3 Search Results for "Liu, Tian Rui"

Document
DOI: 10.4230/LIPIcs.TIME.2024.17
Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN

Authors: Ibrahim Delibasoglu and Fredrik Heintz

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Anomaly detection in time series data is a critical task in various domains, including finance, healthcare, cybersecurity and industry. Traditional methods, such as time series decomposition, clustering, and density estimation, have provided robust solutions for identifying anomalies that exhibit distinct patterns or significant deviations from normal data distributions. Recent advancements in machine learning and deep learning have further enhanced these capabilities. This paper introduces a novel method for anomaly detection that combines the strengths of autoencoders and recurrent neural networks (RNNs) with an reconstruction error feedback mechanism based on Mean Squared Error. We compare our method against classical techniques and recent approaches like OmniAnomaly, which leverages stochastic recurrent neural networks, and the Anomaly Transformer, which introduces association discrepancy to capture long-range dependencies and DCDetector using contrastive representation learning with multi-scale dual attention. Experimental results demonstrate that our method achieves superior overall performance in terms of precision, recall, and F1 score. The source code is available at http://github.com/mribrahim/AE-FAR
Cite as

Ibrahim Delibasoglu and Fredrik Heintz. Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 17:1-17:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{delibasoglu_et_al:LIPIcs.TIME.2024.17,
  author =	{Delibasoglu, Ibrahim and Heintz, Fredrik},
  title =	{{Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{17:1--17:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.17},
  URN =		{urn:nbn:de:0030-drops-212244},
  doi =		{10.4230/LIPIcs.TIME.2024.17},
  annote =	{Keywords: Time series, Anomaly, Neural networks}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2024.57
Non-Linear Paging

Authors: Ilan Doron-Arad and Joseph (Seffi) Naor

Published in: LIPIcs, Volume 297, 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)

Abstract
We formulate and study non-linear paging - a broad model of online paging where the size of subsets of pages is determined by a monotone non-linear set function of the pages. This model captures the well-studied classic weighted paging and generalized paging problems, and also submodular and supermodular paging, studied here for the first time, that have a range of applications from virtual memory to machine learning. Unlike classic paging, the cache threshold parameter k does not yield good competitive ratios for non-linear paging. Instead, we introduce a novel parameter 𝓁 that generalizes the notion of cache size to the non-linear setting. We obtain a tight deterministic 𝓁-competitive algorithm for general non-linear paging and a o(log²𝓁)-competitive lower bound for randomized algorithms. Our algorithm is based on a new generic LP for the problem that captures both submodular and supermodular paging, in contrast to LPs used for submodular cover settings. We finally focus on the supermodular paging problem, which is a variant of online set cover and online submodular cover, where sets are repeatedly requested to be removed from the cover. We obtain polylogarithmic lower and upper bounds and an offline approximation algorithm.
Cite as

Ilan Doron-Arad and Joseph (Seffi) Naor. Non-Linear Paging. In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 297, pp. 57:1-57:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{doronarad_et_al:LIPIcs.ICALP.2024.57,
  author =	{Doron-Arad, Ilan and Naor, Joseph (Seffi)},
  title =	{{Non-Linear Paging}},
  booktitle =	{51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)},
  pages =	{57:1--57:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-322-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{297},
  editor =	{Bringmann, Karl and Grohe, Martin and Puppis, Gabriele and Svensson, Ola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2024.57},
  URN =		{urn:nbn:de:0030-drops-202000},
  doi =		{10.4230/LIPIcs.ICALP.2024.57},
  annote =	{Keywords: paging, competitive analysis, non-linear paging, submodular and supermodular functions}
}
Document
DOI: 10.4230/LIPIcs.WABI.2019.19
Faster Pan-Genome Construction for Efficient Differentiation of Naturally Occurring and Engineered Plasmids with Plaster

Authors: Qi Wang, R. A. Leo Elworth, Tian Rui Liu, and Todd J. Treangen

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)

Abstract
As sequence databases grow, characterizing diversity across extremely large collections of genomes requires the development of efficient methods that avoid costly all-vs-all comparisons [Marschall et al., 2018]. In addition to exponential increases in the amount of natural genomes being sequenced, improved techniques for the creation of human engineered sequences is ushering in a new wave of synthetic genome sequence databases that grow alongside naturally occurring genome databases. In this paper, we analyze the full diversity of available sequenced natural and synthetic plasmid genome sequences. This diversity can be represented by a data structure that captures all presently available nucleotide sequences, known as a pan-genome. In our case, we construct a single linear pan-genome nucleotide sequence that captures this diversity. To process such a large number of sequences, we introduce the plaster algorithmic pipeline. Using plaster we are able to construct the full synthetic plasmid pan-genome from 51,047 synthetic plasmid sequences as well as a natural pan-genome from 6,642 natural plasmid sequences. We demonstrate the efficacy of plaster by comparing its speed against another pan-genome construction method as well as demonstrating that nearly all plasmids align well to their corresponding pan-genome. Finally, we explore the use of pan-genome sequence alignment to distinguish between naturally occurring and synthetic plasmids. We believe this approach will lead to new techniques for rapid characterization of engineered plasmids. Applications for this work include detection of genome editing, tracking an unknown plasmid back to its lab of origin, and identifying naturally occurring sequences that may be of use to the synthetic biology community. The source code for fully reconstructing the natural and synthetic plasmid pan-genomes as well for plaster are publicly available and can be downloaded at https://gitlab.com/qiwangrice/plaster.git.
Cite as

Qi Wang, R. A. Leo Elworth, Tian Rui Liu, and Todd J. Treangen. Faster Pan-Genome Construction for Efficient Differentiation of Naturally Occurring and Engineered Plasmids with Plaster. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 19:1-19:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{wang_et_al:LIPIcs.WABI.2019.19,
  author =	{Wang, Qi and Elworth, R. A. Leo and Liu, Tian Rui and Treangen, Todd J.},
  title =	{{Faster Pan-Genome Construction for Efficient Differentiation of Naturally Occurring and Engineered Plasmids with Plaster}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{19:1--19:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.19},
  URN =		{urn:nbn:de:0030-drops-110492},
  doi =		{10.4230/LIPIcs.WABI.2019.19},
  annote =	{Keywords: comparative genomics, sequence alignment, pan-genome, engineered plasmids}
}
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