"... In PAGE 4: ...Figure 3: CPU time in seconds (left) and size in 1000 nodes (right) for relaxing bound lb-sifting are given in Table2 . Columns 2-6 and 7-11 give informations about the behavior of the algorithms for result- ing BDD size and runtime, respectively.... ..."

"... In PAGE 9: ...Number of shares per node Table4 and Figure 5 show how the processing time and the message size vary with the number of shares per node. The CKMSS scheme deviates from linear behavior in the computations.... In PAGE 9: ... The nonlinear cost, however, is justifiable as it results in increased security levels. In Table4 , the pairing of multicast message sizes for 2i and 2i+1 (i=2,3,4) shares is due to the characteristics of AES encryption, which generates ciphertext in 16-byte blocks.... ..."

"... In PAGE 9: ...9 Number of shares per node Table4 and Figure 5 show how the processing time and the message size vary with the number of shares per node. The CKMSS scheme deviates from linear behavior in the computations.... In PAGE 9: ... The nonlinear cost, however, is justifiable as it results in increased security levels. In Table4 , the pairing of multicast message sizes for 2i and 2i+1 (i=2,3,4) shares is due to the characteristics of AES encryption, which generates ciphertext in 16-byte blocks.... ..."

"... In PAGE 13: ... All other nodes represent valid-next-steps. Bolded nodes depict achievement of this subgoal ( Table4 ). For example, in Fig.... In PAGE 18: ....4. Updating the graph From the initial state, each subsequent student action is translated into an event that propagates through the DSG and alters its structure. When the event propagates, alterations to individual nodes are specific to the type of node ( Table4 ). For example, after a correct identification of a feature (student action matches a particular Identify-Fea- Figure 6 Expert model ture node exactly) the graph updates by (1) chan- ging the state of the Identify-Feature node to identified, (2) adding Assert-Hypothesis nodes which are supported by this feature, (3) creating arcs between the Identify-Feature and each Assert- Hypothesis node supported that feature, (4) updat- ing the evidence cluster, and (5) calculating the new best-next-step.... ..."

"... In PAGE 5: ... For each study, the accuracy and RT data from the WM and the recognition tasks were analyzed using 2-factor ANOVAs, with sex (male versus female) as a between- subjects factor and material (verbal versus nonverbal) as a within- subjects factor. Results Behavioral data Study 1 The means and standard deviations for all behavioral data are shown in Table2 . For Study 1, the accuracy ANOVA for working memory indicated a marginal main effect of material type (F(1,45) = 3.... ..."

"... In PAGE 14: ... After running through the pre-experimental speci- fications, the model ends up with behavioral and af- fective connections that are positive for the attractive toy and negative for threat. The behavioral effect of surveillance was negligible, whereas its emotional ef- fect was negative (see Table3 ). These connections are intuitive plausible.... In PAGE 15: ... The mild threat activation without surveillance was insuf- ficient to justify and anticipate prohibition of play. (As can be seen in Table3 , the sum of the mean weights of mild threat [50% of -1.00 = -.... In PAGE 16: ... Without this specifi- cation, the simulation is not able to reproduce the major results of Gerard and Mathewson. After running the feedforward model through these pre-experimental specifications, the behavioral and af- fective connections were positive for the group and negative for the shock (see Table3 ). The initiation pro- cedure had negligible behavioral consequences, but a negative emotional impact.... In PAGE 16: ... The person apos;s willing- ness to undergo aversive treatment comes as a sur- prise to the model because the negative connection weights prior to the experiment did not predict this response. (As Table3 reveals, the sum of the mean weights of the group [+0.85], initiation [-0.... In PAGE 16: ... In contrast, the mild shock was felt as only moderately aversive (neutral), and this re- sulted in some dissonance. (As shown in Table3 , the sum of the mean weights of the group [+0.85] and the mild shock [70% of -1.... In PAGE 17: ... The broken line shows the attitude prior to the experiment. Thehuman dataarefrom Table3 in quot;Decisionfreedom as a deter- minant ofthe role ofincentive magnitude in attitude change, quot; by D.... In PAGE 17: ... Small payment was insufficient to antici- pate the person apos;s choice to engage in the counter- attitudinal behavior. (As can be seen in Table3 , the weight of low payment [20% of +0.46 = +.... In PAGE 20: ...54.30, unlike the human data. The feedforward mechanism underlying these changes is compensatory adjustments. The positive change in the difficult-low condition for the chosen un- attractive poster is because its choice came as a sur- prise to the network because its pre-experimental mean weight is zero (see Table3 ). This underestimation led to a compensatory upward adjustment.... In PAGE 22: ... All the other pre-experimental specifications are identical to the forced compliance simulation discussed earlier (see Table 2). After running these pre-experimental specifica- tions through the model, the connections with the es- say topic were negligible, but enforcement had a pos- itive connection with behavior and a negative one with affect (see Table3 ). More important, although the behavioral connections of both types of drugs were zero, the affective connections were positive for the pleasant drug and negative for the unpleasant drug.... ..."