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Spam Mimic is a generation technique that takes a covert message and uses an English grammar rule set to generate text that looks like spam. Spam Mimic creates a grammar tree showing the different possible words that can be used and, based on the covert message, selects words from it.

In order to nonetheless ensure seamless operation of the sensor network, temporal coordination of the sleep periods among sensor nodes may be required Another important service for sensor network applications is temporal message ordering (eg, ref [4]) Many data-fusion algorithms have to process sensor readings ordered by the time of occurrence (eg, the approach for velocity estimation just sketched) However, the highly variable message delays in sensor networks imply that messages from distributed sensor nodes may often not arrive at a receiver in the order in which they were sent Reordering messages according to the time of sensor readout requires temporal coordination among sensor nodes Methods for localization of sensor nodes based on the measurement of time of ight or difference of arrival time of certain signals also require synchronized time (eg, ref [5]) 712 Revisiting Time Synchronization for Sensor Networks.

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The Execute button also has a procedure to handle its click event:

VAR(X), the squared standard error of the sample mean, with s 2 /n, where s 2 is the sample variance based on the n observations left after outliers are discarded However, there are two concerns with this approach First, it results in using the wrong standard error; second, discarding outliers in some manner is often met with incredulity because it seems counterintuitive based on what has become traditional training in statistics In particular, it might seem that this must result in less accurate results and less power First consider the issue of accuracy and power when some of the smallest and largest observations are discarded To take an extreme case, consider the usual sample median, which discards all but the middle one or two values As illustrated in Figure 32, it can be more accurate on average versus the mean, as was rst noted by Laplace in 1775.

The front of ce manager should consider developing procedures for front desk clerks to follow that encourage a guest to book additional reservations during the check-in process. Suggesting additional reservations during registration may remind the businessperson of the need for room accommodations the following week, when he or she will visit a city with a hotel that is af liated with the same chain. It may inspire the traveler who has not made reservations for the rest of his or her trip and nds your rates very attractive to stay in a chain member property. This promotion of member properties can be a very pro table marketing concept. Front of ce managers in independent hotels will also nd this concept pro table. Independent hotels have the advantage of offering unique

(d) Stain with Safranin-O/Fast Green to assess the presence of proteoglycans. (e) Immunostain with the antibodies of interest (type II collagen, type I collagen, aggrecan, link protein, etc) with the Histostain-SP kit, following the manufacturer s protocol.

By 1818 Laplace was aware of more general conditions under which the median beats the mean in accuracy To provide some sense of why this occurs, imagine that 20 observations are randomly sampled from a standard normal distribution Now put these values in ascending order and label the results X (1) X (20) It can be shown that with probability 983, the smallest value will be less than 09 That is, P(X (1) 09) = 983 Similarly, P(X (20) 09) = 983 That is, there is a high probability that the smallest and largest observations will be relatively far from the population mean, the value we are trying to estimate.

From these contribution calculations, the ordering (C0, C4, C1) yields the highest contribution and therefore is chosen. Figure 2.20 depicts the nal ordering of af nity information for the columns of table T within the clustered af nity matrix. Once the columns have been put in the right order, we order the rows similarly, ending up with the nal clustered af nity matrix as shown in Figure 2.21.

Sub Test3() Dim ChartData As Variant ChartData = Sheets( Sheet1 ).ChartObjects(1). _ Chart.SeriesCollection(1).Values For i = 1 To UBound(ChartData)

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