74,446 Works

Energy consumption measurement with a multichannel measurement system on a machine tool

Lukas Weiss, Konrad Wegener & Adam Gontarz

Decomposing variations and co-variations in discrete travel choice behavior with multilevel cross-classified logit model

Makoto Chikaraishi, Akimasa Fujiwara, Kay W. Axhausen & Junyi Zhang

FAST: an efficient decision procedure for deduction and static equivalence

Bruno Conchinha, David A. Basin & Carlos Caleiro
7th International Workshop on Formal Aspects of Security and Trust - FAST 2010

Untersuchungen zum Fliess- und Erosionsverhalten granularer Murgänge

Daniel Weber
Zugl. Diss., Technische Wissenschaften, Eidgenössische Technische Hochschule ETH Zürich, Nr. 15321, 2004

Sauerstoff- und Kohlenstoff-Isotopensystematik schwach metamorpher Sedimentgesteine des Oberhalbsteins (Graubünden, Schweiz)

Herbert Eppel
Diss. Naturwiss. ETH Zürich, Nr. 12369, 1997. Ref.: A. B. Thompson ; Korref.: G. L. Bernasconi Green ; Korref.: M. Burkhard

Performance Evaluation of a Vertical Line Descriptor for Omnidirectional Images

Cédric Pradalier, Davide Scaramuzza & Roland Yves Siegwart

<> New Method and Toolbox for Easily Calibrating Omnidirectional Cameras

Roland Yves Siegwart & Davide Scaramuzza

Introducing stress random fields of polycrystalline aggregates into the local approach to fracture

Bruno Sudret, X.H. Dang, A. Zeghadi, T. Yalamas & M. Berveiller

Automatic Detection of Checkerboards on Blurred and Distorted Images

Davide Scaramuzza, Roland Yves Siegwart & Martin Rufli

Assessing the representational accuracy of data-driven models: The case of the effect of urban green infrastructure on temperature

Marius Zumwald, Christoph Baumberger, David N. Bresch & Reto Knutti
Data-driven modelling with machine learning (ML) is already being used for predictions in environmental science. However, it is less clear to what extent data-driven models that successfully predict a phenomenon are representationally accurate and thus increase our understanding of the phenomenon. Besides empirical accuracy, we propose three criteria to indirectly assess the relationships learned by the ML algorithms and how they relate to a phenomenon under investigation: first, consistency of the outcomes with background knowledge;...

Characterization and Polymer Electrolyte Fuel Cell Application of Bimetallic Aerogels

Sebastian Michael Henning

Globally Consistent Assessment of Climate-related Physical Risk: A Conceptual Framework and its Application in Asset Valuation

Samuel Eberenz
Climate change poses increasing risks to what is valuable to humans around the globe. Changing, often intensifying, weather and climate extremes can increasingly be attributed to anthropogenic climate change, and change is projected to accelerate throughout the 21st century. Against this backdrop, risk awareness is growing across sectors and so is the demand for research and tools supporting efforts to mitigate climate change and adapt to its adverse consequences. Over the past decades, more and...

Robotic embankment: Free-form autonomous formation in terrain with HEAP

Dominic Jud, Ilmar Hurkxkens, Christophe Girot & Marco Hutter
Automating earth-moving tasks has the potential to resolve labour-shortage, allow for unseen designs and foster sustainability through using on-site materials. In this interdisciplinary project involving robotics and landscape architecture, we combine our previous work on autonomous excavation of free-form shapes, dynamic landscape design and terrain modelling tools into a robotic landscape system. It tightly connects survey, design and fabrication to exchange information in real-time during fabrication. We purposely built a LiDAR survey drone for tight...

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

David Hoeller, Lorenz Wellhausen, Farbod Farshidian & Marco Hutter
In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using...

Nano-vault architecture mitigates stress in silicon-based anodes for lithium-ion batteries

Marta Haro, Pawan Kumar, Junlei Zhao, Panagiotis Koutsogiannis, Alexander J. Porkovich, Zakaria Ziadi, Theodoros Bouloumis, Vidyadhar Singh, Emilio J. Juarez-Perez, Evropi Toulkeridou, Kai Nordlund, Flyura Djurabekova, Mukhles Sowwan & Panagiotis Grammatikopoulos
Nanomaterials undergoing cyclic swelling-deswelling benefit from inner void spaces that help accommodate significant volumetric changes. Such flexibility, however, typically comes at a price of reduced mechanical stability, which leads to component deterioration and, eventually, failure. Here, we identify an optimised building block for silicon-based lithium-ion battery (LIB) anodes, fabricate it with a ligand- and effluent-free cluster beam deposition method, and investigate its robustness by atomistic computer simulations. A columnar amorphous-silicon film was grown on a...

ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis

Karen Manalastas-Cantos, Petr V. Konarev, Nelly R. Hajizadeh, Alexey G. Kikhney, Dmitry S. Molodenskiy, Alejandro Panjkovich, Haydyn D. T. Mertens, Andrey Gruzinov & Clemente Borges
The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair...

Oligoproline-Based Mono- and Bivalent Minigastrin and Octreotide Analogues: Synthesis, in Vitro, and in Vivo Characterization

Andreas Ritler
Medullary thyroid cancer (MTC) and small cell lung cancer (SCLC) are both neuroendocrine tumours (NETs) with only very limited therapy options. The overexpression of peptide binding G-protein coupled receptors on the surface of cancer cells can be used for selective tumour targeting with radioactively labelled peptides for imaging and therapy. However, peptidic radioligands for the so-called peptide receptor radionuclide therapy (PRRT) often have limitations in terms of stability, specificity, bioavailability, and tumour tissue retention. The...

Simple, admissible, and accurate approximants of the inverse Langevin and Brillouin functions, relevant for strong polymer deformations and flows

Martin Kröger
Approximants to the inverse Langevin and Brillouin functions appear in diverse contexts such as polymer science, molecular dynamics simulations, turbulence modeling, magnetism, theory of rubber. The exact inverses have no analytic representations, and are typically not implemented in software distributions. Various approximants for the inverse Langevin function L-1 had been proposed in the literature. After proving asymptotic features of the inverse functions, that had apparently been overlooked in the past, we use these properties to...

Disconnection by level sets of the discrete Gaussian free field and entropic repulsion

Maximilian Nitzschner
We derive asymptotic upper and lower bounds on the large deviation probability that the level set of the Gaussian free field on Zd, d≥3, below a level α, disconnects the discrete blow-up of a compact set A from the boundary of the discrete blow-up of a box that contains A, when the level set of the Gaussian free field above α is in a strongly percolative regime. These bounds substantially strengthen the results of [21],...

Disentangling the high redshift universe

Carlton Baugh

Numerical schemes for G-Expectations

Yan Dolinsky
We consider a discrete time analog of G-expectations and we prove that in the case where the time step goes to zero the corresponding values converge to the original G-expectation. Furthermore we provide error estimates for the convergence rate. This paper is continuation of Dolinsky, Nutz, and Soner (2012). Our main tool is a strong approximation theorem which we derive for general discrete time martingales.

First stars and quasars: what are their effects on the IGM?

Michael Shull

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