The Topological Non-connectivity Threshold and magnetic phase transitions in classical anisotropic long-range interacting spin system

We analyze from the dynamical point of view the classical characteristics of the Topological Non-connectivity Threshold (TNT), recently introduced in F.Borgonovi, G.L.Celardo, M.Maianti, E.Pedersoli, J.Stat.Phys.,116,516(2004). This shows interesting connections among Topology, Dynamics, and Thermo-Statistics of ferro/paramagnetic phase transition in classical spin systems, due to the combined effect of anisotropy and long-range interactions.Comment: 6 revtex pages, 4 .eps figures Contribution presented at the 3rd Conference NEXT-Sigma-Phi News, Expectations, and Trends in Statistical Physics, August 13-18 2005, Kolymbari, Crete. For related results see also cond-mat/0402270 cond-mat/0410119 cond-mat/0505209 cond-mat/0506233 cond-mat/051007

The Topological Non-connectivity Threshold in quantum long-range interacting spin systems

Quantum characteristics of the Topological Non-connectivity Threshold (TNT), introduced in F.Borgonovi, G.L.Celardo, M.Maianti, E.Pedersoli, J. Stat. Phys., 116, 516 (2004), have been analyzed in the hard quantum regime. New interesting perspectives in term of the possibility to study the intriguing quantum-classical transition through Macroscopic Quantum Tunneling have been addressed.Comment: contribution to NEXTSIGMAPHI 3r

Mastering the Spatio-Temporal Knowledge Discovery Process

The thesis addresses a topic of great importance: a framework for data mining positioning data collected by personal mobile devices. The main contribution of this thesis is the creation of a theoretical and practical framework in order to manage the complex Knowledge discovery process on mobility data. Hence the creation of such framework leads to the integration of very different aspects of the process with their assumptions and requirements. The result is a homogeneous system which gives the possibility to exploit the power of all the components with the same flexibilities of a database such as a new way to use the ontology for an automatic reasoning on trajectory data. Furthermore two extensions are invented and developed and then integrated in the system to confirm the extensibility of it: a innovative way to reconstruct the trajectories considering the uncertainty of the path followed and a Location prediction algorithm called WhereNext. Another important contribution of the thesis is the experimentation on a real case of study on analysis of mobility data. It has been shown the usefulness of the system for a mobility manager who is provided with a knowledge discovery framework

ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery

Il contributo di questa tesi è il disegno e lo sviluppo di un sistema di Knoledge Discovery denominato ConQueSt. Basato sul paradigma del Pattern Discovery guidato dai vincoli, ConQueSt segue la visione dell’Inductive Database: • il mining è visto come forma più complessa di querying, • il sistema quindi è equipaggiato con un data mining query language, e strettamente collegato con un DBMS • i pattern estratti con query di mining diventano cittadini di prima classe e, seguendo il principio di chiusura, vengono materializzati accanto ai dati nel DBMS. ConQueSt è già stato presentato con successo al workshop internazionale della comunità IDB, e alla prestigiosa conferenza IEEE International Conference on Data Mining Engineering (ICDE 2006). A giugno sarà presentato alla conferenaz italiana di basi di dati (SEBD 2006). E’ attualmente in corso la sottomissione ad una prestigiosa rivista

A workflow language for research e-infrastructures

AbstractResearch e-infrastructures are "systems of systems," patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of "reproducibility of science" and "transparent assessment of science." This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of "manually executable actions," i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and "machine-executable actions," i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to (i) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and (ii) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms

The RASSCALS: An X-ray and Optical Study of 260 Galaxy Groups

We describe the ROSAT All-Sky Survey-Center for Astrophysics Loose Systems (RASSCALS), the largest X-ray and optical survey of low mass galaxy groups to date. We draw 260 groups from the combined Center for Astrophysics and Southern Sky Redshift Surveys, covering one quarter of the sky to a limiting Zwicky magnitude of 15.5. We detect 61 groups (23%) as extended X-ray sources. The statistical completeness of the sample allows us to make the first measurement of the X-ray selection function of groups, along with a clean determination of their fundamental scaling laws. We find robust evidence of similarity breaking in the relationship between the X-ray luminosity and velocity dispersion. Groups with sigma < 340 km/s are overluminous by several orders of magnitude compared to the familiar LX ~ sigma^4 law for higher velocity dispersion systems. An understanding of this break depends on the detailed structure of groups with small velocity dispersions sigma < 150 km/s.Comment: 16 pages, including 6 figures. To appear in The Astrophysical Journa

PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems

Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data

A global descriptor of spatial pattern interaction in the galaxy distribution

We present the function J as a morphological descriptor for point patterns formed by the distribution of galaxies in the Universe. This function was recently introduced in the field of spatial statistics, and is based on the nearest neighbor distribution and the void probability function. The J descriptor allows to distinguish clustered (i.e. correlated) from ``regular'' (i.e. anti-correlated) point distributions. We outline the theoretical foundations of the method, perform tests with a Matern cluster process as an idealised model of galaxy clustering, and apply the descriptor to galaxies and loose groups in the Perseus-Pisces Survey. A comparison with mock-samples extracted from a mixed dark matter simulation shows that the J descriptor can be profitably used to constrain (in this case reject) viable models of cosmic structure formation.Comment: Significantly enhanced version, 14 pages, LaTeX using epsf, aaspp4, 7 eps-figures, accepted for publication in the Astrophysical Journa