Zobrazeno 1 - 10
of 15 962
pro vyhledávání: '"ZHANG, YE"'
Autor:
Zhang, Ye
Publikováno v:
Comptes Rendus. Mathématique, Vol 361, Iss G7, Pp 1107-1114 (2023)
In this note we show that the gradient estimate of the heat semigroup, or more precisely the H.-Q. Li inequality, is preserved under tensorization, some suitable group epimorphism, and central sum. We also establish the Riemannian counterpart of the
Externí odkaz:
https://doaj.org/article/e167ffd17ece444f9a6d6fb4e6183e7a
Autor:
Sun, Yuxuan, Si, Yixuan, Zhu, Chenglu, Gong, Xuan, Zhang, Kai, Chen, Pingyi, Zhang, Ye, Shui, Zhongyi, Lin, Tao, Yang, Lin
The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learne
Externí odkaz:
http://arxiv.org/abs/2412.12077
In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning framework. Our approach centers on optimizing autonomous task assignme
Externí odkaz:
http://arxiv.org/abs/2412.09877
The steep dose gradients obtained with pencil beam scanning allow for precise tumor targeting at the cost of high sensitivity to uncertainties. Robust optimization is commonly applied to mitigate uncertainties in density and patient setup, while its
Externí odkaz:
http://arxiv.org/abs/2411.16230
We investigate the effect of counter-rotating-wave terms on nonlocality and entanglement for three qubits coupled with a common bath for strong and ultrastrong coupling regimes beyond the traditional treatment of Born-Markovian, perturbative and rota
Externí odkaz:
http://arxiv.org/abs/2411.13905
Multimodal Magnetic Resonance Imaging (MRI) provides essential complementary information for analyzing brain tumor subregions. While methods using four common MRI modalities for automatic segmentation have shown success, they often face challenges wi
Externí odkaz:
http://arxiv.org/abs/2411.08305
Autor:
Song, Xianxin, Fang, Yuan, Wang, Feng, Ren, Zixiang, Yu, Xianghao, Zhang, Ye, Liu, Fan, Xu, Jie, Ng, Derrick Wing Kwan, Zhang, Rui, Cui, Shuguang
This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless enviro
Externí odkaz:
http://arxiv.org/abs/2411.06687
Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing methods usually adopt Kullback-Leibler divergence to estimate the uncertai
Externí odkaz:
http://arxiv.org/abs/2411.00826
Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucial for the profitability of a bank'sbank's credit card business. In the
Externí odkaz:
http://arxiv.org/abs/2408.03497
Autor:
Li, Junyu, Zhang, Ye, Shu, Wen, Feng, Xiaobing, Wang, Yingchun, Yan, Pengju, Li, Xiaolin, Sha, Chulin, He, Min
Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers
Externí odkaz:
http://arxiv.org/abs/2407.17267