### Table 3 - Restated Assumptions based upon Literature

1996

"... In PAGE 21: ... A summary of the plausibility of these assumptions is shown in Table 2 indicating that four of the assumptions were completely inconsistent with documented experience of other technologies and the remaining five assumptions were partially inconsistent with documented experience. Given the implausibility of so many human factors assumptions, what should CE tool designers do? First, we believe there is sufficient evidence from implementation experiences in related computer- based technologies to restate the assumptions ( Table3 ) to reflect the impact of humans and organizations in facilitating multi-disciplinary design. Given these restated assumptions, the CE tool community needs to develop strategies for creating tools that refocus their development work on the restated assumptions.... ..."

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### Table 1: Descriptive elements of the sample in the database. Other statistics available upon request.

2006

"... In PAGE 4: ...bviously writen in another language (e.g. python, precisely). In others, specialy in gc, there is a large portion of test files. For each of the 10 projects, we computed descriptive data similar to what is available for various open-source projects18, reported partialy in Table1 . Then, more specificaly for the purpose of studying colaborative maintenance, for each file that was studied and for each month we computed how many distinct maintainers had comited a change to that file during that period, and how many comits the file had received during the same period.... ..."

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### Table 1. Overview of adherence of selected systems to the general principles of classification (based upon Duhamel, 1998). 6

"... In PAGE 5: ... However, the analysis was based upon a number of legends, hence indicating gaps in the completeness of land-use classes and parameters used. Duhamel (1998) clearly identified that the above- mentioned studies and some selected national class sets suffer from the lack of systematic analysis of what defines land use, in addition to the insufficient adherence to the fundamental principles of classification mentioned earlier ( Table1 ). Insert Table 1.... In PAGE 8: ...3 Statistical sample unit Statistics are often based upon a selection of areas that are representative for a much larger area, the so- called statistical sample unit. In Table1 for instance the TER-UTI class set uses an area of 9m2 distributed in a systematic manner over the country territory to do annual systematic observations. This methodology has also been applied in Bulgaria besides France.... ..."

### Table 1. Effects of Cutout

1983

"... In PAGE 5: ...ffected by the cutout algorithm. Note that, with a 20.1 m/s (45 mph) cutout, almost all of the available energy is captured while a substantial fraction of the fatigue damage is still eliminated. Table1 lists the blade fatigue life expectancy and annual energy capture associated with edf apos;s and ddf apos;s for the blade-to-tower joints on the DOE 100-kW turbine at Bushland, TX, shown in Figures 5 and 4, respectively. Control Algorithm Effects The purpose of the cutout algorithm is therefore to balance the extension of fatigue life with the reduction in the annual energy capture, both of which result from shutting down the turbine in high winds.... ..."

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### Table 1. Production and Length of Why- and Other Wh- Questions by One Child

"... In PAGE 5: ... Mother: neat hey? Nathaniel: why next Saturday is gonna be April? Mother: (be)cause tomorrow is the last day of March. What is going on here? Examining the emergence of why-ques- tions in the language of one child reveals that such questions ap- pear rather suddenly right around his third birthday, and then de- cline in frequency after about a year (see Table1 ). The vast majority of these questions, about 80% overall, were one-word utterances, simply why? The structural simplicity of most of the questions clarifies why many have assumed that such questions primarily serve the function of keeping the child in the conversa- tion, rather than indicating a sudden explosion of metaphysical capacity.... ..."

### Table 1: Base sample.

2002

"... In PAGE 2: ...ertain. The selling price is sp cents per paper. For a speci c problem, whose weekly demand is shown below, the cost of each paper is c = 20 cents and the selling price is sp = 25 cents. Solve the problem, if the news vendor knows the demand uncertainties but does not know the demand curve for the com- ing week a-priori ( Table1 ). Assume no salvage value s = 0, so that any papers bought in excess of demand are simply discarded with no return.... In PAGE 2: ... Assume no salvage value s = 0, so that any papers bought in excess of demand are simply discarded with no return. Table1 : Weekly demand and its uncertainties. Weekly demand Uncertainty i Day Demand j Demand Probability (di) (dj) (pj) 1 Monday 50 1 50 5/7 2 Tuesday 50 2 100 1/7 3 Wednesday 50 3 140 1/7 4 Thursday 50 5 Friday 50 6 Saturday 100 7 Sunday 140 Solution: In this problem, we want to nd how many papers the vendor must buy (x) to maximize the pro t.... In PAGE 2: ... Our rst instinct to solve this problem is to nd the average demand and nd the optimal sup- ply x corresponding this demand. Since the average demand from the Table1 is 70 papers, x = 70 should be the solution. Let us see if this represents the op- timal solution for the problem.... In PAGE 3: ... Therefore, the value of stochastic solution, VSS, is 1750 ( 50) = 1800 cents per week. Now consider the case where the vendor knows the exact demand ( Table1 ) a-priori. This is the perfect information problem where we want to nd the solution xi for each day i.... In PAGE 12: ... For instance, for k = 1, uk = 0 ( rst discrete value), then problem (20) would become: z = Min v+ 11 + v+ 12 + v 11 + v 12 s: t: v11 + v12 v13 + v14 + v+ 11 v 11 = 2:5 v11 + v12 + v13 v14 + v+ 12 v 12 = 2:25 v11; v12; v13; v14 gt;0 v+ 11; v+ 12; v 11; v 12 gt;0 (21) The solution to (21) is z = 0. The results of all of the problems (k = 1 11) are summarized in Table1 . Variables not shown are equal to zero.... In PAGE 13: ...13 Table1 : Determining feasibility of the second stage. k uk zk vk2 vk4 1 0 0 2.... In PAGE 16: ... A new variant of stochastic an- nealing, HSTA (Hammersley stochastic annealing), therefore, incorporates (i) HSS for the generation probability Gij, (ii) HSS in the inner sampling loop for Nsamp determination, and (iii) the HSS-speci c error bandwidth ( HSS) in the penalty term. Table1 : E ciency improvements. Stochastic algorithm Total moves SA + xed Nsamp 274,200 ESA + xed Nsamp 170,000 STA 5,670 ESTA 3,265 HSTA 1,793 Test function : z = P10 i=1 ui xi i 10 2 + P10 i=1 ui y2 i Q10 i=1 cos(4 ui yi) Table 1 shows total number of con gurational moves of di erent stochastic optimization methods.... In PAGE 16: ... Table 1: E ciency improvements. Stochastic algorithm Total moves SA + xed Nsamp 274,200 ESA + xed Nsamp 170,000 STA 5,670 ESTA 3,265 HSTA 1,793 Test function : z = P10 i=1 ui xi i 10 2 + P10 i=1 ui y2 i Q10 i=1 cos(4 ui yi) Table1 shows total number of con gurational moves of di erent stochastic optimization methods. The rst two algorithms are (conventional) stochas- tic optimization algorithms with a xed Nsamp while the last three algorithms are stochastic annealing al- gorithms with a varying Nsamp.... ..."

### Table 1. The language syntax

2005

"... In PAGE 3: ... By exploiting a- priori knowledge that a particular function terminates, and that the (polynomial) degree of the particular function is bounded, we can derive a formula which gives precisely the time or space cost of the program. 3 Preliminaries The language in Table1 is in some sense an abstract language, omitting any parts not relevant to the runtime. In addition, the expressions are given as if they were all integer values, when in fact they refer to expressions based on the size of the data types of the language.... ..."

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### Table 1. Probabilistic User Model

2006

"... In PAGE 4: ... In these studies, users marked 85% of formula cells on average when testing and debugging spreadsheets, often placing check-marks on cells, and rarely placing a23-marks on cells. Of the cells that users marked, users in our earlier studies made mistakes according to the probabilities given in Table1 , so for our study, we simulated user behavior based on these probabilities. The bold numbers in Table 1 highlight false positive (check on incorrect value) and false negative (a23 on correct value) oracle mistakes.... ..."

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### Table 12 Predictive content of MZM in nominal income growth regressions based upon chained personal income deflator 1976:1-1998:12

"... In PAGE 20: ... The lagged M2 growth terms are also significant in portending positive growth in nominal income. The test statistics in Table12 summarize the case for predictive content using an analogous VECM specification for MZM. The sample and equilibrium estimates correspond to Table 10.... In PAGE 21: ... The evidence remains weaker for MZM even when we conduct the predictive content experiments (not depicted) with the long sample (1964-1998, including ratchet) or a shorter, post-1983 sample. The evidence in the long sample is about the same as that portrayed for the post-1976 sample in Table12 . The evidence for the post-1983 sample is slightly stronger with two of the scale measures, industrial production and personal income, yielding specifications that portend predictive content.... ..."

### Table 1: A collection of probabilistic-logical models together with their underlying probabilistic and logical formalism.

"... In PAGE 1: ... Each subarea focuses on its own language con- cepts. Consider Table1 which lists a subset of proposed for- malisms 1. The language concepts vary from acyclic to cyclic models, from logically structured dependencies among ran- dom variables to states, from finite to continuous random variables, and from functor-free languages to Prolog.... ..."