10 research outputs found
Computational Nuclear Physics and Post Hartree-Fock Methods
We present a computational approach to infinite nuclear matter employing
Hartree-Fock theory, many-body perturbation theory and coupled cluster theory.
These lectures are closely linked with those of chapters 9, 10 and 11 and serve
as input for the correlation functions employed in Monte Carlo calculations in
chapter 9, the in-medium similarity renormalization group theory of dense
fermionic systems of chapter 10 and the Green's function approach in chapter
11. We provide extensive code examples and benchmark calculations, allowing
thereby an eventual reader to start writing her/his own codes. We start with an
object-oriented serial code and end with discussions on strategies for porting
the code to present and planned high-performance computing facilities.Comment: 82 pages, to appear in Lecture Notes in Physics (Springer), "An
advanced course in computational nuclear physics: Bridging the scales from
quarks to neutron stars", M. Hjorth-Jensen, M. P. Lombardo, U. van Kolck,
Editor
Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium
Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa
An appraisal of environmental conflict management provisions in New Zealand's Resource Management Act 1991
End-to-End models for the analysis of marine ecosystems : challenges, issues, and next steps
P.0035 Structural brain correlates of childhood inhibited temperament: rationale and methodology for an ENIGMA-Anxiety mega-analysis
NWORubicon 019.201SG.022Pathways through Adolescenc