Zobrazeno 1 - 10
of 8 856
pro vyhledávání: '"Wang,Shanshan"'
Autor:
Luo, Zheng, Feng, Ming, Gao, Zijian, Yu, Jinyang, Hu, Liang, Wang, Tao, Xue, Shenao, Zhou, Shen, Ouyang, Fangping, Feng, Dawei, Xu, Kele, Wang, Shanshan
The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when proce
Externí odkaz:
http://arxiv.org/abs/2410.17631
Autor:
Galib, Shaikat, Wang, Shanshan, Xu, Guanshuo, Pfeiffer, Pascal, Chesler, Ryan, Landry, Mark, Ambati, Sri Satish
Smaller vision-language models (VLMs) are becoming increasingly important for privacy-focused, on-device applications due to their ability to run efficiently on consumer hardware for processing enterprise commercial documents and images. These models
Externí odkaz:
http://arxiv.org/abs/2410.13611
The Knowledge Graph-to-Text Generation task aims to convert structured knowledge graphs into coherent and human-readable natural language text. Recent efforts in this field have focused on enhancing pre-trained language models (PLMs) by incorporating
Externí odkaz:
http://arxiv.org/abs/2409.10294
Domain generalization (DG) task aims to learn a robust model from source domains that could handle the out-of-distribution (OOD) issue. In order to improve the generalization ability of the model in unseen domains, increasing the diversity of trainin
Externí odkaz:
http://arxiv.org/abs/2409.04699
Autor:
Huang, Wenqiang, Jin, Yuchen, Li, Zhemin, Yao, Lin, Chen, Yun, Luo, Zheng, Zhou, Shen, Lin, Jinguo, Liu, Feng, Gao, Zhifeng, Cheng, Jun, Zhang, Linfeng, Ouyang, Fangping, Zhang, Jin, Wang, Shanshan
Unveiling atomic structures is significant for the relationship construction between microscopic configurations and macroscopic properties of materials. However, we still lack a rapid, accurate, and robust approach to automatically resolve complex pa
Externí odkaz:
http://arxiv.org/abs/2408.16802
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and eddy current, leading to detail loss in reconstructing the DTI-derived parametric maps especia
Externí odkaz:
http://arxiv.org/abs/2408.10236
Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Curren
Externí odkaz:
http://arxiv.org/abs/2408.01944
Publikováno v:
J. Phys. Complex. 5 045003 (2024)
The causal connection between congestions and velocity changes at different locations induces various statistical features, which we identify and measure in detail. We carry out an empirical analysis of large-scale traffic data on a local motorway ne
Externí odkaz:
http://arxiv.org/abs/2406.17724
Autor:
Liu, Mianxin, Ding, Jinru, Xu, Jie, Hu, Weiguo, Li, Xiaoyang, Zhu, Lifeng, Bai, Zhian, Shi, Xiaoming, Wang, Benyou, Song, Haitao, Liu, Pengfei, Zhang, Xiaofan, Wang, Shanshan, Li, Kang, Wang, Haofen, Ruan, Tong, Huang, Xuanjing, Sun, Xin, Zhang, Shaoting
Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese cont
Externí odkaz:
http://arxiv.org/abs/2407.10990
Autor:
Lángi, Zsolt, Wang, Shanshan
The Honeycomb Conjecture states that among tilings with unit area cells in the Euclidean plane, the average perimeter of a cell is minimal for a regular hexagonal tiling. This conjecture was proved by L. Fejes T\'oth for convex tilings, and by Hales
Externí odkaz:
http://arxiv.org/abs/2406.10622