28 Search Results for "Liu, Zhen"

Document
DOI: 10.4230/LITES.11.1.1
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
Research
DOI: 10.4230/TGDK.4.1.1
On the Computational Cost of Knowledge Graph Embeddings

Authors: Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann

Published in: TGDK, Volume 4, Issue 1 (2026). Transactions on Graph Data and Knowledge, Volume 4, Issue 1

Abstract
Over a decade, numerous Knowledge Graph Embedding (KGE) models have been designed and evaluated on reference datasets, always with increasing performance. In this paper, we re-evaluate these models with respect to their computational efficiency during training, by estimating the computational cost of the procedure expressed in floating-point operations. We design a cost model based on analytical expressions and apply it on a collection of 20 KGE models, representative of the state-of-the-art. We show that dimensionality or parameter efficiency, used in the literature to compare models with each other, are not suitable to evaluate the true cost of models. Through fixed-budget experiments, a novel approach to evaluate KGE models based on cost estimates, we re-assess the relative performance of model families compared to the state-of-the-art. Bilinear models such as ComplEx underperform with a low computational budget while hyperbolic linear models appear to offer no particular benefit compared to simpler Euclidian models, especially the MuRE model. Neural models, such as ConvE or CompGCN, achieve reasonable performance in the literature but their high computational cost appears unnecessary when compared with other models. The trade-off between efficiency and expressivity of both linear and neural models is to be further explored.
Cite as

Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann. On the Computational Cost of Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 1:1-1:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{charpenay_et_al:TGDK.4.1.1,
  author =	{Charpenay, Victor and Zoubeirou A Mayaki, Mansour and Zimmermann, Antoine},
  title =	{{On the Computational Cost of Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:30},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.1.1},
  URN =		{urn:nbn:de:0030-drops-256863},
  doi =		{10.4230/TGDK.4.1.1},
  annote =	{Keywords: Knowledge Graph Embedding, Parameter Efficiency, Computational Budget, Green AI}
}
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
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
DX Competition
DOI: 10.4230/OASIcs.DX.2025.15
Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition)

Authors: Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, and Gautam Biswas

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

Abstract
Fault detection and isolation are becoming increasingly important as modern systems become more complex. To encourage the development of new fault detection solutions that can operate with limited noisy data and an incomplete mathematical model, the DX 2025 LiU-ICE competition for diagnosis of the air path of an internal combustion engine was introduced. In this paper, we present our winning solution to this competition. Our fault detection architecture starts with a semi-supervised Transformer Autoencoder trained to reconstruct nominal data. Detected faults are then passed through a rule-based fault persistence filter that aims to suppress false positives. Once a fault is detected, we use four neural networks trained to estimate features determined from structural analysis of a partial system model. The residuals of these networks are fed to a supervised fault classification network that estimates the fault probabilities. With this architecture, we achieved an 87% detection rate with a 0% false alarm rate on the provided competition data. Additionally, our isolation architecture assigned the correct fault 73.8% probabilty on average. On unseen competition data from a new driving cycle, we achieved a 100% detection rate and assigned the correct fault 66.2% probability on average. On the other hand, the Transformer Autoencoder failed to transfer to the new driving conditions, causing many false alarms. We discuss ways future work can reduce this.
Cite as

Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, and Gautam Biswas. Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{coursey_et_al:OASIcs.DX.2025.15,
  author =	{Coursey, Austin and Diaz-Gonzalez, Abel and Quinones-Grueiro, Marcos and Biswas, Gautam},
  title =	{{Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{15:1--15:17},
  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.2025.15},
  URN =		{urn:nbn:de:0030-drops-248043},
  doi =		{10.4230/OASIcs.DX.2025.15},
  annote =	{Keywords: fault detection, fault isolation, autoencoder}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.2
Zero-Knowledge Authenticator for Blockchain: Policy-Private and Obliviously Updateable

Authors: Kostas Kryptos Chalkias, Deepak Maram, Arnab Roy, Joy Wang, and Aayush Yadav

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

Abstract
Transaction details and participant identities on the blockchain are often publicly exposed. In this work, we posit that blockchain’s transparency should not come at the cost of privacy. To that end, we introduce zero-knowledge authenticators (zkAt), a new cryptographic primitive for privacy-preserving authentication on public blockchains. zkAt utilizes zero-knowledge proofs to enable users to authenticate transactions, while keeping the underlying authentication policies private. Prior solutions for such policy-private authentication required the use of threshold signatures, which can only hide the threshold access structure itself. In comparison, zkAt provides privacy for arbitrarily complex authentication policies, and offers a richer interface even within the threshold access structure by, for instance, allowing for the combination of signatures under distinct signature schemes. In order to construct zkAt, we design a compiler that transforms the popular Groth16 non-interactive zero knowledge (NIZK) proof system into a NIZK with equivocable verification keys, a property that we define in this work. Then, for any zkAt constructed using proof systems with this new property, we show that all public information must be independent of the policy, thereby achieving policy-privacy. Next, we give an extension of zkAt, called zkAt^+ wherein, assuming a trusted authority, policies can be updated obliviously in the sense that a third-party learns no new information when a policy is updated by the policy issuer. We also give a theoretical construction for zkAt^+ using recursive NIZKs, and explore the integration of zkAt into modern blockchains. Finally, to evaluate their feasibility, we implement both our schemes for a specific threshold access structure. Our findings show that zkAt achieves comparable performance to traditional threshold signatures, while also attaining privacy for significantly more complex policies with very little overhead.
Cite as

Kostas Kryptos Chalkias, Deepak Maram, Arnab Roy, Joy Wang, and Aayush Yadav. Zero-Knowledge Authenticator for Blockchain: Policy-Private and Obliviously Updateable. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 2:1-2:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{kryptoschalkias_et_al:LIPIcs.AFT.2025.2,
  author =	{Kryptos Chalkias, Kostas and Maram, Deepak and Roy, Arnab and Wang, Joy and Yadav, Aayush},
  title =	{{Zero-Knowledge Authenticator for Blockchain: Policy-Private and Obliviously Updateable}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{2:1--2:23},
  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.2025.2},
  URN =		{urn:nbn:de:0030-drops-247218},
  doi =		{10.4230/LIPIcs.AFT.2025.2},
  annote =	{Keywords: Blockchain privacy, authentication schemes, threshold wallets, zero knowledge proofs}
}
Document
DOI: 10.4230/LIPIcs.ESA.2025.62
Polynomial-Time Constant-Approximation for Fair Sum-Of-Radii Clustering

Authors: Sina Bagheri Nezhad, Sayan Bandyapadhyay, and Tianzhi Chen

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

Abstract
In a seminal work, Chierichetti et al. [Chierichetti et al., 2017] introduced the (t,k)-fair clustering problem: Given a set of red points and a set of blue points in a metric space, a clustering is called fair if the number of red points in each cluster is at most t times and at least 1/t times the number of blue points in that cluster. The goal is to compute a fair clustering with at most k clusters that optimizes certain objective function. Considering this problem, they designed a polynomial-time O(1)- and O(t)-approximation for the k-center and the k-median objective, respectively. Recently, Carta et al. [Carta et al., 2024] studied this problem with the sum-of-radii objective and obtained a (6+ε)-approximation with running time O((k log_{1+ε}(k/ε))^k n^O(1)), i.e., fixed-parameter tractable in k. Here n is the input size. In this work, we design the first polynomial-time O(1)-approximation for (t,k)-fair clustering with the sum-of-radii objective, improving the result of Carta et al. Our result places sum-of-radii in the same group of objectives as k-center, that admit polynomial-time O(1)-approximations. This result also implies a polynomial-time O(1)-approximation for the Euclidean version of the problem, for which an f(k)⋅n^O(1)-time (1+ε)-approximation was known due to Drexler et al. [Drexler et al., 2023]. Here f is an exponential function of k. We are also able to extend our result to any arbitrary 𝓁 ≥ 2 number of colors when t = 1. This matches known results for the k-center and k-median objectives in this case. The significant disparity of sum-of-radii compared to k-center and k-median presents several complex challenges, all of which we successfully overcome in our work. Our main contribution is a novel cluster-merging-based analysis technique for sum-of-radii that helps us achieve the constant-approximation bounds.
Cite as

Sina Bagheri Nezhad, Sayan Bandyapadhyay, and Tianzhi Chen. Polynomial-Time Constant-Approximation for Fair Sum-Of-Radii Clustering. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 62:1-62:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{bagherinezhad_et_al:LIPIcs.ESA.2025.62,
  author =	{Bagheri Nezhad, Sina and Bandyapadhyay, Sayan and Chen, Tianzhi},
  title =	{{Polynomial-Time Constant-Approximation for Fair Sum-Of-Radii Clustering}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{62:1--62:16},
  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.2025.62},
  URN =		{urn:nbn:de:0030-drops-245309},
  doi =		{10.4230/LIPIcs.ESA.2025.62},
  annote =	{Keywords: fair clustering, sum-of-radii clustering, approximation algorithms}
}
Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.6
Toward an Earth-Independent System for EVA Mission Planning: Integrating Physical Models, Domain Knowledge, and Agentic RAG to Provide Explainable LLM-Based Decision Support

Authors: Kaisheng Li and Richard S. Whittle

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
We propose a unified framework for an Earth‑independent AI system that provides explainable, context‑aware decision support for EVA mission planning by integrating six core components: a fine‑tuned EVA domain LLM, a retrieval‑augmented knowledge base, a short-term memory store, physical simulation models, an agentic orchestration layer, and a multimodal user interface. To ground our design, we analyze the current roles and substitution potential of the Mission Control Center - identifying which procedural and analytical functions can be automated onboard while preserving human oversight for experiential and strategic tasks. Building on this framework, we introduce RASAGE (Retrieval & Simulation Augmented Guidance Agent for Exploration), a proof‑of‑concept toolset that combines Microsoft Phi‑4‑mini‑instruct with a FAISS (Facebook AI Similarity Search)‑powered EVA knowledge base and custom A* path planning and hypogravity metabolic models to generate grounded, traceable EVA plans. We outline a staged validation strategy to evaluate improvements in route efficiency, metabolic prediction accuracy, anomaly response effectiveness, and crew trust under realistic communication delays. Our findings demonstrate the feasibility of replicating key Mission Control functions onboard, enhancing crew autonomy, reducing cognitive load, and improving safety for deep‑space exploration missions.
Cite as

Kaisheng Li and Richard S. Whittle. Toward an Earth-Independent System for EVA Mission Planning: Integrating Physical Models, Domain Knowledge, and Agentic RAG to Provide Explainable LLM-Based Decision Support. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 6:1-6:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{li_et_al:OASIcs.SpaceCHI.2025.6,
  author =	{Li, Kaisheng and Whittle, Richard S.},
  title =	{{Toward an Earth-Independent System for EVA Mission Planning: Integrating Physical Models, Domain Knowledge, and Agentic RAG to Provide Explainable LLM-Based Decision Support}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{6:1--6:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.6},
  URN =		{urn:nbn:de:0030-drops-239967},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.6},
  annote =	{Keywords: Human-AI Interaction for Space Exploration, Extravehicular Activities, Cognitive load and Human Performance Issues, Human Systems Exploration, Lunar Exploration, LLM}
}
Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.25
NEREUS: An Assistive Decision Support System for Real-Time, Adaptive Route Guidance in Extravehicular Navigation Activities on the Lunar Surface

Authors: Jasmine Q. Wu, Andrew J. Hwang, and Matthew J. Bietz

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
Extravehicular Activity (EVA) is one of the most complex operational endeavors during human lunar exploration. A key aspect of successful operations involves adapting procedures to address unexpected hazards on the lunar surface. Current route mapping systems rely heavily on static navigation planning around craters, high elevations, and extreme weather conditions to accomplish pre-defined mission objectives. However, the high-resolution data necessary for reliable route mapping is often unavailable. To address this challenge, we have designed NEREUS, a Decision Support System (DSS) that helps EVA operators on the ground respond to anomalies faster by simulating multiple alternative routes in parallel and visualizing trade-offs in consumable resources, speed, and safety as well as impact on overall mission timeline. The system offloads computationally intensive tasks like calculating the impact of evolving hazard data, allowing operators to focus on higher-level decision-making.
Cite as

Jasmine Q. Wu, Andrew J. Hwang, and Matthew J. Bietz. NEREUS: An Assistive Decision Support System for Real-Time, Adaptive Route Guidance in Extravehicular Navigation Activities on the Lunar Surface. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 25:1-25:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{wu_et_al:OASIcs.SpaceCHI.2025.25,
  author =	{Wu, Jasmine Q. and Hwang, Andrew J. and Bietz, Matthew J.},
  title =	{{NEREUS: An Assistive Decision Support System for Real-Time, Adaptive Route Guidance in Extravehicular Navigation Activities on the Lunar Surface}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{25:1--25:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.25},
  URN =		{urn:nbn:de:0030-drops-240158},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.25},
  annote =	{Keywords: Human Computer Interaction (HCI), Adaptive Navigation, Decision Support, Cognitive Load Analysis, Decision Support System, Extravehicular Activity}
}
Document
DOI: 10.4230/OASIcs.Programming.2025.8
Fuzzing as Editor Feedback

Authors: Marcel Garus, Jens Lincke, and Robert Hirschfeld

Published in: OASIcs, Volume 134, Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)

Abstract
Live programming requires concrete examples, but coming up with examples takes effort. However, there are ways to execute code without specifying examples, such as fuzzing. Fuzzing is a technique that synthesizes program inputs to find bugs in security-critical software. While fuzzing focuses on finding crashes, it also produces valid inputs as a byproduct. Our approach is to make use of this to show examples, including edge cases, directly in the editor. To provide examples for individual pieces of code, we implement fuzzing at the granularity of functions. We integrate it into the compiler pipeline and language tooling of Martinaise, a custom programming language with a limited feature set. Initially, our examples are random and then mutate based on coverage feedback to reach interesting code locations and become smaller. We evaluate our tool in small case studies, showing generated examples for numbers, strings, and composite objects. Our fuzzed examples still feel synthetic, but since they are grounded in the dynamic behavior of code, they can cover the entire execution and show edge cases.
Cite as

Marcel Garus, Jens Lincke, and Robert Hirschfeld. Fuzzing as Editor Feedback. In Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025). Open Access Series in Informatics (OASIcs), Volume 134, pp. 8:1-8:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{garus_et_al:OASIcs.Programming.2025.8,
  author =	{Garus, Marcel and Lincke, Jens and Hirschfeld, Robert},
  title =	{{Fuzzing as Editor Feedback}},
  booktitle =	{Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)},
  pages =	{8:1--8:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-382-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{134},
  editor =	{Edwards, Jonathan and Perera, Roly and Petricek, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Programming.2025.8},
  URN =		{urn:nbn:de:0030-drops-242926},
  doi =		{10.4230/OASIcs.Programming.2025.8},
  annote =	{Keywords: Fuzzing, Example-based Programming, Babylonian Programming, Dynamic Analysis, Code Coverage, Randomized Testing, Function-Level Fuzzing}
}
Document
DOI: 10.4230/LIPIcs.WADS.2025.49
On the I/O Complexity of the Cocke-Younger-Kasami Algorithm and of a Family of Related Dynamic Programming Algorithms

Authors: Lorenzo De Stefani and Vedant Gupta

Published in: LIPIcs, Volume 349, 19th International Symposium on Algorithms and Data Structures (WADS 2025)

Abstract
Asymptotically tight lower bounds are derived for the Input/Output (I/O) complexity of a class of dynamic programming algorithms, including matrix chain multiplication, optimal polygon triangulation, and the construction of optimal binary search trees. Assuming no recomputation of intermediate values, we establish an Ω(n³/(√M B)) I/O lower bound, where n denotes the size of the input and M denotes the size of the available fast memory (cache). When recomputation is allowed, we show that the same bound holds for M < cn, where c is a positive constant. In the case where M ≥ 2n, we show an Ω(n/B) I/O lower bound. We also discuss algorithms for which the number of executed I/O operations matches asymptotically each of the presented lower bounds, which are thus asymptotically tight. Additionally, we refine our general method to obtain a lower bound for the I/O complexity of the Cocke-Younger-Kasami algorithm, where the size of the grammar impacts the I/O complexity. An upper bound with asymptotically matching performance in many cases is also provided.
Cite as

Lorenzo De Stefani and Vedant Gupta. On the I/O Complexity of the Cocke-Younger-Kasami Algorithm and of a Family of Related Dynamic Programming Algorithms. In 19th International Symposium on Algorithms and Data Structures (WADS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 349, pp. 49:1-49:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{destefani_et_al:LIPIcs.WADS.2025.49,
  author =	{De Stefani, Lorenzo and Gupta, Vedant},
  title =	{{On the I/O Complexity of the Cocke-Younger-Kasami Algorithm and of a Family of Related Dynamic Programming Algorithms}},
  booktitle =	{19th International Symposium on Algorithms and Data Structures (WADS 2025)},
  pages =	{49:1--49:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-398-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{349},
  editor =	{Morin, Pat and Oh, Eunjin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WADS.2025.49},
  URN =		{urn:nbn:de:0030-drops-242800},
  doi =		{10.4230/LIPIcs.WADS.2025.49},
  annote =	{Keywords: I/O complexity, Dynamic Programming Algorithms, Lower Bounds, Recomputation, Cocke-Younger-Kasami}
}
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.WABI.2025.11
Mutational Signature Refitting on Sparse Pan-Cancer Data

Authors: Gal Gilad, Teresa M. Przytycka, and Roded Sharan

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

Abstract
Mutational processes shape cancer genomes, leaving characteristic marks that are termed signatures. The level of activity of each such process, or its signature exposure, provides important information on the disease, improving patient stratification and the prediction of drug response. Thus, there is growing interest in developing refitting methods that decipher those exposures. Previous work in this domain was unsupervised in nature, employing algebraic decomposition and probabilistic inference methods. Here we provide a supervised approach to the problem of signature refitting and show its superiority over current methods. Our method, SuRe, leverages a neural network model to capture correlations between signature exposures in real data. We show that SuRe outperforms previous methods on sparse mutation data from tumor type specific data sets, as well as pan-cancer data sets, with an increasing advantage as the data become sparser. We further demonstrate its utility in clinical settings.
Cite as

Gal Gilad, Teresa M. Przytycka, and Roded Sharan. Mutational Signature Refitting on Sparse Pan-Cancer Data. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 11:1-11:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{gilad_et_al:LIPIcs.WABI.2025.11,
  author =	{Gilad, Gal and Przytycka, Teresa M. and Sharan, Roded},
  title =	{{Mutational Signature Refitting on Sparse Pan-Cancer Data}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{11:1--11:23},
  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.2025.11},
  URN =		{urn:nbn:de:0030-drops-239374},
  doi =		{10.4230/LIPIcs.WABI.2025.11},
  annote =	{Keywords: mutational signatures, signature refitting, cancer genomics, genomic data analysis, somatic mutations}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.34
DynamicSAT: Dynamic Configuration Tuning for SAT Solving

Authors: Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu

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

Abstract
Boolean Satisfiability (SAT) problem serves as a foundation for solving numerous real-world challenges. As problem complexity increases, so does the demand for sophisticated SAT solvers, which incorporate a variety of heuristics tailored to optimize performance for specific problem instances. However, a major limitation persists: a configuration that performs well on one instance may lead to inefficiencies on others. While previous approaches to automatic algorithm configuration set parameters prior to runtime, they fail to adapt to the dynamic evolution of problem characteristics during the solving process. We introduce DynamicSAT, a novel SAT solver framework that dynamically tunes configuration parameters during solving process. By adjusting parameters on-the-fly, DynamicSAT adapts to changes arising from clause learning, elimination, and other transformations, thus improving efficiency and robustness across diverse SAT instances. We demonstrate that DynamicSAT achieves significant performance gains over the state-of-the-art solver on 2024 SAT Competition Benchmark.
Cite as

Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu. DynamicSAT: Dynamic Configuration Tuning for SAT Solving. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 34:1-34:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{shi_et_al:LIPIcs.CP.2025.34,
  author =	{Shi, Zhengyuan and Jiang, Wentao and Zhang, Xindi and Luo, Jin and Liang, Yun and Chu, Zhufei and Xu, Qiang},
  title =	{{DynamicSAT: Dynamic Configuration Tuning for SAT Solving}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{34:1--34:23},
  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.2025.34},
  URN =		{urn:nbn:de:0030-drops-238952},
  doi =		{10.4230/LIPIcs.CP.2025.34},
  annote =	{Keywords: Boolean satisfiability problem, configuration tuning, multi-armed bandit}
}
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}
}
  • Refine by Type
  • 28 Document/PDF
  • 22 Document/HTML

  • Refine by Publication Year
  • 2 2026
  • 17 2025
  • 6 2023
  • 2 2022
  • 1 2015

  • Refine by Author
  • 2 Dumbrava, Stefania
  • 2 Jiménez-Ruiz, Ernesto
  • 2 Lissandrini, Matteo
  • 2 Monnin, Pierre
  • 1 Arnaboldi, Luca
  • Show More...

  • Refine by Series/Journal
  • 11 LIPIcs
  • 5 OASIcs
  • 4 LITES
  • 8 TGDK

  • Refine by Classification
  • 4 Information systems → Graph-based database models
  • 3 Computing methodologies → Neural networks
  • 3 Computing methodologies → Semantic networks
  • 2 Computer systems organization → Embedded and cyber-physical systems
  • 2 Computing methodologies → Knowledge representation and reasoning
  • Show More...

  • Refine by Keyword
  • 3 Large Language Models
  • 2 Explainable AI
  • 2 Knowledge Graphs
  • 2 Knowledge graphs
  • 2 LLM
  • 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
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact