no code implementations • COLING 2022 • Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu
Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text.
no code implementations • 6 May 2024 • Tong Nie, Guoyang Qin, Wei Ma, Jian Sun
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system.
no code implementations • 29 Apr 2024 • Kairui Feng, Dazhi Xi, Wei Ma, Cao Wang, Yuanlong Li, Xuanhong Chen
The advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards.
no code implementations • 9 Apr 2024 • ZhiHao Lin, Wei Ma, Tao Lin, Yaowen Zheng, Jingquan Ge, Jun Wang, Jacques Klein, Tegawende Bissyande, Yang Liu, Li Li
We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security.
no code implementations • 11 Feb 2024 • Yihong Tang, Zhaokai Wang, Ao Qu, Yihao Yan, Kebing Hou, Dingyi Zhuang, Xiaotong Guo, Jinhua Zhao, Zhan Zhao, Wei Ma
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural language.
no code implementations • 29 Jan 2024 • Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Wei Ma, Lyuye Zhang, Miaolei Shi, Yang Liu
Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including those requiring human- level intelligence, such as vulnerability detection.
1 code implementation • 4 Dec 2023 • Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun
The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it versatile for a variety of imputation problems.
no code implementations • 20 Oct 2023 • Xuechun Li, Paula M. Burgi, Wei Ma, Hae Young Noh, David J. Wald, Susu Xu
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses.
no code implementations • 29 Jul 2023 • Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Yves Le Traon
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data.
no code implementations • 6 Jul 2023 • Fuxiang Tao, Wei Ma, Xuri Ge, Anna Esposito, Alessandro Vinciarelli
The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors.
1 code implementation • 4 Jul 2023 • Tong Nie, Guoyang Qin, Lijun Sun, Wei Ma, Yu Mei, Jian Sun
Spatiotemporal urban data (STUD) displays complex correlational patterns.
no code implementations • 20 May 2023 • Wei Ma, Shangqing Liu, ZhiHao Lin, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, Li Li, Yang Liu
We break down the abilities needed for artificial intelligence~(AI) models to address SE tasks related to code analysis into three categories: 1) syntax understanding, 2) static behavior understanding, and 3) dynamic behavior understanding.
no code implementations • 10 May 2023 • Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu
Furthermore, the perturbation of adversarial examples introduced by RNNS is smaller compared to the baselines in terms of the number of replaced variables and the change in variable length.
1 code implementation • 29 Apr 2023 • Hanyu Sun, Xiao Huang, Wei Ma
In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend a parking space for a driver.
no code implementations • 29 Mar 2023 • Jinxiao Du, Wei Ma
Specifically, we aim to search for the optimal time headway between AVs on each link that achieves the network-wide system optimal dynamic traffic assignment (SO-DTA).
no code implementations • 4 Mar 2023 • Zijian Hu, Wei Ma
This study considers two representative control approaches: ramp metering for freeways and perimeter control for homogeneous urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks.
no code implementations • 20 Dec 2022 • Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhan Wang, Qiang Hu, Jie Zhang, Yang Liu
These structures are fundamental to understanding code.
no code implementations • 26 Sep 2022 • Junjia Huang, Wei Ma, Rong Li, Na Zhao, Tao Zhou
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
no code implementations • 5 Jun 2022 • Xiaohui Liu, Sean Qian, Hock-Hai Teo, Wei Ma
Curb space is one of the busiest areas in urban road networks.
no code implementations • 20 Apr 2022 • Wei Ma, Sean Qian
The proposed framework is cast into the computational graph and a reparametrization trick is developed to estimate the mean and standard deviation of the probabilistic dynamic OD demand simultaneously.
1 code implementation • 8 Apr 2022 • Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis, Yves Le Traon
The results reveal that 1) data with distribution shifts happen more disagreements than without.
1 code implementation • 8 Mar 2022 • Mingxi Li, Yihong Tang, Wei Ma
Currently, most of the state-of-the-art prediction models are based on graph neural networks (GNNs), and the required training samples are proportional to the size of the traffic network.
1 code implementation • 8 Feb 2022 • Yihong Tang, Ao Qu, Andy H. F. Chow, William H. K. Lam, S. C. Wong, Wei Ma
To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.
no code implementations • 4 Nov 2021 • Ao Qu, Yihong Tang, Wei Ma
In view of this, this paper first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to "cheat" DRL-based ATCS in order to save their total travel time.
1 code implementation • 29 Oct 2021 • Zijian Hu, William H. K. Lam, S. C. Wong, Andy H. F. Chow, Wei Ma
The proposed framework consists of two major components: camera calibration and vehicle detection.
no code implementations • 21 Oct 2021 • Wei Ma, Qin Xie, Jianhang Zhang, Shiliang Li, Youjun Xu, Xiaobing Deng, Weilin Zhang
Howerver, a majority of compouds with low docking scores could waste most of the computational resources.
no code implementations • 19 Aug 2021 • Jinlei Zhang, Feng Chen, Lixing Yang, Wei Ma, Guangyin Jin, Ziyou Gao
This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.
1 code implementation • 19 Apr 2021 • Lyuyi Zhu, Kairui Feng, Ziyuan Pu, Wei Ma
The diffusion attack aims to select and attack a small set of nodes to degrade the performance of the entire prediction model.
no code implementations • 21 Sep 2020 • Qiuyuan Wang, Zike Yan, Junqiu Wang, Fei Xue, Wei Ma, Hongbin Zha
To address these problems, we leverage a line flow to encode the coherence of line segment observations of the same 3D line along the temporal dimension, which has been neglected in prior SLAM systems.
no code implementations • 8 Aug 2020 • Jinlei Zhang, Hongshu Che, Feng Chen, Wei Ma, Zhengbing He
The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.
1 code implementation • NeurIPS 2019 • Wei Ma, George H. Chen
Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing.
1 code implementation • 6 Oct 2019 • Wei Ma, Sean Qian
The last decades have witnessed the breakthrough of autonomous vehicles (AVs), and the perception capabilities of AVs have been dramatically improved.
no code implementations • 30 Apr 2019 • Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, Yves Le Traon
This raises the question of how we can automatically select candidate test data to test deep learning models.
no code implementations • 12 Mar 2019 • Wei Ma, Xidong Pi, Sean Qian
Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge.
2 code implementations • 30 Jan 2019 • Wei Ma, Feng Cheng, Yihao Xu, Qinlong Wen, Yongmin Liu
To better unveil this implicit relationship and thus facilitate metamaterial design, we propose to represent metamaterials and model the inverse design problem in a probabilistically generative manner.
Optics
1 code implementation • 26 Jan 2019 • Wei Ma, Zhen, Qian
A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem.
1 code implementation • 21 Jan 2019 • Shuguan Yang, Wei Ma, Xidong Pi, Sean Qian
The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations.
no code implementations • 31 May 2018 • Yuchen Yang, Shuo Liu, Wei Ma, Qiuyuan Wang, Zheng Liu
The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.
no code implementations • 27 Apr 2018 • Jun Lu, Wei Ma, Boi Faltings
We explored $CompNet$, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact.
1 code implementation • 4 Dec 2017 • Wei Ma, Jun Lu
The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend.