"... In PAGE 16: ...5 RESULTS Conclusion-1: It is difficult to achieve meaningful clustering of sentences with traditional distance-based clustering methods. Table1 shows the granularity and tightness of clusters achieved at various thresholds. A threshold of 1.... In PAGE 20: ...3 to 0.5 ( Table1 ). It can be seen that for larger thresholds the clusters contain just one sentence each (which essentially means that clustering does not take place).... ..."

"... In PAGE 19: ... 5.2 Disaggregated Market Power Findings for High Job Concentration Detailed experimental ndings for high job concentration (JCON = 2) and job capacity JCAP varying from tight to excess are reported in Table7 . For reasons clari ed below, these ndings generally support the job capacity hypotheses H1 and H3, and this support is particularly strong when attention is restricted to dominant distance clusters.... In PAGE 19: ... On the other hand, these ndings strongly contradict the job concentration hypothesis H2(a) under conditions of excess job capacity. Insert Table7 About Here Consider, rst, the potential economy E characterized by high job concentration (JCON=2) and tight job capacity (JCAP=1/2). As shown in Table 4, for this E there are twelve work suppliers who each control one work o er and six employers who each control one job opening,... In PAGE 20: ...17 According to hypotheses H1(a) and H2(a), work suppliers should be disadvantaged relative to employers in this potential economy E regarding their ability to exercise market power. Experi- mental ndings for the twenty sample economies (s; E) run for this E are reported in Table7 (a). As seen, these sample economies can be partitioned into three distinct distance (Do) clusters sup- porting three distinct types of behavioral and utility outcomes.... In PAGE 20: ...18 In each distance cluster, however, the mean market power level attained by work suppliers is lower than the mean market power level attained by employers as predicted by hypotheses H1(a) and H2(a). The distance cluster 0-7 in Table7 (a) includes 25% of the twenty sample economies run for this potential economy E. By de nition of the distance measure Do, these are the sample economies whose persistent networks are closest to the competitive network class Ko(E).... In PAGE 20: ... Since the repeat defector pro les can thus be inferred from the reported p-inactive and p-nice pro les, they are omitted to conserve space. A similar remark holds for all remaining distance cluster results reported in Table7 and in subsequent tables.... In PAGE 21: ... Any work supplier red for shirking can thus expect to incur high job search costs (negative refusal payo s) as he attempts to secure new employment. Indeed, as seen in Table7 (a), 40% of the work suppliers in distance cluster 0-7 are ultimately p-inactive (unemployed). These work suppliers become so discouraged by the high job search costs they incur in their attempts to secure employment that the expected utility they assign to each employer eventually becomes negative, discouraging any further work o ers.... In PAGE 21: ... The question then arises whether employers could do even better if they took advantage of the weak structural position of work suppliers by more frequent defections. The next distance cluster 10-13 reported in Table7 (a), comprising 45% of the sample economies for this E, reveals that employers can indeed do better on average by engaging in more aggressive behavior in their initial worksite relationships and more repeat defection behavior in their persistent worksite relationships. As depicted in Figure 1(b), the typical persistent network that evolves in this dominant distance cluster consists of six disjoint pairs of work suppliers and employers in persistent latched relationships together with six p-inactive work suppliers.... In PAGE 22: ...Table7 (a), comprising 30% of the sample economies for this E, cautions that too determined an e ort by employers to exercise market power through aggression and repeat defections can be self-defeating. Work suppliers interacting with strongly aggressive and predacious employers tend to evolve worksite behavioral rules that are aggressive and predacious in turn, whatever their structural situation.... In PAGE 22: ... This widespread coordination failure dramatically decreases the mean utility and market power levels attained by work suppliers and employers alike. In short, the results reported in Table7 (a) provide strong support for hypotheses H1(a) and H2(a); employers are advantaged relative to work suppliers with regard to their ability to exercise market power in conditions of high job concentration and tight job capacity. Yet a closer exami- nation of the data reveals that the mobility and behavioral exibility (provocability) of the work suppliers provides them with some degree of protection against exploitive behavior by employers.... In PAGE 22: ... Work suppliers must incur job search costs despite balanced job capacity and, in addition, jobs are concentrated in the hands of relatively few employers. Indeed, in each of the three distance clusters reported in Table7 (b) for this E, the mean market power level attained by work suppliers is lower than the mean market power level attained by employers. In particular, the Table 7(b) ndings reveal that 80% of the sample economies observed for... In PAGE 22: ... Indeed, in each of the three distance clusters reported in Table 7(b) for this E, the mean market power level attained by work suppliers is lower than the mean market power level attained by employers. In particular, the Table7 (b) ndings reveal that 80% of the sample economies observed for... In PAGE 23: ... The utility levels that would be attained by work suppliers and employers over the course of a trade cycle loop for this E under competitive market conditions are given by the competitive utility pro le U (E) = (1:40; 2:80). As seen in Table7 (c), 25% of the sample economies for this E lie in a distance cluster 0-5 close to the competitive network class (Do=0). The persistent networks that evolve for these sample economies are largely recurrent in form, and the large majority of work suppliers and employers are p-nice.... In PAGE 23: ... Thus, hypothesis H1(b) is supported and hypothesis H2(a) is contradicted in this distance cluster. Table7 (c) also shows that the remaining 75% of the sample economies observed for this E lie in a distance cluster 12-14. As depicted in Figure 1(d), the typical persistent network that evolves in this dominant distance cluster consists of disjoint components comprising from one to four work suppliers latched to a single employer who engage in frequent defections against this employer.... In PAGE 24: ... Speci cally, in contradiction to the prediction of H3(b), employers on average do not experience a decline in their ability to exercise market power as job capacity increases from tight to balanced. The disaggregated ndings reported in Table7 show this is a misleading conclusion. Omitting the relatively small number of sample economies in Table 7(a) for which widespread coordination failure occurs and both agent types do extremely poorly { i.... In PAGE 24: ... The disaggregated ndings reported in Table 7 show this is a misleading conclusion. Omitting the relatively small number of sample economies in Table7 (a) for which widespread coordination failure occurs and both agent types do extremely poorly { i.e.... In PAGE 28: ... This pooling problem is indicated in Table 6 by the high standard deviations for the mean market power levels attained by work suppliers and employers under conditions of balanced job concentration and tight job capacity. Finally, the job concentration sensitivity hypothesis H4 predicts that work suppliers should be better o and employers worse o operating under conditions of balanced job concentration, as reported in Table 8, than work suppliers and employers operating under conditions of high job concentration as reported in Table7 , for any given job capacity level. The ndings reported in Table 7 and Table 8 provide weak support for hypothesis H4 under conditions of tight or excess job capacity, and for hypothesis H4(b) under conditions of balanced job capacity, in the sense that these hypotheses hold when attention is restricted to dominant distance clusters.... In PAGE 28: ... Finally, the job concentration sensitivity hypothesis H4 predicts that work suppliers should be better o and employers worse o operating under conditions of balanced job concentration, as reported in Table 8, than work suppliers and employers operating under conditions of high job concentration as reported in Table 7, for any given job capacity level. The ndings reported in Table7 and Table 8 provide weak support for hypothesis H4 under conditions of tight or excess job capacity, and for hypothesis H4(b) under conditions of balanced job capacity, in the sense that these hypotheses hold when attention is restricted to dominant distance clusters. On the other hand, H4(a) is not even weakly supported under conditions of balanced job capacity.... In PAGE 28: ... On the other hand, H4(a) is not even weakly supported under conditions of balanced job capacity. Speci cally, restricting attention to the dominant distance clusters Do=12 in Table7 (b) and Table 8(b), work suppliers are actually worse o as job concentration is decreased from high to balanced. The latter outcome is due to the higher frequencies of repeat defection behavior on the part of both work suppliers and employers in the balanced job concentration case.... In PAGE 44: ... Table7 (b): Balanced Job Capacity (JCAP=1) Do % of AGGRESSIVE P-INACTIVE P-NICE UTILITY MPOWER Runs w e w e w e w e w e 0-5 25% 25% 0% 2% 0% 75% 70% 1.09 2.... In PAGE 44: ...31) (.76) (22%) (27%) Table7 (c): Excess Job Capacity (JCAP=2) Table 7: Experimental Findings for High Job Concentration... In PAGE 44: ...31) (.76) (22%) (27%) Table 7(c): Excess Job Capacity (JCAP=2) Table7 : Experimental Findings for High Job Concentration... ..."

"... In PAGE 11: ... The vector closest to the mean of each of these clusters was then automatically chosen as a representative model, as defined in Step 11 of Algorithm 1. These three chosen sets of values are shown in Table2 . These parameter sets, or models, can be seen intuitively to work well on overcast scenes (Model 1), partly cloudy scenes (Model 2), and sunny scenes (Model 3).... ..."

"... In PAGE 37: ...Each row of Table11 reports the design decisions taken to produce two designs from the same specification. In each case, one design was produced by CODA, while the other design was produced by a human designer and reported in the literature.... In PAGE 38: ... No module structuring information is reported for the remote temperature sensor because the human designers allocated AdaTM packages rather than information-hiding modules; thus, these results cannot be compared legitimately with the module design generated by CODA. In the absence of assistance, as reported in the last row of Table11 , CODA relies solely on its built-in knowledge to resolve ambiguous or incomplete situations. These situations generally fall into four classes: (1) decisions about merging tasks, (2) decisions about merging modules, (3) decisions about the synchronization requirements between tasks, and (4) decisions about assigning priorities to inter-task messages.... In PAGE 38: ... In some run-time systems, queued messages can take longer to exchange than tightly coupled messages. As the last row of Table11 shows, even without assistance from an experienced designer, CODA can produce designs fairly similar (.95) to those produced solely by human designers.... ..."

"... In PAGE 8: ... The subjects were free to select any number of photographs they considered similar enough to pass their criteria. Typically, the journalists applied quite tight criteria and selected only a few photographs (see Table1 ). Very small similarity sets may induce uncontrollable deviation in the results on the performance of different feature parameters.... ..."

"... In PAGE 4: ... Metrics for both of the code segments in Figure 3 should re ect approximately the same level of data cohesion since the relationships among the elements are similar. This is not the case as shown in Table2 . Note the considerable di erences in the Overlap, Tightness, and MaxCoverage metric values.... In PAGE 6: ... If one com- putes metric slices for the modules in Figure 4, the metric slices all span the entire length of the module. The metrics shown in Table2 are based on metric slices obtained for these examples. The values are now identical for the example pairs of communicational and sequential code segments.... ..."

"... In PAGE 30: ...29 knowledge that CODA does not possess and cannot elicit, or where CODA takes predetermined strategies when a human designer might choose among a wide range of options. Table11 presents a quantitative look at the similarity between designs generated by CODA and designs developed by human designers. Table 11.... In PAGE 30: ...Each row of Table11 reports the design decisions taken to produce two designs from the same specification. In each case, one design was produced by CODA, while the other design was produced by a human designer and reported in the literature.... In PAGE 31: ... No module structuring information is reported for the remote temperature sensor because the human designers allocated AdaTM packages rather than information-hiding modules; thus, these results cannot be compared legitimately with the module design generated by CODA. In the absence of assistance, as reported in the last row of Table11 , CODA relies solely on its built-in knowledge to resolve ambiguous or incomplete situations. These situations generally fall into four classes: (1) decisions about merging tasks, (2) decisions about merging modules, (3) decisions about the synchronization requirements between tasks, and (4) decisions about assigning priorities to inter-task messages.... In PAGE 31: ... In some run-time systems, queued messages can take longer to exchange than tightly coupled messages. As the last row of Table11 shows, even without assistance from an experienced designer, CODA can produce designs fairly similar (.95) to those produced solely by human designers.... ..."

"... In PAGE 7: ... For comparison, the inset of each plot shows a negative control with genes ordered randomly along the matrix axes, and accordingly, no apparent block color-structure for the correlation values. In addition to this visual evidence for the functional affin- ity of coexpressed gene pairs, genes linked in the coexpres- sion networks were found to have significantly higher GO similarities, on average, than seen for all pairs of genes ( Table3 ). In addition, statistically significant positive cor- relations were detected between the pairwise coexpression r-values and GO similarity values for all three coexpres- sion networks, indicating that more tightly coexpressed gene pairs tend to be more functionally related (Table 4).... ..."

"... In PAGE 10: ... Reachability analysis of the automaton usually reduces runtime, but it does not help in reducing the values of m and n. Table3 compares tight to non-tight Bcurrency1 uchi automata when searching for a simple path. The column labeled St in this table indicates whether each property passes (P), or remains undecided (U).... In PAGE 10: ... All properties in this table are passing properties. The column labeled St has the same meaning as in Table3 ; the column labeled tl, when present, reports the ter- mination length. Tables 5 and 6 show the results of applying different methods when handling multiple fairness conditions.... ..."