Zefang Liu, Weston M. Stacey
arXiv preprint arXiv:2408.14404, 2024
A neural ODE-based burning plasma dynamics model, NeuralPlasmaODE, simulates energy transfer in ITER plasmas for both inductive and non-inductive scenarios.
Zefang Liu, Weston M. Stacey
ICLR 2024 Workshop on AI4DifferentialEquations in Science, 2024
A novel Neural ODE-based model is introduced for simulating tokamak plasma dynamics, offering precise energy transfer analysis crucial for advancing controlled thermonuclear fusion.
Zefang Liu, Jialei Shi, John F. Buford
AAAI 2024 Workshop on Artificial Intelligence for Cyber Security, 2024
CyberBench introduces a benchmark for evaluating large language models in cybersecurity, alongside CyberInstruct, a fine-tuned LLM that performs competitively in this domain.
Zefang Liu
arXiv preprint arXiv:2312.15838, 2023
SecQA introduces a specialized dataset for evaluating the performance of large language models in computer security through multiple-choice questions.
Zefang Liu, John F. Buford
Conference on Applied Machine Learning in Information Security 2023, 2023
This paper leverages DistilBERT for anomaly detection in Unix shell sessions using both unsupervised and supervised methods, demonstrating effective detection of anomalous behavior with minimal data labeling.
Zefang Liu, Weston M. Stacey
64th Annual Meeting of the APS Division of Plasma Physics, 2022
This paper develops a multi-region, multi-timescale transport model to simulate burning plasma dynamics in tokamaks, addressing energy transport and radiation effects to prevent thermal runaway instability in ITER scenarios.
Zefang Liu, Shuran Wen, Yinzhu Quan
arXiv preprint arXiv:2106.06900, 2021
This paper introduces a Deep Reinforcement learning-based Group Recommender System (DRGR) using actor-critic networks and the deep deterministic policy gradient algorithm.
Zefang Liu
arXiv preprint arXiv:2012.06690, 2020
This paper predicts Yelp restaurant ratings using both traditional machine learning models and transformer-based models.