An improved measurement of baryon acoustic oscillations from the correlation function of galaxy clusters at z∼0.3z \sim 0.3

We detect the peak of baryon acoustic oscillations (BAO) in the two-point correlation function of a spectroscopic sample of $25226$ clusters selected from the Sloan Digital Sky Survey. Galaxy clusters, as tracers of massive dark matter haloes, are highly biased structures. The linear bias $b$ of the sample considered in this work, that we estimate from the projected correlation function, is $b \sigma_8 = 1.72 \pm 0.03$. Thanks to the high signal in the cluster correlation function and to the accurate spectroscopic redshift measurements, we can clearly detect the BAO peak and determine its position, $s_p$, with high accuracy, despite the relative paucity of the sample. Our measurement, $s_p = 104 \pm 7 \, \mathrm{Mpc} \, h^{-1}$, is in good agreement with previous estimates from large galaxy surveys, and has a similar uncertainty. The BAO measurement presented in this work thus provides a new strong confirmation of the concordance cosmological model and demonstrates the power and promise of galaxy clusters as key probes for cosmological applications based on large scale structures.Comment: 10 pages, 7 figure, accepted for publication in MNRA

Modeling the QSO luminosity and spatial clustering at low redshifts

We investigate the ability of hierarchical models of QSO formation and evolution to match the observed luminosity, number counts and spatial clustering of quasars at redshift z<2. These models assume that the QSO emission is triggered by galaxy mergers, that the mass of the central black hole correlates with halo properties and that quasars shine at their Eddington luminosity except, perhaps, during the very early stages of evolution. We find that models based on simple analytic approximations successfully reproduce the observed B-band QSO luminosity function at all redshifts, provided that some mechanisms is advocated to quench mass accretion within haloes larger than about 1e13 Msun that host bright quasars. These models also match the observed strength of QSO clustering at z~0.8. At larger redshifts, however, they underpredict the QSO biasing which, instead, is correctly reproduced by semi-analytic models in which the halo merger history and associated BHs are followed by Monte Carlo realizations of the merger hierarchy. We show that the disagreement between the luminosity function predicted by semi-analytic models and observations can be ascribed to the use of B-band data, which are a biased tracer of the quasar population, due to obscuration.Comment: 13 pages, 9 figures. Accepted by MNRA

The cosmological co-evolution of supermassive black holes, AGN and galaxies

We model the cosmological co-evolution of galaxies and their central supermassive black holes (BHs) within a semi-analytical framework developed on the outputs of the Millennium Simulation (Croton et al., 2006; De Lucia & Blaizot, 2007). In this work, we analyze the model BH scaling relations, fundamental plane and mass function, and compare them with the most recent observational data. Furthermore, we extend the original code developed by Croton et al. (2006) to follow the evolution of the BH mass accretion and its conversion into radiation, and compare the derived AGN bolometric luminosity function with the observed one. We find, for the most part, a very good agreement between predicted and observed BH properties. Moreover, the model is in good agreement with the observed AGN number density in 0<z<5, provided it is assumed that the cold gas fraction accreted by BHs at high redshifts is larger than at low redshifts (Marulli et al., 2008).Comment: Proceedings of "The Central Kiloparsec: Active Galactic Nuclei and Their Hosts", Ierapetra, Crete, 4-6 June, 2008. To appear in Volume 79 of the Memorie della Societa' Astronomica Italiana. 5 pages, 4 figure

Neural networks for driver behavior analysis

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering positionbased features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results

Cyber resilience meta-modelling: The railway communication case study

Recent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements

Exploring data and model poisoning attacks to deep learning-based NLP systems

Natural Language Processing (NLP) is being recently explored also to its application in supporting malicious activities and objects detection. Furthermore, NLP and Deep Learning have become targets of malicious attacks too. Very recent researches evidenced that adversarial attacks are able to affect also NLP tasks, in addition to the more popular adversarial attacks on deep learning systems for image processing tasks. More precisely, while small perturbations applied to the data set adopted for training typical NLP tasks (e.g., Part-of-Speech Tagging, Named Entity Recognition, etc..) could be easily recognized, models poisoning, performed by the means of altered data models, typically provided in the transfer learning phase to a deep neural networks (e.g., poisoning attacks by word embeddings), are harder to be detected. In this work, we preliminary explore the effectiveness of a poisoned word embeddings attack aimed at a deep neural network trained to accomplish a Named Entity Recognition (NER) task. By adopting the NER case study, we aimed to analyze the severity of such a kind of attack to accuracy in recognizing the right classes for the given entities. Finally, this study represents a preliminary step to assess the impact and the vulnerabilities of some NLP systems we adopt in our research activities, and further investigating some potential mitigation strategies, in order to make these systems more resilient to data and models poisoning attacks

Exploring the impact of data poisoning attacks on machine learning model reliability

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area

Acquired tracheoesophageal fistula repair, due to prolonged mechanical ventilation, in patient with double incomplete aortic arch

We report a case of the repair of an acquired benign tracheoesophageal fistula (TEF) after prolonged mechanical invasive ventilation. Patient had an unknown double incomplete aortic arch determining a vascular ring above trachea and esophagus. External tracheobronchial compression, caused by the vascular ring, increasing the internal tracheoesophageal walls pressure determined by endotracheal and nasogastric tubes favored an early TEF development. The fistula was repaired through an unusual left thoracotomy and vascular ring dissection. TEFs are a heterogeneous group of diseases affecting critically ill patients. Operative closure is necessary to avoid further complications related to this condition. Pre-opera-tive study is mandatory to plan an adequate surgical approach

Numerical Simulation of Compressible Vortical Flows Using a Conservative Unstructured-Grid Adaptive Scheme

A two-dimensional numerical scheme for the compressible Euler equations is presented and applied here to the simulation of exemplary compressible vortical flows. The proposed approach allows to perform computations on unstructured moving grids with adaptation, which is required to capture complex features of the flow-field. Grid adaptation is driven by suitable error indicators based on the Mach number and by element-quality constraints as well. At the new time level, the computational grid is obtained by a suitable combination of grid smoothing, edge-swapping, grid refinement and de-refinement. The grid modifications-including topology modification due to edge-swapping or the insertion/deletion of a new grid node-are interpreted at the flow solver level as continuous (in time) deformations of suitably-defined node-centered finite volumes. The solution over the new grid is obtained without explicitly resorting to interpolation techniques, since the definition of suitable interface velocities allows one to determine the new solution by simple integration of the Arbitrary Lagrangian-Eulerian formulation of the flow equations. Numerical simulations of the steady oblique-shock problem, of the steady transonic flow and of the start-up unsteady flow around the NACA 0012 airfoil are presented to assess the scheme capabilities to describe these flows accurately

Demography of obscured and unobscured AGN: prospects for a Wide Field X-ray Telescope

We discuss some of the main open issues in the evolution of Active Galactic Nuclei which can be solved by the sensitive, wide area surveys to be performed by the proposed Wide Field X-ray Telescope mission.Comment: Proceedings of "The Wide Field X-ray Telescope Workshop", held in Bologna, Italy, Nov. 25-26 2009. To appear in Memorie della Societa' Astronomica Italiana 2010 (arXiv:1010.5889