In recent years, auction theory has been extensively studied and many state-of-the-art solutions have been proposed aiming at allocating scarce resources. However, most of these studies assume that the auctioneer is a...In recent years, auction theory has been extensively studied and many state-of-the-art solutions have been proposed aiming at allocating scarce resources. However, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. Besides the privacy-preserving issue, the performance guarantee of social efficiency maximization is also crucial for auction mechanism design. In this paper, we study the auction mechanisms that consider the above two aspects. We discuss two multi-unit auction models： the identical multiple-items auction and the distinct multiple-items auction.Since the problem of determining a multi-unit auction mechanism that can maximize its social efficiency is NPhard, we design a series of nearly optimal multi-unit auction mechanisms for the proposed models. We prove that the proposed auction mechanisms are strategyproof. Moreover, we also prove that the privacy of bid value from each bidder can be preserved in the auction mechanisms. To the best of our knowledge, this is the first work on the strategyproof multi-unit auction mechanisms that simultaneously consider privacy preservation and social efficiency maximization. The extensive simulations show that the proposed mechanisms have low computation and communication overheads.展开更多
Nuclear magnetic resonance(NMR)spectroscopy is a key method for molecular structure elucidation.However,interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral dat...Nuclear magnetic resonance(NMR)spectroscopy is a key method for molecular structure elucidation.However,interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space.Here we introduce DiffNMR,a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra.DiffNMR refines molecular graphs iteratively through a diffusion-based generative process,ensuring global consistency and mitigating error accumulation inherent in autoregressive methods.The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via a diffusion autoencoder and contrastive learning.It also incorporates retrieval initialization and similarity filtering during inference.Our experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation,especially outperforming autoregressive models in domain generalization and robustness,thereby offering an efficient and robust solution for automated molecular analysis.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61572342 and 61672369)the Natural Science Foundation of Jiangsu Province (Nos. BK20151240 and BK20161258)China Postdoctoral Science Foundation (Nos. 2015M580470 and 2016M591920)
文摘In recent years, auction theory has been extensively studied and many state-of-the-art solutions have been proposed aiming at allocating scarce resources. However, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. Besides the privacy-preserving issue, the performance guarantee of social efficiency maximization is also crucial for auction mechanism design. In this paper, we study the auction mechanisms that consider the above two aspects. We discuss two multi-unit auction models： the identical multiple-items auction and the distinct multiple-items auction.Since the problem of determining a multi-unit auction mechanism that can maximize its social efficiency is NPhard, we design a series of nearly optimal multi-unit auction mechanisms for the proposed models. We prove that the proposed auction mechanisms are strategyproof. Moreover, we also prove that the privacy of bid value from each bidder can be preserved in the auction mechanisms. To the best of our knowledge, this is the first work on the strategyproof multi-unit auction mechanisms that simultaneously consider privacy preservation and social efficiency maximization. The extensive simulations show that the proposed mechanisms have low computation and communication overheads.
基金supported by the National Science and Technology Major Project(Grants No.2023ZD0120702)Basic Research Program of Jiangsu(BK20231215)+1 种基金National Natural Science Foundation of China(Grant No.82401075)Natural Science Foundation of Jiangsu Province Major Project(BK20232012).
文摘Nuclear magnetic resonance(NMR)spectroscopy is a key method for molecular structure elucidation.However,interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space.Here we introduce DiffNMR,a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra.DiffNMR refines molecular graphs iteratively through a diffusion-based generative process,ensuring global consistency and mitigating error accumulation inherent in autoregressive methods.The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via a diffusion autoencoder and contrastive learning.It also incorporates retrieval initialization and similarity filtering during inference.Our experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation,especially outperforming autoregressive models in domain generalization and robustness,thereby offering an efficient and robust solution for automated molecular analysis.
