Search results for "abstract"

showing 10 items of 1959 documents

RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

2016

Two extensions to the AMR smatch scoring script are presented. The first extension com-bines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a manually crafted wrapper fixing the identified CAMR parser errors. The second extension combines a per-sentence smatch with an en-semble method for selecting the best AMR graph among the set of AMR graphs for the same sentence. This second modification au-tomatically yields further 0.4% gain when ap-plied to outputs of two nondeterministic…

FOS: Computer and information sciencesParsingComputer Science - Computation and LanguageComputer sciencebusiness.industry02 engineering and technologyExtension (predicate logic)computer.software_genreSemEvalSet (abstract data type)Nondeterministic algorithm020204 information systemsTest setClassifier (linguistics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerComputation and Language (cs.CL)Natural language processingSentence
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Extracting Backbones in Weighted Modular Complex Networks

2020

AbstractNetwork science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we pro…

FOS: Computer and information sciencesPhysics - Physics and SocietyTheoretical computer scienceComputer scienceMathematics and computingComplex systemComplex networkslcsh:MedicineFOS: Physical sciencesNetwork science02 engineering and technologyPhysics and Society (physics.soc-ph)[INFO] Computer Science [cs]01 natural sciencesArticle010305 fluids & plasmasSet (abstract data type)020204 information systems0103 physical sciences0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]lcsh:ScienceAuthor CorrectionComputingMilieux_MISCELLANEOUSConnected componentSocial and Information Networks (cs.SI)Multidisciplinarybusiness.industryPhysicslcsh:RCommunity structureComputer Science - Social and Information NetworksComplex networkModular designlcsh:Qbusiness
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A permutation code preserving a double Eulerian bistatistic

2016

Visontai conjectured in 2013 that the joint distribution of ascent and distinct nonzero value numbers on the set of subexcedant sequences is the same as that of descent and inverse descent numbers on the set of permutations. This conjecture has been proved by Aas in 2014, and the generating function of the corresponding bistatistics is the double Eulerian polynomial. Among the techniques used by Aas are the M\"obius inversion formula and isomorphism of labeled rooted trees. In this paper we define a permutation code (that is, a bijection between permutations and subexcedant sequences) and show the more general result that two $5$-tuples of set-valued statistics on the set of permutations an…

FOS: Computer and information sciencesPolynomialDiscrete Mathematics (cs.DM)0102 computer and information sciences01 natural sciencesBijective proofCombinatoricsSet (abstract data type)symbols.namesakeEquidistributed sequence[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]FOS: MathematicsDiscrete Mathematics and CombinatoricsMathematics - Combinatorics0101 mathematicsComputingMilieux_MISCELLANEOUSMathematicsConjectureMathematics::CombinatoricsApplied Mathematics010102 general mathematicsGenerating functionEulerian path010201 computation theory & mathematicssymbolsBijectionCombinatorics (math.CO)Computer Science - Discrete Mathematics
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Quantum finite multitape automata

1999

Quantum finite automata were introduced by C.Moore, J.P. Crutchfield, and by A.Kondacs and J.Watrous. This notion is not a generalization of the deterministic finite automata. Moreover, it was proved that not all regular languages can be recognized by quantum finite automata. A.Ambainis and R.Freivalds proved that for some languages quantum finite automata may be exponentially more concise rather than both deterministic and probabilistic finite automata. In this paper we introduce the notion of quantum finite multitape automata and prove that there is a language recognized by a quantum finite automaton but not by a deterministic or probabilistic finite automata. This is the first result on …

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Computational ComplexityTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESTheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFormal Languages and Automata Theory (cs.FL)FOS: Physical sciencesComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)Quantum Physics (quant-ph)Nonlinear Sciences::Cellular Automata and Lattice GasesComputer Science::Formal Languages and Automata Theory
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Automata and Quantum Computing

2015

Quantum computing is a new model of computation, based on quantum physics. Quantum computers can be exponentially faster than conventional computers for problems such as factoring. Besides full-scale quantum computers, more restricted models such as quantum versions of finite automata have been studied. In this paper, we survey various models of quantum finite automata and their properties. We also provide some open questions and new directions for researchers. Keywords: quantum finite automata, probabilistic finite automata, nondeterminism, bounded error, unbounded error, state complexity, decidability and undecidability, computational complexity

FOS: Computer and information sciencesQuantum PhysicsTheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFormal Languages and Automata Theory (cs.FL)FOS: Physical sciencesTheoryofComputation_GENERALComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)68Q10 68Q12 68Q15 68Q19 68Q45Computer Science - Computational ComplexityTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputerSystemsOrganization_MISCELLANEOUSQuantum Physics (quant-ph)Computer Science::Formal Languages and Automata Theory
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Fast Graph Filters for Decentralized Subspace Projection

2020

A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal number of iterations. Building upon tools from graph signal processing, the problem is cast as the design of a graph filter which, in turn, is reduced to the design of a suitable graph shift operator. Exploiting the eigenstructure of the projection and shift matrices leads to an objective whose…

FOS: Computer and information sciencesSignal processingComputer scienceMatrix normConvex relaxationRegular polygon020206 networking & telecommunications02 engineering and technologyShift operatorStatistics - ComputationGraphsymbols.namesakeMatrix (mathematics)Approximation errorKronecker deltaSignal Processing0202 electrical engineering electronic engineering information engineeringsymbolsGraph (abstract data type)Electrical and Electronic EngineeringAlgorithmComputation (stat.CO)Subspace topologyEigenvalues and eigenvectorsIEEE Transactions on Signal Processing
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Fractal surfaces from simple arithmetic operations

2015

Fractal surfaces ('patchwork quilts') are shown to arise under most general circumstances involving simple bitwise operations between real numbers. A theory is presented for all deterministic bitwise operations on a finite alphabet. It is shown that these models give rise to a roughness exponent $H$ that shapes the resulting spatial patterns, larger values of the exponent leading to coarser surfaces.

FOS: Computer and information sciencesStatistics and ProbabilityDiscrete mathematicsOther Computer Science (cs.OH)Condensed Matter Physics01 natural sciences010305 fluids & plasmasSelf-affinityFractalSimple (abstract algebra)Computer Science - Other Computer Science0103 physical sciencesRoughness exponentExponentStatistical physicsAlphabet010306 general physicsBitwise operationReal numberMathematics
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Isotonic regression for metallic microstructure data: estimation and testing under order restrictions

2021

Investigating the main determinants of the mechanical performance of metals is not a simple task. Already known physical inspired qualitative relations between 2D microstructure characteristics and 3D mechanical properties can act as the starting point of the investigation. Isotonic regression allows to take into account ordering relations and leads to more efficient and accurate results when the underlying assumptions actually hold. The main goal in this paper is to test order relations in a model inspired by a materials science application. The statistical estimation procedure is described considering three different scenarios according to the knowledge of the variances: known variance ra…

FOS: Computer and information sciencesStatistics and ProbabilityMathematical optimizationgeometrically necessary dislocationsComputer science0211 other engineering and technologiesG.302 engineering and technology01 natural sciencesStatistics - ApplicationsMethodology (stat.ME)010104 statistics & probabilitySimple (abstract algebra)Isotonic regressionApplications (stat.AP)0101 mathematicsbootstraporder restrictionsStatistics - Methodology021103 operations researchlikelihood ratio testMicrostructurealternating iterative methodOrder (business)Geometrically necessary dislocationsLikelihood-ratio testStatistics Probability and UncertaintyIsotonic regression62F30 62F03 97K80
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Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison

2020

Abstract Nonparametric and machine learning methods are flexible methods for obtaining accurate predictions. Nowadays, data sets with a large number of predictors and complex structures are fairly common. In the presence of item nonresponse, nonparametric and machine learning procedures may thus provide a useful alternative to traditional imputation procedures for deriving a set of imputed values used next for the estimation of study parameters defined as solution of population estimating equation. In this paper, we conduct an extensive empirical investigation that compares a number of imputation procedures in terms of bias and efficiency in a wide variety of settings, including high-dimens…

FOS: Computer and information sciencesStatistics and ProbabilityStatistics::ApplicationsEmpirical comparisonbusiness.industryComputer scienceApplied MathematicsNonparametric statisticsMachine learningcomputer.software_genreStatistics - ComputationVariety (cybernetics)Methodology (stat.ME)Set (abstract data type)Statistics::MethodologyImputation (statistics)Artificial intelligenceStatistics Probability and UncertaintybusinesscomputerStatistics - MethodologyComputation (stat.CO)Social Sciences (miscellaneous)Journal of Survey Statistics and Methodology
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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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