Institution
University of Zurich
Education•Zurich, Switzerland•
About: University of Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Medicine. The organization has 50842 authors who have published 124042 publications receiving 5304521 citations. The organization is also known as: UZH & Uni Zurich.
Topics: Population, Medicine, Context (language use), Gene, Transplantation
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls.
683 citations
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TL;DR: RedMaPPer as discussed by the authors is a new red sequence cluster finder designed to make optimal use of ongoing and near-future large photometric surveys, which can iteratively self-train the red sequence model based on a minimal spectroscopic training sample.
Abstract: We describe redMaPPer, a new red sequence cluster finder specifically designed to make optimal use of ongoing and near-future large photometric surveys. The algorithm has multiple attractive features: (1) it can iteratively self-train the red sequence model based on a minimal spectroscopic training sample, an important feature for high-redshift surveys. (2) It can handle complex masks with varying depth. (3) It produces cluster-appropriate random points to enable large-scale structure studies. (4) All clusters are assigned a full redshift probability distribution P(z). (5) Similarly, clusters can have multiple candidate central galaxies, each with corresponding centering probabilities. (6) The algorithm is parallel and numerically efficient: it can run a Dark Energy Survey-like catalog in ~500 CPU hours. (7) The algorithm exhibits excellent photometric redshift performance, the richness estimates are tightly correlated with external mass proxies, and the completeness and purity of the corresponding catalogs are superb. We apply the redMaPPer algorithm to ~10, 000 deg2 of SDSS DR8 data and present the resulting catalog of ~25,000 clusters over the redshift range z [0.08, 0.55]. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter σ z ≈ 0.006 at z ≈ 0.1, increasing to σ z ≈ 0.02 at z ≈ 0.5 due to increased photometric noise near the survey limit. The median value for |Δz|/(1 + z) for the full sample is 0.006. The incidence of projection effects is low (≤5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and Sunyaev-Zel'dovich catalogs are presented in a companion paper.
683 citations
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17 Dec 2016TL;DR: This work proposes a different approach to perceive forest trials based on a deep neural network used as a supervised image classifier that outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task.
Abstract: We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.
682 citations
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TL;DR: The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe, and is detailed in this paper.
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
681 citations
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TL;DR: A combination of two further approaches: family level inference and Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure are proposed.
Abstract: Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.
680 citations
Authors
Showing all 51384 results
Name | H-index | Papers | Citations |
---|---|---|---|
Richard A. Flavell | 231 | 1328 | 205119 |
Peer Bork | 206 | 697 | 245427 |
Thomas C. Südhof | 191 | 653 | 118007 |
Stuart H. Orkin | 186 | 715 | 112182 |
Ruedi Aebersold | 182 | 879 | 141881 |
Tadamitsu Kishimoto | 181 | 1067 | 130860 |
Stanley B. Prusiner | 168 | 745 | 97528 |
Yang Yang | 164 | 2704 | 144071 |
Tomas Hökfelt | 158 | 1033 | 95979 |
Dan R. Littman | 157 | 426 | 107164 |
Hans Lassmann | 155 | 724 | 79933 |
Matthias Egger | 152 | 901 | 184176 |
Lorenzo Bianchini | 152 | 1516 | 106970 |
Robert M. Strieter | 151 | 612 | 73040 |
Ashok Kumar | 151 | 5654 | 164086 |