The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
Anand Srivastav - One of the best experts on this subject based on the ideXlab platform.
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STACS - Recursive Randomized Coloring Beats Fair Dice Random Colorings
STACS 2001, 2001Co-Authors: Benjamin Doerr, Anand SrivastavAbstract:We investigate a refined recursive coloring approach to construct balanced colorings for hypergraphs. A coloring is called balanced if each hyperedge has (roughly) the same number of vertices in each color. We provide a recursive randomized algorithm that colors an arbitrary hypergraph (n vertices, m edges) with c colors with discrepancy at most O(√n/c logm). The algorithm has expected running time O(nm log c). This result improves the bound of O(√n log(cm)) achieved with probability at least 1/2 by a random coloring that independently chooses a random color for each vertex (Fair Dice coloring). Our approach also lowers the current best upper bound for the c-color discrepancy in the case n = m to O(√n/c log c) and extends the algorithm of Matousek, Welzl and Wernisch for hypergraphs having bounded dual shatter function to arbitrary numbers of colors.
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recursive randomized coloring beats Fair Dice random colorings
Lecture Notes in Computer Science, 2001Co-Authors: Benjamin Doerr, Anand SrivastavAbstract:We investigate a refined recursive coloring approach to construct balanced colorings for hypergraphs. A coloring is called balanced if each hyperedge has (roughly) the same number of vertices in each color. We provide a recursive randomized algorithm that colors an arbitrary hypergraph (n vertices, m edges) with c colors with discrepancy at most O(√n/c log m). The algorithm has expected running time O(nm log c). This result improves the bound of O( √n log (cm)) achieved with probability at least by a random coloring that independently chooses a random color for each vertex (Fair Dice coloring). Our approach also lowers the current best upper bound for the c-color discrepancy in the case n = m to O(√n/c log c) and extends the algorithm of Matousek, Welzl and Wernisch for hypergraphs having bounded dual shatter function to arbitrary numbers of colors.
Benjamin Doerr - One of the best experts on this subject based on the ideXlab platform.
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STACS - Recursive Randomized Coloring Beats Fair Dice Random Colorings
STACS 2001, 2001Co-Authors: Benjamin Doerr, Anand SrivastavAbstract:We investigate a refined recursive coloring approach to construct balanced colorings for hypergraphs. A coloring is called balanced if each hyperedge has (roughly) the same number of vertices in each color. We provide a recursive randomized algorithm that colors an arbitrary hypergraph (n vertices, m edges) with c colors with discrepancy at most O(√n/c logm). The algorithm has expected running time O(nm log c). This result improves the bound of O(√n log(cm)) achieved with probability at least 1/2 by a random coloring that independently chooses a random color for each vertex (Fair Dice coloring). Our approach also lowers the current best upper bound for the c-color discrepancy in the case n = m to O(√n/c log c) and extends the algorithm of Matousek, Welzl and Wernisch for hypergraphs having bounded dual shatter function to arbitrary numbers of colors.
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recursive randomized coloring beats Fair Dice random colorings
Lecture Notes in Computer Science, 2001Co-Authors: Benjamin Doerr, Anand SrivastavAbstract:We investigate a refined recursive coloring approach to construct balanced colorings for hypergraphs. A coloring is called balanced if each hyperedge has (roughly) the same number of vertices in each color. We provide a recursive randomized algorithm that colors an arbitrary hypergraph (n vertices, m edges) with c colors with discrepancy at most O(√n/c log m). The algorithm has expected running time O(nm log c). This result improves the bound of O( √n log (cm)) achieved with probability at least by a random coloring that independently chooses a random color for each vertex (Fair Dice coloring). Our approach also lowers the current best upper bound for the c-color discrepancy in the case n = m to O(√n/c log c) and extends the algorithm of Matousek, Welzl and Wernisch for hypergraphs having bounded dual shatter function to arbitrary numbers of colors.
U. Vaccaro - One of the best experts on this subject based on the ideXlab platform.
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Efficient generation of Fair Dice with few biased coins
IEEE Transactions on Information Theory, 1999Co-Authors: L. Gargano, U. VaccaroAbstract:Given a random variable X which takes n equiprobable values, we consider several algorithmic questions related to the classical problem of simulating the outcomes of X by using a limited number of biased coins.
Steven R Weller - One of the best experts on this subject based on the ideXlab platform.
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towards a Fair Dice iam combining Dice and Fair models
IFAC-PapersOnLine, 2018Co-Authors: Timm Faulwasser, Robin Nydestedt, Christopher M Kellett, Steven R WellerAbstract:Abstract The estimation of the Social Cost of Carbon Dioxide (SC-CO2) is one of the essential purposes of Integrated Assessment Models (IAMs) used in the economics of climate change. One of the most widely used IAMs in this context is Dice. However, the Dice geophysical subsystem fails to account for feedback from the climate subsystem to the carbon subsystem, an effect recently observed in climate physics. This paper investigates how to combine the recently proposed Fair climate model with the socioeconomic subsystem of Dice. Based on an analysis of its differential-algebraic structure, we propose an efficient discretization of Fair that provides a new discrete-time hybrid of Dice and Fair denoted as Fair-Dice. Finally, we compare estimates of the SC-CO2 obtained with Dice2013 with those obtained via Fair-Dice.
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Towards a Fair-Dice IAM: Combining Dice and Fair Models ⁎ ⁎TF acknowledges support from the Daimler Benz Foundation. TF, CMK, and SRW are supported by the Australian Research Council under ARC-DP180103026.
IFAC-PapersOnLine, 2018Co-Authors: Timm Faulwasser, Robin Nydestedt, Christopher M Kellett, Steven R WellerAbstract:Abstract The estimation of the Social Cost of Carbon Dioxide (SC-CO2) is one of the essential purposes of Integrated Assessment Models (IAMs) used in the economics of climate change. One of the most widely used IAMs in this context is Dice. However, the Dice geophysical subsystem fails to account for feedback from the climate subsystem to the carbon subsystem, an effect recently observed in climate physics. This paper investigates how to combine the recently proposed Fair climate model with the socioeconomic subsystem of Dice. Based on an analysis of its differential-algebraic structure, we propose an efficient discretization of Fair that provides a new discrete-time hybrid of Dice and Fair denoted as Fair-Dice. Finally, we compare estimates of the SC-CO2 obtained with Dice2013 with those obtained via Fair-Dice.
H.-w. Gellersen - One of the best experts on this subject based on the ideXlab platform.
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Fair Dice: A Tilt and Motion-Aware Cube with a Conscience
26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW'06), 2006Co-Authors: K. Van Laerhoven, H.-w. GellersenAbstract:As an example of sensory augmentation of a tiny object, a small cube-sized die is presented that perceives rolls and records what face it lands on. It is thus able to detect bias for unFair behaviour due to its physical imperfections. On a deeper level, this case study demonstrates the integration of energy-efficient sensor fusion, combination of classifiers, and a wireless interface to adaptive classification heuristics.
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ICDCS Workshops - Fair Dice: A Tilt and Motion-Aware Cube with a Conscience
26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW'06), 2006Co-Authors: K. Van Laerhoven, H.-w. GellersenAbstract:As an example of sensory augmentation of a tiny object, a small cube-sized die is presented that perceives rolls and records what face it lands on. It is thus able to detect bias for unFair behaviour due to its physical imperfections. On a deeper level, this case study demonstrates the integration of energy-efficient sensor fusion, combination of classifiers, and a wireless interface to adaptive classification heuristics.
