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This meant that a corporation could be sued in a jurisdiction where it had no place of business, if it had committed acts sufficient to subject it to long-arm jurisdiction Given that many infringing companies commit their nefarious acts on a nationwide basis, this change in the law is indeed liberating to plaintiffs (Note: This does not mean that such an infringer may be sued anywhere the plaintiff may choose; other requirements must still be satisfied Consult an attorney).

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Bialystok councilmen, my father included, were advised to leave the city lest they be taken as hostages by the Nazis My father had to make the painfully dif cult decision to escape and leave his wife and only child behind Fortunately he did It was the rst of many life-or-death decisions made by Isaac Melamdovich over the course of the next two years They were decisions in a chain of events that led the Melamdoviches out of the clutches of both the Nazis and the Bolsheviks, across the steppes of Siberia, through Japan courtesy of Ambassador Sugihara, and eventually to the freedom of these shores But no transition of this magnitude is without pain Although Chicago was a far cry from what was experienced by the generations of immigrants who were thrust into the tenements of New York s Lower East Side, there were many similarities and common ordeals.





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that are unreachable with any form of DSL that has interesting capacities presents a signi cant competitive threat to the telcos. Both DSL and cable modems provide a vivid example of how throwing every conceivable form of signal processing at a medium with many pathologies can make a usable silk purse out of a particularly unpromising sow s ear. Among the many phenomena af icting the copper plant are certainly its limited usable bandwidth (roughly 0.75 GHz for coax) and the non atness of its attenuation spectrum, but also pickup of impulse noise and hum, and the fact that such interferences accumulate going upstream in any P2MP topology. The coax-based HFC architecture bears a very close resemblance to that of the optical PON. This is true in its P2MP geometry and also in many of the protocol details. However, there is a very important distinction. As the reader will see in 3, sophisticated signal processing plays essentially no role in a PON because of the ber medium s low attenuation, wide bandwidth, and lack of any forms of noise other than white Gaussian noise in the receiver front end. Signal processing, however, is key to HFC performance. The only real complication is managing the bandwidth sharing in the upstream direction, an issue that PONs share with HFC and for which HFC architects provided the template [Ransom].

This was especially true for the children who, like myself, were unmercifully immersed in a foreign environment and left to cope with the unvarnished and often cruel demands of their fellow compatriots Indeed, I was a struggling, unhappy, greenhorn kid until I entered Roosevelt High School on the North Side of Chicago..

It is straightforward to compute the Bayes predictive density using the Monte Carlo technique of (5.13) and the data from Lagakos (1976). We can use the suf cient statistics from Lagakos (1976) to evaluate the terms of the posterior. The posterior distributions are 1 |DI (19, 300), 2 |DI (5, 300), 3 |DI (18, 254), 4 |DI I (30, 680), 5 |DI I (8, 680), and 6 |DI I (26, 271). We consider treatment as a simple covariate and perform analyses for treatments 1 and 2. Figure 5.4 gives the Bayes predictive densities for both treatments. There does not appear to be any difference between the two treatments, a conclusion supported by Lagakos (1976). The solid lines are the predictive densities for treatment 1 and treatment 2 based on averaging the exact waiting time density given of (5.17) over random samples from the posterior of . We can compute (5.17) only because we have exponential waiting times that allow this calculation. These predictive distributions are estimated with m = 400 samples of 1 , . . . , 6 from their posterior. In general, we cannot compute the exact conditional density fZ (z| ) and we use the saddlepoint approximation to compute the Bayes predictive density using (5.13). This computation, using m = 400 values from the posterior, gives the dashed lines in Figure 5.4. Note that we never actually get the exact predictive density; we estimate it via the random samples. The solid lines estimate the predictive density using

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