### Table 1. ECHO simulation results

"... In PAGE 8: ... 2.3 Summary of Results A general summary of the results of the four simulations can be found in Table1 . This table presents the final (asymptotic) activation values for the total network and the four propositions representing the possible final verdicts.... ..."

### Table 5-1 General State Systems Design Requirements Checklist

"... In PAGE 42: ...afety regulations. States may form regional alliances to support these functions. Each state coordinates with other states, regional alliances, and CVISN Core Infrastructure systems to support nationwide access to safety information for administrative and enforcement functions. Table5 -2 State Safety Information Exchange and Safety Assurance Systems Design Requirements Checklist Commit Level (F/P/N) Item # Compatibility Criteria Req Level (L1/E/C) Op Test Date IOC Date FOC Date Comments 5.2.... In PAGE 44: ...unctions supporting credentials and tax regulations. States may form regional alliances to support these functions. Each state coordinates with other states, regional alliances, and CVISN Core Infrastructure systems to support nationwide access to credentials information for administrative and enforcement functions. Table5 -3 State CV Administration Systems Design Requirements Checklist Commit Level (F/P/N) Item # Compatibility Criteria Req Level (L1/E/C) Op Test Date IOC Date FOC Date Comments 5.3.... In PAGE 48: ... The systems perform roadside functions supporting automated carrier, vehicle, and driver identification and associated look-ups in infrastructure-supplied data for credentials and safety checks. Table5 -4 State Roadside Systems Design Requirements Checklist Commit Level (F/P/N) Item # Compatibility Criteria Req Level (L1/E/C) Op Test Date IOC Date FOC Date Comments 5.4.... ..."

### Table 1. Robustness of Neurogenic Network Models to Parameter Variation

2002

"... In PAGE 3: ... Any set of randomly chosen parameter values that allows the model to pass some functional test we call a solution . Allthreemodelsexhibitedlateralinhibitioninthe2-cell test, but the standard model had a much lower fre- quency of solutions than the augmented model, while the reduced model had the highest solution frequency of thethree ( Table1 ).By this naive measure,the reduced model seems the most robust of the three.... In PAGE 3: ... However if p is the probability of picking a good value for each parameter independently, we expect that the solution frequency for models with equivalent robustness per parameter would scale as p^n, where n is the number of parameters. p is highest for the augmented model ( Table1 ). That measure makes the augmented network most robust.... In PAGE 3: ... That measure makes the augmented network most robust. All three networks could also mediate lat- eral inhibition between a central neural cell and six sur- rounding cells (7-cell pattern, Figure 2), with similar solu- tion frequencies as for the 2-cell pattern ( Table1 ). As with the segment polarity network [9], cooperativity (Hill coefficients H11022 1) in transcriptional activation and repres- sion is essential for the network to function (Table 1).... In PAGE 3: ... We tion of N activation (red lines in Figure 1). Both are sup- run the model for 300 min and measure whether the initially higher portedbyexperiments,andbothcontributetoimproving cell(s) achieve(s) a high concentration of AC (greater than 20% of the solution frequency ( Table1 ), but E(SPL) autoinhibi- the highest steady-state concentration possible) and whether the initially lower cell(s) turn off (below 2% of maximum steady state). tion contributes more than cis-inhibition of N by DL.... In PAGE 5: ... But if differences in Dl levels are conducted another random parameter search inside important to the mechanism, how can the process still these newrestricted ranges. The successrate increased succeed in most cases despite constitutive Dl expres- almost 1,000-fold inside these (still very broad) ranges sion [13]? ( Table1 ). Using the same restricted range found with We added a constitutive input (with tunable parame- the 2-cell pattern, the success rate with the 7-cell test ters determining that input rate) to the dl promoter in also increased more than 100-fold.... In PAGE 6: ...33 0.25 ofoffspringsetsatpassingourtestsgenerallycorrelates with the frequencies of finding solutions through a ran- dom search of parameter space ( Table1 ). As with ran- The Shape of the Working Region dom sampling, the recombination success rate was of Parameter Space much higheramong solutions found withinthe restricted Clearly, simple but realistic representations of the core parameter ranges.... ..."

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### Table 12: Identification of different current authentication states

2002

"... In PAGE 22: ...20 von 98 T-Systems-DSZ-ITSEC-04067-2002 Figures Figure 1: Card Life Cycle Phases and Transitions between them 5 Figure 2: Threat Scenario (in case of method of use Office IFD only ) 13 Figure 4: Logical decision-tree diagram 44 Figure 5: State transition diagram 47 Tables Table 1: Components of the TOE 3 Table 3: Logical initialization and personalization of the SigG Application 6 Table 4: Assumptions about the environment 9 Table 5: Security Threats 14 Table 6: Security objectives 15 Table 7: Subjects 20 Table 8: Security-relevant-events 22 Table 9: Objects and related access-types 25 Table 10: Access-set acy(s,o) of SEF AC1 31 Table 11: Access-set acn(o,s) of SEF AC1 32 Table12 : Identification of different current authentication states 43 Table 13: State transition table 45 Table 14: Access-sets ssy(o,t) defined in terms of the security states 48 Table 15: Access-sets ssn(o,t) defined in terms of the security states 49 Table 16: Security mechanisms 50 Table 17: Mapping between the threats, the security objectives and the SEF 55 ... In PAGE 64: ...1. Security state The current internal state is the tuple of (i) the current authentication state CAS (see Table12 ) reflecting the assumption about the subjects currently using the TOE and (ii) the retry counters (values of RC-PIN and RC-PUK). The parameter assumption about the subjects currently using the TOE depends on (i) the currently selected application context (e.... ..."

### Table 8 Translation from neural network into system identification.

in IN

"... In PAGE 9: ...able 7 Parameter estimation results after pruning .......................................................... 67 Table8... ..."

### Table 2 Nonlinear models.

1998

"... In PAGE 16: ... Much of the emphasis will be on the choice of bandwidth and the new aspects brought in by using local polynomial approximation. A power experiment on a wide class of nonlinear models listed in Table2 has been conducted in Section 6.3.... In PAGE 18: ...Table2 , however, where M1(x) is approximately quadratic (see Figure 1), as can be expected the best result is achieved with T = 2 and h = 1. For the ^ L(V1)-tests the size tends to be too low.... In PAGE 18: ... If no corrections are made for this e ect, it will lead to conservative tests. Figure 5 shows the power of the ^ L(V )-tests for model la) of Table2 , and we see the same general trend as for the ^ L(M)-tests; the optimal h increases with T and the derivative. Here ^ L1(V1) also has some power for h = 1 because the variance is constant, not only linear, under the null hypothesis.... In PAGE 18: ... Here ^ L1(V1) also has some power for h = 1 because the variance is constant, not only linear, under the null hypothesis. ^ L0(V1) is much more robust than ^ L0(M1), and this is the case for the other models listed in Table2 as well. 6.... In PAGE 18: ... In particular when we have a nonlinear model, we do of course not want h = 1 to be chosen when T = 0 or T = 1, but with a small autocorrelation, this may well happen for T = 0. In fact h = 1 was chosen in 136 of 500 realizations of model lc) of Table2 which is clearly nonlinear (cf. Figure 1).... In PAGE 19: ... 6.3 A power experiment for a wide set of models We have performed a power experiment for the models listed in Table2 , where t N(0; 0:62) in model ld) - lf), t N(0; 0:72) in lg) - lj) and t N(0; 1) in the other models. Models la) - lj), aa) - ag) and Aa) - Ag) are discussed in Luukkonen et al.... In PAGE 36: ...Figure 1-2: Plots of ^ M1(x) (Figure 1) and ^ V1(e) (Figure 2) for the models listed in Table2 with n = 100 000. The kernel estimator with bandwidth h = 0:2 is used and each plot consists of two realizations.... In PAGE 36: ... The possible values for h is given at the vertical axes. Figure 7: The gure is based on 500 realizations of the models in Table2 . It shows the power of ^ LT (M1) with h cross-validated and n = 100, 250 and 204 for models la) - li), aa) - ag) and Aa) - Ag), respectively.... In PAGE 36: ...ower achieved in Hjellvik and Tj stheim (1995). The nominal size is 0.05. Figure 8: The gure is based on 500 realizations of the models in Table2 and shows the power of ^ LT (V1) with h cross-validated and n = 100, 250 and 204 for models la), aa) - ag) and Aa) - Ag), respectively.... In PAGE 37: ....05 for the standard normal distribution has been used. The model is Xt = t, the bandwidth is h = n?1=9 and the number of realizations are 500. Table2 : Various nonlinear models. Models la) - lj), aa) - ag) and Aa) - Ag) are discussed in Luukkonen et al.... ..."

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### Table 1: Comparison of ECHO and Pearl networks.

2000

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### Table 8. Number of different states using Nonlinear Elimination.

"... In PAGE 18: ... The observation should be made that the trend of changes remained constant for all three multiplication factors. Table8 shows that the biggest reduction in sensitivity was obtained when we used the Nonlinear Elimination algorithm. For 6R general and Puma manipulators the sensitivity to units was 4.... In PAGE 21: ... Now, solving this subsystem of equations is an attempt in orienting the manipulator in the work space without paying attention to positioning. A degree of success of such an approach is depicted in Table8 . The sensitivity to units for Puma manipulator was completely removed while for 6R general manipulator it was reduced to about ... ..."

### Table 2. Echo canceller performance.

"... In PAGE 3: ... The system exceeds the CCITT G.165 recommendation in simulated echo paths ( Table2 ). The ERLE is grater than 41dB in just 80ms in a simulated echo path (Figure 5).... ..."

### Table 6: Identification of network externalities using concentration and size

"... In PAGE 24: ... 4.2 Results Using Excluded Size and Concentration Method of Identification Table6 presents our results based on the concentration method of identification. All specifications include time fixed effects, assets, assets squared, deposits and deposits squared.... In PAGE 26: ... Since we are using the Summary of Deposits database, we do not have assets for this estimation. We also included a linear probability model specification to compare the results to the instrumental variables results from Table6 . For both estimation results, we have given regular standard errors as well as robust standard errors that account for clustering based on the fact that adoption decisions for the same bank in different periods may be correlated.... In PAGE 26: ...511 which is somewhat smaller than the magnitude of 0.9 from the instrumental variables regressions in Table6 Models 4 and 5. The smaller size is likely due to the fact that our sample consists of banks in isolated, rural areas, which are likely to have a... ..."