Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Novel bimetallic 1%M-Fe/Al2O3-Cr2O3 (2:1) (M = Ru, Au, Pt, Pd) catalysts for Fischer-Tropsch synthesis

The main objective of this work was to study the physicochemical and catalytic properties of bimetallic supported catalysts [1%M-Fe/Al2O3-Cr2O3 (2:1) (M = Ru, Au, Pt, Pd)] in Fischer-Tropsch synthesis. Furthermore, the study investigated the effect of noble metal addition to iron-supported catalysts on their physicochemical properties and reactivity. The physicochemical properties of the catalysts were studied using a range of characterization techniques such as X-ray diffraction (XRD), temperature-programmed reduction (TPR-H2), temperature-programmed desorption of ammonia (TPD-NH3) and BET (Brunauer – Emmett - Teller method). The activity tests were performed by Fischer-Tropsch synthesis in a high-pressure fixed-bed reactor using a gas mixture of H2 and CO with a molar ratio of 1:1. The correlation between the physicochemical properties of the investigated catalysts and their catalytic performance in CO hydrogenation was also investigated. The reactivity results showed that the most active system exhibited a high specific surface area, the highest total acidity and was the most reducible catalyst compared to the other catalysts tested. In addition, the Au–Fe system showed high selectivity towards liquid product formation during CO hydrogenation

Reentrant behavior of superconducting alloys

A dirty BCS superconductor with magnetic impurities is studied. Asymptotic solution of the thermodynamics of such superconductor with spin $1/2$ and $7/2$ magnetic impurities, is found. To this end, the system's free energy $f(H, \beta)$ is bounded from above and below by mean-field type bounds, which are shown to coalesce almost exactly in the thermodynamic limit, provided the impurity concentration is sufficiently small. The resulting mean-field equations for the gap $\Delta$ and a parameter $\nu$, characterizing the impurity subsystem, are solved and the solution minimizing $f$ is found for various values of magnetic coupling constant $g$ and impurity concentration $x$. The phase diagrams of the system are depicted with five distinct phases: the normal phase, unperturbed superconducting phase, perturbed superconducting phase with nonzero gap in the excitation spectrum, perturbed gapless superconducting phase and impurity phase with completely suppressed superconductivity. Furthermore, evidence of reentrant superconductivity and Jaccarino-Peter compensation is found. The credibility of the theory is verified by testing the dependence of the superconducting transition temperature $T_{\text{c}}$ on $x$. Very good quantitative agreement with experimental data is obtained for several alloys: (La$_{1-x}$Ce$_{x}$)Al$_{2}$, (La$_{1-x}$Gd$_{x}$)Al$_{2}$ and (La$_{0.8-x}$Y$_{0.20}$)Ce$_{x}$. The theory presented improves earlier developments in this field.Comment: 15 pages, 8 figures, full length articl

Nonparametric Information Geometry

The differential-geometric structure of the set of positive densities on a given measure space has raised the interest of many mathematicians after the discovery by C.R. Rao of the geometric meaning of the Fisher information. Most of the research is focused on parametric statistical models. In series of papers by author and coworkers a particular version of the nonparametric case has been discussed. It consists of a minimalistic structure modeled according the theory of exponential families: given a reference density other densities are represented by the centered log likelihood which is an element of an Orlicz space. This mappings give a system of charts of a Banach manifold. It has been observed that, while the construction is natural, the practical applicability is limited by the technical difficulty to deal with such a class of Banach spaces. It has been suggested recently to replace the exponential function with other functions with similar behavior but polynomial growth at infinity in order to obtain more tractable Banach spaces, e.g. Hilbert spaces. We give first a review of our theory with special emphasis on the specific issues of the infinite dimensional setting. In a second part we discuss two specific topics, differential equations and the metric connection. The position of this line of research with respect to other approaches is briefly discussed.Comment: Submitted for publication in the Proceedings od GSI2013 Aug 28-30 2013 Pari

The Generalized Second Law of Thermodynamics in Cosmology

A classical and quantum mechanical generalized second law of thermodynamics in cosmology implies constraints on the effective equation of state of the universe in the form of energy conditions, obeyed by many known cosmological solutions, and is compatible with entropy bounds which forbid certain cosmological singularities. In string cosmology the second law provides new information about the existence of non-singular solutions, and the nature of the graceful exit transition from dilaton-driven inflation.Comment: 12 pages, no figure

Integrating biomass into energy supply chain networks

During a period of transition towards decarbonised energy networks, maintaining a reliable and secure energy supply whilst increasing efficiency and reducing cost will be key aims for all energy supply chain (ESC) networks. Renewable energy sources, such as biomass, will play an important role in future ESCs as climate change mitigation becomes an increasingly important priority. This paper seeks to address these requirements by presenting an optimisation model for the design and planning of biomass integration into the ESC networks. A supply chain model was derived and the governing equations were solved using the General Algebraic Modelling System software (GAMS) to achieve an optimal solution. The results of the study indicate that a reduction in the emissions cost of up to 4.32% is achievable on integration of 5–8% of biomass into the ESC network. However, a 4.57% increase in the total cost of the ESC network was recorded at the biomass fraction in the mixed fuel of 7.9%, with the fixed assets cost having the largest impact on the total cost of the ESC network. It has been shown that the cost increment in the assets and operational costs of a biomass and coal co-fired combined heat and power plant can be offset by the cost reductions obtained from reduced carbon dioxide emissions. Economic arguments for dual-fuel plants, therefore, require the introduction of effective carbon pricing legislation. It is concluded that such policy implementations can be effective at mitigating the effects of climate change and would assist in achieving a global carbon neutral economy

Differences in genotype and virulence among four multidrug-resistant <i>Streptococcus pneumoniae</i> isolates belonging to the PMEN1 clone

We report on the comparative genomics and characterization of the virulence phenotypes of four &lt;i&gt;S. pneumoniae&lt;/i&gt; strains that belong to the multidrug resistant clone PMEN1 (Spain&lt;sup&gt;23F&lt;/sup&gt; ST81). Strains SV35-T23 and SV36-T3 were recovered in 1996 from the nasopharynx of patients at an AIDS hospice in New York. Strain SV36-T3 expressed capsule type 3 which is unusual for this clone and represents the product of an in vivo capsular switch event. A third PMEN1 isolate - PN4595-T23 - was recovered in 1996 from the nasopharynx of a child attending day care in Portugal, and a fourth strain - ATCC700669 - was originally isolated from a patient with pneumococcal disease in Spain in 1984. We compared the genomes among four PMEN1 strains and 47 previously sequenced pneumococcal isolates for gene possession differences and allelic variations within core genes. In contrast to the 47 strains - representing a variety of clonal types - the four PMEN1 strains grouped closely together, demonstrating high genomic conservation within this lineage relative to the rest of the species. In the four PMEN1 strains allelic and gene possession differences were clustered into 18 genomic regions including the capsule, the blp bacteriocins, erythromycin resistance, the MM1-2008 prophage and multiple cell wall anchored proteins. In spite of their genomic similarity, the high resolution chinchilla model was able to detect variations in virulence properties of the PMEN1 strains highlighting how small genic or allelic variation can lead to significant changes in pathogenicity and making this set of strains ideal for the identification of novel virulence determinant

Predictability of evolutionary trajectories in fitness landscapes

Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.Comment: 14 pages, 7 figure

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference