Issue
Dagstuhl Reports, Volume 12, Issue 3
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Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep)
Event
Dagstuhl Seminars 22101, 22102, 22111, 22121, 22131, 22132Publication Details
- published at: 2022-11-14
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports12
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Complete Issue
Dagstuhl Reports, Volume 12, Issue 3, March 2022, Complete Issue
Abstract
Dagstuhl Reports, Volume 12, Issue 3, March 2022, Complete Issue
Cite as
Dagstuhl Reports, Volume 12, Issue 3, pp. 1-157, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{DagRep.12.3,
title = {{Dagstuhl Reports, Volume 12, Issue 3, March 2022, Complete Issue}},
pages = {1--157},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3},
URN = {urn:nbn:de:0030-drops-172653},
doi = {10.4230/DagRep.12.3},
annote = {Keywords: Dagstuhl Reports, Volume 12, Issue 3, March 2022, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 12, Issue 3, 2022
Abstract
Dagstuhl Reports, Table of Contents, Volume 12, Issue 3, 2022
Cite as
Dagstuhl Reports, Volume 12, Issue 3, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{DagRep.12.3.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 12, Issue 3, 2022}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.i},
URN = {urn:nbn:de:0030-drops-172662},
doi = {10.4230/DagRep.12.3.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22101 "Tensor Computations: Applications and Optimization". Tensors are higher dimensional analogs of matrices, and represent a key data abstraction for many applications in computational science and data science. Widely used shared infrastructure exists for linear algebra, while, in contrast, for tensor computations, there is no consensus on standard building blocks. This Dagstuhl Seminar aimed to bring together users, and performance optimization specialists, to build such foundations.
We present the abstracts of the 5 tutorials and 14 talks given. The working groups and their outcomes so far are then presented.
Cite as
Paolo Bientinesi, David Ham, Furong Huang, Paul H. J. Kelly, P. (Saday) Sadayappan, and Edward Stow. Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101). In Dagstuhl Reports, Volume 12, Issue 3, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{bientinesi_et_al:DagRep.12.3.1,
author = {Bientinesi, Paolo and Ham, David and Huang, Furong and Kelly, Paul H. J. and Sadayappan, P. (Saday) and Stow, Edward},
title = {{Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101)}},
pages = {1--14},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Bientinesi, Paolo and Ham, David and Huang, Furong and Kelly, Paul H. J. and Sadayappan, P. (Saday) and Stow, Edward},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.1},
URN = {urn:nbn:de:0030-drops-172674},
doi = {10.4230/DagRep.12.3.1},
annote = {Keywords: Tensor, Optimisation, Linear Algebra, Compilers, Benchmark, Domain Specific Language}
}
Document
Computational Models of Human-Automated Vehicle Interaction (Dagstuhl Seminar 22102)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22102 "Computational Models of Human-Automated Vehicle Interaction". At this Dagstuhl Seminar, we discussed how computational (cognitive) models can be used to model human-automated vehicle interaction. The seminar is motivated by developments in the field of semi-automated driving where humans and vehicles interact as teams to either both contribute to the drive (partnership) or to have safe transitions of control from vehicle to human and vice-versa. Computational (cognitive) models can be used in these situations to simulate or model human behavior and thought. Such models can be used among others to better understand human behavior, to test "what if" scenarios to guide design, or to even provide input to the vehicle about the human’s potential behavior and thoughts. The seminar was attended by experts in various fields including computer science, cognitive science, engineering, automotive UI, human-computer interaction, and human factors. They represented academia, industry, and government organizations. With the attendees, we discussed five challenges of the field during panel discussion sessions:
- Challenge 1: How can models inform design and governmental policy?
- Challenge 2: What phenomena and driving scenarios need to be captured?
- Challenge 3: What technical capabilities do computational models possess?
- Challenge 4: How can models benefit from advances in AI while avoiding pitfalls?
- Challenge 5: What insights are needed for and from empirical research?
The attendees then split off into smaller working groups to discuss aspects of these challenges in more depth. Based on these discussions and other input from the attendees, this Dagstuhl report reports the following:
- an executive summary of the seminar
- position perspectives of all the attendees (section: "Talks")
- summaries of the various working groups (section: "Working Groups")
- summaries of the five panels (section: "Panel Discussions")
- an overview of relevant papers (section: "Open Problems")
- a research agenda with some of the most important developments and needs we identified for the field (section: "Open Problems") All in all, we believe the seminar has shown that this field has lots of potential for development and an active community to tackle pressing issues. We can't wait to see what results the participants of the seminar will bring to the field in the future.
Cite as
Christian P. Janssen, Martin Baumann, Antti Oulasvirta, Shamsi Tamara Iqbal, and Luisa Heinrich. Computational Models of Human-Automated Vehicle Interaction (Dagstuhl Seminar 22102). In Dagstuhl Reports, Volume 12, Issue 3, pp. 15-81, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{janssen_et_al:DagRep.12.3.15,
author = {Janssen, Christian P. and Baumann, Martin and Oulasvirta, Antti and Iqbal, Shamsi Tamara and Heinrich, Luisa},
title = {{Computational Models of Human-Automated Vehicle Interaction (Dagstuhl Seminar 22102)}},
pages = {15--81},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Janssen, Christian P. and Baumann, Martin and Oulasvirta, Antti and Iqbal, Shamsi Tamara and Heinrich, Luisa},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.15},
URN = {urn:nbn:de:0030-drops-172680},
doi = {10.4230/DagRep.12.3.15},
annote = {Keywords: artificial intelligence, automated vehicles, cognitive science, computational models, human-automation interaction, human-computer interaction, semi-automated vehicles, user models}
}
Document
Database Indexing and Query Processing (Dagstuhl Seminar 22111)
Abstract
The Dagstuhl Seminar 22111 on "Database indexing and query processing", held from March 13 to March 18 2022, brought together researchers from academia and industry to discuss robustness in database management systems. This seminar was a continuation of previous seminars on the topic of Robust Query Processing, where we included indexing as a general topic and also discussed aspects that have not been addressed by the previous instances of the seminar. This article summarizes the main discussion topics, and presents the summary of the outputs of three work groups that discussed: i) storage architectures, ii) robust operators, and iii) indexing for data warehousing.
Cite as
Renata Borovica-Gajic, Goetz Graefe, Allison Lee, Caetano Sauer, and Pinar Tözün. Database Indexing and Query Processing (Dagstuhl Seminar 22111). In Dagstuhl Reports, Volume 12, Issue 3, pp. 82-96, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{borovicagajic_et_al:DagRep.12.3.82,
author = {Borovica-Gajic, Renata and Graefe, Goetz and Lee, Allison and Sauer, Caetano and T\"{o}z\"{u}n, Pinar},
title = {{Database Indexing and Query Processing (Dagstuhl Seminar 22111)}},
pages = {82--96},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Borovica-Gajic, Renata and Graefe, Goetz and Lee, Allison and Sauer, Caetano and T\"{o}z\"{u}n, Pinar},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.82},
URN = {urn:nbn:de:0030-drops-172698},
doi = {10.4230/DagRep.12.3.82},
annote = {Keywords: database, execution, hardware, optimization, performance, query}
}
Document
3D Morphable Models and Beyond (Dagstuhl Seminar 22121)
Abstract
3D Morphable Models are models separating shape from appearance variation. Typically, they are used as a statistical prior in computer graphics and vision. Recent success with neural representations have caused a resurgence of interest in visual computing problems, leading to more accurate, higher fidelity, more expressive, and memory-efficient solutions. This report documents the program and the outcomes of Dagstuhl Seminar 22121, "3D Morphable Models and Beyond". This meeting of 39 researchers covered various topics, including 3D morphable models, implicit neural representations, physics-inspired approaches, and more. We summarise the discussions, presentations and results of this workshop.
Cite as
James Gardner, Bernhard Egger, William Smith, Christian Theobalt, and Stefanie Wuhrer. 3D Morphable Models and Beyond (Dagstuhl Seminar 22121). In Dagstuhl Reports, Volume 12, Issue 3, pp. 97-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{gardner_et_al:DagRep.12.3.97,
author = {Gardner, James and Egger, Bernhard and Smith, William and Theobalt, Christian and Wuhrer, Stefanie},
title = {{3D Morphable Models and Beyond (Dagstuhl Seminar 22121)}},
pages = {97--116},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Gardner, James and Egger, Bernhard and Smith, William and Theobalt, Christian and Wuhrer, Stefanie},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.97},
URN = {urn:nbn:de:0030-drops-172701},
doi = {10.4230/DagRep.12.3.97},
annote = {Keywords: 3D Computer Vision, Generative Models, Neural Rendering, Implicit Representations, Computer Graphics, Statistical Modelling}
}
Document
Framing in Communication: From Theories to Computation (Dagstuhl Seminar 22131)
Abstract
Framing has become recognised as a powerful communication strategy for winning debates and shaping opinions and decisions. Entman defines framing as an action of selecting "some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described". Instead of engaging in costly and difficult exchanges of argument and counter-argument, a politician or a journalist can then try to reframe a dialogue on, for example, fracking from economic benefits to environmental hazards, or a dialogue on abortion from pro-life to pro-choice. Introduced in 1960’s sociology, framing has been imported into communication sciences and media studies as an attempt to address the ways in which news is reported and, thus, a way in which to tackle manipulation and fake news. The topic has spread to other disciplines such as psychology, philosophy, semantics, pragmatics, political science, journalism, and, most recently – to computational linguistics and artificial intelligence. This seminar aims to pave the way to synthesising definitions developed in these theoretically and empirically driven areas and then to operationalise them in computational and applied areas by means of cross-disciplinary hands-on exchanges in facilitated discussions. Our goal is to support the development of innovative technologies, which can help us to quantify framing phenomena, to study framing at scale, and to deploy computational techniques in order to intervene against malicious attempts to influence opinions and decisions of the general public.
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Katarzyna Budzynska, Chris Reed, Manfred Stede, Benno Stein, and Zhang He. Framing in Communication: From Theories to Computation (Dagstuhl Seminar 22131). In Dagstuhl Reports, Volume 12, Issue 3, pp. 117-140, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{budzynska_et_al:DagRep.12.3.117,
author = {Budzynska, Katarzyna and Reed, Chris and Stede, Manfred and Stein, Benno and He, Zhang},
title = {{Framing in Communication: From Theories to Computation (Dagstuhl Seminar 22131)}},
pages = {117--140},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Budzynska, Katarzyna and Reed, Chris and Stede, Manfred and Stein, Benno and He, Zhang},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.117},
URN = {urn:nbn:de:0030-drops-172713},
doi = {10.4230/DagRep.12.3.117},
annote = {Keywords: Communication Strategies, Discourse and Dialogue, Computational Argumentation, Natural Language Processing}
}
Document
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)
Abstract
Vectorial representations of graphs and relational structures, so-called graph embeddings, make it possible to apply standard tools from data mining, machine learning, and statistics to the graph domain. In particular, graph embeddings aim to capture important information about, both, the graph structure and available side information as a vector, to enable downstream tasks such as classification, regression, or clustering. Starting from the 1960s in chemoinformatics, research in various communities has resulted in a plethora of approaches, often with recurring ideas. However, most of the field advancements are driven by intuition and empiricism, often tailored to a specific application domain. Until recently, the area has received little stimulus from theoretical computer science, graph theory, and learning theory. The Dagstuhl Seminar 22132 "Graph Embeddings: Theory meets Practice", was aimed to gather leading applied and theoretical researchers in graph embeddings and adjacent areas, such as graph isomorphism, bio- and chemoinformatics, and graph theory, to stimulate an increased exchange of ideas between these communities.
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Martin Grohe, Stephan Günnemann, Stefanie Jegelka, and Christopher Morris. Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). In Dagstuhl Reports, Volume 12, Issue 3, pp. 141-155, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Article{grohe_et_al:DagRep.12.3.141,
author = {Grohe, Martin and G\"{u}nnemann, Stephan and Jegelka, Stefanie and Morris, Christopher},
title = {{Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)}},
pages = {141--155},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {3},
editor = {Grohe, Martin and G\"{u}nnemann, Stephan and Jegelka, Stefanie and Morris, Christopher},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.3.141},
URN = {urn:nbn:de:0030-drops-172727},
doi = {10.4230/DagRep.12.3.141},
annote = {Keywords: Machine Learning For Graphs, GNNs, Graph Embedding}
}