Kavli Affiliate: Irfan Siddiqi | First 5 Authors: Bingcheng Qing, Long B. Nguyen, Xinyu Liu, Hengjiang Ren, William P. Livingston | Summary: Quantum-limited Josephson parametric amplifiers play a pivotal role in advancing the field of circuit quantum electrodynamics by enabling the fast and high-fidelity measurement of weak microwave signals. Therefore, it is necessary to develop […]
Kavli Affiliate: Xiang Zhang | First 5 Authors: Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen, Xiang Zhang | Summary: Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box nature of GNNs presents a significant challenge in terms of comprehending […]
Kavli Affiliate: Joel E. Moore | First 5 Authors: Veronika Sunko, Chunxiao Liu, Marc Vila, Ilyoun Na, Yuchen Tang | Summary: Several optical experiments have shown that in magnetic materials the principal axes of response tensors can rotate in a magnetic field. Here we offer a microscopic explanation of this effect, and propose a closely […]
Kavli Affiliate: Feng Wang | First 5 Authors: Meng Lei, Mingchao Cai, Feng Wang, , | Summary: In this work, we introduce an iterative decoupled algorithm designed for addressing the quasi-static multiple-network poroelasticity problem. This problem pertains to the simultaneous modeling of fluid flow and deformations within an elastic porous medium permeated by multiple fluid […]
Kavli Affiliate: Feng Wang | First 5 Authors: Mingchao Cai, Meng Lei, Jingzhi Li, Jiaao Sun, Feng Wang | Summary: In this work, we present an iteratively decoupled algorithm for solving the quasi-static multiple-network poroelastic model. Our approach employs a total-pressure-based formulation with solid displacement, total pressure, and network pressures as primary unknowns. This reformulation […]
Kavli Affiliate: Xiang Zhang | First 5 Authors: Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang, | Summary: Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully […]
Kavli Affiliate: Xiang Zhang | First 5 Authors: Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang, | Summary: Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully […]
Kavli Affiliate: Xiang Zhang | First 5 Authors: Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang, | Summary: Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully […]
Kavli Affiliate: Felix Fischer | First 5 Authors: Javier Cembrano, Felix Fischer, Max Klimm, , | Summary: We study functions that produce a ranking of $n$ individuals from $n$ such rankings and are impartial in the sense that the position of an individual in the output ranking does not depend on the input ranking submitted […]
Kavli Affiliate: Felix Fischer | First 5 Authors: Javier Cembrano, Felix Fischer, Max Klimm, , | Summary: We study functions that produce a ranking of $n$ individuals from $n$ such rankings and are impartial in the sense that the position of an individual in the output ranking does not depend on the input ranking submitted […]