Correlations of random classifiers on large data sets

Classification 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

A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features

A 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

The Water Properties of the Site in Capo Passero using the LED Beacon of the Prototype Tower

In that work we study the scattering parameters of the water on the KM3Net site in Capo Passero. To this purpose we compare the real data from the time calibration runs of the detector to the results of a simulations of the light emission, propagation and detection according to the expeimental apparatus

The system to fill with oil the empty Break Out Boxes (BOBs) of the KM3NeT Detection Units

The KM3NeT collaboration aims to construct the largest underwater neutrino telescope in the Mediterranean sea. The detector is located in two sites, one in front of Toulon at 2500m sea depth and one SE Capo Passero in Sicily at 3500m depth. On both sites, one or two blocks of 115 Detection Units (DU) are connected to shore , each Du being composed by a Vertical Electro-Optical Cable (VEOC) connecting 18 Digital Optical Modules (DOMs). In this report we describe the integration of the DU carried out in our INFN laboratory in Genova, in particular one important phase of the process where some components in the VEOC have to be carefully filled with oil before connection to the DOMs

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses