Issue
Dagstuhl Reports, Volume 14, Issue 11
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Dagstuhl Reports, Volume 14
Journal: Dagstuhl Reports (DagRep)
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Dagstuhl Seminars 24451, 24452 (Perspectives Workshop), 24461, 24462, 24471, 24472Publication Details
- published at: 2025-04-28
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Complete Issue
Dagstuhl Reports, Volume 14, Issue 11, November 2024, Complete Issue
Abstract
Dagstuhl Reports, Volume 14, Issue 11, November 2024, Complete Issue
Cite as
Dagstuhl Reports, Volume 14, Issue 11, pp. 1-121, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.14.11,
title = {{Dagstuhl Reports, Volume 14, Issue 11, November 2024, Complete Issue}},
pages = {1--121},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11},
URN = {urn:nbn:de:0030-drops-230437},
doi = {10.4230/DagRep.14.11},
annote = {Keywords: Dagstuhl Reports, Volume 14, Issue 11, November 2024, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 14, Issue 11, 2024
Abstract
Dagstuhl Reports, Table of Contents, Volume 14, Issue 11, 2024
Cite as
Dagstuhl Reports, Volume 14, Issue 11, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.14.11.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 14, Issue 11, 2024}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.i},
URN = {urn:nbn:de:0030-drops-228163},
doi = {10.4230/DagRep.14.11.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451)
Abstract
Dagstuhl Seminar 24451 focused on how machine learning (ML) is revolutionizing computational biology and chemistry by enhancing the prediction and design of protein-protein and protein-ligand interactions. Key topics included integrating biological and chemical knowledge into ML models, addressing data quality and availability issues, and fostering interdisciplinary collaborations. Theoretical discussions explored representation learning, generative models, and protein language models as efficient alternatives to traditional methods. Practical sessions emphasized the importance of experimental constraints in ML workflows and proposed standards for balanced datasets. The seminar concluded by encouraging collaboration between computational and wet-lab researchers, setting the groundwork for future innovations in protein science and drug discovery.
Cite as
Anne-Florence Bitbol, Jennifer Listgarten, Tomas Pluskal, Anton Bushuiev, and Roman Bushuiev. Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451). In Dagstuhl Reports, Volume 14, Issue 11, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{bitbol_et_al:DagRep.14.11.1,
author = {Bitbol, Anne-Florence and Listgarten, Jennifer and Pluskal, Tomas and Bushuiev, Anton and Bushuiev, Roman},
title = {{Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451)}},
pages = {1--15},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {Bitbol, Anne-Florence and Listgarten, Jennifer and Pluskal, Tomas and Bushuiev, Anton and Bushuiev, Roman},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.1},
URN = {urn:nbn:de:0030-drops-228223},
doi = {10.4230/DagRep.14.11.1},
annote = {Keywords: biological machine learning, ligand, molecular interactions, protein}
}
Document
Reframing Technical Debt (Dagstuhl Perspectives Workshop 24452)
Abstract
Technical Debt has undeniably become part of the everyday vocabulary of software engineers and has shaped the way both industry and academia think of tradeoffs made in software development, accounting for both value and cost: taking shortcuts to expedite software release (value) at the potential risk of higher cost for changes in the long term (cost). This trade-off is not to be taken lightly, as Technical Debt is considered by many software organizations to be the "silent killer" of software projects. To avoid being caught off guard, the industry is increasingly incorporating Technical Debt Management practices into their development processes. The research community has also produced a substantial body of knowledge on the topic of Technical Debt.
However, the industry requires more capable software tools that can manage both legacy and AI-generated code and specifically target Technical Debt. In addition, the industry needs practices and techniques that better support understanding the fundamental tradeoff: the value from incurring Technical Debt against Technical Debt’s long-term costs. Progress on understanding this tradeoff is hindered due to a lack of high-quality datasets, as well as the comparatively small research effort focused on human and social aspects of Technical Debt.
Despite significant early progress in developing a common understanding of the concept of Technical Debt and code-related aspects, the research community and the software-intensive industry need to re-engage. Focusing on how Technical Debt Management is currently practiced and where to focus future research efforts was the goal of this Dagstuhl Perspectives Workshop 24452. The workshop brought together researchers, software tool vendors, and software practitioners to address open challenges and reframe the field of Technical Debt with a concrete and actionable manifesto.
The Dagstuhl Report documents the goals, format, and several discussions held during the workshop. The report also includes some of the outputs of the workshop, as well as the abstracts of the talks given by the participants. A key output of the workshop is a report that summarized a manifesto including values, beliefs, and principles of managing technical debt, titled Reframing Technical Debt and released as a separate document in the Dagstuhl Manifestos series.
Cite as
Paris Avgeriou, Ipek Ozkaya, Heiko Koziolek, Zadia Codabux, and Neil Ernst. Reframing Technical Debt (Dagstuhl Perspectives Workshop 24452). In Dagstuhl Reports, Volume 14, Issue 11, pp. 16-39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{avgeriou_et_al:DagRep.14.11.16,
author = {Avgeriou, Paris and Ozkaya, Ipek and Koziolek, Heiko and Codabux, Zadia and Ernst, Neil},
title = {{Reframing Technical Debt (Dagstuhl Perspectives Workshop 24452)}},
pages = {16--39},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {Avgeriou, Paris and Ozkaya, Ipek and Koziolek, Heiko and Codabux, Zadia and Ernst, Neil},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.16},
URN = {urn:nbn:de:0030-drops-228217},
doi = {10.4230/DagRep.14.11.16},
annote = {Keywords: Technical Debt, Software Maintenance and Evolution, Software Architecture, Software Economics, Software Quality, Socio-Technical Aspects of Software Development, AI-Augmented Software Development}
}
Document
Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461)
Abstract
Despite the recent success of large-scale deep learning, these systems still fall short in terms of their reliability and trustworthiness. They often lack the ability to estimate their own uncertainty in a calibrated way, encode meaningful prior knowledge, avoid catastrophic failures, and also reason about their environments to avoid such failures. Since its inception, Bayesian deep learning (BDL) has harbored the promise of achieving these desiderata by combining the solid statistical foundations of Bayesian inference with the practically successful engineering solutions of deep learning methods. This was intended to provide a principled mechanism to add the benefits of Bayesian learning to the framework of deep neural networks. However, compared to its promise, BDL methods often do not live up to the expectation and underdeliver in terms of real-world impact. This is due to many fundamental challenges related to, for instance, computation of approximate posteriors, unavailability of flexible priors, but also lack of appropriate testbeds and benchmarks. To make things worse, there are also numerous misconceptions about the scope of Bayesian methods, and researchers often end up expecting more than what they can get out of Bayes. By bringing together researchers from diverse communities, such as machine learning, statistics, and deep learning practice, in a personal and interactive seminar environment featuring debates, round tables, and brainstorming sessions, our Dagstuhl Seminar "Rethinking the Role of Bayesianism in the Age of Modern AI" (24461) has discussed these questions from a variety of angles and charted a path for future research to innovate, enhance, and strengthen meaningful real-world impact of Bayesian deep learning.
Cite as
Vincent Fortuin, Mohammad Emtiyaz Khan, Mark van der Wilk, Zoubin Ghahramani, and Katharine Fisher. Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). In Dagstuhl Reports, Volume 14, Issue 11, pp. 40-59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{fortuin_et_al:DagRep.14.11.40,
author = {Fortuin, Vincent and Khan, Mohammad Emtiyaz and van der Wilk, Mark and Ghahramani, Zoubin and Fisher, Katharine},
title = {{Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461)}},
pages = {40--59},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {Fortuin, Vincent and Khan, Mohammad Emtiyaz and van der Wilk, Mark and Ghahramani, Zoubin and Fisher, Katharine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.40},
URN = {urn:nbn:de:0030-drops-228200},
doi = {10.4230/DagRep.14.11.40},
annote = {Keywords: Bayesian machine learning, deep learning, foundation models, model selection, uncertainty estimation}
}
Document
Research Infrastructures and Tools for Collaborative Networked Systems Research (Dagstuhl Seminar 24462)
Abstract
This report presents the program and outcomes of Dagstuhl Seminar "Research Infrastructures and Tools for Collaborative Networked Systems Research" (24462). The seminar brought together experts from the network and distributed systems testbed community, scientists who rely on testbeds for their research, and representatives from funding agencies. It focused on bridging the gap between the services provided by large-scale testbed infrastructures and the needs of researchers conducting cutting-edge experiments. Discussions centered on enhancing the value and impact of research infrastructures by improving collaboration, streamlining experiment workflows, and developing testbed-agnostic tools. The goal was to make experimental research more modular, adaptable, and reproducible, ensuring that experiments and evaluation software can be easily modified, extended, and ported across different testbed environments. Key topics included strategies to improve research quality, reproducibility, and reusability, enhance the discovery process, and maximize the efficient use of research infrastructure resources.
Cite as
Georg Carle, Serge Fdida, Kate Keahey, Henning Schulzrinne, and Sebastian Gallenmüller. Research Infrastructures and Tools for Collaborative Networked Systems Research (Dagstuhl Seminar 24462). In Dagstuhl Reports, Volume 14, Issue 11, pp. 60-91, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{carle_et_al:DagRep.14.11.60,
author = {Carle, Georg and Fdida, Serge and Keahey, Kate and Schulzrinne, Henning and Gallenm\"{u}ller, Sebastian},
title = {{Research Infrastructures and Tools for Collaborative Networked Systems Research (Dagstuhl Seminar 24462)}},
pages = {60--91},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {Carle, Georg and Fdida, Serge and Keahey, Kate and Schulzrinne, Henning and Gallenm\"{u}ller, Sebastian},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.60},
URN = {urn:nbn:de:0030-drops-228197},
doi = {10.4230/DagRep.14.11.60},
annote = {Keywords: Research Infrastructures, Testbeds, Reproducibility, \{FAIR: Findability, Accessibility, Interoperability, and Reuse of digital assets\}, Infrastructure usage and sharing, Artifact Evaluation, Optimizing reuse of data}
}
Document
Graph Algorithms: Distributed Meets Dynamic (Dagstuhl Seminar 24471)
Abstract
This report contains the program and outcomes of the Dagstuhl Seminar "Graph Algorithms: Distributed Meets Dynamic". The field of dynamic graph algorithms address recomputation following edge/vertex insertions/deletions in the input graph. Distributed graph algorithms focus on computing when the input resides across multiple machines, that need to communicate for their joint computation. The seminar brought together researchers from the two communities of dynamic graph algorithms and distributed computing. The aim was to transfer knowledge and techniques between the dynamic and distributed settings, build new collaborations and to explore research directions on computational models of the combined distributed dynamic setting.
Cite as
Keren Censor-Hillel, Yasamin Nazari, Eva Rotenberg, Thatchaphol Saranurak, and Martín Costa. Graph Algorithms: Distributed Meets Dynamic (Dagstuhl Seminar 24471). In Dagstuhl Reports, Volume 14, Issue 11, pp. 92-107, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{censorhillel_et_al:DagRep.14.11.92,
author = {Censor-Hillel, Keren and Nazari, Yasamin and Rotenberg, Eva and Saranurak, Thatchaphol and Costa, Mart{\'\i}n},
title = {{Graph Algorithms: Distributed Meets Dynamic (Dagstuhl Seminar 24471)}},
pages = {92--107},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {Censor-Hillel, Keren and Nazari, Yasamin and Rotenberg, Eva and Saranurak, Thatchaphol and Costa, Mart{\'\i}n},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.92},
URN = {urn:nbn:de:0030-drops-228186},
doi = {10.4230/DagRep.14.11.92},
annote = {Keywords: distributed algorithms, dynamic algorithms}
}
Document
Regular Expressions: Matching and Indexing (Dagstuhl Seminar 24472)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24472 "Regular Expressions: Matching and Indexing".
Cite as
Inge Li Gørtz, Sebastian Maneth, Gonzalo Navarro, and Nicola Prezza. Regular Expressions: Matching and Indexing (Dagstuhl Seminar 24472). In Dagstuhl Reports, Volume 14, Issue 11, pp. 108-119, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{gortz_et_al:DagRep.14.11.108,
author = {G{\o}rtz, Inge Li and Maneth, Sebastian and Navarro, Gonzalo and Prezza, Nicola},
title = {{Regular Expressions: Matching and Indexing (Dagstuhl Seminar 24472)}},
pages = {108--119},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {11},
editor = {G{\o}rtz, Inge Li and Maneth, Sebastian and Navarro, Gonzalo and Prezza, Nicola},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.108},
URN = {urn:nbn:de:0030-drops-228177},
doi = {10.4230/DagRep.14.11.108},
annote = {Keywords: finite automata, regular expressions, complex patterns, text indexing, graph matching and indexing}
}