In this paper, we address the problem of face aging: generating past or
future facial images by incorporating age-related changes to the given face.
Previous aging methods rely solely on human facial image datasets and are thus
constrained by their inherent scale and bias. This restricts their application
to a limited generatable age range and the inability to handle large age gaps.
We propose FADING, a novel approach to address Face Aging via DIffusion-based
editiNG. We go beyond existing methods by leveraging the rich prior of
large-scale language-image diffusion models. First, we specialize a pre-trained
diffusion model for the task of face age editing by using an age-aware
fine-tuning scheme. Next, we invert the input image to latent noise and obtain
optimized null text embeddings. Finally, we perform text-guided local age
editing via attention control. The quantitative and qualitative analyses
demonstrate that our method outperforms existing approaches with respect to
aging accuracy, attribute preservation, and aging quality.

本文提出了一种名为 FADING 的基于扩散编辑的面部老化方法，通过利用大规模语言 - 图像扩散模型的丰富先验信息，通过特定的预训练扩散模型和年龄感知微调策略，将输入图像逆向编码到潜在噪声并获得优化的空文本嵌入，最后通过注意力控制进行基于文本的局部年龄编辑，定量和定性分析表明，该方法在老化准确性、属性保护和老化质量方面优于现有方法。

基于扩散编辑的人脸老化

Face Aging via Diffusion-based Editing

The handover process is one of the most critical functions in a cellular
network, and is in charge of maintaining seamless connectivity of user
equipments (UEs) across multiple cells. It is usually based on signal
measurements from the neighboring base stations (BSs), and it is adversely
affected by the time and frequency selectivity of the radio propagation
channel. In this paper, we introduce a new model for analyzing handover
performance in heterogeneous networks (HetNets) as a function of vehicular user
velocity, cell size, and mobility management parameters. In order to
investigate the impact of shadowing and fading on handover performance, we
extract relevant statistics obtained from a 3rd Generation Partnership Project
(3GPP)-compliant HetNet simulator, and subsequently, we integrate these
statistics into our analytical model to analyze handover failure probability
under fluctuating channel conditions. Computer simulations validate the
analytical findings, which show that fading can significantly degrade the
handover performance in HetNets with vehicular users.

本文介绍了一个新的模型，分析了异构网络中的切换性能。本文的计算机模拟验证了分析结果，表明淡化对具有车载用户的 HetNet 中的切换性能具有显著的恶化影响。