Selected work

Representative Papers

Fully Dynamic Rooted Spanning Tree on GPU

Jul 22, 2026

This work addresses the problem of efficiently maintaining rooted spanning forests in dynamic graphs by proposing four novel GPU-oriented fully dynamic parallel algorithms that support batch edge insertions and deletions without requiring forest reconstruction after each update. As the first approach specifically designed for GPUs to maintain fully dynamic rooted spanning trees, it fills a critical gap in the literature. By integrating dynamic graph update strategies with GPU parallelism, the system achieves throughputs of up to 2 million insertions and 1.4 million deletions per second on real-world datasets, significantly outperforming existing static parallel algorithms.

0 citationsRead paper

Stop Denoising Your Blurs

May 24, 2026

This work addresses a fundamental limitation in conventional diffusion models for image deblurring, which erroneously model blur degradation as additive noise while neglecting its inherently convolutional nature. To overcome this, the authors propose ConvDiff, a novel framework that aligns the forward diffusion process with the physical convolutional mechanism of blur. By constructing a degradation trajectory consistent with real-world blur through frequency-domain decomposition and integrating Gaussian blur modeling with diffusion inversion algorithms, ConvDiff transcends the restrictive additive noise assumption. This approach yields deblurring results that better adhere to physical principles and establishes a scalable diffusion-based paradigm applicable to diverse blur types.

0 citationsRead paper

Wavelet Based Time Series Models with Time-Varying Thresholds

May 18, 2026

This study addresses the challenge that traditional threshold time series models struggle to simultaneously capture both abrupt shifts and smooth evolution in threshold parameters. To overcome this limitation, the authors propose a time-varying threshold autoregressive model based on wavelet series expansion, which flexibly approximates irregular jumps and continuous variations in the threshold function. By leveraging the localized time-frequency properties of wavelet bases, the approach circumvents the limitations of Fourier-based methods in modeling local dynamics. Integrating wavelet expansion, a threshold mechanism, and an autoregressive structure, the proposed model demonstrates superior performance in both simulation studies and empirical analyses, achieving significantly higher fitting accuracy and forecasting capability compared to existing methods, thereby offering a novel framework for modeling complex nonlinear time series.

0 citationsRead paper

Beyond BFS: A Comparative Study of Rooted Spanning Tree Algorithms on GPUs

Mar 12, 2026

This work addresses the limitations of traditional BFS-based rooted spanning tree (RST) construction, which suffers from O(D) step complexity and poor parallel scalability on high-diameter and power-law graphs. The authors present the first GPU-optimized implementation of the Path Reversal RST (PR-RST) algorithm, integrating the GConn connectivity framework with Euler tour-based rooting, and introducing GPU-tailored optimizations including pointer jumping and broadcast enhancements. Experimental evaluation across more than ten real-world graph datasets demonstrates that the proposed method achieves up to 300× speedup over highly optimized BFS baselines, with particularly pronounced gains on high-diameter graphs. These results substantiate the efficiency and scalability of O(log n) step-complexity connectivity strategies in modern parallel architectures.

0 citationsRead paper

A Flexible Modeling of Extremes in the Presence of Inliers

Feb 05, 2026

This study addresses the challenges of modeling extremes in data containing zero-valued interior points, where conventional methods struggle to accurately estimate thresholds and tail characteristics. To overcome these limitations, the paper proposes the first unified mixture model for extreme value analysis that simultaneously captures the interior distribution, tail behavior, and their relative proportions. Parameter inference is performed via maximum likelihood estimation, and model validation is comprehensively assessed using mean excess plots, parameter stability plots, and Pickands plots. Extensive simulations and real-data experiments demonstrate that the proposed approach significantly outperforms existing methods in threshold selection, tail parameter estimation, and overall model stability, effectively mitigating the inadequacies of traditional models in representing interior point structures.

0 citationsRead paper

Recent publications

Latest Papers

Fully Dynamic Rooted Spanning Tree on GPU

Jul 22, 2026

This work addresses the problem of efficiently maintaining rooted spanning forests in dynamic graphs by proposing four novel GPU-oriented fully dynamic parallel algorithms that support batch edge insertions and deletions without requiring forest reconstruction after each update. As the first approach specifically designed for GPUs to maintain fully dynamic rooted spanning trees, it fills a critical gap in the literature. By integrating dynamic graph update strategies with GPU parallelism, the system achieves throughputs of up to 2 million insertions and 1.4 million deletions per second on real-world datasets, significantly outperforming existing static parallel algorithms.

0 citationsRead paper

Stop Denoising Your Blurs

May 24, 2026

This work addresses a fundamental limitation in conventional diffusion models for image deblurring, which erroneously model blur degradation as additive noise while neglecting its inherently convolutional nature. To overcome this, the authors propose ConvDiff, a novel framework that aligns the forward diffusion process with the physical convolutional mechanism of blur. By constructing a degradation trajectory consistent with real-world blur through frequency-domain decomposition and integrating Gaussian blur modeling with diffusion inversion algorithms, ConvDiff transcends the restrictive additive noise assumption. This approach yields deblurring results that better adhere to physical principles and establishes a scalable diffusion-based paradigm applicable to diverse blur types.

0 citationsRead paper

Wavelet Based Time Series Models with Time-Varying Thresholds

May 18, 2026

This study addresses the challenge that traditional threshold time series models struggle to simultaneously capture both abrupt shifts and smooth evolution in threshold parameters. To overcome this limitation, the authors propose a time-varying threshold autoregressive model based on wavelet series expansion, which flexibly approximates irregular jumps and continuous variations in the threshold function. By leveraging the localized time-frequency properties of wavelet bases, the approach circumvents the limitations of Fourier-based methods in modeling local dynamics. Integrating wavelet expansion, a threshold mechanism, and an autoregressive structure, the proposed model demonstrates superior performance in both simulation studies and empirical analyses, achieving significantly higher fitting accuracy and forecasting capability compared to existing methods, thereby offering a novel framework for modeling complex nonlinear time series.

0 citationsRead paper

Beyond BFS: A Comparative Study of Rooted Spanning Tree Algorithms on GPUs

Mar 12, 2026

This work addresses the limitations of traditional BFS-based rooted spanning tree (RST) construction, which suffers from O(D) step complexity and poor parallel scalability on high-diameter and power-law graphs. The authors present the first GPU-optimized implementation of the Path Reversal RST (PR-RST) algorithm, integrating the GConn connectivity framework with Euler tour-based rooting, and introducing GPU-tailored optimizations including pointer jumping and broadcast enhancements. Experimental evaluation across more than ten real-world graph datasets demonstrates that the proposed method achieves up to 300× speedup over highly optimized BFS baselines, with particularly pronounced gains on high-diameter graphs. These results substantiate the efficiency and scalability of O(log n) step-complexity connectivity strategies in modern parallel architectures.

0 citationsRead paper

A Flexible Modeling of Extremes in the Presence of Inliers

Feb 05, 2026

This study addresses the challenges of modeling extremes in data containing zero-valued interior points, where conventional methods struggle to accurately estimate thresholds and tail characteristics. To overcome these limitations, the paper proposes the first unified mixture model for extreme value analysis that simultaneously captures the interior distribution, tail behavior, and their relative proportions. Parameter inference is performed via maximum likelihood estimation, and model validation is comprehensively assessed using mean excess plots, parameter stability plots, and Pickands plots. Extensive simulations and real-data experiments demonstrate that the proposed approach significantly outperforms existing methods in threshold selection, tail parameter estimation, and overall model stability, effectively mitigating the inadequacies of traditional models in representing interior point structures.

0 citationsRead paper