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Scientific Applications of Language Methods.

By: Series: Mathematics, Computing, Language, and Life: Frontiers in Mathematical Linguistics and Language Theory SerPublisher: Singapore : Imperial College Press, 2010Copyright date: ©2011Description: 1 online resource (750 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781848165458
Subject(s): Genre/Form: Additional physical formats: Print version:: Scientific Applications of Language MethodsDDC classification:
  • 410.151
LOC classification:
  • QA267.3 -- .M37 2011eb
Online resources:
Contents:
Intro -- Contents -- Preface -- Chapter 1 Descriptional Complexity - An Introductory Survey -- 1.1 Introduction -- 1.2 Descriptional Systems and Complexity Measures -- 1.3 Measuring Sizes -- 1.3.1 Descriptional Systems for Regular Languages -- 1.3.1.1 Finite Automata -- 1.3.1.2 Regular Expressions -- 1.3.2 Pushdown Automata and Context-Free Grammars -- 1.3.2.1 Pushdown Automata -- 1.3.2.2 Context-Free Grammars -- 1.4 Measuring Resources -- 1.5 Non-Recursive Trade-Offs -- 1.5.1 Proving Non-Recursive Trade-Offs -- 1.5.2 A Compilation of Non-Recursive Trade-Offs -- References -- Chapter 2 Classifying All Avoidable Sets of Partial Words of Size Two -- 2.1 Introduction -- 2.2 Preliminaries -- 2.3 Unavoidable Sets of Partial Words of Size Two -- 2.4 Canonical Forms -- 2.5 The Answer to the Conjectures -- 2.5.1 The m ≡ n2 − n1 − 1 mod 2m + 2 case -- 2.5.2 The m ≡ 2n1 + n2 + 2 mod 2m + 2 and m ≡ 2n2 + n1 + 2 mod 2m + 2 cases -- 2.5.3 The n1 + n2 + 2 = 0 mod 2m + 2, n1 + 1 = 0 mod 2m + 2, and n2 + 1 = 0 mod 2m + 2 cases -- 2.5.4 The m = 6 case -- 2.6 The Classification -- 2.7 Conclusion -- Acknowledgments -- References -- Chapter 3 On Glushkov K-graphs -- 3.1 Introduction -- 3.2 Definitions -- 3.2.1 Classical Notions -- 3.2.2 Extended Glushkov Construction -- 3.2.3 Normal Forms and Casting Operation -- 3.2.4 Characterization of Glushkov Automata in the Boolean Case -- 3.2.5 The Problem of Reduction Rules -- 3.3 Acyclic Glushkov WFA Properties -- 3.3.1 K-rules -- 3.3.2 Confluence for K-rules -- 3.3.3 K-reducibility -- 3.3.4 Several Examples of Use for K-rules -- 3.4 Glushkov K-graph with Orbits -- 3.5 Algorithm for Orbit Reduction -- 3.6 Conclusion -- References -- Chapter 4 Natural Language Dictionaries Implemented as Finite Automata -- 4.1 Dictionaries as Finite Automata -- 4.1.1 Simple and Complex, Static and Dynamic -- 4.1.1.1 Implementing a Dictionary.
4.1.2 Variants of Finite-State Machines Relevant to Dictionary Data Structure Implementation -- 4.1.2.1 Deterministic Finite-State Automaton -- 4.1.2.2 Trie -- 4.1.2.3 Minimal Deterministic Finite Automaton -- 4.1.2.4 Recursive Automaton -- 4.1.2.5 Nondeterministic FA and ε - NDFA -- 4.1.2.6 Transducer -- 4.1.2.7 Implementing Dictionaries with FSMs -- 4.2 Automata as Mappings -- 4.2.1 Perfect Hashing -- 4.2.2 Morphological Analysis and Synthesis -- 4.2.3 Spelling Correction and Restoration of Diacritics -- 4.2.4 Gazetteers and Information Extraction -- 4.2.4.1 Pure DFA Approach -- 4.2.4.2 Indexing Automaton Approach -- 4.3 Construction Methods -- 4.3.1 Construction from Strings -- 4.3.2 Construction from Strings with Some Cyclic Structures -- 4.3.3 Construction from Smaller Automata -- 4.3.4 Construction from Other Sources -- 4.4 Internal Structure and Compression -- 4.4.1 Representation of Automata -- 4.4.1.1 Transition Matrix -- 4.4.1.2 Transition Lists -- 4.4.1.3 Compressed Transition Matrix -- 4.4.1.4 Comparison -- 4.4.2 Compression Techniques -- 4.4.2.1 A Space Optimized Representation of RA -- 4.4.2.2 Dictionary Compression -- 4.4.2.3 Compact Representation of Dynamic Dictionaries -- 4.4.3 Conclusions and Further Reading -- References -- Chapter 5 Tree-Language Based Querying of Hierarchically Structured and Semi-Structured Data -- 5.1 Introduction -- 5.1.1 Motivation -- 5.1.2 Scope and Outline -- 5.2 Preliminaries -- 5.2.1 Regular Expressions -- 5.2.2 Trees and Forests -- 5.2.3 XML Basics -- 5.3 Regular Forest Languages -- 5.3.1 Specifying Regular Forest Languages -- 5.3.1.1 Forest Grammars -- 5.3.1.2 Practical Extensions -- 5.3.1.3 Forest Grammars and XML Schema Languages -- 5.3.2 Recognizing Regular Forest Languages -- 5.3.2.1 Pushdown Forest Automata -- 5.3.2.2 From Forest Grammars to Pushdown Forest Automata -- 5.3.3 Bibliographic Notes.
5.4 Grammar Queries -- 5.4.1 Specifying Queries -- 5.4.1.1 Unary Queries -- 5.4.1.2 K-ary Queries -- 5.4.2 Expressive Power of Grammar Queries -- 5.4.3 Recognizing Unary Queries -- 5.4.3.1 Pushdown Forest Automata as Relabelings -- 5.4.3.2 Locating Unary Matches -- 5.4.4 Recognizing Binary Queries -- 5.4.4.1 Recognizing Simple Binary Queries -- 5.4.4.2 Recognizing General Binary Queries -- 5.4.5 Recognizing K-ary Queries -- 5.4.6 Implementation Issues -- 5.4.7 Bibliographic Notes -- 5.4.7.1 Forest Grammar Queries -- 5.4.7.2 XPath Evaluation and Expressiveness -- 5.4.7.3 Query Automata -- 5.4.7.4 Selecting Tree Automata -- 5.4.7.5 Attribute Grammar Queries -- 5.4.7.6 Regular Hedge Expressions -- 5.4.7.7 Tree-walking Automata -- 5.4.7.8 Pure Logic Formalisms -- 5.4.7.9 Numerical Document Queries -- 5.4.7.10 Tree Queries -- 5.5 Practical Application: A Pattern Language for XML Querying -- 5.5.1 Paths -- 5.5.1.1 Regular Path Expressions -- 5.5.1.2 Boolean Connectives for Paths -- 5.5.2 Qualifiers -- 5.5.2.1 Structure Qualifiers -- 5.5.2.2 Context Qualifiers -- 5.5.3 Binary Patterns -- 5.5.4 From Patterns to Grammar Queries -- 5.5.5 Comparison with XPath -- 5.5.5.1 XPath's Paths -- 5.5.5.2 XPath's Qualifiers -- 5.5.5.3 Differences in Expressiveness -- 5.5.6 Bibliographic Notes -- 5.6 Online Querying -- 5.6.1 Right-ignoring Queries -- 5.6.2 Arbitrary Queries -- 5.6.2.1 Idea -- 5.6.2.2 Recognizing Matches -- 5.6.2.3 Complexity -- 5.6.3 Bibliographical Notes -- 5.7 Summary and Outlook -- 5.8 Proofs of Theorems -- 5.8.1 Proof of Theorem 5.21 -- 5.8.1.1 Proof of (i1) -- 5.8.1.2 Proof of (i2) -- 5.8.2 Proof of Theorem 5.22 -- 5.8.3 Proof of Theorem 5.32 -- References -- Chapter 6 Quotient Monoids and Concurrent Behaviours -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Partial Orders and Order Structures -- 6.4 Mazurkiewicz Traces -- 6.5 Comtraces.
6.6 Generalised Comtraces -- 6.7 Elementary Net Systems -- 6.8 EN-systems with Inhibitor and Mutex Arcs -- 6.9 Concluding Remarks -- Acknowledgments -- References -- Chapter 7 Correction Queries in Active Learning -- 7.1 Introduction -- 7.2 Preliminaries -- 7.3 Learning Models -- 7.3.1 Query Learning -- 7.3.2 Gold-style Learning -- 7.3.3 A Hierarchy of Learning Models -- 7.4 Learning with Correction Queries -- 7.4.1 Learning with Prefix Correction Queries -- 7.4.1.1 Necessary and Sufficient Conditions -- 7.4.1.2 Learning with PCQs versus Learning with MQs -- 7.4.1.3 Learning with PCQs versus Gold-style Learning Models -- 7.4.2 Learning with Length Bounded Correction Queries -- 7.4.3 Learning with Edit Distance Correction Queries -- 7.4.4 Remarks and Further Research -- 7.5 Polynomial Time Learning with Correction Queries -- 7.5.1 Polynomial Time Learning with PCQs -- 7.5.1.1 The Class of Pattern Languages -- 7.5.1.2 The Class of k-Reversible Languages -- 7.5.1.3 Polynomial Time Learning with PCQs versus MQs -- 7.5.2 Polynomial Time Learning with LBCQs -- 7.5.3 Polynomial Time Learning with EDCQs -- 7.5.4 Remarks and Further Research -- References -- Chapter 8 Applications of Grammatical Inference in Software Engineering: Domain Specific Language Development -- 8.1 Introduction -- 8.2 Analysis Phase of DSL Development -- 8.3 Design Phase of DSL Development -- 8.4 Grammatical Inference and Language Design -- 8.4.1 GenInc -- 8.4.2 MAGIc -- 8.4.2.1 Local Search -- 8.4.2.2 Mutation -- 8.4.2.3 Generalization -- 8.4.2.4 Selection -- 8.4.2.5 Example -- 8.4.2.6 Limitations -- 8.5 Case Study -- 8.6 Related Work -- 8.7 Conclusion -- Acknowledgments -- References -- Chapter 9 Small Size Insertion and Deletion Systems -- 9.1 Introduction -- 9.2 Definitions -- 9.2.1 Insertion-Deletion Systems -- 9.2.2 Graph-Controlled Insertion-Deletion Systems.
9.3 Basic Simulation Principles -- 9.4 Insertion-Deletion Systems with Rules of Small Size -- 9.5 Context-Free Insertion-Deletion Systems -- 9.5.1 Non-completeness Results -- 9.6 One-Sided Contextual Insertion-Deletion Systems -- 9.6.1 Non-completeness Results -- 9.7 Pure Insertion Systems -- 9.8 Graph-Controlled Insertion-Deletion Systems -- 9.9 Graph-Controlled Insertion-Deletion Systems with Priorities -- 9.10 Bibliographical Remarks -- References -- Chapter 10 Accepting Networks of Evolutionary Word and Picture Processors: A Survey -- 10.1 Introduction -- 10.2 Basic Definitions -- 10.2.1 Preliminaries -- 10.2.2 Accepting Networks of Evolutionary Processors -- 10.2.3 Accepting Networks of Evolutionary Processors with Filtered Connections -- 10.2.4 Timed Accepting Networks of Evolutionary Processors -- 10.2.5 Computational Complexity Classes -- 10.3 Computational Power -- 10.3.1 Computational Power of ANEPs/ANEPFCs -- 10.3.2 The Role of Evolutionary Operations in ANEPs -- 10.3.3 Complexity Classes and Size Complexity -- 10.4 Universal ANEPs and ANEPFCs -- 10.5 A Direct Simulation -- 10.6 Accepting Networks of Splicing Processors -- 10.6.1 Computational Power and Complexity Results for ANSPs/ANSPFCs -- 10.7 Problem Solving with ANEPs/ANEPFCs -- 10.8 Accepting Networks of Picture Processors -- 10.8.1 Pictures and Picture Languages -- 10.8.2 Picture Processors -- 10.8.3 Accepting Networks of Evolutionary Picture Processors -- 10.8.4 Computational Power -- 10.8.5 Solving Picture Matching with ANEPPs -- References -- Chapter 11 Quantum Automata and Periodic Events -- 11.1 Introduction -- 11.2 Preliminaries -- 11.2.1 Linear Algebra -- 11.2.2 Quantum Finite Automata -- 11.3 Testing Periodicity on Unary 1qfa's -- 11.4 Synthesis of 1qfa's Inducing Periodic Events -- 11.4.1 The Time Complexity of our Synthesis Algorithm -- 11.4.2 Efficient Synthesis.
11.5 Application to Periodic Languages.
Summary: Presenting interdisciplinary research at the forefront of present advances in information technologies and their foundations, "Scientific Applications of Language Methods" is a multi-author volume containing pieces of work (either original research or surveys) exemplifying the application of formal language tools in several fields, including logic and discrete mathematics, natural language processing, artificial intelligence, natural computing and bioinformatics.
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Intro -- Contents -- Preface -- Chapter 1 Descriptional Complexity - An Introductory Survey -- 1.1 Introduction -- 1.2 Descriptional Systems and Complexity Measures -- 1.3 Measuring Sizes -- 1.3.1 Descriptional Systems for Regular Languages -- 1.3.1.1 Finite Automata -- 1.3.1.2 Regular Expressions -- 1.3.2 Pushdown Automata and Context-Free Grammars -- 1.3.2.1 Pushdown Automata -- 1.3.2.2 Context-Free Grammars -- 1.4 Measuring Resources -- 1.5 Non-Recursive Trade-Offs -- 1.5.1 Proving Non-Recursive Trade-Offs -- 1.5.2 A Compilation of Non-Recursive Trade-Offs -- References -- Chapter 2 Classifying All Avoidable Sets of Partial Words of Size Two -- 2.1 Introduction -- 2.2 Preliminaries -- 2.3 Unavoidable Sets of Partial Words of Size Two -- 2.4 Canonical Forms -- 2.5 The Answer to the Conjectures -- 2.5.1 The m ≡ n2 − n1 − 1 mod 2m + 2 case -- 2.5.2 The m ≡ 2n1 + n2 + 2 mod 2m + 2 and m ≡ 2n2 + n1 + 2 mod 2m + 2 cases -- 2.5.3 The n1 + n2 + 2 = 0 mod 2m + 2, n1 + 1 = 0 mod 2m + 2, and n2 + 1 = 0 mod 2m + 2 cases -- 2.5.4 The m = 6 case -- 2.6 The Classification -- 2.7 Conclusion -- Acknowledgments -- References -- Chapter 3 On Glushkov K-graphs -- 3.1 Introduction -- 3.2 Definitions -- 3.2.1 Classical Notions -- 3.2.2 Extended Glushkov Construction -- 3.2.3 Normal Forms and Casting Operation -- 3.2.4 Characterization of Glushkov Automata in the Boolean Case -- 3.2.5 The Problem of Reduction Rules -- 3.3 Acyclic Glushkov WFA Properties -- 3.3.1 K-rules -- 3.3.2 Confluence for K-rules -- 3.3.3 K-reducibility -- 3.3.4 Several Examples of Use for K-rules -- 3.4 Glushkov K-graph with Orbits -- 3.5 Algorithm for Orbit Reduction -- 3.6 Conclusion -- References -- Chapter 4 Natural Language Dictionaries Implemented as Finite Automata -- 4.1 Dictionaries as Finite Automata -- 4.1.1 Simple and Complex, Static and Dynamic -- 4.1.1.1 Implementing a Dictionary.

4.1.2 Variants of Finite-State Machines Relevant to Dictionary Data Structure Implementation -- 4.1.2.1 Deterministic Finite-State Automaton -- 4.1.2.2 Trie -- 4.1.2.3 Minimal Deterministic Finite Automaton -- 4.1.2.4 Recursive Automaton -- 4.1.2.5 Nondeterministic FA and ε - NDFA -- 4.1.2.6 Transducer -- 4.1.2.7 Implementing Dictionaries with FSMs -- 4.2 Automata as Mappings -- 4.2.1 Perfect Hashing -- 4.2.2 Morphological Analysis and Synthesis -- 4.2.3 Spelling Correction and Restoration of Diacritics -- 4.2.4 Gazetteers and Information Extraction -- 4.2.4.1 Pure DFA Approach -- 4.2.4.2 Indexing Automaton Approach -- 4.3 Construction Methods -- 4.3.1 Construction from Strings -- 4.3.2 Construction from Strings with Some Cyclic Structures -- 4.3.3 Construction from Smaller Automata -- 4.3.4 Construction from Other Sources -- 4.4 Internal Structure and Compression -- 4.4.1 Representation of Automata -- 4.4.1.1 Transition Matrix -- 4.4.1.2 Transition Lists -- 4.4.1.3 Compressed Transition Matrix -- 4.4.1.4 Comparison -- 4.4.2 Compression Techniques -- 4.4.2.1 A Space Optimized Representation of RA -- 4.4.2.2 Dictionary Compression -- 4.4.2.3 Compact Representation of Dynamic Dictionaries -- 4.4.3 Conclusions and Further Reading -- References -- Chapter 5 Tree-Language Based Querying of Hierarchically Structured and Semi-Structured Data -- 5.1 Introduction -- 5.1.1 Motivation -- 5.1.2 Scope and Outline -- 5.2 Preliminaries -- 5.2.1 Regular Expressions -- 5.2.2 Trees and Forests -- 5.2.3 XML Basics -- 5.3 Regular Forest Languages -- 5.3.1 Specifying Regular Forest Languages -- 5.3.1.1 Forest Grammars -- 5.3.1.2 Practical Extensions -- 5.3.1.3 Forest Grammars and XML Schema Languages -- 5.3.2 Recognizing Regular Forest Languages -- 5.3.2.1 Pushdown Forest Automata -- 5.3.2.2 From Forest Grammars to Pushdown Forest Automata -- 5.3.3 Bibliographic Notes.

5.4 Grammar Queries -- 5.4.1 Specifying Queries -- 5.4.1.1 Unary Queries -- 5.4.1.2 K-ary Queries -- 5.4.2 Expressive Power of Grammar Queries -- 5.4.3 Recognizing Unary Queries -- 5.4.3.1 Pushdown Forest Automata as Relabelings -- 5.4.3.2 Locating Unary Matches -- 5.4.4 Recognizing Binary Queries -- 5.4.4.1 Recognizing Simple Binary Queries -- 5.4.4.2 Recognizing General Binary Queries -- 5.4.5 Recognizing K-ary Queries -- 5.4.6 Implementation Issues -- 5.4.7 Bibliographic Notes -- 5.4.7.1 Forest Grammar Queries -- 5.4.7.2 XPath Evaluation and Expressiveness -- 5.4.7.3 Query Automata -- 5.4.7.4 Selecting Tree Automata -- 5.4.7.5 Attribute Grammar Queries -- 5.4.7.6 Regular Hedge Expressions -- 5.4.7.7 Tree-walking Automata -- 5.4.7.8 Pure Logic Formalisms -- 5.4.7.9 Numerical Document Queries -- 5.4.7.10 Tree Queries -- 5.5 Practical Application: A Pattern Language for XML Querying -- 5.5.1 Paths -- 5.5.1.1 Regular Path Expressions -- 5.5.1.2 Boolean Connectives for Paths -- 5.5.2 Qualifiers -- 5.5.2.1 Structure Qualifiers -- 5.5.2.2 Context Qualifiers -- 5.5.3 Binary Patterns -- 5.5.4 From Patterns to Grammar Queries -- 5.5.5 Comparison with XPath -- 5.5.5.1 XPath's Paths -- 5.5.5.2 XPath's Qualifiers -- 5.5.5.3 Differences in Expressiveness -- 5.5.6 Bibliographic Notes -- 5.6 Online Querying -- 5.6.1 Right-ignoring Queries -- 5.6.2 Arbitrary Queries -- 5.6.2.1 Idea -- 5.6.2.2 Recognizing Matches -- 5.6.2.3 Complexity -- 5.6.3 Bibliographical Notes -- 5.7 Summary and Outlook -- 5.8 Proofs of Theorems -- 5.8.1 Proof of Theorem 5.21 -- 5.8.1.1 Proof of (i1) -- 5.8.1.2 Proof of (i2) -- 5.8.2 Proof of Theorem 5.22 -- 5.8.3 Proof of Theorem 5.32 -- References -- Chapter 6 Quotient Monoids and Concurrent Behaviours -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Partial Orders and Order Structures -- 6.4 Mazurkiewicz Traces -- 6.5 Comtraces.

6.6 Generalised Comtraces -- 6.7 Elementary Net Systems -- 6.8 EN-systems with Inhibitor and Mutex Arcs -- 6.9 Concluding Remarks -- Acknowledgments -- References -- Chapter 7 Correction Queries in Active Learning -- 7.1 Introduction -- 7.2 Preliminaries -- 7.3 Learning Models -- 7.3.1 Query Learning -- 7.3.2 Gold-style Learning -- 7.3.3 A Hierarchy of Learning Models -- 7.4 Learning with Correction Queries -- 7.4.1 Learning with Prefix Correction Queries -- 7.4.1.1 Necessary and Sufficient Conditions -- 7.4.1.2 Learning with PCQs versus Learning with MQs -- 7.4.1.3 Learning with PCQs versus Gold-style Learning Models -- 7.4.2 Learning with Length Bounded Correction Queries -- 7.4.3 Learning with Edit Distance Correction Queries -- 7.4.4 Remarks and Further Research -- 7.5 Polynomial Time Learning with Correction Queries -- 7.5.1 Polynomial Time Learning with PCQs -- 7.5.1.1 The Class of Pattern Languages -- 7.5.1.2 The Class of k-Reversible Languages -- 7.5.1.3 Polynomial Time Learning with PCQs versus MQs -- 7.5.2 Polynomial Time Learning with LBCQs -- 7.5.3 Polynomial Time Learning with EDCQs -- 7.5.4 Remarks and Further Research -- References -- Chapter 8 Applications of Grammatical Inference in Software Engineering: Domain Specific Language Development -- 8.1 Introduction -- 8.2 Analysis Phase of DSL Development -- 8.3 Design Phase of DSL Development -- 8.4 Grammatical Inference and Language Design -- 8.4.1 GenInc -- 8.4.2 MAGIc -- 8.4.2.1 Local Search -- 8.4.2.2 Mutation -- 8.4.2.3 Generalization -- 8.4.2.4 Selection -- 8.4.2.5 Example -- 8.4.2.6 Limitations -- 8.5 Case Study -- 8.6 Related Work -- 8.7 Conclusion -- Acknowledgments -- References -- Chapter 9 Small Size Insertion and Deletion Systems -- 9.1 Introduction -- 9.2 Definitions -- 9.2.1 Insertion-Deletion Systems -- 9.2.2 Graph-Controlled Insertion-Deletion Systems.

9.3 Basic Simulation Principles -- 9.4 Insertion-Deletion Systems with Rules of Small Size -- 9.5 Context-Free Insertion-Deletion Systems -- 9.5.1 Non-completeness Results -- 9.6 One-Sided Contextual Insertion-Deletion Systems -- 9.6.1 Non-completeness Results -- 9.7 Pure Insertion Systems -- 9.8 Graph-Controlled Insertion-Deletion Systems -- 9.9 Graph-Controlled Insertion-Deletion Systems with Priorities -- 9.10 Bibliographical Remarks -- References -- Chapter 10 Accepting Networks of Evolutionary Word and Picture Processors: A Survey -- 10.1 Introduction -- 10.2 Basic Definitions -- 10.2.1 Preliminaries -- 10.2.2 Accepting Networks of Evolutionary Processors -- 10.2.3 Accepting Networks of Evolutionary Processors with Filtered Connections -- 10.2.4 Timed Accepting Networks of Evolutionary Processors -- 10.2.5 Computational Complexity Classes -- 10.3 Computational Power -- 10.3.1 Computational Power of ANEPs/ANEPFCs -- 10.3.2 The Role of Evolutionary Operations in ANEPs -- 10.3.3 Complexity Classes and Size Complexity -- 10.4 Universal ANEPs and ANEPFCs -- 10.5 A Direct Simulation -- 10.6 Accepting Networks of Splicing Processors -- 10.6.1 Computational Power and Complexity Results for ANSPs/ANSPFCs -- 10.7 Problem Solving with ANEPs/ANEPFCs -- 10.8 Accepting Networks of Picture Processors -- 10.8.1 Pictures and Picture Languages -- 10.8.2 Picture Processors -- 10.8.3 Accepting Networks of Evolutionary Picture Processors -- 10.8.4 Computational Power -- 10.8.5 Solving Picture Matching with ANEPPs -- References -- Chapter 11 Quantum Automata and Periodic Events -- 11.1 Introduction -- 11.2 Preliminaries -- 11.2.1 Linear Algebra -- 11.2.2 Quantum Finite Automata -- 11.3 Testing Periodicity on Unary 1qfa's -- 11.4 Synthesis of 1qfa's Inducing Periodic Events -- 11.4.1 The Time Complexity of our Synthesis Algorithm -- 11.4.2 Efficient Synthesis.

11.5 Application to Periodic Languages.

Presenting interdisciplinary research at the forefront of present advances in information technologies and their foundations, "Scientific Applications of Language Methods" is a multi-author volume containing pieces of work (either original research or surveys) exemplifying the application of formal language tools in several fields, including logic and discrete mathematics, natural language processing, artificial intelligence, natural computing and bioinformatics.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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