### Table 12: Groups of classes for placement in data base -1 are represented. Multiplication by two full scale values will never produce an over ow although the product of -1 -1 will be 1 - 1/32768 and not 1 which cannot be represented. You can write nodes that will work with both simulators but you must be careful with normalization when using integer arithmetic. You can de ne arithmetic operations that exactly match the hardware of your target processor and create a new simulator for that arithmetic. That is outside the scope of this manual. The les to do this for the existing sim- ulators are in directory $OPD_ROOT/src/dsp/arith and subdirectories of this directory. The header les are in directories under ObjProFlt and ObjProInt16 which are subdirectories of $OPD_ROOT/src/include.

### Table 2: Approximated optimal values for and g( ) ` (1 ? c( ; `)) in case of spherical Cauchy and Gaussian mutation vectors for increasing dimension `. As conjectured, the optimal step size scaling parameters quickly stabilize as the dimension gets large. Then the p.d.f. of the ith order statistic is fYi: (x) =

1997

"... In PAGE 9: ... Then the optimal value of can be approximated via univariate numerical optimization over with large xed `. Table2 summarizes the results of this approach for the spherical Cauchy as well as the Gaussian mutation distribution. Evidently, the value of ` (1 ? c( ; `)) already stabilizes for both distributions when ` 100.... In PAGE 9: ... But even ` = 30 yields reasonable results. In case of the Gaussian distribution there is a tiny discrepancy between the approximation in Table2 and the theoretical values obtained previously. 2.... In PAGE 15: ...utation. But notice that the normalizing constants a` di er. At this point a cautionary remark is necessary: It is not guaranteed that the accuracy of the approximations for given ` is equally good for both types of Cauchy mutations. But if the approximations are equally good (which is assumed for the moment) then the optimal choice for g( ) = E[ minfL; 1g ] may be taken from Table2 . Thus, = 0:713 with g( ) = 0:2756 as in case of spherical Cauchy mutations.... ..."

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### Table 1 Summary of Exact and Approximate Quantitative Predictions Made by the Stochastic Optimized-Sttbmovement Model

1988

"... In PAGE 9: ...MEYER, ABRAMS, KORNBLUM, WRIGHT, AND SMITH dictions. Also included in Table1 are illustrative numerical val- ues derived from these predictions for representative target dis- tances and widths. Complete derivations of the model apos;s predic- tions appear in the Appendix.... In PAGE 9: ... Also, because VD/W increases monotonically at a decreasing rate as D/W increases, Equation 4 exhibits a shape similar to log2(2Z gt;/ W), mimicking Pitts apos; law. The degree of sim- ilarity is illustrated in Table1 , where we have fit Equation 1 (Pitts apos; law) to illustrative numerical values derived from the sto- chastic optimized-submovement model. Here it can be seen that the square-root and logarithmic trade-offs come fairly close to each other (r = .... In PAGE 9: ...urvature is greater than that of a linear function (i.e., one where x = 1) but less than that of the corresponding logarithmic function. 14 A square-root function comes closer than a logarithmic function to some of Pitts apos; (1954, Table1 , p. 385) own data.... In PAGE 28: ... Following our general discussion, it would be interesting to conduct studies on the effects of explicit movement-training techniques designed to promote the opti- mality of subjects apos; performance during spatially constrained movement tasks. The model apos;s predictions ( Table1 ) could pro- vide a useful benchmark against which to assess the efficacy of alternative instructional formats and practice protocols. By comparing these predictions with data collected under various real-world conditions, one may eventually achieve significant improvements of people apos;s performance in practical situations requiring skilled movement.... ..."

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### Table 1: The Boolean viral marketing profit results for the two network effect models

"... In PAGE 7: ... All the parameters of the Boolean viral marketing model were set similarly with those in [2]. Table1 shows the viral marketing pro-fit results for the two network effect models. It can be seen that viral marketing using the influence models had obtained some increase in profit over the case using the webs of ... ..."

### Table 2.1: Key bindings in SML Make If you have found something in SML Make that you think is a bug (ouch!), press lt;C-c b gt; and you will get into Emacs mail-mode. The message is automatically addressed to the authors, you just have to present the bug. When you are ready, press lt;C-c C-c gt; and the message will be sent. You can also use this function if you have any ideas on improving SML Make or have a question, and appreciations are always welcome.

### Table 1: Data Categories. The data for our study are broken down into whether or not they were targeted for the marketing campaign, and whether or not they were on the viral list. The quot;relative size quot; value shows the number ofprospects that show up in each group, relative to the Non-viral -- r

"... In PAGE 16: ...till able to observe the take rates of these prospects. We call these consumers viral non-targets. In summary, we have four categories of potential customers at the time of the mailer, viral targets, non-viral targets, viral non-targets and the rest of the prospect universe, the non-viral non-targets. Table1 summarizes the four categories and shows their relative sizes, using the viral non-targets as the reference group. In the sections that follow, we describe in detail the data we had available to investigate the impact of viral marketing on targeted sales.... ..."

### Table 5. Do You Expect to Live with Your Children When You Can No Longer Care For Yourself?

### Table 6. Do You Expect To Receive Care from Your Children When You Can No Longer Care for Yourself?

### Table 10 Unrestricted LA/AIDS Approximate Exact Approximate Exact

"... In PAGE 21: ... The results indicate the following: The approximate price changes can be a very poor guide relative to exact price changes. The numbers in parentheses are the common number of approximate and exact price increases which are statistically different from zero or whose 90% confidence interval Insert Table10 Here Table 10: Unrestricted LA/AIDS Price Simulations... In PAGE 21: ... The results indicate the following: The approximate price changes can be a very poor guide relative to exact price changes. The numbers in parentheses are the common number of approximate and exact price increases which are statistically different from zero or whose 90% confidence interval Insert Table 10 Here Table10 : Unrestricted LA/AIDS Price Simulations... ..."

### Table 7 Exact values of MIPLIB problems

2007

"... In PAGE 6: ...approximate dual solutions to prune the branch- and-bound search tree when possible. In Table7 we report the optimal values for six instances from the MIPLIB 2003 collection. Other problems from MIPLIB can also be solved with this code, but, in general, running times are larger than commercial branch and bound implementations by two or three orders of magnitude.... In PAGE 6: ... Other problems from MIPLIB can also be solved with this code, but, in general, running times are larger than commercial branch and bound implementations by two or three orders of magnitude. The running times for the six instances in Table7 ranged from 58 sec- onds for mann81 to 23 hours for gesa2 o. 5.... ..."

Cited by 1