Zobrazeno 1 - 10
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pro vyhledávání: '"Feng, Wei"'
The origin of the binary black hole mergers observed by LIGO-Virgo-KAGRA (LVK) remains an open question. We calculate the merger rate from primordial black holes (PBHs) within the density spike around supermassive black holes (SMBHs) at the center of
Externí odkaz:
http://arxiv.org/abs/2411.05065
The achievement of spectral super-resolution sensing is critically important for a variety of applications, such as radar, remote sensing, and wireless communication. However, in compressed spectrum sensing, challenges such as spectrum leakage and th
Externí odkaz:
http://arxiv.org/abs/2411.02700
Autor:
Fang, Xinran, Lei, Chengleyang, Feng, Wei, Chen, Yunfei, Xiao, Ming, Ge, Ning, Wang, Chengxiang
Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous task
Externí odkaz:
http://arxiv.org/abs/2410.18382
To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control ($\mathbf{SC}^3$) loops. In each $\mathbf{SC}^3$ cycle, th
Externí odkaz:
http://arxiv.org/abs/2410.18370
Autor:
Chen, Houlun, Wang, Xin, Chen, Hong, Zhang, Zeyang, Feng, Wei, Huang, Bin, Jia, Jia, Zhu, Wenwu
Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding, which hinders precise video moment localization when given fine-grained queries. In this paper, we propose a more challenging fine-grained VCMR benchmark requir
Externí odkaz:
http://arxiv.org/abs/2410.08593
Open-vocabulary multi-object tracking (OVMOT) represents a critical new challenge involving the detection and tracking of diverse object categories in videos, encompassing both seen categories (base classes) and unseen categories (novel classes). Thi
Externí odkaz:
http://arxiv.org/abs/2410.08529
Large-scale multimodal models have shown excellent performance over a series of tasks powered by the large corpus of paired multimodal training data. Generally, they are always assumed to receive modality-complete inputs. However, this simple assumpt
Externí odkaz:
http://arxiv.org/abs/2410.06558
Autor:
Liang, Wanchao, Liu, Tianyu, Wright, Less, Constable, Will, Gu, Andrew, Huang, Chien-Chin, Zhang, Iris, Feng, Wei, Huang, Howard, Wang, Junjie, Purandare, Sanket, Nadathur, Gokul, Idreos, Stratos
The development of large language models (LLMs) has been instrumental in advancing state-of-the-art natural language processing applications. Training LLMs with billions of parameters and trillions of tokens require sophisticated distributed systems
Externí odkaz:
http://arxiv.org/abs/2410.06511
Autor:
Das, Swagat R., Merello, Manuel, Bronfman, Leonardo, Liu, Tie, Garay, Guido, Stutz, Amelia, Mardones, Diego, Zhou, Jian-Wen, Sanhueza, Patricio, Liu, Hong-Li, Vázquez-Semadeni, Enrique, Gómez, Gilberto C., Palau, Aina, Tej, Anandmayee, Xu, Feng-Wei, Baug, Tapas, Dewangan, Lokesh K., He, Jinhua, Zhu, Lei, Li1, Shanghuo, Juvela, Mika, Saha, Anindya, Issac, Namitha, Hwang, Jihye, Nazeer, Hafiz, Toth, L. Viktor
Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a deta
Externí odkaz:
http://arxiv.org/abs/2409.19204
Sign language videos are an important medium for spreading and learning sign language. However, most existing human image synthesis methods produce sign language images with details that are distorted, blurred, or structurally incorrect. They also pr
Externí odkaz:
http://arxiv.org/abs/2409.16709