From Stoner to Local Moment Magnetism in Atomically Thin Cr2Te3
Kavli Affiliate: Cheng Peng | First 5 Authors: Yong Zhong, Cheng Peng, Haili Huang, Dandan Guan, Jinwoong Hwang | Summary: The field of two-dimensional (2D) ferromagnetism has been proliferating over the past few years, with ongoing interests in basic science and potential applications in spintronic technology. However, a high-resolution spectroscopic study of the 2D ferromagnet […]
Continue.. From Stoner to Local Moment Magnetism in Atomically Thin Cr2Te3
Simplicity of AdS Super Yang-Mills at One Loop
Kavli Affiliate: Xinan Zhou | First 5 Authors: Zhongjie Huang, Bo Wang, Ellis Ye Yuan, Xinan Zhou, | Summary: We perform a systematic bootstrap analysis of four-point one-loop Mellin amplitudes for super gluons in $mathrm{AdS}_5timesmathrm{S}^3$ with arbitrary Kaluza-Klein weights. The analysis produces the general expressions for these amplitudes at extremalities two and three, as well […]
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Edge-aware Feature Aggregation Network for Polyp Segmentation
Kavli Affiliate: Yi Zhou | First 5 Authors: Tao Zhou, Yizhe Zhang, Geng Chen, Yi Zhou, Ye Wu | Summary: Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve […]
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DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
Kavli Affiliate: Zheng Zhu | First 5 Authors: Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiwen Lu | Summary: World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential for the generation of high-quality driving videos, and […]
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Stability of Taylor-Couette Flow with Odd Viscosity
Kavli Affiliate: Rudolf Podgornik | First 5 Authors: Guangle Du, Rudolf Podgornik, , , | Summary: Odd viscosity can emerge in 3D hydrodynamics when the time reversal symmetry is broken and anisotropy is introduced. Its ramifications on the stability of the prototypical Taylor-Couette flow in curved geometries have remained unexplored. Here, we investigate the effects […]
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Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks
Kavli Affiliate: Bo Gu | First 5 Authors: Xu Zhang, Ziqi Lin, Shimin Gong, Bo Gu, Dusit Niyato | Summary: Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale […]
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Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
Kavli Affiliate: Cheng Peng | First 5 Authors: Jixun K. Ding, Luhang Yang, Wen O. Wang, Ziyan Zhu, Cheng Peng | Summary: In a lattice model subject to a perpendicular magnetic field, when the lattice constant is comparable to the magnetic length, one enters the "Hofstadter regime," where continuum Landau levels become fractal magnetic Bloch […]
Continue.. Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
Kavli Affiliate: Cheng Peng | First 5 Authors: Jixun K. Ding, Luhang Yang, Wen O. Wang, Ziyan Zhu, Cheng Peng | Summary: In a lattice model subject to a perpendicular magnetic field, when the lattice constant is comparable to the magnetic length, one enters the "Hofstadter regime," where continuum Landau levels become fractal magnetic Bloch […]
Continue.. Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
Introspective Deep Metric Learning
Kavli Affiliate: Zheng Zhu | First 5 Authors: Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu | Summary: This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore […]
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