3 Search Results for "Maurer JR., Calvin R."

Document
DOI: 10.4230/LIPIcs.DISC.2025.30
Team Formation and Applications

Authors: Yuval Emek, Shay Kutten, Ido Rafael, and Gadi Taubenfeld

Published in: LIPIcs, Volume 356, 39th International Symposium on Distributed Computing (DISC 2025)

Abstract
A novel long-lived distributed problem, called Team Formation (TF), is introduced together with a message- and time-efficient randomized algorithm. The problem is defined over the asynchronous model with a complete communication graph, using bounded size messages, where a certain fraction of the nodes may experience a generalized, strictly stronger, version of initial failures. The goal of a TF algorithm is to assemble tokens injected by the environment, in a distributed manner, into teams of size σ, where σ is a parameter of the problem. The usefulness of TF is demonstrated by using it to derive efficient algorithms for many distributed problems. Specifically, we show that various (one-shot as well as long-lived) distributed problems reduce to TF. This includes well-known (and extensively studied) distributed problems such as several versions of leader election and threshold detection. For example, we are the first to break the linear message complexity bound for asynchronous implicit leader election. We also improve the time complexity of message-optimal algorithms for asynchronous explicit leader election. Other distributed problems that reduce to TF are new ones, including matching players in online gaming platforms, a generalization of gathering, constructing a perfect matching in an induced subgraph of the complete graph, and more. To complement our positive contribution, we establish a tight lower bound on the message complexity of TF algorithms.
Cite as

Yuval Emek, Shay Kutten, Ido Rafael, and Gadi Taubenfeld. Team Formation and Applications. In 39th International Symposium on Distributed Computing (DISC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 356, pp. 30:1-30:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{emek_et_al:LIPIcs.DISC.2025.30,
  author =	{Emek, Yuval and Kutten, Shay and Rafael, Ido and Taubenfeld, Gadi},
  title =	{{Team Formation and Applications}},
  booktitle =	{39th International Symposium on Distributed Computing (DISC 2025)},
  pages =	{30:1--30:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-402-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{356},
  editor =	{Kowalski, Dariusz R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2025.30},
  URN =		{urn:nbn:de:0030-drops-248474},
  doi =		{10.4230/LIPIcs.DISC.2025.30},
  annote =	{Keywords: asynchronous message-passing, complete communication graph, initial failures, leader election, matching}
}
Document
DOI: 10.4230/DagSemProc.06311.1
06311 Abstracts Collection – Sensor Data and Information Fusion in Computer Vision and Medicine

Authors: Joachim Denzler, Joachim Hornegger, Josef Kittler, and Calvin R. Maurer JR.

Published in: Dagstuhl Seminar Proceedings, Volume 6311, Sensor Data and Information Fusion in Computer Vision and Medicine (2007)

Abstract
From 30.07.06 to 04.08.06, the Dagstuhl Seminar 06311 ``Sensor Data and Information Fusion in Computer Vision and Medicine'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. Sensor data fusion is of increasing importance for many research fields and applications. Multi-modal imaging is routine in medicine, and in robitics it is common to use multi-sensor data fusion. During the seminar, researchers and application experts working in the field of sensor data fusion presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. The second part briefly summarizes the contributions.
Cite as

Joachim Denzler, Joachim Hornegger, Josef Kittler, and Calvin R. Maurer JR.. 06311 Abstracts Collection – Sensor Data and Information Fusion in Computer Vision and Medicine. In Sensor Data and Information Fusion in Computer Vision and Medicine. Dagstuhl Seminar Proceedings, Volume 6311, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{denzler_et_al:DagSemProc.06311.1,
  author =	{Denzler, Joachim and Hornegger, Joachim and Kittler, Josef and Maurer JR., Calvin R.},
  title =	{{06311 Abstracts Collection – Sensor Data and Information Fusion in Computer Vision and Medicine}},
  booktitle =	{Sensor Data and Information Fusion in Computer Vision and Medicine},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6311},
  editor =	{Joachim Denzler and Joachim Hornegger and Josef Kittler and Calvin R. Maurer JR},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06311.1},
  URN =		{urn:nbn:de:0030-drops-8552},
  doi =		{10.4230/DagSemProc.06311.1},
  annote =	{Keywords: multi-sensor fusion, multi-modal perception, multiple expert fusion, fusion paradigms, multi-modal and intra-modal experts, non-rigid registration, human robot interaction, attention systems, computer vision, image processing, medical image analysis, multi-modal tissue classification, intensity correction, real-time tracking, non-parmetric density estimation, assignment problem, artificial voice}
}
Document
DOI: 10.4230/DagSemProc.06311.2
06311 Executive Summary – Sensor Data and Information Fusion in Computer Vision and Medicine

Authors: Joachim Denzler, Joachim Hornegger, Josef Kittler, and Calvin R. Maurer JR.

Published in: Dagstuhl Seminar Proceedings, Volume 6311, Sensor Data and Information Fusion in Computer Vision and Medicine (2007)

Abstract
Today many technical systems are equipped with multiple sensors and information sources, like cameras, ultrasound sensors or web data bases. It is no problem to generate an exorbitantly large amount of data, but it is mostly unsolved how to take advantage of the expectation that the collected data provide more information than the sum of its parts. The design and analysis of algorithms for sensor data and information acquisition and fusion as well as the usage in a differentiated application field was the major focus of the Seminar held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. 24 researchers, practitioners, and application experts from different areas met to summarize the current state-of-the-art technology in data and information fusion, to discuss current research problems in fusion, and to envision future demands of this challenging research field. The considered application scenarios for data and information fusion were in the fields of computer vision and medicine.
Cite as

Joachim Denzler, Joachim Hornegger, Josef Kittler, and Calvin R. Maurer JR.. 06311 Executive Summary – Sensor Data and Information Fusion in Computer Vision and Medicine. In Sensor Data and Information Fusion in Computer Vision and Medicine. Dagstuhl Seminar Proceedings, Volume 6311, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

Copy BibTex To Clipboard

@InProceedings{denzler_et_al:DagSemProc.06311.2,
  author =	{Denzler, Joachim and Hornegger, Joachim and Kittler, Josef and Maurer JR., Calvin R.},
  title =	{{06311 Executive Summary – Sensor Data and Information Fusion in Computer Vision and Medicine}},
  booktitle =	{Sensor Data and Information Fusion in Computer Vision and Medicine},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6311},
  editor =	{Joachim Denzler and Joachim Hornegger and Josef Kittler and Calvin R. Maurer JR},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06311.2},
  URN =		{urn:nbn:de:0030-drops-8542},
  doi =		{10.4230/DagSemProc.06311.2},
  annote =	{Keywords: Sensor and data fusion, adaptive fusion, multimodal fusion, multiple classifier fusion, computer vision, robotics, medical imaging}
}
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