Zobrazeno 1 - 10
of 378
pro vyhledávání: '"LI Ziyun"'
Autor:
Jiang Xinyi, Li Ziyun, Pan Chixing, Fang Heng, Xu Wang, Chen Zeling, Zhu Junjiang, He Linling, Fang Miaoxian, Chen Chunbo
Publikováno v:
Journal of Medical Biochemistry, Vol 43, Iss 4, Pp 574-586 (2024)
Background: Considerable morbidity and death are associated with acute kidney damage (AKI) following total aortic arch replacement (TAAR). The relationship between AKI following TAAR and serum magnesium levels remains unknown. The intention of this r
Externí odkaz:
https://doaj.org/article/e3e60aa7df814b438c01c29828c282ca
Publikováno v:
Shanghai Jiaotong Daxue xuebao. Yixue ban, Vol 44, Iss 1, Pp 108-115 (2024)
Objective·To investigate the effect of Astragali Radix on T lymphocyte subsets and cytokine expression in Hashimoto's thyroiditis patients with normal thyroid function.Methods·A total of 120 Hashimoto′s thyroiditis patients with normal thyroid fu
Externí odkaz:
https://doaj.org/article/7d48294e63a847cf8c49df29407c56fb
Autor:
Hsieh, He-Yen, Li, Ziyun, Zhang, Sai Qian, Ting, Wei-Te Mark, Chang, Kao-Den, De Salvo, Barbara, Liu, Chiao, Kung, H. T.
We present GazeGen, a user interaction system that generates visual content (images and videos) for locations indicated by the user's eye gaze. GazeGen allows intuitive manipulation of visual content by targeting regions of interest with gaze. Using
Externí odkaz:
http://arxiv.org/abs/2411.04335
Autor:
Zhao, Yiwei, Li, Ziyun, Khwa, Win-San, Sun, Xiaoyu, Zhang, Sai Qian, Sarwar, Syed Shakib, Stangherlin, Kleber Hugo, Lu, Yi-Lun, Gomez, Jorge Tomas, Seo, Jae-Sun, Gibbons, Phillip B., De Salvo, Barbara, Liu, Chiao
Low-Latency and Low-Power Edge AI is essential for Virtual Reality and Augmented Reality applications. Recent advances show that hybrid models, combining convolution layers (CNN) and transformers (ViT), often achieve superior accuracy/performance tra
Externí odkaz:
http://arxiv.org/abs/2410.08326
Autor:
Prasad, Arpan Suravi, Scherer, Moritz, Conti, Francesco, Rossi, Davide, Di Mauro, Alfio, Eggimann, Manuel, Gómez, Jorge Tómas, Li, Ziyun, Sarwar, Syed Shakib, Wang, Zhao, De Salvo, Barbara, Benini, Luca
Extended reality (XR) applications are Machine Learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end), and power-constrained (low tens of mW average power). While ML perfor
Externí odkaz:
http://arxiv.org/abs/2312.14750
Generalized class discovery (GCD) aims to infer known and unknown categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising known classes. Existing research implicitly/explicitly assumes that the frequency of occurrenc
Externí odkaz:
http://arxiv.org/abs/2401.05353
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set comprising recognized classes. A significant limitation in prevailing
Externí odkaz:
http://arxiv.org/abs/2401.05352
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given that most existing literature focuses primarily on utilizing supervised k
Externí odkaz:
http://arxiv.org/abs/2306.03648
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodolog
Externí odkaz:
http://arxiv.org/abs/2209.09120
Autor:
Zhao, Yang, Li, Ziyun, Fu, Yonggan, Zhang, Yongan, Li, Chaojian, Wan, Cheng, You, Haoran, Wu, Shang, Ouyang, Xu, Boominathan, Vivek, Veeraraghavan, Ashok, Lin, Yingyan
We present a first-of-its-kind ultra-compact intelligent camera system, dubbed i-FlatCam, including a lensless camera with a computational (Comp.) chip. It highlights (1) a predict-then-focus eye tracking pipeline for boosted efficiency without compr
Externí odkaz:
http://arxiv.org/abs/2206.08141