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InclusionExclusion and Segre Classes
, 2002
"... We propose a variation of the notion of Segre class, by forcing a naive `inclusionexclusion' principle to hold. The resulting class is computationally tractable, and is closely related to ChernSchwartzMacPherson classes. We deduce several general properties of the new class from this rela ..."
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Cited by 6 (4 self)
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We propose a variation of the notion of Segre class, by forcing a naive `inclusionexclusion' principle to hold. The resulting class is computationally tractable, and is closely related to ChernSchwartzMacPherson classes. We deduce several general properties of the new class from
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Understanding and using the Implicit Association Test: I. An improved scoring algorithm
 Journal of Personality and Social Psychology
, 2003
"... behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures by Implic ..."
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Cited by 592 (92 self)
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behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures. Parallel explicit (i.e., selfreport) measures, available in 156 of these samples (13,068 subjects), also predicted effectively (average r=.361), but with much greater variability of effect size. Predictive validity of selfreport was impaired for socially sensitive topics, for which impression management may distort selfreport responses. For 32 samples with criterion measures involving Black–White interracial behavior, predictive validity of IAT measures significantly exceeded that of selfreport measures. Both IAT and selfreport measures displayed incremental validity, with each measure predicting criterion variance beyond that predicted by the other. The more highly IAT and selfreport measures were intercorrelated, the greater was the predictive validity of each.
The Viterbi algorithm
 Proceedings of the IEEE
, 1973
"... vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A ..."
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Cited by 985 (3 self)
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vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A799A804, 1964. [9] T. C. Mo, “Theory of electrodynamics in media in noninertial frames and applications, ” J. Math. Phys., vol. 11, pp. 25892610, 1970.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
, 2000
"... In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in conver ..."
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Cited by 605 (39 self)
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In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly
Approximate InclusionExclusion
 Combinatorica
, 1993
"... The InclusionExclusion formula expresses the size of a union of a family of sets in terms of the sizes of intersections of all subfamilies. This paper considers approximating the size of the union when intersection sizes are known for only some of the subfamilies, or when these quantities are giv ..."
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Cited by 52 (4 self)
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The InclusionExclusion formula expresses the size of a union of a family of sets in terms of the sizes of intersections of all subfamilies. This paper considers approximating the size of the union when intersection sizes are known for only some of the subfamilies, or when these quantities
INCLUSION–EXCLUSION BASED ALGORITHMS FOR
"... Abstract. We present a deterministic algorithm producing the number of kcolourings of a graph on n vertices in time 2nnO(1). We also show that the chromatic number can be found by a polynomial space algorithm running in time O(2.2461n). Finally, we present a family of polynomial space approximatio ..."
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Abstract. We present a deterministic algorithm producing the number of kcolourings of a graph on n vertices in time 2nnO(1). We also show that the chromatic number can be found by a polynomial space algorithm running in time O(2.2461n). Finally, we present a family of polynomial space approxi
InclusionExclusion Algorithms for . . .
, 2006
"... Given a set U with n elements and a family of subsets S ⊆ 2 U we show how to count the number of kpartitions S1 ∪···∪Sk = U into subsets Si ∈ S in time 2 n n O(1). The only assumption on S is that it can be enumerated in time 2 n n O(1). In effect we get exact algorithms in time 2 n n O(1) for seve ..."
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Given a set U with n elements and a family of subsets S ⊆ 2 U we show how to count the number of kpartitions S1 ∪···∪Sk = U into subsets Si ∈ S in time 2 n n O(1). The only assumption on S is that it can be enumerated in time 2 n n O(1). In effect we get exact algorithms in time 2 n n O(1
Results 1  10
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300,096