Joint forecasts on primordial fluctuations and neutrino physics from future CMB and galaxy surveys

This work is dedicated to the forecasts of the precision in the measurements of cosmological parameters for the ESA M5 proposal Cosmic Origin Explorer (CORE), a satellite dedicated to the observation of the Cosmic Microwave Background (CMB) in temperature and polarization. Together with the CORE forecasts it is also studied the combination of CMB and Large Scale Structure (LSS) data with the study of the combination of CORE with the Euclid satellite, whose launch is scheduled in 2020. For comparison we include also the predictions for the combination of Euclid with the Planck satellite, the third generation mission dedicated to the CMB, launched in 2009. In order to derive the forecasts it is applied a Fisher information matrix approach, which is based on a computational structure that is lighter with respect to the standard Markov Chain Monte Carlo (MCMC) usually applied in CMB forecasts. Together with the standard cosmological model (LCDM), several interesting extensions are considered in the analysis and in particular, a possible scale dependence of the spectral index of primordial fluctuations (both running and running of running spectral index are studied), the spatial curvature and the neutrino sector, for both the number of effective relativistic species and the total neutrino mass. The results show that the CORE will be able to strongly improve current constraints on cosmological parameters and in particular, its combination with the LSS from Euclid is capable of breaking parameter degeneracies and give very precise measurements especially for extended models like the neutrino sector. The forecasts derived with the Fisher matrix approach have been compared with those based on a MCMC approach published in the Core Collaboration ECO papers dedicated to cosmological parameters and inflation. The comparison show a very good agreement between the two methods demonstrating the validity of the Fisher approach for forecasting the capabilities of future mission

Anyonic tight-binding models of parafermions and of fractionalized fermions

Parafermions are emergent quasi-particles which generalize Majorana fermions and possess intriguing anyonic properties. The theoretical investigation of effective models hosting them is gaining considerable importance in view of present-day condensed-matter realizations where they have been predicted to appear. Here we study the simplest number-conserving model of particle-like Fock parafermions, namely a one-dimensional tight-binding model. By means of numerical simulations based on exact diagonalization and on the density-matrix renormalization group, we prove that this quadratic model is nonintegrable and displays bound states in the spectrum, due to its peculiar anyonic properties. Moreover, we discuss its many-body physics, characterizing anyonic correlation functions and discussing the underlying Luttinger-liquid theory at low energies. In the case when Fock parafermions behave as fractionalized fermions, we are able to unveil interesting similarities with two counter-propagating edge modes of two neighboring Laughlin states at filling 1/3.Comment: 13 pages, 11 figures. Updated version after publication in PR

Optimal Persistent Currents for Interacting Bosons on a Ring with a Gauge Field

We study persistent currents for interacting one-dimensional bosons on a tight ring trap, subjected to a rotating barrier potential, which induces an artificial U(1) gauge field. We show that, at intermediate interactions, the persistent current response is maximal, due to a subtle interplay of effects due to the barrier, the interaction and quantum fluctuations. These results are relevant for ongoing experiments with ultracold atomic gases on mesoscopic rings.Comment: 5 pages + supplemental material, 6 figure

Bayesian nonparametric graphical models for time-varying parameters VAR

Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between the variables of interest. Recently, Bayesian nonparametric techniques have become popular for modelling complex phenomena in a flexible and efficient manner, but only few attempts have been made in econometrics. In this paper, we provide an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. We include a Bayesian nonparametric dependent prior specification on the matrix of coefficients and the covariance matrix by mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio et al. (2019), our hierarchical prior overcomes over-parametrization and over-fitting issues by clustering the vector autoregressive (VAR) coefficients into groups and by shrinking the coefficients of each group toward a common location. Our BNP timevarying VAR model is based on a spike-and-slab construction coupled with dependent Dirichlet Process prior (DPP) and allows to: (i) infer time-varying Granger causality networks from time series; (ii) flexibly model and cluster non-zero time-varying coefficients; (iii) accommodate for potential non-linearities. In order to assess the performance of the model, we study the merits of our approach by considering a well-known macroeconomic dataset. Moreover, we check the robustness of the method by comparing two alternative specifications, with Dirac and diffuse spike prior distributions

A comparison between lean and visibility approach in supply chain planning

Nowadays, competition increases more and more in the market and it is moved from firm vs firm to supply chain vs supply chain. Therefore, supply chains (SC) are always looking to improve their efficiency to excel in the market. In order to do that, SC managers pay much attention to the coordination among SC members. SC planning allows the coordination among the SC players. In the literature, many SC planning approaches have been developed and analyzed, but up to now, the debate on which is the best approach is an open issue. On the other hand, lean approach is becoming more and more popular among SC managers. Both practitioners and academics have recognized the importance of Lean approach for single firm efficiency. This paper aim at evaluating the impact of Lean approach implementation in supply chain planning tasks. It provides an in-depth analysis of Lean SC planning policy impact on SC performances and compare it with traditional EOQ and Visibility policies. The influence of SC planning policies and of external parameters is assessed in a DES simulation study. The simulation model tests a multi-product three-echelon supply chain. Lean "pull" principle is developed through Kanban system implementation and Lean "create the flow" principle is developed through setup time and batch size reductions. The simulation study analyses inventory level, transportation performance and service level performances. According to simulation outputs a total SC logistic costs have been evaluated for each scenario. The results provide new insights suggesting that Lean supply chain planning policy gives competitive advantages. The results have important consequences for implementation of Lean concepts in practice in SC planning tasks

Static and Dynamic BART for Rank-Order Data

Ranking lists are often provided at regular time intervals by one or multiple rankers in a range of applications, including sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are introduced, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, and the advantages of the dynamic model are demonstrated by forecasts of weekly pollster rankings of NCAA football teams.Comment: The Supplementary Material is available upon request to the author

Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions

Reduced-rank regression recognises the possibility of a rank-deficient matrix of coefficients, which is particularly useful when the data is high-dimensional. We propose a novel Bayesian model for estimating the rank of the rank of the coefficient matrix, which obviates the need of post-processing steps, and allows for uncertainty quantification. Our method employs a mixture prior on the regression coefficient matrix along with a global-local shrinkage prior on its low-rank decomposition. Then, we rely on the Signal Adaptive Variable Selector to perform sparsification, and define two novel tools, the Posterior Inclusion Probability uncertainty index and the Relevance Index. The validity of the method is assessed in a simulation study, then its advantages and usefulness are shown in real-data applications on the chemical composition of tobacco and on the photometry of galaxies

Organizational culture and lean practices: analysis through a real case study

Nowadays, lean researchers are focused on the role of organizational culture and contingencies factors in the success and sustainability of lean management. This research aims at contributing to the academia debate by analysing through a deep case study whether organizational culture based on lean management can enable companies in overcoming differences related to the national culture. Moreover, the study wants to provide evidences that companies can leverage on lean practice in order to spread organizational culture among different country-based plants