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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Undecidability of Quantized State Feedback Control for Discrete Time Linear Hybrid Systems." In Theoretical Aspects of Computing – ICTAC 2012, edited by A. Roychoudhury and M. D'Souza, 243–258. Lecture Notes in Computer Science 7521. Springer Berlin Heidelberg, 2012. ISBN: 978-3-642-32942-5. DOI: 10.1007/978-3-642-32943-2_19.
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Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Enrico Tronci, and Marisa Venturini Zilli. "Finite Horizon Analysis of Stochastic Systems with the Mur$\varphi$ Verifier." In Theoretical Computer Science, 8th Italian Conference, ICTCS 2003, Bertinoro, Italy, October 13-15, 2003, Proceedings, edited by C. Blundo and C. Laneve, 58–71. Lecture Notes in Computer Science 2841. Springer, 2003. ISSN: 3-540-20216-1. DOI: 10.1007/978-3-540-45208-9_6.
Abstract: Many reactive systems are actually Stochastic Processes. Automatic analysis of such systems is usually very difficult thus typically one simplifies the analysis task by using simulation or by working on a simplified model (e.g. a Markov Chain). We present a Finite Horizon Probabilistic Model Checking approach which essentially can handle the same class of stochastic processes of a typical simulator. This yields easy modeling of the system to be analyzed together with formal verification capabilities. Our approach is based on a suitable disk based extension of the Mur$\varphi$ verifier. Moreover we present experimental results showing effectiveness of our approach.
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Flavio Chierichetti, Silvio Lattanzi, Federico Mari, and Alessandro Panconesi. "On Placing Skips Optimally in Expectation." In Web Search and Web Data Mining (WSDM 2008), edited by M. Najork, A. Z. Broder and S. Chakrabarti, 15–24. Acm, 2008. DOI: 10.1145/1341531.1341537.
Abstract: We study the problem of optimal skip placement in an inverted list. Assuming the query distribution to be known in advance, we formally prove that an optimal skip placement can be computed quite efficiently. Our best algorithm runs in time O(n log n), n being the length of the list. The placement is optimal in the sense that it minimizes the expected time to process a query. Our theoretical results are matched by experiments with a real corpus, showing that substantial savings can be obtained with respect to the tra- ditional skip placement strategy, that of placing consecutive skips, each spanning sqrt(n) many locations.
Keywords: Information Retrieval
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