University of Copenhagen

About: University of Copenhagen is a education organization based out in Copenhagen, Denmark. It is known for research contribution in the topics: Population & Galaxy. The organization has 57645 authors who have published 149740 publications receiving 5903093 citations. The organization is also known as: Copenhagen University & Københavns Universitet.

Expert consensus document: Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA)

Abstract: Cholangiocarcinoma (CCA) is a heterogeneous group of malignancies with features of biliary tract differentiation. CCA is the second most common primary liver tumour and the incidence is increasing worldwide. CCA has high mortality owing to its aggressiveness, late diagnosis and refractory nature. In May 2015, the "European Network for the Study of Cholangiocarcinoma" (ENS-CCA: www.enscca.org or www.cholangiocarcinoma.eu) was created to promote and boost international research collaboration on the study of CCA at basic, translational and clinical level. In this Consensus Statement, we aim to provide valuable information on classifications, pathological features, risk factors, cells of origin, genetic and epigenetic modifications and current therapies available for this cancer. Moreover, future directions on basic and clinical investigations and plans for the ENS-CCA are highlighted.

The German Traffic Sign Recognition Benchmark: A multi-class classification competition

Abstract: The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

Long γ-ray bursts and core-collapse supernovae have different environments

Abstract: When massive stars exhaust their fuel, they collapse and often produce the extraordinarily bright explosions known as core-collapse supernovae. On occasion, this stellar collapse also powers an even more brilliant relativistic explosion known as a long-duration γ-ray burst. One would then expect that these long γ-ray bursts and core-collapse supernovae should be found in similar galactic environments. Here we show that this expectation is wrong. We find that the γ-ray bursts are far more concentrated in the very brightest regions of their host galaxies than are the core-collapse supernovae. Furthermore, the host galaxies of the long γ-ray bursts are significantly fainter and more irregular than the hosts of the core-collapse supernovae. Together these results suggest that long-duration γ-ray bursts are associated with the most extremely massive stars and may be restricted to galaxies of limited chemical evolution. Our results directly imply that long γ-ray bursts are relatively rare in galaxies such as our own Milky Way.

Introductory statistics with R

Abstract: Basics. - The R environment. - Probability and statistics. - Descriptive statistics and graphics. - One and two sample tests. - Regression and correlation. - ANOVA and Kruskal-Wallis. - Tabular data. - Power and the computation of sample size. - Advanced data handling. - Multiple regression. - Linear models. - Logistic regression. - Survival analysis. - Rates and Poisson regression. - Nonlinear curve-fitting. - Obtaining and installing R and the ISwR package. - Data sets in the ISwR package. - Compendium. - Answers to exercises. - Index.