First Results from AMANDA using the TWR System

The Antarctic Muon And Neutrino Detector Array (AMANDA) has been taking data since 2000 and its data acquisition system was upgraded in January 2003 to read out the complete digitized waveforms from the buried Photomultipliers (PMTs) using Transient Waveform Recorders (TWR). This system currently runs in parallel with the standard AMANDA data acquisition system. Once AMANDA is incorporated into the 1 km^3 detector IceCube, only the TWR system will be kept. We report results from a first atmospheric neutrino analysis on data collected in 2003 with TWR. Good agreement in event rate and angular distribution verify the performance of the TWR system. A search of the northern hemisphere for localized event clusters shows no statistically significant excess, thus a flux limit is calculated, which is in full agreement with previous results based on the standard AMANDA data acquisition system. We also update the status of a search for diffusely distributed neutrinos with ultra high energy (UHE) using data collected by the TWR system.Comment: 11 pages, 15 figures, to be published in Proceedings of International School of Cosmic Ray Astrophysics, 15th Course: "Astrophysics at Ultra-high Energies", Erice, Italy, 20-27 June 2006. Minor change: corrected y-label of fig.7 (right

"STUDIO DEL POTENZIAMENTO DELLE INFRASTRUTTURE FERROVIARIE A SERVIZIO DEL PORTO DI LIVORNO PER IL TRASPORTO CONTAINERS"

La tesi si pone lo scopo di incrementera la quota su ferro del traffico containers attuale del porto di Livorno in due fasi relazionate alle ipotesi di sviluppo del porto stesso. Si è studiato lo stato attuale della capacità delle infrastrutture presenti ,quelle di progetto nella prima fase ,2010 e della seconda fase ,2015,relative alla realizzazione della darsena Europa. E' presente un adeguamento a normativa di una intersezione a livelli sfalsati. Si è studiata la dispersione territoriale dei containers sulle infrastrutture viarie. Infine sono presenti informazioni sulle MdM attuali e quelle previste in progetto. La conclusione prevede un massiccio incremento di quota ferroviaria dei contenitori,decongestionando la rete viaria e diminuendo l'impatto ambiental

Information Spreading in Stationary Markovian Evolving Graphs

Markovian evolving graphs are dynamic-graph models where the links among a fixed set of nodes change during time according to an arbitrary Markovian rule. They are extremely general and they can well describe important dynamic-network scenarios. We study the speed of information spreading in the "stationary phase" by analyzing the completion time of the "flooding mechanism". We prove a general theorem that establishes an upper bound on flooding time in any stationary Markovian evolving graph in terms of its node-expansion properties. We apply our theorem in two natural and relevant cases of such dynamic graphs. "Geometric Markovian evolving graphs" where the Markovian behaviour is yielded by "n" mobile radio stations, with fixed transmission radius, that perform independent random walks over a square region of the plane. "Edge-Markovian evolving graphs" where the probability of existence of any edge at time "t" depends on the existence (or not) of the same edge at time "t-1". In both cases, the obtained upper bounds hold "with high probability" and they are nearly tight. In fact, they turn out to be tight for a large range of the values of the input parameters. As for geometric Markovian evolving graphs, our result represents the first analytical upper bound for flooding time on a class of concrete mobile networks.Comment: 16 page

Constraints on Extragalactic Point Source Flux from Diffuse Neutrino Limits

We constrain the maximum flux from extragalactic neutrino point sources by using diffuse neutrino flux limits. We show that the maximum flux from extragalactic point sources is E^2(dN/dE) < 1.4 x 10^-9 (L_nu/2x10^43 erg/s)^1/3 GeV cm-^2 s^-1 from individual point sources with average neutrino luminosity per decade, L_nu. It depends only slightly on factors such as the inhomogeneous matter density distribution in the local universe, the luminosity distribution, and the assumed spectral index. The derived constraints are at least one order of magnitude below the current experimental limits from direct searches. Significant constraints are also derived on the number density of neutrino sources and on the total neutrino power density.Comment: 7 pages, 3 figures, and 2 table

Drug-resistant chronic cluster headache successfully treated with supraorbital plus occipital nerve stimulation. A rare case report

Chronic cluster headache (CCH) is a rare and extremely disabling headache syndrome with a recent clinical systematization of its clinical frame from the European Headache Federation [1]. We present a case of a young man affected by drug-resistant chronic CH (rCCH) who showed improvement after a two-time combined supraorbital and occipital nerve stimulation (S-ONS). The clinical improvement was still present at 6-month follow-up

Community Membership Hiding as Counterfactual Graph Search via Deep Reinforcement Learning

Community detection techniques are useful tools for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore, some users may wish to safeguard their anonymity and opt out of community detection for various reasons, such as affiliation with political or religious organizations. In this study, we address the challenge of community membership hiding, which involves strategically altering the structural properties of a network graph to prevent one or more nodes from being identified by a given community detection algorithm. We tackle this problem by formulating it as a constrained counterfactual graph objective, and we solve it via deep reinforcement learning. We validate the effectiveness of our method through two distinct tasks: node and community deception. Extensive experiments show that our approach overall outperforms existing baselines in both tasks