DROPS

Document

A Survey of Real-Time Support, Analysis, and Advancements in ROS 2

Authors: Daniel Casini, Jian-Jia Chen, Jing Li, Federico Reghenzani, and Harun Teper

Published in: LITES, Volume 11, Issue 1 (2026). Leibniz Transactions on Embedded Systems, Volume 11, Issue 1

Abstract

The Robot Operating System 2 (ROS 2) has emerged as a relevant middleware framework for robotic applications, offering modularity, distributed execution, and communication. In the last six years, ROS 2 has drawn increasing attention from the real-time systems community and industry. This survey presents a comprehensive overview of research efforts that analyze, enhance, and extend ROS 2 to support real-time execution. We first provide a detailed description of the internal scheduling mechanisms of ROS 2 and its layered architecture, including the interaction with DDS-based communication and other communication middleware. We then review key contributions from the literature, covering timing analysis for both single- and multi-threaded executors, metrics such as response time, reaction time, and data age, and different communication modes. The survey also discusses community-driven enhancements to the ROS 2 runtime, including new executor algorithm designs, real-time GPU management, and microcontroller support via micro-ROS. Furthermore, we summarize techniques for bounding DDS communication delays, message filters, and profiling tools that have been developed to support analysis and experimentation. To help systematize this growing body of work, we introduce taxonomies that classify the surveyed contributions based on different criteria. This survey aims to guide both researchers and practitioners in understanding and improving the real-time capabilities of ROS 2.

Cite as

Daniel Casini, Jian-Jia Chen, Jing Li, Federico Reghenzani, and Harun Teper. A Survey of Real-Time Support, Analysis, and Advancements in ROS 2. In LITES, Volume 11, Issue 1 (2026). Leibniz Transactions on Embedded Systems, Volume 11, Issue 1, pp. 1:1-1:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{casini_et_al:LITES.11.1.1,
  author =	{Casini, Daniel and Chen, Jian-Jia and Li, Jing and Reghenzani, Federico and Teper, Harun},
  title =	{{A Survey of Real-Time Support, Analysis, and Advancements in ROS 2}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{1:1--1:37},
  ISSN =	{2199-2002},
  year =	{2026},
  volume =	{11},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.11.1.1},
  URN =		{urn:nbn:de:0030-drops-257914},
  doi =		{10.4230/LITES.11.1.1},
  annote =	{Keywords: ROS 2, middleware, real-time, timing predictability, publish-subscribe}
}

Document

DOI: 10.4230/LIPIcs.ITCS.2026.21

Differential Privacy from Axioms

Authors: Guy Blanc, William Pires, and Toniann Pitassi

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract

Differential privacy (DP) is the de facto notion of privacy both in theory and in practice. However, despite its popularity, DP imposes strict requirements which guard against strong worst-case scenarios. For example, it guards against seemingly unrealistic scenarios where an attacker has full information about all but one point in the data set, and still nothing can be learned about the remaining point. While preventing such a strong attack is desirable, many works have explored whether average-case relaxations of DP are easier to satisfy [Hall et al., 2013; Wang et al., 2016; Bassily and Freund, 2016; Liu et al., 2023]. In this work, we are motivated by the question of whether alternate, weaker notions of privacy are possible: can a weakened privacy notion still guarantee some basic level of privacy, and on the other hand, achieve privacy more efficiently and/or for a substantially broader set of tasks? Our main result shows the answer is no: even in the statistical setting, any reasonable measure of privacy satisfying nontrivial composition is equivalent to DP. To prove this, we identify a core set of four axioms or desiderata: pre-processing invariance, prohibition of blatant non-privacy, strong composition, and linear scalability. Our main theorem shows that any privacy measure satisfying our axioms is equivalent to DP, up to polynomial factors in sample complexity. We complement this result by showing our axioms are minimal: removing any one of our axioms enables ill-behaved measures of privacy.

Cite as

Guy Blanc, William Pires, and Toniann Pitassi. Differential Privacy from Axioms. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{blanc_et_al:LIPIcs.ITCS.2026.21,
  author =	{Blanc, Guy and Pires, William and Pitassi, Toniann},
  title =	{{Differential Privacy from Axioms}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{21:1--21:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.21},
  URN =		{urn:nbn:de:0030-drops-253081},
  doi =		{10.4230/LIPIcs.ITCS.2026.21},
  annote =	{Keywords: Differential Privacy, Privacy Amplification, Composition}
}

Document

DOI: 10.4230/LIPIcs.ITCS.2026.64

Optimal White-Box Adversarial Streaming Lower Bounds for Approximating LIS Length

Authors: Anna Gal, Gillat Kol, Raghuvansh R. Saxena, and Huacheng Yu

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract

The space complexity of deterministic streaming algorithms for approximating the length of the longest increasing subsequence (LIS) in a string of length n has been known to be Θ̃(√n) for almost two decades. In contrast, the space complexity of this problem for randomized streaming algorithms remains one of the few longstanding open problems in one-pass streaming. In fact, no better than Ω(log n) lower bounds are known, and the best upper bounds are no better than their deterministic counterparts. In this paper, we push the limits of our understanding of the streaming space complexity of the approximate LIS length problem by studying it in the white-box adversarial streaming model. This model is an intermediate model between deterministic and randomized streaming algorithms that has recently attracted attention. In the white-box model, the streaming algorithm can draw fresh randomness when processing each incoming element, but an adversary generating the stream observes all previously used randomness and adaptively chooses the subsequent elements of the stream. We prove a tight (up to logarithmic factors) Ω(√n) space lower bound for any white-box streaming algorithm that approximates the length of the LIS of a stream of length n to within a factor better than 1.1. Thus, for this problem, white-box algorithms offer no improvement over deterministic ones.

Cite as

Anna Gal, Gillat Kol, Raghuvansh R. Saxena, and Huacheng Yu. Optimal White-Box Adversarial Streaming Lower Bounds for Approximating LIS Length. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 64:1-64:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{gal_et_al:LIPIcs.ITCS.2026.64,
  author =	{Gal, Anna and Kol, Gillat and Saxena, Raghuvansh R. and Yu, Huacheng},
  title =	{{Optimal White-Box Adversarial Streaming Lower Bounds for Approximating LIS Length}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{64:1--64:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.64},
  URN =		{urn:nbn:de:0030-drops-253519},
  doi =		{10.4230/LIPIcs.ITCS.2026.64},
  annote =	{Keywords: White-bos streaming, Longest increasing subsequence}
}

Document

DOI: 10.4230/LIPIcs.ITCS.2026.35

Symmetric Quantum Computation

Authors: Davi Castro-Silva, Tom Gur, and Sergii Strelchuk

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract

We introduce a systematic study of symmetric quantum circuits, a new restricted model of quantum computation that preserves the symmetries of the problems it solves. This model is well-adapted for studying the role of symmetry in quantum speedups, extending a central notion of symmetric computation studied in the classical setting. Our results establish that symmetric quantum circuits are fundamentally more powerful than their classical counterparts. First, we give efficient symmetric circuits for key quantum techniques such as amplitude amplification, phase estimation and linear combination of unitaries. In addition, we show how the task of symmetric state preparation can be performed efficiently in several natural cases. Finally, we demonstrate an exponential separation in the symmetric setting for the problem XOR-SAT, which requires exponential-size symmetric classical circuits but can be solved by polynomial-size symmetric quantum circuits.

Cite as

Davi Castro-Silva, Tom Gur, and Sergii Strelchuk. Symmetric Quantum Computation. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 35:1-35:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{castrosilva_et_al:LIPIcs.ITCS.2026.35,
  author =	{Castro-Silva, Davi and Gur, Tom and Strelchuk, Sergii},
  title =	{{Symmetric Quantum Computation}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{35:1--35:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.35},
  URN =		{urn:nbn:de:0030-drops-253223},
  doi =		{10.4230/LIPIcs.ITCS.2026.35},
  annote =	{Keywords: Quantum computing, complexity theory, symmetries}
}

Document

Research

DOI: 10.4230/TGDK.3.3.4

Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web

Authors: Florian Ruosch, Cristina Sarasua, and Abraham Bernstein

Published in: TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3

Abstract

In Argument Mining, predicting argumentative relations between texts (or spans) remains one of the most challenging aspects, even more so in the cross-document setting. This paper makes three key contributions to advance research in this domain. We first extend an existing dataset, the Sci-Arg corpus, by annotating it with explicit inter-document argumentative relations, thereby allowing arguments to be distributed over several documents forming an Argument Web; these new annotations are published using Semantic Web technologies (RDF, OWL). Second, we explore and evaluate three automated approaches for predicting these inter-document argumentative relations, establishing critical baselines on the new dataset. We find that a simple classifier based on discourse indicators with access to context outperforms neural methods. Third, we conduct a comparative analysis of these approaches for both intra- and inter-document settings, identifying statistically significant differences in results that indicate the necessity of distinguishing between these two scenarios. Our findings highlight significant challenges in this complex domain and open crucial avenues for future research on the Argument Web of Science, particularly for those interested in leveraging Semantic Web technologies and knowledge graphs to understand scholarly discourse. With this, we provide the first stepping stones in the form of a benchmark dataset, three baseline methods, and an initial analysis for a systematic exploration of this field relevant to the Web of Data and Science.

Cite as

Florian Ruosch, Cristina Sarasua, and Abraham Bernstein. Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 4:1-4:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@Article{ruosch_et_al:TGDK.3.3.4,
  author =	{Ruosch, Florian and Sarasua, Cristina and Bernstein, Abraham},
  title =	{{Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:33},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.4},
  URN =		{urn:nbn:de:0030-drops-252159},
  doi =		{10.4230/TGDK.3.3.4},
  annote =	{Keywords: Argument Mining, Large Language Models, Knowledge Graphs, Link Prediction}
}

Document

Invited Paper

DOI: 10.4230/OASIcs.RW.2024/2025.1

Rule-Based Knowledge Graph Completion (Invited Paper)

Authors: Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt

Published in: OASIcs, Volume 138, Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025)

Abstract

The field of knowledge graph completion is concerned with augmenting knowledge graphs with missing information. Symbolic rule-based approaches are not only efficient and interpretable but also competitive with embedding-based methods in regard to predictive quality. Rule-based knowledge graph completion can be separated into two stages, the learning stage and the application stage, which are both individually challenging. In the learning stage, horn rules are mined from a given knowledge graph. Given the vast size of the space of all possible rules, the mining approach must select relevant rules effectively. In the application stage, the mined rules are used to make new predictions which are assigned with plausibility scores. These scores need to be set by aggregating individual confidence values of rules that have the same consequence. This tutorial covers the fundamental aspects required to build a symbolic rule-based approach for knowledge graph completion. It will discuss the different rule types, mining strategies, and how to effectively apply the rules in different scenarios. Finally, we discuss practical examples for rule application by using the Python-based PyClause library.

Cite as

Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt. Rule-Based Knowledge Graph Completion (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 1:1-1:45, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{betz_et_al:OASIcs.RW.2024/2025.1,
  author =	{Betz, Patrick and Meilicke, Christian and Stuckenschmidt, Heiner},
  title =	{{Rule-Based Knowledge Graph Completion}},
  booktitle =	{Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 \& RW 2025)},
  pages =	{1:1--1:45},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-405-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{138},
  editor =	{Artale, Alessandro and Bienvenu, Meghyn and Garc{\'\i}a, Yazm{\'\i}n Ib\'{a}\~{n}ez and Murlak, Filip},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.RW.2024/2025.1},
  URN =		{urn:nbn:de:0030-drops-250461},
  doi =		{10.4230/OASIcs.RW.2024/2025.1},
  annote =	{Keywords: Knowledge Graph Completion, Rule Learning, Symbolic AI}
}

Document

DOI: 10.4230/LIPIcs.GD.2025.22

NNP-NET: Accelerating t-SNE Graph Drawing for Very Large Graphs by Neural Networks

Authors: Ilan Hartskeerl, Tamara Mchedlidze, Simon van Wageningen, Peter Vangorp, and Alexandru Telea

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

Abstract

tsNET is a recent graph drawing (GD) method that creates high quality layouts but suffers from a very high runtime. We present a new GD method, NNP-NET, which reduces tsNET’s time complexity to generate layouts for very large graphs in seconds. Additionally, we extend tsNET to support drawing graphs with edge weights. We accomplish this by replacing tsNET’s t-SNE projection with Neural Network Projection (NNP), a fast dimensionality reduction (DR) method that can imitate any given DR method. Our experiments show that NNP-NET gets good quality results when compared to other state-of-the art GD methods while yielding a better computational scalability.

Cite as

Ilan Hartskeerl, Tamara Mchedlidze, Simon van Wageningen, Peter Vangorp, and Alexandru Telea. NNP-NET: Accelerating t-SNE Graph Drawing for Very Large Graphs by Neural Networks. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 22:1-22:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{hartskeerl_et_al:LIPIcs.GD.2025.22,
  author =	{Hartskeerl, Ilan and Mchedlidze, Tamara and van Wageningen, Simon and Vangorp, Peter and Telea, Alexandru},
  title =	{{NNP-NET: Accelerating t-SNE Graph Drawing for Very Large Graphs by Neural Networks}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{22:1--22:22},
  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.2025.22},
  URN =		{urn:nbn:de:0030-drops-250087},
  doi =		{10.4230/LIPIcs.GD.2025.22},
  annote =	{Keywords: supervised graph drawing, dimensionality reduction, t-SNE}
}

Document

PhD Panel

DOI: 10.4230/OASIcs.DX.2025.16

Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems (PhD Panel)

Authors: Kélian Poujade, Louise Travé-Massuyès, Jérémy Pirard, and Laure Vieillevigne

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract

Modern complex systems, such as radiotherapy machines, require robust strategies for fault detection, diagnosis, and prognosis to ensure operational continuity and patient safety. While data-driven methods have gained traction, few studies address diagnostic and prognostic tasks using multimodal operational data under unsupervised or semi-supervised learning settings. This gap is particularly critical given the scarcity of labeled failure data in real-world environments. This work aims to design a unified approach for fault detection, diagnosis, and prognosis using multimodal data in the absence of complete labeling. To this end, autoencoders (AEs) are employed due to their suitability for unsupervised and self-supervised learning, flexibility in handling heterogeneous data, and ability to construct latent representations optimized for various downstream tasks. A specific implementation based on a Long Short-Term Memory β-Variational Autoencoder (LSTM-β-VAE) was developed to detect anomalies in machine logs. This framework is applied to TomoTherapy® systems - a highly complex and under-explored use case within the radiotherapy domain. Initial results demonstrate strong anomaly detection performance on both a public benchmark dataset (HDFS) and a proprietary dataset derived from real-world TomoTherapy® machine faults. Beyond methodology, the paper includes a concise literature review of multimodal learning and data-driven diagnosis and prognosis with a focus on AEs. Based on this review, key research directions are identified for the continuation of the thesis, especially the integration of explainable AI as a means to enhance diagnosis capabilities in the absence of labeled faults.

Cite as

Kélian Poujade, Louise Travé-Massuyès, Jérémy Pirard, and Laure Vieillevigne. Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems (PhD Panel). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 16:1-16:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{poujade_et_al:OASIcs.DX.2025.16,
  author =	{Poujade, K\'{e}lian and Trav\'{e}-Massuy\`{e}s, Louise and Pirard, J\'{e}r\'{e}my and Vieillevigne, Laure},
  title =	{{Unsupervised Multimodal Learning for Fault Diagnosis and Prognosis - Application to Radiotherapy Systems}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{16:1--16:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.16},
  URN =		{urn:nbn:de:0030-drops-248058},
  doi =		{10.4230/OASIcs.DX.2025.16},
  annote =	{Keywords: Artificial Intelligence, Diagnosis, Prognosis, Radiotherapy machines}
}

Document

DOI: 10.4230/OASIcs.ATMOS.2025.15

Exact and Heuristic Dynamic Taxi Sharing with Transfers Using Shortest-Path Speedup Techniques

Authors: Johannes Breitling and Moritz Laupichler

Published in: OASIcs, Volume 137, 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025)

Abstract

We introduce a first-of-its-kind efficient, exact algorithm for the dynamic taxi-sharing problem with single-transfer journeys, i.e., a dispatcher that assigns traveler requests to a fleet of shared taxi-like vehicles allowing transfers between vehicles. We extend an existing no-transfer solution by collecting all viable pickup and dropoff vehicles for a request and computing the optimal transfer point for every pair of vehicles. We analyze underlying shortest-path problems and employ state-of-the-art routing algorithms to compute distances on-the-fly, which serves as the basis of dispatching requests with exact and up-to-date travel time information. We utilize constraints on existing routes, pruning techniques for transfer points, and both instruction- and thread-level parallelism to speed up the computation of the best assignment for every traveler. In addition to the exact variant, we propose a tunable heuristic approach that sacrifices solution quality in favor of improved running time. We evaluate our algorithm on a large road network with realistic input sets (up to 150000 requests). We demonstrate the effectiveness of our speedup techniques and the heuristic. We show first results on the benefits of transfers for taxi sharing on dense request sets, proving that our algorithm is well suited for the analysis of taxi sharing with transfers on large input instances.

Cite as

Johannes Breitling and Moritz Laupichler. Exact and Heuristic Dynamic Taxi Sharing with Transfers Using Shortest-Path Speedup Techniques. In 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025). Open Access Series in Informatics (OASIcs), Volume 137, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{breitling_et_al:OASIcs.ATMOS.2025.15,
  author =	{Breitling, Johannes and Laupichler, Moritz},
  title =	{{Exact and Heuristic Dynamic Taxi Sharing with Transfers Using Shortest-Path Speedup Techniques}},
  booktitle =	{25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025)},
  pages =	{15:1--15:22},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-404-8},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{137},
  editor =	{Sauer, Jonas and Schmidt, Marie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2025.15},
  URN =		{urn:nbn:de:0030-drops-247718},
  doi =		{10.4230/OASIcs.ATMOS.2025.15},
  annote =	{Keywords: Dynamic taxi sharing, ride pooling, dial-a-ride problem, transfers, route planning}
}

Document

Survey

DOI: 10.4230/TGDK.3.2.1

Resilience in Knowledge Graph Embeddings

Authors: Arnab Sharma, N'Dah Jean Kouagou, and Axel-Cyrille Ngonga Ngomo

Published in: TGDK, Volume 3, Issue 2 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 2

Abstract

In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have demonstrated their utility in effectively representing structured knowledge. To further facilitate the application of machine learning techniques, knowledge graph embedding models have been developed. Such models can transform entities and relationships within knowledge graphs into vectors. However, these embedding models often face challenges related to noise, missing information, distribution shift, adversarial attacks, etc. This can lead to sub-optimal embeddings and incorrect inferences, thereby negatively impacting downstream applications. While the existing literature has focused so far on adversarial attacks on KGE models, the challenges related to the other critical aspects remain unexplored. In this paper, we, first of all, give a unified definition of resilience, encompassing several factors such as generalisation, in-distribution generalization, distribution adaption, and robustness. After formalizing these concepts for machine learning in general, we define them in the context of knowledge graphs. To find the gap in the existing works on resilience in the context of knowledge graphs, we perform a systematic survey, taking into account all these aspects mentioned previously. Our survey results show that most of the existing works focus on a specific aspect of resilience, namely robustness. After categorizing such works based on their respective aspects of resilience, we discuss the challenges and future research directions.

Cite as

Arnab Sharma, N'Dah Jean Kouagou, and Axel-Cyrille Ngonga Ngomo. Resilience in Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 2, pp. 1:1-1:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@Article{sharma_et_al:TGDK.3.2.1,
  author =	{Sharma, Arnab and Kouagou, N'Dah Jean and Ngomo, Axel-Cyrille Ngonga},
  title =	{{Resilience in Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:38},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.2.1},
  URN =		{urn:nbn:de:0030-drops-248117},
  doi =		{10.4230/TGDK.3.2.1},
  annote =	{Keywords: Knowledge graphs, Resilience, Robustness}
}

Document

Research

DOI: 10.4230/TGDK.3.2.3

GraphRAG on Technical Documents - Impact of Knowledge Graph Schema

Authors: Henri Scaffidi, Melinda Hodkiewicz, Caitlin Woods, and Nicole Roocke

Published in: TGDK, Volume 3, Issue 2 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 2

Abstract

Retrieval Augmented Generation (RAG) is seeing rapid adoption in industry to enable employees to query information captured in proprietary data for their organisation. In this work, we test the impact of domain-relevant knowledge graph schemas on the results of Microsoft’s GraphRAG pipeline. Our approach aims to address the poor quality of GraphRAG responses on technical reports rich in domain-specific terms. The use case involves technical reports about geology, chemistry and mineral processing published by the Minerals Research Institute of Western Australia (MRIWA). Four schemas are considered: a simple five-class minerals domain expert-developed schema, an expanded minerals domain schema, the Microsoft GraphRAG auto-generated schema, and a schema-less GraphRAG. These are compared to a conventional baseline RAG. Performance is evaluated using a scoring approach that accounts for the mix of correct, incorrect, additional, and missing content in RAG responses. The results show that the simple five-class minerals domain schema extracts approximately 10% more entities from the MRIWA reports than the other schema options. Additionally, both the five-class and the expanded eight-class minerals domain schemas produce the most factually correct answers and the fewest hallucinations. We attribute this to the minerals-specific schemas extracting more relevant, domain-specific information during the Indexing stage. As a result, the Query stage’s context window includes more high-value content. This contributes to the observed improvement in answer quality compared to the other pipelines. In contrast, pipelines with fewer domain-related entities in the KG retrieve less valuable information, leaving more room for irrelevant content in the context window. Baseline RAG responses were typically shorter, less complete, and contained more hallucinations compared to our GraphRAG pipelines. We provide a complete set of resources at https://github.com/nlp-tlp/GraphRAG-on-Minerals-Domain/tree/main. These resources include links to the MRIWA reports, a set of questions (from simple to challenging) along with domain-expert curated answers, schemas, and evaluations of the pipelines.

Cite as

Henri Scaffidi, Melinda Hodkiewicz, Caitlin Woods, and Nicole Roocke. GraphRAG on Technical Documents - Impact of Knowledge Graph Schema. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 2, pp. 3:1-3:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@Article{scaffidi_et_al:TGDK.3.2.3,
  author =	{Scaffidi, Henri and Hodkiewicz, Melinda and Woods, Caitlin and Roocke, Nicole},
  title =	{{GraphRAG on Technical Documents - Impact of Knowledge Graph Schema}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:24},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.2.3},
  URN =		{urn:nbn:de:0030-drops-248131},
  doi =		{10.4230/TGDK.3.2.3},
  annote =	{Keywords: RAG, minerals, local search, global search, entity extraction, competency questions}
}

Document

DOI: 10.4230/LIPIcs.AFT.2025.35

On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation

Authors: Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract

Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (≈ $0.18) for linear models, 143,647 gas (≈ $0.49) for CNN(F2, K1), and 506,397 gas (≈ $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.

Cite as

Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy. On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 35:1-35:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{alhaidari_et_al:LIPIcs.AFT.2025.35,
  author =	{Alhaidari, Abdulrahman and Palanisamy, Balaji and Krishnamurthy, Prashant},
  title =	{{On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{35:1--35:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.35},
  URN =		{urn:nbn:de:0030-drops-247548},
  doi =		{10.4230/LIPIcs.AFT.2025.35},
  annote =	{Keywords: DeFi attacks, on-chain machine learning, decentralized learning, real-time defense}
}

Document

DOI: 10.4230/LIPIcs.AFT.2025.33

Cuttlefish: A Fair, Predictable Execution Environment for Cloud-Hosted Financial Exchanges

Authors: Liangcheng Yu, Prateesh Goyal, Ilias Marinos, and Vincent Liu

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract

Recent years have seen a rising interest in cloud-hosted financial exchanges. While the public cloud platforms promise a cost-effective and more accessible option to traders, unfortunately, achieving fairness in cloud environments is challenging due to non-deterministic network latencies and execution times. This work presents Cuttlefish, a fair-by-design cloud execution environment for algorithmic trading. The idea behind Cuttlefish is the efficient and robust mapping of real operations to a novel formulation of "virtual time". With it, Cuttlefish abstracts out the variances of the underlying network communication and computation hardware. Our implementation and evaluation not only validate the practicality of Cuttlefish, but also show its operational efficiency on public cloud platforms.

Cite as

Liangcheng Yu, Prateesh Goyal, Ilias Marinos, and Vincent Liu. Cuttlefish: A Fair, Predictable Execution Environment for Cloud-Hosted Financial Exchanges. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 33:1-33:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{yu_et_al:LIPIcs.AFT.2025.33,
  author =	{Yu, Liangcheng and Goyal, Prateesh and Marinos, Ilias and Liu, Vincent},
  title =	{{Cuttlefish: A Fair, Predictable Execution Environment for Cloud-Hosted Financial Exchanges}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{33:1--33:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.33},
  URN =		{urn:nbn:de:0030-drops-247521},
  doi =		{10.4230/LIPIcs.AFT.2025.33},
  annote =	{Keywords: Cloud-hosted exchanges, Financial exchanges, Computation and communication variances, Virtual time overlay}
}

Document

DOI: 10.4230/LIPIcs.ESA.2025.55

Safe Sequences via Dominators in DAGs for Path-Covering Problems

Authors: Francisco Sena, Romeo Rizzi, and Alexandru I. Tomescu

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

Abstract

A path-covering problem on a directed acyclic graph (DAG) requires finding a set of source-to-sink paths that cover all the nodes, all the arcs, or subsets thereof, and additionally they are optimal with respect to some function. In this paper we study safe sequences of nodes or arcs, namely sequences that appear in some path of every path cover of a DAG. We show that safe sequences admit a simple characterization via cutnodes. Moreover, we establish a connection between maximal safe sequences and leaf-to-root paths in the source- and sink-dominator trees of the DAG, which may be of independent interest in the extensive literature on dominators. With dominator trees, safe sequences admit an O(n)-size representation and a linear-time output-sensitive enumeration algorithm running in time O(m + o), where n and m are the number of nodes and arcs, respectively, and o is the total length of the maximal safe sequences. We then apply maximal safe sequences to simplify Integer Linear Programs (ILPs) for two path-covering problems, LeastSquares and MinPathError, which are at the core of RNA transcript assembly problems from bioinformatics. On various datasets, maximal safe sequences can be computed in under 0.1 seconds per graph, on average, and ILP solvers whose search space is reduced in this manner exhibit significant speed-ups. For example on graphs with a large width, average speed-ups are in the range 50-250× for MinPathError and in the range 80-350× for LeastSquares. Optimizing ILPs using safe sequences can thus become a fast building block of practical RNA transcript assembly tools, and more generally, of path-covering problems.

Cite as

Francisco Sena, Romeo Rizzi, and Alexandru I. Tomescu. Safe Sequences via Dominators in DAGs for Path-Covering Problems. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 55:1-55:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{sena_et_al:LIPIcs.ESA.2025.55,
  author =	{Sena, Francisco and Rizzi, Romeo and Tomescu, Alexandru I.},
  title =	{{Safe Sequences via Dominators in DAGs for Path-Covering Problems}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{55:1--55:17},
  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.2025.55},
  URN =		{urn:nbn:de:0030-drops-245230},
  doi =		{10.4230/LIPIcs.ESA.2025.55},
  annote =	{Keywords: directed acyclic graph, path cover, dominator tree, integer linear programming, least squares, minimum path error}
}

Document

DOI: 10.4230/LIPIcs.ESA.2025.24

Fréchet Distance in Unweighted Planar Graphs

Authors: Ivor van der Hoog, Thijs van der Horst, Eva Rotenberg, and Lasse Wulf

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

Abstract

The Fréchet distance is a distance measure between trajectories in ℝ^d or walks in a graph G. Given constant-time shortest path queries, the Discrete Fréchet distance D_G(P, Q) between two walks P and Q can be computed in O(|P|⋅|Q|) time using a dynamic program. Driemel, van der Hoog, and Rotenberg [SoCG'22] show that for weighted planar graphs this approach is likely tight, as there can be no strongly-subquadratic algorithm to compute a 1.01-approximation of D_G(P, Q) unless the Orthogonal Vector Hypothesis (OVH) fails. Such quadratic-time conditional lower bounds are common to many Fréchet distance variants. However, they can be circumvented by assuming that the input comes from some well-behaved class: There exist (1+ε)-approximations, both in weighted graphs and in ℝ^d, that take near-linear time for c-packed or κ-straight walks in the graph. In ℝ^d there also exists a near-linear time algorithm to compute the Fréchet distance whenever all input edges are long compared to the distance. We consider computing the Fréchet distance in unweighted planar graphs. We show that there exist no strongly-subquadratic 1.25-approximations of the discrete Fréchet distance between two disjoint simple paths in an unweighted planar graph in strongly subquadratic time, unless OVH fails. This improves the previous lower bound, both in terms of generality and approximation factor. We subsequently show that adding graph structure circumvents this lower bound: If the graph is a regular tiling with unit-weighted edges, then there exists an Õ((|P|+|Q|)^{1.5})-time algorithm to compute D_G(P, Q). Our result has natural implications in the plane, as it allows us to define a new class of well-behaved curves that facilitate (1+ε)-approximations of their discrete Fréchet distance in subquadratic time.

Cite as

Ivor van der Hoog, Thijs van der Horst, Eva Rotenberg, and Lasse Wulf. Fréchet Distance in Unweighted Planar Graphs. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 24:1-24:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{vanderhoog_et_al:LIPIcs.ESA.2025.24,
  author =	{van der Hoog, Ivor and van der Horst, Thijs and Rotenberg, Eva and Wulf, Lasse},
  title =	{{Fr\'{e}chet Distance in Unweighted Planar Graphs}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{24:1--24:16},
  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.2025.24},
  URN =		{urn:nbn:de:0030-drops-244924},
  doi =		{10.4230/LIPIcs.ESA.2025.24},
  annote =	{Keywords: Fr\'{e}chet distance, planar graphs, lower bounds, approximation algorithms}
}

72 Search Results for "Zheng, Yu"

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Abstract

Cite as

Thanks for your feedback!

Could not send message