142 citations
- Oak Ridge National LaboratoryUS15 papers
- University of MichiganUS10 papers
- Los Alamos National LaboratoryUS9 papers
- Louisiana State UniversityUS9 papers
- Vanderbilt UniversityUS9 papers
- The University of Texas at AustinUS7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Observatoire de ParisFR6 papers
- University of AugsburgDE6 papers
- University of Tennessee at KnoxvilleUS6 papers
- Center for Astrophysics Harvard & SmithsonianUS5 papers
- Chinese Academy of SciencesCN5 papers
8 papers · 1 filter
Nonequilibrium DMFT+CPA for Correlated Disordered Systems
Eric Dohner, Hanna Terletska, Ka-Ming Tam +2
We present a solution for the nonequilibrium dynamics of an interacting disordered system. The approach adapts the combination of the equilibrium dynamical mean field theory (DMFT)…
Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning
Xin Yang, Ning Zhang, Donglin Wang
The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to cla…
Dynamical Cluster Approximation Study of Electron Localization in the Extended Hubbard Model
Hanna Terletska, Sergei Iskakov, Thomas Maier +1
We perform a detailed study of the phase transitions and mechanisms of electron localization in the extended Hubbard model using the dynamical cluster approximation on a $2\times 2…
Dynamical mean-field theory of the Anderson-Hubbard model with local and non-local disorder in tensor formulation
A. Weh, Y. Zhang, A. Östlin +7
To explore correlated electrons in the presence of local and non-local disorder, the Blackman-Esterling-Berk method for averaging over off-diagonal disorder is implemented into dyn…
Non-local corrections to the typical medium theory of Anderson localization
H. Terletska, A. Moilanen, K. -M. Tam +6
We use the recently developed finite cluster typical medium approach to study the Anderson localization transition in three dimensions. Applying our method to the box and binary al…
Data-driven deep learning algorithms for time-varying infection rates of COVID-19 and mitigation measures
K. D. Olumoyin, A. Q. M. Khaliq, K. M. Furati
Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during…