Latent space modeling of seismic data: An overview

2009 ◽  
Vol 28 (12) ◽  
pp. 1454-1459 ◽  
Author(s):  
Bradley C. Wallet ◽  
Marcilio C. de Matos ◽  
J. Timothy Kwiatkowski ◽  
Yoscel Suarez
Keyword(s):  
Seismic Data ◽  
Latent Space ◽  
Space Modeling

scholarly journals Fratricide in rebel movements: A network analysis of Syrian militant infighting

2019 ◽  
Vol 56 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Emily Kalah Gade ◽  
Mohammed M Hafez ◽  
Michael Gabbay
Keyword(s):  
Network Analysis ◽  
Civil Wars ◽  
Violent Conflict ◽  
Rebel Groups ◽  
Syrian Civil War ◽  
Latent Space ◽  
Space Modeling ◽  
Using Data ◽  
The Relationship

Violent conflict among rebels is a common feature of civil wars and insurgencies. Yet, not all rebel groups are equally prone to such infighting. While previous research has focused on the systemic causes of violent conflict within rebel movements, this article explores the factors that affect the risk of conflict between pairs of rebel groups. We generate hypotheses concerning how differences in power, ideology, and state sponsors between rebel groups impact their propensity to clash and test them using data from the Syrian civil war. The data, drawn from hundreds of infighting claims made by rebel groups on social media, are used to construct a network of conflictual ties among 30 rebel groups. The relationship between the observed network structure and the independent variables is evaluated using network analysis metrics and methods including assortativity, community structure, simulation, and latent space modeling. We find strong evidence that ideologically distant groups have a higher propensity for infighting than ideologically proximate ones. We also find support for power asymmetry, meaning that pairs of groups of disparate size are at greater risk of infighting than pairs of equal strength. No support was found for the proposition that sharing state sponsors mitigates rebels’ propensity for infighting. Our results provide an important corrective to prevailing theory, which discounts the role of ideology in militant factional dynamics within fragmented conflicts.

scholarly journals Seismic inversion by Newtonian machine learning

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA185-WA200
Author(s):  
Yuqing Chen ◽  
Gerard T. Schuster
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wave Equation ◽  
Seismic Data ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Low Rank ◽  
Inversion Method ◽  
Model Parameters ◽  
Latent Space

We present a wave-equation inversion method that inverts skeletonized seismic data for the subsurface velocity model. The skeletonized representation of the seismic traces consists of the low-rank latent-space variables predicted by a well-trained autoencoder neural network. The input to the autoencoder consists of seismic traces, and the implicit function theorem is used to determine the Fréchet derivative, i.e., the perturbation of the skeletonized data with respect to the velocity perturbation. The gradient is computed by migrating the shifted observed traces weighted by the skeletonized data residual, and the final velocity model is the one that best predicts the observed latent-space parameters. We denote this as inversion by Newtonian machine learning (NML) because it inverts for the model parameters by combining the forward and backward modeling of Newtonian wave propagation with the dimensional reduction capability of machine learning. Empirical results suggest that inversion by NML can sometimes mitigate the cycle-skipping problem of conventional full-waveform inversion (FWI). Numerical tests with synthetic and field data demonstrate the success of NML inversion in recovering a low-wavenumber approximation to the subsurface velocity model. The advantage of this method over other skeletonized data methods is that no manual picking of important features is required because the skeletal data are automatically selected by the autoencoder. The disadvantage is that the inverted velocity model has less resolution compared with the FWI result, but it can serve as a good initial model for FWI. Our most significant contribution is that we provide a general framework for using wave-equation inversion to invert skeletal data generated by any type of neural network. In other words, we have combined the deterministic modeling of Newtonian physics and the pattern matching capabilities of machine learning to invert seismic data by NML.

A Latent Space Modeling Approach to Interfirm Relationship Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 1-44
Author(s):  
Ka Chung Ng ◽  
Mike K. P. So ◽  
Kar Yan Tam
Keyword(s):  
Diffusion Of Innovations ◽  
Dynamic Networks ◽  
Interfirm Relationships ◽  
Alliance Formation ◽  
Relationship Analysis ◽  
Interactive Behavior ◽  
Latent Space ◽  
Space Modeling ◽  
Interfirm Network ◽  
Space Approach

Interfirm relationships are crucial to our understanding of firms’ collective and interactive behavior. Many information systems-related phenomena, including the diffusion of innovations, standard alliances, technology collaboration, and outsourcing, involve a multitude of relationships between firms. This study proposes a latent space approach to model temporal change in a dual-view interfirm network. We assume that interfirm relationships depend on an underlying latent space; firms that are close to each other in the latent space are more likely to develop a relationship. We construct the latent space by embedding two dynamic networks of firms in an integrated manner, resulting in a more comprehensive view of an interfirm relationship. We validate our approach by introducing three business measures derived from the latent space model to study alliance formation and stock comovement. We illustrate how the trajectories of firms provide insights into alliance activities. We also show that our proposed measures have strong predictive power on stock comovement. We believe the proposed approach enriches the methodology toolbox of IS researchers in studying interfirm relationships.

scholarly journals Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Azadeh Rezazadeh Hamedani ◽  
Mohammad Hossein Moattar ◽  
Yahya Forghani
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Geodesic Distance ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Latent Space ◽  
Space Modeling

AbstractDissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.

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.”

Sign in / Sign up

Export Citation Format

Share Document