scholarly journals Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow

2020 ◽  
Vol 39 (8) ◽  
pp. 15-25
Author(s):  
S. Wiewel ◽  
B. Kim ◽  
V. C. Azevedo ◽  
B. Solenthaler ◽  
N. Thuerey
Keyword(s):  
Fluid Flow ◽  
Space Subdivision ◽  
Latent Space

scholarly journals Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow

2019 ◽  
Vol 38 (2) ◽  
pp. 71-82 ◽  
Author(s):  
S. Wiewel ◽  
M. Becher ◽  
N. Thuerey
Keyword(s):  
Fluid Flow ◽  
Temporal Evolution ◽  
Space Physics ◽  
Latent Space

The Dark Tetrad

2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović
Keyword(s):  
Personality Traits ◽  
Antisocial Behavior ◽  
Second Order ◽  
The Other ◽  
Negative Pole ◽  
Latent Space ◽  
Order Factor

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

Short stroke linear motor controls fluid flow

1998 ◽  
Vol 08 (PR2) ◽  
pp. Pr2-805-Pr2-808
Author(s):  
A. Basak
Keyword(s):  
Fluid Flow ◽  
Linear Motor ◽  
Motor Controls
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