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While there is plenty of information available about the luminaries of science, here we discuss the relative lack of information about ordinary researchers. Luckily, because of recent advances in name disambiguation, the career histories of everyday scientists can now be analyzed, changing the way we think about scientific creativity entirely. We describe how the process of shuffling a career – moving the works a scientist publishes around randomly in time – helped us discover what we call the “random impact rule,” which dictates that, when we adjust for productivity, the highest impact work in a career can occur at any time. We also see that the probability of landmark work follows a cumulative distribution, meaning that the random impact rule holds true not just for the highest impact work in any career but also for other important works, too. While there is precedent for this rule in the literature – Simonton proposed the “constant probability of success” model in the 1970s – until recently we didn’t have the data on hand to test it. The random impact rule allows us to decouple age and creativity, instead linking periods of high productivity to creative breakthroughs.
In this chapter, a novel framework is proposed to address critical mobility management challenges, including frequent handovers (HOs), handover failure (HOF), and excessive energy consumption for seamless HO in emerging dense wireless cellular networks. In particular, we develop a model that exploits broadband mmW connectivity whenever available to cache content that MUEs are interested in. Thus it will enable the MUEs to use the cached content and avoid unnecessary HO to small cell base stations (SCBSs) with relatively small cell sizes. First, we develop a geometric model to derive tractable, closed-form expressions for key performance metrics, such as the probability of caching, cumulative distribution function of caching duration, and the average data rate for content caching over an mmW link. In addition, we provide insight on the performance gains that caching in mmW–mW networks can yield in terms of reducing the number of HOs and the average HOF.
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