Zobrazeno 1 - 10
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pro vyhledávání: '"ChEN, Jun"'
Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcity due to the sensitivity of su
Externí odkaz:
http://arxiv.org/abs/2411.17672
Autor:
Chen, Jun Yu, Gao, Tao
We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous approaches that primarily concentrate on skill-based
Externí odkaz:
http://arxiv.org/abs/2411.17255
Large multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning over a large number of images. Existing benchmarks for multi-image que
Externí odkaz:
http://arxiv.org/abs/2411.16740
Autor:
Xiang, Jun, Chen, Jun
Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety of UAVs. This type of planning can be represented as a Constrained Sh
Externí odkaz:
http://arxiv.org/abs/2411.14427
Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing manag
Externí odkaz:
http://arxiv.org/abs/2411.14403
Autor:
Xiang, Jun, Chen, Jun
Probabilistic Reachable Set (PRS) plays a crucial role in many fields of autonomous systems, yet efficiently generating PRS remains a significant challenge. This paper presents a learning approach to generating 2-dimensional PRS for states in a dynam
Externí odkaz:
http://arxiv.org/abs/2411.14356
Autor:
Wu, Sean, Chen, Jun Yu, Mohammadzadeh, Vahid, Besharati, Sajad, Lee, Jaewon, Nouri-Mahdavi, Kouros, Caprioli, Joseph, Fei, Zhe, Scalzo, Fabien
Perimetric measurements provide insight into a patient's peripheral vision and day-to-day functioning and are the main outcome measure for identifying progression of visual damage from glaucoma. However, visual field data can be noisy, exhibiting hig
Externí odkaz:
http://arxiv.org/abs/2411.12146
Autor:
Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, Zhu, Yi-Fan
DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High re
Externí odkaz:
http://arxiv.org/abs/2411.09345
Autor:
Chen, Jun-Kun, Wang, Yu-Xiong
This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene editing is roo
Externí odkaz:
http://arxiv.org/abs/2411.05006
This paper investigates a novel lossy compression framework operating under logarithmic loss, designed to handle situations where the reconstruction distribution diverges from the source distribution. This framework is especially relevant for applica
Externí odkaz:
http://arxiv.org/abs/2410.21666