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Fixed parameter set splitting, linear kernel and improved running time
 In Broersma et al. [BJS05
"... We study the problem kSet Splitting in fixed parameter complexity. We show that the problem can be solved in time O ∗ (2.6494 k), improving on the best currently known running time of O ∗ (8 k). This is done by showing that a nontrivial instance must have a small minimal Set Cover, and using this ..."
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Cited by 3 (1 self)
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We study the problem kSet Splitting in fixed parameter complexity. We show that the problem can be solved in time O ∗ (2.6494 k), improving on the best currently known running time of O ∗ (8 k). This is done by showing that a nontrivial instance must have a small minimal Set Cover, and using
Exploiting Hierarchical Configuration to Improve RunTime MPSoC Task Assignment ∗
"... Runtime assignment of a set of communicating tasks onto a heterogeneous multiprocessor systemonchip (MPSoC) platform is a challenging task. Having FPGA fabric tiles in such MPSoC platform increases performance and flexibility of the platform. Such FPGA tiles can not only run tasks in hardware bu ..."
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generic runtime task assignment heuristic. We show that adding a hierarchical configuration significantly improves task assignment performance (i.e. success rate and assignment quality). In several cases, the performance of a heuristic with a hierarchical configuration extends beyond the capabilities
Improving RunTime Scheduling for GeneralPurpose Parallel Code
"... Today, almost all desktop and laptop computers are sharedmemory multicores, but the code they run is overwhelmingly serial. High level language extensions and libraries (e.g., OpenMP, Cilk++, TBB) make it much easier for programmers to write parallel code than previous approaches (e.g., MPI), in ..."
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Today, almost all desktop and laptop computers are sharedmemory multicores, but the code they run is overwhelmingly serial. High level language extensions and libraries (e.g., OpenMP, Cilk++, TBB) make it much easier for programmers to write parallel code than previous approaches (e.g., MPI
Fibonacci Heaps and Their Uses in Improved Network optimization algorithms
, 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized tim ..."
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Cited by 739 (18 self)
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time and all other standard heap operations in o ( 1) amortized time. Using Fheaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worstcase bounds, where n is the number of vertices and m the number of edges
A LinearTime Heuristic for Improving Network Partitions
, 1982
"... An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning. To d ..."
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Cited by 524 (0 self)
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An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
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Cited by 534 (4 self)
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The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a
Risks for the long run: A potential resolution of asset pricing puzzles
 JOURNAL OF FINANCE
, 1994
"... We model consumption and dividend growth rates as containing (i) a small longrun predictable component and (ii) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin’s (1989) preferences, can explain ke ..."
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Cited by 761 (63 self)
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We model consumption and dividend growth rates as containing (i) a small longrun predictable component and (ii) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin’s (1989) preferences, can explain
Realtime human pose recognition in parts from single depth images
 IN CVPR
, 2011
"... We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler p ..."
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Cited by 568 (17 self)
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local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate
Mining Sequential Patterns: Generalizations and Performance Improvements
 RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH
, 1995
"... The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a userspecified ..."
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Cited by 759 (5 self)
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The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a user
Pfinder: Realtime tracking of the human body
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... Pfinder is a realtime system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D ..."
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Cited by 1482 (48 self)
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Pfinder is a realtime system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2
Results 1  10
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183,869