Zobrazeno 1 - 10
of 7 571
pro vyhledávání: '"WANG, Can"'
Autor:
Rong, Dongke, Chen, Xiuqi, Chen, Shengru, Zhang, Jingfeng, Xu, Yue, Shang, Yanxing, Hong, Haitao, Cui, Ting, Wang, Qianying, Ge, Chen, Wang, Can, Zheng, Qiang, Zhang, Qinghua, Wang, Lingfei, Deng, Yu, Jin, Kuijuan, Liu, Gang-Qin, Guo, Er-Jia
A wealth of remarkable behaviors is observed at the interfaces between magnetic oxides due to the coexistence of Coulomb repulsion and interatomic exchange interactions. While previous research has focused on bonded oxide heterointerfaces, studies on
Externí odkaz:
http://arxiv.org/abs/2412.03007
Autor:
Yang, Weiqin, Chen, Jiawei, Xin, Xin, Zhou, Sheng, Hu, Binbin, Feng, Yan, Chen, Chun, Wang, Can
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metr
Externí odkaz:
http://arxiv.org/abs/2411.00163
Autor:
Choi, Songhee, Jin, Qiao, Zi, Xian, Rong, Dongke, Fang, Jie, Zhang, Jinfeng, Zhang, Qinghua, Li, Wei, Xu, Shuai, Chen, Shengru, Hong, Haitao, Ting, Cui, Wang, Qianying, Tang, Gang, Ge, Chen, Wang, Can, Chen, Zhiguo, Gu, Lin, Li, Qian, Wang, Lingfei, Wang, Shanmin, Hong, Jiawang, Jin, Kuijuan, Guo, Er-Jia
The integration of ferroelectrics with semiconductors is crucial for developing functional devices, such as field-effect transistors, tunnel junctions, and nonvolatile memories. However, the synthesis of high-quality single-crystalline ferroelectric
Externí odkaz:
http://arxiv.org/abs/2410.16987
Large Language Model (LLM) services exhibit impressive capability on unlearned tasks leveraging only a few examples by in-context learning (ICL). However, the success of ICL varies depending on the task and context, leading to heterogeneous service q
Externí odkaz:
http://arxiv.org/abs/2410.07737
Autor:
Wang, Zhe, Zhao, Tianjian, Zhang, Zhen, Chen, Jiawei, Zhou, Sheng, Feng, Yan, Chen, Chun, Wang, Can
Dynamic Graph Neural Networks (DyGNNs) have garnered increasing research attention for learning representations on evolving graphs. Despite their effectiveness, the limited expressive power of existing DyGNNs hinders them from capturing important evo
Externí odkaz:
http://arxiv.org/abs/2410.01367
Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient and high-quality synthesis, various distillation-based accelerated sam
Externí odkaz:
http://arxiv.org/abs/2409.19681
Autor:
Zhan, Zheyuan, Chen, Defang, Mei, Jian-Ping, Zhao, Zhenghe, Chen, Jiawei, Chen, Chun, Lyu, Siwei, Wang, Can
Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to
Externí odkaz:
http://arxiv.org/abs/2409.19365
Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To add
Externí odkaz:
http://arxiv.org/abs/2409.16938
Autor:
Chen, Wang, Hu, Mengli, Zong, Junyu, Xie, Xuedong, Ren, Wei, Meng, Qinghao, Yu, Fan, Tian, Qichao, Jin, Shaoen, Qiu, Xiaodong, Wang, Kaili, Wang, Can, Liu, Junwei, Li, Fang-Sen, Wang, Li, Zhang, Yi
The transition metal dichalcogenides (TMDCs) with a 1T' structural phase are predicted to be two-dimensional topological insulators at zero temperature. Although the quantized edge conductance of 1T'-WTe$_2$ has been confirmed to survive up to 100 K,
Externí odkaz:
http://arxiv.org/abs/2409.09698
Autor:
Lu, Zekun, Chen, Feng, Guo, J. H., Ding, M. D., Wang, Can, Yu, Haocheng, Ni, Y. W., Xia, Chun
The periodic coronal rain and in-phase radiative intensity pulsations have been observed in multiple wavelengths in recent years. However, due to the lack of three-dimensional coronal magnetic fields and thermodynamic data in observations, it remains
Externí odkaz:
http://arxiv.org/abs/2408.16988