The Noisy Neighbor Effect: How Negative Advertising in One State Influences Viewers Next Door

2019 ◽  
Author(s):  
Stan Oklobdzija
Keyword(s):  
Negative Advertising ◽  
Neighbor Effect

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

Reexamining the Effects of Electoral Competition on Negative Advertising

2021 ◽  
pp. 1532673X2110420
Author(s):  
Kevin K. Banda
Keyword(s):  
Contextual Factors ◽  
Electoral Competition ◽  
Strategic Choices ◽  
Negative Advertising ◽  
Open Seat ◽  
Campaign Advertising

Prior research suggests that campaigns become more negative when the election environment becomes more competitive. Much of this research suffers from data and design limitations. I replicate and extend prior analyses using a much larger number of cases. Using advertising data drawn from 374 U.S. Senate and gubernatorial campaigns contested from 2000 through 2018, I find evidence that electoral competition encourages candidates to engage in more negative advertising campaigns and that incumbency status conditions these effects. Incumbents of both parties use more negative messaging strategies as competition increases. The effects of competition among challengers and open seat candidates is mixed. These results add to what we know about campaign advertising behavior and suggest that researchers should take care to avoid ignoring important contextual factors that underlie candidates’ strategic choices.

scholarly journals Negative Advertising and Political Competition

2015 ◽  
Vol 32 (3) ◽  
pp. 433-477 ◽  
Author(s):  
Amit Gandhi ◽  
Daniela Iorio ◽  
Carly Urban
Keyword(s):  
Political Competition ◽  
Negative Advertising

scholarly journals Negative Campaign Advertising: Demobilizer or Mobilizer?

1999 ◽  
Vol 93 (4) ◽  
pp. 891-899 ◽  
Author(s):  
Martin P. Wattenberg ◽  
Craig Leonard Brians
Keyword(s):  
Survey Data ◽  
Real World ◽  
Presidential Campaign ◽  
Political Campaigns ◽  
Experimental Setting ◽  
Data Set ◽  
Negative Advertising ◽  
Campaign Advertising

As political campaigns become increasingly adversarial, scholars are giving some much-needed attention to the effect of negative advertising on turnout. In a widely recognized Review article and subsequent book, Ansolabehere and his colleagues (1994, 1995) contend that attack advertising drives potential voters away from the polls. We dispute the generalizability of this claim outside the experimental setting. Using NES survey data as well as aggregate sources, we subject their research to rigorous real-world testing. The survey data directly contradict their findings, yielding no evidence of a turnout disadvantage for those who recollected negative presidential campaign advertising. In attempting to replicate Ansolabehere et al.'s earlier aggregate results we uncover quite substantial discrepancies and inconsistencies in their data set. We conclude that their aggregate study is deeply flawed and that Ansolabehere et al. exaggerated the demobilization dangers posed by attack advertising, at least in voters' own context.

scholarly journals The Effect of Artificial Mowing on the Competition of Phragmites australis and Spartina alterniflora in the Yangtze Estuary

Scientifica ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yue Yuan ◽  
Chao Zhang ◽  
Dezhi Li
Keyword(s):  
Spartina Alterniflora ◽  
Phragmites Australis ◽  
Native Species ◽  
Interspecific Interactions ◽  
Interaction Strength ◽  
Yangtze Estuary ◽  
The Yangtze Estuary ◽  
Neighbor Effect ◽  
Index Effect

Spartina alterniflora Loisel. is one of the most invasive species in the world. However, little is known about the role of artificial mowing in its invasiveness and competiveness. In this work, we studied the effect of mowing on its interspecific interactions with native species Phragmites australis (Cav.) Trin ex Steud of the Yangtze Estuary, China. We calculated their relative neighbor effect (RNE) index, effect of relative crowding (Dr) index, and interaction strength (I) index. The results showed that the RNE of Phragmites australis and Spartina alterniflora was 0.354 and 0.619, respectively, and they have competitive interactions. The mowing treatments can significantly influence the RNE of Phragmites australis and Spartina alterniflora on each other. Concretely, the RNE of Spartina alterniflora in the removal treatments was significantly higher than the value in the controls. But the RNE of Phragmites australis in the removal treatments was significantly lower than the value in the controls. Meanwhile, Dr of the two species on the targets was higher in the removal treatments than that in the controls, and the opposite was for I. We concluded that artificial mowing could promote the invasion of Spartina alterniflora by increasing its competitive performance compared with native species.

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