Enhanced superconductivity by near-neighbor attraction in the doped Hubbard model

Kavli Affiliate: Cheng Peng | First 5 Authors: Cheng Peng, Yao Wang, Jiajia Wen, Young Lee, Thomas Devereaux | Summary: Recent experiment has unveiled an anomalously strong electron-electron attraction in one-dimensional copper-oxide chain Ba$_{2-x}$Sr$_x$CuO$_{3+delta}$. While the near-neighbor electron attraction $V$ in the one-dimensional extended Hubbard chain has been examined recently, its effect in the Hubbard […]


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Minimising statistical errors in calibration of quantum-gate sets

Kavli Affiliate: David Gross | First 5 Authors: Yaiza Aragonés-Soria, René Otten, Tobias Hangleiter, Pascal Cerfontaine, David Gross | Summary: Calibration of quantum gates is a necessary hurdle to overcome on the way to a reliable quantum computer. In a recent paper, a protocol called Gate Set Calibration protocol (GSC) has been introduced and used […]


Continue.. Minimising statistical errors in calibration of quantum-gate sets

Minimising statistical errors in calibration of quantum-gate sets

Kavli Affiliate: David Gross | First 5 Authors: Yaiza Aragonés-Soria, René Otten, Tobias Hangleiter, Pascal Cerfontaine, David Gross | Summary: Calibration of quantum gates is a necessary hurdle to overcome on the way to a reliable quantum computer. In a recent paper, a protocol called Gate Set Calibration protocol (GSC) has been introduced and used […]


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OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

Kavli Affiliate: Zheng Zhu | First 5 Authors: Wanhua Li, Xiaoke Huang, Zheng Zhu, Yansong Tang, Xiu Li | Summary: This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and […]


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APES: Articulated Part Extraction from Sprite Sheets

Kavli Affiliate: Matthew Fisher | First 5 Authors: Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat | Summary: Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such […]


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NeMF: Neural Motion Fields for Kinematic Animation

Kavli Affiliate: Yi Zhou | First 5 Authors: Chengan He, Jun Saito, James Zachary, Holly Rushmeier, Yi Zhou | Summary: We present an implicit neural representation to learn the spatio-temporal space of kinematic motions. Unlike previous work that represents motion as discrete sequential samples, we propose to express the vast motion space as a continuous […]


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Collective effects in an incompressible electronic liquid

Kavli Affiliate: Yi Zhou | First 5 Authors: Jian-Jian Miao, Hui-Ke Jin, Yi Zhou, , | Summary: Starting from the Landau’s kinetic equation, we show that an electronic liquid in $d=2,3$ dimensions depicted by a Landau type effective theory will become incompressible on condition that the Landau parameters satisfy either (i) $1+F_{1}^{s}/d=0$ or (ii) $F_{0}^{s}to{}+infty$. […]


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Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

Kavli Affiliate: Zheng Zhu | First 5 Authors: Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng | Summary: Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain […]


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Improving Subgraph Representation Learning via Multi-View Augmentation

Kavli Affiliate: Yi Zhou | First 5 Authors: Yili Shen, Jiaxu Yan, Cheng-Wei Ju, Jun Yi, Zhou Lin | Summary: Subgraph representation learning based on Graph Neural Network (GNN) has broad applications in chemistry and biology, such as molecule property prediction and gene collaborative function prediction. On the other hand, graph augmentation techniques have shown […]


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