Zobrazeno 1 - 10
of 1 935
pro vyhledávání: '"Xu, Duo"'
Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs) to estima
Externí odkaz:
http://arxiv.org/abs/2410.07032
One of the fundamental challenges in reinforcement learning (RL) is to take a complex task and be able to decompose it to subtasks that are simpler for the RL agent to learn. In this paper, we report on our work that would identify subtasks by using
Externí odkaz:
http://arxiv.org/abs/2410.01929
This paper considers learning online (implicit) nonlinear model predictive control (MPC) laws using neural networks and Laguerre functions. Firstly, we parameterize the control sequence of nonlinear MPC using Laguerre functions, which typically yield
Externí odkaz:
http://arxiv.org/abs/2409.09436
Autor:
Xu, Duo, Lazar, Mircea
This paper considers the design of finite control set model predictive control (FCS-MPC) for discrete-time switched affine systems. Existing FCS-MPC methods typically pursue practical stability guarantees, which ensure convergence to a bounded invari
Externí odkaz:
http://arxiv.org/abs/2407.07615
Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is n
Externí odkaz:
http://arxiv.org/abs/2405.14238
In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention. This paper introduces CityLLaVA, a novel fine-tuning framework
Externí odkaz:
http://arxiv.org/abs/2405.03194
A learning management system streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an e-lea
Externí odkaz:
http://arxiv.org/abs/2403.13822
Computational aesthetic evaluation has made remarkable contribution to visual art works, but its application to music is still rare. Currently, subjective evaluation is still the most effective form of evaluating artistic works. However, subjective e
Externí odkaz:
http://arxiv.org/abs/2402.08300
Autor:
Law, C-Y, Tan, Jonathan C., Skalidis, Raphael, Morgan, Larry, Xu, Duo, Alves, Felipe de Oliveira, Barnes, Ashley T., Butterfield, Natalie, Caselli, Paola, Cosentino, Giuliana, Fontani, Francesco, Henshaw, Jonathan D., Jimenez-Serra, Izaskun, Lim, Wanggi
Magnetic fields may play a crucial role in setting the initial conditions of massive star and star cluster formation. To investigate this, we report SOFIA-HAWC+ $214\:\mu$m observations of polarized thermal dust emission and high-resolution GBT-Argus
Externí odkaz:
http://arxiv.org/abs/2401.11560
Autor:
Qian, Yikai, Wang, Tianle, Tong, Xinyi, Jin, Xin, Xu, Duo, Zheng, Bo, Ge, Tiezheng, Yu, Feng, Zhu, Song-Chun
In addressing the challenge of interpretability and generalizability of artificial music intelligence, this paper introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and
Externí odkaz:
http://arxiv.org/abs/2401.02678