arXiv subjects

Yuming Han

Publications and source records attributed to Yuming Han.

3 recordsLinked to original sources

arXiv2026

Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access

Polyanskiy proposed a framework for the unsourced multiple access channel (MAC) problem where users employ a common codebook in the finite blocklength regime. However, existing approaches handle channel noise before the joint decoder. In this work, we introduce a decoder compatible diffusion denoiser as a lightweight analysis within joint decoding. The score network is trained on samples drawn from the channel output distribution, making the method easy to integrate with existing code designs. In our theoretical analysis, we derive a diffusion-denoiser random-coding achievable bound that is strictly tighter. Simulations on existing decoders, including FASURA, MSUG-MRA and pilot-based method, show consistent performance gains with at least a $0.5$ $\mathrm{dB}$ improvement in required $\mathrm{E_b/N_0}$ at a fixed error target.

cs.IT↗

arXiv2026

A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression

Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers valuable structural details for urban monitoring and disaster assessment, but large-area datasets can easily reach hundreds of gigabytes, creating significant challenges for storage and long-term management. In this paper, we propose a PPO-based bitrate allocation Conditional Diffusion Compression (PCDC) framework. PCDC integrates a conditional diffusion decoder with a PPO-based block-wise bitrate allocation strategy to achieve high compression ratios while maintaining strong perceptual performance. We also release a high-resolution drone image dataset with richer structural details at a consistent low altitude over residential neighborhoods in coastal urban areas. Experimental results show compression ratios of 19.3x on DIV2K and 21.2x on the drone image dataset. Moreover, downstream object detection experiments demonstrate that the reconstructed images preserve task-relevant information with negligible performance loss.

cs.CV↗

arXiv2024

Markovian Analysis of Information Cascades with Fake Agents

People often learn from other's actions when they make decisions while doing online shopping. This kind of observational learning may lead to information cascades, which means agents might ignore their own signals and follow the 'trend' created collectively by the actions of their predecessors. It is well-known that with rational agents, such a cascade model can result in either correct or incorrect cascades. In this paper, we additionally consider the presence of fake agents who always take fixed actions and we investigate their influence on the outcome of these cascades. We propose an infinite Markov Chain sequence structure and a tree structure to analyze how the fraction and the type of such fake agents impacts behavior of the upcoming agents. We show that an increase in the fraction of fake agents may reduce the chances of their preferred outcome, and also there is a certain lower bound for the probability of a wrong cascade. In particular, we discuss the probability of an agent being fake tends to 1 and the effect of a constant portion of fake agents.

cs.SI↗