5 Search Results for "Schultz, Thomas"


Document
Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)

Authors: Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang

Published in: Dagstuhl Reports, Volume 8, Issue 10 (2019)


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18442, "Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy", which was attended by 30 international researchers, both junior and senior. Directional preferences or anisotropies are encountered across many different disciplines and spatial scales. These disciplines share a need for modeling, processing, and visualizing anisotropic quantities, which poses interesting challenges to applied computer science. With the goal of identifying open problems, making practitioners aware of existing solutions, and discovering synergies between different applications in which anisotropy arises, this seminar brought together researchers working on different aspects of computer science with experts from neuroimaging and astronomy. This report gathers abstracts of the talks held by the participants, as well as an account of topics raised within the breakout sessions.

Cite as

Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang. Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442). In Dagstuhl Reports, Volume 8, Issue 10, pp. 148-172, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@Article{fuster_et_al:DagRep.8.10.148,
  author =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  title =	{{Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)}},
  pages =	{148--172},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.8.10.148},
  URN =		{urn:nbn:de:0030-drops-103524},
  doi =		{10.4230/DagRep.8.10.148},
  annote =	{Keywords: Anisotropy, astronomy, diffusion-weighted imaging (DWI), geometry processing, tensor fields, topology, visualization, uncertainty, shape modeling, microstructure imaging, statistical analysis}
}
Document
Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142)

Authors: Ingrid Hotz, Evren Özarslan, and Thomas Schultz

Published in: Dagstuhl Reports, Volume 6, Issue 4 (2016)


Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 16142, "Multidisciplinary Approaches to Multivalued Data: Modelling, Visualization, Analysis", which was attended by 27 international researchers, both junior and senior. Modelling multivalued data using tensors and higher-order descriptors has become common practice in neuroscience, engineering, and medicine. Novel tools for image analysis, visualization, as well as statistical hypothesis testing and machine learning are required to extract value from such data, and can only be developed within multidisciplinary collaborations. This report gathers abstracts of the talks held by participants on recent advances and open questions related to these challenges, as well as an account of topics raised within two of the breakout sessions.

Cite as

Ingrid Hotz, Evren Özarslan, and Thomas Schultz. Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142). In Dagstuhl Reports, Volume 6, Issue 4, pp. 16-38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


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@Article{hotz_et_al:DagRep.6.4.16,
  author =	{Hotz, Ingrid and \"{O}zarslan, Evren and Schultz, Thomas},
  title =	{{Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142)}},
  pages =	{16--38},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{4},
  editor =	{Hotz, Ingrid and \"{O}zarslan, Evren and Schultz, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.4.16},
  URN =		{urn:nbn:de:0030-drops-61517},
  doi =		{10.4230/DagRep.6.4.16},
  annote =	{Keywords: visualization, image processing, statistical analysis, machine learning, tensor fields, higher-order descriptors, diffusion-weighted imaging (DWI), structural mechanics, fluid dynamics, microstructure imaging, connectomics, uncertainty visualization, feature extraction}
}
Document
Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences

Authors: Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke

Published in: OASIcs, Volume 34, German Conference on Bioinformatics 2013


Abstract
With the advent of metatranscriptomics it has now become possible to study the dynamics of microbial communities. The analysis of environmental RNA-Seq data implies several challenges for the development of efficient tools in bioinformatics. One of the first steps in the computational analysis of metatranscriptomic sequencing reads requires the separation of rRNA and mRNA fragments to ensure that only protein coding sequences are actually used in a subsequent functional analysis. In the context of the rRNA filtering task it is desirable to have a broad spectrum of different methods in order to find a suitable trade-off between speed and accuracy for a particular dataset. We introduce a machine learning approach for the detection of rRNA in metatranscriptomic sequencing reads that is based on support vector machines in combination with dinucleotide distance histograms for feature representation. The results show that our SVM-based approach is at least one order of magnitude faster than any of the existing tools with only a slight degradation of the detection performance when compared to state-of-the-art alignment-based methods.

Cite as

Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke. Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 80-89, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)


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@InProceedings{klingenberg_et_al:OASIcs.GCB.2013.80,
  author =	{Klingenberg, Heiner and Martinjak, Robin and Gl\"{o}ckner, Frank Oliver and Daniel, Rolf and Lingner, Thomas and Meinicke, Peter},
  title =	{{Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{80--89},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2013.80},
  URN =		{urn:nbn:de:0030-drops-42324},
  doi =		{10.4230/OASIcs.GCB.2013.80},
  annote =	{Keywords: Metatranscriptomics, metagenomics, rRNA detection, distance histograms}
}
Document
Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond

Authors: Thomas Schultz

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)


Abstract
By measuring the anisotropic self-diffusion rates of water, Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) provides a unique noninvasive probe of fibrous tissue. In particular, it has been explored widely for imaging nerve fiber tracts in the human brain. Geometric features provide a quick visual overview of the complex datasets that arise from DW-MRI. At the same time, they build a bridge towards quantitative analysis, by extracting explicit representations of structures in the data that are relevant to specific research questions. Therefore, features in DWMRI data are an active research topic not only within scientific visualization, but have received considerable interest from the medical image analysis, neuroimaging, and computer vision communities. It is the goal of this paper to survey contributions from all these fields, concentrating on streamline clustering, edge detection and segmentation, topological methods, and extraction of anisotropy creases. We point out interrelations between these topics and make suggestions for future research.

Cite as

Thomas Schultz. Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 322-345, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)


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@InCollection{schultz:DFU.Vol2.SciViz.2011.322,
  author =	{Schultz, Thomas},
  title =	{{Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{322--345},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.322},
  URN =		{urn:nbn:de:0030-drops-33010},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.322},
  annote =	{Keywords: Diffusion-Weighted MRI, dMRI, DT-MRI, DTI, HARDI, Streamline Clustering, Edge Detection, DW-MRI Segmentation, Tensor Topology, Crease Surfaces}
}
Document
Network Discovery and Verification

Authors: Zuzana Beerliova, Felix Eberhard, Thomas Erlebach, Alexander Hall, Michael Hoffmann, Matus Mihalak, and L. Shankar Ram

Published in: Dagstuhl Seminar Proceedings, Volume 5031, Algorithms for Optimization with Incomplete Information (2005)


Abstract
Consider the problem of discovering (or verifying) the edges and non-edges of a network, modelled as a connected undirected graph, using a minimum number of queries. A query at a vertex v discovers (or verifies) all edges and non-edges whose endpoints have different distance from v. In the network discovery problem, the edges and non-edges are initially unknown, and the algorithm must select the next query based only on the results of previous queries. We study the problem using competitive analysis and give a randomized on-line algorithm with competitive ratio O(sqrt(n*log n)) for graphs with n vertices. We also show that no deterministic algorithm can have competitive ratio better than 3. In the network verification problem, the graph is known in advance and the goal is to compute a minimum number of queries that verify all edges and non-edges. This problem has previously been studied as the problem of placing landmarks in graphs or determining the metric dimension of a graph. We show that there is no approximation algorithm for this problem with ratio o(log n) unless P=NP. Furthermore, we prove that the optimal number of queries for d-dimensional hypercubes is Theta(d/log d).

Cite as

Zuzana Beerliova, Felix Eberhard, Thomas Erlebach, Alexander Hall, Michael Hoffmann, Matus Mihalak, and L. Shankar Ram. Network Discovery and Verification. In Algorithms for Optimization with Incomplete Information. Dagstuhl Seminar Proceedings, Volume 5031, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)


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@InProceedings{beerliova_et_al:DagSemProc.05031.17,
  author =	{Beerliova, Zuzana and Eberhard, Felix and Erlebach, Thomas and Hall, Alexander and Hoffmann, Michael and Mihalak, Matus and Ram, L. Shankar},
  title =	{{Network Discovery and Verification}},
  booktitle =	{Algorithms for Optimization with Incomplete Information},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5031},
  editor =	{Susanne Albers and Rolf H. M\"{o}hring and Georg Ch. Pflug and R\"{u}diger Schultz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05031.17},
  URN =		{urn:nbn:de:0030-drops-594},
  doi =		{10.4230/DagSemProc.05031.17},
  annote =	{Keywords: on-line algorithms , set cover , landmarks , metric dimension}
}
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