University of Tübingen

About: University of Tübingen is a education organization based out in Tübingen, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 40555 authors who have published 84108 publications receiving 3015320 citations. The organization is also known as: Eberhard Karls University & Eberhard-Karls-Universität Tübingen.

Geometry of the Time-Dependent Variational Principle in Quantum Mechanics

Abstract: The time-dependent variational principle (TDVP).- Lie algebras and symplectic structures.- Symplectic structure on group representation spaces.- Geometry of the TDVP.- Simple applications.- The unitary group and examples from many-body physics.- The group SU(1,1) and rotationally invariant Hamiltonians.- Summary.

Prevention and early detection of prostate cancer

Abstract: Prostate cancer is a common malignancy in men and the worldwide burden of this disease is rising. Lifestyle modifications such as smoking cessation, exercise, and weight control offer opportunities to reduce the risk of developing prostate cancer. Early detection of prostate cancer by prostate-specific antigen (PSA) screening is controversial, but changes in the PSA threshold, frequency of screening, and the use of other biomarkers have the potential to minimise the overdiagnosis associated with PSA screening. Several new biomarkers for individuals with raised PSA concentrations or those diagnosed with prostate cancer are likely to identify individuals who can be spared aggressive treatment. Several pharmacological agents such as 5α-reductase inhibitors and aspirin could prevent development of prostate cancer. In this Review, we discuss the present evidence and research questions regarding prevention, early detection of prostate cancer, and management of men either at high risk of prostate cancer or diagnosed with low-grade prostate cancer.

Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Abstract: Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 33 x 33 px features and Alexnet performance for 17 x 17 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.

Reactor electron antineutrino disappearance in the Double Chooz experiment

Abstract: The Double Chooz experiment has observed 8,249 candidate electron antineutrino events in 227.93 live days with 33.71 GW-ton-years (reactor power x detector mass x livetime) exposure using a 10.3 cubic meter fiducial volume detector located at 1050 m from the reactor cores of the Chooz nuclear power plant in France. The expectation in case of theta13 = 0 is 8,937 events. The deficit is interpreted as evidence of electron antineutrino disappearance. From a rate plus spectral shape analysis we find sin^2 2{\theta}13 = 0.109 \pm 0.030(stat) \pm 0.025(syst). The data exclude the no-oscillation hypothesis at 99.9% CL (3.1{\sigma}).

Compound-specific stable isotope analysis of organic contaminants in natural environments: a critical review of the state of the art, prospects, and future challenges

Abstract: Compound-specific stable isotope analysis (CSIA) using gas chromatography-isotope ratio mass spectrometry (GC/IRMS) has developed into a mature analytical method in many application areas over the last decade. This is in particular true for carbon isotope analysis, whereas measurements of the other elements amenable to CSIA (hydrogen, nitrogen, oxygen) are much less routine. In environmental sciences, successful applications to date include (i) the allocation of contaminant sources on a local, regional, and global scale, (ii) the identification and quantification of (bio)transformation reactions on scales ranging from batch experiments to contaminated field sites, and (iii) the characterization of elementary reaction mechanisms that govern product formation. These three application areas are discussed in detail. The investigated spectrum of compounds comprises mainly n-alkanes, monoaromatics such as benzene and toluene, methyl tert-butyl ether (MTBE), polycyclic aromatic hydrocarbons (PAHs), and chlorinated hydrocarbons such as tetrachloromethane, trichloroethylene, and polychlorinated biphenyls (PCBs). Future research directions are primarily set by the state of the art in analytical instrumentation and method development. Approaches to utilize HPLC separation in CSIA, the enhancement of sensitivity of CSIA to allow field investigations in the µg L−1 range, and the development of methods for CSIA of other elements are reviewed. Furthermore, an alternative scheme to evaluate isotope data is outlined that would enable estimates of position-specific kinetic isotope effects and, thus, allow one to extract mechanistic chemical and biochemical information.