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Researcher profile

Yuhua Wang

Yuhua Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine Learningphysics.optics

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Published work

2 published item(s)

preprint2026arXiv

Modulating anomalous thermal quenching behavior of stimulation luminescence via high-orbit electronic satellite-stabilized Trap state in germanate-based phosphors for 5D optical data storage

Persistent luminescence (PersL) materials, widely used in emergency lighting and information storage, are primarily employed at room temperature. However, their luminescent performance deteriorates sharply at high temperatures. Herein, a serials of Mg2GeO4:Ti4+,Ln3+ (Ln = Tb, Eu) phosphors demonstrated anomalous thermal quenching PersL due to the temperature-dependent Fermi-Dirac distribution of bound charge carriers of Ti4+Mg2+ as remote electron traps and VMg2+ as hole traps. The high carrier retention rate of phosphors is attributed to the ability of Ti4+Mg2+ positive charge center to strongly trap non-bonding electrons over a long range (about 20 angstroms) as the electronic satellite for its stable operation. Under external optical/thermal stimulation, the released electrons and holes recombine at the different luminescent levels of Tb3+, resulting in the emission of different PersL branching ratios. Using these phosphors, we have developed 5D optical data storage (2D plane + trap depth + temperature + time) and the encrypted engine program for high-temperature aerospace engines. This study reveals the energy storage process of long-range trapping and releasing electrons by Ti4+ electron traps, and provides a new design concept for the design of PersL materials.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning

Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses privacy risks. A common defense involves per-example $\ell_2$ clipping before prototype computation to bound sensitivity, followed by isotropic Gaussian noise to enforce Local Differential Privacy (LDP). However, Isotropic Gaussian Prototype Perturbation (IGPP) typically over-perturbs discriminative dimensions and struggles to balance the clipping threshold with representation fidelity. In this paper, we propose VPDR, a client-side privacy plug-in that seamlessly integrates into existing ProtoPFLs. Motivated by the observation that dimension-wise class variance reflects discriminability, we introduce Variance-adaptive Prototype Perturbation (VPP), which allocates less noise to discriminative subspaces, preserving semantic separability while ensuring privacy. We further develop Distillation-guided Clipping Regularization (DCR), which enables feature norms to adaptively concentrate near the predefined clipping threshold while maintaining prediction consistency. Theoretical analysis shows that our groupwise mechanism provides privacy guarantees no weaker than the isotropic baseline under the same privacy constraints. Extensive experiments on multi-domain benchmarks demonstrate that VPDR achieves a superior privacy-utility trade-off, outperforming IGPP in personalized federated fine-tuning without sacrificing robustness against realistic attacks.

0 citations0 reviewsNo code linkedOpen access