34 Search Results for "Chen, Xue"

Document
DOI: 10.4230/LIPIcs.ESA.2025.111
Hardness of Median and Center in the Ulam Metric

Authors: Nick Fischer, Elazar Goldenberg, Mursalin Habib, and Karthik C. S.

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
The classical rank aggregation problem seeks to combine a set X of n permutations into a single representative "consensus" permutation. In this paper, we investigate two fundamental rank aggregation tasks under the well-studied Ulam metric: computing a median permutation (which minimizes the sum of Ulam distances to X) and computing a center permutation (which minimizes the maximum Ulam distance to X) in two settings. - Continuous Setting: In the continuous setting, the median/center is allowed to be any permutation. It is known that computing a center in the Ulam metric is NP-hard and we add to this by showing that computing a median is NP-hard as well via a simple reduction from the Max-Cut problem. While this result may not be unexpected, it had remained elusive until now and confirms a speculation by Chakraborty, Das, and Krauthgamer [SODA '21]. - Discrete Setting: In the discrete setting, the median/center must be a permutation from the input set. We fully resolve the fine-grained complexity of the discrete median and discrete center problems under the Ulam metric, proving that the naive Õ(n² L)-time algorithm (where L is the length of the permutation) is conditionally optimal. This resolves an open problem raised by Abboud, Bateni, Cohen-Addad, Karthik C. S., and Seddighin [APPROX '23]. Our reductions are inspired by the known fine-grained lower bounds for similarity measures, but we face and overcome several new highly technical challenges.
Cite as

Nick Fischer, Elazar Goldenberg, Mursalin Habib, and Karthik C. S.. Hardness of Median and Center in the Ulam Metric. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 111:1-111:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{fischer_et_al:LIPIcs.ESA.2025.111,
  author =	{Fischer, Nick and Goldenberg, Elazar and Habib, Mursalin and Karthik C. S.},
  title =	{{Hardness of Median and Center in the Ulam Metric}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{111:1--111:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.111},
  URN =		{urn:nbn:de:0030-drops-245809},
  doi =		{10.4230/LIPIcs.ESA.2025.111},
  annote =	{Keywords: Ulam distance, median, center, rank aggregation, fine-grained complexity}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.12
Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing

Authors: Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Millions of biological sample records collected in the last few centuries archived in natural history collections are un-georeferenced. Georeferencing complex locality descriptions associated with these collection samples is a highly labour-intensive task collection agencies struggle with. None of the existing automated methods exploit maps that are an essential tool for georeferencing complex relations. We present preliminary experiments and results of a novel method that exploits multi-modal capabilities of recent Large Multi-Modal Models (LMM). This method enables the model to visually contextualize spatial relations it reads in the locality description. We use a grid-based approach to adapt these auto-regressive models for this task in a zero-shot setting. Our experiments conducted on a small manually annotated dataset show impressive results for our approach (∼1 km Average distance error) compared to uni-modal georeferencing with Large Language Models and existing georeferencing tools. The paper also discusses the findings of the experiments in light of an LMM’s ability to comprehend fine-grained maps. Motivated by these results, a practical framework is proposed to integrate this method into a georeferencing workflow.
Cite as

Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones. Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 12:1-12:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{wijegunarathna_et_al:LIPIcs.GIScience.2025.12,
  author =	{Wijegunarathna, Kalana and Stock, Kristin and Jones, Christopher B.},
  title =	{{Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{12:1--12:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.12},
  URN =		{urn:nbn:de:0030-drops-238412},
  doi =		{10.4230/LIPIcs.GIScience.2025.12},
  annote =	{Keywords: Large Multi-Modal Models, Large Language Models, LLM, Georeferencing, Natural History collections}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.18
U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping

Authors: Vit Kostejn, Yamil Essus, Jenna Abrahamson, and Ranga Raju Vatsavai

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
In recent years, large pre-trained models, commonly referred to as foundation models, have become increasingly popular for various tasks leveraging transfer learning. This trend has expanded to remote sensing, where transformer-based foundation models such as Prithvi, msGFM, and SatSwinMAE have been utilized for a range of applications. While these transformer-based models, particularly the Prithvi model, exhibit strong generalization capabilities, they have limitations on capturing fine-grained details compared to convolutional neural network architectures like U-Net in segmentation tasks. In this paper, we propose a novel architecture, U-Prithvi, which combines the strengths of the Prithvi transformer with those of U-Net. We introduce a RandomHalfMaskLayer to ensure balanced learning from both models during training. Our approach is evaluated on the Sen1Floods11 dataset for flood inundation mapping, and experimental results demonstrate better performance of U-Prithvi over both individual models, achieving improved performance on out-of-sample data. While this principle is illustrated using the Prithvi model, it is easily adaptable to other foundation models.
Cite as

Vit Kostejn, Yamil Essus, Jenna Abrahamson, and Ranga Raju Vatsavai. U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 18:1-18:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{kostejn_et_al:LIPIcs.GIScience.2025.18,
  author =	{Kostejn, Vit and Essus, Yamil and Abrahamson, Jenna and Vatsavai, Ranga Raju},
  title =	{{U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{18:1--18:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.18},
  URN =		{urn:nbn:de:0030-drops-238479},
  doi =		{10.4230/LIPIcs.GIScience.2025.18},
  annote =	{Keywords: GeoAI, flood mapping, foundation model, U-Net, Prithvi}
}
Document
DOI: 10.4230/LIPIcs.WABI.2025.10
DiVerG: Scalable Distance Index for Validation of Paired-End Alignments in Sequence Graphs

Authors: Ali Ghaffaari, Alexander Schönhuth, and Tobias Marschall

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Determining the distance between two loci within a genomic region is a recurrent operation in various tasks in computational genomics. A notable example of this task arises in paired-end read mapping as a form of validation of distances between multiple alignments. While straightforward for a single genome, graph-based reference structures render the operation considerably more involved. Given the sheer number of such queries in a typical read mapping experiment, an efficient algorithm for answering distance queries is crucial. In this paper, we introduce DiVerG, a compact data structure as well as a fast and scalable algorithm, for constructing distance indexes for general sequence graphs on multi-core CPU and many-core GPU architectures. DiVerG is based on PairG [Jain et al., 2019], but overcomes the limitations of PairG by exploiting the extensive potential for improvements in terms of scalability and space efficiency. As a consequence, DiVerG can process substantially larger datasets, such as whole human genomes, which are unmanageable by PairG. DiVerG offers faster index construction time and consistently faster query time with gains proportional to the size of the underlying compact data structure. We demonstrate that our method performs favorably on multiple real datasets at various scales. DiVerG achieves superior performance over PairG; e.g. resulting to 2.5-4x speed-up in query time, 44-340x smaller index size, and 3-50x faster construction time for the genome graph of the MHC region, as a particularly variable region of the human genome. The implementation is available at: https://github.com/cartoonist/diverg
Cite as

Ali Ghaffaari, Alexander Schönhuth, and Tobias Marschall. DiVerG: Scalable Distance Index for Validation of Paired-End Alignments in Sequence Graphs. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 10:1-10:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{ghaffaari_et_al:LIPIcs.WABI.2025.10,
  author =	{Ghaffaari, Ali and Sch\"{o}nhuth, Alexander and Marschall, Tobias},
  title =	{{DiVerG: Scalable Distance Index for Validation of Paired-End Alignments in Sequence Graphs}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{10:1--10:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.10},
  URN =		{urn:nbn:de:0030-drops-239369},
  doi =		{10.4230/LIPIcs.WABI.2025.10},
  annote =	{Keywords: Sequence graph, distance index, read mapping, sparse matrix}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.28
SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability

Authors: Ole Lübke and Jeremias Berg

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

Abstract
Maximum Satisfiability (MaxSAT), the constraint paradigm of minimizing a linear expression over Boolean (0-1) variables subject to a set of propositional clauses, is today used for solving NP-hard combinatorial optimization problems in various domains. Especially anytime MaxSAT solvers that compute low-cost solutions within a limited available computational time have significantly improved in recent years. Such solvers can be divided into SAT-based methods that use sophisticated reasoning, and stochastic local search (SLS) methods that heuristically explore the search space. The two are complementary; roughly speaking, SLS struggles with finding feasible solutions, and SAT-based methods with minimizing cost. Consequently, most state-of-the-art anytime MaxSAT solvers run SLS before a SAT-based algorithm with minimal communication between the two. In this paper, we aim to harness the complementary strengths of SAT-based, and SLS approaches in the context of anytime MaxSAT. More precisely, we describe several ways to enhance the performance of the so-called core-boosted linear search algorithm for anytime MaxSAT with SLS techniques. Core-boosted linear search is a three-phase algorithm where each phase uses different types of reasoning. Beyond MaxSAT, core-boosted search has also been successful in the related paradigms of pseudo-boolean optimization and constraint programming. We describe how an SLS approach to MaxSAT can be tightly integrated with all three phases of the algorithm, resulting in non-trivial information exchange in both directions between the SLS algorithm and the reasoning methods. We evaluate our techniques on standard benchmarks from the latest MaxSAT Evaluation and demonstrate that our techniques can noticeably improve on implementations of core-boosted search and SLS.
Cite as

Ole Lübke and Jeremias Berg. SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 28:1-28:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{lubke_et_al:LIPIcs.CP.2025.28,
  author =	{L\"{u}bke, Ole and Berg, Jeremias},
  title =	{{SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{28:1--28:20},
  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.2025.28},
  URN =		{urn:nbn:de:0030-drops-238897},
  doi =		{10.4230/LIPIcs.CP.2025.28},
  annote =	{Keywords: Maximum Satisfiability, MaxSAT, SAT, SLS, Anytime Optimization}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.32
Efficient Certified Reasoning for Binarized Neural Networks

Authors: Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Neural networks have emerged as essential components in safety-critical applications - these use cases demand complex, yet trustworthy computations. Binarized Neural Networks (BNNs) are a type of neural network where each neuron is constrained to a Boolean value; they are particularly well-suited for safety-critical tasks because they retain much of the computational capacities of full-scale (floating-point or quantized) deep neural networks, but remain compatible with satisfiability solvers for qualitative verification and with model counters for quantitative reasoning. However, existing methods for BNN analysis suffer from either limited scalability or susceptibility to soundness errors, which hinders their applicability in real-world scenarios. In this work, we present a scalable and trustworthy approach for both qualitative and quantitative verification of BNNs. Our approach introduces a native representation of BNN constraints in a custom-designed solver for qualitative reasoning, and in an approximate model counter for quantitative reasoning. We further develop specialized proof generation and checking pipelines with native support for BNN constraint reasoning, ensuring trustworthiness for all of our verification results. Empirical evaluations on a BNN robustness verification benchmark suite demonstrate that our certified solving approach achieves a 9× speedup over prior certified CNF and PB-based approaches, and our certified counting approach achieves a 218× speedup over the existing CNF-based baseline. In terms of coverage, our pipeline produces fully certified results for 99% and 86% of the qualitative and quantitative reasoning queries on BNNs, respectively. This is in sharp contrast to the best existing baselines which can fully certify only 62% and 4% of the queries, respectively.
Cite as

Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel. Efficient Certified Reasoning for Binarized Neural Networks. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 32:1-32:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{yang_et_al:LIPIcs.SAT.2025.32,
  author =	{Yang, Jiong and Tan, Yong Kiam and Soos, Mate and Myreen, Magnus O. and Meel, Kuldeep S.},
  title =	{{Efficient Certified Reasoning for Binarized Neural Networks}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{32:1--32:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.32},
  URN =		{urn:nbn:de:0030-drops-237665},
  doi =		{10.4230/LIPIcs.SAT.2025.32},
  annote =	{Keywords: Neural network verification, proof certification, SAT solving, approximate model counting}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.27
Streamlining Distributed SAT Solver Design

Authors: Dominik Schreiber, Niccolò Rigi-Luperti, and Armin Biere

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Distributed clause-sharing SAT solvers have recently been established as powerful automated reasoning tools that can conquer previously infeasible instances. A common design of distributed SAT solvers is to run many off-the-shelf sequential solvers in parallel, employ some diversification (e.g., restart intervals or decision orders), and share conflict clauses among the solver threads. This approach, naïvely, adopts all best practices of sequential solver design for distributed solving, where these practices may be less useful or even actively detrimental. In this work we diagnose such shortcomings in the state-of-the-art system MallobSat and propose first effective mitigations. In particular, we replace the redundant pre- and inprocessing at all threads with single-core preprocessing that runs next to the parallel search, remove LBD values from the clause-sharing operation, and slim down solver diversification to very few lightweight and uniform methods. Experimental evaluations on up to 3072 cores (64 nodes) confirm that our measures improve performance while also drastically simplifying the SAT solving program that is run in parallel.
Cite as

Dominik Schreiber, Niccolò Rigi-Luperti, and Armin Biere. Streamlining Distributed SAT Solver Design. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{schreiber_et_al:LIPIcs.SAT.2025.27,
  author =	{Schreiber, Dominik and Rigi-Luperti, Niccol\`{o} and Biere, Armin},
  title =	{{Streamlining Distributed SAT Solver Design}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{27:1--27:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.27},
  URN =		{urn:nbn:de:0030-drops-237615},
  doi =		{10.4230/LIPIcs.SAT.2025.27},
  annote =	{Keywords: Satisfiability, parallel SAT solving, distributed computing, preprocessing}
}
Document
DOI: 10.4230/OASIcs.SLATE.2025.6
Beyond the Score: Exploring the Intersection Between Sociodemographics and Linguistic Features in English (L1) Writing Placement

Authors: Miguel Da Corte and Jorge Baptista

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
This study examines the intersection of sociodemographic characteristics, linguistic features, and writing placement outcomes at a community college in the United States of America. It focuses on 210 anonymized writing samples from native English speakers (L1) that were automatically classified by Accuplacer and independently assessed by two trained raters. Disparities across gender and race using 40 top-ranked linguistic features selected from Coh-Metrix, CTAP, and Developmental Education-Specific (DES) sets were analyzed. Three statistical tests were used: one-way ANOVA, Tukey’s HSD, and Chi-square. ANOVA results showed racial differences in nine linguistic features, especially those tied to syntactic complexity, discourse markers, and lexical precision. Gender differences were more limited, with only one feature reaching significance (Positive Connectives, p = 0.007). Tukey’s HSD pairwise tests showed no significant gender group variation but revealed sensitivity in DES features when comparing racial groups. Chi-square analysis indicated no significant association between gender and placement outcomes but suggested a possible link between race and human-assigned levels (χ² = 9.588, p = 0.048). These findings suggest that while automated systems assess general writing skills, human-devised linguistic features and demographic insights can support more equitable placement practices for all students entering college-level programs.
Cite as

Miguel Da Corte and Jorge Baptista. Beyond the Score: Exploring the Intersection Between Sociodemographics and Linguistic Features in English (L1) Writing Placement. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{dacorte_et_al:OASIcs.SLATE.2025.6,
  author =	{Da Corte, Miguel and Baptista, Jorge},
  title =	{{Beyond the Score: Exploring the Intersection Between Sociodemographics and Linguistic Features in English (L1) Writing Placement}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{6:1--6:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.6},
  URN =		{urn:nbn:de:0030-drops-236861},
  doi =		{10.4230/OASIcs.SLATE.2025.6},
  annote =	{Keywords: Developmental Education (DevEd), sociolinguistic variation, text classification, Machine Learning, placement equity}
}
Document
DOI: 10.4230/OASIcs.SLATE.2025.9
Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework

Authors: Jorge Baptista, Izabela Müller, and Sónia Reis

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
Semantic parsing serves as a crucial interface between natural language and formal meaning representations, enabling computational systems to capture the underlying semantic structure of linguistic expressions. This paper addresses a relatively understudied area in both linguistic theory and natural language processing: the semantic representation of adverbs. We conduct a comparative analysis of annotation guidelines and practices across two semantic representation frameworks: Lexicalized Meaning Representation (LMR), applied to the European Portuguese edition of the novella "O Principezinho" by Antoine de Saint-Exupéry (1943); and Abstract Meaning Representation (AMR), applied to the Brazilian Portuguese edition, "O Pequeno Príncipe". The study reveals significant limitations in AMR’s handling of adverbial constructions, particularly when assessed against contemporary syntactic-semantic advances in linguistic theory. Furthermore, it highlights the theoretical and practical challenges that LMR continues to face in this domain.
Cite as

Jorge Baptista, Izabela Müller, and Sónia Reis. Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 9:1-9:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{baptista_et_al:OASIcs.SLATE.2025.9,
  author =	{Baptista, Jorge and M\"{u}ller, Izabela and Reis, S\'{o}nia},
  title =	{{Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{9:1--9:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.9},
  URN =		{urn:nbn:de:0030-drops-236891},
  doi =		{10.4230/OASIcs.SLATE.2025.9},
  annote =	{Keywords: Semantic representation, Adverbs, Lexicalized Meaning Representation (LMR), Abstract Meaning Representation (AMR), Annotation guidelines, European Portuguese, Brazilian Portuguese, Comparative analysis, The Little Prince, Corpus linguistics, Natural Language Processing (NLP), Multi-word expressions, Syntactic-semantic interface, Linguistic theory}
}
Document
DOI: 10.4230/LIPIcs.SEA.2025.11
CluStRE: Streaming Graph Clustering with Multi-Stage Refinement

Authors: Adil Chhabra, Shai Dorian Peretz, and Christian Schulz

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
We present CluStRE, a novel streaming graph clustering algorithm that balances computational efficiency with high-quality clustering using multi-stage refinement. Unlike traditional in-memory clustering approaches, CluStRE processes graphs in a streaming setting, significantly reducing memory overhead while leveraging re-streaming and evolutionary heuristics to improve solution quality. Our method dynamically constructs a quotient graph, enabling modularity-based optimization while efficiently handling large-scale graphs. We introduce multiple configurations of CluStRE to provide trade-offs between speed, memory consumption, and clustering quality. Experimental evaluations demonstrate that CluStRE improves solution quality by 89.8%, operates 2.6× faster, and uses less than two-thirds of the memory required by the state-of-the-art streaming clustering algorithm on average. Moreover, our strongest mode enhances solution quality by up to 150% on average. With this, CluStRE achieves comparable solution quality to in-memory algorithms, i.e. over 96% of the quality of clustering approaches, including Louvain, effectively bridging the gap between streaming and traditional clustering methods.
Cite as

Adil Chhabra, Shai Dorian Peretz, and Christian Schulz. CluStRE: Streaming Graph Clustering with Multi-Stage Refinement. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{chhabra_et_al:LIPIcs.SEA.2025.11,
  author =	{Chhabra, Adil and Dorian Peretz, Shai and Schulz, Christian},
  title =	{{CluStRE: Streaming Graph Clustering with Multi-Stage Refinement}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{11:1--11:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.11},
  URN =		{urn:nbn:de:0030-drops-232493},
  doi =		{10.4230/LIPIcs.SEA.2025.11},
  annote =	{Keywords: graph clustering, community, streaming, online, memetic, evolutionary}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.9
DAMA: A Dual Arbitration Mechanism for Mixed-Criticality Applications

Authors: Wafic Lawand and Rodolfo Pellizzoni

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
We discuss hardware resource management in mixed-criticality systems, where requestors may issue latency-critical (LTC) and non-latency-critical (NLTC) requests. LTC requests must adhere to strict latency bounds imposed by safety-critical applications, but timely servicing NLTC requests is necessary to maximize overall system performance in the average case. In this paper, we address this tradeoff for a shared memory resource by proposing DAMA, a dual arbitration mechanism that imposes an upper bound on the cumulative latency of LTC requests without unduly impacting NLTC performance. DAMA comprises a high-performance arbiter, a real-time arbiter, and a mechanism that constantly monitors the cumulative latency of requests suffered by each requestor. DAMA primarily executes in high-performance mode and only switches to real-time mode in the rare instances when its incorporated mechanism detects a violation of a task’s timing guarantee. We demonstrate the effectiveness of our arbitration scheme by adapting a predictable prefetcher that issues NLTC requests and attaching it to the L1 caches of our cores. We show both formally and experimentally that DAMA provides timing guarantees for LTC requests while processing other NLTC requests. We also demonstrate that with a negligible overhead of less than 1.5% on the cumulative latency bound of LTC requests, DAMA can achieve an equivalent average performance to a prefetcher that processes requests under a high-performance arbitration scheme.
Cite as

Wafic Lawand and Rodolfo Pellizzoni. DAMA: A Dual Arbitration Mechanism for Mixed-Criticality Applications. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 9:1-9:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{lawand_et_al:LIPIcs.ECRTS.2025.9,
  author =	{Lawand, Wafic and Pellizzoni, Rodolfo},
  title =	{{DAMA: A Dual Arbitration Mechanism for Mixed-Criticality Applications}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{9:1--9:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.9},
  URN =		{urn:nbn:de:0030-drops-235875},
  doi =		{10.4230/LIPIcs.ECRTS.2025.9},
  annote =	{Keywords: Real-time Systems, Mixed-criticality Applications, Memory controllers, Prefetchers}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.FSCD.2025.2
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk)

Authors: Matthew L. Daggitt, Wen Kokke, Robert Atkey, Ekaterina Komendantskaya, Natalia Slusarz, and Luca Arnaboldi

Published in: LIPIcs, Volume 337, 10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025)

Abstract
Neuro-symbolic programs, i.e. programs containing both machine learning components and traditional symbolic code, are becoming increasingly widespread. Finding a general methodology for verifying such programs is challenging due to both the number of different tools involved and the intricate interface between the "neural" and "symbolic" program components. In this paper we present a general decomposition of the neuro-symbolic verification problem into parts, and examine the problem of the embedding gap that occurs when one tries to combine proofs about the neural and symbolic components. To address this problem we then introduce Vehicle - standing as an abbreviation for a "verification condition language" - an intermediate programming language interface between machine learning frameworks, automated theorem provers, and dependently-typed formalisations of neuro-symbolic programs. Vehicle allows users to specify the properties of the neural components of neuro-symbolic programs once, and then safely compile the specification to each interface using a tailored typing and compilation procedure. We give a high-level overview of Vehicle’s overall design, its interfaces and compilation & type-checking procedures, and then demonstrate its utility by formally verifying the safety of a simple autonomous car controlled by a neural network, operating in a stochastic environment with imperfect information.
Cite as

Matthew L. Daggitt, Wen Kokke, Robert Atkey, Ekaterina Komendantskaya, Natalia Slusarz, and Luca Arnaboldi. Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk). In 10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 337, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{daggitt_et_al:LIPIcs.FSCD.2025.2,
  author =	{Daggitt, Matthew L. and Kokke, Wen and Atkey, Robert and Komendantskaya, Ekaterina and Slusarz, Natalia and Arnaboldi, Luca},
  title =	{{Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs}},
  booktitle =	{10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025)},
  pages =	{2:1--2:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-374-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{337},
  editor =	{Fern\'{a}ndez, Maribel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSCD.2025.2},
  URN =		{urn:nbn:de:0030-drops-236172},
  doi =		{10.4230/LIPIcs.FSCD.2025.2},
  annote =	{Keywords: Neural Network Verification, Types, Interactive Theorem Provers}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.69
Approximation Algorithms for Optimal Hopsets

Authors: Michael Dinitz, Ama Koranteng, and Yasamin Nazari

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
For a given graph G, a hopset H with hopbound β and stretch α is a set of edges such that between every pair of vertices u and v, there is a path with at most β hops in G ∪ H that approximates the distance between u and v up to a multiplicative stretch of α. Hopsets have found a wide range of applications for distance-based problems in various computational models since the 90s. More recently, there has been significant interest in understanding these fundamental objects from an existential and structural perspective. But all of this work takes a worst-case (or existential) point of view: How many edges do we need to add to satisfy a given hopbound and stretch requirement for any input graph? We initiate the study of the natural optimization variant of this problem: given a specific graph instance, what is the minimum number of edges that satisfy the hopbound and stretch requirements? We give approximation algorithms for a generalized hopset problem which, when combined with known existential bounds, lead to different approximation guarantees for various regimes depending on hopbound, stretch, and directed vs. undirected inputs. We complement our upper bounds with a lower bound that implies Label Cover hardness for directed hopsets and shortcut sets with hopbound at least 3.
Cite as

Michael Dinitz, Ama Koranteng, and Yasamin Nazari. Approximation Algorithms for Optimal Hopsets. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 69:1-69:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{dinitz_et_al:LIPIcs.ICALP.2025.69,
  author =	{Dinitz, Michael and Koranteng, Ama and Nazari, Yasamin},
  title =	{{Approximation Algorithms for Optimal Hopsets}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{69:1--69:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.69},
  URN =		{urn:nbn:de:0030-drops-234464},
  doi =		{10.4230/LIPIcs.ICALP.2025.69},
  annote =	{Keywords: Hopsets, Approximation Algorithms}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.34
Taming and Dissecting Recursions Through Interprocedural Weak Topological Ordering

Authors: Jiawei Yang, Xiao Cheng, Bor-Yuh Evan Chang, Xiapu Luo, and Yulei Sui

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Abstract interpretation provides a foundational framework for approximating program semantics by interpreting code through abstract domains using semantic functions over ordered sets along a program’s control flow graph (CFG). To facilitate fixpoint computation in abstract interpretation, weak topological ordering (WTO) is an effective strategy for handling loops, as it identifies strategic control points in the CFG where widening and narrowing operations should be applied. However, existing abstract interpreters still face challenges when extending WTO computation in the presence of recursive programs. Computing a precise whole-program WTO requires full context-sensitive analysis which is not scalable for large programs, while context-insensitive analysis introduces spurious cycles that compromise precision. Current approaches either ignore recursion (resulting in unsoundness) or rely on conservative approximations, sacrificing precision by adopting the greatest elements of abstract domains and applying widening at function boundaries without subsequent narrowing refinements. These can lead to undesired results for downstream tasks, such as bug detection. To address the above limitations, we present RecTopo, a new technique to boost the efficiency of precise abstract interpretation in the presence of recursive programs through interprocedural weak topological ordering (IWTO). Rather than pursuing an expensive whole-program WTO analysis, RecTopo employs an on-demand approach that strategically decomposes programs at recursion boundaries and constructs targeted IWTOs for each recursive component. RecTopo dissects and analyzes (nested) recursions through interleaved widening and narrowing operations. This approach enables precise control over interpretation ordering within recursive structures while eliminating spurious recursions through systematic correlation of control flow and call graphs. We implemented RecTopo and evaluated its effectiveness using an assertion-based checking client focused on buffer overflow detection, comparing it against three popular open-source abstract interpreters (IKOS, Clam, CSA). The experiments on 8312 programs from the NIST dataset demonstrate that, on average, RecTopo is 31.99% more precise and achieves a 17.49% higher recall rate compared to three other tools. Moreover, RecTopo exhibits an average precision improvement of 46.51% and a higher recall rate of 32.98% compared to our baselines across ten large open-source projects. Further ablation studies reveal that IWTO reduces spurious widening operations compared to whole-program WTO, resulting in a 12.83% reduction in analysis time.
Cite as

Jiawei Yang, Xiao Cheng, Bor-Yuh Evan Chang, Xiapu Luo, and Yulei Sui. Taming and Dissecting Recursions Through Interprocedural Weak Topological Ordering. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 34:1-34:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{yang_et_al:LIPIcs.ECOOP.2025.34,
  author =	{Yang, Jiawei and Cheng, Xiao and Chang, Bor-Yuh Evan and Luo, Xiapu and Sui, Yulei},
  title =	{{Taming and Dissecting Recursions Through Interprocedural Weak Topological Ordering}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{34:1--34:31},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.34},
  URN =		{urn:nbn:de:0030-drops-233265},
  doi =		{10.4230/LIPIcs.ECOOP.2025.34},
  annote =	{Keywords: Abstract interpretation, recursion, weak topological ordering}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.36
Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees

Authors: Guanqin Zhang, Kota Fukuda, Zhenya Zhang, H.M.N. Dilum Bandara, Shiping Chen, Jianjun Zhao, and Yulei Sui

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch-and-bound (BaB) is a "divide-and-conquer" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive "first-come-first-served" manner, thereby suffering from an issue of inefficiency to reach a verification conclusion. To bridge this gap, we introduce an order over different sub-problems produced by BaB, concerning with their different likelihoods of containing counterexamples. Based on this order, we propose a novel verification framework Oliva that explores the sub-problem space by prioritizing those sub-problems that are more likely to find counterexamples, in order to efficiently reach the conclusion of the verification. Even if no counterexample can be found in any sub-problem, it only changes the order of visiting different sub-problems and so will not lead to a performance degradation. Specifically, Oliva has two variants, including Oliva^GR, a greedy strategy that always prioritizes the sub-problems that are more likely to find counterexamples, and Oliva^SA, a balanced strategy inspired by simulated annealing that gradually shifts from exploration to exploitation to locate the globally optimal sub-problems. We experimentally evaluate the performance of Oliva on 690 verification problems spanning over 5 models with datasets MNIST and CIFAR-10. Compared to the state-of-the-art approaches, we demonstrate the speedup of Oliva for up to 25× in MNIST, and up to 80× in CIFAR-10.
Cite as

Guanqin Zhang, Kota Fukuda, Zhenya Zhang, H.M.N. Dilum Bandara, Shiping Chen, Jianjun Zhao, and Yulei Sui. Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 36:1-36:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{zhang_et_al:LIPIcs.ECOOP.2025.36,
  author =	{Zhang, Guanqin and Fukuda, Kota and Zhang, Zhenya and Bandara, H.M.N. Dilum and Chen, Shiping and Zhao, Jianjun and Sui, Yulei},
  title =	{{Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{36:1--36:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.36},
  URN =		{urn:nbn:de:0030-drops-233281},
  doi =		{10.4230/LIPIcs.ECOOP.2025.36},
  annote =	{Keywords: neural network verification, branch and bound, counterexample potentiality, simulated annealing, stochastic optimization}
}
  • Refine by Type
  • 34 Document/PDF
  • 27 Document/HTML

  • Refine by Publication Year
  • 25 2025
  • 3 2023
  • 3 2022
  • 1 2021
  • 1 2020
  • Show More...

  • Refine by Author
  • 3 Chen, Xue
  • 2 Baptista, Jorge
  • 2 Sui, Yulei
  • 2 Suri, Subhash
  • 2 Xue, Jie
  • Show More...

  • Refine by Series/Journal
  • 24 LIPIcs
  • 4 OASIcs
  • 2 LITES
  • 4 TGDK

  • Refine by Classification
  • 3 Computing methodologies → Artificial intelligence
  • 3 Computing methodologies → Neural networks
  • 3 Theory of computation → Computational geometry
  • 3 Theory of computation → Design and analysis of algorithms
  • 3 Theory of computation → Streaming, sublinear and near linear time algorithms
  • Show More...

  • Refine by Keyword
  • 4 Large Language Models
  • 1 AC⁰
  • 1 Abstract Meaning Representation (AMR)
  • 1 Abstract interpretation
  • 1 Adverbs
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact