Suppose a $k$-uniform hypergraph $H$ that satisfies a certain regularity instance (that is, there is a partition of $H$ given by the hypergraph regularity lemma into a bounded number of quasirandom subhypergraphs of prescribed densities). We prove that with high probability a large enough uniform random sample of the vertex set of $H$ also admits the same regularity instance. Here the crucial feature is that the error term measuring the quasirandomness of the subhypergraphs requires only an arbitrarily small additive correction. This has applications to combinatorial property testing. The graph case of the sampling result was proved by Alon, Fischer, Newman and Shapira.

翻译：假设一个符合某种规律性实例(即高光学常态Lemma给高光成像灵敏度给出的1美元分解成一连串规定密度的准兰地次高血压次高)的美元,我们证明,高概率情况下,一个足够统一的脊椎抽样(H$)的庞大统一随机样本也承认同一规律性实例。这里的关键特征是测量次精密度值的错误术语只需要一个任意的小型添加剂修正即可。这有组合属性测试的应用。抽样结果的图表案例由Alon、Fischer、Newman和Shapira证明。