Controllable stroke-based sketch synthesis from a self-organized latent space

2021 ◽  
Vol 137 ◽  
pp. 138-150
Author(s):  
Sicong Zang ◽  
Shikui Tu ◽  
Lei Xu
2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.


2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović

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.”


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

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.


Sign in / Sign up

Export Citation Format

Share Document