Zobrazeno 1 - 10
of 3 557
pro vyhledávání: '"REN Chao"'
Autor:
HAN Qingqing, LI Tuo, XING Haiqun, REN Chao, LIU Jiahui, WANG Yu, MA Wenbin, CHENG Xin, HUO Li
Publikováno v:
罕见病研究, Vol 3, Iss 1, Pp 102-107 (2024)
Gliomas are the most common primary intracranial tumors in adults, among which high-grade glioma patients are characterized by short survival and poor prognosis. The diagnosis, treatment, evaluation of effective treatments, and prognosis prediction o
Externí odkaz:
https://doaj.org/article/f760c22f761b49e2a668244d934beac3
Publikováno v:
罕见病研究, Vol 1, Iss 1, Pp 72-77 (2022)
Transthyretin-related amyloid cardiomyopathy (ATTR-CM) is a disease caused by the depo-sition of insoluble amyloid fibers formed by the misfolding of transthyretin precursor protein in the intercellular space of cardiomyocytes. This lesion may lead t
Externí odkaz:
https://doaj.org/article/c8fe244ef6834d2095e7a25ecdb557dc
Autor:
Li, Anran, Chen, Yuanyuan, Ren, Chao, Wang, Wenhan, Hu, Ming, Li, Tianlin, Yu, Han, Chen, Qingyu
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is scaling to large-scale graphs, which typically incurs high computation an
Externí odkaz:
http://arxiv.org/abs/2409.14655
Autor:
Lin, Xin, Zhou, Yuyan, Yue, Jingtong, Ren, Chao, Chan, Kelvin C. K., Qi, Lu, Yang, Ming-Hsuan
Unsupervised restoration approaches based on generative adversarial networks (GANs) offer a promising solution without requiring paired datasets. Yet, these GAN-based approaches struggle to surpass the performance of conventional unsupervised GAN-bas
Externí odkaz:
http://arxiv.org/abs/2408.09241
In recent years, self-supervised denoising methods have gained significant success and become critically important in the field of image restoration. Among them, the blind spot network based methods are the most typical type and have attracted the at
Externí odkaz:
http://arxiv.org/abs/2407.06514
To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the existing semi-decentralized schemes, which adaptively adjust the collabor
Externí odkaz:
http://arxiv.org/abs/2406.19002
Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the
Externí odkaz:
http://arxiv.org/abs/2406.00488
Publikováno v:
BIO Web of Conferences, Vol 59, p 03004 (2023)
Cell type identification is a vital step in the analysis of scRNA-seq data. Transcriptome subtype pivotal information such as alternative polyadenylation (APA) obtained from standard scRNA-seq data can also provide valid clues for cell type identific
Externí odkaz:
https://doaj.org/article/b18c24a308c2439dbe73a91ad4364716
The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of
Externí odkaz:
http://arxiv.org/abs/2405.18205
Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (non-IID) local data. Existing MHPFL methods focus on achieving client-l
Externí odkaz:
http://arxiv.org/abs/2404.17847