Freezing in random graph ferromagnets

Using T=0 Monte Carlo and simulated annealing simulation, we study the energy relaxation of ferromagnetic Ising and Potts models on random graphs. In addition to the expected exponential decay to a zero energy ground state, a range of connectivities for which there is power law relaxation and freezing to a metastable state is found. For some connectivities this freezing persists even using simulated annealing to find the ground state. The freezing is caused by dynamic frustration in the graphs, and is a feature of the local search-nature of the Monte Carlo dynamics used. The implications of the freezing on agent-based complex systems models are briefly considered.Comment: Published version: 1 reference deleted, 1 word added. 4 pages, 5 figure

An integrated framework for the geographic surveillance of chronic disease

<p>Abstract</p> <p>Background</p> <p>Geographic public health surveillance is concerned with describing and disseminating geographic information about disease and other measures of health to policy makers and the public. While methodological developments in the geographical analysis of disease are numerous, few have been integrated into a framework that also considers the effects of case ascertainment bias on the effectiveness of chronic disease surveillance.</p> <p>Results</p> <p>We present a framework for the geographic surveillance of chronic disease that integrates methodological developments in the spatial statistical analysis and case ascertainment. The framework uses an hierarchical approach to organize and model health information derived from an administrative health data system, and importantly, supports the detection and analysis of case ascertainment bias in geographic data. We test the framework on asthmatic data from Alberta, Canada. We observe high prevalence in south-western Alberta, particularly among Aboriginal females. We also observe that persons likely mistaken for asthmatics tend to be distributed in a pattern similar to asthmatics, suggesting that there may be an underlying social vulnerability to a variety of respiratory illnesses, or the presence of a diagnostic practice style effect. Finally, we note that clustering of asthmatics tends to occur at small geographic scales, while clustering of persons mistaken for asthmatics tends to occur at larger geographic scales.</p> <p>Conclusion</p> <p>Routine and ongoing geographic surveillance of chronic diseases is critical to developing an understanding of underlying epidemiology, and is critical to informing policy makers and the public about the health of the population.</p

The effect of conflicting public health guidance on smokers' and vapers’ e-cigarette harm perceptions

BACKGROUND: E-cigarettes are increasingly being viewed, incorrectly, as more harmful than cigarettes. This may discourage smokers from switching to e-cigarettes. One potential explanation for these increasingly harmful attitudes is conflicting information presented in the media and online, and from public health bodies. AIMS AND METHODS: In this prospectively registered online study, we aimed to examine the impact of conflicting public health information on smokers’ and vapers’ e-cigarette harm perceptions. Daily UK smokers who do not vape (n = 334) and daily UK vapers (n = 368) were randomized to receive either: (1) a consistent harm reduction statement from two different public health bodies (Harm Reduction), (2) a consistent negative statement about e-cigarette harms from two different public health bodies (Negative), (3) a harm reduction statement from one public health body and a negative statement from another (Conflict), and (4) a statement of the risks of smoking followed by a harm reduction statement from one public health body and a negative statement from another (Smoking Risk + Conflict). Participants then answered questions regarding their perceptions of e-cigarette harm. RESULTS: The Negative condition had the highest e-cigarette harm perceptions, significantly higher than the Smoking Risk + Conflict condition (MD = 5.4, SE = 1.8, p < .016, d = 0.3 [CI 0.73 to 10.04]), which did not differ from the Conflict condition (MD = 1.5, SE = 1.8, p = .836, d = 0.1 [CI −3.14 to 6.17]). The Conflict condition differed from the Harm Reduction condition, where harm perceptions were lowest (MD = 5.4, SE = 1.8, p = .016, d = 0.3 [CI 0.74 to 10.07]). CONCLUSIONS: These findings are the first to demonstrate that, compared to harm reduction information, conflicting information increases e-cigarette harm perceptions amongst vapers, and smokers who do not vape. IMPLICATIONS: This research provides the first empirical evidence that conflicting information increases smokers’ and vapers’ e-cigarette harm perceptions, compared to harm reduction information. This may have a meaningful impact on public health as e-cigarette harm perceptions can influence subsequent smoking and vaping behavior. Conflicting information may dissuade smokers, who have the most to gain from accurate e-cigarette harm perceptions, from switching to e-cigarettes. These findings indicate that public health communications that are consensus-based can lower harm perceptions of e-cigarettes, and have the potential to reduce morbidity and mortality attributable to tobacco smoking

Dynamical frustration in ANNNI model and annealing

Zero temperature quench in the Axial Next Nearest Neighbour Ising (ANNNI) model fails to bring it to its ground state for a certain range of values of the frustration parameter $\kappa$, the ratio of the next nearest neighbour antiferromagnetic interaction strength to the nearest neighbour one. We apply several annealing methods, both classical and quantum, and observe that the behaviour of the residual energy and the order parameter depends on the value of $\kappa$ strongly. Classical or thermal annealing is found to be adequate for small values of $\kappa$. However, neither classical nor quantum annealing is effective at values of $\kappa$ close to the fully frustrated point $\kappa=0.5$, where the residual energy shows a very slow algebraic decay with the number of MCS.Comment: 6 pages,10 figures, to be published in Proceedings of " The International Workshop on Quantum annealing and other Optimization Methods

Ising model in small-world networks

The Ising model in small-world networks generated from two- and three-dimensional regular lattices has been studied. Monte Carlo simulations were carried out to characterize the ferromagnetic transition appearing in these systems. In the thermodynamic limit, the phase transition has a mean-field character for any finite value of the rewiring probability p, which measures the disorder strength of a given network. For small values of p, both the transition temperature and critical energy change with p as a power law. In the limit p -> 0, the heat capacity at the transition temperature diverges logarithmically in two-dimensional (2D) networks and as a power law in 3D.Comment: 6 pages, 7 figure

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.

Recent developments allowed generating multiple high-quality \u27omics\u27 data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values

Using the stated preference method for the calculation of social discount rate

The aim of this paper is to build the stated preference method into the social discount rate methodology. The first part of the paper presents the results of a survey about stated time preferences through pair-choice decision situations for various topics and time horizons. It is assumed that stated time preferences differ from calculated time preferences and that the extent of stated rates depends on the time period, and on how much respondents are financially and emotionally involved in the transactions. A significant question remains: how can the gap between the calculation and the results of surveys be resolved, and how can the real time preferences of individuals be interpreted using a social time preference rate. The second part of the paper estimates the social time preference rate for Hungary using the results of the survey, while paying special attention to the pure time preference component. The results suggest that the current method of calculation of the pure time preference rate does not reflect the real attitudes of individuals towards future generations

The clustering of the first galaxy halos

We explore the clustering properties of high redshift dark matter halos, focusing on halos massive enough to host early generations of stars or galaxies at redshift 10 and greater. Halos are extracted from an array of dark matter simulations able to resolve down to the "mini-halo" mass scale at redshifts as high as 30, thus encompassing the expected full mass range of halos capable of hosting luminous objects and sources of reionization. Halo clustering on large-scales agrees with the Sheth, Mo & Tormen halo bias relation within all our simulations, greatly extending the regime where large-scale clustering is confirmed to be "universal" at the 10-20% level (which means, for example, that 3sigma halos of cluster mass at z=0 have the same large-scale bias with respect to the mass distribution as 3sigma halos of galaxy mass at z=10). However, on small-scales, the clustering of our massive halos (> ~10^9 Msun/h) at these high redshifts is stronger than expected from comparisons with small-scale halo clustering extrapolated from lower redshifts. This implies "non-universality" in the scale-dependence of halo clustering, at least for the commonly used parameterizations of the scale-dependence of bias that we consider. We provide a fit for the scale-dependence of bias in our results. This study provides a basis for using extraordinarily high redshift galaxies (redshift ~10) as a probe of cosmology and galaxy formation at its earliest stages. We show also that mass and halo kinematics are strongly affected by finite simulation volumes. This suggests the potential for adverse affects on gas dynamics in hydrodynamic simulations of limited volumes, such as is typical in simulations of the formation of the "first stars", though further study is warranted.Comment: MNRAS accepte

Introducing Small-World Network Effect to Critical Dynamics

We analytically investigate the kinetic Gaussian model and the one-dimensional kinetic Ising model on two typical small-world networks (SWN), the adding-type and the rewiring-type. The general approaches and some basic equations are systematically formulated. The rigorous investigation of the Glauber-type kinetic Gaussian model shows the mean-field-like global influence on the dynamic evolution of the individual spins. Accordingly a simplified method is presented and tested, and believed to be a good choice for the mean-field transition widely (in fact, without exception so far) observed on SWN. It yields the evolving equation of the Kawasaki-type Gaussian model. In the one-dimensional Ising model, the p-dependence of the critical point is analytically obtained and the inexistence of such a threshold p_c, for a finite temperature transition, is confirmed. The static critical exponents, gamma and beta are in accordance with the results of the recent Monte Carlo simulations, and also with the mean-field critical behavior of the system. We also prove that the SWN effect does not change the dynamic critical exponent, z=2, for this model. The observed influence of the long-range randomness on the critical point indicates two obviously different hidden mechanisms.Comment: 30 pages, 1 ps figures, REVTEX, accepted for publication in Phys. Rev.