Zobrazeno 1 - 10
of 1 404
pro vyhledávání: '"HUANG, YINING"'
Autor:
Zhang, Zuobai, Notin, Pascal, Huang, Yining, Lozano, Aurélie, Chenthamarakshan, Vijil, Marks, Debora, Das, Payel, Tang, Jian
Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models
Externí odkaz:
http://arxiv.org/abs/2412.01108
Autor:
Xu, Zhihao, Gong, Shengjie, Tang, Jiapeng, Liang, Lingyu, Huang, Yining, Li, Haojie, Huang, Shuangping
We present a novel approach for synthesizing 3D facial motions from audio sequences using key motion embeddings. Despite recent advancements in data-driven techniques, accurately mapping between audio signals and 3D facial meshes remains challenging.
Externí odkaz:
http://arxiv.org/abs/2409.01113
Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, beyond generating new random molecules, efficient exploration and a compreh
Externí odkaz:
http://arxiv.org/abs/2405.03987
High Spectral-Efficiency, Ultra-low MIMO SDM Transmission over a Field-Deployed Multi-Core OAM Fiber
Autor:
Liu, Junyi, Xu, Zengquan, Mo, Shuqi, Huang, Yuming, Huang, Yining, Li, Zhenhua, Guo, Yuying, Shen, Lei, Xu, Shuo, Gao, Ran, Du, Cheng, Feng, Qian, Luo, Jie, Liu, Jie, Yu, Siyuan
Few-mode multi-core fiber (FM-MCF) based Space-Division Multiplexing (SDM) systems possess the potential to maximize the number of multiplexed spatial channels per fiber by harnessing both the space (fiber cores) and mode (optical mode per core) dime
Externí odkaz:
http://arxiv.org/abs/2407.01552
Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These m
Externí odkaz:
http://arxiv.org/abs/2404.15777
Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural Language Processing (NLP), showing potential in traditional tasks such as Named Entity Recognition (NER). Our study explores a three-phase training strategy that harnesses G
Externí odkaz:
http://arxiv.org/abs/2402.09282
Autor:
Zhang, Zaixi, Yan, Jiaxian, Huang, Yining, Liu, Qi, Chen, Enhong, Wang, Mengdi, Zitnik, Marinka
Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical modeling and domain expertise, are often resource-intensive. Recent advan
Externí odkaz:
http://arxiv.org/abs/2306.11768
Autor:
Du, Yuanqi, Wang, Yingheng, Huang, Yining, Li, Jianan Canal, Zhu, Yanqiao, Xie, Tian, Duan, Chenru, Gregoire, John M., Gomes, Carla P.
We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machin
Externí odkaz:
http://arxiv.org/abs/2307.05378
Autor:
Wu, Keyi, Fan, Weidong, Wei, Jing, Lu, Jianyun, Ma, Xiaowei, Yuan, Zelin, Huang, Zhiwei, Zhong, Qi, Huang, Yining, Zou, Fei, Wu, Xianbo
Publikováno v:
In Ecotoxicology and Environmental Safety 15 January 2025 290
Autor:
Qiu, Minhao, Huang, Yining, Zhou, Xiaoying, Yu, Junyu, Li, Jianmin, Wang, Wei, Zippi, Maddalena, Fiorino, Sirio, Hong, Wandong
Publikováno v:
In Cellular Signalling January 2025 125