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相关概念视频

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Mutation, Gene Flow, and Genetic Drift01:09

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Optimal Foraging00:48

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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BITS:在开放环境中使用比特可扩展的增量哈希.

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    本研究介绍了比特可扩展增量哈希 (BITS),这是一个用于开放环境图像检索的新方法. BITS支持在线增量编码和动态比特长度调整,而不忘记知识,在动态场景中优于传统方法.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 传统的图像检索哈希模型假设封闭环境,限制了现实世界的应用性.
    • 在线增量学习和适应不断扩大的概念空间对于实际的图像检索系统至关重要.

    研究的目的:

    • 开发一个散列范式,支持数据流的在线增量编码.
    • 为了在开放环境中实现适应性代码长度和位扩展,而无需知识遗忘.
    • 为了解决大规模图像检索中封闭集假设的局限性.

    主要方法:

    • 提议比特可扩展增量哈希 (BITS),一种基于遇到的类别动态调整比特长度的方法.
    • 实施一个为高效增量散列的三阶段框架,包括在线连续编码和比特扩展.
    • 使用模糊的增量设置来模拟现实的开放环境场景,对数据增量和类增量设置进行评估.

    主要成果:

    • 通过BITS实现了与线下哈希方法可比的性能,同时显著降低了计算成本.
    • 在6个基准数据集的动态场景中表现出优越性.
    • 有效地处理在线增量编码和自适应代码长度调整,而不会遗忘知识.

    结论:

    • BITS提供了一种有效的解决方案,用于在具有动态概念空间的开放环境中检索图像.
    • 拟议的方法平衡了实际应用的性能和计算效率.
    • 通过支持自适应和可扩展的哈希代码,BITS在增量哈希领域取得了进展.