Correlation effects in a simple model of small-world network

We analyze the effect of correlations in a simple model of small world network by obtaining exact analytical expressions for the distribution of shortest paths in the network. We enter correlations into a simple model with a distinguished site, by taking the random connections to this site from an Ising distribution. Our method shows how the transfer matrix technique can be used in the new context of small world networks.Comment: 10 pages, 3 figure

Percolation on two- and three-dimensional lattices

In this work we apply a highly efficient Monte Carlo algorithm recently proposed by Newman and Ziff to treat percolation problems. The site and bond percolation are studied on a number of lattices in two and three dimensions. Quite good results for the wrapping probabilities, correlation length critical exponent and critical concentration are obtained for the square, simple cubic, HCP and hexagonal lattices by using relatively small systems. We also confirm the universal aspect of the wrapping probabilities regarding site and bond dilution.Comment: 15 pages, 6 figures, 3 table

Percolation model for structural phase transitions in Li1−x_{1-x}Hx_xIO3_3 mixed crystals

A percolation model is proposed to explain the structural phase transitions found in Li$_{1-x}$H$_x$IO$_3$ mixed crystals as a function of the concentration parameter $x$. The percolation thresholds are obtained from Monte Carlo simulations on the specific lattices occupied by lithium atoms and hydrogen bonds. The theoretical results strongly suggest that percolating lithium vacancies and hydrogen bonds are indeed responsible for the solid solution observed in the experimental range $0.22 < x < 0.36$.Comment: 4 pages, 2 figure

Evolution of the Corticotropin-releasing Hormone Signaling System and Its Role in Stress-induced Phenotypic Plasticity

Developing animals respond in variation in their habitats by altering their rules of development and/or their morphologies (i.e., they exhibit phenotypic plasticity). In vertebrates, one mechanism by which plasticity is expressed is through activation of the neuroendocrine system, which transduces environmental information into a physiological response. Recent findings of ours with amphibians and of others with mammals show that the primary vertebrate stress neuropeptide, corticotropin-releasing hormone (CRH), is essential for adaptive developmental responses to environmental stress. For instance, CRH-dependent mechanisms cause accelerated metamorphosis in response to pond-drying in some amphibian species, and intrauterine fetal stress syndromes in humans precipitate preterm birth. CRH may be a phylogenetically ancient developmental signaling molecule that allows developing organisms to escape deleterious changes in their larval/fetal habitat. The response to CRH is mediated by at least two different receptor subtypes and may also be modulated by a secreted binding protein.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73287/1/j.1749-6632.1999.tb07877.x.pd

Cosolvent flushing for the remediation of PAHs from former manufactured gas plants

Cosolvent flushing is a technique that has been proposed for the removal of hydrophobic organic contaminants in the subsurface. Cosolvents have been shown to dramatically increase the solubility of such compounds compared to the aqueous solubility; however, limited data are available on the effectiveness of cosolvents for field-contaminated media. In this work, we examine cosolvent flushing for the removal of polycyclic aromatic hydrocarbons (PAHs) in soil from a former manufactured gas plant (FMGP). Batch studies confirmed that the relationship between the soil-cosolvent partitioning coefficient (Ki) and the volume fraction of cosolvent (fc) followed a standard log-linear equation. Using methanol at an fc of 0.95, column studies were conducted at varying length scales, ranging from 11.9 to 110 cm. Removal of PAH compounds was determined as a function of pore volumes (PVs) of cosolvent flushed. Despite using a high fc, rate and chromatographic effects were observed in all the columns. PAH effluent concentrations were modeled using a common two-site sorption model. Model fits were improved by using MeOH breakthrough curves to determine fitted dispersion coefficients. Fitted mass-transfer rates were two to three orders of magnitude lower than predicted values based on published data using artificially contaminated sands

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

Dragon-kings: mechanisms, statistical methods and empirical evidence

This introductory article presents the special Discussion and Debate volume "From black swans to dragon-kings, is there life beyond power laws?" published in Eur. Phys. J. Special Topics in May 2012. We summarize and put in perspective the contributions into three main themes: (i) mechanisms for dragon-kings, (ii) detection of dragon-kings and statistical tests and (iii) empirical evidence in a large variety of natural and social systems. Overall, we are pleased to witness significant advances both in the introduction and clarification of underlying mechanisms and in the development of novel efficient tests that demonstrate clear evidence for the presence of dragon-kings in many systems. However, this positive view should be balanced by the fact that this remains a very delicate and difficult field, if only due to the scarcity of data as well as the extraordinary important implications with respect to hazard assessment, risk control and predictability.Comment: 20 page

Red Queen Coevolution on Fitness Landscapes

Species do not merely evolve, they also coevolve with other organisms. Coevolution is a major force driving interacting species to continuously evolve ex- ploring their fitness landscapes. Coevolution involves the coupling of species fit- ness landscapes, linking species genetic changes with their inter-specific ecological interactions. Here we first introduce the Red Queen hypothesis of evolution com- menting on some theoretical aspects and empirical evidences. As an introduction to the fitness landscape concept, we review key issues on evolution on simple and rugged fitness landscapes. Then we present key modeling examples of coevolution on different fitness landscapes at different scales, from RNA viruses to complex ecosystems and macroevolution.Comment: 40 pages, 12 figures. To appear in "Recent Advances in the Theory and Application of Fitness Landscapes" (H. Richter and A. Engelbrecht, eds.). Springer Series in Emergence, Complexity, and Computation, 201

Effect of left ventricular hypertrophy on long-term survival of patients with coronary artery disease following percutaneous coronary intervention

The impact of left ventricular hypertrophy (LVH) on survival among patients with established coronary artery disease (CAD) is not well understood. We sought to evaluate the effect of LVH on the survival of patients with CAD following percutaneous coronary intervention (PCI). Three hospitals in New York City contributed prospectively defined data on 4284 consecutive patients undergoing PCI. Allcause mortality at a mean follow-up of three years was the primary endpoint. LVH was present in 383 patients (8.9%). LVH patients had a greater prevalence of hypertension (88% vs. 68%, p<0.001), vascular disease (21% vs. 6.6%, p=0.001), and prior heart failure (10% vs. 5.5%, p<0.001). LVH patients presented less often with one-vessel disease (38% vs. 50%, p=0.040) and more often with two- (34% vs. 29%, p=0.014) or three-vessel (22% vs. 18%, p=0.044) disease. Ejection fractions and angiographic success were similar in both groups. In-hospital mortality did not differ between groups. At three-year follow-up, the survival rate for patients with LVH was 86% vs. 91% in patients without LVH (log-rank p=0.001). However, after adjustment for differences in baseline characteristics using Cox proportional hazards analysis, LVH was found not to be an independent predictor of mortality (hazard ratio, 0.93; 95% confidence interval, 0.68–1.28; p=0.67). We conclude that LVH at the time of PCI is not independently associated with an increase in the hazard of death at three years