Issue
Dagstuhl Reports, Volume 11, Issue 7
-
Part of:
Volume:
Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep)
Event
Dagstuhl Seminars 21331, 21332, 21342, 21351, 21352Publication Details
- published at: 2021-12-22
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports11
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Document
Complete Issue
Dagstuhl Reports, Volume 11, Issue 7, August 2021, Complete Issue
Abstract
Dagstuhl Reports, Volume 11, Issue 7, July 2021, Complete Issue
Cite as
Dagstuhl Reports, Volume 11, Issue 7, pp. 1-180, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{DagRep.11.7,
title = {{Dagstuhl Reports, Volume 11, Issue 7, August 2021, Complete Issue}},
pages = {1--180},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7},
URN = {urn:nbn:de:0030-drops-155861},
doi = {10.4230/DagRep.11.7},
annote = {Keywords: Dagstuhl Reports, Volume 11, Issue 7, July 2021, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 11, Issue 7, 2021
Abstract
Dagstuhl Reports, Table of Contents, Volume 11, Issue 7, 2021
Cite as
Dagstuhl Reports, Volume 11, Issue 7, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{DagRep.11.7.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 11, Issue 7, 2021}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.i},
URN = {urn:nbn:de:0030-drops-155870},
doi = {10.4230/DagRep.11.7.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Coalition Formation Games (Dagstuhl Seminar 21331)
Abstract
There are many situations in which individuals will choose to act as a group, or coalition. Examples include social clubs, political parties, partnership formation, and legislative voting. Coalition formation games are a class of cooperative games where the aim is to partition a set of agents into coalitions, according to some criteria, such as coalitional stability or maximization of social welfare. In our seminar we discussed applications, results, and new directions of research in the field of coalition formation games.
Cite as
Edith Elkind, Judy Goldsmith, Anja Rey, and Jörg Rothe. Coalition Formation Games (Dagstuhl Seminar 21331). In Dagstuhl Reports, Volume 11, Issue 7, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{elkind_et_al:DagRep.11.7.1,
author = {Elkind, Edith and Goldsmith, Judy and Rey, Anja and Rothe, J\"{o}rg},
title = {{Coalition Formation Games (Dagstuhl Seminar 21331)}},
pages = {1--15},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
editor = {Elkind, Edith and Goldsmith, Judy and Rey, Anja and Rothe, J\"{o}rg},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.1},
URN = {urn:nbn:de:0030-drops-155885},
doi = {10.4230/DagRep.11.7.1},
annote = {Keywords: Coalition Formation, Cooperative Games}
}
Document
Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)
Abstract
Two key changes are driving an immediate need for deeper understanding of I/O workloads in high-performance computing (HPC): applications are evolving beyond the traditional bulk-synchronous models to include integrated multistep workflows, in situ analysis, artificial intelligence, and data analytics methods; and storage systems designs are evolving beyond a two-tiered file system and archive model to complex hierarchies containing temporary, fast tiers of storage close to compute resources with markedly different performance properties. Both of these changes represent a significant departure from the decades-long status quo and require investigation from storage researchers and practitioners to understand their impacts on overall I/O performance. Without an in-depth understanding of I/O workload behavior, storage system designers, I/O middleware developers, facility operators, and application developers will not know how best to design or utilize the additional tiers for optimal performance of a given I/O workload. The goal of this Dagstuhl Seminar was to bring together experts in I/O performance analysis and storage system architecture to collectively evaluate how our community is capturing and analyzing I/O workloads on HPC systems, identify any gaps in our methodologies, and determine how to develop a better in-depth understanding of their impact on HPC systems. Our discussions were lively and resulted in identifying critical needs for research in the area of understanding I/O behavior. We document those discussions in this report.
Cite as
Philip Carns, Julian Kunkel, Kathryn Mohror, and Martin Schulz. Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332). In Dagstuhl Reports, Volume 11, Issue 7, pp. 16-75, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{carns_et_al:DagRep.11.7.16,
author = {Carns, Philip and Kunkel, Julian and Mohror, Kathryn and Schulz, Martin},
title = {{Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)}},
pages = {16--75},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
editor = {Carns, Philip and Kunkel, Julian and Mohror, Kathryn and Schulz, Martin},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.16},
URN = {urn:nbn:de:0030-drops-155891},
doi = {10.4230/DagRep.11.7.16},
annote = {Keywords: I/O performance measurement, Understanding user I/O patterns, HPC I/O, I/O characterization}
}
Document
Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 21342)
Abstract
Edge computing, a key part of the 5G networks and beyond, promises to decentralize cloud applications while providing more bandwidth and reducing latencies. The promises are delivered by moving application-specific computations between the cloud, the data-producing devices, and the network infrastructure components at the edges of wireless and fixed networks. However, the current AI/ML methods assume computations are conducted in a powerful computational infrastructure, such as a homogeneous cloud with ample computing and data storage resources available. In this seminar, we discussed and developed presumptions for a comprehensive view of AI methods and capabilities in the context of edge computing, and provided a roadmap to bring together enablers and key aspects for edge computing and applied AI/ML fields.
Cite as
Aaron Ding, Ella Peltonen, Sasu Tarkoma, and Lars Wolf. Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 21342). In Dagstuhl Reports, Volume 11, Issue 7, pp. 76-88, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{ding_et_al:DagRep.11.7.76,
author = {Ding, Aaron and Peltonen, Ella and Tarkoma, Sasu and Wolf, Lars},
title = {{Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 21342)}},
pages = {76--88},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
editor = {Ding, Aaron and Peltonen, Ella and Tarkoma, Sasu and Wolf, Lars},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.76},
URN = {urn:nbn:de:0030-drops-155906},
doi = {10.4230/DagRep.11.7.76},
annote = {Keywords: artificial intelligence, communication networks, edge computing, intelligent networking}
}
Document
Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics (Dagstuhl Seminar 21351)
Abstract
Computational linguistics builds models that can usefully process and produce language and that can increase our understanding of linguistic phenomena. From the computational perspective, language data are particularly challenging notably due to their variable degree of idiosyncrasy (unexpected properties shared by few peer objects), and the pervasiveness of non-compositional phenomena such as multiword expressions (whose meaning cannot be straightforwardly deduced from the meanings of their components, e.g. red tape, by and large, to pay a visit and to pull one’s leg) and constructions (conventional associations of forms and meanings). Additionally, if models and methods are to be consistent and valid across languages, they have to face specificities inherent either to particular languages, or to various linguistic traditions.
These challenges were addressed by the Dagstuhl Seminar 21351 entitled "Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics", which took place on 30-31 August 2021. Its main goal was to create synergies between three distinct though partly overlapping communities: experts in typology, in cross-lingual morphosyntactic annotation and in multiword expressions. This report documents the program and the outcomes of the seminar. We present the executive summary of the event, reports from the 3 Working Groups and abstracts of individual talks and open problems presented by the participants.
Cite as
Timothy Baldwin, William Croft, Joakim Nivre, and Agata Savary. Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics (Dagstuhl Seminar 21351). In Dagstuhl Reports, Volume 11, Issue 7, pp. 89-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{baldwin_et_al:DagRep.11.7.89,
author = {Baldwin, Timothy and Croft, William and Nivre, Joakim and Savary, Agata},
title = {{Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics (Dagstuhl Seminar 21351)}},
pages = {89--138},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
editor = {Baldwin, Timothy and Croft, William and Nivre, Joakim and Savary, Agata},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.89},
URN = {urn:nbn:de:0030-drops-155911},
doi = {10.4230/DagRep.11.7.89},
annote = {Keywords: computational linguistics, morphosyntax, multiword expressions, language universals, idiosyncrasy}
}
Document
Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)
Abstract
Graph and network models are essential for data science applications in computer science, social sciences, and life sciences. They help to detect patterns in data on dyadic relations between pairs of genes, humans, or documents, and have improved our understanding of complex networks across disciplines. While the advantages of graph models of relational data are undisputed, we often have access to data with multiple types of higher-order relations not captured by simple graphs. Such data arise in social systems with non-dyadic or group-based interactions, multi-modal transportation networks with multiple connection types, or time series containing specific sequences of nodes traversed on paths. The complex relational structure of such data questions the validity of graph-based data mining and modelling, and jeopardises interdisciplinary applications of network analysis and machine learning.
To address this challenge, researchers in topological data analysis, network science, machine learning, and physics recently started to generalise network analysis to higher-order graph models that capture more than dyadic relations. These higher-order models differ from standard network analysis in assumptions, applications, and mathematical formalisms. As a result, the emerging field lacks a shared terminology, common challenges, benchmark data and metrics to facilitate fair comparisons. By bringing together researchers from different disciplines, Dagstuhl Seminar 21352 "Higher-Order Graph Models: From Theoretical Foundations to Machine Learning" aimed at the development of a common language and a shared understanding of key challenges in the field that foster progress in data analytics and machine learning for data with complex relational structure. This report documents the program and the outcomes of this seminar.
Cite as
Tina Eliassi-Rad, Vito Latora, Martin Rosvall, and Ingo Scholtes. Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352). In Dagstuhl Reports, Volume 11, Issue 7, pp. 139-178, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
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@Article{eliassirad_et_al:DagRep.11.7.139,
author = {Eliassi-Rad, Tina and Latora, Vito and Rosvall, Martin and Scholtes, Ingo},
title = {{Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)}},
pages = {139--178},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2021},
volume = {11},
number = {7},
editor = {Eliassi-Rad, Tina and Latora, Vito and Rosvall, Martin and Scholtes, Ingo},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.139},
URN = {urn:nbn:de:0030-drops-155929},
doi = {10.4230/DagRep.11.7.139},
annote = {Keywords: (Social) Network analysis, Graph mining, Graph theory, Network science, Machine Learning, Statistical relational learning, Topological data analysis}
}