Search Results for author: Peter S. Dodds

Found 4 papers, 2 papers with code

More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy Policies

1 code implementation17 Feb 2023 Juniper Lovato, Philip Mueller, Parisa Suchdev, Peter S. Dodds

In this study, we examine a large textual dataset of privacy policies from 1997-2019 in order to investigate the data collection activities of data brokers and data processors.

Sirius: Visualization of Mixed Features as a Mutual Information Network Graph

1 code implementation9 Jun 2021 Jane L. Adams, Todd F. Deluca, Christopher M. Danforth, Peter S. Dodds, Yuhang Zheng, Konstantinos Anastasakis, Boyoon Choi, Allison Min, Michael M. Bessey

Data scientists across disciplines are increasingly in need of exploratory analysis tools for data sets with a high volume of features of mixed data type (quantitative continuous and discrete categorical).

Dimensionality Reduction feature selection +1

What we write about when we write about causality: Features of causal statements across large-scale social discourse

no code implementations20 Apr 2016 Thomas C. McAndrew, Joshua C. Bongard, Christopher M. Danforth, Peter S. Dodds, Paul D. H. Hines, James P. Bagrow

Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium.

Sentiment Analysis Topic Models

Nonlinear functional mapping of the human brain

no code implementations8 Sep 2015 Nicholas Allgaier, Tobias Banaschewski, Gareth Barker, Arun L. W. Bokde, Josh C. Bongard, Uli Bromberg, Christian Büchel, Anna Cattrell, Patricia J. Conrod, Christopher M. Danforth, Sylvane Desrivières, Peter S. Dodds, Herta Flor, Vincent Frouin, Jürgen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Scott Mackey, Jean-Luc Martinot, Kevin Murphy, Frauke Nees, Dimitri Papadopoulos-Orfanos, Luise Poustka, Michael N. Smolka, Henrik Walter, Robert Whelan, Gunter Schumann, Hugh Garavan, IMAGEN Consortium

In the present study, we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI from a 242-subject subset of the IMAGEN project, a European study of adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data.

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