Zobrazeno 1 - 10
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pro vyhledávání: '"Yu, Li"'
Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy free. Therefore, representations of those ideal synthetic point clouds h
Externí odkaz:
http://arxiv.org/abs/2409.06956
The detailed study of the strong gravitational lensing of a Kerr black hole within Quantum Einstein Gravity (QEG) is performed. We calculate the photon sphere, the deflection angle of light, and observables on the equatorial plane under the strong de
Externí odkaz:
http://arxiv.org/abs/2409.03975
State Space Models (SSMs), especially Mamba, have shown great promise in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, accurate medical image segmentation requires the
Externí odkaz:
http://arxiv.org/abs/2408.13735
Autor:
Shi, Lianzheng, Zhang, Jianhua, Yu, Li, Zhang, Yuxiang, Zhang, Zhen, Cai, Yichen, Liu, Guangyi
Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method
Externí odkaz:
http://arxiv.org/abs/2408.06558
Autor:
Dai, Pimeng, Yu, Li
We determine which simplicial complexes have the maximum or minimum sum of Betti numbers and sum of bigraded Betti numbers with a given number of vertices in each dimension.
Comment: 22 pages, 1 figure
Comment: 22 pages, 1 figure
Externí odkaz:
http://arxiv.org/abs/2407.19423
Incomplete multi-modal image segmentation is a fundamental task in medical imaging to refine deployment efficiency when only partial modalities are available. However, the common practice that complete-modality data is visible during model training i
Externí odkaz:
http://arxiv.org/abs/2407.14796
Federated learning has emerged as a compelling paradigm for medical image segmentation, particularly in light of increasing privacy concerns. However, most of the existing research relies on relatively stringent assumptions regarding the uniformity a
Externí odkaz:
http://arxiv.org/abs/2407.02280
Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumption is task homogeneity where ea
Externí odkaz:
http://arxiv.org/abs/2406.18995
In this paper, an anti-eavesdropping estimation problem is investigated. A linear encryption scheme is utilized, which first linearly transforms innovation via an encryption matrix and then encrypts some components of the transformed innovation. To r
Externí odkaz:
http://arxiv.org/abs/2406.10677
We model the TESS light curve of the binary system RX Dra, and also first calculate a lot of theoretical models to fit the g-mode frequencies previously detected from the TESS series of this system. The mass ratio is determined to be $q$=0.9026$^{+0.
Externí odkaz:
http://arxiv.org/abs/2405.15940