On higher-derivative gauge theories

In this work we study the main properties and the one-loop renormalization of a Yang-Mills theory in which the kinetic term contains also a fourth-order differential operator; in particular, we add to the Yang-Mills Lagrangian the most general contribution of mass dimension six, weighted with a dimensionful parameter. This model is renormalizable; in the literature two values for the beta function for the gauge coupling have been reported, one obtained using the heat kernel approach and one with Feynman diagrams. In this work we repeat the computation using heat kernel techniques confirming the latter result. We also considered coupling with matter. We then study the supersymmetric extension of the model; this is a nontrivial task because of the complicate structure of the higher-derivative term. Some partial results were known, but a computation of the beta functions for the full supersymmetric non-Abelian higher-derivative gauge theory was missing. We make use of the (unextended) supersymmetric higher-derivative Lagrangian density for the Yang-Mills field in six spacetime dimensions derived in arXiv:hep-th/0505082; by dimensional reduction we obtain the N=1 and N=2 supersymmetric higher-derivative super-Yang-Mills Lagrangian in four spacetime dimensions, whose beta function we evaluate using heat kernels. We also deduce the beta function for N=4 supersymmetry.Comment: Based on the thesis prepared as final dissertation for the MSc degree in Physics at the University of Padova. 68 pages; added reference in 1.

Using Enamel Matrix Derivative to Improve Treatment Efficacy in Periodontal Furcation Defects

Purpose Furcations are complicated periodontal defects. Untreated furcations lead to loss of the involved teeth and supporting tissues. It has been demonstrated that regenerative biomaterials are beneficial in reconstruction of the bone surrounding furcation‐affected teeth. These biomaterials range from bone grafts and nonresorbable/resorbable barrier membranes to biologics that are able to trigger inactive regenerative processes in periodontal tissues. Selection of appropriate material(s) to treat furcations is challenging. The aim of this article is to provide a comparative outlook on different biomaterials applicable in regeneration of furcations with a focus on enamel matrix derivative (EMD). Methods Scientific databases including PubMed/MEDLINE, ScienceDirect, and EMBASE were searched, and 28 articles were found primarily for this specific study. Full texts were studied to identify relevant studies; 17 studies were excluded because of irrelevancy, while 11 main studies were ultimately selected. Other references have been used for general statements. Results EMD is a protein complex widely used in the regeneration of different periodontal defects. To assess the effects of EMD for treatment of root furcations, clinical studies involving EMD with and without barrier membranes and bone grafts were selected and compared. Briefly, this study reveals that when EMD is combined with open flap debridement (OFD), guided tissue regeneration (GTR), or bone grafting (BG), the amount of class II furcations converted to class I increases significantly. EMD also reduces tissue swelling and patient discomfort after treatment. Conclusions This study provides evidence to find the best combination of biomaterials to treat furcation defects. The best results are obtained if EMD is combined with β‐TCP/HA alloplastic bone grafts

An international review of cultural consumption research

Despite the effects of the crisis, several studies show that there has been an increase in cultural production in all the most important western countries over the last twenty years. Nevertheless, the dimensions of the flows of demand are changing: the lowering of the threshold of perceived accessibility to the cultural contents on offer is resulting in new population segments using them. The modalities of cultural product consumption are also changing, and are increasingly influenced by the direct involvement of the consumer in the creative processes. On the other side, the competition to conquer consumersÕ free time has intensified because more figures are now involved, both from the cultural industry and outside. The cultural offer has multiplied and become more differentiated. But while this consumption is changing dimensions and modality, a gap is emerging in the information and knowledge of cultural consumption behaviour, mainly due to a lack of innovative official statistical measurements. The present paper wants to understand how academic literature reacted to the need for information on cultural consumption, that became widespread during 2000. Our main objective is to offer an initial overview of scientific literature of the fist decade of the twenty-first century, while trying to understand the future research trends. The analysis showed that great attention is still dedicated to the segmentation of cultural demand, but the analysis of motivations underlying cultural consumption is significantly acquiring more importance. Moreover, we identified vast research areas in which cultural consumption has only been partially studied, such as: social consumption, studies on individual businesses, methodological triangulation, and the operative implications for business management.Cultural consumption; Marketing research; Segmentation; Motivations

Conformal Anomaly for Non-Conformal Scalar Fields

We give a general definition of the conformal anomaly for theories that are not classically Weyl invariant and show that this definition yields a quantity that is both finite and local. As an example we study the conformal anomaly for a non-minimally coupled massless scalar and show that our definition coincides with results obtained using the heat kernel method.Comment: 9 page

Bayesian nonparametric sparse VAR models

High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in real-world networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ---- Supplementary Material available on reques

Matrix-State Particle Filter for Wishart Stochastic Volatility Processes

This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We focus on a special class of models recently proposed in the literature and assume that the covariance matrix is a latent variable which follows an autoregressive Wishart process. We review two alternative stochastic representations of the Wishart process and propose Markov-Switching Wishart processes to capture different regimes in the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for matrix-valued distributions and allows us to sequentially estimate both the parameters and the latent variables.Multivariate Stochastic Volatility; Matrix-State Particle Filters; Sequential Monte Carlo; Wishart Processes, Markov Switching.

Beta-Product Poisson-Dirichlet Processes

Time series data may exhibit clustering over time and, in a multiple time series context, the clustering behavior may differ across the series. This paper is motivated by the Bayesian non--parametric modeling of the dependence between the clustering structures and the distributions of different time series. We follow a Dirichlet process mixture approach and introduce a new class of multivariate dependent Dirichlet processes (DDP). The proposed DDP are represented in terms of vector of stick-breaking processes with dependent weights. The weights are beta random vectors that determine different and dependent clustering effects along the dimension of the DDP vector. We discuss some theoretical properties and provide an efficient Monte Carlo Markov Chain algorithm for posterior computation. The effectiveness of the method is illustrated with a simulation study and an application to the United States and the European Union industrial production indexes

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author

Efficient Gibbs Sampling for Markov Switching GARCH Models

We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi-point samplers, such as the Multiple-Try Metropolis (MTM) and the Multiple trial Metropolize Independent Sampler, in combination with FFBS for the MS-GARCH process. In this sense we ex- tend to the MS state space models the work of So [2006] on efficient MTM sampler for continuous state space models. Finally, we suggest to further improve the sampler efficiency by introducing the antithetic sampling of Craiu and Meng [2005] and Craiu and Lemieux [2007] within the FFBS. Our simulation experiments on MS-GARCH model show that our multi-point and multi-move strategies allow the sampler to gain efficiency when compared with single-move Gibbs sampling.Comment: 38 pages, 7 figure

Bayesian Model Selection for Beta Autoregressive Processes

We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the constraints on the parameter space. We provide a full Bayesian approach to the estimation and include the parameter restrictions in the inference problem by a suitable specification of the prior distributions. Moreover in a Bayesian framework parameter estimation and model choice can be solved simultaneously. In particular we suggest a Markov-Chain Monte Carlo (MCMC) procedure based on a Metropolis-Hastings within Gibbs algorithm and solve the model selection problem following a reversible jump MCMC approach