Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Dong, Zehao"'
Autor:
Dong, Zehao, Zhang, Yang, Chiu, Chun-Chien, Lu, Sicheng, Zhang, Jianbing, Liu, Yu-Chen, Liu, Suya, Yang, Jan-Chi, Yu, Pu, Wang, Yayu, Chen, Zhen
Real-space imaging of three-dimensional atomic structures is a critical yet challenging task in materials science. Although scanning transmission electron microscopy has achieved sub-angstrom lateral resolution through techniques like electron ptycho
Externí odkaz:
http://arxiv.org/abs/2406.04252
Autor:
Lian, Zichen, Wang, Yongchao, Wang, Yongqian, Feng, Yang, Dong, Zehao, Yang, Shuai, Xu, Liangcai, Li, Yaoxin, Fu, Bohan, Li, Yuetan, Jiang, Wanjun, Liu, Chang, Zhang, Jinsong, Wang, Yayu
The interplay between nontrivial band topology and layered antiferromagnetism in MnBi2Te4 has opened up a new avenue for exploring topological phases of matter. Representative examples include the quantum anomalous Hall effect and axion insulator sta
Externí odkaz:
http://arxiv.org/abs/2405.08686
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation learning through neighborhood aggregation and achieve impressive results
Externí odkaz:
http://arxiv.org/abs/2404.13655
Autor:
Dong, Zehao, Zhao, Qihang, Payne, Philip R. O., Province, Michael A, Cruchaga, Carlos, Zhang, Muhan, Zhao, Tianyu, Chen, Yixin, Li, Fuhai
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model
Externí odkaz:
http://arxiv.org/abs/2402.07268
Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy
Autor:
Dong, Zehao, Chen, Yixin, Gay, Hiram, Hao, Yao, Hugo, Geoffrey D., Samson, Pamela, Zhao, Tianyu
Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radio
Externí odkaz:
http://arxiv.org/abs/2402.07167
Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy that facilitate treatment planning, dose evaluation, plan comparison and etc. It helps to increase the ability to deliver precise and effective radiation treatments while mana
Externí odkaz:
http://arxiv.org/abs/2402.01076
Autor:
Dong, Zehao, Huo, Mengwu, Li, Jie, Li, Jingyuan, Li, Pengcheng, Sun, Hualei, Lu, Yi, Wang, Meng, Wang, Yayu, Chen, Zhen
The recent discovery of superconductivity in La3Ni2O7-{\delta} under high pressure with a transition temperature around 80 K has sparked extensive experimental and theoretical efforts. Several key questions regarding the pairing mechanism remain to b
Externí odkaz:
http://arxiv.org/abs/2312.15727
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained parallelism, low-power consu
Externí odkaz:
http://arxiv.org/abs/2309.16022
Autor:
Ye, Shusen, Zhao, Jianfa, Yao, Zhiheng, Chen, Sixuan, Dong, Zehao, Li, Xintong, Shi, Luchuan, Liu, Qingqing, Jin, Changqing, Wang, Yayu
The parent compound of cuprates is a charge-transfer-type Mott insulator with strong hybridization between the Cu $3d_{\mathrm x^2-y^2}$ and O $2p$ orbitals. A key question concerning the pairing mechanism is the behavior of doped holes in the antife
Externí odkaz:
http://arxiv.org/abs/2309.09260
Autor:
Dong, Zehao, Zhang, Muhan, Payne, Philip R. O., Province, Michael A, Cruchaga, Carlos, Zhao, Tianyu, Li, Fuhai, Chen, Yixin
The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopt
Externí odkaz:
http://arxiv.org/abs/2309.00738