University of Iceland

About: University of Iceland is a education organization based out in Reykjavik, Suðurnes, Iceland. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 5423 authors who have published 16199 publications receiving 694762 citations. The organization is also known as: Háskóli Íslands.

Researching with children: Ethical tensions

Abstract: There is a need to reflect on both the processes and outcomes of the range of approaches aimed at promoting children’s engagement in research, with the specific intent of listening to children’s vo...

"I have turned into a foreman here at home." Families and work-life balance in times of Covid-19 in a gender equality paradise.

Abstract: This article explores the gendered realities of work-life balance in Iceland during the Covid-19 pandemic, in particular how these societal changes reflect and affect the gendered division of unpaid labor, such as childcare and household chores. The study draws on open ended real-time diary entries, collected for two weeks during the peak of the pandemic in Iceland. The entries represent the voices of 37 mothers in heteronormative relationships. The findings imply that, during the pandemic, the mothers took on greater mental work than before. They also described intense emotional labor, as they tried to keep everyone calm and safe. The division of tasks at home lay on their shoulders, causing them stress and frustration. The findings suggest that, even in a country that has been at the top of the Gender Gap Index for several years, an unprecedented situation like Covid-19 can reveal and exaggerate strong gender norms and expectations towards mothers. This article is protected by copyright. All rights reserved.

Semisupervised Self-Learning for Hyperspectral Image Classification

Abstract: Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the context of hyperspectral image classification.

surface changes in the north Atlantic meridional overturning circulation during the last millennium

Abstract: Despite numerous investigations, the dynamical origins of the Medieval Climate Anomaly and the Little Ice Age remain uncertain. A major unresolved issue relating to internal climate dynamics is the mode and tempo of Atlantic meridional overturning circulation variability, and the significance of decadal-to-centennial scale changes in Atlantic meridional overturning circulation strength in regulating the climate of the last millennium. Here we use the time-constrained high-resolution local radiocarbon reservoir age offset derived from an absolutely dated annually resolved shell chronology spanning the past 1,350 years, to reconstruct changes in surface ocean circulation and climate. The water mass tracer data presented here from the North Icelandic shelf, combined with previously published data from the Arctic and subtropical Atlantic, show that surface Atlantic meridional overturning circulation dynamics likely amplified the relatively warm conditions during the Medieval Climate Anomaly and the relatively cool conditions during the Little Ice Age within the North Atlantic sector. Palaeoclimate proxies, such as shells, record past ocean changes. A radiocarbon study based on a shell chronology from the Icelandic shelf is used to track changes in ocean circulation and climate for the past 1,350 years, suggesting a declining influence of North Atlantic surface waters on the Icelandic shelf over the last millennium.

Peri-implantitis susceptibility as it relates to periodontal therapy and supportive care

Abstract: To assess the long-term survival of implants inserted in periodontally susceptible patients and to investigate the influence of residual pockets on the incidence of peri-implantitis and implant loss.