A database for publications published by researchers and students at SimulaMet.
Research area
Publication type
- All (359)
- Journal articles (130) Remove Journal articles <span class="counter">(130)</span> filter
- Books (2)
- Edited books (1)
- Proceedings, refereed (161)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (7)
- Talks, invited (18)
- Talks, contributed (15)
- Public outreach (3)
- Miscellaneous (8)
Journal articles
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
Algorithms, no. 1 (2023): 30.Status: Published
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Algorithms |
Issue | 1 |
Pagination | 30 |
Date Published | Jan-01-2023 |
Publisher | MDPI |
URL | https://www.mdpi.com/1999-4893/16/1/30 |
DOI | 10.3390/a16010030 |
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Trauma, Violence, & Abuse (2023).Status: Accepted
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Livestreaming of child sexual abuse is an established form of online child sexual exploitation
and abuse. However, only a limited body of research has examined this issue. The Covid-19
pandemic has accelerated internet use and user knowledge of livestreaming services
emphasising the importance of understanding this crime. In this scoping review, existing
literature was brought together through an iterative search of eight databases containing peer-
reviewed journal articles, as well as grey literature. Records were eligible for inclusion if the
primary focus was on livestream technology and online child sexual exploitation and abuse,
the child being defined as eighteen years or younger. Fourteen of the 2,218 records were
selected. The data were charted and divided into four categories: victims, offenders,
legislation, and technology. Limited research, differences in terminology, study design, and
population inclusion criteria present a challenge to drawing general conclusions on the
current state of livestreaming of child sexual abuse. The records show that victims are
predominantly female. The average livestream offender was found to be older than the
average online child sexual abuse offender. Therefore, it is unclear whether the findings are
representative of the global population of livestream offenders. Furthermore, there appears to
be a gap in what the records show on platforms and payment services used and current digital
trends. The lack of a legal definition and privacy considerations pose a challenge to
investigation, detection, and prosecution. The available data allow some insights into a
potentially much larger issue.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Trauma, Violence, & Abuse |
Publisher | SAGE Publications |
MatCoupLy: Learning coupled matrix factorizations with Python
SoftwareX 21, no. 101294 (2023).Status: Published
MatCoupLy: Learning coupled matrix factorizations with Python
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | SoftwareX |
Volume | 21 |
Issue | 101294 |
Date Published | Feb-01-2023 |
Publisher | Elsevier |
ISSN | 2352-7110 |
URL | https://linkinghub.elsevier.com/retrieve/pii/Shttps://www.sciencedirect.... |
DOI | 10.1016/j.softx.2022.101292 |
PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment
Nature Scientific Data 10 (2023).Status: Published
PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Nature Scientific Data |
Volume | 10 |
Publisher | Nature |
URL | https://doi.org/10.1038/s41597-023-01981-y |
DOI | 10.1038/s41597-023-01981-y |
Journal articles
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 |
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 |
Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning
Geophysical Journal International 230, no. 2 (2022): 1305-1317.Status: Published
Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning
Seismic signals generated by iceberg calving can be used to monitor ice loss at tidewater glaciers with high temporal resolution and independent of visibility. We combine the Empirical Matched Field (EMF) method and machine learning using Convolutional Neural Networks (CNNs) for calving event detection at the SPITS seismic array and the single broadband station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals generated by events in a confined target region similar to single P and/or S phase templates by assessing the beam power obtained using empirical phase delays between the array stations. The false detection rate depends on threshold settings and therefore needs appropriate tuning or, alternatively, post-processing. We combine the EMF detector at the SPITS array, as well as an STA/LTA detector at the KBS station, with a post-detection classification step using CNNs. The CNN classifier uses waveforms of the three-component record at KBS as input. We apply the methodology to detect and classify calving events at tidewater glaciers close to the KBS station in the Kongsfjord region in Northwestern Svalbard. In a previous study, a simpler method was implemented to find these calving events in KBS data, and we use it as the baseline in our attempt to improve the detection and classification performance. The CNN classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples, and regional tectonic seismic events. Subsequently, we process continuous data of 6 months in 2016. We test different CNN architectures and data augmentations to deal with the limited training data set available. Targeting Kronebreen, one of the most active glaciers in the Kongsfjord region, we show that the best performing models significantly improve the baseline classifier. This result is achieved for both the STA/LTA detection at KBS followed by CNN classification, as well as EMF detection at SPITS combined with a CNN classifier at KBS, despite of SPITS being located at 100 km distance from the target glacier in contrast to KBS at 15 km distance. Our results will further increase confidence in estimates of ice loss at Kronebreen derived from seismic observations which in turn can help to better understand the impact of climate change in Svalbard.
Afilliation | Scientific Computing, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Geophysical Journal International |
Volume | 230 |
Issue | 2 |
Pagination | 1305–1317 |
Date Published | 09/2022 |
Publisher | Oxford University Press |
ISSN | 0956-540X |
URL | https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggac117/655... |
DOI | 10.1093/gji/ggac117 |
Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness
IEEE Signal Processing Magazine 39, no. 4 (2022): 8-24.Status: Published
Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness
Afilliation | Machine Learning |
Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | IEEE Signal Processing Magazine |
Volume | 39 |
Issue | 4 |
Pagination | 8-24 |
Date Published | 06/2022 |
Publisher | IEEE |
DOI | 10.1109/MSP.2022.3163870 |
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Information Sciences (2022).Status: Published
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Information Sciences |
Publisher | arXiv |
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection
Medical Image Analysis (2022).Status: Published
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Medical Image Analysis |
Publisher | Medical Image Analysis |