3 Search Results for "Han, Runchao"

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.AFT.2025.19
Two-Tier Black-Box Blockchains and Application to Instant Layer-1 Payments

Authors: Michele Ciampi, Yun Lu, Rafail Ostrovsky, and Vassilis Zikas

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

Abstract
Common blockchain protocols are monolithic, i.e., their security relies on a single assumption, e.g., honest majority of hashing power (Bitcoin) or stake (Cardano, Algorand, Ethereum). In contrast, so-called optimistic approaches (Thunderella, Meshcash) rely on a combination of assumptions to achieve faster transaction liveness. We revisit, redesign, and augment the optimistic paradigm to a tiered approach. Our design assumes a primary (Tier 1) and a secondary (Tier 2, also referred to as fallback) blockchain, and achieves full security also in a tiered fashion: If the assumption underpinning the primary chain holds, then we guarantee safety, liveness and censorship resistance, irrespectively of the status of the fallback chain. And even if the primary assumption fails, all security properties are still satisfied (albeit with a temporary slow down) provided the fallback assumption holds. To our knowledge, no existing optimistic or tiered approach preserves both safety and liveness when any one of its underlying blockchain (assumptions) fails. The above is achieved by a new detection-and-recovery mechanism that links the two blockchains, so that any violation of safety, liveness, or censorship resistance on the (faster) primary blockchain is temporary - it is swiftly detected and recovered on the secondary chain - and thus cannot result in a persistent fork or halt of the blockchain ledger. We instantiate the above paradigm using a primary chain based on proof of reputation (PoR) and a fallback chain based on proof of stake (PoS). Our construction uses the PoR and PoS blockchains in a mostly black-box manner - where rather than assuming a concrete construction we distil abstract properties on the two blockchains that are sufficient for applying our tiered methodology. In fact, choosing reputation as the resource of the primary chain opens the door to an incentive mechanism - which we devise and analyze - that tokenizes reputation in order to deter cheating and boost participation (on both the primary/PoR and the fallback/PoS blockchain). As we demonstrate, such tokenization in combination with interpreting reputation as a built-in system-wide credit score, allows for embedding in our two-tiered methodology a novel mechanism which provides collateral-free, multi-use payment-channel-like functionality where payments can be instantly confirmed.
Cite as

Michele Ciampi, Yun Lu, Rafail Ostrovsky, and Vassilis Zikas. Two-Tier Black-Box Blockchains and Application to Instant Layer-1 Payments. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 19:1-19:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ciampi_et_al:LIPIcs.AFT.2025.19,
  author =	{Ciampi, Michele and Lu, Yun and Ostrovsky, Rafail and Zikas, Vassilis},
  title =	{{Two-Tier Black-Box Blockchains and Application to Instant Layer-1 Payments}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{19:1--19:24},
  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.2025.19},
  URN =		{urn:nbn:de:0030-drops-247380},
  doi =		{10.4230/LIPIcs.AFT.2025.19},
  annote =	{Keywords: Fault tolerant blockchain, instantly confirmed payments}
}
Document
DOI: 10.4230/OASIcs.Tokenomics.2021.2
General Congestion Attack on HTLC-Based Payment Channel Networks

Authors: Zhichun Lu, Runchao Han, and Jiangshan Yu

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

Abstract
Payment Channel Networks (PCNs) have been a promising approach to scale blockchains. However, PCNs have limited liquidity: large-amount or multi-hop payments may fail. The major threat of PCNs liquidity is payment griefing, where the adversary who acts as the payee keeps withholding the payment, so that coins involved in the payment cannot be used for routing other payments before the payment expires. Payment griefing gives adversaries a chance to launch the congestion attack, where the adversary griefs a large number of payments and paralyses the entire PCN. Understanding congestion attacks, including their strategies and impact, is crucial for designing PCNs with better liquidity guarantees. However, existing research has only focused on the specific attacking strategies and specific aspects of their impact on PCNs. We fill this gap by studying the general congestion attack. Compared to existing attack strategies, in our framework each step serves an orthogonal purpose and is customisable, allowing the adversary to focus on different aspects of the liquidity. To evaluate the attack’s impact, we propose a generic method of quantifying PCNs' liquidity and effectiveness of the congestion attacks. We evaluate our general congestion attacks on Bitcoin’s Lightning Network, and show that with direct channels to 1.5% richest nodes, and ∼ 0.0096 BTC of cost, the adversary can launch a congestion attack that locks 47% (∼280 BTC) coins in the network; reduces success rate of payments by 16.0%∼60.0%; increases fee of payments by 4.5%∼16.0%; increases average attempts of payments by 42.0%∼115.3%; and increase the number of bankruptcy nodes (i.e., nodes with insufficient balance for making normal-size payments) by 26.6%∼109.4%, where the amounts of payments range from 0.001 to 0.019 BTC.
Cite as

Zhichun Lu, Runchao Han, and Jiangshan Yu. General Congestion Attack on HTLC-Based Payment Channel Networks. In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lu_et_al:OASIcs.Tokenomics.2021.2,
  author =	{Lu, Zhichun and Han, Runchao and Yu, Jiangshan},
  title =	{{General Congestion Attack on HTLC-Based Payment Channel Networks}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{2:1--2:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.2},
  URN =		{urn:nbn:de:0030-drops-158990},
  doi =		{10.4230/OASIcs.Tokenomics.2021.2},
  annote =	{Keywords: Blockchain, PCN, Congestion}
}
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