"... In PAGE 17: ...902 14.943 Table 3: occurence of the numbers in the ternary sequence for the Calgary Corpus a a a d a a a a d a a a a g a a a a d a a a 0 0 0 3 1 0 0 0 1 1 0 0 0 5 1 0 0 0 2 1 0 0 0 0 0 2 1 0 0 0 1 1 0 0 0 2 1 0 0 0 2 1 0 0 The occurence of the numbers are presented in Table2 . Now let us start with the following modified rule: Let us consider the context the .... ..."

"... In PAGE 18: ...20 40 60 80 100 0 1 2 3 4 5 6 7 8 9 book1 without RLE pic without RLE pic with RLE Figure 1: Relative frequencies of characters [0, 9] Therefore most symbols are encoded only with the ternary sequence (see Table3 for the modified MTF rule). To increase further the number of zeros we change the rule again: In addition to the privious rule we move a symbol at position 1 only to position 0 if the last produced number was different from zero.... In PAGE 19: ... In [8] and [2] grouping was also used, but in a different way. Table3 illustrates that typically the zero is the most likely symbol. To reduce the number of free parameters we like to encode as many symbols as possible with as few parameters as possible.... ..."

"... In PAGE 9: ... The rst class of equivalences is shown in Example 2. For the second class let A1 = A; B1 = B: NowP1 = 1 2(A1 + B1) = 1 2(A ? B) = Q; Q1 = 12(A1 ? B1) = 1 2(A + B) = P: 2 Results: Table1 shows some results obtained. Shorter multiplications (m = 3; 4) were a matter of seconds or minutes on the computer while longer multiplications (m = 7) took several CPU{days of computer{time.... In PAGE 10: ...f Examples m f Examples 3 4 P A Q 6 10 AB QP B A P B Q AB QP B A 3 5 AQB 6 10 A Q B BP A AP B A Q B BP A 4 5 P QA Q 7 8 PP PA Q Q Q P BP Q P P P BQ Q Q 5 8 P A AB Q 7 8 PP Q AP Q Q P A BB Q P P Q BP Q Q 5 8 PB ABQ 7 8 P Q PB QP Q P A B A Q P Q P AQP Q 5 8 P AA AQ 7 10 A QAQ Q QB P B B BQ A P PP B P B 6 8 Q AP QBP 7 10 Q AAP QBP Q AP Q B P Q AP QB B P 6 8 Q A P P BQ 7 10 PQA ABP Q Q B P P A Q P Q A BBP Q 6 8 QP AB P Q 7 10 QP QAAB P Q P A B P Q Q ABB P Q P 6 8 P P AB QQ 7 10 P QP ABAQ P P A B Q Q PB A B Q P Q 6 10 AAPQBB 7 10 P BQPBA Q A AP QB B PB A QP A Q 6 10 AQB APB 7 10 PAP A QBQ A Q B APB P A P B QBQ Table1 : Multiplications obtained via computer.... ..."

"... In PAGE 18: ... The symbolic trees have been abbreviated to fit. which had two readings) as shown in Table3 . This representation is meant to capture the flavor of a recursive (ACTION AGENT OBJECT) case system.... In PAGE 27: ... bits distinguish the members. The patterns are displayed in Table 6. A sequential cas- caded network consisting of a 10-10-16 function network and a 16-286 context network was trained using sequences of these bit patterns corresponding to the sentences in Table 1. The initial context vectors were all zeroes, and the desired final states were the compressed 16-dimensional representations devised by the 48-16-48 RAAM for the trees in Table3 (not including 10b). This system is the closest thing yet to a barely adequate connectionist system for processing language: Given a variable-length sequence of words, the network returns, in linear time, a 16-dimensional vector, which can be decoded into a quot;meaning quot; by a RAAM, and can perhaps be operated upon by associative inference engines.... ..."

"... In PAGE 18: ... The symbolic trees have been abbreviated to fit. which had two readings) as shown in Table3 . This representation is meant to capture the flavor of a recursive (ACTION AGENT OBJECT) case system.... In PAGE 27: ... bits distinguish the members. The patterns are displayed in Table 6. A sequential cas- caded network consisting of a 10-10-16 function network and a 16-286 context network was trained using sequences of these bit patterns corresponding to the sentences in Table 1. The initial context vectors were all zeroes, and the desired final states were the compressed 16-dimensional representations devised by the 48-16-48 RAAM for the trees in Table3 (not including 10b). This system is the closest thing yet to a barely adequate connectionist system for processing language: Given a variable-length sequence of words, the network returns, in linear time, a 16-dimensional vector, which can be decoded into a quot;meaning quot; by a RAAM, and can perhaps be operated upon by associative inference engines.... ..."

"... In PAGE 3: ...een attributed to the frontal lobe (Shimamura et al., 1995). The purpose of the current work is to isolate relational from associative effects by contrasting lists with different index lengths but the same number of items and item associates. Table1 (c and d) provides an example of two relations with different length indexes and the same number of items and item associates. Materials and methods Subjects were required to perform a pair recognition task, similar to Phillips and Niki (2002) Experiment 5; that is, subjects were given a sequence of kanji pairs and a test pair for which they were required to make a recognition judgment: Was the test pair in the previous list? Kanji are generally word units, often with multiple semantic and phonetic components.... ..."

"... In PAGE 3: ...of 64 constructed from seed sets of sizes 4, 8 and 16 for different ZCZ sizes are illustrated in Table1 . As well as the ZCZ values being based upon the aperiodic correlation functions as opposed to just the periodic such as the sequences based on ternary preferred pairs (TPP), the new method produces larger family sizes, although the maximum ZCZ given in [3] by (0.... ..."

"... In PAGE 21: ... Since the expressions for conditional probabilities were given earlier, we described the coding algorithm for zn 1 entirely. In Table5 we illustrate the rates for the encoding of the ternary sequence for the original MTF rule (marked as MTF0), for the first modified one (marked as MTF1) and for the second modified one (marked as MTF2). 5.... ..."