Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:
If some unusually forward-thinking gynecologist suggested egg freezing to her, the modal response would be "wait, why are you telling me this?" The same goes for women in their 20s, it's only in the late 20s and early 30s that egg freezing is taken seriously as a possibility. Before that, the women who are strongly pro-natal are confident that they can get kids the old fashioned way (and usually succeed) while those more lukewarm think that they still have some time and it's not a major priority. This doesn't strike me as necessarily irrational. That means that the woman you implicitly target, in their late teens or 20s, but is confident they need egg freezing, is a rare breed. But of course, if you do want to find them, LessWrong is far from the worst bet.
。新收录的资料是该领域的重要参考
保险人与被保险人未约定保险价值的，保险价值按照下列规定计算：
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在週二的講話中，習近平還說，解放軍已「有效應對各種風險挑戰」，許多軍中人員經歷了「政治整訓」。