A database for publications published by researchers and students at SimulaMet.
Status
Research area
Publication type
- All (835) Remove All <span class="counter">(835)</span> filter
- Journal articles (238)
- Books (9)
- Edited books (2)
- Proceedings, refereed (262)
- Book chapters (13)
- Talks, keynote (17)
- PhD theses (4)
- Proceedings, non-refereed (19)
- Posters (12)
- Technical reports (11)
- Manuals (1)
- Talks, invited (161)
- Talks, contributed (20)
- Public outreach (49)
- Miscellaneous (17)
Journal articles
A new symmetric linearly implicit exponential integrator preserving polynomial invariants or Lyapunov functions for conservative or dissipative systems
Journal of Computational Physics 449 (2022): 110800.Status: Published
A new symmetric linearly implicit exponential integrator preserving polynomial invariants or Lyapunov functions for conservative or dissipative systems
A new symmetric linearly implicit exponential integrator that preserves the polynomial first integrals or the Lyapunov functions for the conservative and dissipative stiff equations, respectively, is proposed in this work. The method is tested by both oscillated ordinary differential equations and partial differential equations, e.g., an averaged system in wind-induced oscillation, the Fermi–Pasta–Ulam systems, and the polynomial pendulum oscillators. The numerical simulations confirm the conservative properties of the proposed method and demonstrate its good behavior in superior running speed when compared with fully implicit schemes for long-time simulations.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Journal of Computational Physics |
| Volume | 449 |
| Pagination | 110800 |
| Date Published | Jan-15-2022 |
| Publisher | Journal of Computational Physics |
| ISSN | 00219991 |
| URL | https://arxiv.org/abs/2104.12118 |
| DOI | 10.1016/j.jcp.2021.110800 |
Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder
PLOS ONE (2022).Status: Accepted
Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | PLOS ONE |
| Publisher | PLOS ONE |
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
BMC Bioinformatics 23 (2022).Status: Published
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
Background: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data.
Results: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.
Conclusion: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | BMC Bioinformatics |
| Volume | 23 |
| Number | Article 31 |
| Date Published | 2022 |
| Publisher | Springer |
| DOI | 10.1186/s12859-021-04550-5 |
When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?
Information and Software Technology 143 (2022).Status: Accepted
When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?
Context: The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals.
Objective: This paper examines conditions for proper use of MMRE in effort estimation contexts.
Method: We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE.
Results: We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data.
Conclusion: MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management, EDOS: Effective Digitalization of Public Sector |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Information and Software Technology |
| Volume | 143 |
| Date Published | 03/2022 |
| Publisher | Elsevier |
When 2 + 2 should be 5: The summation fallacy in time prediction
Journal of Behavioral Decision Making (2022).Status: Accepted
When 2 + 2 should be 5: The summation fallacy in time prediction
Predictions of time (e.g., work hours) are often based on the aggregation of estimates of elements (e.g., activities, subtasks). The only types of estimates that can be safely aggregated by summation are those reflecting predicted average outcomes (expected values). The sums of other types of estimates, such as bounds of confidence intervals or estimates of the mode, do not have the same interpretation as their components (e.g., the sum of the 90% upper bounds is not the appropriate 90% upper bound of the sum). This can be a potential source of bias in predictions of time, as shown in Studies 1 and 2, where professionals with experience in estimation provided total estimates of time that were inconsistent with their estimates of individual tasks. Study 3 shows that this inconsistency can be attributed to improper aggregation of time estimates and demonstrates how this can produce both over- and underestimation—and also time prediction intervals that are far too wide. Study 4 suggests that the results may reflect a more general fallacy in the aggregation of probabilistic quantities. Our observations are consistent with that inconsistencies and biases are driven by a tendency towards applying a naïve summation (2+2=4) of probabilistic (stochastic) values, in situations where this is not appropriate. This summation fallacy may be in particular consequential in a context where informal estimation methods (expert-judgment based estimation) are used.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Journal of Behavioral Decision Making |
| Publisher | Wiley |
Realizing benefits in public IT projects: A multiple case study
a journal (2022).Status: Submitted
Realizing benefits in public IT projects: A multiple case study
IT investments in the public sector are large, and it is essential that they lead to benefits for the organizations themselves and for the wider society. While there is evidence suggesting a positive connection between the existence of benefits management practices and benefits realization, less is known about how to implement such practices effectively. The paper aims to provide insights into when benefits are most likely to be realized, and how benefits management practices and roles should be implemented in order to have a positive effect on the projects’ success in terms of realizing benefits. The authors collected data relating to ten Norwegian public IT projects. For each project, they collected data on benefits management from project documents, by interviewing the project owners and benefits owners, and follow-up surveys. The benefits internal to the organization were those with the highest degree of realization, while the societal benefits were those with the lowest degree. Projects assessed to have more specific, measurable, accountable, and realistically planned benefits were more successful in realizing benefits. Benefits owners were most effective when they were able to attract attention towards the benefits to be realized, had a strong mandate, and had domain expertise.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | a journal |
| Publisher | x |
Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels
IEEE Transactions on Vehicular Technology (2022).Status: Published
Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels
Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel state information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the art alternatives.
| Afilliation | Communication Systems |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Vehicular Technology |
| Publisher | IEEE |
| Place Published | IEEE Transactions on Vehicular Technology |
| ISSN | 0018-9545 |
| DOI | 10.1109/TVT.2022.3145011 |
DPER: Direct Parameter Estimation for Randomly missing data
Knowledge-Based Systems 240 (2022): 108082.Status: Published
DPER: Direct Parameter Estimation for Randomly missing data
{Parameter estimation is an important problem with applications in discriminant analysis, hypothesis testing, etc. Yet, when there are missing values in the data sets, commonly used imputation-based techniques are usually needed before further parameter estimation since works in direct parameter estimation exists in only limited settings. Unfortunately, such two-step procedures (imputation-parameter estimation) can be computationally expensive. Therefore, it motivates us to propose novel algorithms that directly find the maximum likelihood estimates (MLEs) for an arbitrary one-class/multiple-class randomly missing data set under some mild assumptions. Furthermore, due to the direct computation, our algorithms do not require multiple iterations through the data, thus promising to be less time-consuming while maintaining superior estimation performance than state-of-the-art methods under comparisons. We validate these claims by empirical results on various data sets of different sizes.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Knowledge-Based Systems |
| Volume | 240 |
| Pagination | 108082 |
| Publisher | Elsevier |
| ISSN | 0950-7051 |
| Keywords | MLEs, parameter estimation, Randomly missing data |
| URL | https://www.sciencedirect.com/science/article/pii/S0950705121011540 |
| DOI | 10.1016/j.knosys.2021.108082 |
Artificial Intelligence for Colonoscopy: Past, Present, and Future
IEEE Journal of Biomedical and Health Informatics (2022): 1.Status: Published
Artificial Intelligence for Colonoscopy: Past, Present, and Future
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Pagination | 1 - 1 |
| Date Published | Jan-01-2022 |
| Publisher | IEEE |
| ISSN | 2168-2194 |
| URL | https://ieeexplore.ieee.org/document/9739863/http://xplorestaging.ieee.o... |
| DOI | 10.1109/JBHI.2022.3160098 |
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
IEEE Journal of Biomedical and Health Informatics (2022).Status: Published
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests that also achieved the highest DSC score with 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Date Published | 12/2021 |
| Publisher | IEEE |
| ISSN | 2168-2194 |
| URL | https://ieeexplore.ieee.org/document/9662196 |
| DOI | 10.1109/JBHI.2021.3138024 |
