TY - CHAP A1 - Kaiser, Benjamin A1 - Clausecker, Robert T1 - CleanMaple T2 - Proceedings of SAT Competition 2021 : Solver and Benchmark Descriptions Y1 - 2021 UR - http://hdl.handle.net/10138/333647 SP - 24 ER - TY - CHAP A1 - Kaiser, Benjamin A1 - Clausecker, Robert T1 - CleanMaple_PriPro, CaDiCaL_PriPro and CaDiCaL_PriPro_no_bin T2 - Proceedings of SAT Competition 2021 : Solver and Benchmark Descriptions Y1 - 2021 UR - http://hdl.handle.net/10138/333647 N1 - CaDiCaL_PriPro won the Special Innovation Price at the SAT Competition 2021 SP - 25 ER - TY - CHAP A1 - Fürst, Steffen A1 - Rakow, Christian T1 - Parallelization of large-scale agent-based epidemiological simulations T2 - Proceedings of the 34th European Modeling & Simulation Symposium N2 - Agent-based epidemiological models have been applied widely successfully during the SARS-CoV-2 pandemic and assisted policymakers in assessing the effectiveness of intervention strategies. The computational complexity of agent-based models is still challenging, and therefore it is important to utilize modern multi-core systems as good as possible. In this paper, we are presenting our work on parallelizing the epidemiological simulation model MATSim Episim. Episim combines a large-scale person-centric human mobility model with a mechanistic model of infection and a person-centric disease progression model. In general, the parallelization of agent-based models with an inherent sequential structure — in the case of epidemiological models, the temporal order of the individual movements of the agents — is challenging. Especially when the underlying social network is irregular and dynamic, they require frequent communication between the processing elements. In Episim, however, we were able to take advantage of the fact that people are not contagious on the same day they become infected, and therefore immediate health synchronization is not required. By parallelizing some of the most computationally intensive submodels, we are now able to run MATSim Episim simulations up to eight times faster than the serial version. This makes it feasible to increase the number of agents, e.g. to run simulations for the whole of Germany instead of just Berlin as before. Y1 - 2022 U6 - https://doi.org/https://doi.org/10.46354/i3m.2022.emss.008 ER - TY - CHAP A1 - Shinano, Yuji A1 - Tateiwa, N. A1 - Nakamura, S. A1 - Yoshida, A. A1 - Yasuda, M. A1 - Kaji, S. A1 - Fujisawa, K. T1 - Massive Parallelization for Finding Shortest Lattice Vectors Based on Ubiquity Generator Framework T2 - 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC) N2 - Lattice-based cryptography has received attention as a next-generation encryption technique, because it is believed to be secure against attacks by classical and quantum computers. Its essential security depends on the hardness of solving the shortest vector problem (SVP). In the cryptography, to determine security levels, it is becoming significantly more important to estimate the hardness of the SVP by high-performance computing. In this study, we develop the world’s first distributed and asynchronous parallel SVP solver, the MAssively Parallel solver for SVP (MAP-SVP). It can parallelize algorithms for solving the SVP by applying the Ubiquity Generator framework, which is a generic framework for branch-and-bound algorithms. The MAP-SVP is suitable for massive-scale parallelization, owing to its small memory footprint, low communication overhead, and rapid checkpoint and restart mechanisms. We demonstrate its performance and scalability of the MAP-SVP by using up to 100,032 cores to solve instances of the Darmstadt SVP Challenge. Y1 - 2020 U6 - https://doi.org/10.1109/SC41405.2020.00064 SP - 834 EP - 848 ER - TY - CHAP A1 - Pfetsch, Marc A1 - Pokutta, Sebastian T1 - IPBoost – Non-Convex Boosting via Integer Programming T2 - Proceedings of ICML Y1 - 2020 N1 - URL of the Code: https://www2.mathematik.tu-darmstadt.de/~pfetsch/ipboost.html N1 - URL of the Slides: https://app.box.com/s/8dpvmls88suouy11bkpwufhu7iiz6dxl N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2020/02/13/ipboost-abstract.html ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Singh, M. A1 - Torrico, A. T1 - On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness T2 - Proceedings of ICML Y1 - 2020 N1 - URL of the Poster: https://app.box.com/s/24vbh1s2vib11upqyepzen3lzdl13skr N1 - URL of the Slides: https://app.box.com/s/fmjxhfdpiqubm28upf563ohy8t8fgl3a N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2020/06/03/unreasonable-abstract.html N1 - https://www.youtube.com/watch?v=VB1e0HrDmVo ER - TY - CHAP A1 - Diakonikolas, Jelena A1 - Carderera, Alejandro A1 - Pokutta, Sebastian T1 - Locally Accelerated Conditional Gradients T2 - Proceedings of AISTATS Y1 - 2020 N1 - URL of the Code: https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O N1 - URL of the PDF: http://proceedings.mlr.press/v108/diakonikolas20a/diakonikolas20a.pdf N1 - URL of the Slides: https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2019/07/04/LaCG-abstract.html N1 - https://slideslive.com/38930107/locally-accelerated-conditional-gradients?ref=account-folder-52123-folders ER - TY - CHAP A1 - Combettes, Cyrille W. A1 - Pokutta, Sebastian T1 - Boosting Frank-Wolfe by Chasing Gradients T2 - Proceedings of ICML Y1 - 2020 N1 - Additional Note: github code: https://github.com/cyrillewcombettes/boostfw N1 - URL of the Code: https://colab.research.google.com/drive/1TSOVjDFF1X2ADBo_adHLsUVrblSutRKw N1 - URL of the Slides: https://app.box.com/s/wwj247r5d456q0778p9b9y1jm6txuifb N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2020/03/16/boostFW.html N1 - https://www.youtube.com/watch?v=BfyV0C5FRbE ER - TY - CHAP A1 - Pokutta, Sebastian T1 - Restarting Algorithms: Sometimes there is Free Lunch T2 - Proceedings of CPAIOR Y1 - 2020 N1 - URL of the Slides: https://app.box.com/s/8ps8bhjv2phhy182xn55iurk9t3o1xc3 N1 - https://www.youtube.com/watch?v=v3Xzoda2_gQ ER - TY - CHAP A1 - Berner, Julius A1 - Richter, Lorenz A1 - Ullrich, Karen T1 - An optimal control perspective on diffusion-based generative modeling T2 - Transactions on Machine Learning Research N2 - We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs) such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to transfer methods from optimal control theory to generative modeling. First, we show that the evidence lower bound is a direct consequence of the well-known verification theorem from control theory. Further, we develop a novel diffusion-based method for sampling from unnormalized densities -- a problem frequently occurring in statistics and computational sciences. Y1 - 2024 UR - https://openreview.net/forum?id=oYIjw37pTP ER - TY - CHAP A1 - Richter, Lorenz A1 - Berner, Julius T1 - Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning T2 - Proceedings of the 39th International Conference on Machine Learning, PMLR N2 - The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements. Y1 - 2022 VL - 162 SP - 18649 EP - 18666 ER - TY - CHAP A1 - Krüger, Jan A1 - Manley, Phillip A1 - Zschiedrich, Lin A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Köning, Rainer A1 - Bodermann, Bernd T1 - Untersuchung des Schwingungseinflusses auf gemessene Punktspreizfunktionen in der optischen Mikroskopie T2 - Proc. DGaO Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0287-2022-P005-6 SP - P5 ER - TY - CHAP A1 - Sharma, Kartikey A1 - Hendrych, Deborah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe T2 - Proceedings of INFORMS Optimization Society Conference Y1 - 2024 ER - TY - CHAP A1 - Prodanov, Dimiter A1 - Vohra, Sumit Kumar T1 - Active Segmentation: Differential Geometry meets Machine Learning T2 - Proceedings of the 23rd International Conference on Computer Systems and Technologies N2 - Image segmentation is an active area of research for more than 30 years. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. This paper presents a project supported in part by the International Neuroinformatics Coordination Facility through the Google Summer of code. The project resulted in an automated image segmentation and classification platform, called Active Segmentation for ImageJ (AS/IJ). The platform integrates a set of filters computing differential geometrical invariants and combines them with machine learning approaches. Y1 - 2022 U6 - https://doi.org/10.1145/3546118.3546154 SP - 1 EP - 6 ER - TY - CHAP A1 - Matuschke, Jannik A1 - Bley, Andreas A1 - Müller, Benjamin ED - Bodlaender, Hans L. ED - Italiano, Giuseppe F. T1 - Approximation Algorithms for Facility Location with Capacitated and Length-Bounded Tree Connections N2 - We consider a generalization of the uncapacitated facility location problem that occurs in planning of optical access networks in telecommunications. Clients are connected to open facilities via depth-bounded trees. The total demand of clients served by a tree must not exceed a given tree capacity. We investigate a framework for combining facility location algorithms with a tree-based clustering approach and derive approximation algorithms for several variants of the problem, using techniques for approximating shallow-light Steiner trees via layer graphs, simultaneous approximation of shortest paths and minimum spanning trees, and greedy coverings. Y1 - 2013 U6 - https://doi.org/10.1007/978-3-642-40450-4_60 VL - Algorithms -- ESA 2013 SP - 707 EP - 718 PB - Springer ER - TY - CHAP A1 - Schummel, Philip A1 - Teichmann, Malte A1 - Gonnermann-Müller, Jana T1 - Specifying ten roles of using chatgpt in secondary education: a teacher´s perspective T2 - Proceedings of the European Conference on Information Systems N2 - ChatGPT can benefit education; however, challenges arise that demand competent and responsible usage. Competence development needs to start in schools, with the teacher being the gatekeeper for integrating it into the teaching process. Existing research suggests that schoolteachers lack specific guidance on integrating ChatGPT into teaching, as current discussions remain high-level and omit specific use cases throughout the entire teaching process. To guide an informed usage, this paper presents the results of a systematic literature review, analyzing 38 selected papers on investigating ChatGPT in schools. This selection is based on rigorously defined criteria, adhering to the PRISMA guidelines, initially screening 1075 papers. The central value of this paper is to aggregate research into ten roles for secondary teachers on using ChatGPT in schools, presenting corresponding use cases throughout the teaching process. Future research validates the ten roles, conducting interviews with teachers to present an exhaustive framework for integrating ChatGPT in schoolteacher practice. Y1 - 2025 UR - https://aisel.aisnet.org/ecis2025/education/education/3/ ER - TY - CHAP A1 - Lasby, Mike A1 - Zimmer, Max A1 - Pokutta, Sebastian A1 - Schultheis, Erik T1 - Compressed sparse tiles for memory-efficient unstructured and semi-structured sparsity T2 - Proceedings of the ICLR Workshop on Sparsity in LLMs Y1 - 2025 UR - https://openreview.net/forum?id=iso0KV2HVq ER - TY - CHAP A1 - Fukuda, Takuma A1 - Kera, Hiroshi A1 - Kawamoto, Kazuhiko T1 - Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning T2 - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Y1 - 2025 U6 - https://doi.org/10.1109/CVPR52734.2025.00460 SP - 4884 EP - 4893 ER - TY - CHAP A1 - Haase, Jennifer A1 - Hanel, Paul H. P. A1 - Pokutta, Sebastian T1 - S-DAT: a multilingual, GenAI-driven framework for automated divergent thinking assessment T2 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) Y1 - 2025 VL - 8 SP - 1194 EP - 1205 ER - TY - CHAP A1 - Villim, Ján A1 - Nguyen, Martin A1 - Bobik, Pavol A1 - Genci, Jan A1 - Gecášek, Daniel T1 - New Version of the COR Simulation Engine T2 - Proceedings of 39th International Cosmic Ray Conference — PoS(ICRC2025) N2 - The COR simulation engine is a tool devoted to evaluating cosmic ray trajectories in Earth's magnetosphere. It is part of the COR System, available at https://cor.crmodels.org, and it also functions as a standalone command line tool. The former tool was published in 2022. We report the new version of the tool status with improved performance, precision, new functions/features, and refactored code. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.22323/1.501.1377 VL - 501 PB - Sissa Medialab CY - Trieste, Italy ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Schulte, Anton A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Stabernack, Benno T1 - A Flexible Open-Source Framework for FPGA-based Network-Attached Accelerators using SpinalHDL T2 - Architecture of Computing Systems - 39th International Conference, ARCS 2026, Mainz, Germany, March 24-26, 2026, Proceedings. N2 - Domain-specific accelerators are increasingly vital in heterogeneous computing systems, driven by the demand for higher computational capacity and especially energy efficiency. Network-attached FPGAs promise a scalable and flexible alternative to closely coupled FPGAs for integrating accelerators into computing environments. While the advantages of specialized hardware implementations are apparent, traditional hardware development and integration remain time-consuming and complex. We present an open-source framework which combines a hardware shell with supporting software libraries, which enables fast development and deployment of FPGA-based network-attached accelerators. In contrast to traditional approaches using VHDL or Verilog, we leverage generative programming with SpinalHDL, providing a flexible hardware description with multi-level abstractions. This work eases the integration of accelerators into existing network infrastructures and simplifies adaptation to different FPGAs, eliminating complex and lengthy top-level hardware descriptions. Y1 - 2026 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Sahling, Kristina A1 - Haase, Jennifer T1 - Let's Be Realistic: AI-Recommender Use in a Complex Management Setting T2 - CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems Y1 - 2025 U6 - https://doi.org/10.1145/3706599.3720131 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Hutzler, Merit A1 - Kreowsky, Philipp A1 - De Lucia, Marco A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Spazier, Johannes A1 - Stabernack, Benno A1 - Yahdzhyiev, Serhii T1 - Using FPGA-based Network-Attached Accelerators for Energy-Efficient AI Training in HPC Datacenters T2 - 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) N2 - FPGA-based Network-Attached Accelerators offer a disaggregated alternative to closely-coupled FPGAs or GPUs, but their adoption is very limited due to missing hardware/software frameworks. However, the usability and energy-efficiency of FPGAs for HPC use-cases has been demonstrated previously. Yet, the support by HPC infrastructure for those devices is lacking behind other accelerators. This paper addresses this shortcoming and demonstrates a full-stack approach that seamlessly integrates Network-Attached Accelerators in HPC datacenters and allows flexible and scalable usage of FPGAs. The presented work includes the according framework, integration steps and a show-case application from the geoscience domain. We evaluate our approach by comparing the training of a Physics-Informed Neural Network on the NAA against two GPU implementations. The NAA reduces total energy consumption by about 50% and 17% compared to the Keras and CUDA implementation, respectively. These results demonstrate that FPGA-based NAAs can be successfully integrated into HPC environments and are a viable path toward more energy-efficient AI training. Y1 - 2026 ER - TY - CHAP A1 - Sunkara, Vikram A1 - Rostami, Atefe A1 - von Tycowicz, Christoph A1 - Schütte, Christof T1 - Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI T2 - The 4th World Conference on Explainable Artificial Intelligence (XAI-2026) Y1 - 2026 ER -