algorithms and Computation : 10Th International Symposium, Isaac'99, Chennai, India, December 16-18, 1999 ; Proceedings by Isaac (Conference); Aggarwal, Alok; Pandu Rangan, C., 1955-; published by Berlin ; London ...
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algorithms and Computation : 10Th International Symposium, Isaac'99, Chennai, India, December 16-18, 1999 ; Proceedings by Isaac (Conference); Aggarwal, Alok; Pandu Rangan, C., 1955-; published by Berlin ; London : Springer
Algorithm Engineering and Experimentation : International Workshop Alenex '99, Baltimore, Md, Usa, January 15-16, 1999 : Selected Papers by Alenex '99 (1999 : Baltimore, MD.); Goodrich, Michael T; McGeoch, Cat...
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Algorithm Engineering and Experimentation : International Workshop Alenex '99, Baltimore, Md, Usa, January 15-16, 1999 : Selected Papers by Alenex '99 (1999 : Baltimore, MD.); Goodrich, Michael T; McGeoch, Catherine C., 1959-; published by Berlin ; New York : Springer
Algorithmic Learning Theory : 10Th International Conference, Alt'99, Tokyo, Japan, December 6-8, 1999 : Proceedings by Alt'99 (1999 : Tokyo, Japan); Watanabe, Osamu, 1958-; Yokomori, Takashi; published by Berl...
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Algorithmic Learning Theory : 10Th International Conference, Alt'99, Tokyo, Japan, December 6-8, 1999 : Proceedings by Alt'99 (1999 : Tokyo, Japan); Watanabe, Osamu, 1958-; Yokomori, Takashi; published by Berlin ; New York : Springer
Algorithmic Learning Theory : 9Th International Conference, Alt '98, Otzenhausen, Germany, October 8-10, 1998 : Proceedings by Alt '98 (1998 : Otzenhausen, Germany); Richter, Michael M., 1938-; published by Be...
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Algorithmic Learning Theory : 9Th International Conference, Alt '98, Otzenhausen, Germany, October 8-10, 1998 : Proceedings by Alt '98 (1998 : Otzenhausen, Germany); Richter, Michael M., 1938-; published by Berlin ; New York : Springer
The article presents a speech by computer scientist Leslie Lamport, a computer scientist and the 2013 recipient of the Association for Computing Machinery's (ACM's) A.M. Turing Award, entitled 'The Compute...
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The article presents a speech by computer scientist Leslie Lamport, a computer scientist and the 2013 recipient of the Association for Computing Machinery's (ACM's) A.M. Turing Award, entitled 'The computer Science of Concurrency: The Early Years'. It discusses the history of concurrent algorithms in the 1960s and 1970s. Topics discussed include mutual exclusion, producer-consumer synchronization, and distributed algorithms.
A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare alg...
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A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds and to describe the possibility spaces associated with these processes. By doing this, we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained in subsequently asking a how-actually question. Once one has explained how-possibly, there is little left to do.
Confocal microscopy of colloids combined with digital image processing has become a powerful tool in soft matter physics and materials science. Together, these techniques enable locating and tracking of more than half...
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Confocal microscopy of colloids combined with digital image processing has become a powerful tool in soft matter physics and materials science. Together, these techniques enable locating and tracking of more than half a million individual colloidal particles at once. However, despite improvements in locating algorithms that improve position accuracy, it remains challenging to locate all particles in a densely packed, three dimensional colloid without erroneously identifying the same particle more than once. We present a simple iterative algorithm that mitigates both the "missed particle" and "double counting" problems while simultaneously reducing sensitivity to the specific choice of input parameters. It is also useful for analyzing images with spatially varying brightness in which a single set of input parameters is not appropriate for all particles. The algorithm is easy to implement and compatible with existing particle locating software. Published by AIP Publishing.
Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wire...
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Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD) method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.
We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center,...
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We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.
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