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Table 1. Posterior mean of heritabilities for single test days, total yield,andpersistencyforthetwotraitsmilkyieldanddiseaseliability for models [1.1] and [1.2].1
2003
"... In PAGE 5: ... The figure indicates that the ranking of the models stabilize to a consistent pattern when sample size reaches 40,000 and that the Monte Carlo estimators show little change when sample size is larger than 100,000. Posterior Mean of Heritabilities Monte Carlo estimates of posterior mean of heritabil- ities for single TD milk yields and for 305-d milk produc- tion are shown in Table1 for models [1.... In PAGE 5: ...or both models were (0.12, 0.29), which is indicative of sharp posterior distributions. Estimates of posterior means of heritabilities for lia- bility to disease at d 5, 25, 85, 165, 285, and summed over 305 d are shown in Table1 for models [1.1] and [1.... ..."
Table 6: User activity over 10-second and 10-minute intervals. The EECS and CAMPUS numbers are for the single day 10/22/2001. The quantities in parenthesis are standard deviations. The numbers for the Windows NT, Sprite, and BSD columns are taken from earlier trace studies[3, 1, 9].
in A- / $ 9
2002
"... In PAGE 8: ... In these cases, our heuristic will overestimate the number of users. This means that that average number of users reported in Table6 is slightly high (especially the maximum number of users per time period), and so all the per-user I/O rates are slightly low. Nonetheless, we believe that they are close enough to provide a meaningful basis for comparison with earlier studies.... In PAGE 9: ... It is clear that the CAMPUS system is an order of magnitude busier than any of the other systems, particularly in terms of the amount of data read and written. However, as shown in Table6 , the per-user statistics have not changed significantly. One thing to note is the apparent contradiction that the average hourly read/write ratio is not the same as the read/write ratio for the entire trace period, particularly for the EECS data.... ..."
Table 2. Posterior mean of genetic correlations for single test days, 305-d yield, and persistency for milk yield and disease liability; model [1.1].1
2003
"... In PAGE 5: ...qual to (0.12; 0.31). Posterior Mean of Genetic Correlations Estimates of posterior means of genetic correlations between milk yield and disease liability for all combina- tions of DIM 5, 25, 85, 165, 285, total 305-d yield, and persistency are shown in Table2 and Table 3 for model [1.... In PAGE 6: ....25 to 0.57 and are close to the correlation estimates between single TD of milk production and disease lia- bility. Estimates of posterior means of the genetic correla- tions between disease liability summed over the com- plete lactation and milk yield at d 5, 25, 85, 165, and 285, respectively, are also shown in Table2 and Table 3. For model [1.... ..."
Table 3. Posterior mean of genetic correlations for single test days, 305-d yield, and persistency for milk yield and disease liability; model [1.2].
2003
"... In PAGE 5: ...qual to (0.12; 0.31). Posterior Mean of Genetic Correlations Estimates of posterior means of genetic correlations between milk yield and disease liability for all combina- tions of DIM 5, 25, 85, 165, 285, total 305-d yield, and persistency are shown in Table 2 and Table3 for model [1.... ..."
Table 3 presents the valuation effects around profit upgrade announcements made by ASX-listed firms during the sample period. Panel A reports the average abnormal return for the cross-sectionally combined observations for each day during the event window. None of the single day average abnormal returns prior to the announcement are significant. A highly
"... In PAGE 26: ...1714 0.9844 Note: * denotes statistical significance at the 10% level ** denotes statistical significance at the 5% level *** denotes statistical significance at the 1% level Table3 , Panel B reports CAAR for profit upgrades over select multi-day intervals. All CAAR leading up to the announcement date are positive, but small, and none are significant.... ..."
Table 2. (a) Total penalties and (b) percentage improvement over greedy search of hill climbing methods, backtracking methods, and branch and bound methods with a decomposition into sub-problems of a single day. For branch and bound a time limit per sub-problem of either 2 hours or 1 minute was used.
"... In PAGE 9: ... The existing system, which is based on greedy search, took about 15 seconds to solve each of the instances (all experiments were run on 450 MHz Pentium III apos;s with 256 Megabytes of memory). Table2 summarizes the results for the three methods. In all of the reported results, each problem instance was divided into one day sub-problems.... ..."
Table 5 presents the volume effects around profit upgrades made by ASX-listed firms during the sample period. Panel A reports the average abnormal volume for the cross- sectionally combined observations for each day during the event window, whereas Panel B reports CAAVOL for profit warnings over select multi-day intervals. None of the single day abnormal volumes before the announcement are significant, and most observations are in fact below the adopted benchmark. Highly significant abnormal volume observed following the announcement is consistent with Kim and Verrecchia (1991), demonstrating that abnormal trading volumes are proportional to the degree of price change at the time of an unanticipated announcement.
Table 2. Statistics of 4.5-day ERS-1 orbits based on SLR tracking and single satellite altimeter crossover di erences
in ERS-1 precise orbit determination using TOPEX/ERS-1 dual satellite altimeter crossover differences
"... In PAGE 6: ... This leads to a better apos;statistical averaging apos;. As can be seen in Table2 many more apos;duals apos; are available than apos;singles apos;: 76054 compared to 6240. Whenever there is an ERS-1 pass of altimeter data, this pass will cross with a TOPEX pass and apos;duals apos; can be obtained (because a complete TOPEX cycle of altimeter measurements has been selected).... ..."
Table 6, Panel B reports CAAVOL for profit warnings over select multi-day intervals. The findings generally support those expressed for single day observations prior to the announcement day. All CAAVOL are positive, and most are significant. Specifically, CAAVOL of 0.37% for the event window [-20, -1] supports the proposition of substantial irregular trading activity during the three-week period leading up the release of profit warnings by ASX-listed firms during the sample period. These findings are generally inconsistent with Collett (2004) who also examines trading volume prior to the release of negative trading statements by UK firms. Collett reports no evidence of significant abnormal trading volume for the five-day period prior to announcement.
"... In PAGE 35: ...50% 0.60% -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 Eve nt Day Daily Average Abnormal Volume Table6 presents the volume effects around profit warnings made by ASX-listed firms during the sample period. Panel A reports the average abnormal volume for the cross- sectionally combined observations for each day during the event window.... ..."
Table 2 Elapsed time per model day and computational rate at T170 resolution on the Paragon and SP2 for single and double precision
"... In PAGE 11: ....2. Performance Results. Figures 3{5 and Table2 present PCCM2 perfor- mance at di erent resolutions (T42 and T170, both with 18 vertical levels), on di erent numbers of nodes, and at both single and double precision. We show both execution times, expressed as elapsed seconds per model day, and sustained computational rate, expressed both as G op/sec and as M op/sec/node.... ..."
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