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4. The noti cation usage for different users follows a Zipf-like distribution, with a of 0.44 0.46. A small number of users consume a signi cant fraction of system and network resources. For example, 5% of users receive around 25% noti cations, and 10% of users receive around 40% noti cations. 5. There is geographic locality in noti cation content. Users in the same city have more sharing in noti cation content compared to the users picked at random. 6. On average the server sends less than 0.5 million event noti cations with a total of 40 MB per day. Thus, the network load is low but other activities such as database updates can still keep the noti cation servers busy. As the noti cation service gains more popularity, the system and network load will increase.



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8

Then, again, if we select = A , R, = X i , and Q = qXiI, for some positive scalar p n i and some scalar 0 << X 5 1 used to introduce exponential ~ e i g h t i n g , 'then the recursions ~ of Alg. 31.1 would reduce to the following. Start with 201-1 = 0 and Pol-1 = X-'n-' and repeat for i 2 0:

(10.153)





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that are not in MST by a loop-breakage procedure, which iteratively follows dangling edges from leaves to LMST loops, and breaks loops by eliminating their longest edges, until the procedure nishes at a single node (as a by-product, this single node can also be considered as an elected leader of the network). The leader so elected also learns the longest MST edge in the process, and can broadcast it to other nodes. Note that this procedure operates only in two dimensions, since it is based on a face routing scheme (cf. ref. [23]). Santi and Blough [4] showed that, in two and three dimensions, the transmission range can be reduced signi cantly if weaker requirements on connectivity are acceptable. Halving the critical transmission range, the longest connected component contains 90% of nodes approximately. This means that a considerable amount of energy is spent in order to connect relatively few nodes. It has also been shown in ref. [40] that, as the common transmission range changes, the probability that the network is connected exhibits a sharp transition within a relatively short interval (see Fig. 10.21). Similar transition phenomena also exist for k-connectivity [19,39].

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Our intelligence, like our activity, presents two very different forms: on the one hand, are sensations and sensory tendencies; on the other, conceptual thought and moral activity. Each of these two parts of ourselves represents a separate pole of our being, and these two poles are not only distinct from one another but are opposed to one another. Our sensory appetites are necessarily egoistic: they have our individuality and it alone as their object. When we satisfy our hunger, our thirst, and so on, without bringing any other tendency into play, it is ourselves, and ourselves alone, that we satisfy. [Conceptual thought] and moral activity are, on the contrary, distinguished by the fact that the rules of conduct to which they conform can be universalized. Therefore, by de nition, they pursue impersonal ends. Morality begins with disinterest, with attachment to something other than ourselves. . . . These two aspects of our psychic life are, therefore, opposed to each other as are the personal and the impersonal. There is in us a being that represents everything in relation to itself and from its own point of view; in everything that it does, this being has no other object but itself. There is another being in us, however, which knows things sub specie aeternitatis, as if it were participating in some thought other than its own, and which, in its acts, tends to accomplish ends that surpass its own. The old formula homo duplex is therefore veri ed by the facts. Far from being simple, our inner life has something that is like a double center of gravity. On the one hand is our individuality and, more particularly,

address change. If the VPN Client is not able to contact the VPN Router, then the tunnel will be brought down.

Figure 32-2

Now that we understand how Windows CE represents bands, adding bands to a rebar control is straightforward.

Figure 22.3 shows data in C7:C33 being smoothed by the formulas in D8:D33. Cell D4 contains the smoothing factor . Begin the first cell or cells in the forecast with actual data or an average of the first few data values. Cell D7 does this with

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