74 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Fudan UniversityCN2 papers
- Nankai UniversityCN2 papers
- National University of Defense TechnologyCN2 papers
- Shanghai Jiao Tong UniversityCN2 papers
- Aalto UniversityFI1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- Beijing Advanced Sciences and Innovation CenterCN1 paper
- Beijing Institute of TechnologyCN1 paper
- Central South UniversityCN1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Chinese Academy of SciencesCN1 paper
- Collaborative Innovation Center of Quantum MatterCN1 paper
5 papers · 1 filter
Magnetic-field-induced nonlinear transport in HfTe5
Cheng Zhang, Jinshan Yang, Zhongbo Yan +8
The interplay of electron correlations and topological phases gives rise to various exotic phenomena including fractionalization, excitonic instability, and axionic excitation. Rec…
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
Weijie Li, Yang Wei, Tianpeng Liu +5
The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automati…
Doped Holographic Superconductors in Gubser-Rocha model
Ziyi Zhao, Wenhe Cai, Shuta Ishigaki
We construct a doped holographic superconductor in the Gubser-Rocha model, and realize a superconducting dome in the middle of the temperature-doping phase diagram. It is worth not…
AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on Protein Tertiary Structure Prediction
Zhongju Yuan, Tao Shen, Sheng Xu +3
Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly advanced protein tertiary structure prediction, achieving results comparable to real biological experim…
Phase Diagram of Initial Condensation for Two-layer Neural Networks
Zhengan Chen, Yuqing Li, Tao Luo +2
The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research. In this paper, based on the ea…