arXiv subjects

Yuming Hu

Publications and source records attributed to Yuming Hu.

4 recordsLinked to original sources

arXiv2026

PEMI: Transparent Performance Enhancements for QUIC

QUIC, as the transport layer of the next-generation Web stack (HTTP/3), natively provides security and performance improvements over TCP-based stacks. However, since QUIC provides end-to-end encryption for both data and packet headers, in-network assistance like Performance-Enhancing Proxy (PEP) is unavailable for QUIC. To achieve the similar optimization as TCP, some works seek to collaborate endpoints and middleboxes to provide in-network assistance for QUIC. But involving both host and in-network devices increases the difficulty of deployment in the Internet. In this paper, by analyzing the QUIC standard, implementations, and the locality of application traffic, we identify opportunities for transparent middleboxes to measure RTT and infer packet loss for QUIC connections, despite the absence of plaintext ACK information. We then propose PEMI as a concrete system that continuously measures RTT and infers lost packets, enabling fast retransmissions for QUIC. PEMI enables performance enhancement for QUIC in a completely transparent manner, without requiring any explicit cooperation from the endpoints. To keep fairness, PEMI employs a delay-based congestion control and utilizes feedback-based methods to enforce CWND. Extensive evaluation results, including Mininet and trace-driven dynamic experiments, show that PEMI can significantly improve the performance of QUIC. For example, in the Mininet experiments, PEMI increases the goodput of file transfers by up to 2.5$\times$, and reduces the 90th percentile jitter of RTC frames by 20-75%.

cs.NI↗

arXiv2024

A posteriori error estimation for an interior penalty virtual element method of Kirchhoff plates

In this paper, we develop a residual-type a posteriori error estimation for an interior penalty virtual element method (IPVEM) for the Kirchhoff plate bending problem. Building on the work in \cite{FY2023IPVEM}, we adopt a modified discrete variational formulation that incorporates the $ H^1 $-elliptic projector in the jump and average terms. This allows us to simplify the numerical implementation by including the $ H^1 $-elliptic projector in the computable error estimators. We derive the reliability and efficiency of the a posteriori error bound by constructing an enriching operator and establishing some related error estimates that align with $C^0$-continuous interior penalty finite element methods. As observed in the a priori analysis, the interior penalty virtual elements exhibit similar behaviors to $C^0$-continuous elements despite its discontinuity. This observation extends to the a posteriori estimate since we do not need to account for the jumps of the function itself in the discrete scheme and the error estimators. As an outcome of the error estimator, an adaptive VEM is introduced by means of the mesh refinement strategy with the one-hanging-node rule. Numerical results from several benchmark tests confirm the robustness of the proposed error estimators and show the efficiency of the resulting adaptive VEM.

math.NA↗

arXiv2019

Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning

The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation. Another aim of the project is to find a model that would be generalizable across medical imaging datasets. We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose. Specifically, we implemented three models (2-D U-Net, 3-D U-Net, and DenseNet), and evaluated them on four datasets (Automated Cardiac Diagnosis Challenge, MICCAI 2009 LV, Sunnybrook Cardiac Data, MICCAI 2012 RV). While maintaining a consistent preprocessing strategy, we tested the performance of each model when trained on data from the same dataset as the test data, and when trained on data from a different dataset than the test dataset. Data augmentation was also used to increase the adaptability of the models. The results were compared to determine performance and generalizability.

eess.IV↗

arXiv2018

Age of Information in Poisson Networks

The age of information (AoI) has been extensively studied in recent years. However, few works have focused on the analysis of AoI in large wireless networks. In this work, we analyze this metric to characterize timeliness in a Poisson bipolar network, in which spatial distribution, fading, and interference are taken into consideration. We further study the effect of deadline constraint on the AoI. We derive upper and lower bounds for the cumulative distribution function (cdf) of the average age in network. Numerical results show that the inclusion of deadline constraint can substantially improve the performance of the system.

cs.IT↗