### Table 2. Information of Various Adversaries

2005

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### Table 2. Information of Various Adversaries

2005

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### Table 2: Informal summary of adversaries/simulators used in the proof.

2005

"... In PAGE 26: ...Table2... ..."

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### Table 2: Informal summary of adversaries/simulators used in the proof.

2005

"... In PAGE 26: ...Table2... ..."

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### Table 1. Product sharing information.

"... In PAGE 3: ... Step 1. The product sharing information is summarized in Table1 . The table shows the occurrence of all product terms in different output functions and the total number of occurrence of each product term.... ..."

### Table 1. Information Sharing Structures

2003

"... In PAGE 3: ...Adapted from C. C. Poirier, Advanced Supply Chain Management and e-Business, CRM Today, July 24, 2002; used by permission.) Information Sharing Structures Table1 (inspired by Hong 2002; Kumar and van Dissel 1996) gives a three-part typology for interorganizational information systems (IOS) based on interorganizational interdependencies: sequential, reciprocal, and hub-and-spoke. The corresponding structures are as follows (see Table 1): (1) Sequential information sharing: In this structure, the output of one partner apos;s activity will flow into the next trading partner as its input.... In PAGE 9: ... Shared Deployment Although the CPFR guidelines have identified two types of deployment scenarios, shared deployment and peer-to-peer deployment (VICS 2002), shared deployment is the easier way. Figure 6 shows a typical shared deployment scenario similar to the hub-and-spoke model of Table1 . In this model, two partners rely on the same application for specific CPFR functionality.... ..."

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### Table 1. The true gradient of the expected return and its MC and BQ estimates for two versions of the simple bandit problem corresponding to two di erent reward functions.

"... In PAGE 7: ... As a re- sult the probability of a path is also Gaussian with the same mean and variance: Pr( ) = (ajx) N(0; 1). The score function of the path = a and the Fisher information matrix G are computed as follows: r log Pr( ) = a a2 1 ; G = 1 0 0 2 Table1 shows the exact gradient of the expected re- turn and its MC and BQ estimates (using 10 and 100 samples) for two versions of the simple bandit prob- lem corresponding to two di erent reward functions r(a) = a and r(a) = a2. The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table 1.... In PAGE 7: ... The score function of the path = a and the Fisher information matrix G are computed as follows: r log Pr( ) = a a2 1 ; G = 1 0 0 2 Table 1 shows the exact gradient of the expected re- turn and its MC and BQ estimates (using 10 and 100 samples) for two versions of the simple bandit prob- lem corresponding to two di erent reward functions r(a) = a and r(a) = a2. The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table1 . The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table 1 for comparison purposes.... In PAGE 7: ... The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table 1. The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table1 for comparison purposes. As shown in Table 1, the BQ estimate has much lower standard deviation than the MC estimate for both small and large sample sizes.... In PAGE 7: ... The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table 1 for comparison purposes. As shown in Table1 , the BQ estimate has much lower standard deviation than the MC estimate for both small and large sample sizes.... ..."

### Table 1. The true gradient of the expected return and its MC and BQ estimates for two versions of the simple bandit problem corresponding to two di erent reward functions.

"... In PAGE 7: ... As a re- sult the probability of a path is also Gaussian with the same mean and variance: Pr( ) = (ajx) N(0; 1). The score function of the path = a and the Fisher information matrix G are computed as follows: r log Pr( ) = a a2 1 ; G = 1 0 0 2 Table1 shows the exact gradient of the expected re- turn and its MC and BQ estimates (using 10 and 100 samples) for two versions of the simple bandit prob- lem corresponding to two di erent reward functions r(a) = a and r(a) = a2. The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table 1.... In PAGE 7: ... The score function of the path = a and the Fisher information matrix G are computed as follows: r log Pr( ) = a a2 1 ; G = 1 0 0 2 Table 1 shows the exact gradient of the expected re- turn and its MC and BQ estimates (using 10 and 100 samples) for two versions of the simple bandit prob- lem corresponding to two di erent reward functions r(a) = a and r(a) = a2. The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table1 . The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table 1 for comparison purposes.... In PAGE 7: ... The average over 104 runs of the MC and BQ estimates and their standard devia- tions are reported in Table 1. The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table1 for comparison purposes. As shown in Table 1, the BQ estimate has much lower standard deviation than the MC estimate for both small and large sample sizes.... In PAGE 7: ... The gradient is analyt- ically computable in this problem and is reported as \Exact quot; in Table 1 for comparison purposes. As shown in Table1 , the BQ estimate has much lower standard deviation than the MC estimate for both small and large sample sizes.... ..."

### TABLE I SUMMARY OF TRUST RELATIONSHIPS FOR INFORMATION SHARING.

2004

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