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比利·克里斯托、本·施瓦茨(《谎言屋》)加盟喜剧片[我们都不满意](We Are Unsatisfied，暂译)。马特·拉特纳([我的初恋女孩]制片)首执导筒。一个加州喜剧演员(施瓦茨饰)回到老家长岛后与他的酒鬼皮肤科医生(克里斯托饰)不打不相识，影片围绕二人之间的奇葩友情故事展开。该片下月纽约开机。Focus and devotion are critical//189大千世界包罗万象，十四部短片，十四个社会切面，十四种无畏表达惊喜感、新鲜感，一切皆可拍。扭蛋机里装着无限可能。这里所有短片都如同机器里那些内容未知的扭蛋。直到被打开前，你都不知道将遇见怎样的精彩，一场属于青年创作者的freestyle。无限精彩，等你解锁。一人问道：杨公子定就是了。所以他没有错么？他还是错了，神仙的事归神仙，人间的事归人间，他寻求神仙来指点人间的事，这就是错的，一来这是对神仙，对信仰的不尊重，二来人间的事，神仙不会来评，所谓神仙的旨意，不过是人为捏造而来的。For codes of the same length, theoretically, the further the coding distance between any two categories, the stronger the error correction capability. Therefore, when the code length is small, the theoretical optimal code can be calculated according to this principle. However, it is difficult to effectively determine the optimal code when the code length is slightly larger. In fact, this is an NP-hard problem. However, we usually do not need to obtain theoretical optimal codes, because non-optimal codes can often produce good enough classifiers in practice. On the other hand, it is not that the better the theoretical properties of coding, the better the classification performance, because the machine learning problem involves many factors, such as dismantling multiple classes into two "class subsets", and the difficulty of distinguishing the two class subsets formed by different dismantling methods is often different, that is, the difficulty of the two classification problems caused by them is different. Therefore, one theory has a good quality of error correction, but it leads to a difficult coding for the two-classification problem, which is worse than the other theory, but it leads to a simpler coding for the two-classification problem, and it is hard to say which is better or weaker in the final performance of the model.