Zobrazeno 1 - 10
of 762
pro vyhledávání: '"Li, Yizhou"'
This paper presents a recurrent neural network approach to simulating mechanical ventilator pressure. The traditional mechanical ventilator has a control pressure that is monitored by a medical practitioner and can behave incorrectly if the proper pr
Externí odkaz:
http://arxiv.org/abs/2410.06552
Autor:
Li, Yizhou, Polak, Pawel
We incorporate the conditional value-at-risk (CVaR) quantity into a generalized class of Pickands estimators. By introducing CVaR, the newly developed estimators not only retain the desirable properties of consistency, location, and scale invariance
Externí odkaz:
http://arxiv.org/abs/2409.15677
With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve th
Externí odkaz:
http://arxiv.org/abs/2406.12298
One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation tech
Externí odkaz:
http://arxiv.org/abs/2406.05170
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA2024)
Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching. While some previous learning-based methods integrated a phys
Externí odkaz:
http://arxiv.org/abs/2402.18181
Autor:
Li, Yizhou, Polak, Pawel
The Pickands estimator for the extreme value index is beneficial due to its universal consistency, location, and scale invariance, which sets it apart from other types of estimators. However, similar to many extreme value index estimators, it is mark
Externí odkaz:
http://arxiv.org/abs/2401.11096
Publikováno v:
Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2024)
Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck. Due to the limited receptive field, existing CNN-based metho
Externí odkaz:
http://arxiv.org/abs/2312.14650
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-drive
Externí odkaz:
http://arxiv.org/abs/2305.16114
The paper considers the problem of modeling a univariate random variable. Main contributions: (i) Suggested a new family of distributions with quantile defined by a linear combination of some basis quantiles. This family of distributions has a high s
Externí odkaz:
http://arxiv.org/abs/2305.00081
Removing raindrops in images has been addressed as a significant task for various computer vision applications. In this paper, we propose the first method using a Dual-Pixel (DP) sensor to better address the raindrop removal. Our key observation is t
Externí odkaz:
http://arxiv.org/abs/2210.13321