A novel method for estimating group-representative functional networks from multi-subject fMRI data.
arXiv research
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The paper introduces group-representative clustering to ensure fair representation of different groups in clusters.
We study geodesics on a planar Riemann surface of infinite type having a single infinite end. Of particular interest is the class of geodesics that go out the infinite end in a most efficient manner. We investigate properties of these geodesics and relate them to the structure of the boundary of a Dirichlet polygon for…
Quadratic-time algorithm computes stretch factors and foliations for pseudo-Anosov mapping classes.
We consider the following question: Which parameters in the extension of a rational pleating ray across the boundary of $\Cal M$, the Maskit embedding of the Teichmüller space of once punctured tori correspond to a Kleinian group? Using methods of Keen and Series and Wright we prove a local result, stating that on each…
There are multiple sides to every story, and while statistical topic models have been highly successful at topically summarizing the stories in corpora of text documents, they do not explicitly address the issue of learning the different sides, the viewpoints, expressed in the documents. In this paper, we show how thes…
Factor analysis provides linear factors that describe relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also na…
For a oriented genus g surface with one boundary component, S, the Torelli group is the group of orientation preserving homeomorphisms of S that induce the identity on homology. The Magnus representation of the Torelli group represents the action on F/F" where F=pi_1(S) and F" is the second term of the derived series. …
The paper proves rigidity results for Anosov flows and their orbit equivalences.
Bayesian model transfers knowledge across different engineering fleets.
DMTG groups tasks for multi-task learning in one shot.
A new algorithm reduces frequentist regret in multi-agent bandit problems with sparse hypergraphs.