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We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study "transient scene attributes" -- high level properties which affect scene appearance, such as "snow", "autumn", "dusk", "fog". We define 40 transient attributes and use crowdsourcing to annotate thousands of(More)
Recombination (or called <i>crossover</i>) operators are a kind of characterizing feature of evolutionary algorithms (EAs). The usefulness of recombination operators has been verified empirically in many practical applications, and has also been theoretically studied in single-objective optimization. For multi-objective optimization, however, there lacks(More)
Ensemble learning is among the state-of-the-art learning techniques, which trains and combines many base learners. Ensemble pruning removes some of the base learners of an ensemble, and has been shown to be able to further improve the generalization performance. However, the two goals of ensemble pruning, i.e., maximizing the generalization performance and(More)
Running time analysis of metaheuristic search algorithms has attracted a lot of attention. When studying a metaheuristic algorithm over a problem class, a natural question is what are the easiest and the hardest cases of the problem class. The answer can be helpful for simplifying the analysis of an algorithm over a problem class as well as understanding(More)
A new and optimized procedure for the allylic oxidation of Δ(5)-steroids with t-butyl hydroperoxide in the presence of catalytic amounts of N-hydroxyphthalimide (NHPI) under mild conditions was developed, showing excellent regioselectivity and chemoselectivity (functional group compatibility). It was found that Co(OAc)2 could enhance the catalytic ability(More)
Recombination (also called crossover) operators are widely used in EAs to generate offspring solutions. Although the usefulness of recombination has been well recognized, theoretical analysis on recombination operators remains a hard problem due to the irregularity of the operators and their complicated interactions to mutation operators. In this paper, as(More)
—Evolutionary algorithms are a large family of heuristic optimization algorithms. They are problem independent, and have been applied in various optimization problems. Thus general analysis tools are especially appealing for guiding the analysis of evolutionary algorithms in various situations. This paper develops the switch analysis approach for running(More)
In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness(More)