Zobrazeno 1 - 10
of 457
pro vyhledávání: '"Wu Zongze"'
Autor:
Wu Zongze, Liu Quan, Wageh Swelm, Sun Zhe, Al-Hartomy Omar A., Al-Sehemi Abdullah G., Yan Lesen, Chen Jiaojuan, Zhang Wenjian, Yang Jilin, Zhang Han, Liu Liping
Publikováno v:
Nanophotonics, Vol 12, Iss 1, Pp 81-98 (2022)
Photodynamic therapy (PDT) is a highly promising modality against cancer, but its efficacy is severely limited by the low oxygen content in solid tumors. In this study, a smart photosensitive NiPS3 nanosheet was developed to solve the problem of low
Externí odkaz:
https://doaj.org/article/b6aa069be2cd4984918517f5af59136d
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data. Hypergraphs, which can model complex beyond-pairwise relationships, o
Externí odkaz:
http://arxiv.org/abs/2410.10083
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and
Externí odkaz:
http://arxiv.org/abs/2408.08332
Autor:
Sun Zhe, Fan Taojian, Liu Quan, Huang Luodan, Hu Weibin, Shi Lulin, Wu Zongze, Yang Qinhe, Liu Liping, Zhang Han
Publikováno v:
Nanophotonics, Vol 10, Iss 9, Pp 2519-2535 (2021)
Personalized therapeutic vaccines against immune desert tumors are an increasingly important field in current cancer immunotherapy. However, limitations in neoantigen recognition, impotent immune cells, and a lack of intratumoral infiltrated lymphocy
Externí odkaz:
https://doaj.org/article/4a8e9c2a249f42768a5c69217ab9a4ad
Few-shot anomaly detection methods can effectively address data collecting difficulty in industrial scenarios. Compared to 2D few-shot anomaly detection (2D-FSAD), 3D few-shot anomaly detection (3D-FSAD) is still an unexplored but essential task. In
Externí odkaz:
http://arxiv.org/abs/2406.18941
In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains largely unexplored. This paper proposes a novel and valuable research task
Externí odkaz:
http://arxiv.org/abs/2406.05645
Publikováno v:
E3S Web of Conferences, Vol 409, p 02017 (2023)
This paper aims to clarify the conditions and specific details of data collection by establishing an economic benefit analysis model, and provide corresponding economic analysis and support for the construction of anaerobic digestion plants and PTG f
Externí odkaz:
https://doaj.org/article/8105bf3deab941a38f8f4cdd3dfa189b
Publikováno v:
E3S Web of Conferences, Vol 409, p 05014 (2023)
The planning and transformation of existing energy systems through renewable energy sources and the cleanest fossil fuels is considered to be one of the most promising and effective strategies for achieving the transition to a low-carbon world. At th
Externí odkaz:
https://doaj.org/article/6c622dee24554fcfb0bae24e6e40452a
Autor:
Nitzan, Yotam, Wu, Zongze, Zhang, Richard, Shechtman, Eli, Cohen-Or, Daniel, Park, Taesung, Gharbi, Michaël
We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, starting from a blank canvas or an image, a user specifies a sequence o
Externí odkaz:
http://arxiv.org/abs/2404.12382
Autor:
Pei, Wenjie, Xu, Weina, Wu, Zongze, Li, Weichao, Wang, Jinfan, Lu, Guangming, Wang, Xiangrong
The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation
Externí odkaz:
http://arxiv.org/abs/2401.00755