Linköping University

About: Linköping University is a education organization based out in Linköping, Sweden. It is known for research contribution in the topics: Population & Health care. The organization has 15671 authors who have published 50013 publications receiving 1542189 citations.

Niraparib Maintenance Therapy in Platinum-Sensitive, Recurrent Ovarian Cancer

Abstract: Tesaro; Amgen; Genentech; Roche; AstraZeneca; Myriad Genetics; Merck; Gradalis; Cerulean; Vermillion; ImmunoGen; Pfizer; Bayer; Nu-Cana BioMed; INSYS Therapeutics; GlaxoSmithKline; Verastem; Mateon Therapeutics; Pharmaceutical Product Development; Clovis Oncology; Janssen/Johnson Johnson; Eli Lilly; Merck Sharp Dohme

Fullerene-Free Polymer Solar Cells with over 11% Efficiency and Excellent Thermal Stability

Abstract: A nonfullerene-based polymer solar cell (PSC) that significantly outperforms fullerene-based PSCs with respect to the power-conversion efficiency is demonstrated for the first time. An efficiency of >11%, which is among the top values in the PSC field, and excellent thermal stability is obtained using PBDB-T and ITIC as donor and acceptor, respectively.

Intrauterine growth curves based on ultrasonically estimated foetal weights.

Abstract: Available standard intrauterine growth curves based on birthweights underestimate foetal growth in preterm period. New growth curves are presented based on data from four Scandinavian centres for 759 ultrasonically estimated foetal weights in 86 uncomplicated pregnancies. Mean weight of boys exceeded that of girls by 2-3%. A uniform SD value of 12% of the mean weight was adopted for the standard curves as the true SD varied non-systematically between 9.1 and 12.4%. Applied to an unselected population of 8663 singleton births, before 210 days of gestation, 32% of birthweights were classified as small-for-gestational age (SGA; i.e. below mean - 2 SD); the corresponding figures were 11.1% for gestational ages between 210 and 258 days, and 2.6% for ages of 259 days or longer. The new growth curves reveal better the true distribution of SGA foetuses and neonates, and are suggested for use in perinatological practice.

Learning Spatially Regularized Correlation Filters for Visual Tracking

Abstract: Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model. We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.