S. Ochsenfeld and S. Schlichting, “Hydrodynamic and Non-hydrodynamic Excitations in Kinetic Theory -- A  Numerical Analysis in Scalar Field Theory,” arXiv:2308.04491. 2023.
LibreCat: /record/50172
arXiv: 2308.04491

M. Pleines, M. Pallasch, F. Zimmer, and M. Preuss, “Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of  Agents,” arXiv:2309.17207. 2023.
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arXiv: 2309.17207

T. Schmalofski, M. Kroll, H. Dette, and R. Janisch, “Towards active learning: A stopping criterion for the sequential  sampling of grain boundary degrees of freedom,” arXiv:2302.01603. 2023.
LibreCat: /record/50147
arXiv: 2302.01603

P. Carzon, M. Martinez, J. N.-H. Jacquelyn Noronha-Hostler, P. Plaschke, S. Schlichting, and M. Sievert, “Pre-Equilibrium Evolution of Conserved Charges with ICCING Initial  Conditions,” arXiv:2301.04572. 2023.
LibreCat: /record/50174
arXiv: 2301.04572

H. Dawid, P. Harting, and M. Neugart, “Implications of Algorithmic Wage Setting on Online Labor Platforms: a Simulation-Based Analysis,”10.2139/ssrn.4405558__SSRN Electronic Journal, 2023, doi:10.2139/ssrn.4405558 .
LibreCat: /record/50145
DOI: 10.2139/ssrn.4405558

X. Du, S. Ochsenfeld, and S. Schlichting, “Universality of energy-momentum response in kinetic theories,” arXiv:2306.09094. 2023.
LibreCat: /record/50173
arXiv: 2306.09094

M. Bauer, “Ultrafast two-colour X-ray emission spectroscopy reveals excited state landscape in a base metal dyad,”10.48550/ARXIV.2301.04425__Ultrafast two-colour X-ray emission spectroscopy reveals excited state landscape in a base metal dyad, 2023, doi:10.48550/ARXIV.2301.04425 .
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DOI: 10.48550/ARXIV.2301.04425

L. W. de Jong, M. M. Nejad, E. Yoon, S. Cheng, and K. Diba, “Optogenetics reveals paradoxical network stabilizations in hippocampal C and C,” Current Biology, 2023.
LibreCat: /record/50222

P. Müller et al., “Investigation of the Compatibility between Warheads and Peptidomimetic Sequences of Protease Inhibitors—A Comprehensive Reactivity and Selectivity Study,”10.3390/ijms24087226__International Journal of Molecular Sciences, vol. 24, no. 8, Art. no. 7226, 2023, doi:10.3390/ijms24087226 .
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DOI: 10.3390/ijms24087226

A. Nebel, A. Kling, R. Willamowski, and T. Schell, “Recalibration of limits to growth: An update of the World3 model,”10.1111/jiec.13442__Journal of Industrial Ecology, 2023, doi:10.1111/jiec.13442 .
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DOI: 10.1111/jiec.13442

C. Weckert, L. Solis-Vasquez, J. Oppermann, A. Koch, and O. Sinnen, “Altis-SYCL: Migrating Altis Benchmarking Suite from CUDA to SYCL for GPUs and FPGAs,”10.1145/3624062.3624542__2023, doi:10.1145/3624062.3624542 .
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DOI: 10.1145/3624062.3624542

N. Contini et al., “Enabling Reconfigurable HPC through MPI-based Inter-FPGA Communication,”10.1145/3577193.3593720__2023, doi:10.1145/3577193.3593720 .
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DOI: 10.1145/3577193.3593720

C. Alt et al., “Shallow Water DG Simulations on FPGAs: Design and Comparison of a Novel Code Generation Pipeline,” in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023.
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DOI: 10.1007/978-3-031-32041-5_5

L. Van Hirtum et al., “A computation of D(9) using FPGA Supercomputing,” arXiv:2304.03039. 2023.
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arXiv: 2304.03039

O. G.-M. Oscar Garcia-Montero, A. Mazeliauskas, P. Plaschke, and S. Schlichting, “Pre-equilibrium photons from the early stages of heavy-ion collisions,” arXiv:2308.09747. 2023.
LibreCat: /record/50171
arXiv: 2308.09747

N. C. Banh and I. Scharlau, “First steps towards real-time assessment of attentional weights and capacity according to TVA,”10.23668/PSYCHARCHIVES.12945__in Abstracts of the 65th TeaP, Trier, Germany, 2023, doi:10.23668/PSYCHARCHIVES.12945 .
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DOI: 10.23668/PSYCHARCHIVES.12945

J. Faj, T. Kenter, S. Faghih-Naini, C. Plessl, and V. Aizinger, “Scalable Multi-FPGA Design of a Discontinuous Galerkin Shallow-Water Model on Unstructured Meshes,”10.1145/3592979.3593407__2023, doi:10.1145/3592979.3593407 .
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DOI: 10.1145/3592979.3593407

C. Prouveur, M. Haefele, T. Kenter, and N. Voss, “FPGA Acceleration for HPC Supercapacitor Simulations,”10.1145/3592979.3593419__2023, doi:10.1145/3592979.3593419 .
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DOI: 10.1145/3592979.3593419

F. Jentzsch, “Hardware-Aware AutoML for Exploration of Custom FPGA Accelerators for RadioML,”10.1109/fpl60245.2023.00066__2023, doi:10.1109/fpl60245.2023.00066 .
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DOI: 10.1109/fpl60245.2023.00066

F. Simon-Mertens, Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation. Paderborn University, 2023.
LibreCat: /record/45762