Search results for "string [quantum chromodynamics]"

showing 10 items of 166 documents

Multi-Dimensional Pattern Matching with Dimensional Wildcards: Data Structures and Optimal On-Line Search Algorithms

1997

We introduce a new multidimensional pattern matching problem that is a natural generalization of string matching, a well studied problem1. The motivation for its algorithmic study is mainly theoretical. LetA1:n1,?,1:nd be a text matrix withN=n1?ndentries andB1:m1,?,1:mr be a pattern matrix withM=m1?mrentries, whered?r?1 (the matrix entries are taken from an ordered alphabet ?). We study the problem of checking whether somer-dimensional submatrix ofAis equal toB(i.e., adecisionquery).Acan be preprocessed andBis given on-line. We define a new data structure for preprocessingAand propose CRCW-PRAM algorithms that build it inO(logN) time withN2/nmaxprocessors, wherenmax=max(n1,?,nd), such that …

Control and OptimizationSuffix treeBlock matrixWildcard characterString searching algorithmcomputer.file_formatData structurelaw.inventionCombinatoricsComputational MathematicsMatrix (mathematics)Computational Theory and MathematicsSearch algorithmlawPattern matchingcomputerMathematicsJournal of Algorithms
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Analytic second derivatives for general coupled-cluster and configuration-interaction models.

2004

Analytic second derivatives of energy for general coupled-cluster (CC) and configuration-interaction (CI) methods have been implemented using string-based many-body algorithms. Wave functions truncated at an arbitrary excitation level are considered. The presented method is applied to the calculation of CC and CI harmonic frequencies and nuclear magnetic resonance chemical shifts up to the full CI level for some selected systems. The present benchmarks underline the importance of higher excitations in high-accuracy calculations.

Coupled clusterChemistryChemical shiftMathematical analysisC++ string handlingGeneral Physics and AstronomyPhysical and Theoretical ChemistryAtomic physicsConfiguration interactionWave functionEnergy (signal processing)ExcitationSecond derivativeThe Journal of chemical physics
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Spin-Wave Driven Bidirectional Domain Wall Motion in Kagome Antiferromagnets

2021

We predict a mechanism to controllably manipulate domain walls in kagome antiferromagnets via a single linearly polarized spin-wave source. We show by means of atomistic spin dynamics simulations of antiferromagnets with kagome structure that the speed and direction of the domain wall motion can be regulated by only tuning the frequency of the applied spin-wave. Starting from microscopics, we establish an effective action and derive the corresponding equations of motion for the spin-wave-driven domain wall. Our analytical calculations reveal that the coupling of two spin-wave modes inside the domain wall explains the frequency-dependent velocity of the spin texture. Such a highly tunable sp…

CouplingPhysicsCondensed Matter - Materials ScienceCondensed Matter - Mesoscale and Nanoscale PhysicsCondensed matter physicsLinear polarizationMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesGeneral Physics and AstronomyEquations of motionPhysik (inkl. Astronomie)Domain wall (string theory)Spin waveMesoscale and Nanoscale Physics (cond-mat.mes-hall)Domain (ring theory)AntiferromagnetismCondensed Matter::Strongly Correlated ElectronsSpin-½
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Periodicity and repetitions in parameterized strings

2008

AbstractOne of the most beautiful and useful notions in the Mathematical Theory of Strings is that of a Period, i.e., an initial piece of a given string that can generate that string by repeating itself at regular intervals. Periods have an elegant mathematical structure and a wealth of applications [F. Mignosi and A. Restivo, Periodicity, Algebraic Combinatorics on Words, in: M. Lothaire (Ed.), Cambridge University Press, Cambridge, pp. 237–274, 2002]. At the hearth of their theory, there are two Periodicity Lemmas: one due to Lyndon and Schutzenberger [The equation aM=bNcP in a free group, Michigan Math. J. 9 (1962) 289–298], referred to as the Weak Version, and the other due to Fine and …

Discrete mathematicsLemma (mathematics)Algebraic combinatoricsCombinatorics on wordsSettore INF/01 - InformaticaApplied MathematicsParameterized complexityParameterized stringsString searching algorithmString (physics)Periodic functionCombinatoricsCombinatorics on wordsDiscrete Mathematics and CombinatoricsString periodicityUniquenessCombinatorics on Words AlgorithmsMathematics
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On Table Arrangements, Scrabble Freaks, and Jumbled Pattern Matching

2010

Given a string s, the Parikh vector of s, denoted p(s), counts the multiplicity of each character in s. Searching for a match of Parikh vector q (a “jumbled string”) in the text s requires to find a substring t of s with p(t) = q. The corresponding decision problem is to verify whether at least one such match exists. So, for example for the alphabet Σ = {a, b, c}, the string s = abaccbabaaa has Parikh vector p(s) = (6,3,2), and the Parikh vector q = (2,1,1) appears once in s in position (1,4). Like its more precise counterpart, the renown Exact String Matching, Jumbled Pattern Matching has ubiquitous applications, e.g., string matching with a dyslectic word processor, table rearrangements, …

Discrete mathematicsParikh vectors jumbled pattern matching scrabble approximate pattern matching000AnagramParikh vectorsString searching algorithmApproximate string matchingDecision problemalgorithmsData structureJumbled Pattern MatchingSubstringscrabbleapproximate pattern matchingString MatchingWavelet TreePattern matchingMathematics
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Effect of Hamstring Tightness and Fatigue on Dynamic Stability and Agility in Physically Active Young Men

2023

Hamstring extensibility has been defined as a factor to diminished dynamic stability and therefore increased risk of injury. The purpose of this study was to analyse the effects of hamstring tightness and fatigue on dynamic stability and agility. Nineteen participants were divided between the normal extensibility group (NEG) (n = 9, 82.2° ± 12.4°) and hamstrings tightness group (HTG) (n = 10, 64° ± 4.9°) using the passive straight leg raise test. To analyse dynamic stability and agility, they performed the modified Star Excursion Balance Test (mSEBT) and Dynamic Postural Stability Index (DPSI), and hexagon agility test, respectively, before and after a fatigue …

Esportshamstring shorteningflexibilityMúsculslandingElectrical and Electronic EngineeringBiochemistryInstrumentationone-leg balanceAtomic and Molecular Physics and OpticsAnalytical ChemistrySensors
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On the Greedy Algorithm for the Shortest Common Superstring Problem with Reversals

2015

We study a variation of the classical Shortest Common Superstring (SCS) problem in which a shortest superstring of a finite set of strings $S$ is sought containing as a factor every string of $S$ or its reversal. We call this problem Shortest Common Superstring with Reversals (SCS-R). This problem has been introduced by Jiang et al., who designed a greedy-like algorithm with length approximation ratio $4$. In this paper, we show that a natural adaptation of the classical greedy algorithm for SCS has (optimal) compression ratio $\frac12$, i.e., the sum of the overlaps in the output string is at least half the sum of the overlaps in an optimal solution. We also provide a linear-time implement…

FOS: Computer and information sciences0102 computer and information sciences02 engineering and technologyInformation System01 natural sciencesString (physics)Theoretical Computer ScienceCombinatoricsHigh Energy Physics::TheoryAnalysis of algorithmGreedy algorithmComputer Science - Data Structures and Algorithms0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)Greedy algorithmFinite setAnalysis of algorithmsMathematicsSuperstring theoryShortest Common SuperstringComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsReversalShortest Path Faster Algorithm010201 computation theory & mathematicsCompression ratioSignal Processing020201 artificial intelligence & image processingK shortest path routingInformation Systems
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Novel Results on the Number of Runs of the Burrows-Wheeler-Transform

2021

The Burrows-Wheeler-Transform (BWT), a reversible string transformation, is one of the fundamental components of many current data structures in string processing. It is central in data compression, as well as in efficient query algorithms for sequence data, such as webpages, genomic and other biological sequences, or indeed any textual data. The BWT lends itself well to compression because its number of equal-letter-runs (usually referred to as $r$) is often considerably lower than that of the original string; in particular, it is well suited for strings with many repeated factors. In fact, much attention has been paid to the $r$ parameter as measure of repetitiveness, especially to evalua…

FOS: Computer and information sciencesBurrows–Wheeler transformSettore INF/01 - InformaticaCombinatorics on wordsFormal Languages and Automata Theory (cs.FL)Computer scienceString (computer science)Search engine indexingCompressed data structuresComputer Science - Formal Languages and Automata TheoryString indexingData structureMeasure (mathematics)Burrows-Wheeler-TransformRepetitivenessCombinatorics on wordsBurrows-Wheeler-Transform Compressed data structures String indexing Repetitiveness Combinatorics on wordsTransformation (function)Computer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)AlgorithmData compression
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Adaptive learning of compressible strings

2020

Suppose an oracle knows a string $S$ that is unknown to us and that we want to determine. The oracle can answer queries of the form "Is $s$ a substring of $S$?". In 1995, Skiena and Sundaram showed that, in the worst case, any algorithm needs to ask the oracle $\sigma n/4 -O(n)$ queries in order to be able to reconstruct the hidden string, where $\sigma$ is the size of the alphabet of $S$ and $n$ its length, and gave an algorithm that spends $(\sigma-1)n+O(\sigma \sqrt{n})$ queries to reconstruct $S$. The main contribution of our paper is to improve the above upper-bound in the context where the string is compressible. We first present a universal algorithm that, given a (computable) compre…

FOS: Computer and information sciencesCentroid decompositionGeneral Computer ScienceString compressionAdaptive learningKolmogorov complexityContext (language use)Data_CODINGANDINFORMATIONTHEORYString reconstructionTheoretical Computer ScienceCombinatoricsString reconstruction; String learning; Adaptive learning; Kolmogorov complexity; String compression; Lempel-Ziv; Centroid decomposition; Suffix treeSuffix treeIntegerComputer Science - Data Structures and AlgorithmsOrder (group theory)Data Structures and Algorithms (cs.DS)Adaptive learning; Centroid decomposition; Kolmogorov complexity; Lempel-Ziv; String compression; String learning; String reconstruction; Suffix treeTime complexityComputer Science::DatabasesMathematicsLempel-ZivSettore INF/01 - InformaticaLinear spaceString (computer science)SubstringBounded functionString learningTheoretical Computer Science
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A subquadratic algorithm for minimum palindromic factorization

2014

We give an $\mathcal{O}(n \log n)$-time, $\mathcal{O}(n)$-space algorithm for factoring a string into the minimum number of palindromic substrings. That is, given a string $S [1..n]$, in $\mathcal{O}(n \log n)$ time our algorithm returns the minimum number of palindromes $S_1,\ldots, S_\ell$ such that $S = S_1 \cdots S_\ell$. We also show that the time complexity is $\mathcal{O}(n)$ on average and $\Omega(n\log n)$ in the worst case. The last result is based on a characterization of the palindromic structure of Zimin words.

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)PalindromeCharacterization (mathematics)Binary logarithmOmegaSubstringTheoretical Computer ScienceString algorithmComputational Theory and MathematicsFactorizationComputer Science - Data Structures and AlgorithmsC++ string handlingPalindromeDiscrete Mathematics and CombinatoricsData Structures and Algorithms (cs.DS)FactorizationTime complexityAlgorithmMathematicsComputer Science - Discrete Mathematics
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