A database for publications published by researchers and students at SimulaMet.
Research area
Book chapters
Multilinear Models, Iterative Methods☆
In Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier, 2020.Status: Published
Multilinear Models, Iterative Methods☆
In this section, multilinear models for multi-way arrays requiring iterative fitting algorithms are outlined. Among them: the PARAFAC (PARAllel FACtor analysis) model and one of its variants (the PARAFAC2 model); Tucker models in which one or more modes are reduced (viz., the N-way Tucker-N and Tucker-m models); hybrid models having intermediate properties between PARAFAC and Tucker ones; and coupled matrix and tensor decompositions (CMTF) which simultaneously decomposes multiple tensors. Five examples are included as to illustrate some practical aspects concerning the use of these models on analytical data.
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Book Chapter |
| Year of Publication | 2020 |
| Book Title | Reference Module in Chemistry, Molecular Sciences and Chemical Engineering |
| Publisher | Elsevier |
| ISBN Number | 978-0-12-409547-2 |
| Keywords | -way Tucker models, CANDECOMP, Curve resolution, Exploratory analysis, Least squares, Linked mode PARAFAC, Multi-way analysis, Multi-way array, Multilinear model, PARAFAC, PARAFAC2, PARALIND, Restricted Tucker models, Tensor decomposition, Tensor-matrix factorization |
| URL | http://www.sciencedirect.com/science/article/pii/B9780124095472146098 |
| DOI | 10.1016/B978-0-12-409547-2.14609-8 |
Journal articles
Cross-product penalized component analysis (X-CAN)
Chemometrics and Intelligent Laboratory Systems 203 (2020): 104038.Status: Published
Cross-product penalized component analysis (X-CAN)
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| Volume | 203 |
| Pagination | 104038 |
| Date Published | 06/2020 |
| Publisher | Elsevier |
| ISSN | 01697439 |
| DOI | 10.1016/j.chemolab.2020.104038 |
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
Cognitive Neurodynamics (2020): 1-18.Status: Published
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems, No Simula project |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Cognitive Neurodynamics |
| Pagination | 1–18 |
| Publisher | Springer |
Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions
Frontiers in Applied Mathematics and Statistics 6 (2020).Status: Published
Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Frontiers in Applied Mathematics and Statistics |
| Volume | 6 |
| Number | 41 |
| Date Published | Aug-09-2020 |
| Publisher | Frontiers |
| Keywords | CANDECOMP/PARAFAC, independent component analysis (ICA), local field potential (LFP), Neuroscience, population rate model, principal component analysis (PCA), tensor decompositions |
| URL | https://www.frontiersin.org/article/10.3389/fams.2020.00041/fullhttps://... |
| DOI | 10.3389/fams.2020.00041 |
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Scientific Data (2020).Status: Published
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Scientific Data |
| Date Published | 08/2020 |
| Publisher | Springer Nature |
| Keywords | dataset, GI, Machine learning |
| URL | http://www.nature.com/articles/s41597-020-00622-y |
| DOI | 10.1038/s41597-020-00622-y |
Proceedings, refereed
Kvasir-SEG: A Segmented Polyp Dataset
In MMM2020. Daejeon, Korea: MMM 2020 26TH INTERNATIONAL CONFERENCE ON MULTIMEDIA MODELING, 2020.Status: Published
Kvasir-SEG: A Segmented Polyp Dataset
Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep learning based CNN approach. This work will be valuable for researchers to reproduce results and compare their methods in the future. By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | MMM2020 |
| Publisher | MMM 2020 26TH INTERNATIONAL CONFERENCE ON MULTIMEDIA MODELING |
| Place Published | Daejeon, Korea |
| Keywords | Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet Fuzzy c-mean clustering, Semantic segmentation |
The Influence of Players Strategy on the perceived delay
In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020.Status: Accepted
The Influence of Players Strategy on the perceived delay
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) |
Quality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions
In ACM Multimedia Systems Conference 2020 (MMSys 2020), 2020.Status: Accepted
Quality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | ACM Multimedia Systems Conference 2020 (MMSys 2020) |
A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience
In ACM Multimedia Systems Conference 2020 (MMSys 2020), 2020.Status: Accepted
A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | ACM Multimedia Systems Conference 2020 (MMSys 2020) |
Assessing Interactive Gaming Quality of Experience Using a Crowdsourcing Approach
In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020.Status: Accepted
Assessing Interactive Gaming Quality of Experience Using a Crowdsourcing Approach
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) |