Zobrazeno 1 - 10
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pro vyhledávání: '"Wu, Nian"'
Motion and deformation analysis of cardiac magnetic resonance (CMR) imaging videos is crucial for assessing myocardial strain of patients with abnormal heart functions. Recent advances in deep learning-based image registration algorithms have shown p
Externí odkaz:
http://arxiv.org/abs/2407.02229
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning n
Externí odkaz:
http://arxiv.org/abs/2402.18507
Here, we present a study combining Bayesian optimisation structural inference with the machine learning interatomic potential NequIP to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules $\bet
Externí odkaz:
http://arxiv.org/abs/2311.16750
The block Kaczmarz method and its variants are designed for solving the over-determined linear system. They involve iteratively projecting the current point onto the solution space of a subset of constraints. In this work, by alternately dealing with
Externí odkaz:
http://arxiv.org/abs/2311.00199
Autor:
Wu, Nian, Zhang, Miaomiao
This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation spaces generated by a well known Euler-Poincar\'e differential equation (EPDiff). To achieve this, we develop a neural operator that for the first time learn
Externí odkaz:
http://arxiv.org/abs/2303.07115
With the growth of data, it is more important than ever to develop an efficient and robust method for solving the consistent matrix equation AXB=C. The randomized Kaczmarz (RK) method has received a lot of attention because of its computational effic
Externí odkaz:
http://arxiv.org/abs/2301.12753
Autor:
Wu, Nian-Ci, Liu, Chengzhi
Publikováno v:
Applied Mathematics and Computation, 2024
For large-scale data fitting, the least-squares progressive iterative approximation is a widely used method in many applied domains because of its intuitive geometric meaning and efficiency. In this work, we present a randomized progressive iterative
Externí odkaz:
http://arxiv.org/abs/2212.06398
Publikováno v:
Numerical Linear Algebra with Applications, 2024
For solving a consistent system of linear equations, the classical row-action (also known as Kaczmarz) method is a simple while really effective iteration solver. Based on the greedy index selection strategy and Polyak's heavy-ball momentum accelerat
Externí odkaz:
http://arxiv.org/abs/2212.06358
Autor:
Wu, Nian-Ci, Liu, Cheng-Zhi
Publikováno v:
Computer Aided Geometric Design, 2024
For large-scale data fitting, the least-squares progressive-iterative approximation (LSPIA) methods were proposed by Lin et al. (SIAM Journal on Scientific Computing, 2013, 35(6):A3052-A3068) and Deng et al. (Computer-Aided Design, 2014, 47:32-44), w
Externí odkaz:
http://arxiv.org/abs/2211.06556
The randomized row method is a popular representative of the iterative algorithm because of its efficiency in solving the overdetermined and consistent systems of linear equations. In this paper, we present an extended randomized multiple row method
Externí odkaz:
http://arxiv.org/abs/2210.03478