"... In PAGE 6: ... Formally, a network con guration is a pair hA; vi,whereA=A1j:::jAn is a network and v =(v1;:::;vn) is a delay vector, indicating how much each component of the network has been delayed. The transitions between network con gurations are given by Table2 , where for d 2 R gt;0, v + d denotes the delay vector (v1 + d;:::;vn+d), and v[vi := 0] denotes the delay vector obtained by replacing vi with 0 in the vector v. Example 1.... ..."

"... In PAGE 8: ....2. Results and analyses Our analysis of these semantic networks focuses on five properties: sparsity, connectedness, short path-lengths, high neighborhood clustering, and power-law degree distributions. The statistics related to these properties are shown in Table2 (under the Data columns), and the estimated degree distributions for each network are plotted in Figure 5. To provide a benchmark for small-world analyses, we also computed the average shortest-path lengths (Lrandom) ... In PAGE 10: ...01 and 3.19 (see Table2 ). The high-connectivity words at the tail of the power-law distribution can be thought of as the hubs of the semantic network.... In PAGE 13: ... Incorporating additional processes would surely make the model more realistic but would also entail adding in more free parameters, corresponding to the relative weights of those mechanisms. Given that the data we wish to account for consists of only the few summary statistics in Table2 and the degree distributions in Figure 5, it is essential to keep the number of free parameters to an absolute minimum. Our model for undirected networks (model A) has no free numerical parameters, while our model for directed networks (model B) has just one free parameter.... In PAGE 16: ...95, corresponding to the reasonable assumption that on average, 19 out of 20 new directed connections point from a new node towards an existing node. We evaluated the models by calculating the same statistical properties (see Table2 ) and degree distributions (see Figure 5) discussed above for the real semantic networks. Because the growing models are stochastic, results vary from simulation to simulation.... ..."

"... In PAGE 48: ...ut vessels... should ask the boss, then a whole white lung there. Extracted schemata in bed film no visible left evolutionary-like pleuro-parenchymatous lesion no pleural effusion no right attraction of mediastinum no right-to-left shift of mediastinum micro-nodules pneumonectomy right white lung missing a little trachea cut vessels Comments on the choices _______________Insert Table9... In PAGE 62: ...62 Table9 continues Verbalization Comment .... ..."

"... In PAGE 17: ... We used this procedure to construct a graph with the same density as the undirected word-association graph and subjected it to the same analyses. The results of these analyses are presented in Table1 . The degree of individual nodes in the LSA graph is weakly correlated with the degree of nodes in the association graph (H9267H11005.... ..."

"... In PAGE 2: ... BX is therefore classified into the inter-relationships between objects and objects, objects and actions, actions and actions. Table2 describes inter- relationship examples in each category. We introduce a new concept called domain knowledge slice in the context of DKBA.... ..."

"... In PAGE 10: ... These weights can be assigned through expert experience or learned from the data. Table2 shows a step-by-step procedure for an example. Table 2.... ..."

"... In PAGE 9: ...elevance and identifying power. Terms with low precision, i.e., terms that retrieved many irrelevant documents, were eliminated. The resulting search strategy ( Table1 ) was then executed to retrieve all articles that satisfied the selected criteria. ... ..."