### Table 1: Deterministic sampling using aBDD (static and dynamic)

1999

"... In PAGE 5: ...Experiment 1 ( Table1 , and Figure 2): First, we use the order computed by sampling to build the BDD statically. Except for slightly inferior orderings on c499 and c1355 (both circuits are functionally equiva- lent) we find that our methods always produce better variable orderings than those produced by DFS search based static techniques (Table 1).... In PAGE 5: ...Experiment 1 (Table 1, and Figure 2): First, we use the order computed by sampling to build the BDD statically. Except for slightly inferior orderings on c499 and c1355 (both circuits are functionally equiva- lent) we find that our methods always produce better variable orderings than those produced by DFS search based static techniques ( Table1 ). For many industrial examples we find that DFS-MIN cannot even process the circuits.... In PAGE 5: ... It is easy to see that window based sampling gives much better results than cube based methods. Interestingly, for EX3 and EX6, aBDD based methods can create a small BDD for the output function, but cube based sampling fails for some of the runs! Experiment 2 ( Table1 and Figure 3) show the utility of window based sampling in a dynamic vari- able ordering scheme. That is, we show how dynamic reordering techniques can be significantly improved if they are supplied with an initial variable ordering generated using a window based sampling technique.... In PAGE 5: ... That is, we show how dynamic reordering techniques can be significantly improved if they are supplied with an initial variable ordering generated using a window based sampling technique. In Table1 , we find that we can produce far smaller graphs than the traditional dynamic reordering meth- ods (sift, sift-convergence). Also, for most of the large circuits we take less time.... ..."

Cited by 1

### Table 3: Mode transition tables for deterministic temperature control system.

1993

"... In PAGE 6: ... Therefore, the designer is responsible for deciding whether and how a nondeterministic speci cation should be made deterministic. The temperature control system can be made nondeterministic by forcing condition Running to be a when condition of all transitions among modes Inactive, Heat, and AC (see Table3 ). One of the results of this change to the speci cation is that there are no sequences of instantaneous mode transitions.... ..."

Cited by 137

### Table 1 Deterministic chaos versus stochastic chaos

"... In PAGE 8: ... Following earlier convention, we will use the terminology Stochas- tic Chaos (SC) for the description of aperiodic behavior in brain dynamics which has been mod- eled by KIII (Freeman, 2000b; Kozma and Free- man, 2001; Werbos, 2000). Table1 summarizes our present understanding on the relation between deterministic chaos and stochastic chaos. SR has three main components, a bi- or multi- stable energy function, weak periodic) input sig- nal, and a noise component (Gammaitoni et al.... ..."

### Table 3: Axiom system DB for deterministic agent bisimilarity.

### Table 5.3: Deterministic disturbance system results

### Table 6.2: Dynamic and deterministic tests selection. Test SCs Total In

### lable. Kinematically controllable dynamic systems

### Table 1. Parameter Values for the Calibrated Deterministic Stationary State

2003

"... In PAGE 31: ... Mendoza (2002) calibrates the liquidity requirements model to Mexican data and produces numerical simulations to examine the effects of the borrowing constraint on macroeconomic dynamics and welfare. The calibration parameters are reproduced in Table1 . Figure 4 plots the ergodic distributions of foreign bond holdings with and without the liquidity requirement.... ..."

Cited by 4

### Table 1: Statistics for estimations of the largest Lyapunov exponent of di erent dynamical systems. All exponents are measured in bits. See text for detailed explanations.

"... In PAGE 8: ... We generated a time series of length 1000 and applied the method described in the previous subsection for embedding dimensions m = 1; 5; 10; 15. The results are summarized in Table1 . For all embedding dimensions, the estimated mean value is close to the true Lyapunov exponent = 1:0 which is contained in the corresponding con dence intervals.... In PAGE 10: ... From the clouds of points it is very hard to determine in which case the skeleton (the deterministic part) is periodic and in which case it is chaotic. The estimated Lyapunov exponents and con dence intervals (see Table1 ) are also very simi- lar. In other words, the algorithm estimates the Lyapunov exponent very reliably for the disturbed chaotic system, but it also returns a positive value for the disturbed periodic system.... In PAGE 10: ...isible in Fig. 3. First, we estimate the largest Lyapunov exponent (with con dence intervals) for the ATX data set for embedding dimensions m = 1; 5; 10; 15. The results are summarized in Table1 and depicted on the left-hand side of Fig. 4.... ..."

### Table 4. System dynamic inconsistency diagnosis report.

"... In PAGE 10: ...2 Dynamic inconsistency diagnosis by BITs In case a dynamic inconsistency is detected, source and type of the inconsistency can be diagnosed and allocated based on the dynamic inconsistency detection report shown in the form of Table 3. An example diagnosis report for tracing the dynamic inconsistency is shown in Table4 . By this approach, detailed dynamic inconsistency can be allocated by the corresponding BITs at run-time.... In PAGE 11: ........ Subsystemk The diagnosis result shows the accurate sources and reasons of system dynamic inconsistency. Observing Table4 it can be found that one subsystem, two classes, five objects and seven functions have been allocated for a specific or hybrid dynamic inconsistency. 3.... ..."