3 Search Results for "Spagnuolo, Michela"

Document
Poster Abstract
DOI: 10.4230/LIPIcs.GD.2025.50
Reeb Lobsters Are 1-Planar (Poster Abstract)

Authors: Maarten Löffler, Miriam Münch, and Ignaz Rutter

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
Very recently, Chambers, Fasy, Hosseini Sereshgi and Löffler [Erin W. Chambers et al., 2025] showed that every Reeb caterpillar admits a crossing-free drawing. It turns out that this does not hold for Reeb lobsters but we show that these graphs admit drawings with at most one crossing per edge.
Cite as

Maarten Löffler, Miriam Münch, and Ignaz Rutter. Reeb Lobsters Are 1-Planar (Poster Abstract). In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 50:1-50:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{loffler_et_al:LIPIcs.GD.2025.50,
  author =	{L\"{o}ffler, Maarten and M\"{u}nch, Miriam and Rutter, Ignaz},
  title =	{{Reeb Lobsters Are 1-Planar}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{50:1--50:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.50},
  URN =		{urn:nbn:de:0030-drops-250365},
  doi =		{10.4230/LIPIcs.GD.2025.50},
  annote =	{Keywords: Reeb graphs, layered drawings, local crossing number}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.22
Learning to Bound for Maximum Common Subgraph Algorithms

Authors: Buddhi W. Kothalawala, Henning Koehler, and Qing Wang

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Identifying the maximum common subgraph between two graphs is a computationally challenging NP-hard problem. While the McSplit algorithm represents a state-of-the-art approach within a branch-and-bound (BnB) framework, several extensions have been proposed to enhance its vertex pair selection strategy, often utilizing reinforcement learning techniques. Nonetheless, the quality of the upper bound remains a critical factor in accelerating the search process by effectively pruning unpromising branches. This research introduces a novel, more restrictive upper bound derived from a detailed analysis of the McSplit algorithm’s generated partitions. To enhance the effectiveness of this bound, we propose a reinforcement learning approach that strategically directs computational effort towards the most promising regions within the search space.
Cite as

Buddhi W. Kothalawala, Henning Koehler, and Qing Wang. Learning to Bound for Maximum Common Subgraph Algorithms. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 22:1-22:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kothalawala_et_al:LIPIcs.CP.2025.22,
  author =	{Kothalawala, Buddhi W. and Koehler, Henning and Wang, Qing},
  title =	{{Learning to Bound for Maximum Common Subgraph Algorithms}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{22:1--22:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.22},
  URN =		{urn:nbn:de:0030-drops-238837},
  doi =		{10.4230/LIPIcs.CP.2025.22},
  annote =	{Keywords: Combinatorial Search, Branch and Bound, Graph Theory}
}
Document
DOI: 10.4230/DagSemProc.06171.10
Structural Descriptors for 3D Shapes

Authors: Michela Spagnuolo, Silvia Biasotti, Bianca Falcidieno, and Simone Marini

Published in: Dagstuhl Seminar Proceedings, Volume 6171, Content-Based Retrieval (2006)

Abstract
Assessing the similarity among 3D shapes is a very complex and challenging research topic. While human perception have been widely studied and produced theories that received a large consensus, the computational aspects of 3D shape retrieval and matching have been only recently addressed. The majority of the methods proposed in the literature mainly focus on the geometry of shapes, in the sense of considering its spatial distribution or extent in the 3D space. From a practical point of view, the main advantage of these methods is that they do not make specific assumption on the topology of the digital models, usually triangle meshes or even triangle soups. Moreover, these methods are also computationally efficient. There is a growing consensus, however, that shapes are recognized and coded mentally in terms of relevant parts and their spatial configuration, or structure. Methods approaching the problem from a geometric point of view do not take into account the structure of the shape and generally the similarity distance between two objects depends on their spatial embedding. The presentation will discuss the definition and use of structural descriptions for assessing shape similarity. The idea is to define a shape description framework based on results of differential topology which deal with the description of shapes by means of the properties of one, or more, real-valued functions defined over the shape. Studying these properties, several topological descriptions of the shape can be defined, which may also encode different geometric and morphological attributes that globally and locally describe the shape. Examples and results will be discussed and ongoing work outlined. This work is partially supported by the EU Newtwork of Excellence AIM{@}SHAPE.
Cite as

Michela Spagnuolo, Silvia Biasotti, Bianca Falcidieno, and Simone Marini. Structural Descriptors for 3D Shapes. In Content-Based Retrieval. Dagstuhl Seminar Proceedings, Volume 6171, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{spagnuolo_et_al:DagSemProc.06171.10,
  author =	{Spagnuolo, Michela and Biasotti, Silvia and Falcidieno, Bianca and Marini, Simone},
  title =	{{Structural Descriptors for 3D Shapes}},
  booktitle =	{Content-Based Retrieval},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6171},
  editor =	{Tim Crawford and Remco C. Veltkamp},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06171.10},
  URN =		{urn:nbn:de:0030-drops-6532},
  doi =		{10.4230/DagSemProc.06171.10},
  annote =	{Keywords: 3D shape descriptors, computational topology}
}
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