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1. ITSY: Initial Trigger-Based PFC Deadlock Detection in the Data Plane

   Wu, Xinyu Crystal; Ng, T. S. (May 2021, IEEE International Workshop on Intelligent Cloud Computing and Networking)

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2. ITSY: Initial Trigger-Based PFC Deadlock Detection in the Data Plane

   Wu, Xinyu Crystal; Ng, T. S. (May 2021, IEEE International Workshop on Intelligent Cloud Computing and Networking)

   null (Ed.)
3. Detecting and Resolving PFC Deadlocks with ITSY Entirely in the Data Plane

   https://doi.org/10.1109/INFOCOM48880.2022.9796798  

   Wu, Xinyu Crystal; Eugene Ng, T. S. (May 2022, IEEE INFOCOM 2022 - IEEE Conference on Computer Communications)

   The Priority-based Flow Control (PFC) protocol is adopted to guarantee zero packet loss in many high-performance data centers. PFC, however, can induce deadlocks and in severe cases cause the entire network to be blocked. Existing solutions have focused on deadlock avoidance; unfortunately, they are not foolproof. Therefore, deadlock detection is a necessity. We propose ITSY, a novel system that correctly detects and resolves deadlocks entirely in the data plane. It works with any network topologies and routing algorithms. Unique to ITSY is the use of deadlock initial triggers, which contributes to efficient deadlock detection, mitigation, and recurrence prevention. ITSY provides three deadlock resolution mechanisms with different trade-off options. We implement ITSY for programmable switches in the P4 language. Experiments show that ITSY detects and resolves deadlocks rapidly with minimal overheads. 

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4. Predicting the Conducted Emission of a CRM PFC Below 1MHz by Transient Modeling

   https://doi.org/10.1109/EMCSI38923.2020.9191520  

   Kim, Hongseok; He, Jiayi; Wu, Chunyu; Erickson, Nicholas; Cho, Sangho; Kim, Dohyung; Hur, Yeong; Fan, Jun (July 2020, 2020 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI))

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5. Classifying Subjects with PFC Lesions from Healthy Controls during Working Memory Encoding via Graph Convolutional Networks

   https://doi.org/10.1109/NER52421.2023.10123793  

   Balaji, Sai Sanjay; Parhi, Keshab K. (April 2023, 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER))

   This paper describes a group-level classification of 14 patients with prefrontal cortex (pFC) lesions from 20 healthy controls using multi-layer graph convolutional networks (GCN) with features inferred from the scalp EEG recorded from the encoding phase of working memory (WM) trials. We first construct undirected and directed graphs to represent the WM encoding for each trial for each subject using distance correlation- based functional connectivity measures and differential directed information-based effective connectivity measures, respectively. Centrality measures of betweenness centrality, eigenvector centrality, and closeness centrality are inferred for each of the 64 channels from the brain connectivity. Along with the three centrality measures, each graph uses the relative band powers in the five frequency bands - delta, theta, alpha, beta, and gamma- as node features. The summarized graph representation is learned using two layers of GCN followed by mean pooling, and fully connected layers are used for classification. The final class label for a subject is decided using majority voting based on the results from all the subject's trials. The GCN-based model can correctly classify 28 of the 34 subjects (82.35% accuracy) with undirected edges represented by functional connectivity measure of distance correlation and classify all 34 subjects (100% accuracy) with directed edges characterized by effective connectivity measure of differential directed information. 

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