### Table 1: Outline of simultaneous interpreting corpus type of speech monologue(lecture)

"... In PAGE 2: ... Moreover, the transcript is segmented into utterance units by the pause for 200ms or more, or that for 50ms after the sentence break, and the starting time and ending time have been provided for every utterance unit. Table1 shows the outline of the simultaneous interpreting corpus. The database consists of wave files, transcription files and environment data files and contain about 569,000 morphemes in 26,000 utterance sentences.... ..."

### Table 2: Statistics of the simultaneous interpreting corpus item English E-to-J Japanese J-to-E

### Table 9. Simultaneous Workspaces

2006

"... In PAGE 4: ... For instance, an average of two means that for each revision of a file the developer per- formed on average two commands, likely, because of two simultaneous workspaces. In Table9 we show the distri- bution of these averages. Although most developers seem to have one workspaces, there is strong evidence that sev- eral developers have more than one workspaces for the same module and the same branch.... ..."

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### Table 1. Simultaneous, multiple actuator

"... In PAGE 4: ... When multiple actuators are poked at the same time, the resulting mirror de#0Dection does not equal the sum of the individual pokes. Table1 and #0Cgure 6 demonstrate this nonlinearity. The major di#0Berence between the interferogram pairs shown in the #0Cgure is not in the shape, but in the magnitude of the de#0Dection.... ..."

### Table 2. Symptom key-stroke list

in Summary The Effects of Promethazine on Human Performance, Mood States, and Motion Sickness Tolerance

1996

### Table 3: Solution for simultaneous LCA with heterogeneous class sizes and homogeneous conditional probabilities, model with 5 latent classes. The latent class probabilities add up to 1 rowwise. For the conditional probabilities only the estimates for the probabilities to commit the anti-social behaviour are reported. For example, for variable 1, given that one falls into latent class 1, the estimated probability to say apos;yes apos; is .000, and hence the estimated probability to say apos;no apos; is 1.000. Over all latent classes, the (weighted) average probability to say apos;yes apos; is equal to the observed proportion of saying apos;yes apos;, which is .012 for variable 1.

"... In PAGE 8: ... We now interpret the parameter estimates of the partly heterogeneous simultaneous latent class model with five latent classes in Table 2b and the completely heterogeneous simultaneous latent class model with two latent classes in Table 2c. In Table3 we find the solution for simultaneous LCA with five latent classes, with heterogeneous class size estimates at the top and homogeneous conditional probabilities at the bottom. The homogeneous conditional probabilities are more easily interpreted by comparing them with the observed proportions of the various forms of anti-social behaviour.... ..."

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### Table 1: Terms and their interpretation

1989

"... In PAGE 8: ...Extending the language In this section we discuss how to interpret terms with any nite number of variables (instead of exactly one as in Table1 ) and how datatypes relate to computations. We will consider only product and functional types, because sum types are completely straightforward5.... In PAGE 8: ... If T were IdC, then [[x1: 1 ` (let x2=e2 in e): ]] would be hid 1; g2i; g. In the general case, Table1 says that ; above is indeed composition in the Kleisli category, therefore hid 1; g2i; g becomes hid 1; g2i; T g; . But in hid 1; g2i; T g; there is a type mismatch, since the codomain of hid 1; g2i is 1 T 2, while the domain of T g is T ( 1 2).... In PAGE 18: ...SYNTAX SEMANTICS var x1; : : : ; xn ` xi = n i ; V let x ` e1 = g1 x; x ` e2 = g2 x ` (let x=e1 in e2) = hidV n; g1i; tV n;V ; T g2; V x; x ` e = g x ` ( x:e) = T V;V;V n(g); G; V V T app x ` e1 = g1 x ` e = g x ` e(e1) = hg; g1i; appv Table 9: call-by-value interpretation RULE SYNTAX SEMANTICS var x1; : : : ; xn ` xi = n i let x ` e1 = g1 x; x ` e2 = g2 x ` (let x=e1 in e2) = hid(TN)n; g1i; t(TN)n;N; T (id(TN)n N); T g2; N x; x ` e = g x ` ( x:e) = T TN;N;(TN)n(g); G; NTN T app x ` e1 = g1 x ` e = g x ` e(e1) = hg; g1i; appn Table1 0: call-by-name interpretation... ..."

Cited by 369

### Table 3: Terms and their interpretation

1989

Cited by 369