A database for publications published by researchers and students at SimulaMet.
Research area
Publication type
- All (390)
- Journal articles (142)
- Books (2)
- Edited books (1)
- Proceedings, refereed (175)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (9)
- Talks, invited (20)
- Talks, contributed (15) Remove Talks, contributed <span class="counter">(15)</span> filter
- Public outreach (3)
- Master's theses (1)
- Miscellaneous (8)
Talks, contributed
A Flexible Framework for Coupled Matrix/Tensor Factorizations
In TRICAP: Three-way methods In Chemistry And Psychology, 2022.Status: Published
A Flexible Framework for Coupled Matrix/Tensor Factorizations
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | TRICAP: Three-way methods In Chemistry And Psychology |
Mono- and multi-infection patterns of HPV and the risk of cervical intraepithelial neoplasia
In ANCR Symposium, the Faroe Islands. ANCR Symposium, 2022.Status: Published
Mono- and multi-infection patterns of HPV and the risk of cervical intraepithelial neoplasia
| Afilliation | Machine Learning |
| Project(s) | DeCipher |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | ANCR Symposium, the Faroe Islands |
| Publisher | ANCR Symposium |
An AO-ADMM approach to constrained PARAFAC2
In Nordic AI Meet, 2022.Status: Published
An AO-ADMM approach to constrained PARAFAC2
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Nordic AI Meet |
Fully Constrained PARAFAC2 with AO-ADMM
In SIAM Conference on Parallel Processing for Scientific Computing, 2022.Status: Published
Fully Constrained PARAFAC2 with AO-ADMM
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | SIAM Conference on Parallel Processing for Scientific Computing |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In NuGOweek 2022 in Spain, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | NuGOweek 2022 in Spain |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In Nordic Metabolomics 2022, Copenhagen, Denmark, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Nordic Metabolomics 2022, Copenhagen, Denmark |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In Norwegian Bioinformatics Days, Sundvolden, Norway, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Norwegian Bioinformatics Days, Sundvolden, Norway |
Characterizing postprandial metabolomics response using multi-way data analysis
In Annual NORBIS Conference, 2022.Status: Published
Characterizing postprandial metabolomics response using multi-way data analysis
Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.
In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test. We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.
Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Annual NORBIS Conference |
Talks, contributed
A new linearly implicit energy-preserving exponential method for conservative or dissipative systems.
In In Manifolds and Geometric Integration Colloquia, Norway, 2021.Status: Published
A new linearly implicit energy-preserving exponential method for conservative or dissipative systems.
In this work, we propose a linearly implicit exponential integrator that preserves the invariant or the Lyapunov functions for the conservative or dissipative systems by combining the idea of exponential integrators and discrete gradient methods. Numerical simulations are shown to confirm the conservative properties of the methods, and to demonstrate the efficiency of the methods when compared to other fully implicit schemes.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | In Manifolds and Geometric Integration Colloquia, Norway |
Tracing Dynamic Networks through Constrained Parafac2 Decomposition
In SIAM Conference on Applied Linear Algebra (LA21), Virtual Conference. SIAM, 2021.Status: Published
Tracing Dynamic Networks through Constrained Parafac2 Decomposition
Time-evolving data analysis is crucial for understanding complex systems such as the brain. Methods that assume static networks have successfully recovered spatial networks of connectivity from neuroimaging data. Still, discovering both underlying networks and their evolution is a challenging task.
To capture temporal evolution of connectivity networks, we arrange dynamic data as a tensor and use a tensor factorization method called PARAFAC2. PARAFAC2 deciphers the hidden structure of dynamic networks and yields unique and interpretable components. Preliminary results using PARAFAC2 in neuroimaging data analysis are promising. However, the constant cross-product constraint on the time-evolving mode hinders the use of additional constraints or regularization (e.g. spatial smoothness) on this mode. Currently, the only way to regularize the time-evolving mode of a PARAFAC2 model is with a flexible coupling approach, which finds the solution through regularized least-squares subproblems. Instead, we use an alternating direction method of multipliers (ADMM) based approach to widen the possible regularization penalties to any proximable function.
Our numerical experiments demonstrate that the proposed ADMM-based algorithmic approach for PARAFAC2 can accurately recover the underlying evolving components, is flexible in terms of imposing constraints and also computationally efficient.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | SIAM Conference on Applied Linear Algebra (LA21), Virtual Conference |
| Publisher | SIAM |