"... In PAGE 15: ...degree gt; lt;Data gt;0.9 1.0 lt;/Data gt; lt;/degree gt; lt;Atom gt; lt;op gt; lt;Rel gt;have_money lt;/Rel gt; lt;/op gt; lt;/Atom gt; lt;/And gt; lt;/And gt; lt;/body gt; lt;/Implies gt; The remaining annotation-based languages are treated similarly. A summary of the proposed encodings can be found in Table1 , where the syntax of lt;Implies gt; is specified for some of the existing languages. Notice that for annotation-based languages the lt;degree gt; element is not present, like in the previous example.... ..."

"... In PAGE 8: ...The remaining annotation-based languages are treated sim- ilarly. A summary of the proposed encodings can be found in Table1 , where the syntax of lt;Implies gt; is specified for some of the existing languages. Notice that for annotation- based languages the lt;degree gt; element is not present, like in the previous example.... ..."

"... In PAGE 7: ...Table2 specify an operational semantics for ! exn. The problem is that we have only introduced some notions of reduction without de ning any reduction strategy.... In PAGE 7: ...orm, when it exists, is unique. This is the purpose of the next proposition. Proposition 5.1 (Church-Rosser Property) Let ! be the reduction relation induced by the notions of reduction of Table2 (that is the least re exive, transitive relation containing ! and compatible with the expression formation rules). If A, B, C are expressions such that A ! B and A ! C then there exists an expression D such that B ! D and C ! D.... In PAGE 7: ...2 (Subject Reduction Property) Let ? be a typing environment, A be an expression, and be a type such that ? ? A : . If A ! B, according to the one-step reduction relation induced by the notions of reduction of Table2 , then ? ? B : . Proof.... In PAGE 9: ... 7 CPS-Interpretation The modi ed semantics appears now as a conservative extension of the ML-like ones. Never- theless, one may still wonder if Table2 is just an ad-hoc adaptation of the ML-like reduction rules, designed to make Proposition 6.4 hold, or if there is anything canonical in the modi- ed semantics.... ..."

"... In PAGE 42: ...1. Ideal Functional Form of Detector In Chapter 1 we introduced an ideal classifier as a large table as shown in Table5 below. This classifier is ideal in several ways.... In PAGE 45: ... To use such a representation, we must build a table that stores two values for each input, P(image | object) and P(image | non-object), as shown below in Table 7. There are several conse- quences of using a decision rule derived from this generalized form instead of one directly derived from the concise form in Table5 . The generalized form will work just as well if our probability estimates are accurate representations of the true probabilities.... In PAGE 51: ... This underscores the problem with naive Bayes classifiers or any classifier in general; that is, we will have errors when we do not model important relationships, such as that between x and y when P(x | y, object) and P(x | y, non-object) are very different functions. No classifier is immune to this problem except the ideal model, Table5 , which is infeasible. In a naive Bayes classifier, the relationships that are not modeled are very explicit.... ..."

"... In PAGE 31: ... Table 5. Region-wide Average Annual Deliveries by Source Base Case (taf/yr) Unconstrained (taf/yr) Water Source Agricultural Urban Total Agricultural Urban Total Surface Water 3,408 748 4,156 3,406 764 4,170 Groundwater 1,492 676 2,168 1,492 676 2,168 Total 4,900* 1,424 6,324 4,898* 1,440 6,338 Note: *Deliveries may differ from the demands reported in Table3 because some water supplies are recycled. Scarcity and Operating Costs As stated earlier, CALVIN attempts to maximize economic benefit by minimizing both the cost of water scarcity and operating costs to the system.... ..."