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what lies before us … Pessimism vs. Optimism

SPLICING LANGUAGES: AGL and INFERENCE onward J.H.M.Dassen(1996) Molecular Computation and Splicing System(MA Thesis). what lies before us … Pessimism vs. Optimism Practical proposal vs. Proof-of-concepts Perfect vs. Error resilient.

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what lies before us … Pessimism vs. Optimism

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  1. SPLICING LANGUAGES:AGL and INFERENCE onwardJ.H.M.Dassen(1996) Molecular Computation and Splicing System(MA Thesis) clc

  2. what lies before us… Pessimism vs. Optimism Practical proposal vs. Proof-of-concepts Perfect vs. Error resilient clc

  3. how will I go along Ⅰ Formal Language Theory in DNA Computation Ⅱ Parallelism b/w HPP solving and AGL Ⅲ From AGL to Inference Ⅳ Limitations & Discussion clc

  4. Ⅰ.1 Splicing Systems Hitherto • H-system • Lipton[94-95a-95b] Formalization & generalization of Adleman’s approach to other NP-complete problems. • Ex H-system Circular H-system Sticker system P-system clc

  5. Ⅰ.2 Possible Contributions a. For computational strength -Turing Equivalence Expansion Finiteness & Regularity -More Operator Formalization b. To confirm homogeneity -HPP solving & AGL clc

  6. a. For Full Computation • Is Splicing Language Finite? • Is Splicing Language Regular? • Is Splicing Language Reflexive? clc

  7. b. Operators Hybridization, Ligation, Annealing. OR PCR(2n), Gel electrophoresis, Antibody affinity(Bead Extract) clc

  8. Restriction Enzymes Shin(2000:13) clc

  9. Recognition in situ: INITIAL/FINAL CLUSTER Filtering -Hae3 -strohpej# Distance from recognition site: SUBJACENCY Filtering -EcoR1 : GAATTC CTTAAG And if DNA strings: …GCTACTAGAATTCGCGCTA… …CGATGATCTTAAGCGCGAT… then the enzyme cuts like; …GCTACTAGAATTCGCGCTA… …CGATGATCTTAAGCGCGAT… -Hha1: GCGC CGCG And if DNA strings: …GCTACTAGAATTCGCGCTA… …CGATGATCTTAAGCGCGAT… -#strpnighn - Billi saw [a picture of hisi mother] *Billi saw [John’s picture of hisi mother] *Whoi Bill see [John’s picture of ti ] clc

  10. HPP Solving Experiment Design 1.Generate a random path through the graph 2.DNA sequence encoding 3.Apply a (polynomial-time) verification algorithm to it 4.Find a candidate solution path and discard the others Zhang(2002a:288,290) clc

  11. Artificial Grammar Learning Cleermans (1998:408) clc

  12. Why Segmentation? • Findings in clinical neuroscience Shanks et al(2002:65) clc

  13. Workload comparison Dogil et al.(2002:72-73) clc

  14. 2. From L1, L2, FL researches Boundary Unit Activation Christiansen(1998:240) clc

  15. 3. From Statistic observations Redington et al. (Lee&Kim1999: 11) SRN clc

  16. Acquisition Difficulty in aphasics Statistical Pushover plural 1 2 2 Prog –ing 2 1 1 Past irregular 3 7 7 Past regular 4 6 3 ‘A’, ‘the’ 5 5 6 Contractable copulars 6 3 4 Uncontractable copulars 7 4 5 3rd regular -s 8 8 8 Order Comparison clc

  17. Employment from the achievements 1. Optimal Sequence generation 2. Initial phase 3. Synthesis 4. Separation 5. Extraction Shin(2000:26) clc

  18. Within the Limitation • n22n →n(n(n-1))/2=n3 [Graph Size Volume Requirements ] 7 vertices 1/50th of a teaspoon 37 vertices 25,000 gallons 70 vertices 10 to the 25 kilograms of nucleotides which is roughly the mass of the earth! (Linial and Linial 1995) ‘Floccinaucinihilipilification’(≤102) vs. ‘A’(20x24!)3 • Time: Polynomial vs. Exponential (monomial) NG clc

  19. Splicing System Splicing System =(V,A,R), where V consists of N and T; N,T: disjoint set of alphabet N: non-terminal elements T: terminal elements A N multiset over V* R: a finite set of ordered pairs (u.v)  u, v: words over N∪T, u contains at least one letter of N. : regular R  V*#V*$V*#V* (L)={w  V*  (x,y)├r (w,z) or (x,y) ├r (z,w), for some x,y  L, r  R, z  V* power set of V* string aaaa generation Kim(1997:1291) clc

  20. AGL Processes Initial cluster constraints Subjacency Final cluster constraints clc

  21. left unanswered • Sequence Optimization • Efficacy • Pushover future expectation • Rehabilitant Policy • L2 Learning Architecture • Coinage Referent Index • soa-Related Interpretation in Discourse clc

  22. Reference Christiansen, Morten, et al.(1998) “Learning to segment Using Multiple Cues: A Connectionist Model.”Language and Cognitive Processes. 13(2/3). 221-268. Cleermans, Axel, Arnaud Destrebecqz, and Maud Boyer.(1998) “Implicit Learning: News form the Front.”Trends in Cognitive science, 2:10. 406-416. Dassen, J.H.M.(1996) Molecular Computation & Splicing Systems, Msc Thesis, Leiden Univ. Dogile,G. et al.(2002) “The Speaking Brain.”J. of Neurolinguistics. 15. 59-90. Kim, S.M.(1997) “Computational Modeling for splicing Systems.”SIAM J. COMPUT. 26:5, 184-1309. Redington, Martin and Nick Chater. Probabilistic and Distributional approaches to Language Acquisition. PPT by Lee & Kim(1999). Shanks, David R.(2002) Modularity and Artificial Grammar Learning. Hove, East Sussex. Shin, S.Y. (2000) DNA 컴퓨팅 기법을 이용한 조합 최적화 문제의 해결. 서울대 대학원 컴퓨터공학과 석사학위 논문. Zhang, B.T.(2002a) “분자 정보처리기술.” 『전자공학회지』29:3. 286-298. Zhang, B.T.(2002b) Molecular Intelligence Course Tutorial PPT. clc

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