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Table 3. Inference Rules of the Closure Operation.
1997
"... In PAGE 34: ... The closure operation consists of the function Closure : TSSpec;A ! TSSpec;A such that Closure(TS) is the least xed point which results from the application on TS of the inference rules Seq, Sim, Alt-1, Alt-2, and Sync given in Table 3. The inference rules of Table3 can be informally formulated as follows: The Seq rule infers the sequence of two transitions provided that the mark- ings shared between m0 1 and m2 are equal. The double arrow under the e1 event forces that e1 leads to a stable state.... ..."
Cited by 18
Table 2: Four kinds of DNA code operators.
"... In PAGE 4: ..., 1991). After overlapping the 2-D pixel image coded by DNA bases with the standard operating template, each pixel undergoes a kind of DNA code mutation ( Table2 ). We select one-dimensional (1-D) DNA string from this mutation result.... ..."
Table 2. Sub{Word Operations.
1997
Table 4: Converting a Horn Clause to a Closure Operator ^ .
1991
"... In PAGE 41: ... We can convert this math- ematics for ATMS computation into pseudo-code in the way Tables 1 and 2 did for version spaces. Pseudo-code for the operation 7! ^ , which takes a Horn clause to the corresponding closure operator on la- bel functions, is given in Table4 . In the program there, the following functions are taken from the anti-chains interface: upper_union upper_homogeneous_intersection singleton empty The rst two of these we have encountered before.... In PAGE 42: ... The Common Lisp function apply is similar but does not use the `end value apos; x. In Table4 , the end value used is f;g (which is not to be confused with the empty set itself). In particular, note the case in which has the form ) a so that S is the emptyset.... ..."
Cited by 6
Table I: Execution Times and Code Size creation of closure objects. The semantics of AspectJ dictate that in a cyclical graph, the order of the execution of advice methods is determined by the dynamic residues before any advice is executed. Because those residues and the resulting execution order can be arbitrarily complex, we decided that closures offer a clean, general solution. As observed previously, cycles in the advice-on-advice application graph are very rare and usually pathological. It therefore seems unnecessary to try to avoid closures under these circumstances. We have, however, strived to minimise the cost of closures; we create specialised closure classes with fields matching the types of context values, whereas ajc uses an expensive object array to store context values (requiring boxing of primitive types).
2004
Table I: Execution Times and Code Size creation of closure objects. The semantics of AspectJ dictate that in a cyclical graph, the order of the execution of advice methods is determined by the dynamic residues before any advice is executed. Because those residues and the resulting execution order can be arbitrarily complex, we decided that closures offer a clean, general solution. As observed previously, cycles in the advice-on-advice application graph are very rare and usually pathological. It therefore seems unnecessary to try to avoid closures under these circumstances. We have, however, strived to minimise the cost of closures; we create specialised closure classes with fields matching the types of context values, whereas ajc uses an expensive object array to store context values (requiring boxing of primitive types).
2004
Table 1: Application of the operators
"... In PAGE 4: ... It can be shown that the sequential execution of these three operators results in a perfect unshu e over the initial index. Table1 shows an example of the application of the previous methodology for mapping a trellis with 16 states onto an architecture composed of two PE (four ACS units) with two I/O paths each. Therefore, the index of each state has three bits (8 states), with 1 PE bit (2 PEs), 1 CYCLE bit and 1 PATH bit (2 paths).... ..."
Table 1. Complexity of sample applications
2006
"... In PAGE 10: ... Finally, AcousticLocalization is able to determine the distance of neighboring sensor nodes by taking advantage of the difference in speed of radio waves and sound. Table1 gives details about the complexity of the three applications showing the respective code size, the number of nesC components and the number of binary components. Table 1.... ..."
Cited by 17
Table 1. Comparison of DNA computers with conventional computers
"... In PAGE 3: ... Computing with DNA molecules has many advantages over conventional computing methods that utilize solid-state semiconductors. The properties of DNA computing compared with conventional computers are summarized in Table1 . Though DNA computing performs individual operations slowly, it can execute billions of operations simultaneously.... ..."
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