Semi-analytic galaxy formation in coupled dark energy cosmologies

Among the possible alternatives to the standard cosmological model ($\Lambda$CDM), coupled Dark Energy models postulate that Dark Energy (DE), seen as a dynamical scalar field, may interact with Dark Matter (DM), giving rise to a "fifth-force", felt by DM particles only. In this paper, we study the impact of these cosmologies on the statistical properties of galaxy populations by combining high-resolution numerical simulations with semi-analytic models (SAM) of galaxy formation and evolution. New features have been implemented in the reference SAM in order to have it run self-consistently and calibrated on these cosmological simulations. They include an appropriate modification of the mass temperature relation and of the baryon fraction in DM haloes, due to the different virial scalings and to the gravitational bias, respectively. Our results show that the predictions of our coupled-DE SAM do not differ significantly from theoretical predictions obtained with standard SAMs applied to a reference $\Lambda$CDM simulation, implying that the statistical properties of galaxies provide only a weak probe for these alternative cosmological models. On the other hand, we show that both galaxy bias and the galaxy pairwise velocity distribution are sensitive to coupled DE models: this implies that these probes might be successfully applied to disentangle among quintessence, $f(R)$-Gravity and coupled DE models.Comment: 8 pages, 1 Table, 5 Figures, MNRAS submitte

A min-cut approach to functional regionalization, with a case study of the Italian local labour market areas

In several economical, statistical and geographical applications, a territory must be subdivided into functional regions. Such regions are not fixed and politically delimited, but should be identified by analyzing the interactions among all its constituent localities. This is a very delicate and important task, that often turns out to be computationally difficult. In this work we propose an innovative approach to this problem based on the solution of minimum cut problems over an undirected graph called here transitions graph. The proposed procedure guarantees that the obtained regions satisfy all the statistical conditions required when considering this type of problems. Results on real-world instances show the effectiveness of the proposed approach

Correlation Clustering with Adaptive Similarity Queries

In correlation clustering, we are given $n$ objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity score can be learned by making a query, and the goal is to minimise both the disagreements and the total number of queries. On the one hand, we describe simple active learning algorithms, which provably achieve an almost optimal trade-off while giving cluster recovery guarantees, and we test them on different datasets. On the other hand, we prove information-theoretical bounds on the number of queries necessary to guarantee a prescribed disagreement bound. These results give a rich characterization of the trade-off between queries and clustering error

On the Troll-Trust Model for Edge Sign Prediction in Social Networks

In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the troll-trust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple probabilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.Comment: v5: accepted to AISTATS 201

Inter-municipal Co-operation: the Managerial Perspective of Local Authorities

The paper aims at contributing to the body of knowledge referred to Public Administrations co-operation. In particular, the research is focused on Local Public Administration (LPA), intensely influenced by the global economic crisis. The study regards to how LPAs could reach more efficiency and effectiveness in providing services to its final users (citizen, companies and other PAs), as well as provide new services, especially on the cooperation among LPAs, called inter- LPA cooperation (ILPAC). Having analysed LPA paradigm, it has been possible to isolate some relevant trends characterizing LPA and some open scientific literature gaps about ILPAC: nowadays, in ILPAC phenomenon, some weak points can be highlighted, especially in start-up and in management phases. Consequently, in collaboration with the eGovernment Observatory of the Milan University of Technology, the research has inquired the reasons that lead to activate an ILPAC and develop a decision making framework for the governance of shared functions in the LPA. Particularly, it has focused on the identification of LPA environmental reasons and LPA proper characteristics pushing LPA to activate an ILPAC of its fundamental functions. Once identified these elements, it has tried to identify the organizational and managerial configurations adopted for ILPAC to manage shared functions. The study has implied the use of several instruments in order to investigate ILPAC phases, from their founding to ordinary management in the Italian context. Results have been analysed using statistical methods, in order to come to light some peculiarities already pointed out by the descriptive examination. In addition, linear regression has been set in order to inquire into ILPAC performances, compared to autonomous municipalities. Using this methodology, the analysis has pointed out some important suggestions pertaining to ILPAC management. Primary considerations has shown the effect of regional different governances that impact on the amount and the dimension of ILPACs in their territories. Secondly, associated municipalities obtain better performances than independent bodies, for instance, considering the One-Stop-Shop proceedings. In addition, linear regression has proven that ILPACs produce benefits in a wider context: the analysis has pointed out some important suggestions pertaining to ILPAC management, as organizational performances increase when the number of associated municipalities increase or both proceeding costs and time improve when large ILPACs formalize and clearly declare their objectives

A Rutherford-like formula for scattering off Kerr-Newman BHs and subleading corrections

By exploiting the Kerr-Schild gauge, we study the scattering of a massive (charged) scalar off a Kerr-Newman black hole. In this gauge, the interactions between the probe and the target involve only tri-linear vertices. We manage to write down the tree-level scattering amplitudes in analytic form, from which we can construct an expression for the eikonal phase which is exact in the spin of the black hole at arbitrary order in the Post-Minkowskian expansion. We compute the classical contribution to the cross-section and deflection angle at leading order for a Kerr black hole for arbitrary orientation of the spin. Finally, we test our method by reproducing the classical amplitude for a Schwarzschild black hole at second Post-Minkowskian order and outline how to extend the analysis to the Kerr-Newman case.Comment: 36 pages, 7 figures. v2: typos corrected and refs added. Version published on JHE