1,721,490 research outputs found
Status and physics results of the KM3NeT experiment
No full textThe KM3NeT submarine telescopes, currently under construction in the depths of the Mediterranean Sea, will enable the study of the Universe using neutrinos, the most elusive subatomic particles, as probe to investigate the depths of the Cosmos. KM3NeT-ARCA will study galactic and extra-galactic sources of very high energy neutrinos, thus operating in the field of the so-called “multi-messenger
astronomy”. The detection of neutrinos from an astrophysical source can provide important information on the emission mechanisms of these objects. KM3NeTORCA will instead deal with a basic physics study: the mass hierarchy of neutrinos.
This contribution will describe the components of the two detectors and the first measurements carried out. In addition, the sensitivities of KM3NeT for several
fundamental measurements will be described, such as cosmic neutrino fluxes from diffuse and point-like astrophysical sources and neutrino mass ordering
Correlations of random classifiers on large data sets
No full textClassification of large data sets by feedforward neural networks is investigated. To deal with unmanageably large sets of classification tasks, a probabilistic model of their relevance is considered. Optimization of networks computing randomly chosen classifiers is studied in terms of correlations of classifiers with network input–output functions. Effects of increasing sizes of sets of data to be classified are analyzed using geometrical properties of high-dimensional spaces. Their consequences on concentrations of values of sufficiently smooth functions of random variables around their mean values are applied. It is shown that the critical factor for suitability of a class of networks for computing randomly chosen classifiers is the maximum of sizes of the mean values of their correlations with network input–output functions. To include cases in which function values are not independent, the method of bounded differences is exploited
Ensemble Aggregation Approaches for Functional Optimization
No full textIn this work we investigate the use of ensemble methods, consisting in the aggregation of several approximating models, in the context of functional optimization. In fact, while ensemble techniques are routinely employed in the machine learning literature for classification and regression, there is little research on their application to general optimization problems. Here we consider two strategies to aggregate different solutions to a functional optimization problem, based on optimized weighted averaging and aggregation over the minimum, the latter also in approximate version. A theoretical analysis of approximate functional optimization in the context of ensemble aggregation is provided. Then, simulation results are reported to showcase the advantages of ensembles for functional optimization, in terms of better accuracy and improved robustness with respect to single solutions
Public Transport Transfers Assessment via Transferable Utility Games and Shapley Value Approximation
No full textThe importance of transfer points in public transport networks is estimated by exploiting an approach based on transferable utility cooperative games, which integrates the network topology and the demands. Transfer points are defined as clusters of nearby stops, from which it is easily possible to switch between routes. The methodology is based on a solution concept from cooperative game theory, known as Shapley value. A special formulation of the game is developed for public transport networks with an emphasis on transfers. Based on such a game, the Shapley value is evaluated as an attribute of each transfer point to measure its relative importance: the greater the associated value, the larger the relevance. Due to the computational requirements of the Shapley value calculation for large-size networks, a Monte Carlo approximation is investigated and adopted. A case study of a real-world network is presented to demonstrate the model’s viability
A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features
No full textA game-theoretic approach based on the framework of transferable-utility cooperative games is developed to assess the reliability of transfer nodes in public transportation networks in the case of stochastic transfer times. A cooperative game is defined, whose model takes into account the public transportation system, the travel times, the transfers and the associated stochastic transfer times, and the users’ demand. The transfer stops are modeled as the players of such a game, and the Shapley value – a solution concept in cooperative game theory – is used to identify their centrality and relative importance. Theoretical properties of the model are analyzed. A two-level Monte Carlo approximation of the vector of Shapley values associated with the nodes is introduced, which is efficient and able to take into account the stochastic features of the transportation network. The performance of the algorithm is investigated, together with that of its distributed computing variation. The usefulness of the proposed approach for planners and policy makers is shown with a simple example and on a case study from the public transportation network of Auckland, New Zealand
W. Szymborska La gioia di scrivere. Tutte le poesie (1945-2009)
No full textThis anthology contains the complete annotated works by Wislawa Szymoborska. Szymborska, currently one of the most well known poets in Italy, was “discovered” by Pietro Marchesani, who began to translate her works in 1996, just before the Polish writer was awarded the Nobel Prize for Literature. The volume is enriched by a detailed foreword by Marchesani on Szymborska’s life and oeuvre
Observation of the cosmic ray shadow of the Sun with the ANTARES neutrino telescope
No full textOne of the main goals of the ANTARES neutrino telescope is the search for point-like neutrino sources. Hence, a reliable method to estimate both the angular resolution and the pointing accuracy of the detector is needed. In this poster we present the study of the Sun "shadow" effect: the shadow is the deficit in the atmospheric muon flux in the direction of the Sun induced by the absorption of the primary cosmic rays. The analysis is based on the ANTARES data sample taken between 2008 and 2017. The Sun shadow effect has been observed with 3.9 statistical significance and the angular resolution of the telescope for downward-going atmospheric muons has been found equal to 0.45 ± 0.12 degrees. The pointing accuracy is consistent with the expectations and no evidence of systematic pointing shifts has been found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
