Zobrazeno 1 - 10
of 74 163
pro vyhledávání: '"LIU, YAN"'
The Kirkwood-Dirac distribution, serving as an informationally complete representation of a quantum state, has recently garnered { increasing} attention. We investigate the Kirkwood-Dirac classicality with respect to mutually unbiased bases. For prim
Externí odkaz:
http://arxiv.org/abs/2411.11666
Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is ye
Externí odkaz:
http://arxiv.org/abs/2411.06159
Autor:
Jiang, Zhi, Yao, Danyang, Ran, Xu, Gao, Yu, Wang, Jianguo, Gan, Xuetao, Liu, Yan, Hao, Yue, Han, Genquan
On chip acousto-optic (AO) modulation represents a significant advancement in the development of highly integrated information processing systems. However, conventional photonic devices face substantial challenges in achieving efficient conversion du
Externí odkaz:
http://arxiv.org/abs/2411.04742
Publikováno v:
IEEE Transactions on Cloud Computing ( Volume: 12, Issue: 2, April-June 2024)
This article presents the design of an open-API-based explainable AI (XAI) service to provide feature contribution explanations for cloud AI services. Cloud AI services are widely used to develop domain-specific applications with precise learning met
Externí odkaz:
http://arxiv.org/abs/2411.03376
The Mixture of Experts (MoE) is an advanced model architecture in the industry that combines multiple specialized expert models from various domains into a single supermodel. This approach enables the model to scale without significantly increasing t
Externí odkaz:
http://arxiv.org/abs/2411.00662
Autor:
Wang, Zerui, Liu, Yan
Transformer-based models have achieved state-of-the-art performance in various computer vision tasks, including image and video analysis. However, Transformer's complex architecture and black-box nature pose challenges for explainability, a crucial a
Externí odkaz:
http://arxiv.org/abs/2411.00630
Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention for its ability to utilize MM data and reduce reliance on labeled images. However, current methods face several challenges: (
Externí odkaz:
http://arxiv.org/abs/2410.17565
Autor:
Wei, Runpu, Yin, Zijin, Liang, Kongming, Min, Min, Pan, Chengwei, Yu, Gang, Huang, Haonan, Liu, Yan, Ma, Zhanyu
Automatic polyp segmentation is helpful to assist clinical diagnosis and treatment. In daily clinical practice, clinicians exhibit robustness in identifying polyps with both location and size variations. It is uncertain if deep segmentation models ca
Externí odkaz:
http://arxiv.org/abs/2410.16732
Autor:
Li, Tian-Ming, Zhang, Jia-Chi, Chen, Bing-Jie, Huang, Kaixuan, Liu, Hao-Tian, Xiao, Yong-Xi, Deng, Cheng-Lin, Liang, Gui-Han, Chen, Chi-Tong, Liu, Yu, Li, Hao, Bao, Zhen-Ting, Zhao, Kui, Xu, Yueshan, Li, Li, He, Yang, Liu, Zheng-He, Yu, Yi-Han, Zhou, Si-Yun, Liu, Yan-Jun, Song, Xiaohui, Zheng, Dongning, Xiang, Zhong-Cheng, Shi, Yun-Hao, Xu, Kai, Fan, Heng
For superconducting quantum processors, stable high-fidelity two-qubit operations depend on precise flux control of the tunable coupler. However, the pulse distortion poses a significant challenge to the control precision. Current calibration methods
Externí odkaz:
http://arxiv.org/abs/2410.15041
Autor:
Schnitzer, Mireille E, Talbot, Denis, Liu, Yan, Berger, David, Wang, Guanbo, O'Loughlin, Jennifer, Sylvestre, Marie-Pierre, Ertefaie, Ashkan
Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not directly applicable to time-varying scenarios. We propose a novel two-s
Externí odkaz:
http://arxiv.org/abs/2410.08283