论文标题
群集扩散散热
Cluster Diffusing Shuffles
论文作者
论文摘要
公正的洗牌算法，例如Fisher-Yates Shuffle，通常用于媒体播放器中的播放。这些算法无论这些项目彼此之间的相似程度如何，都将所有被同样洗牌的项目处理。尽管这对于许多应用可能是可取的，但由于聚类幻觉，这对于洗牌游戏来说是有问题的，这是人类错误地考虑“条纹”或“簇”的趋势，这些趋势可能是由于随机分布的采样而引起的，即非随机。本论文试图通过一种称为群集扩散（CD）的偏见的洗牌算法来解决这个问题，这些算法基于无序的超均匀系统，例如鸡眼中的锥细胞分布，重原子核的能量水平，重型原子核的能量水平，特征值分布的各种随机矩阵的分布以及许多其他类型的物质，以及各种物质，各种物质，以及各种物质，以及各种各样的物质，以及各种各样的物质。这些系统抑制密度波动的大规模波动，而不会像格子一样出现订购，从而使其非常适合洗牌游戏。 CD随机的范围从基于随机矩阵的洗牌范围，该矩阵的$（n^3）$时间和$ o（n^2）$ space到更有效的近似值，这些近似值需要$ o（n）$时间和$ o（n）$ space。
Unbiased shuffling algorithms, such as the Fisher-Yates shuffle, are often used for shuffle play in media players. These algorithms treat all items being shuffled equally regardless of how similar the items are to each other. While this may be desirable for many applications, this is problematic for shuffle play due to the clustering illusion, which is the tendency for humans to erroneously consider 'streaks' or 'clusters' that may arise from samplings of random distributions to be non-random. This thesis attempts to address this issue with a family of biased shuffling algorithms called cluster diffusing (CD) shuffles which are based on disordered hyperuniform systems such as the distribution of cone cells in chicken eyes, the energy levels of heavy atomic nuclei, the eigenvalue distributions of various types of random matrices, and many others which appear in a variety of biological, chemical, physical, and mathematical settings. These systems suppress density fluctuations at large length scales without appearing ordered like lattices, making them ideal for shuffle play. The CD shuffles range from a random matrix based shuffle which takes $O(n^3)$ time and $O(n^2)$ space to more efficient approximations which take $O(n)$ time and $O(n)$ space.
