Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control

Kavli Affiliate: Biao Huang | First 5 Authors: Runze Lin, Yangyang Luo, Xialai Wu, Junghui Chen, Biao Huang | Summary: The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in the process industry, traditional model-based optimization […]


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Spin pumping effect in non-Fermi liquid metals

Kavli Affiliate: Long Zhang | First 5 Authors: Xiao-Tian Zhang, Xu-Ping Yao, Yuya Ominato, Long Zhang, Mamoru Matsuo | Summary: Spin pumping effect is a sensitive and well-established experimental method in two-dimensional (2D) magnetic materials. We propose that spin pumping effect can be a valuable probe for non-Fermi liquid (NFL) behaviors at the 2D interface […]


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Annealing-tunable charge density wave in the kagome antiferromagnet FeGe

Kavli Affiliate: Long Zhang | First 5 Authors: Xueliang Wu, Xinrun Mi, Long Zhang, Chin-Wei Wang, Nour Maraytta | Summary: The unprecedented phenomenon that a charge density wave (CDW) emerges inside the antiferromagnetic (AFM) phase indicates an unusual CDW mechanism associated with magnetism in FeGe. Here, we demonstrate that both the CDW and magnetism of […]


Continue.. Annealing-tunable charge density wave in the kagome antiferromagnet FeGe

Annealing-tunable charge density wave in the kagome antiferromagnet FeGe

Kavli Affiliate: Long Zhang | First 5 Authors: Xueliang Wu, Xinrun Mi, Long Zhang, Chin-Wei Wang, Nour Maraytta | Summary: The unprecedented phenomenon that a charge density wave (CDW) emerges inside the antiferromagnetic (AFM) phase indicates an unusual CDW mechanism associated with magnetism in FeGe. Here, we demonstrate that both the CDW and magnetism of […]


Continue.. Annealing-tunable charge density wave in the kagome antiferromagnet FeGe

Bilayer $t$-$J$-$J_perp$ Model and Magnetically Mediated Pairing in the Pressurized Nickelate La$_3$Ni$_2$O$_7$

Kavli Affiliate: Gang Su | First 5 Authors: Xing-Zhou Qu, Dai-Wei Qu, Jialin Chen, Congjun Wu, Fan Yang | Summary: The recently discovered nickelate superconductor La$_3$Ni$_2$O$_7$ has a high transition temperature near 80 K under pressure, which offers additional avenues of unconventional superconductivity. Here with state-of-the-art tensor-network methods, we study a bilayer $t$-$J$-$J_perp$ model for […]


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GADER: GAit DEtection and Recognition in the Wild

Kavli Affiliate: Cheng Peng | First 5 Authors: Yuxiang Guo, Cheng Peng, Ram Prabhakar, Chun Pong Lau, Rama Chellappa | Summary: Gait recognition holds the promise of robustly identifying subjects based on their walking patterns instead of color information. While previous approaches have performed well for curated indoor scenes, they have significantly impeded applicability in […]


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Magnon spin photogalvanic effect induced by Aharonov-Casher phase

Kavli Affiliate: Gang Su | First 5 Authors: YuanDong Wang, Zhen-Gang Zhu, Gang Su, , | Summary: Magnons are electrically neutral bosonic quasiparticles emerging as collective spin excitations of magnetically ordered materials, and play a central role in the next-generation spintronics owing to its obviating Joule heating. A difficult obstacle for quantum magnonics is that […]


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Generalizable prediction of potential miRNA-disease associations based on heterogeneous graph learning

Kavli Affiliate: Yi Zhou | First 5 Authors: Yi Zhou, Meixuan Wu, Chengzhou Ouyang, Xinyi Wang, Min Zhu | Summary: Biomedical studies have revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). The generalizability is a […]


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Generalizable and explainable prediction of potential miRNA-disease associations based on heterogeneous graph learning

Kavli Affiliate: Yi Zhou | First 5 Authors: Yi Zhou, Meixuan Wu, Chengzhou Ouyang, Min Zhu, | Summary: Biomedical research has revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). However, the generalizability and explainability are […]


Continue.. Generalizable and explainable prediction of potential miRNA-disease associations based on heterogeneous graph learning