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Causal inference is a permanent challenge topic in statistics, data science, and many other applied fields. Existing machine learning methods often focus on the correlations in the data and ignore the...
It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust t...
贫困是罪恶之源,但现有文献尚未探讨当地贫困对域外犯罪的影响。基于1992-2012年中国省级面板数据,本文通过估算空间计量模型,考察贫困的异地犯罪效应。结果发现,贫困不仅会增加本地犯罪,而且会提升其它省份犯罪案件。后者的这种溢出效应在地理位置较近、劳动力流动性大、方言相似度高的省市之间更为明显。此外,本文还检验了助力减贫的低保政策和加强警力投入对降低贫困导致的本地和异地犯罪的影响,发现两者都可以降...
Nonlinear dynamics play a prominent role in many domains and are notoriously difficult to solve. Whereas previous quantum algorithms for general nonlinear equations have been severely limited due to t...
机组组合(UC)作为电力系统优化运行的基本问题之一,其模型的质量严重影响着求解器的求解效率,如何建立高质量的UC问题模型则成为近年研究的热点。为此,我们从大规模电力系统UC问题的模型、算法和应用方面展开研究,主要内容包括:针对单机组组合(1UC)问题,基于Facet、凸包以及动态规划相关理论,研究全新的1UC问题统一建模框架,给出1UC问题一系列适用于不同场景、不同算法的高质量模型;基于滑动窗口、...
We consider numerical approximations of the anisotropic phase-field dendritic crystal growth model. This is a highly complex coupled nonlinear system consisting of the anisotropic Allen-Cahn equation,...
Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples, based on learning the score function (gradient o...
We study multi-period inventory control systems in which managers face seasonal demands with unknown distributions and make inventory decisions based on past demand data. It can be shown that a data-d...
The Ricci flow is a powerful tool in geometry to construct the canonical metric on a given manifold. It can be viewed as a nonlinear heat flow of the Riemannian metric and may develop finite time sing...
In electronic structure calculations, Kohn-Sham equations rank among the most widely adopted mathematical models. However, due to the deficiency of available approximations for exchange-correlation en...
Hypothesis testing on high-dimensional fixed effects is indispensable for investigating the utility of the predictors on response. In this case, the conventional frequentist methods designed for cases...
The problem of using covariates to predict shapes of objects in a regression setting is important in many fields. A formal statistical approach, termed geodesic regression model, is commonly used for ...
In this talk, we will review the past developments on the solutions of the compressible Navier-Stokes equations and reveal the three hidden structures which linked the weak solution to the strong one....
In the 2nd part of the series talks, I will introduce the new phenomena of weak-kink and its interaction with regular peakons which we recently developed. Such models include cubic Camassa-Holm (CH) t...
In this talk, I will majorly focus on the scalar peakon models developed in the last 30 years. Most integrable peakon equations come from the negative order flow in the hierarchy. I will take some exa...

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