@inbook{45895,
  author       = {{Karl, Holger and Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon and Redder, Adrian}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{183--202}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers}}},
  doi          = {{10.5281/zenodo.8068664}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45901,
  author       = {{Blömer, Johannes and Bobolz, Jan and Eidens, Fabian and Jager, Tibor and Kramer, Paul}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{237--246}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Practical Cryptograhic Techniques for Secure and Privacy-Preserving Customer Loyalty Systems}}},
  doi          = {{10.5281/zenodo.8068755}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45897,
  author       = {{Gottschalk, Sebastian and Vorbohle, Christian and Kundisch, Dennis and Engels, Gregor and Wünderlich, Nacy V.}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{203--224}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Architectural Management of OTF Computing Markets}}},
  doi          = {{10.5281/zenodo.8068691}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45891,
  author       = {{Blömer, Johannes and Eidens, Fabian and Jager, Tibor and Niehues, David and Scheideler, Christian}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{145--164}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Robustness and Security}}},
  doi          = {{10.5281/zenodo.8068629}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45882,
  author       = {{Bäumer, Frederik Simon and Chen, Wei-Fan and Geierhos, Michaela and Kersting, Joschka and Wachsmuth, Henning}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{65--84}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation}}},
  doi          = {{10.5281/zenodo.8068456}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45884,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{85--104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45878,
  author       = {{Haake, Claus-Jochen and Hehenkamp, Burkhard and Polevoy, Gleb}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{21--44}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{The Market for Services: Incentives, Algorithms, Implementation}}},
  doi          = {{10.5281/zenodo.8068414}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45886,
  author       = {{Wehrheim, Heike and Hüllermeier, Eyke and Becker, Steffen and Becker, Matthias and Richter, Cedric and Sharma, Arnab}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{105--123}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Composition Analysis in Unknown Contexts}}},
  doi          = {{10.5281/zenodo.8068510}},
  volume       = {{412}},
  year         = {{2023}},
}

@unpublished{45911,
  abstract     = {{Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.}},
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2305.13764}},
  title        = {{{Mitigating Label Noise through Data Ambiguation}}},
  year         = {{2023}},
}

@inbook{45880,
  author       = {{Elrich, Alina and Kaimann, Daniel and Fahr, René and Kundisch, Dennis and Mir Djawadi, Behnud and Müller, Michelle and Poniatowski, Martin and Schäfers, Sabrina and Frick, Bernd}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{45--64}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Empirical Analysis in Markets for OTF Services}}},
  doi          = {{10.5281/zenodo.8068430}},
  volume       = {{412}},
  year         = {{2023}},
}

@article{45816,
  author       = {{Dieter, Peter and Stumpe, Miriam and Ulmer, Marlin Wolf and Schryen, Guido}},
  journal      = {{Transportation Research Part D}},
  title        = {{{Anticipatory Assignment of Passengers to Meeting Points for Taxi-Ridesharing}}},
  volume       = {{121}},
  year         = {{2023}},
}

@techreport{46102,
  author       = {{Leineweber, Jonas}},
  publisher    = {{H-Soz-Kult}},
  title        = {{{Tagungsbericht: 10/20 Jahre UNESCO-Konvention zum Erhalt des Immateriellen Kulturerbes – Auftaktveranstaltung zum Doppeljubiläum}}},
  year         = {{2023}},
}

@inbook{46154,
  author       = {{Janzen, Thomas and Gabel, Stephan and Gampert, Martha and Matz, Frauke and Reckermann, Julia}},
  booktitle    = {{Digitalisierung in der Hochschullehre – Perspektiven und Gestaltungsoptionen}},
  editor       = {{Mrohs, Lorenz and Hess, Miriam and Lindner, Konstantin and Schlüter, Julia and Overhage, Sven}},
  pages        = {{151--154}},
  publisher    = {{University of Bamberg Press}},
  title        = {{{Das DigitELE Tutorial: Eine digitale Lernumgebung in der Englischdidaktik}}},
  year         = {{2023}},
}

@article{38041,
  abstract     = {{<jats:p>While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of these devices. As in the early days of GPUs in HPC, for workloads that can reasonably be decoupled into loosely coupled working sets, multi-accelerator support can be achieved by using standard communication interfaces like MPI on the host side. However, for performance and productivity, some applications can profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities here when extending the dataflow characteristics to their communication interfaces.</jats:p>
          <jats:p>In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA support and three missing benchmarks that particularly characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented for current boards with Intel and Xilinx FPGAs, we established a baseline for multi-FPGA performance. Additionally, for the communication-centric benchmarks, we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is currently only available for one of the boards. The evaluation with parallel execution on up to 26 FPGA boards makes use of one of the largest academic FPGA installations.</jats:p>}},
  author       = {{Meyer, Marius and Kenter, Tobias and Plessl, Christian}},
  issn         = {{1936-7406}},
  journal      = {{ACM Transactions on Reconfigurable Technology and Systems}},
  keywords     = {{General Computer Science}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks}}},
  doi          = {{10.1145/3576200}},
  year         = {{2023}},
}

@article{46243,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{ECML-PKDD}},
  location     = {{Torino}},
  title        = {{{Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras}}},
  year         = {{2023}},
}

@article{46251,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{International Joint Conference on Artificial Intelligence}},
  location     = {{Macau}},
  title        = {{{Neuro-Symbolic Class Expression Learning}}},
  year         = {{2023}},
}

@article{45361,
  abstract     = {{<jats:p> The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms. </jats:p>}},
  author       = {{Schade, Robert and Kenter, Tobias and Elgabarty, Hossam and Lass, Michael and Kühne, Thomas and Plessl, Christian}},
  issn         = {{1094-3420}},
  journal      = {{The International Journal of High Performance Computing Applications}},
  keywords     = {{Hardware and Architecture, Theoretical Computer Science, Software}},
  publisher    = {{SAGE Publications}},
  title        = {{{Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics}}},
  doi          = {{10.1177/10943420231177631}},
  year         = {{2023}},
}

@phdthesis{45780,
  author       = {{Tornede, Alexander}},
  title        = {{{Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions}}},
  doi          = {{10.17619/UNIPB/1-1780 }},
  year         = {{2023}},
}

@article{29240,
  abstract     = {{The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this,
we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position observations only, based on variational backward error analysis.}},
  author       = {{Ober-Blöbaum, Sina and Offen, Christian}},
  issn         = {{0377-0427}},
  journal      = {{Journal of Computational and Applied Mathematics}},
  keywords     = {{Lagrangian learning, variational backward error analysis, modified Lagrangian, variational integrators, physics informed learning}},
  pages        = {{114780}},
  publisher    = {{Elsevier}},
  title        = {{{Variational Learning of Euler–Lagrange Dynamics from Data}}},
  doi          = {{10.1016/j.cam.2022.114780}},
  volume       = {{421}},
  year         = {{2023}},
}

@article{29236,
  abstract     = {{The numerical solution of an ordinary differential equation can be interpreted as the exact solution of a nearby modified equation. Investigating the behaviour of numerical solutions by analysing the modified equation is known as backward error analysis. If the original and modified equation share structural properties, then the exact and approximate solution share geometric features such as the existence of conserved quantities. Conjugate symplectic methods preserve a modified symplectic form and a modified Hamiltonian when applied to a Hamiltonian system. We show how a blended version of variational and symplectic techniques can be used to compute modified symplectic and Hamiltonian structures. In contrast to other approaches, our backward error analysis method does not rely on an ansatz but computes the structures systematically, provided that a variational formulation of the method is known. The technique is illustrated on the example of symmetric linear multistep methods with matrix coefficients.}},
  author       = {{McLachlan, Robert and Offen, Christian}},
  journal      = {{Journal of Geometric Mechanics}},
  keywords     = {{variational integrators, backward error analysis, Euler--Lagrange equations, multistep methods, conjugate symplectic methods}},
  number       = {{1}},
  pages        = {{98--115}},
  publisher    = {{AIMS Press}},
  title        = {{{Backward error analysis for conjugate symplectic methods}}},
  doi          = {{10.3934/jgm.2023005}},
  volume       = {{15}},
  year         = {{2023}},
}