scholarly journals Delayed information cascades in Flickr: Measurement, analysis, and modeling

2012 ◽  
Vol 56 (3) ◽  
pp. 1066-1076 ◽  
Author(s):  
Meeyoung Cha ◽  
Fabrício Benevenuto ◽  
Yong-Yeol Ahn ◽  
Krishna P. Gummadi
Keyword(s):  
Information Cascades ◽  
Delayed Information ◽  
Measurement Analysis

Positive Associations Primacy in the IAT

2011 ◽  
Vol 58 (5) ◽  
pp. 376-384 ◽  
Author(s):  
Pasquale Anselmi ◽  
Michelangelo Vianello ◽  
Egidio Robusto
Keyword(s):  
Implicit Association Test ◽  
Rasch Measurement ◽  
Normal Weight ◽  
Association Test ◽  
Primacy Effect ◽  
Implicit Association ◽  
Obese People ◽  
Measurement Analysis ◽  
Implicit Prejudice

Two studies investigated the different contribution of positive and negative associations to the size of the Implicit Association Test (IAT) effect. A Many-Facet Rasch Measurement analysis was applied for the purpose. Across different IATs (Race and Weight) and different groups of respondents (White, Normal weight, and Obese people) we observed that positive words increase the IAT effect whereas negative words tend to decrease it. Results suggest that the IAT is influenced by a positive associations primacy effect. As a consequence, we argue that researchers should be careful when interpreting IAT effects as a measure of implicit prejudice.

Credit Risk Modeling with Delayed Information

2012 ◽  
Author(s):  
Takanori Adachi ◽  
ryozo miura ◽  
Hidetoshi Nakagawa
Keyword(s):  
Credit Risk ◽  
Risk Modeling ◽  
Delayed Information ◽  
Credit Risk Modeling

Ultrametricity of Information Cascades

2013 ◽  
Author(s):  
Rumi Ghosh ◽  
Bernardo A. Huberman
Keyword(s):  
Information Cascades

Characterizing Non-Myopic Information Cascades in Bayesian Learning

2019 ◽  
Author(s):  
Ilai Bistritz ◽  
Nasimeh Heydaribeni ◽  
Achilleas Anastasopoulos
Keyword(s):  
Bayesian Learning ◽  
Information Cascades

scholarly journals Engine Malfunctioning Conditions Identification through Instantaneous Crankshaft Torque Measurement Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 3522
Author(s):  
Konstantinos-Marios Tsitsilonis ◽  
Gerasimos Theotokatos
Keyword(s):  
Operating Conditions ◽  
Cylinder Pressure ◽  
Dynamics Model ◽  
Engine Model ◽  
Measurement Analysis ◽  
Start Of Injection ◽  
Rate Of Heat Release ◽  
Relationship Of ◽  
Diagnostic Measurement ◽  
The Relationship

In this study a coupled thermodynamics and crankshaft dynamics model of a large two-stroke diesel engine was utilised, to map the relationship of the engine Instantaneous Crankshaft Torque (ICT) with the following frequently occurring malfunctioning conditions: (a) change in Start of Injection (SOI), (b) change in Rate of Heat Release (RHR), (c) change in scavenge air pressure, and (d) blowby. This was performed using frequency analysis on the engine ICT, which was obtained through a series of parametric runs of the coupled engine model, under the various malfunctioning and healthy operating conditions. This process demonstrated that engine ICT can be successfully utilised to identify the distinct effects of malfunctions (c) or (d), as they occur individually in any cylinder. Furthermore by using the same process, malfunctions (a) and (b) can be identified as they occur individually for any cylinder, however there is no distinct effect on the engine ICT among these malfunctions, since their effect on the in-cylinder pressure is similar. As a result, this study demonstrates the usefulness of the engine ICT as a non-intrusive diagnostic measurement, as well as the benefits of malfunctioning conditions mapping, which allows for quick and less resource intensive identification of engine malfunctions.

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

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