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Mikel Garcia de Andoin, Alberto Bottarelli, Sebastian Schmitt, Philipp Hauke, Mikel Sanz , " Formulation of the Electric Vehicle Charging and Routing Problem for a Hybrid Quantum-Classical Search Space Reduction Heuristic", International Conference on Intelligent Transportation Systems, 2024.
AbstractCombinatorial optimization problems have attracted much interest in the quantum computing community in the recent years as a potential testbed to showcase quantum advantage. In this paper, we show how to exploit multilevel carriers of quantum information -- qudits -- for the construction of algorithms for constrained quantum optimization. These systems have been recently introduced in the context of quantum optimization and they allow us to treat...
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Andreas Neofytou, Thiago de Jesus de Araujo Rios, Mariusz Bujny, Stefan Menzel, Hyunsun Alicia Kim , "Level Set Topology Optimization with Sparse Automatic Differentiation", Structural and Multidisciplinary Optimization, 2024.
AbstractAnalytical differentiation for a smooth and accurate sensitivity field is typically used for efficient structural and multidisciplinary optimization. However, it can be challenging for multiphysics and non-linear problems. An alternative approach is automatic differentiation (AD). For large problems with many design variables AD can be computationally expensive and memory demanding and thus its use is still limited. To address some of these chall...
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Christiane Wiebel , "Visual control of action in complex sensorimotor situations", European Conference on Visual Perception, 2024.
AbstractHumans successfully interact with their dynamic environment through a large repertoire of movements. Whether reaching out to an object, walking through a busy street, or even steering a car, humans actively move their eyes and head to direct their gaze to positions of interest, fostering movement planning and control. However, although the properties of the environment, and thus the positions of interest, can rapidly change, humans can direct the...
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Andrea Castellani, Konstantinos Paraschakis, Ioannis Tsamardinos, Giorgos Borboudakis , "Confidence Interval Estimation of Predictive Performance in the Context of AutoML", AutoML24, 2024.
AbstractAny supervised machine learning analysis is required to provide an estimate of the out-of- sample predictive performance. However, it is imperative to also provide a quantification of the uncertainty of this performance in the form of a confidence or credible interval (CI) and not just a point estimate. In an AutoML setting, estimating the CI is challenging due to the “winner’s curse", i.e., the bias of estimation due to cross-validating seve...
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Muhammad Ashfaq, Ahmed Sadik, Tommi Mikkonen, Muhammad Waseem, Niko Mäkitalo , "Holon Programming Model for Adhoc and Scalable System of Systems", The 11th IEEE International Conference on Software Defined Systems (SDS-2024), 2024.
AbstractAs digital ecosystems evolve into increasingly complex networks, harnessing their collective potential becomes paramount. This paper focuses on developing software for smart ecosystems by programming their underlying System-of-Systems (SoS)—a domain ripe with opportunities and challenges. This paper introduces the Holon Programming Model (HPM), a novel conceptual framework designed to enhance the programmability of SoS. An SoS in this context r...
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David Ehrlich, Kyle Poland, Abdullah Makkeh, Felix Lanfermann, Patricia Wollstadt, Michael Wibral , "Partial Information Decomposition for Continuous Variables based on Shared Exclusions: Analytical Formulation and Estimation", Physical Review E, vol. 110, 2024.
AbstractDescribing statistical dependencies is foundational to empirical scientific research. For uncovering intricate and possibly non-linear dependencies between a single target variable and several source variables within a system, a principled and versatile framework can be found in the theory of Partial Information Decomposition (PID). Nevertheless, the majority of existing PID measures are restricted to categorical variables, while many systems of ...
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Judith Dörrenbächer, Tuan Vu Pham, Thomas H Weisswange, Marc Hassenzahl , "Role-Play Methods to Explore Social Dynamics in Human-Robot Groups", ACM Conference on Designing Interactive Systems (DIS 2024): Workshop on Creative Robotics Theatre, 2024.
AbstractWithin the realm of Human-Human-Robot Interaction (HHRI), it has been discussed that robots interacting in groups of people encounter a level of social complexity that can take on an unpredictable life of its own [5,8]. Consequently, we are interested in group-level phenomena. In particular, we investigate socio-emotional aspects (e.g., identity formation [Forsyth & Elliott, 1999], group moods [Kelly & Barsade, 2001], social influence, and group ...
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Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Knowledge Transfer for Dynamic Multi-Objective Optimization With a Changing Number of Objective", IEEE Transactions on Emerging Topics in Computational Intelligence, 2024.
AbstractDifferent from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMO...
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Jonathan Jakob , "Incremental Learning in Regression Contexts", Bielefeld University, 2024.
AbstractIncremental or Online Learning is a Machine Learning paradigm in which models are updated with each new incoming data sample. In the real world, these models are usually deployed on Data Streams, that are potentially infinite in size and therefore cannot be tackled by common Batch or Offline Learning approaches. Other reasons for using incremental algorithms are continuously evolving Data Streams, Concept Drift or specific objectives like Persona...
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Simon Kohaut, Benedict Flade, Devendra Dhami, Julian Eggert, Kristian Kersting , "Towards Probabilistic Clearance, Explanation and Optimization", 2024 International Conference on Unmanned Aircraft Systems (ICUAS), 2024.
AbstractEmploying Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing and challenging task. While UAS have the potential to significantly enhance today's logistics and emergency response capabilities, unmanned flying objects above the heads of unprotected pedestrians induce similarly significant safety risks. In this work, we make strides towards improved safety and legal compliance in applying UAS in two ways. First, we d...
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