no code implementations • 13 Feb 2024 • Xin Jin, Wu Zhou, Jingyu Wang, Duo Xu, Yongsen Zheng
In order to improve the quality of AI music generation and further guide computer music production, synthesis, recommendation and other tasks, we use Birkhoff's aesthetic measure to design a aesthetic model, objectively measuring the aesthetic beauty of music, and form a recommendation list according to the aesthetic feeling of music.
no code implementations • 11 Sep 2023 • Duo Xu, Stella Offner, Robert Gutermuth, Michael Grudic, David Guszejnov, Philip Hopkins
We train DDPMs to estimate the ISRF using synthetic three-band dust emission.
no code implementations • 23 Apr 2023 • Xin Jin, Wu Zhou, Jinyu Wang, Duo Xu, Yiqing Rong, Jialin Sun
In order to guide the generation task of AI music performance, and to improve the performance effect of human performers, this paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty.
1 code implementation • 4 Apr 2023 • Duo Xu, Jonathan C. Tan, Chia-Jung Hsu, Ye Zhu
We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps.
no code implementations • 14 Jan 2023 • Xin Jin, Wu Zhou, Jinyu Wang, Duo Xu, Yiqing Rong, Shuai Cui
Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored.
no code implementations • 8 Jan 2023 • Jiawen Li, Yunqian Huang, Sheng Song, Hongbo Chen, Junni Shi, Duo Xu, Haibin Zhang, Man Chen, Rui Zheng
A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system.
no code implementations • 8 Dec 2022 • Duo Xu, Faramarz Fekri
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations.
1 code implementation • International Conference on Software Engineering 2022 • Yueming Wu, Deqing Zou, Shihan Dou, Wei Yang, Duo Xu, Hai Jin
Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability.
no code implementations • 31 May 2022 • Duo Xu, Sander Damsma, Mircea Lazar
To improve the steady-state behavior of FCS-MPC, in this paper we design a cost function that penalizes the tracking error with respect to a state and input steady-state limit cycle.
no code implementations • 21 Jun 2021 • Duo Xu, Faramarz Fekri
In this work, we propose a new hierarchical framework via symbolic RL, leveraging a symbolic transition model to improve the data-efficiency and introduce the interpretability for learned policy.
Hierarchical Reinforcement Learning Inductive logic programming +2
no code implementations • 22 Mar 2021 • Duo Xu, Faramarz Fekri
In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor-critic RL with HMC, which is termed as {\it Hamiltonian Policy}.
no code implementations • 30 Jun 2020 • Duo Xu, Mohit Agarwal, Ekansh Gupta, Faramarz Fekri, Raghupathy Sivakumar
Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning.
no code implementations • ICLR 2020 • Duo Xu, Mohit Agarwal, Raghupathy Sivakumar, Faramarz Fekri
Building atop the baseline, we then make the following novel contributions in our work: (i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.
no code implementations • 3 Jan 2019 • Duo Xu
State space models (SSM) have been widely applied for the analysis and visualization of large sequential datasets.