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pro vyhledávání: '"XU, YUANYUAN"'
To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (SPET) images from low-dose PET (LPET). However, the success of current
Externí odkaz:
http://arxiv.org/abs/2407.20878
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single labeled source domain to an unlabeled target domain, neglecting the compr
Externí odkaz:
http://arxiv.org/abs/2407.05688
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes). Existing solutions f
Externí odkaz:
http://arxiv.org/abs/2404.14696
To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods
Externí odkaz:
http://arxiv.org/abs/2404.01563
Autor:
Mou, Banasree S., Zhang, Xinshu, Xiang, Li, Xu, Yuanyuan, Zhong, Ruidan, Cava, Robert J., Zhou, Haidong, Jiang, Zhigang, Smirnov, Dmitry, Drichko, Natalia, Winter, Stephen M.
Co-based materials have recently been explored due to potential to realise complex bond-dependent anisotropic magnetism. Prominent examples include Na$_2$Co$_2$TeO$_6$, BaCo$_2$(AsO$_4$)$_2$, Na$_2$BaCo(PO$_4$)$_2$, and CoX$_2$ (X = Cl, Br, I). In or
Externí odkaz:
http://arxiv.org/abs/2403.11980
Autor:
Cui, Jiaqi, Xu, Yuanyuan, Xiao, Jianghong, Fei, Yuchen, Zhou, Jiliu, Peng, Xingcheng, Wang, Yan
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps. However, existing methods fail to derive the desirable radiotherapy parameters that can be directly input into the treatment planning system (
Externí odkaz:
http://arxiv.org/abs/2402.18879
Autor:
Ding, Yiran, Zhang, Li Lyna, Zhang, Chengruidong, Xu, Yuanyuan, Shang, Ning, Xu, Jiahang, Yang, Fan, Yang, Mao
Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to ar
Externí odkaz:
http://arxiv.org/abs/2402.13753
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the
Externí odkaz:
http://arxiv.org/abs/2402.04566
Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items. The retrieval stage retrieves candidate items relevant to user interests for recommendations and has attracte
Externí odkaz:
http://arxiv.org/abs/2402.01253
We prove that the spectral radius of a large random matrix $X$ with independent, identically distributed complex entries follows the Gumbel law irrespective of the distribution of the matrix elements. This solves a long-standing conjecture of Bordena
Externí odkaz:
http://arxiv.org/abs/2312.08325