Two-qubit logic between distant spins in silicon

Kavli Affiliate: Giordano Scappucci | First 5 Authors: Jurgen Dijkema, Xiao Xue, Patrick Harvey-Collard, Maximilian Rimbach-Russ, Sander L. de Snoo | Summary: Direct interactions between quantum particles naturally fall off with distance. For future-proof qubit architectures, however, it is important to avail of interaction mechanisms on different length scales. In this work, we utilize a […]


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WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom

Kavli Affiliate: Wei Gao | First 5 Authors: Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang | Summary: In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media […]


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DyExplainer: Explainable Dynamic Graph Neural Networks

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 […]


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Linear magneto-conductivity as a DC probe of time-reversal symmetry breaking

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 […]


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An Unconditionally Stable Iterative Decoupled Algorithm for Multiple-Network Poroelasticity Model

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 […]


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A ballistic electron source with magnetically-controlled valley polarization in bilayer graphene

Kavli Affiliate: Herre S. J. Van Der Zant | First 5 Authors: Josep Ingla-Aynés, Antonio L. R. Manesco, Talieh S. Ghiasi, Kenji Watanabe, Takashi Taniguchi | Summary: The achievement of valley-polarized electron currents is a cornerstone for the realization of valleytronic devices. Here, we report on ballistic coherent transport experiments where two opposite quantum point […]


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The Oxygen Reduction Pathway for Spinel Metal Oxides in Alkaline Media: An Experimentally Supported Ab Initio Study

Kavli Affiliate: Hector D. Abruna | First 5 Authors: Colin R. Bundschu, Mahdi Ahmadi, Juan F. Méndez-Valderrama, Yao Yang, Héctor D. Abruña | Summary: Precious-metal-free spinel oxide electrocatalysts are promising candidates for catalyzing the oxygen reduction reaction (ORR) in alkaline fuel cells. In this theory-driven study, we use joint density-functional theory in tandem with supporting […]


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Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

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 […]


Continue.. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

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 […]


Continue.. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

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 […]


Continue.. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series