arXiv subjects

Chunya Liu

Publications and source records attributed to Chunya Liu.

3 recordsLinked to original sources

arXiv2025

Impact of the WHO's 90-70-90 Strategy on HPV-Related Cervical Cancer Control: A Mathematical Model Evaluation in China

In August 2020, the World Health Assembly approved the Global Strategy to eliminate cervical cancer, marking the first time that numerous countries committed to eliminating a form of cancer. China introduced the HPV vaccine in 2016 and has made significant advancements in both prevention and treatment strategies. However, due to the relatively late introduction of the vaccine, the burden of cervical cancer in China continues to rise. In light of this, we develop a compartmental model to assess the impact of the WHO's 90-70-90 strategy, along with adult catch-up vaccination, on the control of HPV-induced cervical cancer in China. We analyze the basic properties of the model and provide proofs of the local and global asymptotic stability of the equilibrium points. Additionally, a sensitivity analysis is performed, and we use the MCMC algorithm to fit the number of new cervical cancer cases and deaths in China from 1990 to 2021. The estimated basic reproduction number before and after the introduction of the HPV vaccine in China is 1.5026 (95% CI: 1.4051-1.6002) and 1.0726 (95% CI: 0.9384-1.2067), respectively. The sensitivity analysis reveals that screening, as a non-pharmaceutical intervention, plays a crucial role in controlling the spread of the disease. We apply the 90-70-90 strategy to predict the future number of new cervical cancer cases and deaths in China. The results indicate that prioritizing the 70-90 target combination is the most cost-effective approach and can achieve the goal of zero new cervical cancer cases by 2061. Finally, an optimal control model is developed to explore the best implementation strategies for HPV vaccination and screening under various plausible scenarios.

q-bio.PE↗

arXiv2025

GenPTW: Latent Image Watermarking for Provenance Tracing and Tamper Localization

The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics. Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utility. We propose \textbf{GenPTW}, a \textbf{Gen}eral watermarking framework that unifies \textbf{P}rovenance tracing and \textbf{T}amper localization in latent space. It supports both in-generation and post-generation embedding without altering the generative process, and is plug-and-play compatible with latent diffusion models (LDMs) and visual autoregressive (VAR) models. To achieve precise provenance tracing and tamper localization, we embed the watermark using two complementary mechanisms: cross-attention fusion aligned with latent semantics and spatial fusion providing explicit spatial guidance for edit sensitivity. A tamper-aware extractor jointly conducts provenance tracing and tamper localization by leveraging watermark features together with high-frequency features. Experiments show that GenPTW maintains high visual fidelity and strong robustness against diverse AIGC-editing.

cs.CR↗

arXiv2023

Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation

Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the capacity gap between students and teachers becomes larger, existing KD methods fail to achieve better results. Our work shows that the `prior knowledge' is vital to KD, especially when applying large teachers. Particularly, we propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation. This means that our method also takes the teacher's feature as `input', not just `target'. Besides, we dynamically adjust the ratio of the prior knowledge during the training phase according to the feature gap, thus guiding the student in an appropriate difficulty. To evaluate the proposed method, we conduct extensive experiments on two image classification benchmarks (i.e. CIFAR100 and ImageNet) and an object detection benchmark (i.e. MS COCO. The results demonstrate the superiority of our method in performance under varying settings. Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers. More importantly, DPK provides a fast solution in teacher model selection for any given model.

cs.CV↗