Recent Submissions 

  1. Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity 

    Isert, Clemens; Kromann, Jimmy C.; Stiefl, Nikolaus; et al. (2023)
    ACS Omega
    Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics ...
    Journal Article
  2. Changes in interactions over ecological time scales influence single-cell growth dynamics in a metabolically coupled marine microbial community 

    Daniels, Michael; van Vliet, Simon; Ackermann, Martin (2023)
    The ISME Journal
    Microbial communities thrive in almost all habitats on earth. Within these communities, cells interact through the release and uptake of metabolites. These interactions can have synergistic or antagonistic effects on individual community members. The collective metabolic activity of microbial communities leads to changes in their local environment. As the environment changes over time, the nature of the interactions between cells can ...
    Journal Article
  3. The role of management in lean implementation: evidence from the pharmaceutical industry 

    Januszek, Sven; Macuvele, Julian; Fredli, Thomas; et al. (2022)
    International Journal of Operations & Production Management
    Journal Article
  4. Selecting advanced analytics in manufacturing: a decision support model 

    Lorenz, Rafael; Kraus, Mathias; Wolf, Hergen; et al. (2022)
    Production Planning & Control
    Journal Article
  5. Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning 

    Seeliger, Arne; Weibel, Raphael P.; Feuerriegel, Stefan (2022)
    International Journal of Human-Computer Interaction
    Journal Article
  6. Understanding manufacturing repurposing: a multiple-case study of ad hoc healthcare product production during COVID-19 

    Ho, Wan Ri; Maghazei, Omid; Netland, Torbjörn (2022)
    OPERATIONS MANAGEMENT RESEARCH
    Journal Article
  7. Effect of lean implementation on team psychological safety and learning 

    Fenner, Sophie; Arellano, Maricela C.; von Dzengelevski, Oliver; et al. (2022)
    International Journal of Operations & Production Management
    Journal Article
  8. Natural variation in Avr3D1 from Zymoseptoria sp. contributes to quantitative gene-for-gene resistance and to host specificity 

    Meile, Lukas; Garrido-Arandia, María; Bernasconi, Zoe; et al. (2023)
    New Phytologist
    Successful host colonization by plant pathogens requires the circumvention of host defense responses, frequently through sequence modifications in secreted pathogen proteins known as avirulence factors (Avrs). Although Avr sequences are often polymorphic, the contribution of these polymorphisms to virulence diversity in natural pathogen populations remains largely unexplored. We used molecular genetic tools to determine how natural sequence ...
    Journal Article
  9. Space-time error estimates for deep neural network approximations for differential equations 

    Grohs, Philipp; Hornung, Fabian; Jentzen, Arnulf; et al. (2023)
    Advances in Computational Mathematics
    Over the last few years deep artificial neural networks (ANNs) have very successfully been used in numerical simulations for a wide variety of computational problems including computer vision, image classification, speech recognition, natural language processing, as well as computational advertisement. In addition, it has recently been proposed to approximate solutions of high-dimensional partial differential equations (PDEs) by means of ...
    Journal Article
  10. Entropy Maximization with Depth: A Variational Principle for Random Neural Networks 

    Joudaki, Amir; Daneshmand, Hadi; Bach, Francis (2022)
    arXiv
    To understand the essential role of depth in neural networks, we investigate a variational principle for depth: Does increasing depth perform an implicit optimization for the representations in neural networks? We prove that random neural networks equipped with batch normalization maximize the differential entropy of representations with depth up to constant factors, assuming that the representations are contractive. Thus, representations ...
    Journal Article
  11. Emerging technologies and the use case: A multi-year study of drone adoption 

    Maghazei, Omid; Lewis, Michael A.; Netland, Torbjörn (2022)
    Journal of Operations Management
    Journal Article
  12. New times for international production networks 

    Friedli, Thomas; Netland, Torbjörn (2021)
    Die Unternehmung
    Journal Article
  13. Value certainty and choice confidence are multidimensional constructs that guide decision-making 

    Lee, Douglas G.; Hare, Todd A. (2023)
    Cognitive, Affective and Behavioral Neuroscience
    The degree of certainty that decision-makers have about their evaluations of available choice alternatives and their confidence about selecting the subjectively best alternative are important factors that affect current and future value-based choices. Assessments of the alternatives in a given choice set are rarely unidimensional; their values are usually derived from a combination of multiple distinct attributes. For example, the taste, ...
    Journal Article
  14. On the Identifiability and Estimation of Causal Location-Scale Noise Models 

    Immer, Alexander; Schultheiss, Christoph; Vogt, Julia E.; et al. (2022)
    arXiv
    We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i.e., $Y = f(X) + g(X)N$. Despite the generality of the model class, we show the causal direction is identifiable up to some pathological cases. To empirically validate these theoretical ...
    Journal Article
  15. PAC-Bayesian Meta-Learning: From Theory to Practice 

    Rothfuss, Jonas; Josifoski, Martin; Fortuin, Vincent; et al. (2022)
    arXiv
    Meta-Learning aims to accelerate the learning on new tasks by acquiring useful inductive biases from related data sources. In practice, the number of tasks available for meta-learning is often small. Yet, most of the existing approaches rely on an abundance of meta-training tasks, making them prone to overfitting. How to regularize the meta-learner to ensure generalization to unseen tasks, is a central question in the literature. We provide ...
    Journal Article
  16. Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach 

    Lavaei, Abolfazl; Di Lillo, Luigi; Censi, Andrea; et al. (2022)
    IEEE Transactions on Control of Network Systems
    In this work, we propose a compositional data-driven approach for the formal estimation of collision risks for autonomous vehicles (AVs) while acting in a stochastic multi-agent framework. The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation. In our proposed ...
    Journal Article
  17. Climate impact comparison of electric and gas-powered end-user appliances 

    Dietrich, Florian; Chen, Jia; Shekhar, Ankit; et al. (2023)
    Earth's Future
    Journal Article
  18. Spatiotemporal evolution of global long-term patterns of soil moisture 

    Lal, Preet; Shekhar, Ankit; Gharun, Mana; et al. (2023)
    Science of The Total Environment
    Journal Article
  19. Do COVID-19 containment measures work? Evidence from Switzerland 

    Sturm, Jan-Egbert; Pleninger, Regina; Streicher, Sina (2022)
    Swiss Journal of Economics and Statistics
    Journal Article
  20. A Perspective on Implementation of Technology-Driven Exergames for Adults as Telerehabilitation Services 

    Meulenberg, Cécil J. W.; Marusic, Uros; de Bruin, Eling D. (2022)
    Frontiers in Psychology
    Journal Article

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