Zobrazeno 1 - 10
of 207 954
pro vyhledávání: '"An, Lili"'
Autor:
Wang, Zilong, Chen, Nan, Qiu, Luna K., Yue, Ling, Guo, Geli, Ou, Yang, Jiang, Shiqi, Yang, Yuqing, Qiu, Lili
In recent years, the rapid aging of the global population has led to an increase in cognitive disorders, such as Alzheimer's disease, presenting significant public health challenges. Although no effective treatments currently exist to reverse Alzheim
Externí odkaz:
http://arxiv.org/abs/2410.19733
Recently, the LHAASO Collaboration presented the first very-high-energy gamma-ray catalog, containing 90 TeV sources. Among these sources, 1LHAASO J1929 +1846u* is located 0.3$^\circ$ west of SNR G54.1 +0.3 and it also lies inside a $+53 \, \text{km
Externí odkaz:
http://arxiv.org/abs/2410.19543
Autor:
Zhang, Hengxiang, Gao, Hongfu, Hu, Qiang, Chen, Guanhua, Yang, Lili, Jing, Bingyi, Wei, Hongxin, Wang, Bing, Bai, Haifeng, Yang, Lei
With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous works have introduced several benchmarks to evaluate the safety risk of
Externí odkaz:
http://arxiv.org/abs/2410.18491
Quantum techniques are expected to revolutionize how information is acquired, exchanged, and processed. Yet it has been a challenge to realize and measure their values in practical settings. We present first photon machine learning as a new paradigm
Externí odkaz:
http://arxiv.org/abs/2410.17471
Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-context tasks. Recently, researchers introduced Mamba, an advanced model
Externí odkaz:
http://arxiv.org/abs/2410.15678
Autor:
Hu, Xiang, Fu, Hongyu, Wang, Jinge, Wang, Yifeng, Li, Zhikun, Xu, Renjun, Lu, Yu, Jin, Yaochu, Pan, Lili, Lan, Zhenzhong
Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability i
Externí odkaz:
http://arxiv.org/abs/2410.14255
Large Language Models (LLMs) have shown remarkable capabilities in various natural language processing tasks. However, LLMs may rely on dataset biases as shortcuts for prediction, which can significantly impair their robustness and generalization cap
Externí odkaz:
http://arxiv.org/abs/2410.13343
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance mi
Externí odkaz:
http://arxiv.org/abs/2410.13914
Autor:
Xu, Linfeng, Meng, Fanman, Wu, Qingbo, Pan, Lili, Qiu, Heqian, Wang, Lanxiao, Chen, Kailong, Geng, Kanglei, Qian, Yilei, Wang, Haojie, Zhou, Shuchang, Ling, Shimou, Liu, Zejia, Chen, Nanlin, Xu, Yingjie, Cheng, Shaoxu, Tan, Bowen, Xu, Ziyong, Li, Hongliang
The application of activity recognition in the ``AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manually captured videos and a limited number of activity types, with lit
Externí odkaz:
http://arxiv.org/abs/2410.12337
Science is increasingly global, with international collaboration playing a crucial role in advancing scientific development and knowledge exchange across borders. However, the processes that regulate how scientific labor is distributed among countrie
Externí odkaz:
http://arxiv.org/abs/2410.13020