Zobrazeno 1 - 10
of 32 452
pro vyhledávání: '"Liu dong"'
This paper investigates the structure of Verma modules over the N=1 BMS superalgebra. We provide a detailed classification of singular vectors, establish necessary and sufficient conditions for the existence of subsingular vectors, uncover the struct
Externí odkaz:
http://arxiv.org/abs/2412.17000
In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques
Externí odkaz:
http://arxiv.org/abs/2412.15752
This study delves into the existence of dark matter halo around supermassive black holes in galactic cores using a novel gravitational model. By analyzing gravitational waves emitted during the ringdown phase of black holes under different field pert
Externí odkaz:
http://arxiv.org/abs/2412.07172
We propose to transfer representational knowledge from multiple sources to a target noisy matrix completion task by aggregating singular subspaces information. Under our representational similarity framework, we first integrate linear representation
Externí odkaz:
http://arxiv.org/abs/2412.06233
Autor:
Zheng, Yunzhe, Liu, Dong E.
Magic State Distillation (MSD) has been a research focus for fault-tolerant quantum computing due to the need for non-Clifford resource in gaining quantum advantage. Although many of the MSD protocols so far are based on stabilizer codes with transve
Externí odkaz:
http://arxiv.org/abs/2412.04402
Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessi
Externí odkaz:
http://arxiv.org/abs/2412.04307
With the development of society, time series anomaly detection plays an important role in network and IoT services. However, most existing anomaly detection methods directly analyze time series in the time domain and cannot distinguish some relativel
Externí odkaz:
http://arxiv.org/abs/2412.02474
In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilist
Externí odkaz:
http://arxiv.org/abs/2411.19320
To improve the convergence speed and optimization accuracy of the Dung Beetle Optimizer (DBO), this paper proposes an improved algorithm based on circle mapping and longitudinal-horizontal crossover strategy (CICRDBO). First, the Circle method is use
Externí odkaz:
http://arxiv.org/abs/2411.17738
Autor:
Zhang, Menglin, Luo, Xin, Lan, Yunwei, Liu, Chang, Li, Rui, Zhang, Kaidong, Yang, Ganlin, Liu, Dong
Recent advances in NeRF inpainting have leveraged pretrained diffusion models to enhance performance. However, these methods often yield suboptimal results due to their ineffective utilization of 2D diffusion priors. The limitations manifest in two c
Externí odkaz:
http://arxiv.org/abs/2411.15551