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Thierry Rakotoarivelo - One of the best experts on this subject based on the ideXlab platform.

  • fairness in multiterminal data Compression decomposition of shapley value
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Thierry Rakotoarivelo, Parastoo Sadeghi
    Abstract:

    We consider the Problem of how to attain fairness in the multiterminal data Compression Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as the cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved in a distributed manner by adapting an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

  • fairness in multiterminal data Compression decomposition of shapley value
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Parastoo Sadeghi, Thierry Rakotoarivelo
    Abstract:

    We consider the Problem of how to determine a fair source coding rate allocation method for the lossless data Compression Problem in multiterminal networks, e.g, the wireless sensor network where there are a large number of sources to be encoded. We model this Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We formulate a coalitional game model where the entropy function quantifies the cost incurred due to the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of sources and contributes to a considerable reduction in computational complexity. Experiments demonstrate large complexity reduction when the number of sources becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as a cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved by an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. We then show that the MIF algorithm can be implemented in a distributed manner. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

Ni Ding - One of the best experts on this subject based on the ideXlab platform.

  • fairness in multiterminal data Compression decomposition of shapley value
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Thierry Rakotoarivelo, Parastoo Sadeghi
    Abstract:

    We consider the Problem of how to attain fairness in the multiterminal data Compression Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as the cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved in a distributed manner by adapting an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

  • fairness in multiterminal data Compression decomposition of shapley value
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Parastoo Sadeghi, Thierry Rakotoarivelo
    Abstract:

    We consider the Problem of how to determine a fair source coding rate allocation method for the lossless data Compression Problem in multiterminal networks, e.g, the wireless sensor network where there are a large number of sources to be encoded. We model this Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We formulate a coalitional game model where the entropy function quantifies the cost incurred due to the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of sources and contributes to a considerable reduction in computational complexity. Experiments demonstrate large complexity reduction when the number of sources becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as a cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved by an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. We then show that the MIF algorithm can be implemented in a distributed manner. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

  • Distributed Data Compression in Sensor Clusters: A Maximum Independent Flow Approach
    2018
    Co-Authors: Ni Ding, Sadeghi Parastoo, Smith David, Rakotoarivelo Thierry
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as a cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved by an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. We then show that the MIF algorithm can be implemented in a distributed manner. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.Comment: 5 pages, 4 figure

Parastoo Sadeghi - One of the best experts on this subject based on the ideXlab platform.

  • fairness in multiterminal data Compression decomposition of shapley value
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Thierry Rakotoarivelo, Parastoo Sadeghi
    Abstract:

    We consider the Problem of how to attain fairness in the multiterminal data Compression Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    International Symposium on Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as the cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved in a distributed manner by adapting an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

  • fairness in multiterminal data Compression decomposition of shapley value
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, David R Smith, Parastoo Sadeghi, Thierry Rakotoarivelo
    Abstract:

    We consider the Problem of how to determine a fair source coding rate allocation method for the lossless data Compression Problem in multiterminal networks, e.g, the wireless sensor network where there are a large number of sources to be encoded. We model this Problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We formulate a coalitional game model where the entropy function quantifies the cost incurred due to the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of sources and contributes to a considerable reduction in computational complexity. Experiments demonstrate large complexity reduction when the number of sources becomes large.

  • distributed data Compression in sensor clusters a maximum independent flow approach
    arXiv: Information Theory, 2018
    Co-Authors: Ni Ding, Parastoo Sadeghi, David Smith, Thierry Rakotoarivelo
    Abstract:

    Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and one sensor serves as a cluster header or sink. Here, we formulate the Problem of how to let the sensors encode their observations such that the direction of compressed data is a feasible flow towards the sink. We demonstrate that this Problem can be solved by an existing maximum independent flow (MIF) algorithm in polynomial time. Further, we reveal that this algorithm in fact determines an optimal solution by recursively pushing the remaining randomness in the sources via unsaturated communication links towards the sink. We then show that the MIF algorithm can be implemented in a distributed manner. For those networks with integral communication capacities, we propose an integral MIF algorithm which completes much faster than MIF. Finally, we point out that the nature of the data Compression Problem in a sensor cluster is to seek the maximum independent information flow in the intersection of two submodular polyhedra, which can be further utilized to improve the MIF algorithm in the future.

Mitsutoshi Kuroda - One of the best experts on this subject based on the ideXlab platform.

  • on large strain finite element solutions of higher order gradient crystal plasticity
    International Journal of Solids and Structures, 2011
    Co-Authors: Mitsutoshi Kuroda
    Abstract:

    Abstract A finite-strain higher-order gradient crystal plasticity model accounting for the backstress effect originating from the existence of geometrically necessary dislocations (GNDs) is applied to plane strain finite element analysis. Different element types are tested to seek out an element formulation that is reliable and useful for solving Problems involving severe plastic deformation. In the present finite element formulation, the GND density rates are chosen to be additional nodal degrees of freedom. Different orders of shape functions are employed for the interpolation of displacement rates and GND density rates. Their effects on solutions are examined in detail by considering three boundary value Problems: a simple shear of a constrained layer (a film), a Compression Problem with loading surfaces impenetrable to dislocations, and a tension Problem involving shear band formation. In all the cases, the formulation in which eight-node elements with reduced integration and four-node elements with full integration are used respectively for displacement rates and the GND density rates gives reasonable solutions. In addition to the discussion on the choice of finite elements, detailed behavior in gradient-dependent solids, such as the accumulation of GND density and the distribution of backstress on each slip system, is investigated by utilizing the reliable computational results obtained.

  • crystal plasticity analysis of texture development in magnesium alloy during extrusion
    International Journal of Plasticity, 2011
    Co-Authors: Tsuyoshi Mayama, Masafumi Noda, Ryoichi Chiba, Mitsutoshi Kuroda
    Abstract:

    The texture development mechanism during the extrusion of magnesium alloy is investigated by experimental observation and numerical analysis. First, we perform a finite element analysis of a full extrusion process using a phenomenological constitutive equation, and it is confirmed that the loading condition of the extrusion process near the central axis of the billet is approximated by an equi-biaxial Compression mode. Then, the equi-biaxial Compression Problem is adopted as a simplified boundary value Problem to be solved using a crystal plasticity model to clarify the detailed texture development mechanism during the extrusion process. The crystal plasticity analysis of equi-biaxial Compression successfully reproduces the texture development from an initial random texture to the final experimentally observed texture. The effects of the deformation modes (i.e. slip and twinning systems) implemented in the calculation and the reference stress ratio of basal to nonbasal slip systems on texture development are studied in detail. Finally, the mechanism of texture development during the extrusion process is discussed in terms of the lattice rotation caused by the activated slip systems.

  • effects of texture on shear band formation in plane strain tension Compression and bending
    International Journal of Plasticity, 2007
    Co-Authors: Mitsutoshi Kuroda, Viggo Tvergaard
    Abstract:

    Abstract In this study, effects of typical texture components observed in rolled aluminum alloy sheets on shear band formation in plane strain tension/Compression and bending are systematically studied. The material response is described by a generalized Taylor-type polycrystal model, in which each grain is characterized in terms of an elastic–viscoplastic continuum slip constitutive relation. First, a simple model analysis in which the shear band is assumed to occur in a weaker thin slice of material is performed. From this simple model analysis, two important quantities regarding shear band formation are obtained: i.e. the critical strain at the onset of shear banding and the corresponding orientation of shear band. Second, the shear band development in plane strain tension/Compression is analyzed by the finite element method. Predictability of the finite element analysis is compared to that of the simple model analysis. Third, shear band developments in plane strain pure bending of a sheet specimen with the typical textures are studied. Regions near the surfaces in a bent sheet specimen are approximately subjected to plane strain tension or Compression. From this viewpoint, the bendability of a sheet specimen may be evaluated, using the knowledge regarding shear band formation in plane strain tension/Compression. To confirm this and to encompass overall deformation of a bent sheet specimen, including shear bands, finite element analyses of plane strain pure bending are carried out, and the predicted shear band formation in bent specimens is compared to that in the tension/Compression Problem. Finally, the present results are compared to previous related studies, and the efficiency of the present method for materials design in future is discussed.

Oren Weimann - One of the best experts on this subject based on the ideXlab platform.

  • near optimal Compression for the planar graph metric
    Symposium on Discrete Algorithms, 2018
    Co-Authors: Amir Abboud, Pawel Gawrychowski, Shay Mozes, Oren Weimann
    Abstract:

    The Planar Graph Metric Compression Problem is to compactly encode the distances among k nodes in a planar graph of size n. Two naive solutions are to store the graph using O(n) bits, or to explicitly store the distance matrix with O(k2 log n) bits. The only lower bounds are from the seminal work of Gavoille, Peleg, Prennes, and Raz [SODA'01], who rule out Compressions into a polynomially smaller number of bits, for weighted planar graphs, but leave a large gap for unweighted planar graphs. For example, when [Equation], the upper bound is O(n) and their constructions imply an Ω(n3/4) lower bound. This gap is directly related to other major open questions in labeling schemes, dynamic algorithms, and compact routing. Our main result is a new Compression of the planar graph metric into [Equation] bits, which is optimal up to log factors. Our data structure circumvents an Ω(k2) lower bound of Krauthgamer, Nguyen, and Zondiner [SIDMA'14] for Compression using minors, and the lower bound of Gavoille et al. for Compression of weighted planar graphs. This is an unexpected and decisive proof that weights can make planar graphs inherently more complex. Moreover, we design a new Subset Distance Oracle for planar graphs with [Equation] space, and O(n3/4) query time. Our work carries strong messages to related fields. In particular, the famous O(n1/2) vs. Ω(n1/3) gap for distance labeling schemes in planar graphs cannot be resolved with the current lower bound techniques. On the positive side, we introduce the powerful tool of unit-monge to planar graph algorithms.

  • near optimal Compression for the planar graph metric
    arXiv: Data Structures and Algorithms, 2017
    Co-Authors: Amir Abboud, Pawel Gawrychowski, Shay Mozes, Oren Weimann
    Abstract:

    The Planar Graph Metric Compression Problem is to compactly encode the distances among $k$ nodes in a planar graph of size $n$. Two na\"ive solutions are to store the graph using $O(n)$ bits, or to explicitly store the distance matrix with $O(k^2 \log{n})$ bits. The only lower bounds are from the seminal work of Gavoille, Peleg, Prennes, and Raz [SODA'01], who rule out Compressions into a polynomially smaller number of bits, for {\em weighted} planar graphs, but leave a large gap for unweighted planar graphs. For example, when $k=\sqrt{n}$, the upper bound is $O(n)$ and their constructions imply an $\Omega(n^{3/4})$ lower bound. This gap is directly related to other major open questions in labelling schemes, dynamic algorithms, and compact routing. Our main result is a new Compression of the planar graph metric into $\tilde{O}(\min (k^2 , \sqrt{k\cdot n}))$ bits, which is optimal up to log factors. Our data structure breaks an $\Omega(k^2)$ lower bound of Krauthgamer, Nguyen, and Zondiner [SICOMP'14] for Compression using minors, and the lower bound of Gavoille et al. for Compression of weighted planar graphs. This is an unexpected and decisive proof that weights can make planar graphs inherently more complex. Moreover, we design a new {\em Subset Distance Oracle} for planar graphs with $\tilde O(\sqrt{k\cdot n})$ space, and $\tilde O(n^{3/4})$ query time. Our work carries strong messages to related fields. In particular, the famous $O(n^{1/2})$ vs. $\Omega(n^{1/3})$ gap for distance labelling schemes in planar graphs {\em cannot} be resolved with the current lower bound techniques.