Observational Constraints on Silent Quartessence

We derive new constraints set by SNIa experiments (`gold' data sample of Riess et al.), X-ray galaxy cluster data (Allen et al. Chandra measurements of the X-ray gas mass fraction in 26 clusters), large scale structure (Sloan Digital Sky Survey spectrum) and cosmic microwave background (WMAP) on the quartessence Chaplygin model. We consider both adiabatic perturbations and intrinsic non-adiabatic perturbations such that the effective sound speed vanishes (Silent Chaplygin). We show that for the adiabatic case, only models with equation of state parameter $|\alpha |\lesssim 10^{-2}$ are allowed: this means that the allowed models are very close to \LambdaCDM. In the Silent case, however, the results are consistent with observations in a much broader range, -0.3<\alpha<0.7.Comment: 7 pages, 12 figures, to be submitted to JCA

Artificial Intelligence to improve learning outcomes through online collaborative activities

A key strategic objective of the University courses is the promotion and development of new and innovative teaching activities, also through the e-learning environment, with the aim of providing students with direct involvement in the learning process. Collaborative activities represent important and effective teaching methodologies that allow the improvements of learning outcomes through active learning. Furthermore, they can allow the development of soft skills because they enable learners to work together and practice critical reflection and conflict negotiation. Recently, online learning environments are being used to design and deliver assignments based on student work groups. Indeed, the development of digital technologies allows the organization of these online activities in a flexible way for both students and teachers. The goal of this work is to develop successful collaborative activities for undergraduate students to ensure the improvement of knowledge and soft skills on a specific topic. One of the fundamental factors that influence the success of collaborative learning is the students’ group formation, which consists in the realization of heterogeneous groups in terms of cognitive resources, characteristics, and behaviors, composed by four or five students. However, the correct implementation of groups requires careful profiling of each student’s behavior which can be difficult for the teacher to detect. In this work an intelligent software, developed using Artificial Intelligence algorithms, was used to assist the teacher in the realization of heterogeneous groups of students. It is composed of a Machine Learning model, consisting in clustering techniques applied to Moodle learning analytics performed to return clusters that identifies different students’ profiles, and a specific algorithm that automatically organizes the groups, ensuring the heterogeneity including at least one student from each cluster. At the end of the execution the software returns the list of the heterogeneous groups to the teacher. The software was applied to assignments that required working group within a specific online course for university students, using a Moodle e-learning platform. The quantitative analysis demonstrated the effectiveness of the numerical method for group composition proposed in this work to ensure successful collaborative activities, confirmed also by the perceptions of the students on the course

General CMB and Primordial Trispectrum Estimation

We present trispectrum estimation methods which can be applied to general non-separable primordial and CMB trispectra. We present a general optimal estimator for the connected part of the trispectrum, for which we derive a quadratic term to incorporate the effects of inhomogeneous noise and masking. We describe a general algorithm for creating simulated maps with given arbitrary (and independent) power spectra, bispectra and trispectra. We propose a universal definition of the trispectrum parameter $T_{NL}$, so that the integrated bispectrum on the observational domain can be consistently compared between theoretical models. We define a shape function for the primordial trispectrum, together with a shape correlator and a useful parametrisation for visualizing the trispectrum. We derive separable analytic CMB solutions in the large-angle limit for constant and local models. We present separable mode decompositions which can be used to describe any primordial or CMB bispectra on their respective wavenumber or multipole domains. By extracting coefficients of these separable basis functions from an observational map, we are able to present an efficient estimator for any given theoretical model with a nonseparable trispectrum. The estimator has two manifestations, comparing the theoretical and observed coefficients at either primordial or late times. These mode decomposition methods are numerically tractable with order $l^5$ operations for the CMB estimator and approximately order $l^6$ for the general primordial estimator (reducing to order $l^3$ in both cases for a special class of models). We also demonstrate how the trispectrum can be reconstructed from observational maps using these methods.Comment: 38 pages, 9 figures. In v2 Figures 4-7 are altered slightly and some extra references are included in the bibliography. v3 matches version submitted to journal. Includes discussion of special case

Machine-learning-based software to group heterogeneous students for online peer assessment activities

Since the academic year 2017/2018, a peer assessment activity was included in the online Genomics laboratory for the master’s degree course in Biological Sciences of the University of Camerino, with the aim of improving learning outcomes and soft skills in students, such as team building and critical thinking. Creating groups in university courses is not easy because of the large number of students, that leads teachers to realize groups totally randomly, a procedure that is not always effective. One of the factors that influences the success of collaborative learning is the creation of heterogeneous groups based on the students’ behaviors. Despite little improvements, the online genomics laboratory highlighted some gaps. Random groups didn’t ensure that each group was composed of heterogeneous students, and it leads some students to have a bad perception of the peer review activity, negatively affecting their engagement and motivation. This work proposes a new Machine Learning Approach and the realization of a specific software, able to create effective heterogeneous groups to be involved in the online peer assessment process, in order to improve learning outcomes and satisfaction in the students. The aim is to check the improvement of the peer assessment effectiveness using heterogeneous groups compared to random groups of students. Two editions of the online laboratory of Genomics were analysed, examining the students’ results and perceptions to verify the impact of the Machine Learning approach designed in this work

Dark Energy and Dark Matter

It is a puzzle why the densities of dark matter and dark energy are nearly equal today when they scale so differently during the expansion of the universe. This conundrum may be solved if there is a coupling between the two dark sectors. In this paper we assume that dark matter is made of cold relics with masses depending exponentially on the scalar field associated to dark energy. Since the dynamics of the system is dominated by an attractor solution, the dark matter particle mass is forced to change with time as to ensure that the ratio between the energy densities of dark matter and dark energy become a constant at late times and one readily realizes that the present-day dark matter abundance is not very sensitive to its value when dark matter particles decouple from the thermal bath. We show that the dependence of the present abundance of cold dark matter on the parameters of the model differs drastically from the familiar results where no connection between dark energy and dark matter is present. In particular, we analyze the case in which the cold dark matter particle is the lightest supersymmetric particle.Comment: 4 pages latex, 2 figure

An entirely analytical cosmological model

The purpose of the present study is to show that in a particular cosmological model, with an affine equation of state, one can obtain, besides the background given by the scale factor, Hubble and deceleration parameters, a representation in terms of scalar fields and, more important, explicit mathematical expressions for the density contrast and the power spectrum. Although the model so obtained is not realistic, it reproduces features observed in some previous numerical studies and, therefore, it may be useful in the testing of numerical codes and as a pedagogical tool.Comment: 4 pages (revtex4), 4 figure

Scaling solutions in general non-minimal coupling theories

A class of generalized non-minimal coupling theories is investigated, in search of scaling attractors able to provide an accelerated expansion at the present time. Solutions are found in the strong coupling regime and when the coupling function and the potential verify a simple relation. In such cases, which include power law and exponential functions, the dynamics is independent of the exact form of the coupling and the potential. The constraint from the time variability of $G$, however, limits the fraction of energy in the scalar field to less than 4% of the total energy density, and excludes accelerated solutions at the present.Comment: 10 pages, 3 figures, accepted for publication in Phys. Rev.