@article{45854,
  abstract     = {{In a previous paper the authors developed an algorithm to classify certain quaternary quadratic lattices over totally real fields. The present article applies this algorithm to the classification of binary Hermitian lattices over totally imaginary fields. We use it in particular to classify the 48-dimensional extremal even unimodular lattices over the integers that admit a semilarge automorphism.}},
  author       = {{Kirschmer, Markus and Nebe, Gabriele}},
  issn         = {{1058-6458}},
  journal      = {{Experimental Mathematics}},
  keywords     = {{General Mathematics}},
  number       = {{1}},
  pages        = {{280--301}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Binary Hermitian Lattices over Number Fields}}},
  doi          = {{10.1080/10586458.2019.1618756}},
  volume       = {{31}},
  year         = {{2022}},
}

@misc{45790,
  author       = {{Palushi, Juela}},
  title        = {{{Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks}}},
  year         = {{2022}},
}

@misc{45789,
  author       = {{Budanurmath, Vinaykumar}},
  title        = {{{Propaganda Technique Detection Using Connotation Frames}}},
  year         = {{2022}},
}

@misc{45914,
  author       = {{Manjunatha, Suraj}},
  publisher    = {{Paderborn University }},
  title        = {{{Dealing With Pre-Processing And Feature Extraction Of Time-Series Data In  Predictive Maintenance}}},
  year         = {{2022}},
}

@misc{45915,
  author       = {{Kaur , Parvinder}},
  title        = {{{Analysis of Time-Series Classification in Conditional Monitoring Systems}}},
  year         = {{2022}},
}

@article{34677,
  author       = {{Black, Tobias and Wu, Chunyan}},
  issn         = {{0944-2669}},
  journal      = {{Calculus of Variations and Partial Differential Equations}},
  keywords     = {{Applied Mathematics, Analysis}},
  number       = {{3}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Prescribed signal concentration on the boundary: eventual smoothness in a chemotaxis-Navier–Stokes system with logistic proliferation}}},
  doi          = {{10.1007/s00526-022-02201-y}},
  volume       = {{61}},
  year         = {{2022}},
}

@unpublished{33493,
  abstract     = {{Electronic structure calculations have been instrumental in providing many
important insights into a range of physical and chemical properties of various
molecular and solid-state systems. Their importance to various fields,
including materials science, chemical sciences, computational chemistry and
device physics, is underscored by the large fraction of available public
supercomputing resources devoted to these calculations. As we enter the
exascale era, exciting new opportunities to increase simulation numbers, sizes,
and accuracies present themselves. In order to realize these promises, the
community of electronic structure software developers will however first have
to tackle a number of challenges pertaining to the efficient use of new
architectures that will rely heavily on massive parallelism and hardware
accelerators. This roadmap provides a broad overview of the state-of-the-art in
electronic structure calculations and of the various new directions being
pursued by the community. It covers 14 electronic structure codes, presenting
their current status, their development priorities over the next five years,
and their plans towards tackling the challenges and leveraging the
opportunities presented by the advent of exascale computing.}},
  author       = {{Gavini, Vikram and Baroni, Stefano and Blum, Volker and Bowler, David R. and Buccheri, Alexander and Chelikowsky, James R. and Das, Sambit and Dawson, William and Delugas, Pietro and Dogan, Mehmet and Draxl, Claudia and Galli, Giulia and Genovese, Luigi and Giannozzi, Paolo and Giantomassi, Matteo and Gonze, Xavier and Govoni, Marco and Gulans, Andris and Gygi, François and Herbert, John M. and Kokott, Sebastian and Kühne, Thomas and Liou, Kai-Hsin and Miyazaki, Tsuyoshi and Motamarri, Phani and Nakata, Ayako and Pask, John E. and Plessl, Christian and Ratcliff, Laura E. and Richard, Ryan M. and Rossi, Mariana and Schade, Robert and Scheffler, Matthias and Schütt, Ole and Suryanarayana, Phanish and Torrent, Marc and Truflandier, Lionel and Windus, Theresa L. and Xu, Qimen and Yu, Victor W. -Z. and Perez, Danny}},
  booktitle    = {{arXiv:2209.12747}},
  title        = {{{Roadmap on Electronic Structure Codes in the Exascale Era}}},
  year         = {{2022}},
}

@inproceedings{46193,
  author       = {{Karp, Martin and Podobas, Artur and Kenter, Tobias and Jansson, Niclas and Plessl, Christian and Schlatter, Philipp and Markidis, Stefano}},
  booktitle    = {{International Conference on High Performance Computing in Asia-Pacific Region}},
  publisher    = {{ACM}},
  title        = {{{A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays: Design, Evaluation, and Future Challenges}}},
  doi          = {{10.1145/3492805.3492808}},
  year         = {{2022}},
}

@unpublished{32404,
  abstract     = {{The CP2K program package, which can be considered as the swiss army knife of
atomistic simulations, is presented with a special emphasis on ab-initio
molecular dynamics using the second-generation Car-Parrinello method. After
outlining current and near-term development efforts with regards to massively
parallel low-scaling post-Hartree-Fock and eigenvalue solvers, novel approaches
on how we plan to take full advantage of future low-precision hardware
architectures are introduced. Our focus here is on combining our submatrix
method with the approximate computing paradigm to address the immanent exascale
era.}},
  author       = {{Kühne, Thomas and Plessl, Christian and Schade, Robert and Schütt, Ole}},
  booktitle    = {{arXiv:2205.14741}},
  title        = {{{CP2K on the road to exascale}}},
  year         = {{2022}},
}

@article{33226,
  abstract     = {{A parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The evaluation of a density-matrix functional as the essential part of our approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.}},
  author       = {{Schade, Robert and Bauer, Carsten and Tamoev, Konstantin and Mazur, Lukas and Plessl, Christian and Kühne, Thomas}},
  journal      = {{Phys. Rev. Research}},
  pages        = {{033160}},
  publisher    = {{American Physical Society}},
  title        = {{{Parallel quantum chemistry on noisy intermediate-scale quantum computers}}},
  doi          = {{10.1103/PhysRevResearch.4.033160}},
  volume       = {{4}},
  year         = {{2022}},
}

@article{33684,
  author       = {{Schade, Robert and Kenter, Tobias and Elgabarty, Hossam and Lass, Michael and Schütt, Ole and Lazzaro, Alfio and Pabst, Hans and Mohr, Stephan and Hutter, Jürg and Kühne, Thomas and Plessl, Christian}},
  issn         = {{0167-8191}},
  journal      = {{Parallel Computing}},
  keywords     = {{Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Networks and Communications, Hardware and Architecture, Theoretical Computer Science, Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms}}},
  doi          = {{10.1016/j.parco.2022.102920}},
  volume       = {{111}},
  year         = {{2022}},
}

@article{19941,
  abstract     = {{In backward error analysis, an approximate solution to an equation is compared to the exact solution to a nearby ‘modified’ equation. In numerical ordinary differential equations, the two agree up to any power of the step size. If the differential equation has a geometric property then the modified equation may share it. In this way, known properties of differential equations can be applied to the approximation. But for partial differential equations, the known modified equations are of higher order, limiting applicability of the theory. Therefore, we study symmetric solutions of discretized
partial differential equations that arise from a discrete variational principle. These symmetric solutions obey infinite-dimensional functional equations. We show that these equations admit second-order modified equations which are Hamiltonian and also possess first-order Lagrangians in modified coordinates. The modified equation and its associated structures are computed explicitly for the case of rotating travelling waves in the nonlinear wave equation.}},
  author       = {{McLachlan, Robert I and Offen, Christian}},
  journal      = {{Journal of Geometric Mechanics}},
  number       = {{3}},
  pages        = {{447 -- 471}},
  publisher    = {{AIMS}},
  title        = {{{Backward error analysis for variational discretisations of partial  differential equations}}},
  doi          = {{10.3934/jgm.2022014}},
  volume       = {{14}},
  year         = {{2022}},
}

@article{23382,
  abstract     = {{Hamiltonian systems are differential equations which describe systems in classical mechanics, plasma physics, and sampling problems. They exhibit many structural properties, such as a lack of attractors and the presence of conservation laws. To predict Hamiltonian dynamics based on discrete trajectory observations, incorporation of prior knowledge about Hamiltonian structure greatly improves predictions. This is typically done by learning the system's Hamiltonian and then integrating the Hamiltonian vector field with a symplectic integrator. For this, however, Hamiltonian data needs to be approximated based on the trajectory observations. Moreover, the numerical integrator introduces an additional discretisation error. In this paper, we show that an inverse modified Hamiltonian structure adapted to the geometric integrator can be learned directly from observations. A separate approximation step for the Hamiltonian data avoided. The inverse modified data compensates for the discretisation error such that the discretisation error is eliminated. The technique is developed for Gaussian Processes.}},
  author       = {{Offen, Christian and Ober-Blöbaum, Sina}},
  journal      = {{Chaos: An Interdisciplinary Journal of Nonlinear Science}},
  publisher    = {{AIP}},
  title        = {{{Symplectic integration of learned Hamiltonian systems}}},
  doi          = {{10.1063/5.0065913}},
  volume       = {{32(1)}},
  year         = {{2022}},
}

@inproceedings{40613,
  author       = {{Dröse, Jennifer}},
  booktitle    = {{Beiträge zum Mathematikunterricht}},
  title        = {{{Verstehensgrundlagen diagnostizieren - Diagnostisches Denken von drei Professionalisierungsgruppen}}},
  year         = {{2022}},
}

@inproceedings{46538,
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Vollmers, Daniel and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{ISWC 2022}},
  isbn         = {{978-3-031-19432-0}},
  keywords     = {{colide dice eml4u ngonga raki sherif speaker vollmers zahera}},
  publisher    = {{Springer, Cham}},
  title        = {{{MultPAX: Keyphrase Extraction using Language Models and Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-19433-7_18}},
  year         = {{2022}},
}

@inproceedings{45248,
  author       = {{Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}},
  booktitle    = {{33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland}},
  editor       = {{Klin, Bartek and Lasota, Slawomir and Muscholl, Anca}},
  pages        = {{31:1–31:23}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement}}},
  doi          = {{10.4230/LIPIcs.CONCUR.2022.31}},
  volume       = {{243}},
  year         = {{2022}},
}

@article{27364,
  author       = {{Meyer, Marius and Kenter, Tobias and Plessl, Christian}},
  issn         = {{0743-7315}},
  journal      = {{Journal of Parallel and Distributed Computing}},
  title        = {{{In-depth FPGA Accelerator Performance Evaluation with Single Node Benchmarks from the HPC Challenge Benchmark Suite for Intel and Xilinx FPGAs using OpenCL}}},
  doi          = {{10.1016/j.jpdc.2021.10.007}},
  year         = {{2022}},
}

@inproceedings{47520,
  author       = {{Graf, Lara Marie and Häsel-Weide, Uta and Nührenbörger, Marcus and Höveler, Karina}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2022 - 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik}},
  location     = {{Frankfurt am Main}},
  pages        = {{773--776}},
  title        = {{{Lernwege von fachfremd unterrichtenden Lehrkräften zur Ablösung vom zählenden Rechnen}}},
  year         = {{2022}},
}

@inproceedings{47519,
  author       = {{Schwerin, Imke}},
  booktitle    = {{Beiträge zum Mathematikunterricht 2022 - 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik}},
  location     = {{Frankfurt am Main}},
  pages        = {{401--404}},
  title        = {{{Verdoppeln und Halbieren im 2. Schuljahr - Vorgehensweisen und Verständnis}}},
  year         = {{2022}},
}

@inproceedings{31407,
  abstract     = {{<jats:p>Students are not aware and have little understanding of collecting and processing personal data in their everyday contexts of interaction with data-driven digital artifacts. To be aware of where, how and why data are collected and processed is important to be self-determined. Therefore, we develop and evaluate a teaching sequence to provide reasoning about data as a fundamental aspect of statistical literacy. This teaching sequences deals with the context of interaction with a cellular network where location data are collected. Students get real location data from an unknown person which can be explored with the aim to characterize the person. Students gain different insights by using different basic filters and explain how they achieve these. The results of the exploratory study indicate that students learned to gain insights by exploring given location data and that these insights may describe the person with detailed aspects that may not necessarily be true.</jats:p>}},
  author       = {{Höper, Lukas and Podworny, Susanne and Schulte, Carsten and Frischemeier, Daniel}},
  booktitle    = {{Proceedings of the IASE 2021 Satellite Conference}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Exploration of Location Data: Real Data in the Context of Interaction with a Cellular Network}}},
  doi          = {{10.52041/iase.nkppy}},
  year         = {{2022}},
}

