成果一.
我所Smith Scott A副研究员与其合作者的论文A priori bounds for quasi-linear SPDEs in the full subcritical regime被JOURNAL OF THE EUROPEAN MATHEMATICAL SOCIETY接收发表。
摘要：This paper is concerned with quasi-linear parabolic equations driven by an additive forcing E Ca-2, in the full subcritical regime a E (0, 1). We are inspired by Hairer's regularity structures, however we work with a more parsimonious model indexed by multi-indices rather than trees. This allows us to capture additional symmetries which play a crucial role in our analysis. Assuming bounds on this model, which is modified in agreement with the concept of algebraic renormalization, we prove local a priori estimates on solutions to the quasi-linear equations modified by the corresponding counter-terms.
论文链接： http://dx.doi.org/10.4171/JEMS/1574
成果二.
我所王彬副研究员与其合作者的论文A Theory of Credit Rating Criteria被MANAGEMENT SCIENCE接收发表。
摘要：We propose a theory for rating financial securities in the presence of structural maximization by the issuer in a market with investors who rely on credit rating. Two types of investors, simple investors who price tranches solely based on the ratings and modelbased investors who use the rating information to calibrate models, are considered. Concepts of self-consistency and information gap are proposed to study different rating criteria. In particular, the expected loss criterion used by Moody's satisfies self-consistency, but the probability of default criterion used by Standard & Poor's does not. Moreover, the probability of default criterion typically has a higher information gap than the expected loss criterion. Empirical evidence in the post-Dodd-Frank period is consistent with our theoretical implications. We show that a set of axioms based on self-consistency leads to a tractable representation for all self-consistent rating criteria, which can also be extended to incorporate economic scenarios. New examples of self-consistent and scenario-based rating criteria are suggested.
论文链接： http://dx.doi.org/10.1287/mnsc.2023.01075
成果三.
我所常晋源研究员与其合作者的论文Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling被JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY接收发表。
摘要：In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to the complex optimization typically required for statistical inference using EL. To achieve the first objective, we propose a penalized approach that regularizes the Lagrange multipliers, significantly reducing the dimensionality of the problem while accommodating a comprehensive set of model conditions. For the second objective, our study designs and thoroughly investigates two popular sampling schemes within the BPEL context. We demonstrate that the BPEL framework is highly flexible and efficient, enhancing the adaptability and practicality of EL methods. Our study highlights the practical advantages of using sampling techniques over traditional optimization methods for EL problems, showing rapid convergence to the global optima of posterior distributions and ensuring the effective resolution of complex statistical inference challenges.
论文链接： http://dx.doi.org/10.1093/jrsssb/qkaf009
成果四.
我所张世华研究员与其合作者的论文SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics被PLOS COMPUTATIONAL BIOLOGY接收发表。
摘要：Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.
论文链接： http://dx.doi.org/10.1371/journal.pcbi.1012881
成果五.
我所王勇研究员（方程）与其合作者的论文GLOBAL SOLUTIONS OF THE COMPRESSIBLE EULER--POISSON EQUATIONS FOR PLASMA WITH DOPING PROFILE FOR LARGE INITIAL DATA OF SPHERICAL SYMMETRY被SIAM JOURNAL ON MATHEMATICAL ANALYSIS接收发表。
摘要：We establish the global-in-time existence of solutions of finite relative-energy for the multidimensional compressible Euler--Poisson equations for plasma with doping profile for large initial data of spherical symmetry. Both the total initial energy and the initial mass are allowed to be unbounded, and the doping profile is allowed to be of large variation. This is achieved by adapting a class of degenerate density-dependent viscosity terms, so that a rigorous proof of the inviscid limit of global weak solutions of the Navier--Stokes--Poisson equations with the density-dependent viscosity terms to the corresponding global solutions of the Euler-Poisson equations for plasma with doping profile can be established. New difficulties arise when tackling the nonzero varied doping profile, which have been overcome by establishing some novel estimates for the electric field terms so that the neutrality assumption on the initial data is avoided. In particular, we prove that no concentration is formed in the inviscid limit for the finite relative-energy solutions of the compressible Euler--Poisson equations with large doping profiles in plasma physics.
论文链接： http://dx.doi.org/10.1137/23M1605806
成果六.
我所王勇研究员（运筹）、李雷研究员与其合作者的论文A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data被NATIONAL SCIENCE REVIEW接收发表。
摘要：Continuous glucose monitoring (CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states (MAE = 3.7 mg/dL). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task (AUROC = 0.914 for type 2 diabetes (T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics, CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling (Pearson correlation coefficient = 0.763) and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the early warning of T2D and recommendations for lifestyle modification in diabetes management. This article presents a pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data for diabetes screening, subtyping, risk stratification, and personalized dietary recommendations.
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