Bid shading plays a crucial role in Real-Time Bidding (RTB) by adaptively adjusting the bid to avoid advertisers overspending. Existing mainstream two-stage methods, which first model bid landscapes and then optimize surplus using operations research techniques, are constrained by unimodal assumptions that fail to adapt for non-convex surplus curves and are vulnerable to cascading errors in sequential workflows. Additionally, existing discretization models of continuous values ignore the dependence between discrete intervals, reducing the model's error correction ability, while sample selection bias in bidding scenarios presents further challenges for prediction. To address these issues, this paper introduces Generative Bid Shading (GBS), which comprises two primary components: 1) an end-to-end generative model that utilizes an autoregressive approach to generate shading ratios by stepwise residuals, capturing complex value dependencies without relying on predefined priors; and 2) a reward preference alignment system, which incorporates a channel-aware hierarchical dynamic network (CHNet) as the reward model to extract fine-grained features, along with modules for surplus optimization and exploration utility reward alignment, ultimately optimizing both short-term and long-term surplus using group relative policy optimization (GRPO). Extensive experiments on both offline and online A/B tests validate GBS's effectiveness. Moreover, GBS has been deployed on the Meituan DSP platform, serving billions of bid requests daily.

A novel classification method for polarimetric synthetic aperture radar (PolSAR) images using support vector machine (SVM) with self-paced learning (SPL) optimization is proposed in the paper. In our method, Cloude-Pottier polarimetric decomposition components and the eigenvalues of coherency matrix are used as features. Classification is carried out using SVM, and SPL is used to improve the classifier and achieve a stronger generalization capacity. Under SPL paradigm, the classifier learns the easier samples first and gradually involves more difficult samples into the training process. The proposed method achieves the overall classification accuracies of 89.79% on the Flevoland dataset. Such results are comparable with the compared algorithms.

In recent years, both passive and active (i.e., Synthetic Aperture Radar or SAR) satellite remote sensing has proven to be valuable tools for mapping land cover. Most of the classification algorithms are based on image-intensity and they do not perform well in different coastal zone types, because these terrains have similar optical or radar backscattering signals. In this paper we propose a weighted joint sparse on the three-channel to mine the polarimetric features and texture information. The proposed method can update weights automatically according to the difference of the three channels' contribution and the least residual error. The similarity and distinctiveness of three channels are used to deal with the complex object classification. Hybrid sparse coefficients are input to the support vector machine for fully polarimetric image classification. The proposed algorithm performed well in distinguishing some coastal land-use types. A comparison study is also conducted to show that proposed algorithm outperforms two commonly classification methods.

Polarimetric synthetic aperture radar (PolSAR) is of great importance in the remote sensing, which can be used widely in both civil and military fields. However, existing classification methods cannot effectively utilize the spatial structure information of the SAR data. In this study, a classification method for PolSAR images based on non-negative tensor factorization (NTF) is proposed. The proposed method uses tensor to represent the original data and extract the spacial structure feature by using NTF. Classification results are obtained by using support vector machines (SVM) and the Wishart clustering technology. The results show the validity and accuracy of the proposed method on PolSAR images classification.