Principles of Vascular Surgery

Rob, Charles

doi:10.1007/978-1-4612-5032-6_4

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…The naive (random walk) Autoregressive Integrated Moving Average Model (ARIMA) prediction model from Refs. 7 , 26 ; 2. HDoutliers, 17 an unsupervised algorithm designed to identify anomalies in high-dimensional data, based on a distributional model that allows for probability assignment to an anomaly; and 3.…”

Section: Resultsmentioning

confidence: 99%

Graph neural network-based anomaly detection for river network systems

Buchhorn,

Santos-Fernandez,

Mengersen

et al. 2024

F1000Res

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Background Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model’s efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.

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Section: Resultsmentioning

confidence: 99%

Graph neural network-based anomaly detection for river network systems

Buchhorn,

Santos-Fernandez,

Mengersen

et al. 2024

F1000Res

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“…The author, Charles Rob, selected 27 articles considered to be classics in the literature. 16 The history of vascular trauma is covered extensively in Vascular Trauma by Rich and Spencer, which the SVS has "adopted." 17 The SVS currently anticipates the third edition of Vascular Trauma to be completed by Todd Rasmussen and Nigel Tai of the United States and United Kingdom militaries, respectively, supplementing the second edition with their recent allied experiences in Afghanistan and Iraq.…”

Section: Oral History Of Svs and Vascular Surgerymentioning

confidence: 99%

Interviews with pioneers of vascular surgery

Yao

Gregory

Rich

2012

Journal of Vascular Surgery

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The purpose of the SVS is not just to hold an annual meeting, but also to pursue social, financial, and political responsibilities. In addition, the Society leads in research, training, education, and practice (ie, patient care). The current leadership of the SVS is dedicated to preserving the history of the Society. The History Project Work Group will execute the orders of the Society to develop a series of digital videography recorded interviews with leaders in vascular surgery to be made available to our membership. The information collected will form the basis for a book on the history of the SVS.

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