The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
T.a. Girard - One of the best experts on this subject based on the ideXlab platform.
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SIMPLE dark matter search results
Physics Letters B, 2005Co-Authors: T.a. Girard, F. Giuliani, T Morlat, Mf Costa, Ji Collar, Denis Limagne, Georges Waysand, J Puibasset, Hs Miley, M AugusteAbstract:We report an improved SIMPLE experiment comprising four superheated droplet detectors with a total exposure of 0.42 kgd. The result yields similar to factor 10 improvement in the previously-reported results, and-despite the low exposure-is seen to provide restrictions on the allowed phase space of spin-dependent coupling strengths almost equivalent to those from the significantly larger exposure Naiad-CDMS/ZEPLIN searches. (c) 2005 Elsevier B.V. All rights reserved.
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Exclusion limits on spin dependent WIMP-nucleon couplings from the SIMPLE experiment
Physics Letters B, 2004Co-Authors: F. Giuliani, T.a. GirardAbstract:The SIMPLE experiment is reanalyzed within the context of the WIMP model independent framework of Tovey et al. The results are compared with those of the UK Naiad and NaF experiments as recently reported by the Tokyo group, together with those of the latest UK Naiad results in 2003, and further constrain the allowed parameter space.
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Exclusion limits on spin dependent WIMP-nucleon couplings from the SIMPLE experiment
Physics Letters B, 2004Co-Authors: F. Giuliani, T.a. GirardAbstract:The SIMPLE experiment is reanalyzed within the context of the WIMP model independent framework of Tovey et. al.. The results are compared with those of the UK Naiad and NaF experiments as recently reported by the Tokyo group, together with those of the latest UK Naiad results in 2003, and further constrain the allowed parameter space.Comment: 4 pages, submitted to Phys. Lett.
F. Giuliani - One of the best experts on this subject based on the ideXlab platform.
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SIMPLE dark matter search results
Physics Letters B, 2005Co-Authors: T.a. Girard, F. Giuliani, T Morlat, Mf Costa, Ji Collar, Denis Limagne, Georges Waysand, J Puibasset, Hs Miley, M AugusteAbstract:We report an improved SIMPLE experiment comprising four superheated droplet detectors with a total exposure of 0.42 kgd. The result yields similar to factor 10 improvement in the previously-reported results, and-despite the low exposure-is seen to provide restrictions on the allowed phase space of spin-dependent coupling strengths almost equivalent to those from the significantly larger exposure Naiad-CDMS/ZEPLIN searches. (c) 2005 Elsevier B.V. All rights reserved.
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Exclusion limits on spin dependent WIMP-nucleon couplings from the SIMPLE experiment
Physics Letters B, 2004Co-Authors: F. Giuliani, T.a. GirardAbstract:The SIMPLE experiment is reanalyzed within the context of the WIMP model independent framework of Tovey et al. The results are compared with those of the UK Naiad and NaF experiments as recently reported by the Tokyo group, together with those of the latest UK Naiad results in 2003, and further constrain the allowed parameter space.
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Exclusion limits on spin dependent WIMP-nucleon couplings from the SIMPLE experiment
Physics Letters B, 2004Co-Authors: F. Giuliani, T.a. GirardAbstract:The SIMPLE experiment is reanalyzed within the context of the WIMP model independent framework of Tovey et. al.. The results are compared with those of the UK Naiad and NaF experiments as recently reported by the Tokyo group, together with those of the latest UK Naiad results in 2003, and further constrain the allowed parameter space.Comment: 4 pages, submitted to Phys. Lett.
Martin Abadi - One of the best experts on this subject based on the ideXlab platform.
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Incremental, iterative data processing with timely dataflow
Communications of the ACM, 2016Co-Authors: Derek G Murray, Rebecca Isaacs, Peter Barham, Frank Mcsherry, Michael Isard, Martin AbadiAbstract:We describe the timely dataflow model for distributed computation and its implementation in the Naiad system. The model supports stateful iterative and incremental computations. It enables both low-latency stream processing and high-throughput batch processing, using a new approach to coordination that combines asynchronous and fine-grained synchronous execution. We describe two of the programming frameworks built on Naiad: GraphLINQ for parallel graph processing, and differential dataflow for nested iterative and incremental computations. We show that a general-purpose system can achieve performance that matches, and sometimes exceeds, that of specialized systems.
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SOSP - Naiad: a timely dataflow system
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 2013Co-Authors: Derek G Murray, Rebecca Isaacs, Frank Mcsherry, Michael Isard, Paul Barham, Martin AbadiAbstract:Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.
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Naiad a timely dataflow system
Symposium on Operating Systems Principles, 2013Co-Authors: Derek G Murray, Rebecca Isaacs, Frank Mcsherry, Michael Isard, Paul Barham, Martin AbadiAbstract:Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.
N. J. T. Smith - One of the best experts on this subject based on the ideXlab platform.
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NaI dark matter limits and the Naiad array – a detector with improved sensitivity to WIMPs using unencapsulated NaI
Physics Letters B, 2000Co-Authors: N.j.c. Spooner, V. A. Kudryavtsev, C.d. Peak, J.e. Mcmillan, J.w. Roberts, T. B. Lawson, P. K. Lightfoot, Matthew J. Lehner, Daniel Tovey, N. J. T. SmithAbstract:Re-analysis of published data from the UKDMC NaI Tl dark matter experiment is presented using latest spin factors and comparison is made with the sensitivity predicted for Naiad, a 100 kg NaI detector concept based on unencapsulated . NaI Tl . We present experimental results and Monte Carlo simulations for Naiad and show that a factor of 1.5-2 improvement in energy threshold is achievable over conventional NaI dark matter detectors with consequent ; 50% improvement in nuclear recoil discrimination at 10 keV. An overall improvement in sensitivity to spin dependent WIMP interactions of factor 50, based on 100 kg = yrs of data, is predicted relative to previous UKDMC limits. q 2000 Published by Elsevier Science B.V. All rights reserved.
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nai dark matter limits and the Naiad array a detector with improved sensitivity to wimps using unencapsulated nai
Physics Letters B, 2000Co-Authors: N.j.c. Spooner, V. A. Kudryavtsev, C.d. Peak, J.e. Mcmillan, J.w. Roberts, M. J. Lehner, T. B. Lawson, P. K. Lightfoot, Daniel Tovey, N. J. T. SmithAbstract:Re-analysis of published data from the UKDMC NaI Tl dark matter experiment is presented using latest spin factors and comparison is made with the sensitivity predicted for Naiad, a 100 kg NaI detector concept based on unencapsulated . NaI Tl . We present experimental results and Monte Carlo simulations for Naiad and show that a factor of 1.5-2 improvement in energy threshold is achievable over conventional NaI dark matter detectors with consequent ; 50% improvement in nuclear recoil discrimination at 10 keV. An overall improvement in sensitivity to spin dependent WIMP interactions of factor 50, based on 100 kg = yrs of data, is predicted relative to previous UKDMC limits. q 2000 Published by Elsevier Science B.V. All rights reserved.
Derek G Murray - One of the best experts on this subject based on the ideXlab platform.
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Incremental, iterative data processing with timely dataflow
Communications of the ACM, 2016Co-Authors: Derek G Murray, Rebecca Isaacs, Peter Barham, Frank Mcsherry, Michael Isard, Martin AbadiAbstract:We describe the timely dataflow model for distributed computation and its implementation in the Naiad system. The model supports stateful iterative and incremental computations. It enables both low-latency stream processing and high-throughput batch processing, using a new approach to coordination that combines asynchronous and fine-grained synchronous execution. We describe two of the programming frameworks built on Naiad: GraphLINQ for parallel graph processing, and differential dataflow for nested iterative and incremental computations. We show that a general-purpose system can achieve performance that matches, and sometimes exceeds, that of specialized systems.
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SOSP - Naiad: a timely dataflow system
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 2013Co-Authors: Derek G Murray, Rebecca Isaacs, Frank Mcsherry, Michael Isard, Paul Barham, Martin AbadiAbstract:Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.
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Naiad a timely dataflow system
Symposium on Operating Systems Principles, 2013Co-Authors: Derek G Murray, Rebecca Isaacs, Frank Mcsherry, Michael Isard, Paul Barham, Martin AbadiAbstract:Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.
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Composable Incremental and Iterative Data-Parallel Computation with Naiad
2012Co-Authors: Frank Mcsherry, Rebecca Isaacs, Michael Isard, Derek G MurrayAbstract:We report on the design and implementation of Naiad, a set of declarative data-parallel language extensions and an associated runtime supporting efficient and composable incremental and iterative computation. This combination is enabled by a new computational model we call differential dataflow, in which incremental computation can be performed using a partial, rather than total, order on time. Naiad extends standard batch data-parallel processing models like MapReduce, Hadoop, and Dryad/DryadLINQ, to support efficient incremental updates to the inputs in the manner of a stream processing system, while at the same time enabling arbitrarily nested fixed-point iteration. In this paper, we evaluate a prototype of Naiad that uses shared memory on a single multi-core computer. We apply Naiad to various computations, including several graph algorithms, and observe good scaling properties and efficient incremental recomputation.
