The Experts below are selected from a list of 22194 Experts worldwide ranked by ideXlab platform
Miguel A. L. Nicolelis - One of the best experts on this subject based on the ideXlab platform.
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robust satisficing linear regression performance robustness trade off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
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Robust satisficing linear regression: Performance/robustness trade-off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
Michel Raynal - One of the best experts on this subject based on the ideXlab platform.
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Timed Quorum System for Large-Scale and Dynamic Environments
arXiv: Distributed Parallel and Cluster Computing, 2008Co-Authors: Vincent Gramoli, Michel RaynalAbstract:This paper presents Timed Quorum System (TQS), a new quorum system especially suited for large-scale and dynamic systems. TQS requires that two quorums intersect with high probability if they are used in the same small period of time. It proposed an algorithm that implements TQS and that verifies probabilistic atomicity: a Consistency Criterion that requires each operation to respect atomicity with high probability. This TQS implementation has quorum of size O(\sqrt{nD}) and expected access time of O(log \sqrt{nD}) message delays, where n measures the size of the system and D is a required parameter to handle dynamism.
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Timed Quorum System for Large-Scale Dynamic Environments
2007Co-Authors: Vincent Gramoli, Michel RaynalAbstract:This report presents Timed Quorum System (TQS), a new quorum system especially suited for large-scale and dynamic systems. TQS requires that two quorums intersect with high probability if they are used in the same small period of time. It proposes an algorithm that implements TQS and that verifies probabilistic atomicity: a Consistency Criterion that requires each operation to respect atomicity with high probability. This TQS implementation has quorum of size O(√nD) with expected access time of O(log√nD) message delays, where n measures the size of the system and D is a required parameter to handle dynamism.
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Future Directions in Distributed Computing - Ordering vs timeliness: two facets of Consistency?
Future Directions in Distributed Computing, 2003Co-Authors: Mustaque Ahamad, Michel RaynalAbstract:Distributed applications are characterized by the fact that the processes they are made up of execute on possibly geographically dispersed nodes. An important problem the underlying distributed system has to solve lies in maintaining the Consistency of the state that is shared by such processes. Unfortunately, the non-instantaneity of message transmissions and failure occurrences make this fundamental task far from being trivial. Of course, this difficulty depends on the type of Consistency required by the application. Distributed programming models based on shared variables have been advocated by many researchers. Basically, a Consistency Criterion states which value has to be returned when a process reads a variable of the shared state. We think that there are two basic axes that help characterize Consistency criteria: ordering and timeliness. The ordering axis defines the possible orders in which operations can be executed while returning values for read operations that are permitted by the Consistency Criterion. The timeliness axis defines how soon a value written by one process must become visible to others. By exploring these two axes, one can not only define versatile Consistency criteria that meet the needs of diverse applications, but Consistency levels can also be adapted based on available system resources or changing needs of an application. We believe that the characterization of Consistency criteria using the orthogonal axes of ordering and timeliness helps us understand important issues related to shared objects in distributed systems.
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AN OPTIMISTIC PROTOCOL FOR A LINEARIZABLE DISTRIBUTED SHARED MEMORY SYSTEM
Parallel Processing Letters, 1996Co-Authors: Masaaki Mizuno, Mitchell L. Neilsen, Michel RaynalAbstract:Recently, distributed shared memory systems have received much attention because such an abstraction simplifies programming. In this paper, we present a simple protocol which implements the linearizability Consistency Criterion in a distributed shared memory system. Unlike previously implemented protocols, our protocol is based on an optimistic approach. The protocol eliminates the necessity of potentially expensive synchronization among processors for each write operation, but may require processes to rollback.
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Static and Dynamic Adaptation of Transactional Consistency
1996Co-Authors: Oliver Theel, Michel RaynalAbstract:Consistency criteria adopted for the management of persistent replicated objects in a distributed system define the degree of concurrency allowed among operations accessing objects. Several notions of Consistency are known from the literature, among them are causal Consistency, causal serializability, and serializability. In this paper, we propose a generalizing algorithm for concurrency control in a transaction system that exhibits a clean separation between policy and mechanism. A Consistency Criterion selected is manifested as a set of rules forming the policy. The mechanism, however, remains unchanged regardless of the currently used policy. The mechanism implements causally consistent message delivery and uses tokens and quorums of tokens to enforce access operation ordering according to the specified Consistency Criterion. Since a policy is implemented as a set of rules, switching on-the-fly from one Consistency Criterion to another one can easily be done whenever changes in access patterns or cost/availability requirements suggest a modification. An example of an application exploiting the advantages of switching among various Consistency criteria concludes the paper.
Miriam Zacksenhouse - One of the best experts on this subject based on the ideXlab platform.
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robust satisficing linear regression performance robustness trade off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
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Robust satisficing linear regression: Performance/robustness trade-off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
Sulabha Ramachandran - One of the best experts on this subject based on the ideXlab platform.
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relative risk of a shuffled deck a generalizable logical Consistency Criterion for sample selection in health state valuation studies
Health Economics, 2006Co-Authors: Benjamin M Craig, Sulabha RamachandranAbstract:In a health state valuation study, respondents may be asked to rank a deck of cards, with each card representing a particular health state. A logical inConsistency occurs when a more severe health state card is ranked higher than a less severe card. Occasional inconsistencies may be justified by errors in judgment or measurement. However, when respondents return shuffled decks, their responses must be removed from the sample; otherwise, valuation estimates will be biased toward the median. In this paper, we present a logical Consistency Criterion for sample selection in health state valuation studies. This statistical Criterion is based on the relative risk of a shuffled deck and generalizable to all health state classification systems, subsets (or decks) of health states, and valuation techniques. We applied the Criterion to secondary data collected from 4048 United States and 3395 United Kingdom respondents. In both studies, respondents evaluated 12-card decks of EQ-5D health states using time trade-off and visual analog scale techniques. Among the UK respondents, a small portion (approximately 5%) did not satisfy the Criterion; their exclusion significantly changed the sample characteristics and the mean value estimates of the EQ-5D health states. Similar results were found among the US respondents. Copyright © 2006 John Wiley & Sons, Ltd.
Mikhail A. Lebedev - One of the best experts on this subject based on the ideXlab platform.
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robust satisficing linear regression performance robustness trade off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
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Robust satisficing linear regression: Performance/robustness trade-off and Consistency Criterion
Mechanical Systems and Signal Processing, 2009Co-Authors: Miriam Zacksenhouse, Simona Nemets, Mikhail A. Lebedev, Miguel A. L. NicolelisAbstract:Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance Criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new Criterion, which assesses the Consistency between the observations and the linear model. The proposed Criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.