4 Search Results for "Lim, Wei Quan"

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)

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@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/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)

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@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
Vision
DOI: 10.4230/TGDK.1.1.8
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Authors: Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Cite as

Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{damato_et_al:TGDK.1.1.8,
  author =	{d'Amato, Claudia and Mahon, Louis and Monnin, Pierre and Stamou, Giorgos},
  title =	{{Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{8:1--8:35},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.8},
  URN =		{urn:nbn:de:0030-drops-194824},
  doi =		{10.4230/TGDK.1.1.8},
  annote =	{Keywords: Graph-based Learning, Knowledge Graph Embeddings, Large Language Models, Explainable AI, Knowledge Graph Completion \& Curation}
}
Document
DOI: 10.4230/LIPIcs.DISC.2019.20
Parallel Finger Search Structures

Authors: Seth Gilbert and Wei Quan Lim

Published in: LIPIcs, Volume 146, 33rd International Symposium on Distributed Computing (DISC 2019)

Abstract
In this paper we present two versions of a parallel finger structure FS on p processors that supports searches, insertions and deletions, and has a finger at each end. This is to our knowledge the first implementation of a parallel search structure that is work-optimal with respect to the finger bound and yet has very good parallelism (within a factor of O(log p)^2) of optimal). We utilize an extended implicit batching framework that transparently facilitates the use of FS by any parallel program P that is modelled by a dynamically generated DAG D where each node is either a unit-time instruction or a call to FS. The work done by FS is bounded by the finger bound F_L (for some linearization L of D), i.e. each operation on an item with distance r from a finger takes O(log r+1) amortized work. Running P using the simpler version takes O((T_1+F_L)/p + T_infty + d * ((log p)^2 + log n)) time on a greedy scheduler, where T_1, T_infty are the size and span of D respectively, and n is the maximum number of items in FS, and d is the maximum number of calls to FS along any path in D. Using the faster version, this is reduced to O((T_1+F_L)/p + T_infty + d *(log p)^2 + s_L) time, where s_L is the weighted span of D where each call to FS is weighted by its cost according to F_L. FS can be extended to a fixed number of movable fingers. The data structures in our paper fit into the dynamic multithreading paradigm, and their performance bounds are directly composable with other data structures given in the same paradigm. Also, the results can be translated to practical implementations using work-stealing schedulers.
Cite as

Seth Gilbert and Wei Quan Lim. Parallel Finger Search Structures. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 20:1-20:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{gilbert_et_al:LIPIcs.DISC.2019.20,
  author =	{Gilbert, Seth and Lim, Wei Quan},
  title =	{{Parallel Finger Search Structures}},
  booktitle =	{33rd International Symposium on Distributed Computing (DISC 2019)},
  pages =	{20:1--20:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-126-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{146},
  editor =	{Suomela, Jukka},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2019.20},
  URN =		{urn:nbn:de:0030-drops-113275},
  doi =		{10.4230/LIPIcs.DISC.2019.20},
  annote =	{Keywords: Parallel data structures, Multithreading, Dictionaries, Comparison-based Search, Distribution-sensitive algorithms}
}
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