Supernova constraints on Multi-coupled Dark Energy

The persisting consistency of ever more accurate observational data with the predictions of the standard LCDM cosmological model puts severe constraints on possible alternative scenarios, but still does not shed any light on the fundamental nature of the cosmic dark sector.As large deviations from a LCDM cosmology are ruled out by data, the path to detect possible features of alternative models goes necessarily through the definition of cosmological scenarios that leave almost unaffected the background and -- to a lesser extent -- the linear perturbations evolution of the universe. In this context,the Multi-coupled DE (McDE) model was proposed by Baldi 2012 as a particular realization of an interacting Dark Energy field characterized by an effective screening mechanism capable of suppressing the effects of the coupling at the background and linear perturbation level. In the present paper, for the first time, we challenge the McDE scenario through a direct comparison with real data, in particular with the luminosity distance of Type Ia supernovae. By studying the existence and stability conditions of the critical points of the associated background dynamical system, we select only the cosmologically consistent solutions, and confront their background expansion history with data. Confirming previous qualitative results, the McDE scenario appears to be fully consistent with the adopted sample of Type Ia supernovae, even for coupling values corresponding to an associated scalar fifth-force about four orders of magnitude stronger than standard gravity. Our analysis demonstrates the effectiveness of the McDE background screening, and shows some new non-trivial asymptotic solutions for the future evolution of the universe. Our results show how the background expansion history might be highly insensitive to the fundamental nature and to the internal complexity of the dark sector. [Abridged]Comment: 10 pages, 7 figures. Matches version accepted for publication in JCA

Linear Perturbation constraints on Multi-coupled Dark Energy

The Multi-coupled Dark Energy (McDE) scenario has been recently proposed as a specific example of a cosmological model characterized by a non-standard physics of the dark sector of the universe that nevertheless gives an expansion history which does not significantly differ from the one of the standard $\Lambda$CDM model. In this work, we present the first constraints on the McDE scenario obtained by comparing the predicted evolution of linear density perturbations with a large compilation of recent data sets for the growth rate $f\sigma_{8}$, including 6dFGS, LRG, BOSS, WiggleZ and VIPERS. Confirming qualitative expectations, growth rate data provide much tighter bounds on the model parameters as compared to the extremely loose bounds that can be obtained when only the background expansion history is considered. In particular, the $95\%$ confidence level on the coupling strength $|\beta |$ is reduced from $|\beta |\leq 83$ (background constraints only) to $|\beta |\leq 0.88$ (background and linear perturbation constraints). We also investigate how these constraints further improve when using data from future wide-field surveys such as supernova data from LSST and growth rate data from Euclid-type missions. In this case the $95\%$ confidence level on the coupling further reduce to $|\beta |\leq 0.85$. Such constraints are in any case still consistent with a scalar fifth-force of gravitational strength, and we foresee that tighter bounds might be possibly obtained from the investigation of nonlinear structure formation in McDE cosmologies.[Abridged]Comment: 24 pages, 12 figure

Estimating the wage premium to supervision for middle managers in different contexts: evidence from Germany and the UK

The analysis of wage distribution has attracted scholars from different disciplines seeking to develop theoretical arguments to explain the upward or downward trend. In particular, how the middle management wage premium changes in different contexts is a relatively neglected area of research. This study argues that wage distribution changes in different contexts, representing different forms of capitalism. To shed light on this, we considered the size and the shape of the wage premium to supervision paid to middle managers in Germany and the UK. We find evidence of two forms of context: middle managers are paid differently for the same task according to the economy where they work; of this amount, about half of the difference is related to the context. We frame the analysis within the literature on varieties of capitalism

Nanostructured nickel film deposition on carbon fibers for improving reinforcement-matrix interface in metal matrix composites

The issues in dispersing any form of carbon in metal matrix is the major problem in the field of metal matrix composites with carbon reinforcement (MMCcr). The low wettability of carbon in molten metals and the difference in density are some of the difficulties to obtain a good dispersion of carbon fibers in the matrix and, as a consequence, an improvement of some critical properties for metals in a wide range of application (mechanical properties, electrical properties, optical properties). For this reason, the aim of this work is to obtain a metallic coated carbon fiber to enhance the interaction between the reinforcement and the matrix. Moreover, also the density of carbon fibers could be adjusted depending on the thickness of the coating. Electroless Nickel-Phosphorus Plating (ENP) is one of the candidate to be a coating technique to improve the interaction between the carbon fibers and the metal matrix. Despite of its versatility in terms of complex geometry of the substrate and homogeneity and adhesion of the coating, the presence of the phosphorus in the alloy could create some problems with the metal matrix such as the formation of metal-phosphorus products that can drastically decrease the mechanical properties of the composite. For this reason, in this work, is presented a new way of Electroless Pure Nickel Plating (EPP) without any introduction of phosphorus in the nickel coating. The dependence of the coating thickness and the density of the coated fibers were studied under different plating parameters (temperature of the plating solution, deposition rate and plating solution composition). All the samples were characterized with SEM and XRD and the thickness, density and homogeneity were compared for all the samples obtained

Lightweight metallic matrix composites. Development of new composites material reinforced with carbon structures

Carbon nano/micro-structures used as fillers in metallic lightweight alloys matrix composites are receiving considerable attention in scientific research and industrial applications. Aluminum and magnesium are the most studied light metals used as matrices in metal composites materials principally for their low density (respectively 2.7 g/cm3 and 1.7 g/cm3) and low melting temperature (around 660 °C for both metals). A good interaction between matrix and fillers is the first step to obtain an increase in bulk properties; furthermore, the manufacturing procedure of the composite is fundamental in terms of quality of fillers dispersion. In this work the influence of surface modifications for three classes of carbon fillers for aluminum and magnesium alloy (AZ63) as matrices is studied. In particular, the selected fillers are short carbon micro fibres (SCMFs), carbon woven fabrics (CWF) and unidirectional yarn carbon fibres (UYFs). The surface modification was carried out by a direct coating of pure nickel on fibres. The electroless pure nickel plating was chosen as coating technique and the use of hydrazine as reducing agent has prevented the co-deposition of other elements (such as P or B). SEM and EDS analyses were performed to study the effect of surface modifications. The mechanical properties of manufactured composites were evaluated by four point flexural tests according to ASTM C1161 (room temperature). Results confirm improved interactions between matrix and fillers, and the specific interaction was studied for any chosen reinforcement

Stone-Gelfand duality for metrically complete lattice-ordered groups

We extend Yosida's 1941 version of Stone-Gelfand duality to metrically complete unital lattice-ordered groups that are no longer required to be real vector spaces. This calls for a generalised notion of compact Hausdorff space whose points carry an arithmetic character to be preserved by continuous maps. The arithmetic character of a point is (the complete isomorphism invariant of) a metrically complete additive subgroup of the real numbers containing $1$, namely, either $\frac{1}{n}\mathbb{Z}$ for an integer $n = 1, 2, \dots$, or the whole of $\mathbb{R}$. The main result needed to establish the extended duality theorem is a substantial generalisation of Urysohn's Lemma to such "arithmetic" compact Hausdorff spaces. The original duality is obtained by considering the full subcategory of spaces whose each point is assigned the entire group of real numbers. In the introduction we indicate motivations from and connections with the theory of dimension groups.Comment: 24 pages, 2 figure

T-Norms Driven Loss Functions for Machine Learning

Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data. These approaches can potentially learn competitive solutions with a significant reduction of the amount of supervised data. A large class of neural-symbolic approaches is based on First-Order Logic to represent prior knowledge, relaxed to a differentiable form using fuzzy logic. This paper shows that the loss function expressing these neural-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator. When restricted to supervised learning, the presented theoretical apparatus provides a clean justification to the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by a neural-symbolic method. Therefore, the methodology allows the development of a novel class of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature

Relational Neural Machines

Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available. However, deep learning solely focuses on the accuracy of the predictions, neglecting the reasoning process leading to a decision, which is a major issue in life-critical applications. Probabilistic logic reasoning allows to exploit both statistical regularities and specific domain expertise to perform reasoning under uncertainty, but its scalability and brittle integration with the layers processing the sensory data have greatly limited its applications. For these reasons, combining deep architectures and probabilistic logic reasoning is a fundamental goal towards the development of intelligent agents operating in complex environments. This paper presents Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First--Order Logic based reasoner. A Relational Neural Machine is able to recover both classical learning from supervised data in case of pure sub-symbolic learning, and Markov Logic Networks in case of pure symbolic reasoning, while allowing to jointly train and perform inference in hybrid learning tasks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems. The experiments show promising results in different relational tasks