Search results for "FOS: Mathematics"

showing 10 items of 1448 documents

Transitive reasoning with imprecise probabilities

2015

We study probabilistically informative (weak) versions of transitivity, by using suitable definitions of defaults and negated defaults, in the setting of coherence and imprecise probabilities. We represent p-consistent sequences of defaults and/or negated defaults by g-coherent imprecise probability assessments on the respective sequences of conditional events. Finally, we prove the coherent probability propagation rules for Weak Transitivity and the validity of selected inference patterns by proving the p-entailment for the associated knowledge bases.

FOS: Computer and information sciencesComputer Science - Logic in Computer ScienceArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceProbability (math.PR)FOS: MathematicsComputer Science::Artificial IntelligenceMathematics - ProbabilityLogic in Computer Science (cs.LO)
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Topological Logics with Connectedness over Euclidean Spaces

2013

We consider the quantifier-free languages, Bc and Bc °, obtained by augmenting the signature of Boolean algebras with a unary predicate representing, respectively, the property of being connected, and the property of having a connected interior. These languages are interpreted over the regular closed sets of R n ( n ≥ 2) and, additionally, over the regular closed semilinear sets of R n . The resulting logics are examples of formalisms that have recently been proposed in the Artificial Intelligence literature under the rubric Qualitative Spatial Reasoning. We prove that the satisfiability problem for Bc is undecidable over the regular closed semilinear sets in all dimensions greater than 1,…

FOS: Computer and information sciencesComputer Science - Logic in Computer ScienceGeneral Computer ScienceUnary operationClosed setLogicSocial connectedness0102 computer and information sciencesTopological space68T30 (Primary) 03D15 68Q17 (Secondary)Topology01 natural sciencesTheoretical Computer ScienceMathematics - Geometric TopologyEuclidean geometryFOS: Mathematics0101 mathematicsMathematicsI.2.4; F.4.3; F.2.2Discrete mathematicsI.2.4010102 general mathematicsGeometric Topology (math.GT)Predicate (mathematical logic)Undecidable problemLogic in Computer Science (cs.LO)Computational Mathematics010201 computation theory & mathematicsF.4.3F.2.2Boolean satisfiability problemACM Transactions of Computational Logic
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Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)

2021

Artificial neural network (ANN) is a supervised learning algorithm, where parameters are learned by several back-and-forth iterations of passing the inputs through the network, comparing the output with the expected labels, and correcting the parameters. Inspired by a recent work of Boer and Kramer (2020), we investigate a different problem: Suppose an observer can view how the ANN parameters evolve over many iterations, but the dataset is oblivious to him. For instance, this can be an adversary eavesdropping on a multi-party computation of an ANN parameters (where intermediate parameters are leaked). Can he form a system of equations, and solve it to recover the dataset?

FOS: Computer and information sciencesComputer Science - Machine LearningComputingMethodologies_PATTERNRECOGNITIONComputer Science - Cryptography and SecurityComputer Science::Neural and Evolutionary ComputationFOS: MathematicsNumerical Analysis (math.NA)Mathematics - Numerical AnalysisCryptography and Security (cs.CR)Computer Science::DatabasesMachine Learning (cs.LG)Computer Science::Cryptography and Security
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Deep neural networks to recover unknown physical parameters from oscillating time series.

2022

PLOS ONE 17(5), e0268439 (2022). doi:10.1371/journal.pone.0268439

FOS: Computer and information sciencesComputer Science - Machine LearningMultidisciplinaryTime FactorsPhysics610FOS: Physical sciencesSignal Processing Computer-AssistedNumerical Analysis (math.NA)Machine Learning (cs.LG)KnowledgePhysics - Data Analysis Statistics and ProbabilityFOS: MathematicsHumansMathematics - Numerical Analysisddc:610Neural Networks ComputerData Analysis Statistics and Probability (physics.data-an)PloS one
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Model identification and local linear convergence of coordinate descent

2020

For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g., support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough. Results concerning coordinate descent are scarcer and model identification has only been shown for specific estimators, the support-vector machine for instance. In this work, we show that cyclic coordinate descent achieves model identification in finite time for a wide class of functions. In addition, we prove explicit local linear convergence rates for coordinate descent. Extensive experiments on various estimators and on real datasets demonstrate that thes…

FOS: Computer and information sciencesComputer Science - Machine LearningOptimization and Control (math.OC)Statistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]FOS: Mathematics[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Machine Learning (stat.ML)[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC][MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]Mathematics - Optimization and ControlMachine Learning (cs.LG)
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Right-jumps and pattern avoiding permutations

2015

We study the iteration of the process "a particle jumps to the right" in permutations. We prove that the set of permutations obtained in this model after a given number of iterations from the identity is a class of pattern avoiding permutations. We characterize the elements of the basis of this class and we enumerate these "forbidden minimal patterns" by giving their bivariate exponential generating function: we achieve this via a catalytic variable, the number of left-to-right maxima. We show that this generating function is a D-finite function satisfying a nice differential equation of order~2. We give some congruence properties for the coefficients of this generating function, and we sho…

FOS: Computer and information sciencesD-finite function[ MATH.MATH-CV ] Mathematics [math]/Complex Variables [math.CV]Discrete Mathematics (cs.DM)General Computer Scienceinsertion sort[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM][ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]left-to-right maximumPermutation patternTheoretical Computer Science[ MATH.MATH-NT ] Mathematics [math]/Number Theory [math.NT]Combinatorics[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]FOS: Mathematicsanalytic combinatoricsMathematics - CombinatoricsDiscrete Mathematics and CombinatoricsGolden ratioMathematicsProbability (math.PR)Generating function[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM][MATH.MATH-CV]Mathematics [math]/Complex Variables [math.CV]Function (mathematics)[MATH.MATH-NT]Mathematics [math]/Number Theory [math.NT]Exponential function[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]generating functionPermutation patternExponentAnalytic combinatoricssupercongruenceCombinatorics (math.CO)Maxima[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilityComputer Science - Discrete Mathematics
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Properties of a Class of Toeplitz Words

2021

We study the properties of the uncountable set of Stewart words. These are Toeplitz words specified by infinite sequences of Toeplitz patterns of the form $\alpha\beta\gamma$, where $\alpha,\beta,\gamma$ is any permutation of the symbols 0,1,?. We determine the critical exponent of the Stewart words, prove that they avoid the pattern $xxyyxx$, find all factors that are palindromes, and determine their subword complexity. An interesting aspect of our work is that we use automata-theoretic methods and a decision procedure for automata to carry out the proofs.

FOS: Computer and information sciencesDecision procedureSubword complexityDiscrete Mathematics (cs.DM)Combinatorics on wordsSettore INF/01 - InformaticaGeneral Computer ScienceFormal Languages and Automata Theory (cs.FL)Computer Science - Formal Languages and Automata TheoryToeplitz wordTheoretical Computer ScienceComputer Science::Discrete MathematicsPattern avoidanceFOS: MathematicsAutomatic sequenceMathematics - CombinatoricsCombinatorics (math.CO)Computer Science::Formal Languages and Automata TheoryComputer Science - Discrete Mathematics
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Algorithms for Computing Abelian Periods of Words

2012

Constantinescu and Ilie (Bulletin EATCS 89, 167--170, 2006) introduced the notion of an \emph{Abelian period} of a word. A word of length $n$ over an alphabet of size $\sigma$ can have $\Theta(n^{2})$ distinct Abelian periods. The Brute-Force algorithm computes all the Abelian periods of a word in time $O(n^2 \times \sigma)$ using $O(n \times \sigma)$ space. We present an off-line algorithm based on a $\sel$ function having the same worst-case theoretical complexity as the Brute-Force one, but outperforming it in practice. We then present on-line algorithms that also enable to compute all the Abelian periods of all the prefixes of $w$.

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)Abelian repetitionElementary abelian groupRank of an abelian groupCombinatoricsComputer Science - Data Structures and AlgorithmsFOS: MathematicsDiscrete Mathematics and CombinatoricsMathematics - CombinatoricsData Structures and Algorithms (cs.DS)Abelian groupOnline algorithmMathematicsArithmetic of abelian varietiesDiscrete mathematicsCombinatorics on wordsApplied MathematicsAbelian periodText algorithmWeak repetitionPrefixCombinatorics on wordsDesign of algorithmCombinatorics (math.CO)AlgorithmWord (computer architecture)Computer Science::Formal Languages and Automata TheoryComputer Science - Discrete Mathematics
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Upperbounds on the probability of finding marked connected components using quantum walks

2019

Quantum walk search may exhibit phenomena beyond the intuition from a conventional random walk theory. One of such examples is exceptional configuration phenomenon -- it appears that it may be much harder to find any of two or more marked vertices, that if only one of them is marked. In this paper, we analyze the probability of finding any of marked vertices in such scenarios and prove upper bounds for various sets of marked vertices. We apply the upper bounds to large collection of graphs and show that the quantum search may be slow even when taking real-world networks.

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)FOS: Physical sciences01 natural sciencesUpper and lower bounds010305 fluids & plasmasTheoretical Computer Science0103 physical sciencesFOS: MathematicsMathematics - CombinatoricsQuantum walkElectrical and Electronic Engineering010306 general physicsQuantum computerMathematicsDiscrete mathematicsConnected componentQuantum PhysicsStatistical and Nonlinear PhysicsRandom walkQuantum searchElectronic Optical and Magnetic MaterialsModeling and SimulationSignal ProcessingCombinatorics (math.CO)Quantum Physics (quant-ph)Stationary stateComputer Science - Discrete Mathematics
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Normal, Abby Normal, Prefix Normal

2014

A prefix normal word is a binary word with the property that no substring has more 1s than the prefix of the same length. This class of words is important in the context of binary jumbled pattern matching. In this paper we present results about the number $pnw(n)$ of prefix normal words of length $n$, showing that $pnw(n) =\Omega\left(2^{n - c\sqrt{n\ln n}}\right)$ for some $c$ and $pnw(n) = O \left(\frac{2^n (\ln n)^2}{n}\right)$. We introduce efficient algorithms for testing the prefix normal property and a "mechanical algorithm" for computing prefix normal forms. We also include games which can be played with prefix normal words. In these games Alice wishes to stay normal but Bob wants t…

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)Formal Languages and Automata Theory (cs.FL)Computer Science - Data Structures and AlgorithmsFOS: MathematicsMathematics - CombinatoricsData Structures and Algorithms (cs.DS)Computer Science - Formal Languages and Automata TheoryCombinatorics (math.CO)Data_CODINGANDINFORMATIONTHEORYComputer Science - Discrete Mathematics
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