Issue
Dagstuhl Reports, Volume 12, Issue 2
-
Part of:
Volume:
Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep)
Event
Dagstuhl Seminars 22061, 22062, 22072, 22081, 22082, 22091Publication Details
- published at: 2022-08-23
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports12
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Issue
Dagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issue
Abstract
Dagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issue
Cite as
Dagstuhl Reports, Volume 12, Issue 2, pp. 1-144, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{DagRep.12.2,
title = {{Dagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issue}},
pages = {1--144},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2},
URN = {urn:nbn:de:0030-drops-169279},
doi = {10.4230/DagRep.12.2},
annote = {Keywords: Dagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 12, Issue 2, 2022
Abstract
Dagstuhl Reports, Table of Contents, Volume 12, Issue 2, 2022
Cite as
Dagstuhl Reports, Volume 12, Issue 2, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{DagRep.12.2.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 12, Issue 2, 2022}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.i},
URN = {urn:nbn:de:0030-drops-169287},
doi = {10.4230/DagRep.12.2.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Logic and Random Discrete Structures (Dagstuhl Seminar 22061)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22061 "Logic and Random Discrete Structures". The main topic of this seminar has been the analysis of large random discrete structures, such as trees, graphs, or permutations, from the perspective of mathematical logic. It has brought together both experts and junior researchers from a number of different areas where logic and random structures play a role, with the goal to establish new connections between such areas and to encourage interactions between foundational research and different application areas, including probabilistic databases.
Cite as
Erich Grädel, Phokion G. Kolaitis, Marc Noy, and Matthias Naaf. Logic and Random Discrete Structures (Dagstuhl Seminar 22061). In Dagstuhl Reports, Volume 12, Issue 2, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{gradel_et_al:DagRep.12.2.1,
author = {Gr\"{a}del, Erich and Kolaitis, Phokion G. and Noy, Marc and Naaf, Matthias},
title = {{Logic and Random Discrete Structures (Dagstuhl Seminar 22061)}},
pages = {1--16},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {Gr\"{a}del, Erich and Kolaitis, Phokion G. and Noy, Marc and Naaf, Matthias},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.1},
URN = {urn:nbn:de:0030-drops-169295},
doi = {10.4230/DagRep.12.2.1},
annote = {Keywords: combinatorics, complexity theory, logic, random structures, probabilistic databases}
}
Document
Computation and Reconfiguration in Low-Dimensional Topological Spaces (Dagstuhl Seminar 22062)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22062 "Computation and Reconfiguration in Low-Dimensional Topological Spaces". The seminar consisted of a small collection of introductory talks, an open problem session, and then the seminar participants worked in small groups on problems on reconfiguration within the context of objects as diverse as elimination trees, morphings, curves on surfaces, translation surfaces and Delaunay triangulations.
Cite as
Maike Buchin, Anna Lubiw, Arnaud de Mesmay, Saul Schleimer, and Florestan Brunck. Computation and Reconfiguration in Low-Dimensional Topological Spaces (Dagstuhl Seminar 22062). In Dagstuhl Reports, Volume 12, Issue 2, pp. 17-66, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{buchin_et_al:DagRep.12.2.17,
author = {Buchin, Maike and Lubiw, Anna and de Mesmay, Arnaud and Schleimer, Saul and Brunck, Florestan},
title = {{Computation and Reconfiguration in Low-Dimensional Topological Spaces (Dagstuhl Seminar 22062)}},
pages = {17--66},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {Buchin, Maike and Lubiw, Anna and de Mesmay, Arnaud and Schleimer, Saul and Brunck, Florestan},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.17},
URN = {urn:nbn:de:0030-drops-169305},
doi = {10.4230/DagRep.12.2.17},
annote = {Keywords: Geometric Topology, Computational Geometry, Graph Drawing, Reconfiguration, Dagstuhl Seminar}
}
Document
New Perspectives in Symbolic Computation and Satisfiability Checking (Dagstuhl Seminar 22072)
Abstract
Dagstuhl Seminar 22072 gathered researchers from Symbolic Computation and Satisfiability Checking. These communities have independent histories but worked together in recent years (e.g. Dagstuhl Seminar 15471 and the EU SC-Square Project). We seek to tackle problems which are in the interest of both communities, and require the expertise of both to overcome.
Cite as
Erika Abraham, James H. Davenport, Matthew England, and Alberto Griggio. New Perspectives in Symbolic Computation and Satisfiability Checking (Dagstuhl Seminar 22072). In Dagstuhl Reports, Volume 12, Issue 2, pp. 67-86, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{abraham_et_al:DagRep.12.2.67,
author = {Abraham, Erika and Davenport, James H. and England, Matthew and Griggio, Alberto},
title = {{New Perspectives in Symbolic Computation and Satisfiability Checking (Dagstuhl Seminar 22072)}},
pages = {67--86},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {Abraham, Erika and Davenport, James H. and England, Matthew and Griggio, Alberto},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.67},
URN = {urn:nbn:de:0030-drops-169310},
doi = {10.4230/DagRep.12.2.67},
annote = {Keywords: computer algebra systems, SMT Solvers, verification}
}
Document
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22081 "Theory of Randomized Optimization Heuristics".
This seminar is part of a biennial seminar series. This year, we focused on connections between classical topics of the community, such as Evolutionary Algorithms and Strategies (EA, ES), Estimation-of-Distribution Algorithms (EDA) and Evolutionary Multi-Objective Optimization (EMO), and related fields like Stochastic Gradient Descent (SGD) and Bayesian Optimization (BO). The mixture proved to be extremely successful. Already the first talk turned into a two hour long, vivid and productive plenary discussion. The seminar was smaller than previous versions (due to corona regulations), but its intensity more than made up for the smaller size.
Cite as
Anne Auger, Carlos M. Fonseca, Tobias Friedrich, Johannes Lengler, and Armand Gissler. Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081). In Dagstuhl Reports, Volume 12, Issue 2, pp. 87-102, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{auger_et_al:DagRep.12.2.87,
author = {Auger, Anne and Fonseca, Carlos M. and Friedrich, Tobias and Lengler, Johannes and Gissler, Armand},
title = {{Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081)}},
pages = {87--102},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {Auger, Anne and Fonseca, Carlos M. and Friedrich, Tobias and Lengler, Johannes and Gissler, Armand},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.87},
URN = {urn:nbn:de:0030-drops-169325},
doi = {10.4230/DagRep.12.2.87},
annote = {Keywords: black-box optimization, derivative-free optimization, evolutionary and genetic algorithms, randomized search algorithms, stochastic gradient descent, theoretical computer science}
}
Document
Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082)
Abstract
Given the increasing amount of digital music, the development of computational tools that allow users to find, organize, analyze, and interact with music has become central to the research field known as Music Information Retrieval (MIR). As in general multimedia processing, many of the recent advances in MIR have been driven by techniques based on deep learning (DL). There is a growing trend to relax problem-specific modeling constraints from MIR systems and instead apply relatively generic DL-based approaches that rely on large quantities of data. In the Dagstuhl Seminar 22082, we critically examined this trend, discussing the strengths and weaknesses of these approaches using music as a challenging application domain. We mainly focused on music analysis tasks applied to audio representations (rather than symbolic music representations) to give the seminar cohesion. In this context, we systematically explored how musical knowledge can be integrated into or relaxed from computational pipelines. We then discussed how this choice could affect the explainability of models or the vulnerability to data biases and confounding factors. Furthermore, besides explainability and generalization, we also addressed efficiency, ethical and educational aspects considering traditional model-based and recent data-driven methods. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary describing the main topics, goals, and group activities. Then, we give an overview of the participants' stimulus talks and subsequent discussions (listed alphabetically by the main contributor’s last name) and summarize further activities, including group discussions and music sessions.
Cite as
Meinard Müller, Rachel Bittner, Juhan Nam, Michael Krause, and Yigitcan Özer. Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082). In Dagstuhl Reports, Volume 12, Issue 2, pp. 103-133, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{muller_et_al:DagRep.12.2.103,
author = {M\"{u}ller, Meinard and Bittner, Rachel and Nam, Juhan and Krause, Michael and \"{O}zer, Yigitcan},
title = {{Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082)}},
pages = {103--133},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {M\"{u}ller, Meinard and Bittner, Rachel and Nam, Juhan and Krause, Michael and \"{O}zer, Yigitcan},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.103},
URN = {urn:nbn:de:0030-drops-169333},
doi = {10.4230/DagRep.12.2.103},
annote = {Keywords: Audio signal processing, deep learning, knowledge representation, music information retrieval, user interaction and interfaces}
}
Document
AI for the Social Good (Dagstuhl Seminar 22091)
Abstract
Progress in the field of Artificial intelligence (AI) and machine learning (ML) has not slowed down in recent years. Long-standing challenges like Go have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones. The pace of research progress shows no signs of slowing down, and demand for talent is unprecedented. AI for Social Good in general is trying to ensure that the social good does not become an afterthought, but that society benefits as a whole. In this Dagstuhl Seminar, which can be considered a follow-up edition of Dagstuhl Seminar 19082, we brought together AI and machine learning researchers with non-governmental organisations (NGOs), as they already pursue a social good goal, have rich domain knowledge, and vast networks with (non-)governmental actors in developing countries. Such collaborations benefit both sides: on the one hand, the new techniques can help with prediction, data analysis, modelling, or decision making. On the other hand, the NGOs' domains contain many non-standard conditions, like missing data, side-effects, or multiple competing objectives, all of which are fascinating research challenges in themselves. And of course, publication impact is substantially enhanced when a method has real-world impact. In this seminar, researchers and practitioners from diverse areas of machine learning joined stakeholders from a range of NGOs to spend a week together. We first pursued an improved understanding of each side’s challenges and established a common language, via presentations and discussion groups. Building on this foundation, we organised a hackathon around some existing technical questions within the NGOs to scope the applicability of AI methods and seed collaborations. Finally, we defined guidelines and next steps for future AI for Social Good initiatives.
Cite as
Claudia Clopath, Ruben De Winne, and Tom Schaul. AI for the Social Good (Dagstuhl Seminar 22091). In Dagstuhl Reports, Volume 12, Issue 2, pp. 134-142, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
Copy BibTex To Clipboard
@Article{clopath_et_al:DagRep.12.2.134,
author = {Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
title = {{AI for the Social Good (Dagstuhl Seminar 22091)}},
pages = {134--142},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {2},
editor = {Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.134},
URN = {urn:nbn:de:0030-drops-169345},
doi = {10.4230/DagRep.12.2.134},
annote = {Keywords: Machine Learning, Artificial Intelligence, Social Good, NGO, sustainable development goals, Non-governmental organisation}
}