Zobrazeno 1 - 10
of 2 724
pro vyhledávání: '"Zhang CE"'
Publikováno v:
Translational Neuroscience, Vol 15, Iss 1, Pp 2-10 (2024)
Calmodulin-dependent protein kinases (CaMKs) are widely regarded as “memory molecules” due to their role in controlling numerous neuronal functions in the brain, and the CaMK signaling pathway plays a crucial role in controlling synaptic plastici
Externí odkaz:
https://doaj.org/article/f30335edea0f4a19bac91df403d18464
Autor:
Kan, Zhehan, Zhang, Ce, Liao, Zihan, Tian, Yapeng, Yang, Wenming, Xiao, Junyuan, Li, Xu, Jiang, Dongmei, Wang, Yaowei, Liao, Qingmin
Large Vision-Language Model (LVLM) systems have demonstrated impressive vision-language reasoning capabilities but suffer from pervasive and severe hallucination issues, posing significant risks in critical domains such as healthcare and autonomous s
Externí odkaz:
http://arxiv.org/abs/2411.12713
Autor:
Weber, Maurice, Fu, Daniel, Anthony, Quentin, Oren, Yonatan, Adams, Shane, Alexandrov, Anton, Lyu, Xiaozhong, Nguyen, Huu, Yao, Xiaozhe, Adams, Virginia, Athiwaratkun, Ben, Chalamala, Rahul, Chen, Kezhen, Ryabinin, Max, Dao, Tri, Liang, Percy, Ré, Christopher, Rish, Irina, Zhang, Ce
Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-perfor
Externí odkaz:
http://arxiv.org/abs/2411.12372
Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLM
Externí odkaz:
http://arxiv.org/abs/2410.12790
Autor:
Lou Lei, Wang Lianjie, Zhou Bingyan, Zhao Chen, Zhang Bin, Yan Mingyu, Zhang Ce, Xiang Hongzhi, Cai Yun, Wang Xingbo, Zhao Zifan, Zhou Nan, Liu Jiayi
Publikováno v:
Frontiers in Energy Research, Vol 10 (2022)
The fast reactor is gradually being paid attention to in nuclear power due to a better breeding effect, and its economy is also directly followed. Due to the restriction of the core power density, the core power level is directly related to the volum
Externí odkaz:
https://doaj.org/article/5b63fec6adc5412da91298a3d1755234
Publikováno v:
Science and Engineering of Composite Materials, Vol 28, Iss 1, Pp 160-168 (2021)
A modified vertical braiding machine and closed annular axis mandrels with a special-shaped cross section were used to braid annular axis preforms under four different technical parameters. After measuring the braiding angles and yarn spacing of the
Externí odkaz:
https://doaj.org/article/17a469e512004f3d98b69dea82b488b8
Autor:
Veitch-Michaelis, Josh, Cottam, Andrew, Schweizer, Daniella, Broadbent, Eben N., Dao, David, Zhang, Ce, Zambrano, Angelica Almeyda, Max, Simeon
Accurately quantifying tree cover is an important metric for ecosystem monitoring and for assessing progress in restored sites. Recent works have shown that deep learning-based segmentation algorithms are capable of accurately mapping trees at countr
Externí odkaz:
http://arxiv.org/abs/2407.11743
Autor:
Alexandrov, Anton, Raychev, Veselin, Müller, Mark Niklas, Zhang, Ce, Vechev, Martin, Toutanova, Kristina
As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages. However, such language adaptation is often accompanied by c
Externí odkaz:
http://arxiv.org/abs/2407.08699
Autor:
Zhang, Ce, Eskandarian, Azim
LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR quality assessment methods, leading to uncertainty in detection performan
Externí odkaz:
http://arxiv.org/abs/2406.17265
Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open
Externí odkaz:
http://arxiv.org/abs/2406.04692