"... In PAGE 5: ... We do this to highlight the improvements that can be made over some of the mechanisms traditionally applied to the domain of group decision making. Table1 summarizes the properties of the mechanisms we describe in this section. Most of the mechanisms we consider here take a set of reported agent pref- erences and return one of a set of possible outcomes along with a payment (positive or negative) to or from each agent.... ..."

"... In PAGE 4: ..., 1994). Table1 presents an example of the features and inter-feature causal relationships for one of the six novel categories used in the present research. Lake Victoria Shrimp were described to participants as possessing four distinctive binary features and three inter- feature causal relationships.... In PAGE 5: ...Nisbett, Krantz, Jepson, amp; Kunda, 1983; Proffitt, Coley, amp; Medin, 2000; Smith, Shafir, amp; Osherson, 1993; Thagard amp; Nisbett, 1982), we use blank properties that are unfamiliar to the participants (Osherson, Smith, Wilkie, Lopez, amp; Shafir, 1990). Table1 also includes the blank properties for Lake Victoria Shrimp. For example, participants are posed the hypothetical, Suppose one Lake Victoria Shrimp has been found that has mucus that is slightly acidic , and then asked, What proportion of all Lake Victoria Shrimp have mucus that is slightly acidic? The predicate mucus is slightly acidic was deliberately chosen to be unfamiliar or blank to participants.... In PAGE 5: ... According to our first hypothesis, categories with a common-cause schema may promote inductions because it prompts categorizers to assume that new features are also likely to be produced by the common cause. For example, when instructed on the Lake Victoria Shrimp category of Table1 and a single category member that has acidic mucus, participants might reason that, High amounts of the ACh neurotransmitter cause many properties of Lake Victoria Shrimp, it might cause acidic mucus as well. To test this prediction, performance on the property induction task by participants who were provided with a common-cause causal schema is compared to a control group that was taught the novel category but without the associated knowledge.... In PAGE 16: ...common-cause, chain, and F1-control conditions, and F4 in the common-effect and F4-control conditions. For example, for the category of Table1 participants in the common-cause, chain, and F1-control conditions were told that 100% of Lake Victoria Shrimp have high amounts of ACh neurotransmitter , and that F2,F3, and F4 occurred 75% of the time (e.g.... In PAGE 22: ... In those conditions each category feature F1 was described as occurring 100% of the time in category members, and 0% of the time in members of other categories. For example, for the category of Table1 participants were told that 100% of Acetylcholine Shrimp have high amounts of ACh neurotransmitter, and no other kind of shrimp does. In the common-effect condition and its F4-control condition the categories were named: Fast Ants, Heavy Shrimp, Planet Stars, Reactive Sodium Carbonate, Carbonos, and Bright Computers, and F4 was described as occurring 100% of the time in category members, and 0% of the time in members of other categories.... ..."

"... In PAGE 4: ...ig. 4. Scene text images: Images with variations in skew, perspective, blur, illumination, and alignment. Before we attempt to classify the various techniques used in TIE, it is important to define the commonly used terms and summarize the characteristics2 of text that can be used for TIE algorithms. Table1 shows a list of properties that have been utilized in recently published algorithms [25-30]. Text in images can exhibit many variations with respect to the following properties: 1.... ..."

"... In PAGE 7: ... 3 Some Test Suite Problems Many of the functions that have been used to test genetic algorithms are small (less than 50 bits) or the test functions can be decomposed such that the individual pieces that make up the test function can be solved independently. For example, the DeJong test functions (labeled F1 through F5) are common test functions, as are the Rastrigin (F6), Schwefel (F7) and Griewangk (F8) functions (See Table1 ) [H. 93].... In PAGE 7: ... 3.1 Limitations of Existing Test Problems While all of the test problems in Table1 are nonlinear, for most of these problems the interactions between variables are linear. Such problems are separable in the sense that the optimal value for each parameter can be determined independent of all the other variables.... In PAGE 11: ... 4.1 Composite Functions Many of the test functions in Table1 have been introduced into the literature because of interesting properties, but they lack nonlinear interactions between variables. Simple function composition can be used to convert these functions into a nonlinear function of 2 variables.... In PAGE 28: ...5 245.9 Table1 0: Comparison of number of evaluations required to nd solutions in contrast to time required to reach the stopping criterion. This is further broken down into the incremental evaluations (Inc-Evals) used by the local search component of the Hybrid algorithm and full evaluations (Full-Evals) of strings generated by recombination and mutation.... ..."

"... In PAGE 14: ... The second way, shown in Figure 10, preserves all customers by weakening participation constraints. The I/I properties of this new Customer class are the union of the original I/I properties in Table3 ; whereas, the common... In PAGE 15: ...Table3 . Unless the original classes have exactly the same I/I properties and common properties, this type of integration will always produce an incongruent object class, as it does here.... ..."

"... In PAGE 3: ... Without going into structural or design details of these networks, here we present a short overview of properties that seem crucial for our work. Table1 shows properties of common DHTs for a network with n nodes. #Hops for lookup/store is the expected number of nodes a request for a lookup or store has to pass.... ..."