Shock Breakout in 3-Dimensional Red Supergiant Envelopes

Kavli Affiliate: Lars Bildsten | First 5 Authors: Jared A. Goldberg, Yan-fei Jiang, Lars Bildsten, , | Summary: Using Athena++, we perform 3D Radiation-Hydrodynamic calculations of the radiative breakout of the shock wave in the outer envelope of a red supergiant (RSG) which has suffered core collapse and will become a Type IIP supernova. The […]


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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 […]


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