Zobrazeno 1 - 10
of 1 464
pro vyhledávání: '"Zhang, Yufan"'
Autor:
Yi, Zhonghua, Shi, Hao, Jiang, Qi, Yang, Kailun, Wang, Ze, Gu, Diyang, Zhang, Yufan, Wang, Kaiwei
Event cameras, with high temporal resolution and high dynamic range, have limited research on the inter-modality local feature extraction and matching of event-image data. We propose EI-Nexus, an unmediated and flexible framework that integrates two
Externí odkaz:
http://arxiv.org/abs/2410.21743
The dynamics of dislocations can be formulated in terms of the evolution of continuous variables representing dislocation densities ('continuum dislocation dynamics'). We show for various variants of this approach that the resulting models can be env
Externí odkaz:
http://arxiv.org/abs/2410.17177
This paper proposes a computationally tractable algorithm for learning infinite-horizon average-reward linear mixture Markov decision processes (MDPs) under the Bellman optimality condition. Our algorithm for linear mixture MDPs achieves a nearly min
Externí odkaz:
http://arxiv.org/abs/2410.14992
M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions
Autor:
Datar, Aniket, Pokhrel, Anuj, Nazeri, Mohammad, Rao, Madhan B., Pan, Chenhui, Zhang, Yufan, Harrison, Andre, Wigness, Maggie, Osteen, Philip R., Ye, Jinwei, Xiao, Xuesu
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flig
Externí odkaz:
http://arxiv.org/abs/2410.01105
This paper presents P2U-SLAM, a visual Simultaneous Localization And Mapping (SLAM) system with a wide Field of View (FoV) camera, which utilizes pose uncertainty and point uncertainty. While the wide FoV enables considerable repetitive observations
Externí odkaz:
http://arxiv.org/abs/2409.10143
Autor:
Dong, Guosheng, Pan, Da, Sun, Yiding, Zhang, Shusen, Liang, Zheng, Wu, Xin, Shen, Yanjun, Yang, Fan, Sun, Haoze, Li, Tianpeng, Lin, Mingan, Xu, Jianhua, Zhang, Yufan, Nie, Xiaonan, Su, Lei, Wang, Bingning, Zhang, Wentao, Mao, Jiaxin, Zhou, Zenan, Chen, Weipeng
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a uni
Externí odkaz:
http://arxiv.org/abs/2408.15079
Day-ahead unit commitment (UC) is a fundamental task for power system operators, where generator statuses and power dispatch are determined based on the forecasted nodal net demands. The uncertainty inherent in renewables and load forecasting require
Externí odkaz:
http://arxiv.org/abs/2408.05185
This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs. At the training phase, a bilevel program for parameter estimation is formulated, where the upper level opti
Externí odkaz:
http://arxiv.org/abs/2406.10434
We study the infinite-horizon average-reward reinforcement learning with linear MDPs. Previous approaches either suffer from computational inefficiency or require strong assumptions on dynamics, such as ergodicity, for achieving a regret bound of $\w
Externí odkaz:
http://arxiv.org/abs/2405.15050
Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. While existing deterministic market clearing fails to accommodate the uncertainty, the recently proposed stochastic market clearing struggles t
Externí odkaz:
http://arxiv.org/abs/2405.09004