### Table 2: Illustrating Stage 2 of the algorithm ndings as follows. Theorem 4.7. An arbitrary n-segment instance of the segment visibility problem can be solved in O(log n) time on a mesh with multiple broadcasting of size n n. Furthermore, this is time-optimal.A complete worked example based on the set of segments featured in Figure 2 is presented for the reader apos;s bene t. Figure 3 shows the set of input segments along with the binary tree T that guides the algorithm. The various data items computed in Stage 1 are summarized in Table 1. The results of Stage 2 are captured, in succinct form, in Tables 2 and 3. Speci cally, the solution to the endpoint visibility problem is contained in Table 3. 5 Applications The purpose of this section is to show that the EV and SV problems discussed in the previous section yield time-optimal solutions to a number of problems of import to computer graphics, robotics, and VLSI design.

### Table 3 Tests to reach criterion for test sets

"... In PAGE 11: ... One measure of learning facility is how quickly par- ticipants passed the criterion of a perfect score on one test. As seen in Table3 , the mean number of tests to reach criterion was smaller for experts than for novices on all four test sets. The difference between experts and novices was not significant for any one test set alone, but was significant when all four sets were considered together, t(90) 2.... ..."

### Table 9: Complexity of deciding consistency in augmented qualitative networks.

1995

### Table 7.1: Parameterizations of the augmented crop network.

2006

### Table 1: Product group recommendation network statistics. p: number of products, n: number of nodes, e: number of edges (recommendations), eu: number of unique edges, bb: number of buy bits, be: number of buy edges.

2006

"... In PAGE 3: ... 2.3 Recommendation network summary sta- tistics Table1 shows the sizes of various product group recommen- dation networks. For each product group we took recom- mendations on all products from the group and created a graph.... ..."

Cited by 23

### Table 1: Illustrating Stage 1 of the algorithm NODE BT BA LC RC a

"... In PAGE 13: ... Figure 3 shows the set of input segments along with the binary tree T that guides the algorithm. The various data items computed in Stage 1 are summarized in Table1 . The results of Stage 2 are captured, in succinct form, in Tables 2 and 3.... ..."

### Table 1: Illustrating Stage 1 of the algorithm NODE BT BA LC RC a

"... In PAGE 13: ... Figure 3 shows the set of input segments along with the binary tree T that guides the algorithm. The various data items computed in Stage 1 are summarized in Table1 . The results of Stage 2 are captured, in succinct form, in Tables 2 and 3.... ..."

### TABLE III PROPERTIES OF DATA-SHARING GRAPHS, MEASURED AND MODELED AS UNIMODAL PROJECTION OF AFFILIATION NETWORKS. CLUSTERING COEFFICIENT ARE MEASURED USING EQ. 3 AND MODELED USING EQ. 10 Clustering Average degree

2004

Cited by 35

### Table 1. Wireless data access in heterogeneous wireless network standards.

2005

"... In PAGE 35: ....2.3 Heterogeneous access networks There have been a wide variety of wireless access network technologies developed during the past decade. As described in Table1 , wireless data access can be provided over heterogeneous wireless networks [17]. Some of the wireless network standards have aimed at specific market regions such as North-America, Europe and Asia, especially in cellular networks.... In PAGE 37: ... However, the practical performance of these cellular data systems is usually much lower than the nominal rates, even down to ten times lower than the nominal rate [22]. As shown in Table1 , wireless local and personal area networking standards include IEEE 802.... In PAGE 39: ...ominated by 802.11 standards. In addition, the lack of end-to-end QoS support in the public Internet so far may have lowered the interest for providing QoS support in the local access network. As illustrated in Table1 , there are a wide variety of wireless networks, and many local and personal area standards have emerged. However, whatever the used WLAN system is, it is providing service only locally .... ..."

### Table2. Original Datasets Augmented with Constructed Features

"... In PAGE 6: ...3 Experiments The performed experiments follow the three basic steps of our approach. In the first step, after analyzing each dataset, the user/expert suggested two new features for datasets pima, cmc and smoke and just one new feature for dataset hepatitis as Table2 shows. In the second step, C4.... ..."