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By S. Bassis and C.F. Morabito B. Apolloni

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Read or Download Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy May 28-30 2009, Volume 204 Frontiers in Artificial ... Intelligent Engineering Systems) PDF

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Additional resources for Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy May 28-30 2009, Volume 204 Frontiers in Artificial ... Intelligent Engineering Systems)

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The fitting procedure We solve two orders of inference problems: i) fitting of the intercontact times ti s through (4), by identifying s-parameters a, b, c for each agent; ii) regression of the fitting parameters versus the mobility parameters α and λ (for short m-parameters). We accomplish the first task with a slightly improved variant of the Algorithmic Inference procedure described in a previous paper [2]. Namely, denoting by t(i) the i-th element of the sorted intercontact times and by m the quantity (m+1)/2 , we use the well behaving statistics [4] s1 = t(m) s2 = 1 m (6) m ti − s1 (7) i=1 m s3 = log t(i) (8) i=m Thanks to the sampling mechanism t= FT−1 (u) = ga,b,c (u) = c bu + 1 1−u 1 a −1 (9) relating a realization of the uniform random variable U to a T ’s, we obtain the master equations [5] s1 = ga,b,c (u(m) ) s2 = s3 = 1 m (10) m ga,b,c (ui ) − ga,b,c (u(m) ) (11) i=1 1 ξm log c + 2 a m log i=m bui + 1 1 − ui (12) As usual, we solve them in the s-parameters in correspondence to a large set of randomly drawn seeds {u1 , .

We also clamped to 0 the last 60 seconds of contacts that are artificially generated by the above beaconing control rule. After this preprocessing, we computed for each PTR a log of both its contacts, intraand intercontact times with any other of the PTRs enrolled in the experiment. In this paper we focus exclusively on the latter. f. that we jointly visualize for all devices in LogLog scale in Figure 7(a), and contrast with the confidence region of the median of these curves in picture (b). At first glance this picture indicates a good match between the proposed theoretical model and on-field behavior.

In Figure 5 we 1 see the best fitting we obtain separately on a, b a and c. The first graph denotes a complex trend of a with α that we interpret in this way. On the one hand, the comparative inspection of curves like in Figure 6 shows that an a increase (a↑) in (4) has the effect of moving the elbow between the non-Pareto and Pareto traits back, as for the turning time, and down, as for the corresponding probability, with the twofold effect of reducing both the distribution time scale (t↓) and the rate of contact times (r↓) falling in the second trait (call them the intentional times).

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