My research lies at the intersection of computational chemistry, machine learning, and toxicology, focusing on the development of structural-based computational frameworks to predict and evaluate the hazard profiles of complex chemical space.
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Wenwen Cai, Xuanxuan Wang, Mingqian Zhu, Dengfeng Kuang
Vortex beams carrying orbital angular momentum enable high-capacity optical communication and imaging, yet multiple scattering in dynamic medias such as biological tissues disrupts their wavefront. Brownian motion decorrelates the scattered field and invalidates conventional meth…
Daichang Dong, Yulong Zhao, Zhijun Sun
Miniature reconstructive spectrometers have attracted significant attention for their broad potential applications. However, performance of the spectrometers is heavily dependent on their preset calibration procedure and spectral reconstruction algorithms, and conventional calibr…
<i>Deep learning (DL) methods show promising potential for single-cell data analysis, yet required tremendous efforts in building the models. </i><i>To streamline the application of sequence-based DL methods in single-cell genomics, we established a two-layer CNN model as a basel…
Wenwen Cai, Xuanxuan Wang, Mingqian Zhu, Dengfeng Kuang
Supplementary Materials
Kangjian Di, Fuhao Yu, Silin Chen, Jiashu Li, Andy Liu, Sen Shao, et al.
Optical neural networks (ONNs) promise ultra-fast and energy-efficient computing but are hampered by the critical challenge of on-chip training. Here, we propose an on-chip training distillation-guided optical neural network (DGONN) and introduce a forward distilled algorithm to…