A note on the birefringence angle estimation in CMB data analysis

Parity violating physics beyond the standard model of particle physics induces a rotation of the linear polarization of photons. This effect, also known as cosmological birefringence (CB), can be tested with the observations of the cosmic microwave background (CMB) anisotropies which are linearly polarized at the level of $5-10\%$. In particular CB produces non-null CMB cross correlations between temperature and B mode-polarization, and between E- and B-mode polarization. Here we study the properties of the so called D-estimators, often used to constrain such an effect. After deriving the framework of both frequentist and Bayesian analysis, we discuss the interplay between birefringence and weak-lensing, which, albeit parity conserving, modifies pre-existing TB and EB cross correlation.Comment: 12 pages. Accepted for publication in JCA

From crisis to crisis: emergencies and uncertainties in large metropolitan areas and cities of Southern Europe

This monographic issue collects the works of researchers who questioned the dynamics of the urban and metropolitan areas of southern Europe in the current framework of crisis (first of all, concerning health, migration and geopolitics). Specifically, starting from the cases of Madrid, Lisbon, Rome, Athens, Valencia, Bari and Lecco, the contributions presented analyse the consequences of this climate of emergency on the social, economic and territorial fabric of the urban and metropolitan contexts and the development of a wide range of solutions addressed to mitigate the uncertainties (e.g., solidarity networks and bottom-up initiatives). The various works make extensive use of cross-scale analyses, a combination of research methodologies and different observation perspectives

Big Data, Cognitive Computing and the future of learning managements Systems

Since the early years, when they started to enter the market, Learning Management Systems (LMSs) demonstrated their utility inside learning environments, contributing to the diffusion of e-learning especially in those Institutions with a low budget or no internal knowledge for developing e-learning initiatives. Today, they have reached a high maturity level, providing professional solutions to almost any educational need referring to distance learning. However, in our opinion, there are two important evolutions that should profoundly change the architecture of these pillar software tools. First, the acquisition of an enormous amount of data related to educational tasks will be very interesting for all the actors involved in educational processes (teachers, students, researchers, administrative personnel), and this will be particularly evident when standards like Experience-API (xAPI) will help to provide a more pervasive experience for learners. Second, we are observing the rise of new era for software platforms, characterized by machine learning, deep learning, cognitive computing and many other technologies that substantially give the computer a much more active role in the respective processes. We believe that this new paradigm will apply to education too. What this will entail is mainly related to exponential learning, a process of exponential growth of training demand because new knowledge and skills must be delivered at a speed never seen before, and where big data contexts are fundamental. In this paper, we present an analysis of how LMSs should evolve in the future, in our opinion and according to our experience, in terms of functionalities and services provided to users. We believe that current LMSs and their software architectures, mainly based on traditional multi-tier, relational database-oriented architectures will not be enough to stand the impact of these two new paradigms for modern learning environments. We are in the process of re-designing a virtual community platform that we have created and developed along the years, used in our universities and in several public and private organizations. The platform is oriented towards the support of collaborative processes, where of course e-learning is one of the most important, but not the only one, and where we are adding new services supporting collaboration in different ways. In this paper we will present the software architectural changes and evolution according to the advent of big data and cognitive computing

Intelligent Roundabout Insertion using Deep Reinforcement Learning

An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choices, in terms of time and safety, are taken. For this purpose, we present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver. Our model is trained with a novel implementation of A3C, which we will call Delayed A3C (D-A3C), in a synthetic environment where vehicles move in a realistic manner with interaction capabilities. In addition, the system is trained such that agents feature a unique tunable behavior, emulating real world scenarios where drivers have their own driving styles. Similarly, the maneuver can be performed using different aggressiveness levels, which is particularly useful to manage busy scenarios where conservative rule-based policies would result in undefined waits

A comparison of CMB Angular Power Spectrum Estimators at Large Scales: the TT case

In the context of cosmic microwave background (CMB) data analysis, we compare the efficiency at large scale of two angular power spectrum algorithms, implementing, respectively, the quadratic maximum likelihood (QML) estimator and the pseudo spectrum (pseudo-Cl) estimator. By exploiting 1000 realistic Monte Carlo (MC) simulations, we find that the QML approach is markedly superior in the range l=[2-100]. At the largest angular scales, e.g. l < 10, the variance of the QML is almost 1/3 (1/2) that of the pseudo-Cl, when we consider the WMAP kq85 (kq85 enlarged by 8 degrees) mask, making the pseudo spectrum estimator a very poor option. Even at multipoles l=[20-60], where pseudo-Cl methods are traditionally used to feed the CMB likelihood algorithms, we find an efficiency loss of about 20%, when we considered the WMAP kq85 mask, and of about 15% for the kq85 mask enlarged by 8 degrees. This should be taken into account when claiming accurate results based on pseudo-Cl methods. Some examples concerning typical large scale estimators are provided.Comment: 9 pages, 7 figures. Accepted for publication in MNRA

Planck 2018 constraints on anisotropic birefringence and its cross-correlation with CMB anisotropy

Parity-violating extensions of standard electromagnetism produce cosmic birefringence, the in vacuo rotation of the linear polarisation direction of a photon during propagation. We employ {\it Planck} 2018 CMB polarised data to constrain anisotropic birefringence, modeled by its angular power spectrum $C_{\ell}^{\alpha \alpha}$, and the cross-correlation with CMB temperature maps, $C_{\ell}^{\alpha T}$, at scales larger than $\sim$15 degrees. We present joint limits on the scale invariant quantity, $A^{\alpha \alpha} \equiv \ell (\ell +1) \, C_{\ell}^{\alpha \alpha} / 2 \pi$, and on the analogous amplitude for the cross-correlation, $A^{\alpha T} \equiv \ell (\ell +1) \, C_{\ell}^{\alpha T} / 2 \pi$. We find no evidence of birefringence within the error budget and obtain A^{\alpha \alpha} < 0.104 \, \mbox{[deg^2]} and A^{\alpha T}=1.50^{+2.41}_{-4.10} \, \mbox{[\mu$K$\cdotdeg] both at } 95 \% \mbox{ C.L.}. The latter bound appears competitive in constraining a few early dark energy models recently proposed to alleviate the $H_{0}$ tension. Slicing the joint likelihood at $A^{\alpha T}=0$, the bound on $A^{\alpha \alpha}$ becomes tighter at A^{\alpha \alpha} < 0.085 \, \mbox{[deg^2]} at 95\% \mbox{ C.L.}. In addition we recast the constraints on $A^{\alpha \alpha}$ as a bound on the amplitude of primordial magnetic fields responsible for Faraday rotation, finding B_{1 {\tiny \mbox{Mpc}}} < 26.9 nG and B_{1 {\tiny \mbox{Mpc}}} < 24.3 nG at 95$\%$ C.L. for the marginalised and sliced case respectively.Comment: 38 pages, 34 Figures. References added. Section 6 extended. Accepted for publication in Journal of Cosmology and Astroparticle Physic