Zobrazeno 1 - 10
of 1 550
pro vyhledávání: '"Wu Nian"'
Autor:
Wu, Nian, Zhang, Miaomiao
This paper presents a novel method, named geodesic deformable networks (GDN), that for the first time enables the learning of geodesic flows of deformation fields derived from images. In particular, the capability of our proposed GDN being able to pr
Externí odkaz:
http://arxiv.org/abs/2410.18797
Autor:
Wu, Nian, Aapro, Markus, Jestilä, Joakim S., Drost, Robert, Garcıa, Miguel Martınez, Torres, Tomas, Xiang, Feifei, Cao, Nan, He, Zhijie, Bottari, Giovanni, Liljeroth, Peter, Foster, Adam S.
Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, precise control requires extensiv
Externí odkaz:
http://arxiv.org/abs/2409.20014
This paper presents a novel approach, termed {\em Temporal Latent Residual Network (TLRN)}, to predict a sequence of deformation fields in time-series image registration. The challenge of registering time-series images often lies in the occurrence of
Externí odkaz:
http://arxiv.org/abs/2407.11219
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
Publikováno v:
愛知大学文學論叢. 157:30-42
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=jairo_______::cde968f13b40d4bbfe2941bd7be2a16b
http://id.nii.ac.jp/1082/00010056/
http://id.nii.ac.jp/1082/00010056/
