### Table 3: Insights and Opportunities

in References

"... In PAGE 9: ...Insights and Opportunities Responding to the second question we discuss some of the insights and opportunities that a social perspective on software development provides. Table3 summarizes this discussion. Table 3: Insights and Opportunities... ..."

### Table 1: 25 basic relations and the schematic diagram 3 Di erent Representations of IN DU There have been many di erent methods of visualising the qualitative relation- ships of intervals as proposed in IA. These include (i) expressing the relations in INDU as ORD-clause form [9]; (ii) geometrically representing the intervals in two-dimensional planes and considering the relations as admissible regions in this plane [6], [10] and (iii) representing the relations as a lattice [6]. These interpretation provide a rich insight and better understanding of the expressive power of the language.

1999

"... In PAGE 3: ... Then, these relations can also be expressed as set of inequalities in terms of the end points and duration. For example, Xb lt;Y Xe lt; Y b ^ Xd lt; Y d (see Table1 ). The inde nite qualitative temporal in-... ..."

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### Table 6. Insight evolution sensitivity.

in Projects by

2004

"... In PAGE 10: ...able 5 Default scenario results............................................................................................. 51 Table6 .... In PAGE 62: ...igure 25. Insight evolution curves. Fig. 26 and Table6 show that a faster insight evolution helps to finish the project earlier and reduce the number of errors not detected at the end of the project. It is also observed that changes on the insight have a greater impact in the iterative approach than in the sequential ... ..."

### Table 6: Unexpected, hypothesis, and incorrect insights.

2004

"... In PAGE 5: ...onger (p lt;0.01). In general, Clusterview users finished quickly while GeneSpring users took twice as long. Table6 shows the total number of unexpected insights, hypotheses generated, and incorrect insights from the insight occurrences for each tool. Unexpected Insights: The participants using HCE with the Viral dataset noticed several facts about the data that were completely unrelated to their initial list of questions.... ..."

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### Table 7: Total number of insights in each category

2004

"... In PAGE 6: ...4 Insight Categories Though a wide variety of insights were made, most could be categorized into a few basic groups through a clustering process. Table7 summarizes the number of each type of insight by tool. Overall Gene Expression: These described and compared overall expression distributions for a particular experimental condition.... ..."

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### Table 2 Industry Price Adjustment Under Geometric Frictions a

1999

"... In PAGE 13: ... In the absence of ad hoc modifications, the underlying dynamics of the aggregate price level are determined by a second-order Euler equation.16 Table2 presents summary statistics for second-order pricing rules of the six manufacturing industries, estimated under the prior of geometric frictions. Estimated parameters of the industry decision rules and VAR forecast model parameters were obtained by the maximum likelihood estimator described in the Appendix.... In PAGE 13: ... The same number of lags, p, is imposed on all regressors in an industry VAR.17 The estimated error correction coefficients, a(1) in Table2 , indicate the average quarterly reduction rates planned for the price gap, pt?1 ? p t?1, of each industry. The proportion of explained variation in quarterly price changes can be substantial, with R2 in three industries ranging from .... In PAGE 13: ...anging from .3 to .6. The row in Table2 labeled R2(%) lists the proportion of the R2 that is attributable to industry forecasts of future equilibrium prices, obtained by comparing decision rules to those where the coefficient of h0 zt?1 is constrained to zero. The results indicate that the rational forecast term, h0 zt?1, provides the modal source of explained variation for four industries.... In PAGE 13: ... The results indicate that the rational forecast term, h0 zt?1, provides the modal source of explained variation for four industries. Table2 also contains the estimated mean lag of producer responses to unexpected shocks and the estimated mean lead of responses to anticipated events. The mean lead of the industry planning horizon is typically somewhat smaller than the mean lag response due to discounting of forward events.... In PAGE 14: ... Although it is possible that producers may have serially correlated information that has not been included in the industry forecast models, it is plausible also that residual autocorrelations could be due to misspecifications of the frictions in producer responses. A final indication of potential misspecifications is indicated in the bottom row of Table2 . This row, labeled LR(h jzt?1), lists the rejection probabilities of a likelihood ratio test to determine if the data prefers an unrestricted forecast model of forward equilibrium price changes to the rational forecasts embedded in the geometric frictions version of rational error correction.... In PAGE 14: ... This is due to a significant seasonal pattern in the producer price of motor vehicles which could not be adequately captured by fixed seasonal dummies. Without exception, all of the problems noted for the estimated decision rules under geometric frictions in Table2 are eliminated under polynomial frictions. The percentage of explained variation, R2, is considerably higher for most industries in Table 3; mean lags are more plausible; the assumption of serially uncorrelated residuals is retained in all industries; and the rejection probabilities in the bottom row in Table 3 indicate that the rational expectations overidentifying restrictions are not rejected at confidence levels of 95% or higher.... In PAGE 15: ...rejections of rational expectations overidentifying restrictions are often interpreted as evidence of non-rational forecasting by agents or of inadequate specifications of agent forecast models of forcing terms. Because the only difference between industry model specifications used in the side-by-side comparisons of Table2 and Table 3 is the degree of the Euler equation polynomials, m, the culprit, at least in these examples and for the statistical properties considered, is rigid priors on the specification of dynamic frictions. More intuitive insights into the dynamic effects of the higher-order lag and lead polynomials are obtained by rearranging the Euler equation to define the current period response weights to lags and expected leads of the forcing term, Etfp t+ig, implied by the industry decision rules, Etfptg = Etfa(1)a(L)?1A( )a( F )?1p t g; = Etf 1 X i=?1 wip t+ig; (20) where negative subscripts, i lt; 0, denote responses to lagged events and positive subscripts, i gt; 0, responses to anticipated events.... In PAGE 15: ... The lag and lead weights of the six estimated industry decision rules are displayed in the panels of Figure 1. The dotted lines are the friction weights generated by the two-root decision rules (m = 1) reported in Table2 and the solid lines are the friction weights associated with the 2m-root decision rules (m gt; 1) shown in Table 3.21 Several effects of the generalization of frictions are apparent from the plots of the industry friction weights in Figure 1.... In PAGE 15: ... Larger mean leads require longer planning horizons and are characteristic of the flatter friction weight distributions indicated by the dotted lines in Figure 1 for the two-root decision rules, m = 1. Thus, vertical distances between the two sets of friction weight distributions in each panel are indicative of differences between the industry mean leads of Table2 and the corresponding mean leads of Table 3. As shown in the panels of Figure 1, relatively low-order friction polynomials, a(B)a(BF )?1, can generate a variety of flexible shapes, including the seasonal weights at distances of 4 quarters indicated for the motor vehicles industry, SIC 371.... ..."

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### Table 1. Insight and macro constraint summary

2006

"... In PAGE 7: ... 5. Computational REST architectural style Driven by the insights from the previous sections and summarized in Table1 , we formulated three addi- tional macro constraints for REST to better explain the phenomena we identified. These macro constraints, in turn, lead to a larger body of micro constraints, acting on the level of a single active element (server, cache, client, etc.... ..."

### Table 1: Portfolio insight of the SI customers

in The

1111

"... In PAGE 11: ... Although the fourth SI category only encompasses one particular SI product, we thought it to be appealing to investigate this product as a separate and unique category. INSERT TABLE 1 ABOUT HERE Table1 provides insight into the portfolio of the SI customers during their lifecycle (i.e.... In PAGE 12: ... SI customer. Given the fact that this category is the most popular (i.e. possessed) within the range of SI products (see Table1 ) we can ascertain the need to find the appropriate marketing strategy and actions to revert this SI behavior. - The most retention prone SI customers are those that own high-risk products in the long run (i.... In PAGE 13: ...ince the savings account customers (i.e. SI category 2) not only represent the largest group of customers (cf. Table1 ), but at the same time also the most alarming in terms of defection rates (cf.... ..."

### Table 6. Neuroanatomic and Neuropsychological Correlates of Insight in Dementias

2007

"... In PAGE 8: ...gies (Gainotti 1975; Ownsworth and others 2006; Trouillet and others 2003), educational level, and pro- fessional status before the illness (Spitznagel and Tremont 2005). Another line of research focuses on the associations between anosognosia in AD and neuropsychological dimensions (see Table6 ). For instance, Starkstein and col- leagues (2006) examined a large sample of AD patients with variable severities of dementia and found a significant positive correlation between anosognosia and deficits in verbal memory and verbal comprehension.... ..."