The Experts below are selected from a list of 225849 Experts worldwide ranked by ideXlab platform
Sasu Tarkoma - One of the best experts on this subject based on the ideXlab platform.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Explaining the power-Law Distribution of human mobility through transportation modality decomposition
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma, Pan Hui, Weixiong RaoAbstract:Human mobility has been empirically observed to exhibit Levy flightcharacteristics and behaviour with power-Law distributed jump size. The fundamentalmechanisms behind this behaviour has not yet been fully explained. In thispaper, we propose to explain the Levy walk behaviour observed in humanmobility patterns by decomposing them into different classes according tothe different transportation modes, such as Walk/Run, Bike, Train/Subway orCar/Taxi/Bus. Our analysis is based on two real-life GPS datasets containingapproximately 10 and 20 million GPS samples with transportation mode information.We show that human mobility can be modelled as a mixture of different transportationmodes and that these single movement patterns can be approximated by a lognormalDistribution rather than a power-Law Distribution. Then, we demonstrate thatthe mixture of the decomposed lognormal flight Distributions associated witheach modality is a power-Law Distribution, providing an explanation to theemergence of Levy Walk patterns that characterize human mobility patterns.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
arXiv: Physics and Society, 2014Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.
Kai Zhao - One of the best experts on this subject based on the ideXlab platform.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Explaining the power-Law Distribution of human mobility through transportation modality decomposition
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma, Pan Hui, Weixiong RaoAbstract:Human mobility has been empirically observed to exhibit Levy flightcharacteristics and behaviour with power-Law distributed jump size. The fundamentalmechanisms behind this behaviour has not yet been fully explained. In thispaper, we propose to explain the Levy walk behaviour observed in humanmobility patterns by decomposing them into different classes according tothe different transportation modes, such as Walk/Run, Bike, Train/Subway orCar/Taxi/Bus. Our analysis is based on two real-life GPS datasets containingapproximately 10 and 20 million GPS samples with transportation mode information.We show that human mobility can be modelled as a mixture of different transportationmodes and that these single movement patterns can be approximated by a lognormalDistribution rather than a power-Law Distribution. Then, we demonstrate thatthe mixture of the decomposed lognormal flight Distributions associated witheach modality is a power-Law Distribution, providing an explanation to theemergence of Levy Walk patterns that characterize human mobility patterns.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
arXiv: Physics and Society, 2014Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.
Benjamin Doerr - One of the best experts on this subject based on the ideXlab platform.
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lazy parameter tuning and control choosing all parameters randomly from a power Law Distribution
Genetic and Evolutionary Computation Conference, 2021Co-Authors: Denis Antipov, Maxim Buzdalov, Benjamin DoerrAbstract:Most evolutionary algorithms have multiple parameters and their values drastically affect the performance. Due to the often complicated interplay of the parameters, setting these values right for a particular problem is a challenging task. This task becomes even more complicated when the optimal parameter values change significantly during the run of the algorithm since then a dynamic parameter choice is necessary. In this work, we propose a lazy but effective solution, namely choosing all parameter values in each iteration randomly from a suitably scaled power-Law Distribution. We demonstrate the effectiveness of this approach via runtime analyses of the (1 + (λ, λ)) genetic algorithm with all three parameters chosen in this manner. We show that this algorithm on the one hand can imitate simple hill-climbers like the (1+1) EA, giving the same asymptotic runtime on some simple problems. On the other hand, this algorithm is also very efficient on jump functions, where the best static parameters are very different from those necessary to optimize simple problems. We prove a performance guarantee that is comparable to, and sometimes even better than, the best performance known for static parameters. We complement our theoretical results with a rigorous empirical study confirming what the asymptotic runtime results suggest.
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lazy parameter tuning and control choosing all parameters randomly from a power Law Distribution
arXiv: Neural and Evolutionary Computing, 2021Co-Authors: Denis Antipov, Maxim Buzdalov, Benjamin DoerrAbstract:Most evolutionary algorithms have multiple parameters and their values drastically affect the performance. Due to the often complicated interplay of the parameters, setting these values right for a particular problem (parameter tuning) is a challenging task . This task becomes even more complicated when the optimal parameter values change significantly during the run of the algorithm since then a dynamic parameter choice (parameter control) is necessary. In this work, we propose a lazy but effective solution, namely choosing all parameter values (where this makes sense) in each iteration randomly from a suitably scaled power-Law Distribution. To demonstrate the effectiveness of this approach, we perform runtime analyses of the $(1+(\lambda,\lambda))$ genetic algorithm with all three parameters chosen in this manner. We show this algorithm on the one hand can imitate simple hill-climbers like the $(1+1)$ EA, giving the same asymptotic runtime on problems like OneMax, LeadingOnes, or Minimum Spanning Tree. On the other hand, this algorithm is also very efficient on jump functions, where the best static parameters are very different from those necessary to optimize simple problems. We prove a performance guarantee that is comparable, sometimes even better, than the best performance known for static parameters. We complement our theoretical results with a rigorous empirical study confirming what the asymptotic runtime results suggest.
Francesco Tornabene - One of the best experts on this subject based on the ideXlab platform.
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free vibration analysis of functionally graded conical cylindrical shell and annular plate structures with a four parameter power Law Distribution
Computer Methods in Applied Mechanics and Engineering, 2009Co-Authors: Francesco TornabeneAbstract:Based on the First-order Shear Deformation Theory (FSDT) this paper focuses on the dynamic behavior of moderately thick functionally graded conical, cylindrical shells and annular plates. The last two structures are obtained as special cases of the conical shell formulation. The treatment is developed within the theory of linear elasticity, when materials are assumed to be isotropic and inhomogeneous through the thickness direction. The two-constituent functionally graded shell consists of ceramic and metal. These constituents are graded through the thickness, from one surface of the shell to the other. A generalization of the power-Law Distribution presented in literature is proposed. Two different four-parameter power-Law Distributions are considered for the ceramic volume fraction. Some material profiles through the functionally graded shell thickness are illustrated by varying the four parameters of power-Law Distributions. For the first power-Law Distribution, the bottom surface of the structure is ceramic rich, whereas the top surface can be metal rich, ceramic rich or made of a mixture of the two constituents and on the contrary for the second one. Symmetric and asymmetric volume fraction profiles are presented in this paper. The homogeneous isotropic material can be inferred as a special case of functionally graded materials (FGM). The governing equations of motion are expressed as functions of five kinematic parameters, by using the constitutive and kinematic relationships. The solution is given in terms of generalized displacement components of the points lying on the middle surface of the shell. The discretization of the system equations by means of the Generalized Differential Quadrature (GDQ) method leads to a standard linear eigenvalue problem, where two independent variables are involved without using the Fourier modal expansion methodology. Numerical results concerning six types of shell structures illustrate the influence of the power-Law exponent, of the power-Law Distribution and of the choice of the four parameters on the mechanical behaviour of shell structures considered.
Mirco Musolesi - One of the best experts on this subject based on the ideXlab platform.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Explaining the power-Law Distribution of human mobility through transportation modality decomposition
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explaining the power Law Distribution of human mobility through transportation modality decomposition
Scientific Reports, 2015Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma, Pan Hui, Weixiong RaoAbstract:Human mobility has been empirically observed to exhibit Levy flightcharacteristics and behaviour with power-Law distributed jump size. The fundamentalmechanisms behind this behaviour has not yet been fully explained. In thispaper, we propose to explain the Levy walk behaviour observed in humanmobility patterns by decomposing them into different classes according tothe different transportation modes, such as Walk/Run, Bike, Train/Subway orCar/Taxi/Bus. Our analysis is based on two real-life GPS datasets containingapproximately 10 and 20 million GPS samples with transportation mode information.We show that human mobility can be modelled as a mixture of different transportationmodes and that these single movement patterns can be approximated by a lognormalDistribution rather than a power-Law Distribution. Then, we demonstrate thatthe mixture of the decomposed lognormal flight Distributions associated witheach modality is a power-Law Distribution, providing an explanation to theemergence of Levy Walk patterns that characterize human mobility patterns.
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explaining the power Law Distribution of human mobility through transportation modality decomposition
arXiv: Physics and Society, 2014Co-Authors: Mirco Musolesi, Kai Zhao, Sasu TarkomaAbstract:Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.