Search results for "Encoding"

showing 10 items of 134 documents

Coding Binary Trees by Words over an Alphabet with Four Letters

1992

Abstract We propose a new encoding scheme to represent binary trees with n leaves by words of length n over an alphabet with four letters. We give a characterization of these codewords.

Discrete mathematicsBinary treeData_CODINGANDINFORMATIONTHEORYArithmeticTruncated binary encodingAlphabetComputer Science::Formal Languages and Automata TheoryCoding (social sciences)MathematicsJournal of Information and Optimization Sciences
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Analysis of properties of recombination operators proposed for the node-depth encoding

2011

The node-depth encoding is a representation for evolutionary algorithms applied to tree problems. Its represents trees by storing the nodes and their depth in a proper ordered list. The original formulation of the node-depth encoding has only mutation operators as the search mechanism. Although it is computationally efficient, the exclusive use of mutation restricts the exploration of the search space and the algorithm convergence. Then, this work proposes two specific recombination operators to improve the convergence of the algorithm using the node-depth encoding representation. These operators are based on recombination operators for permutation representations. Analysis of the proposed …

Discrete mathematicsPermutationTree (data structure)Encoding (memory)Mutation (genetic algorithm)Convergence (routing)Evolutionary algorithmQuantitative Biology::Populations and EvolutionNode (circuits)Representation (mathematics)AlgorithmMathematicsProceedings of the 13th annual conference companion on Genetic and evolutionary computation
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A Generalization of Girod’s Bidirectional Decoding Method to Codes with a Finite Deciphering Delay

2012

In this paper we generalize an encoding method due to Girod (cf. [6]) using prefix codes, that allows a bidirectional decoding of the encoded messages. In particular we generalize it to any finite alphabet A, to any operation defined on A, to any code with finite deciphering delay and to any key x ∈ A+ , on a length depending on the deciphering delay. We moreover define, as in [4], a deterministic transducer for such generalized method. We prove that, fixed a code X ∈ A* with finite deciphering delay and a key x ∈ A *, the transducers associated to different operations are isomorphic as unlabelled graphs. We also prove that, for a fixed code X with finite deciphering delay, transducers asso…

Discrete mathematicsPrefix codeStrongly connected componentSettore INF/01 - InformaticaGeneralization020206 networking & telecommunications0102 computer and information sciences02 engineering and technology01 natural sciencesPrefix010201 computation theory & mathematicsEncoding (memory)0202 electrical engineering electronic engineering information engineeringCode (cryptography)AlphabetGirod's encoding codes finite deciphering delayDecoding methodsMathematics
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Burrows-Wheeler transform and Run-Length Enconding

2017

In this paper we study the clustering effect of the Burrows-Wheeler Transform (BWT) from a combinatorial viewpoint. In particular, given a word w we define the BWT-clustering ratio of w as the ratio between the number of clusters produced by BWT and the number of the clusters of w. The number of clusters of a word is measured by its Run-Length Encoding. We show that the BWT-clustering ratio ranges in ]0, 2]. Moreover, given a rational number \(r\,\in \,]0,2]\), it is possible to find infinitely many words having BWT-clustering ratio equal to r. Finally, we show how the words can be classified according to their BWT-clustering ratio. The behavior of such a parameter is studied for very well-…

Discrete mathematicsRational numberBurrows–Wheeler transformComputer scienceComputer Science (all)0102 computer and information sciences02 engineering and technologyBurrows-Wheeler transform01 natural sciencesBurrows-Wheeler transform; Clustering effect; Run-length encoding; Theoretical Computer Science; Computer Science (all)Theoretical Computer ScienceClustering effect010201 computation theory & mathematicsRun-length encoding0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingCluster analysisWord (computer architecture)Run-length encoding
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Three-page encoding and complexity theory for spatial graphs

2004

We construct a series of finitely presented semigroups. The centers of these semigroups encode uniquely up to rigid ambient isotopy in 3-space all non-oriented spatial graphs. This encoding is obtained by using three-page embeddings of graphs into the product of the line with the cone on three points. By exploiting three-page embeddings we introduce the notion of the three-page complexity for spatial graphs. This complexity satisfies the properties of finiteness and additivity under natural operations.

Discrete mathematics[ MATH.MATH-GT ] Mathematics [math]/Geometric Topology [math.GT]Algebra and Number TheoryDegree (graph theory)Semigroup010102 general mathematicsGeometric topologyGeometric Topology (math.GT)01 natural sciences57M25 57M15 57M05Combinatorics010104 statistics & probabilityMathematics - Geometric TopologyCone (topology)Additive functionEncoding (memory)[MATH.MATH-GT]Mathematics [math]/Geometric Topology [math.GT]FOS: Mathematics0101 mathematicsUnit (ring theory)Ambient isotopyMathematics[MATH.MATH-GT] Mathematics [math]/Geometric Topology [math.GT]MathematicsofComputing_DISCRETEMATHEMATICS
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New Encodings of Pseudo-Boolean Constraints into CNF

2009

International audience; This paper answers affirmatively the open question of the existence of a polynomial size CNF encoding of pseudo-Boolean (PB) constraints such that generalized arc consistency (GAC) is maintained through unit propagation (UP). All previous encodings of PB constraints either did not allow UP to maintain GAC, or were of exponential size in the worst case. This paper presents an encoding that realizes both of the desired properties. From a theoretical point of view, this narrows the gap between the expressive power of clauses and the one of pseudo-Boolean constraints.

Discrete mathematics[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]Polynomial021103 operations researchUnit propagation[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]0211 other engineering and technologies[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]02 engineering and technologyComputer Science::Computational ComplexityExpressive powerExponential functionCombinatorics[ INFO.INFO-CC ] Computer Science [cs]/Computational Complexity [cs.CC]Encoding (memory)0202 electrical engineering electronic engineering information engineeringLocal consistency020201 artificial intelligence & image processingPoint (geometry)[INFO.INFO-CC] Computer Science [cs]/Computational Complexity [cs.CC][ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]Mathematics
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Surviving task interruptions: Investigating the implications of long-term working memory theory

2006

Typically, we have several tasks at hand, some of which are in interrupted state while others are being carried out. Most of the time, such interruptions are not disruptive to task performance. Based on the theory of Long-Term Working Memory (LTWM; Ericsson, K.A., Kintsch, W., 1995. Long-term working memory. Psychological Review, 102, 211-245), we posit that unless there are enough mental skills and resources to encode task representations to retrieval structures in long-term memory, the resulting memory traces will not enable reinstating the information, which can lead to memory losses. However, once encoded to LTWM, they are virtually safeguarded. Implications of the theory were tested in…

Elementary cognitive taskWorking memoryLong-term memorybusiness.industryComputer scienceGeneral EngineeringMemory rehearsalShort-term memoryHuman Factors and ErgonomicsEducationTask (project management)Human-Computer InteractionHardware and ArchitectureEncoding (memory)Artificial intelligenceImplicit memoryMemory LossesbusinessSoftwareCognitive psychologyInternational Journal of Human-Computer Studies
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Packet loss recovery in an indoor Free Space Optics link using rateless codes

2014

Free Space Optics (FSO) systems present some important advantages if compared to Radio Frequency links, but they can be affected by several impairments that degrade the link quality and availability. In particular, due to temporary interruptions of the line-of-sight condition between the transmitter and the receiver, packet loss can occur during data transmission. In this work, we present an indoor Free Space Optics link, in which we have systematically generated interruptions of the beam. We demonstrate how the application of the most recent rateless codes, i.e., RaptorQ codes, can strongly improve the link quality by reducing packet loss. In particular, results show that the Packet Error …

EngineeringSettore ING-INF/03 - TelecomunicazioniNetwork packetbusiness.industryTransmitterFree Space Optics (FSO) packet loss rateless codes RaptorQSettore ING-INF/01 - ElettronicaPacket lossEncoding (memory)Electronic engineeringBit error ratebusinessDecoding methodsFree-space optical communicationData transmission2014 16th International Conference on Transparent Optical Networks (ICTON)
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String attractors and combinatorics on words

2019

The notion of \emph{string attractor} has recently been introduced in [Prezza, 2017] and studied in [Kempa and Prezza, 2018] to provide a unifying framework for known dictionary-based compressors. A string attractor for a word $w=w[1]w[2]\cdots w[n]$ is a subset $\Gamma$ of the positions $\{1,\ldots,n\}$, such that all distinct factors of $w$ have an occurrence crossing at least one of the elements of $\Gamma$. While finding the smallest string attractor for a word is a NP-complete problem, it has been proved in [Kempa and Prezza, 2018] that dictionary compressors can be interpreted as algorithms approximating the smallest string attractor for a given word. In this paper we explore the noti…

FOS: Computer and information sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaFormal Languages and Automata Theory (cs.FL)De Brujin wordComputer Science - Formal Languages and Automata TheoryBurrows-Wheeler transformString attractorComputer Science - Data Structures and AlgorithmsThue-Morse wordLempel-Ziv encodingBurrows-Wheeler transform; De Brujin word; Lempel-Ziv encoding; Run-length encoding; String attractor; Thue-Morse wordData Structures and Algorithms (cs.DS)Run-length encoding
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Binary jumbled string matching for highly run-length compressible texts

2012

The Binary Jumbled String Matching problem is defined as: Given a string $s$ over $\{a,b\}$ of length $n$ and a query $(x,y)$, with $x,y$ non-negative integers, decide whether $s$ has a substring $t$ with exactly $x$ $a$'s and $y$ $b$'s. Previous solutions created an index of size O(n) in a pre-processing step, which was then used to answer queries in constant time. The fastest algorithms for construction of this index have running time $O(n^2/\log n)$ [Burcsi et al., FUN 2010; Moosa and Rahman, IPL 2010], or $O(n^2/\log^2 n)$ in the word-RAM model [Moosa and Rahman, JDA 2012]. We propose an index constructed directly from the run-length encoding of $s$. The construction time of our index i…

FOS: Computer and information sciencesString algorithmsStructure (category theory)Binary numberG.2.1Data_CODINGANDINFORMATIONTHEORY0102 computer and information sciences02 engineering and technologyString searching algorithm01 natural sciencesComputer Science - Information RetrievalTheoretical Computer ScienceCombinatoricsdata structuresSimple (abstract algebra)Computer Science - Data Structures and AlgorithmsString algorithms; jumbled pattern matching; prefix normal form; data structures0202 electrical engineering electronic engineering information engineeringParikh vectorData Structures and Algorithms (cs.DS)Run-length encodingMathematics68W32 68P05 68P20String (computer science)prefix normal formSubstringComputer Science Applicationsjumbled pattern matching010201 computation theory & mathematicsData structureSignal ProcessingRun-length encoding020201 artificial intelligence & image processingConstant (mathematics)Information Retrieval (cs.IR)Information SystemsInformation Processing Letters
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