There is a strong demand for multi-attribute auctions in real-world scenarios for non-price attributes that allow participants to express their preferences and the item’s value. However, this also makes it difficult to perform calculations with incomplete information, as a single attribute—price—no longer determines the revenue. At the same time, the mechanism must satisfy individual rationality (IR) and incentive compatibility (IC). This paper proposes an innovative dual network to solve these problems. A shared MLP module is constructed to extract bidder features, and multiple-scale loss is used to determine network status and update. The method was tested on real and extended cases, showing that the approach effectively improves the auctioneer’s revenue without compromising the bidder.
Clear underwater images are necessary in many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. In order to overcome the limitations of the available color correction and deblurring algorithms, this paper proposed a fusion-based image enhancement method for various water areas. We proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval. Subsequently, using these two methods, we took two images from a single underwater image as inputs of the fusion framework. Finally, we obtained a satisfactory underwater image. To validate the effectiveness of the experiment, we tested our method using public datasets. The results showed that the proposed method can adaptively correct color casts and significantly enhance the details and quality of attenuated underwater images.
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