4 Search Results for "Kittler, Josef"

Document
DOI: 10.4230/LIPIcs.ITCS.2025.24
Sampling List Packings

Authors: Evan Camrud, Ewan Davies, Alex Karduna, and Holden Lee

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
We initiate the study of approximately counting the number of list packings of a graph. The analogous problem for usual vertex coloring and list coloring has attracted substantial attention. For list packing the setup is similar, but we seek a full decomposition of the lists of colors into pairwise-disjoint proper list colorings. The existence of a list packing implies the existence of a list coloring, but the converse is false. Recent works on list packing have focused on existence or extremal results of on the number of list packings, but here we turn to the algorithmic aspects of counting and sampling. In graphs of maximum degree Δ and when the number of colors is at least Ω(Δ²), we give a fully polynomial-time randomized approximation scheme (FPRAS) based on rapid mixing of a natural Markov chain (the Glauber dynamics) which we analyze with the path coupling technique. Some motivation for our work is the investigation of an atypical spin system, one where the number of spins for each vertex is much larger than the graph degree.
Cite as

Evan Camrud, Ewan Davies, Alex Karduna, and Holden Lee. Sampling List Packings. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 24:1-24:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{camrud_et_al:LIPIcs.ITCS.2025.24,
  author =	{Camrud, Evan and Davies, Ewan and Karduna, Alex and Lee, Holden},
  title =	{{Sampling List Packings}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{24:1--24:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.24},
  URN =		{urn:nbn:de:0030-drops-226528},
  doi =		{10.4230/LIPIcs.ITCS.2025.24},
  annote =	{Keywords: List packing, Graph colouring, Markov chains, Path coupling}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.11
How Does Knowledge Evolve in Open Knowledge Graphs?

Authors: Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.
Cite as

Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{polleres_et_al:TGDK.1.1.11,
  author =	{Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jim\'{e}nez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
  title =	{{How Does Knowledge Evolve in Open Knowledge Graphs?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{11:1--11:59},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},
  URN =		{urn:nbn:de:0030-drops-194855},
  doi =		{10.4230/TGDK.1.1.11},
  annote =	{Keywords: KG evolution, temporal KG, versioned KG, dynamic KG}
}
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)

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@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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