Issue
Dagstuhl Reports, Volume 12, Issue 8
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Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep)
Publication Details
- published at: 2023-03-02
- 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 8, August 2022, Complete Issue
Abstract
Dagstuhl Reports, Volume 12, Issue 8, August 2022, Complete Issue
Cite as
Dagstuhl Reports, Volume 12, Issue 8, pp. 1-118, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{DagRep.12.8,
title = {{Dagstuhl Reports, Volume 12, Issue 8, August 2022, Complete Issue}},
pages = {1--118},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8},
URN = {urn:nbn:de:0030-drops-177102},
doi = {10.4230/DagRep.12.8},
annote = {Keywords: Dagstuhl Reports, Volume 12, Issue 8, August 2022, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 12, Issue 8, 2022
Abstract
Dagstuhl Reports, Table of Contents, Volume 12, Issue 8, 2022
Cite as
Dagstuhl Reports, Volume 12, Issue 8, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{DagRep.12.8.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 12, Issue 8, 2022}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.i},
URN = {urn:nbn:de:0030-drops-177118},
doi = {10.4230/DagRep.12.8.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Visualization and Decision Making Design Under Uncertainty (Dagstuhl Seminar 22331)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22331 "Visualization and Decision Making Design Under Uncertainty". The seminar brought together 33 researchers and practitioners from different domains concerned with visualization and decision making under uncertainty including visualization, visual analytics, human-computer interaction, artificial intelligence, climate research, geography and geology. The programme was organized in two parts: In the first part which lasted two days, participants gave short talks where they discussed current practices and the uncertainty visualization challenges they encountered in their own research. At the end of day two, participants brainstormed collectively around the main uncertainty visualization research challenges across domains and applications. In the second part, participants voted for the following three main challenges they wished to discuss for the remainder of the seminar (one and a half days): applications, human-centered uncertainty visualization, a design process for uncertainty visualization. Thus three break-out groups were formed to discuss these challenges. Abstracts for the individual talks and the break-out group activities are included in this report.
Cite as
Nadia Boukhelifa, Christopher R. Johnson, and Kristi Potter. Visualization and Decision Making Design Under Uncertainty (Dagstuhl Seminar 22331). In Dagstuhl Reports, Volume 12, Issue 8, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{boukhelifa_et_al:DagRep.12.8.1,
author = {Boukhelifa, Nadia and Johnson, Christopher R. and Potter, Kristi},
title = {{Visualization and Decision Making Design Under Uncertainty (Dagstuhl Seminar 22331)}},
pages = {1--19},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
editor = {Boukhelifa, Nadia and Johnson, Christopher R. and Potter, Kristi},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.1},
URN = {urn:nbn:de:0030-drops-177120},
doi = {10.4230/DagRep.12.8.1},
annote = {Keywords: Decision making, Uncertainty visualization, Visual Analytics, Visualization}
}
Document
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22332 "Differential Equations and Continuous-Time Deep Learning". Neural ordinary-differential equations and similar continuous model architectures have gained interest in recent years, due to the existence of a vast literature in calculus and numerical analysis. Thus, continuous models might lead to architectures with finer control over prior assumptions or theoretical understanding. In this seminar, we have sought to bring together researchers from traditionally disjoint areas - machine learning, numerical analysis, dynamical systems and their "consumers" - to try and develop a joint language about this novel modeling paradigm. Through talks & group discussions, we have identified common interests and we hope that this first seminar is but the first step on a joint journey.
Cite as
David Duvenaud, Markus Heinonen, Michael Tiemann, and Max Welling. Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). In Dagstuhl Reports, Volume 12, Issue 8, pp. 20-30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{duvenaud_et_al:DagRep.12.8.20,
author = {Duvenaud, David and Heinonen, Markus and Tiemann, Michael and Welling, Max},
title = {{Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332)}},
pages = {20--30},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
editor = {Duvenaud, David and Heinonen, Markus and Tiemann, Michael and Welling, Max},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.20},
URN = {urn:nbn:de:0030-drops-177131},
doi = {10.4230/DagRep.12.8.20},
annote = {Keywords: deep learning, differential equations}
}
Document
Power and Energy-Aware Computing on Heterogeneous Systems (PEACHES) (Dagstuhl Seminar 22341)
Abstract
This report documents the program and outcomes of the Dagstuhl Seminar 22341 - Power and Energy-Aware Computing on Heterogeneous Systems (PEACHES). The seminar was held on Aug 21 – Aug 26, 2022, and brought together 35 international experts from different domains across the entire system stack – from system designers to programmers and operators. We present the abstracts of 18 talks and 5 summaries of discussions and active sessions on the principal topic areas: Energy Transparency from Hardware to Software, Energy Optimisation and Management, Computing for Sustainability, Green Computing Hackathon, and Disruptive Paradigms.
Cite as
Kerstin I. Eder, Timo Hönig, Daniel Mosse, Max Plauth, and Maja Hanne Kirkeby. Power and Energy-Aware Computing on Heterogeneous Systems (PEACHES) (Dagstuhl Seminar 22341). In Dagstuhl Reports, Volume 12, Issue 8, pp. 31-59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{eder_et_al:DagRep.12.8.31,
author = {Eder, Kerstin I. and H\"{o}nig, Timo and Mosse, Daniel and Plauth, Max and Kirkeby, Maja Hanne},
title = {{Power and Energy-Aware Computing on Heterogeneous Systems (PEACHES) (Dagstuhl Seminar 22341)}},
pages = {31--59},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
editor = {Eder, Kerstin I. and H\"{o}nig, Timo and Mosse, Daniel and Plauth, Max and Kirkeby, Maja Hanne},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.31},
URN = {urn:nbn:de:0030-drops-177140},
doi = {10.4230/DagRep.12.8.31},
annote = {Keywords: energy, heterogeneous computing, operating systems, power, systems}
}
Document
Privacy in Speech and Language Technology (Dagstuhl Seminar 22342)
Abstract
This report documents the outcomes of Dagstuhl Seminar 22342 "Privacy in Speech and Language Technology". The seminar brought together 27 attendees from 9 countries (Australia, Belgium, France, Germany, the Netherlands, Norway, Portugal, Sweden, and the USA) and 6 distinct disciplines (Speech Processing, Natural Language Processing, Privacy Enhancing Technologies, Machine Learning, Human Factors, and Law) in order to achieve a common understanding of the privacy threats raised by speech and language technology, as well as the existing solutions and the remaining issues in each discipline, and to draft an interdisciplinary roadmap towards solving those issues in the short or medium term.
To achieve these goals, the first day and the morning of the second day were devoted to 3-minute self-introductions by all participants intertwined with 6 tutorials to introduce the terminology, the problems faced, and the solutions brought in each of the 6 disciplines. We also made a list of use cases and identified 6 cross-disciplinary topics to be discussed. The remaining days involved working groups to discuss these 6 topics, collaborative writing sessions to report on the findings of the working groups, and wrap-up sessions to discuss these findings with each other. A hike was organized in the afternoon of the third day.
The seminar was a success: all participants actively participated in the working groups and the discussions, and went home with new ideas and new collaborators. This report gathers the abstracts of the 6 tutorials and the reports of the working groups, which we consider as valuable contributions towards a full-fledged roadmap.
Cite as
Simone Fischer-Hübner, Dietrich Klakow, Peggy Valcke, and Emmanuel Vincent. Privacy in Speech and Language Technology (Dagstuhl Seminar 22342). In Dagstuhl Reports, Volume 12, Issue 8, pp. 60-102, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{fischerhubner_et_al:DagRep.12.8.60,
author = {Fischer-H\"{u}bner, Simone and Klakow, Dietrich and Valcke, Peggy and Vincent, Emmanuel},
title = {{Privacy in Speech and Language Technology (Dagstuhl Seminar 22342)}},
pages = {60--102},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
editor = {Fischer-H\"{u}bner, Simone and Klakow, Dietrich and Valcke, Peggy and Vincent, Emmanuel},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.60},
URN = {urn:nbn:de:0030-drops-177152},
doi = {10.4230/DagRep.12.8.60},
annote = {Keywords: Privacy, Speech and Language Technology, Privacy Enhancing Technologies, Dagstuhl Seminar}
}
Document
Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)
Abstract
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
Cite as
Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke. Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351). In Dagstuhl Reports, Volume 12, Issue 8, pp. 103-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{chau_et_al:DagRep.12.8.103,
author = {Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
title = {{Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)}},
pages = {103--116},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {8},
editor = {Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.8.103},
URN = {urn:nbn:de:0030-drops-177161},
doi = {10.4230/DagRep.12.8.103},
annote = {Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability}
}