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Caching of video files on user devices, combined with file exchange through device-to-device (D2D) communications is a promising method for increasing the throughput of wireless networks. Previous theoretical investigations showed that throughput can be increased by orders of magnitude, but assumed a Zipf distribution for modeling the popularity distribution, which was based on observations in wired networks. Thus the question whether cache-aided D2D video distribution can provide in practice the benefits promised by existing theoretical literature remains open. To answer this question, we provide new results specifically for popularity distributions of video requests of mobile users. Based on an extensive real-world dataset, we adopt a generalized distribution, known as Mandelbrot-Zipf (MZipf) distribution. We first show that this popularity distribution can fit the practical data well. Using this distribution, we analyze the throughput–outage tradeoff of the cache-aided D2D network and show that the scaling law is identical to the case of Zipf popularity distribution when the MZipf distribution is sufficiently skewed, implying that the benefits previously promised in the literature could indeed be realized in practice. To support the theory, practical evaluations using numerical experiments are provided, and show that the cache-aided D2D can outperform the conventional unicasting from base stations.
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Endogenous Popularity: How Perceptions of Support Affect the Popularity of Authoritarian Regimes
Being popular makes it easier for dictators to govern. A growing body of scholarship therefore focuses on the factors that influence authoritarian popularity. However, it is possible that the perception of popularity itself affects incumbent approval in autocracies. We use framing experiments embedded in four surveys in Russia to examine this phenomenon. These experiments reveal that manipulating information—and thereby perceptions—about Russian President Vladimir Putin’s popularity can significantly affect respondents’ support for him. Additional analyses, which rely on a novel combination of framing and list experiments, indicate that these changes in support are not due to preference falsification, but are in fact genuine. This study has implications for research on support for authoritarian leaders and defection cascades in nondemocratic regimes.
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- Award ID(s):
- 2049595
- PAR ID:
- 10510345
- Publisher / Repository:
- American Political Science Review
- Date Published:
- Journal Name:
- American Political Science Review
- Volume:
- 118
- Issue:
- 2
- ISSN:
- 0003-0554
- Page Range / eLocation ID:
- 1046-1052
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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All data is not equally popular. Often, some portion of data is more frequently accessed than the rest, which causes a skew in popularity of the data items. Adapting to this skew can improve performance, and this topic has been studied extensively in the past for disk-based settings. In this work, we consider an in-memory data structure, namely hash table , and show how one can leverage the skew in popularity for higher performance. Hashing is a low-latency operation, sensitive to the effects of caching and code complexity, among other factors. These factors make learning in-the-loop challenging as the overhead of performing additional operations can have significant impact on performance. In this paper, we propose VIP hashing, a hash table method that uses lightweight mechanisms for learning the skew in popularity and adapting the hash table layout on the fly. These mechanisms are non-blocking, i.e, the hash table is operational at all times. The overhead is controlled by sensing changes in the popularity distribution to dynamically switch-on/off the mechanisms as needed. We ran extensive tests against a host of workloads generated by Wiscer , a homegrown benchmarking tool, and we find that VIP hashing improves performance in the presence of skew (22% increase in fetch operation throughput for a hash table with 1M keys under low skew) while adapting to insert and delete operations, and changing popularity distribution of keys on the fly. Our experiments on DuckDB show that VIP hashing reduces the end-to-end execution time of TPC-H query 9 by 20% under low skew.more » « less
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null (Ed.)Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail, less popular, items. The effectiveness of these approaches is often assessed using different metrics to evaluate the extent to which over-concentration on popular items is reduced. However, not much attention has been given to the user-centered evaluation of this bias; how different users with different levels of interest towards popular items are affected by such algorithms. In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users’ perspective and we propose a new metric that can address these limitations. In addition, we present an effective approach that mitigates popularity bias from the user-centered point of view. Finally, we investigate several state-of-the-art approaches proposed in recent years to mitigate popularity bias and evaluate their performances using the existing metrics and also from the users’ perspective. Our experimental results using two publicly-available datasets show that existing popularity bias mitigation techniques ignore the users’ tolerance towards popular items. Our proposed user-centered method can tackle popularity bias effectively for different users while also improving the existing metrics.more » « less
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Children’s understanding of status and group norms influence their expectations about social encounters. However, status is multidimensional and children may perceive status stratification (i.e., high- and low-status) differently across multiple status dimensions (i.e., wealth and popularity). The current study investigated the effect of status level and norms on children’s expectations about intergroup affiliation in wealth and popularity contexts. Participants ( N = 165; age range: 5–10 years; M age = 7.72 years) were randomly assigned to hear two scenarios where a high- or low-status target affiliated with opposite-status groups based on either wealth or popularity. In one scenario, the group expressed an inclusive norm. In the other scenario, the group expressed an exclusive norm. For each scenario, children made predictions about children’s expectations for a target to acquire social resources. Novel findings indicated that children associated wealth status to some extent, but they drew stronger inferences from the wealth dimension than from the popularity dimension. In contrast to previous evidence that children distinguish between high- and low-status groups, we did not find evidence to support this in the context of the current study. In addition, norms of exclusion diminished children’s expectations for acquiring social resources from wealth and popularity groups but this effect was more pronounced between wealth groups. We found age differences in children’s expectations in regards to norms, but not in regards to status. The implications of how these effects, in addition to lack of effects, bear on children’s expectations about acquiring resources are discussed.more » « less
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Streaming video algorithms dynamically select between different versions of a video to deliver the highest quality version that can be viewed without buffering over the client’s connection. To improve the quality for viewers, the backing video service can generate more and/or better versions, but at a significant computational overhead. Processing all videos uploaded to Facebook in the most intensive way would require a prohibitively large cluster. Facebook’s video popularity distribution is highly skewed, however, with analysis on sampled videos showing 1% of them accounting for 83% of the total watch time by users. Thus, if we can predict the future popularity of videos, we can focus the intensive processing on those videos that improve the quality of the most watch time. To address this challenge, we designed CHESS, the first popularity prediction algorithm that is both scalable and accurate. CHESS is scalable because, unlike the state-ofthe- art approaches, it requires only constant space per video, enabling it to handle Facebook’s video workload. CHESS is accurate because it delivers superior predictions using a combination of historical access patterns with social signals in a unified online learning framework. We have built a video prediction service, CHESSVPS, using our new algorithm that can handle Facebook’s workload with only four machines. We find that re-encoding popular videos predicted by CHESSVPS enables a higher percentage of total user watch time to benefit from intensive encoding, with less overhead than a recent production heuristic, e.g., 80% of watch time with one-third as much overhead.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/S0003055423000618
