@inproceedings{45653, author = {{Vernholz, Mats}}, location = {{Stuttgart}}, title = {{{Industrie 4.0 in der beruflichen Bildung – Automatisierter Maschinenbaulernbetrieb Paderborn }}}, doi = {{https://doi.org/10.48513/joted.v11i2.267 }}, year = {{2022}}, } @misc{45715, author = {{Tcheussi Ngayap, Vanessa Ingrid}}, title = {{{FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators}}}, year = {{2022}}, } @inbook{33738, author = {{Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web: ESWC 2022 Satellite Events}}, isbn = {{9783031116087}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings}}}, doi = {{10.1007/978-3-031-11609-4_9}}, year = {{2022}}, } @inproceedings{31257, abstract = {{Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches can grow to the millions for complex learning problems. This often leads to impractical runtimes. We propose to alleviate this problem by predicting the length of target concepts before the exploration of the solution space. By these means, we can prune the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmarks. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with a macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm, which learns ALC concepts, with our concept length predictor. Our extension yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times faster than other state-of-the-art concept learning algorithms for ALC---including CELOE---and achieves significant improvements in the F-measure of the concepts learned on 3 out of 4 datasets. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/dice-group/LearnALCLengths}}, author = {{Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngomo, Ngonga Axel-Cyrille}}, booktitle = {{ESWC}}, keywords = {{dice knowgraphs raki daikiri kouagou heindorf demir ngonga}}, location = {{Hersonissos, Crete, Greece}}, pages = {{236 -- 252}}, publisher = {{Springer}}, title = {{{Learning Concept Lengths Accelerates Concept Learning in ALC}}}, volume = {{13261}}, year = {{2022}}, } @inproceedings{33739, abstract = {{At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.}}, author = {{Bondarenko, Alexander and Wolska, Magdalena and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille and Stein, Benno and Braslavski, Pavel and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{3296–3308}}, publisher = {{International Committee on Computational Linguistics}}, title = {{{CausalQA: A Benchmark for Causal Question Answering}}}, year = {{2022}}, } @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}}, }