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A post_save callback method, for example, is called immediately after the object in question is saved, but before any follow-up processing is done. In the case of MT::Entry, this means that while the entry may have been saved, its categories that are stored with MT::Placement have not yet been saved. Therefore, if the entry is a completely new one, even if categories were selected in the edit entry interface, it would appear to the callback method that this entry has no categories at all. A pre_save callback is called before the object is saved. If the object is new to the system, it will not have a value in the id field.

E{e[t], e[t]} =

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Nonrandomized Controlled Trials of Exercise Interventions for Cancer Survivors Intervention (I ) and Control (C) Arms (I ) Aerobic and resistive gym-based training 3 d/w for 12 w (C) Stretching (I ) Supervised bed ergometer cycling daily during hospitalization (C) Usual care (I ) Unsupervised gym- or home-based aerobic activity 4 d/w for 10 w (C) No intervention (I ) Supervised group physical training 1 d/w for 4 w and information and coping skills for 7 w (C) Usual care Depression (BDI ); anxiety (SSTAI ); self-esteem (RSEI ) Quality of life (2 items); depression (HAD); anxiety (HAD) Psychologic status (POMS, SCL-90-R) Exercise group reduced global psychologic distress; control group did not change, but no group differences analyzed Exercise group had lower depression and anxiety than control group; no change in self-esteem Exercise group increased social activities more than control group; no difference on any other psychosocial outcomes Psychosocial Outcome Measures Quality of life (CARES) Results for Psychosocial Outcomes Exercise group improved more on quality of life

Thus, H (g(X)) H (X). 2.5 Zero conditional entropy. Show that if H (Y |X) = 0, then Y is a function of X [i.e., for all x with p(x) > 0, there is only one possible value of y with p(x, y) > 0]. Conditional mutual information vs. unconditional mutual information. Give examples of joint random variables X, Y , and Z such that (a) I (X; Y | Z) < I (X; Y ). (b) I (X; Y | Z) > I (X; Y ). Coin weighing. Suppose that one has n coins, among which there may or may not be one counterfeit coin. If there is a counterfeit coin, it may be either heavier or lighter than the other coins. The coins are to be weighed by a balance. (a) Find an upper bound on the number of coins n so that k weighings will nd the counterfeit coin (if any) and correctly declare it to be heavier or lighter.

This query prints the name of all customers who have an account at any one of the branches located in city Edina. To optimize the query in our distributed system, we proceed as follows. Phase 1: Tree Selection. Disregarding distribution, as discussed for Example 4.1, there are ve alternatives that are candidates for running this query. In Figure 4.31, we have shown these alternatives again for ease of reference. From these alternatives, we need to eliminate the ones that do not apply the select before the join. Applying this rule leaves us with alternatives 2 and 5 as the only two viable options. Next, we use cost estimation to determine which one of the two alternatives performs better. We select alternative 5 as the candidate tree since it has a smaller processing cost. Up to this point in the discussion, we have not considered distribution. In order to run this query optimally in our two-site distributed system, we need to decide where each operation is performed. We also need to decide which sites are used to access the relations and how the intermediate results will be merged to form the nal results. Phase 2: Data Localization. In this phase, we need to decide which sites we will use to access the relations we need. The rule that we apply here says, Select the sites where the most data is. In our example, Site 1 has a copy of the Customer relation and the Account relation, while Site 2 has a copy of the Customer relation and the Branch relation. We know that there are 500 customers in the database and each customer

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