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will rst try to satisfy the request by allotting the bandwidth from the reserved amount. If the reserved bandwidth has already been used, the request will be allotted the bandwidth from the remaining available bandwidth of the cell. Otherwise, the handoff request is dropped. Let the maximum amount of bandwidth that could be reserved be max (a small percentage of total available bandwidth). The amount of bandwidth reserved for Type 2 handoffs dynamically varies between 0 and max depending on the relative position of the average load line in the previous neighboring cell. To explain the concept of average load line, consider a cell N, and refer to Figure 16.6. Assume that cell N 1 contains k Type 2 handoff calls with residual residence times in cell N 1 denoted by t1 , t2 , , tk such that t1 t2 tk and let the corresponding amounts of bandwidth allocated to the calls be by b1 , b2 , , bk . Let B be the sum total of bj where j ranges from 1 to k. The average load line L is de ned as the average of ti and ti+1 where i is the smallest subscript for which the inequality below holds.

Here a serendipitous accident took place before a genius engaged in a serious, long-term investigation that prepared him to recognize the implications of the accident. On the other hand, the public s perception of the Serendipity Pattern could best be captured by a 1970 Hershey Food Corp. commercial where a person walks along absent-mindedly munching a chocolate bar, while another person dreamily strolls by eating peanut butter. The two collide. Hey, you got peanut butter on my chocolate, says one. And you got chocolate on my peanut butter, says the other.6 They sample the result and, Eureka! they discover Reese s Peanut Butter Cups. Sometimes the most useful ideas come from simple combinations of existing ideas. Nevertheless, this type of invention is rare and by its unexpected nature de es forecasting. S. Harris s cartoon (reprinted with permission of ScienceCartoonPlus.com) encapsulates the dif culty of planning for serendipity.

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Global Positioning Systems, Inertial Navigation, and Integration, Mohinder S. Grewal, Lawrence R. Weill, Angus P. Andrews Copyright # 2001 John Wiley & Sons, Inc. Print ISBN 0-471-35032-X Electronic ISBN 0-471-20071-9

The following command performs a faster, destructive read-write test: WARNING! This will erase all the data on the partition.

def test bond(self): from ppf.date time \ import date, shift, modified following, basis act 360, months pd = date(2008, 01, 01) env = ppf.market.environment(pd) times = numpy.linspace(0, 6, 10) factors = numpy.array([math.exp(-0.05*t) for t in times]) env.add curve("zc.disc.eur" , ppf.market.curve(times, factors, ppf.math.interpolation. loglinear)) expiries, tenors = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, \ 6.0], [0, 90] values = numpy.zeros((9, 2)) values.fill(0.001) env.add surface("ve.term.eur.hw" , ppf.market.surface(expiries, tenors, values)) env.add constant("cv.mr.eur.hw", 0.01) r = ppf.model.hull white.requestor() s = ppf.model.hull white.monte carlo.state(10000) e = ppf.model.hull white.monte carlo.evolve("eur") e.evolve(0.0,3.0,s,r,env) f = ppf.model.hull white.fill(5.0) t = 3.0 T = 4.0 sx = s.fill(t, r, env) ptT = f.numeraire rebased bond(t, T, "eur", env, r, sx) actual = ptT.mean() expected = r.discount factor(T, "eur", env) assert(math.fabs(actual-expected) < 1.0e-3)

The traditional approach, almost to the exclusion of alternatives, focuses on an individual or, sometimes, a small social unit like a family. The risk is understood as

the cases constitute a compact case base. Since RSOM achieves compact clusters, this will enable one to extract nonambiguous rules. As a demonstration of the nature of distribution of the frequency of winning nodes, the results for the horseshoe data, corresponding to SOM and RSOM are shown in Figures 3.13 and 3.14. Separation between the clusters is seen to be more prominent in Figure 3.14. These winning nodes may be viewed as the prototype points (cases) representing the two classes. Unlike the conventional methods, the cases and prototypes selected here are not just a subset of the original data points, but represent some collective information generated by the network after learning the entire data set.

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