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The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging de...
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing...
Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale distributed stochastic ...
The class of strongly quasiconvex functions was introduced in the famous paper of B.T. Polyak in 1966. It is the natural extension of the class of strongly convex functions, its applications emcompass...
Parameter estimation or filtering is one of the important issues in diverse fields including statistical learning, signal processing, system identification and adaptive control. With the development o...
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、...
High-dimensional sampling problems are ubiquitous in statistics, operations research, machine learning, etc. In this talk, I will introduce quantum algorithms for two important problems in high-dimens...
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,...
Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal grad...
Modern neural networks are usually over-parameterized—the number of parameters exceeds the number of training data. In this case the loss functions tend to have many (or even infinite) global minima, ...
System optimization and control have become the key design philosophies in modern control system theories, where reinforcement learning (RL) algorithm has drawn considerable attention in recent litera...
Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization...
Nonlinear partial differential equations (PDEs) are crucial to modelling important problems in science but they are computationally expensive and suffer from the curse of dimensionality. Since quantum...
Algorithms developed in Cornell’s Laboratory for Intelligent Systems and Controls can predict the in-game actions of volleyball players with more than 80% accuracy, and now the lab is collaborating wi...

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