Do foreign investors have an edge? : a study of Norwegian equity mutual funds

This paper examines the performance and investor behavior of Norwegian equity mutual funds in the time periods 1990-2012 and 2006-2012, respectively. The behavioral part try to reveal differences between local (Norwegian) and foreign (non-Norwegian) investors in light of home bias. We find that 94 per cent of the mutual funds are not expected to generate a significant positive alpha, excluding transaction costs. After deducting returns from capital assets, illustrations find that foreigners have more volatile cash flows than locals. We test if these differences are due to irrational biases, different risk profiles or information advantages. We provide significant evidence that three month average historic returns can predict larger changes in foreign capital assets than in local, which indicates that foreigners chase performance more than locals. Furthermore, we find that changes in foreign capital assets predict three- and six month average return better than locals, hence we can exclude the irrational bias story. Finally, after controlling for risk, we find significant positive alpha for foreigners and no significant results for locals, which exclude the different risk profile story. Overall, this suggests that foreign investors generate positive return because they have an information advantage

Calculation of soil volume loss caused by drilling of anchors

Accurate prediction of ground settlements related to deep supported excavations or foundation works are key in risk assessments of vulnerability of neighboring assets. Several studies show that rotary percussive duplex drilling of casings for tieback anchors and piles can cause substantial local soil volume loss (cavities) around the casings resulting in ground settlements. This paper presents FE back-analysis of a well-documented deep supported excavation in soft clay to investigate the influence from such soil volume loss on the surrounding ground. The analysis demonstrates a simple approach to estimate potential installation effects from overburden drilling by modelling volume loss in specified soil clusters. The method can be implemented in early-planning risk assessments in building projects to assess influence areas and suitability of drilling methods.Calculation of soil volume loss caused by drilling of anchorspublishedVersio

More efficient manual review of automatically transcribed tabular data

Machine learning methods have proven useful in transcribing historical data. However, results from even highly accurate methods require manual verification and correction. Such manual review can be time-consuming and expensive, therefore the objective of this paper was to make it more efficient. Previously, we used machine learning to transcribe 2.3 million handwritten occupation codes from the Norwegian 1950 census with high accuracy (97%). We manually reviewed the 90,000 (3%) codes with the lowest model confidence. We allocated those 90,000 codes to human reviewers, who used our annotation tool to review the codes. To assess reviewer agreement, some codes were assigned to multiple reviewers. We then analyzed the review results to understand the relationship between accuracy improvements and effort. Additionally, we interviewed the reviewers to improve the workflow. The reviewers corrected 62.8% of the labels and agreed with the model label in 31.9% of cases. About 0.2% of the images could not be assigned a label, while for 5.1% the reviewers were uncertain, or they assigned an invalid label. 9,000 images were independently reviewed by multiple reviewers, resulting in an agreement of 86.43% and disagreement of 8.96%. We learned that our automatic transcription is biased towards the most frequent codes, with a higher degree of misclassification for the lowest frequency codes. Our interview findings show that the reviewers did internal quality control and found our custom tool well-suited. So, only one reviewer is needed, but they should report uncertainty.Comment: 19 pages, 5 figures, 1 tabl

Occode: an end-to-end machine learning pipeline for transcription of historical population censuses

Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end machine learning pipeline that scales to the dataset size, and a model that achieves high accuracy with few manual transcriptions. In addition, the correctness of the model results must be verified. This paper describes our lessons learned developing, tuning, and using the Occode end-to-end machine learning pipeline for transcribing 7,3 million rows with handwritten occupation codes in the Norwegian 1950 population census. We achieve an accuracy of 97% for the automatically transcribed codes, and we send 3% of the codes for manual verification. We verify that the occupation code distribution found in our result matches the distribution found in our training data which should be representative for the census as a whole. We believe our approach and lessons learned are useful for other transcription projects that plan to use machine learning in production. The source code is available at: https://github.com/uit-hdl/rhd-code

Late onset sensory-motor axonal neuropathy, a novel SLC12A6 related phenotype

We describe five families from different regions in Norway with a late onset autosomal dominant hereditary polyneuropathy sharing a heterozygous variant in the SLC12A6 gene. Mutations in the same gene have previously been described in infants with autosomal recessive hereditary motor and sensory neuropathy with corpus callosum agenesis and mental retardation (Andermann syndrome), and in a few case-reports describing dominantly acting de novo mutations, most of them with onset in childhood. The phenotypes in our families demonstrated heterogeneity. Some of our patients only had subtle to moderate symptoms and some individuals even no complaints. None had central nervous system manifestations. Clinical and neurophysiological evaluations revealed a predominant sensory axonal polyneuropathy with slight to moderate motor components. In all ten patients the identical SLC12A6 missense variant, NM_001365088.1 c.1655G > A p.(Gly552Asp), was identified. For functional characterization, the mutant potassium chloride cotransporter 3 was modelled in Xenopus oocytes. This revealed a significant reduction in potassium influx for the p.(Gly552Asp) substitution. Our findings further expand the spectrum of SLC12A6 disease, from biallelic hereditary motor and sensory neuropathy with corpus callosum agenesis and mental retardation and monoallelic early-onset hereditary motor and sensory neuropathy caused by de novo mutations, to late onset autosomal dominant axonal neuropathy with predominant sensory deficits

Comparison of glioma stem cells to neural stem cells from the adult human brain identifies dysregulated Wnt- signaling and a fingerprint associated with clinical outcome

AbstractGlioblastoma is the most common brain tumor. Median survival in unselected patients is <10 months. The tumor harbors stem-like cells that self-renew and propagate upon serial transplantation in mice, although the clinical relevance of these cells has not been well documented. We have performed the first genome-wide analysis that directly relates the gene expression profile of nine enriched populations of glioblastoma stem cells (GSCs) to five identically isolated and cultivated populations of stem cells from the normal adult human brain. Although the two cell types share common stem- and lineage-related markers, GSCs show a more heterogeneous gene expression. We identified a number of pathways that are dysregulated in GSCs. A subset of these pathways has previously been identified in leukemic stem cells, suggesting that cancer stem cells of different origin may have common features. Genes upregulated in GSCs were also highly expressed in embryonic and induced pluripotent stem cells. We found that canonical Wnt-signaling plays an important role in GSCs, but not in adult human neural stem cells. As well we identified a 30-gene signature highly overexpressed in GSCs. The expression of these signature genes correlates with clinical outcome and demonstrates the clinical relevance of GSCs

Forurensning i de norske havområdene - Barentshavet, Norskehavet og Nordsjøen - Rapport fra Overvåkingsgruppen 2021

Denne rapporten sammenstiller kunnskap om forurensningstilstanden i forvaltningsplanområdene Barentshavet, Norskehavet og Nordsjøen/Skagerrak, med hovedvekt på miljøgifter og radioaktiv forurensning. Rapporten oppsummerer resultater fra overvåkning av Overvåkingsgruppens indikatorsett, som publiseres på miljostatus.no, det vil si 43 indikatorer for forurensning og fire indikatorer for potensielt forurensende menneskelig aktivitet i havområdene. Rapporten inneholder også annen relevant kunnskap. Det er også gjort en evaluering av indikatorsettet og de ulike indikatorenes egnethet til å vurdere tilstand, utvikling og effekter på mattrygghet og miljøkvalitet.publishedVersio

miRNA-mRNA Integrated Analysis Reveals Roles for miRNAs in Primary Breast Tumors

Peer reviewe

First results from the L3+C experiment at CERN

The L3+C experiment combines the high-precision spectrometer of the L3 detector at LEP, CERN, with a small air shower array. The momenta of cosmic ray induced muons can be measured from 20 to 2000 GeV/c. During the 1999 data taking period 5 billion muon events were recorded in the spectrometer. From April until mid Summer 2000 an additional 3 billion muon events have been recorded as well as 25 million air shower events. Here the first results on the muon momentum spectrum and charge ratio will be presented