Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables

Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Quantum teleportation via mixed two-mode squeezed states in the coherent-state representation

Quantum teleportation for continuous variables is generally described in phase space by using the Wigner functions. We study quantum teleportation via a mixed two-mode squeezed state in Hilbert-Schmidt space by using the coherent-state representation and operators. This shows directly how the teleported state is related to the original state.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

CsF and alumina: A mixed homogeneous-heterogeneous catalytic system for the transesterification of sunflower oil with methanol

The activity and nature (i e heterogeneous and/or homogeneous) of catalysts based on CsF supported on alpha-Al2O3 were investigated for the transesterification of vegetable oil with methanol. The effect of the activation temperature, CsF loading and the reusability in a recirculating reactor were first studied CsF/alpha-Al2O3 exhibited the highest activity for a CsF loading of 0 6 mmol/g and when activated at 120 degrees C An important aspect of this study is the effect of CsF leaching into the reaction mixture, which is attributed to the high solubility of CsF in methanol, leading to a complete loss of activity after one run It was Identified that the activity of the catalyst resulted from a synergy between alumina and dissolved CsF, the presence of both compounds being absolutely necessary to observe any conversion The use of an alumina with a higher surface area resulted in a far greater reaction rate, showing that the concentration of surface site on the oxide (probably surface hydroxyl) was rate-limiting in the case of the experiments using the low surface area alpha-Al2O3 This work emphasizes that combined homogeneous-heterogeneous catalytic systems made from the blending of the respective catalysts can be used to obtain high conversion of vegetable oil to biodiesel. Despite the homogeneous/heterogeneous dual character, such a catalytic system may prove valuable in developing a simple and cost-effective continuous catalytic process for biodiesel production (C) 2010 Elsevier B V All rights reserved

Freshwater Reservoir Effect on Re-Dating of Eurasian Steppe Cultures: First Results for Eneolithic and Early Bronze Age North-East Kazakhstan

Freshwater reservoir effects (FRE) can cause a major problem with radiocarbon dating human skeletal material in the Eurasian steppe. We present the first results of research into the extent of the FRE in the sites of Borly 4 (Eneolithic), and Shauke 1 and 8b (Early Bronze Age), North-Eastern Kazakhstan. AMS 14C dating and stable isotope (δ13C, δ15N) analysis of associated groups of samples (32 samples, 11 groups in total) demonstrate that: a) the diet of the humans and fauna analysed was based on the C3 foodchain with no evidence of a C4 plant (such as millet) contribution; aquatic resources apparently were a continuous dietary feature for the humans; b) the first 14C dates obtained for the Upper and Middle Irtysh River region attribute the Eneolithic period of the area to the 34th-30th c. BC, and the Early Bronze Age – to the 25th-20th c. BC; there is a ca. 450 years hiatus between the two periods; c) the maximum fish-herbivore freshwater reservoir offset observed equals 301±47 14C yrs. As such, 14C dates from aquatic and human samples from the area need to be interpreted with caution as they are likely to be affected by the offset (i.e. appear older).
The paper also discusses the effect of a sodium hydroxide (NaOH) wash on δ13C, δ15N, C:Natomic levels and collagen yields of the bone samples. Our results indicate a minor but significant effect of NaOH treatment only on C:Natomic ratios of the samples.