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Cross-sectional study examining the relationships between abstract thinking (i.e., construal level), personality traits, and health behaviors (exercise, diet, and substance use).

Cross-sectional study examining the relationships between abstract thinking (i.e., construal level), personality traits, and health behaviors (exercise, diet, and substance use).

Cross-sectional study examining the relationships between abstract thinking (i.e., construal level), personality traits, and health behaviors (exercise, diet, and substance use).

This paper presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. The question addressed here is how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. A key role is played by the Newton polytope of a statistical model. Our results are applied to the hidden Markov model and to the general Markov model on a binary tree.

Solar radiation data are essential in the design of solar energy conversion devices. In this regard, empirical models were selected to estimate the solar radiation on horizontal and inclined surfaces. We used in this article eleven years of solar radiation over Cairo, Egypt from 1995‐2005, and used the statistical parameters ((MBE), (RMSE), MPE, R2), and t‐ Test statics, in order to differentiate among these models and choose the best model. The Olmo et al. model was applied to the database corresponding to the horizontal solar irradiation to determine values for a south facing surface, and Olmo et al. model is recommended to estimate the global solar radiation on an inclined surface in this study, due to its accuracy, input requirements and simplicity. The RMSE results indicate that the anisotropic models (Hay, Klucher and Perez) show similar performance on an overall basis, but isotropic model and Tamps and Coulson exhibit much larger error. Generally, the observation of the Perez’s and Klucher models describes that the irradiance on inclined plane is more accurate than that in other models.

The report describes (a) an outline of the Kalman-based tracking procedure for reentry trajectories, (b) a detailed derivation of a mathematical model to implement this procedure in radar polar coordinates, and (c) methods for checking, via simulation, the degree of degradation introduced by model simplifications and other changes.

The report describes (a) an outline of the Kalman-based tracking procedure for reentry trajectories, (b) a detailed derivation of a mathematical model to implement this procedure in radar polar coordinates, and (c) methods for checking, via simulation, the degree of degradation introduced by model simplifications and other changes.

We aim to enhance the dashboard of the Planning and Evaluation Group for decision-making based on health data in the State of São Paulo. This review will enable us to identify statistical models to integrate into the dashboard, facilitating the identification of care points more efficiently for budget remodeling, intelligent allocation of resources for endemic disease containment and health promotion, and increased predictability of public spending. Considering these multi-determinations in budget executability, along with the factors recognized in the literature that influence health problems, statistical strategies are needed to guide the budget planning stage.

We aim to enhance the dashboard of the Planning and Evaluation Group for decision-making based on health data in the State of São Paulo. This review will enable us to identify statistical models to integrate into the dashboard, facilitating the identification of care points more efficiently for budget remodeling, intelligent allocation of resources for endemic disease containment and health promotion, and increased predictability of public spending. Considering these multi-determinations in budget executability, along with the factors recognized in the literature that influence health problems, statistical strategies are needed to guide the budget planning stage.

We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithms proposed for this task. We present results for prepositional phrase attachment in both English and Spanish.

Philip Morris Records; publication; illegible

The notion of approximately integrable linear statistical models is introduced to analyze the higher order optimality properties of some common nonparametric estimators. The approximately integrable models suggest a useful approach to a unified treatment of both regular and irregular non-parameter problems. It is shown that with such models any rate of improvement ranging from (log n) to the alpha power/squared to 1/(log...log n) to the alpha power), alpha 0, of the classical non-parametric procedures can be anticipated. Both an example of a first order asymptotically optimal estimator with the unusual rate 1/n log n and an estimator with an extremely slow unimprovable rate of convergence 1(log...log n) the alpha power are presented.

We consider a simple Maier-Saupe statistical model with the inclusion of disorder degrees of freedom to mimic the phase diagram of a mixture of rod-like and disc-like molecules. A quenched distribution of shapes leads to the existence of a stable biaxial nematic phase, in qualitative agreement with experimental findings for some ternary lyotropic liquid mixtures. An annealed distribution, however, which is more adequate to liquid mixtures, precludes the stability of this biaxial phase. We then use a two-temperature formalism, and assume a separation of relaxation times, to show that a partial degree of annealing is already sufficient to stabilize a biaxial nematic structure.

We review recent statistical mechanical approaches to multimode laser theory. The theory has proved very effective to describe standard lasers. We refer of the mean field theory for passive mode locking and developments based on Monte Carlo simulations and cavity method to study the role of the frequency matching condition. The status for a complete theory of multimode lasing in open and disordered cavities is discussed and the derivation of the general statistical models in this framework is presented. When light is propagating in a disordered medium, the system can be analyzed via the replica method. For high degrees of disorder and nonlinearity, a glassy behavior is expected at the lasing threshold, providing a suggestive link between glasses and photonics. We describe in details the results for the general Hamiltonian model in mean field approximation and mention an available test for replica symmetry breaking from intensity spectra measurements. Finally, we summary some perspectives still opened for such approaches.

We review recent statistical mechanical approaches to multimode laser theory. The theory has proved very effective to describe standard lasers. We refer of the mean field theory for passive mode locking and developments based on Monte Carlo simulations and cavity method to study the role of the frequency matching condition. The status for a complete theory of multimode lasing in open and disordered cavities is discussed and the derivation of the general statistical models in this framework is presented. When light is propagating in a disordered medium, the system can be analyzed via the replica method. For high degrees of disorder and nonlinearity, a glassy behavior is expected at the lasing threshold, providing a suggestive link between glasses and photonics. We describe in details the results for the general Hamiltonian model in mean field approximation and mention an available test for replica symmetry breaking from intensity spectra measurements. Finally, we summary some perspectives still opened for such approaches.

We review recent statistical mechanical approaches to multimode laser theory. The theory has proved very effective to describe standard lasers. We refer of the mean field theory for passive mode locking and developments based on Monte Carlo simulations and cavity method to study the role of the frequency matching condition. The status for a complete theory of multimode lasing in open and disordered cavities is discussed and the derivation of the general statistical models in this framework is presented. When light is propagating in a disordered medium, the system can be analyzed via the replica method. For high degrees of disorder and nonlinearity, a glassy behavior is expected at the lasing threshold, providing a suggestive link between glasses and photonics. We describe in details the results for the general Hamiltonian model in mean field approximation and mention an available test for replica symmetry breaking from intensity spectra measurements. Finally, we summary some perspectives still opened for such approaches.

A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the non-adaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.

We discuss several ways of illustrating fundamental concepts in statistical and thermal physics by considering various models and algorithms. We emphasize the importance of replacing students' incomplete mental images by models that are physically accurate. In some cases it is sufficient to discuss the results of an algorithm or the behavior of a model rather than having students write a program.

There exists various methods for genomic prediction developed under different frameworks such as optimizing for its prediction accuracy, speed of generating results and memory consumption. We will explore existing statistical models and methods for genomic prediction and investigate the key concepts and theories that the existing genomic prediction methods are based on. In addition, we will report potential applications and key limitations of each of the method or model.

There exists various methods for genomic prediction developed under different frameworks such as optimizing for its prediction accuracy, speed of generating results and memory consumption. We will explore existing statistical models and methods for genomic prediction and investigate the key concepts and theories that the existing genomic prediction methods are based on. In addition, we will report potential applications and key limitations of each of the method or model.

There exists various methods for genomic prediction developed under different frameworks such as optimizing for its prediction accuracy, speed of generating results and memory consumption. We will explore existing statistical models and methods for genomic prediction and investigate the key concepts and theories that the existing genomic prediction methods are based on. In addition, we will report potential applications and key limitations of each of the method or model.

The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray has been used as a main platform for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are however subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.

The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray has been used as a main platform for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are however subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.

The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray has been used as a main platform for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are however subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.

We propose a new method of analysis for the \lya forest, namely to measure the 1-point and 2-point joint probability distribution of the transmitted flux. The results for a sample of seven observed quasars and from two simulations of structure formation are shown and compared. Statistically significant differences in the 2-point function between the results of the numerical simulations and the observations are easily found. The analysis we suggest is very simple to apply to observed data sets, and we discuss its superiority over the traditional Voigt-profile fitting algorithms for accurate comparison to the predictions of theoretical models.

We study two statistical models for short-length categorical (or ordinal) time series. The first one is a regression model based on generalized linear model. The second one is a parametrized Markovian model, particularizing the discrete autoregressive model to the case of categorical data. These models are used to analyze two data-sets: annual larch cone production and weekly planktonic abundance.

We study two statistical models for short-length categorical (or ordinal) time series. The first one is a regression model based on generalized linear model. The second one is a parametrized Markovian model, particularizing the discrete autoregressive model to the case of categorical data. These models are used to analyze two data-sets: annual larch cone production and weekly planktonic abundance.

This dissertation focuses on effective combination of data-driven natural language processing (NLP) approaches with linguistic knowledge sources that are based on manual text annotation or word grouping according to semantic commonalities. I gainfully apply fine-grained linguistic soft constraints - of syntactic or semantic nature - on statistical NLP models, evaluated in end-to-end state-of-the-art statistical machine translation (SMT) systems. The introduction of semantic soft constraints involves intrinsic evaluation on word-pair similarity ranking tasks, extension from words to phrases, application in a novel distributional paraphrase generation technique and an introduction of a generalized framework of which these soft semantic and syntactic constraints can be viewed as instances, and in which they can be potentially combined. Fine granularity is key in the successful combination of these soft constraints in many cases. I show how to softly constrain SMT models by adding fine-grained weighted features, each preferring translation of only a specific syntactic constituent. Previous attempts using coarse-grained features yielded negative results. I also show how to softly constrain corpus-based semantic models of words (distributional profiles) to effectively create word-sense-aware models, by using semantic word grouping information found in a manually compiled thesaurus. Previous attempts using hard constraints and resulting in aggregated, coarse-grained models, yielded lower gains. A novel paraphrase generation technique incorporating these soft semantic constraints is then also evaluated in a SMT system. This paraphrasing technique is based on the Distributional Hypothesis. The main advantage of this novel technique over current pivoting techniques for paraphrasing is the independence from parallel texts, which are a limited resource.

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.

We introduce a general class of statistical-mechanics models, taking values in an abelian group, which includes examples of both spin and gauge models, both ordered and disordered. The model is described by a set of ``variables'' and a set of ``interactions''. A Gibbs factor is associated to each variable and to each interaction. We introduce a duality transformation for systems in this class. The duality exchanges the abelian group with its dual, the Gibbs factors with their Fourier transforms, and the interactions with the variables. High (low) couplings in the interaction terms are mapped into low (high) couplings in the one-body terms. The idea is that our class of systems extends the one for which the classical procedure 'a la Kramers and Wannier holds, up to include randomness into the pattern of interaction. We introduce and study some physical examples: a random Gaussian Model, a random Potts-like model, and a random variant of discrete scalar QED. We shortly describe the consequence of duality for each example.

For the free models of statistical mechanics on torus, exact asymptotic expansions of the free energy, the internal energy and the specific heat in the vicinity of the critical point are found. It is shown that there is direct relation between the terms of the expansion and the Kronecker's double series. The latter can be expressed in terms of the elliptic theta-functions in all orders of the asymptotic expansion.

We present a simple and efficient approximation scheme which greatly facilitates extension of Wang-Landau sampling (or similar techniques) in large systems for the estimation of critical behavior. The method, presented in an algorithmic approach, is based on a very simple idea, familiar in statistical mechanics from the notion of thermodynamic equivalence of ensembles and the central limit theorem. It is illustrated that, we can predict with high accuracy the critical part of the energy space and by using this restricted part we can extend our simulations to larger systems and improve accuracy of critical parameters. It is proposed that the extensions of the finite size critical part of the energy space, determining the specific heat, satisfy a scaling law involving the thermal critical exponent. The method is applied successfully for the estimation of the scaling behavior of specific heat of both square and simple cubic Ising lattices. The proposed scaling law is verified by estimating the thermal critical exponent from the finite size behavior of the critical part of the energy space. The density of states (DOS) of the zero-field Ising model on these lattices is obtained via a multi-range Wang-Landau sampling.

This paper reviews recent attempts at modelling inequality of wealth as an emergent phenomenon of interacting-agent processes. We point out that recent models of wealth condensation which draw their inspiration from molecular dynamics have, in fact, reinvented a process introduced quite some time ago by Angle (1986) in the sociological literature. We emphasize some problematic aspects of simple wealth exchange models and contrast them with a monetary model based on economic principles of market mediated exchange. The paper also reports new results on the influence of market power on the wealth distribution in statistical equilibrium. As it turns out, inequality increases but market power alone is not sufficient for changing the exponential tails of simple exchange models into Pareto tails.

In this contribution we evaluate the minimal cluster mass for which the effects of an incomplete sampling of the Initial Mass Function (IMF) cannot be neglected. This minimal cluster mass corresponds to the situation in which the integrated luminosity of the cluster modeled equals the luminosity of the most luminous individual star included in the model, and it takes values between 3x10^2 and 6x10^5 Mo depending on the age and the observed band. We show different examples for young (t < 10 Myr) and old (t up to 10 Gyr) stellar populations. We also make here a first release of the spectral energy distribution (SED) from 160 mic. (1.8x10^12 Hz) to 25 keV (6.0x10^18 Hz) for star-forming regions with ages between 0.1 and 10 Myr, and metallicities between Z=0.001 and Z=0.040. The SEDs are available at our WWW server and include the corresponding quantities for the evaluation of sampling effects. These SEDs can be directly used to obtain colors (including the X-rays ones), as input of photoionization codes, or for chi^2 fitting with observed data taking into account the intrinsic uncertainty of the models due to the IMF sampling.

Relativistic effects in the thermodynamical properties of interacting particle systems are investigated within the framework of the relativistic direct interaction theory in various forms of dynamics. In the front form of relativistic dynamics an exactly solvable model of a one-dimensional hard spheres gas is formulated and an equation of state and thermodynamical potentials for such a gas are found. Weakly-relativistic corrections to the thermodynamical functions of the dilute gas with short-range interactions are discussed on the basis of the approximately relativistic Hamiltonian function in the instant form of dynamics.

Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from computer models. Studies to date are most often of a data-assimilation flavour, which implicitly consider univariate statistical models with the flux as the variate of interest. This univariate approach typically assumes that the flux field is either a spatially correlated Gaussian process or a spatially uncorrelated non-Gaussian process with prior expectation fixed using flux inventories (e.g., NAEI or EDGAR in Europe). Here, we extend this approach in three ways. First, we develop a bivariate model for the mole-fraction field and the flux field. The bivariate approach allows optimal prediction of both the flux field and the mole-fraction field, and it leads to significant computational savings over the univariate approach. Second, we employ a lognormal spatial process for the flux field that captures both the lognormal characteristics of the flux field (when appropriate) and its spatial dependence. Third, we propose a new, geostatistical approach to incorporate the flux inventories in our updates, such that the posterior spatial distribution of the flux field is predominantly data-driven. The approach is illustrated on a case study of methane (CH$_4$) emissions in the United Kingdom and Ireland.

Selection and ranking problems have been studied over the last thirty years, generally under one of two formulations: Bechhofer's indifference zone approach and Gupta's subset selection approach. This paper deals with subset selection. Subset selection procedures in multivariate models are briefly reviewed. These include: (1) Procedures for selecting the best component in a multivariate normal population in terms of the component means as well as the component variances; (2) Procedures for selecting the best from several multivariate normal populations in terms of the Mahalanobis distance, the generalized variance, and the multiple correlation coefficient; (3) Procedures (fixed sample size as well as inverse sampling) for selecting the most (least) probable cell in a multinominal distribution; (4) Procedures for selecting the best from several multinomial populations in terms of the Shannon entropy function; and (5) Procedures for choosing the best subset of the predictor variables in a linear regression model. (Author)

Selection and ranking problems have been studied over the last thirty years, generally under one of two formulations: Bechhofer's indifference zone approach and Gupta's subset selection approach. This paper deals with subset selection. Subset selection procedures in multivariate models are briefly reviewed. These include: (1) Procedures for selecting the best component in a multivariate normal population in terms of the component means as well as the component variances; (2) Procedures for selecting the best from several multivariate normal populations in terms of the Mahalanobis distance, the generalized variance, and the multiple correlation coefficient; (3) Procedures (fixed sample size as well as inverse sampling) for selecting the most (least) probable cell in a multinominal distribution; (4) Procedures for selecting the best from several multinomial populations in terms of the Shannon entropy function; and (5) Procedures for choosing the best subset of the predictor variables in a linear regression model. (Author)

The ability to decide objectively between competing material models has always been an important engineering task. For a variety of reasons, simple inspection of the general fit of various models to the appropriate data does not always result in an obvious choice for the superior model, and heretofore there has been no statistical measure that can be used to choose between models (except in the simple case of nested models). Bayes factors overcome these previous limitations and can provide a valid objective statistical measure for choosing between competing material models. This study explores the use of Bayes factors as an objective measure in choosing between competing material models. The focus is on the use of this method by engineers who may or may not be well versed in statistical methods. In addition, the statistical background necessary to gain an in-depth understanding of the Bayes method is included as appendices. Also discussed in some detail is the Random Fatigue Limit s-N model proposed by Pascal and Meeker, which holds considerable promise for describing HCF behavior, as well as the applicability of applying Bayes factors to this model.

The probabilistic analysis of the propagation of small fatigue cracks has been investigated. Monte Carlo simulations have been formulated that can embrace nearly all details of the micromechanics of small crack growth. They calculate the propagation of the entire, irregular crack front as it passes through a random microstructure. The simulations illuminate both the mechanics and the statistics of small crack growth. A computationally efficient probabilistic model has also been formulated as the basis of lifetime prediction in field application. The probabilistic model is sufficiently flexible to allow incorporation of the physical aspects of propagation that have been shown in the Monte Carlo simulations to be necessary for accurate predictions. In particular, the probabilistic model begins with the premise that two independent variables, for example but not necessarily the crack length and the crack shape, are required to account for the observed statistics of growth.

Analyses are carried out in two stages: (1) a bivariate logistic regression model is used to examine the crude association between decision-making autonomy; (2) an adjusted logistic regression is performed to control the covariates (age, education level, employment status, household income, and number of children).

Analyses are carried out in two stages: (1) a bivariate logistic regression model is used to examine the crude association between decision-making autonomy; (2) an adjusted logistic regression is performed to control the covariates (age, education level, employment status, household income, and number of children).

Analyses are carried out in two stages: (1) a bivariate logistic regression model is used to examine the crude association between decision-making autonomy; (2) an adjusted logistic regression is performed to control the covariates (age, education level, employment status, household income, and number of children).

A connection between integrability properties and general statistical properties of the spectra of symmetric transfer matrices of the asymmetric eight-vertex model is studied using random matrix theory (eigenvalue spacing distribution and spectral rigidity). For Yang-Baxter integrable cases, including free-fermion solutions, we have found a Poissonian behavior, whereas level repulsion close to the Wigner distribution is found for non-integrable models. For the asymmetric eight-vertex model, however, the level repulsion can also disappearand the Poisson distribution be recovered on (non Yang--Baxter integrable) algebraic varieties, the so-called disorder varieties. We also present an infinite set of algebraic varieties which are stable under the action of an infinite discrete symmetry group of the parameter space. These varieties are possible loci for free parafermions. Using our numerical criterion we have tested the generic calculability of the model on these algebraic varieties.

A gaussian distribution of binding energies, but conditioned to exploit generally available information on packing in liquids, provides a statistical-thermodynamic theory of liquid water that is structurally non-committal, molecularly realistic, and surprisingly accurate. Neglect of fluctuation contributions to this gaussian model yields a mean-field theory that produces useless results. A refinement that accounts for sharper-than-gaussian behavior at high binding energies recognizes contributions from a discrete number of water molecules and permits a natural matching of numerically exact results. These gaussian models, which can be understood as vigorous simplifications of quasi-chemical theories, are applicable to aqueous environments where the utility of structural models based on geometrical considerations of water hydrogen bonding have not been established.

A gaussian distribution of binding energies, but conditioned to exploit generally available information on packing in liquids, provides a statistical-thermodynamic theory of liquid water that is structurally non-committal, molecularly realistic, and surprisingly accurate. Neglect of fluctuation contributions to this gaussian model yields a mean-field theory that produces useless results. A refinement that accounts for sharper-than-gaussian behavior at high binding energies recognizes contributions from a discrete number of water molecules and permits a natural matching of numerically exact results. These gaussian models, which can be understood as vigorous simplifications of quasi-chemical theories, are applicable to aqueous environments where the utility of structural models based on geometrical considerations of water hydrogen bonding have not been established.

Pusey, Barrett and Rudolph (PBR) have recently given a completely novel argument that restricts the class of possible models for quantum phenomena (arXiv:1111.3328). In these notes the assumptions used by PBR are considerably weakened, to further restrict the class of possible models. The `factorisability' assumption used by PBR is replaced by a far weaker `compatibility' assumption for uncorrelated quantum subsystems which, moreover, does not require the assignation of separate underlying properties to each subsystem (i.e, reductionism). Further, it is shown that an assumption of measurement independence may be dropped to obtain a related result having the same experimental significance (at the expense of a weaker conceptual significance). The latter is a remarkable feature of the PBR approach, given that Bell inequalities, steering inequalities and Kochen-Specker theorems all require an assumption of this type.