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Fan Wu

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#edge computing Book Open access Nov 2026

Joint Incentive Contract and Assignment Design for Mobile Networked Platforms with Endogenous Supply

Mobile networked platforms such as crowdsensing markets, edge-computing exchanges, vehicular service systems, and sponsored-content programs must coordinate two coupled levers: an assignment rule that decides who serves each request and an incentive contract that determines how realized value is shared. In all four set...

Yu-Mou Liu, Zhen-Zhe Zheng, Jiang Rong et al. · 0 citations
#artificial intelligence Review Sep 2026

Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems

AgentX-Model, the next generation of AgentX's model research framework, is presented, which connects proposal development and model experimentation within sandboxes defined by business inputs and prediction tasks within sandboxes defined by business inputs and prediction tasks.

Shuang Yang, Zi-Jie Zhuang, Chang-Xin Lao et al. · 0 citations
#machine learning Preprint Sep 2026

PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning

The proposed PPDL, a novel forecasting framework that integrates physical priors with deep learning, is validated through comprehensive experiments on industrial-scale datasets, and achieves improvements across different backbones and significantly outperforms existing online solutions.

Zi-Bo Zhao, Zheng-Xiong Guan, Chaoli Zhang et al. · 0 citations
#machine learning Preprint Sep 2026

EStream: Fast and Memory-Efficient MoE Prefill through Expert Virtualization on Mobile NPUs

EStream introduces a hardware-aware configuration algorithm that automatically configures the UFS--NPU pipeline and maximizes loading--computation overlap and introduces a hardware-aware configuration algorithm that automatically configures the UFS--NPU pipeline and maximizes loading--computation overlap.

Jun-Ming Zhang, Zhen-Zhe Zheng, Fan Wu et al. · 0 citations
Book Open access Aug 2026

Two-Stage Auctions with Bid Refinement for Online Advertising

This work investigates the design of two-stage auctions that allow advertisers to submit and update their bids, with second-stage bids serving as refinements of the initial ones, and proposes a dynamic two-stage auction that adopts realization-dependent entry fees with discounts.

Yidan Xing, Rui Guo, Yi-Xin Tao et al. · 0 citations

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