a glassy transition, estimating the critical temperature and the value of the critical exponents. Nevertheless, the extrapolation to infinite volume is hampered by strong scaling corrections. We show that there is no ferromagnetic transition in a large temperature range around the glassy critical temperature. We also compare our results with those obtained recently on the “random permutation” Potts glass.

The SEIQS stochastic epidemic model with external source of infection

This paper deals with a stochastic epidemic model for computer viruses with latent and quarantine periods, and two sources of infection: internal and external. All sojourn times are considered random variables which are assumed to be independent and exponentially distributed. For this model extinction and hazard times are analyzed, giving results for their Laplace transforms and moments. The transient behavior is considered by studying the number of times that computers are susceptible, exposed, infectious and quarantined during a period of time (0, t] and results for their joint and marginal distributions, moments and cross moments are presented. In order to give light this analysis, some numerical examples are showed.

Assessing advertising in a hierarchical decision model

Many destination marketing organizations in the United States and elsewhere are facing budget retrenchment for tourism marketing, especially for advertising. This study evaluates a three-stage model using Random Coefficient Logit (RCL) approach which controls for correlations between different non-independent alternatives and considers heterogeneity within individual’s responses to advertising. The results of this study indicate that the proposed RCL model results in a significantly better fit as compared to traditional logit models, and indicates that tourism advertising significantly influences tourist decisions with several variables (age, income, distance and Internet access) moderating these decisions differently depending on decision stage and product type. These findings suggest that this approach provides a better foundation for assessing, and in turn, designing more effective advertising campaigns.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

Fate and Transport of Naproxen in a Sandy Aquifer Material: Saturated Column Studies and Model Evaluation

Naproxen-C14H14O3 is a nonsteroidal anti-inflammatory drug which has been found at detectable concentrations in wastewater, surface water, and groundwater. Naproxen is relatively hydrophilic and is in anionic form at pH between 6 and 8. In this study, column experiments were performed using an unconsolidated aquifer material from an area near Barcelona (Spain) to assess transport and reaction mechanisms of Naproxen in the aquifer matrix under different pore water fluxes. Results were evaluated using HYDRUS-1D, which was used to estimate transport parameters. Batch sorption isotherms for Naproxen conformed with the linear model with a sorption coefficient of 0.42 (cm3 g−1), suggesting a low sorption affinity. Naproxen breakthrough curves (BTCs) measured in soil columns under steady-state, saturated water flow conditions displayed similar behavior, with no apparent hysteresis in sorption or dependence of retardation (R, 3.85-4.24) on pore water velocities. Soil sorption did not show any significant decrease for increasing flow rates, as observed from Naproxen recovery in the effluent. Sorption parameters estimated by the model suggest that Naproxen has a low sorption affinity to aquifer matrix. Most sorption of Naproxen occurred on the instantaneous sorption sites, with the kinetic sorption sites representing only about 10 to 40% of total sorption.

Boston, Massachusetts Region, Digital Elevation Model with Bathymetry

This raster layer represents surface elevation and bathymetry data for the Boston Region, Massachusetts. It was created by merging portions of MassGIS Digital Elevation Model 1:5,000 (2005) data with NOAA Estuarine Bathymetric Digital Elevation Models (30 m.) (1998). DEM data was derived from the digital terrain models that were produced as part of the MassGIS 1:5,000 Black and White Digital Orthophoto imagery project. Cellsize is 5 meters by 5 meters. Each cell has a floating point value, in meters, which represents its elevation above or below sea level.

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.

(Table 1) Deuterium ratios, chloride content and hydrate volume of pore water and hydrate meltwater from ODP Site 164-996

Site 996 is located above the Blake Diapir where numerous indications of vertical fluid migration and the presence of hydrate existed prior to Ocean Drilling Program (ODP) Leg 164. Direct sampling of hydrates and visual observations of hydrate-filled veins that could be traced 30-40 cm along cores suggest a connection between fluid migration and hydrate formation. The composition of pore water squeezed from sediment cores showed large variations due to melting of hydrate during core recovery and influence of saline water from the evaporitic diapir below. Analysis of water released during hydrate decomposition experiments showed that the recovered hydrates contained significant amounts of pore water. Solutions of the transport equations for deuterium (d2H) and chloride (Cl-) were used to determine maximum (d2H) and minimum (Cl-) in situ concentrations of these species. Minimum in situ concentrations of hydrate were estimated by combining these results with Cl- and d2H values measured on hydrate meltwaters and pore waters obtained by squeezing of sediments, by the means of a method based on analysis of distances in the two-dimensional Cl- d2H space. The computed Cl- and d2H distribution indicates that the minimum hydrate amount solutions are representative of the actual hydrate amount. The highest and mean hydrate concentrations estimates from our model are 31% and 10% of the pore space, respectively. These concentrations agree well with visual core observations, supporting the validity of the model assumptions. The minimum in situ Cl- concentrations were used to constrain the rates of upward fluid migration. Simulation of all available data gave a mean flow rate of 0.35 m/k.y. (range: 0.125-0.5 m/k.y.).

Age model of ODP Site 184-1146 (Table 1)

Clay mineral assemblages at ODP Site 1146 in the northern South China Sea are used to investigate sediment source and transport processes and to evaluate the evolution of the East Asian monsoon over the past 2 Myr. Clay minerals consist mainly of illite (22-43%) and smectite (12-48%), with associated chlorite (10-30%), kaolinite (2-18%), and random mixed-layer clays (5-22%). Hydrodynamic and mineralogical studies indicate that illite and chlorite sources include Taiwan and the Yangtze River, that smectite and mixed-layer clays originate predominantly from Luzon and Indonesia, and that kaolinite is primarily derived from the Pearl River. Mineral assemblages indicate strong glacial-interglacial cyclicity, with high illite, chlorite, and kaolinite content during glacials and high smectite and mixed-layer clay content during interglacials. During interglacials, summer enhanced monsoon (southwesterly) currents transport more smectite and mixed-layer clays to Site 1146 whereas during glacials, enhanced winter monsoon (northerly) currents transport more illite and chlorite from Taiwan and the Yangtze River. The ratio (smectite+mixed layers)/(illite+chlorite) was adopted as a proxy for East Asian monsoon variability. Higher ratios indicate strengthened summer-monsoon winds and weakened winter-monsoon winds during interglacials. In contrast, lower ratios indicate a strongly intensified winter monsoon and weakened summer monsoon during glacials. Spectral analysis indicates the mineral ratio was dominantly forced by monsoon variability prior to the development of large-scale glaciation at 1.2 Myr and by both monsoon variability and the effects of changing sea level in the interval 1.2 Myr to present.

Random recursion

Thesis (Ph.D.)--University of Washington, 2016-06

Pore, surface and fillet moisture in centrifuged beds of coarse coal

The moisture content of the coarse coking coal product from the centrifuges of preparation plants was investigated to evaluate the contribution of three types of water: that held internally in pores, that in fillets at points of contacts between the particles, and the moisture covering the surface. A standardised laboratory centrifuge test was used to measure the total non-centrifugable moisture (NCM) content and also the quantity held in internal pores, called NCMi. The fillet moisture NCMf was estimated by means of a formulation which relies on experimentally measured holdup volumes, supplemented by a physical model. The surface moisture NCMs could then be derived by difference. The NCMf, which depends on the body force, the particle size and the surface tension and contact angle of the liquid, ranges from effectively zero for large particles to 10% for fines. The surface moisture NCMs is of the order of 0.5% for high rank coals and increases to 4.5% for lower rank coals. (C) 2002 Elsevier Science Ltd. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.

Mixed model formulations for multi-environment trials

When studying genotype X environment interaction in multi-environment trials, plant breeders and geneticists often consider one of the effects, environments or genotypes, to be fixed and the other to be random. However, there are two main formulations for variance component estimation for the mixed model situation, referred to as the unconstrained-parameters (UP) and constrained-parameters (CP) formulations. These formulations give different estimates of genetic correlation and heritability as well as different tests of significance for the random effects factor. The definition of main effects and interactions and the consequences of such definitions should be clearly understood, and the selected formulation should be consistent for both fixed and random effects. A discussion of the practical outcomes of using the two formulations in the analysis of balanced data from multi-environment trials is presented. It is recommended that the CP formulation be used because of the meaning of its parameters and the corresponding variance components. When managed (fixed) environments are considered, users will have more confidence in prediction for them but will not be overconfident in prediction in the target (random) environments. Genetic gain (predicted response to selection in the target environments from the managed environments) is independent of formulation.