Zobrazeno 1 - 10
of 23 074
pro vyhledávání: '"CHEN, Xiang"'
Autor:
Chen, Xiang
Turbulence transport poses a significant challenge in fusion research. The measurement of turbulent fluctuations is critical for comprehending turbulence transport, predicting its behavior, and ultimately controlling it to maximize fusion gain. Howev
Autor:
Owens, Deonna M., Rossi, Ryan A., Kim, Sungchul, Yu, Tong, Dernoncourt, Franck, Chen, Xiang, Zhang, Ruiyi, Gu, Jiuxiang, Deilamsalehy, Hanieh, Lipka, Nedim
Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite significant advancements in bias mitigation techniques using data augme
Externí odkaz:
http://arxiv.org/abs/2409.13884
In the field of photocatalytic water splitting, no current studies have explicitly investigated the coexistence of multiple band-edge alignments in two-dimensional (2D) materials with intrinsic electric fields. In this Letter, we designed the ternary
Externí odkaz:
http://arxiv.org/abs/2409.09625
Autor:
Zhang, Jintian, Peng, Cheng, Sun, Mengshu, Chen, Xiang, Liang, Lei, Zhang, Zhiqiang, Zhou, Jun, Chen, Huajun, Zhang, Ningyu
Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in directly handling retrieval tasks. However, many practical applicatio
Externí odkaz:
http://arxiv.org/abs/2409.05152
Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the target area to critical anatomical structures during surgery. However, d
Externí odkaz:
http://arxiv.org/abs/2409.05040
Autor:
Zhang, Yuxi, Chen, Xiang, Wang, Jiazheng, Liu, Min, Wang, Yaonan, Liu, Dongdong, Hu, Renjiu, Zhang, Hang
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge. This task focuses on unsupervised registration of anatomical structures in brain MRI images between different patients. The diffi
Externí odkaz:
http://arxiv.org/abs/2409.00917
Autor:
Lyu, Hanjia, Rossi, Ryan, Chen, Xiang, Tanjim, Md Mehrab, Petrangeli, Stefano, Sarkhel, Somdeb, Luo, Jiebo
Large Language Models (LLMs) and Large Multimodal Models (LMMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on te
Externí odkaz:
http://arxiv.org/abs/2408.15172
Reconstruction under adverse rainy conditions poses significant challenges due to reduced visibility and the distortion of visual perception. These conditions can severely impair the quality of geometric maps, which is essential for applications rang
Externí odkaz:
http://arxiv.org/abs/2408.11540
Autor:
Geng, Yuxia, Zhu, Runkai, Chen, Jiaoyan, Chen, Jintai, Chen, Zhuo, Chen, Xiang, Xu, Can, Wang, Yuxiang, Xu, Xiaoliang
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with differe
Externí odkaz:
http://arxiv.org/abs/2408.09786
Modern Artificial Intelligence (AI) applications are increasingly utilizing multi-tenant deep neural networks (DNNs), which lead to a significant rise in computing complexity and the need for computing parallelism. ReRAM-based processing-in-memory (P
Externí odkaz:
http://arxiv.org/abs/2408.04812