no code implementations • 12 Apr 2025 • Jiangong Chen, Yue Xiao, Zhendong Peng, Jing Zhu, Xia Lei, Christos Masouros, Kai-Kit Wong
Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement.
no code implementations • 30 Mar 2025 • Jing Zhu, Mingxuan Ju, Yozen Liu, Danai Koutra, Neil Shah, Tong Zhao
Generative recommendation (GR) has become a powerful paradigm in recommendation systems that implicitly links modality and semantics to item representation, in contrast to previous methods that relied on non-semantic item identifiers in autoregressive models.
no code implementations • 27 Mar 2025 • Jing Zhu, Qu, Luo, Zheng Chu, Gaojie Chen, Pei Xiao, Lixia Xiao, Chaoyun Song
In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM.
no code implementations • 24 Mar 2025 • Qu Luo, Jing Zhu, Zilong Liu, Yanqun Tang, Pei Xiao, Gaojie Chen, Jia Shi
This paper is devoted to enhanced receiver design for multiple input and multiple output AFDM (MIMO-AFDM) systems.
no code implementations • 17 Mar 2025 • Qu Luo, Jing Zhu, Gaojie Chen, Pei Xiao, Rahim Tafazolli
Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks.
no code implementations • 13 Nov 2024 • Qu Luo, Jing Zhu, Gaojie Chen, Pei Xiao, Rahim Tafazolli
This mapping is referred to as nonlinear mapping (codebook) in this paper.
1 code implementation • 30 Oct 2024 • Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang
Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e. g., Wi-Fi, LTE, and 5G) simultaneously.
no code implementations • 26 Sep 2024 • Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra
Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models.
no code implementations • 24 Jun 2024 • Jing Zhu, YuHang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area.
1 code implementation • 19 Jun 2024 • YuHang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang
Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive.
1 code implementation • 7 Jun 2024 • Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra
To address the efficiency challenges at inference time, we introduce a retrieval-reranking scheme.
1 code implementation • 11 Nov 2023 • Jianbin Qin, Sifan Huang, Yaoshu Wang, Jing Zhu, Yifan Zhang, Yukai Miao, Rui Mao, Makoto Onizuka, Chuan Xiao
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
no code implementations • 9 Nov 2023 • Sihan Gao, Jing Zhu, Xiaoxuan Zhuang, Zhaoyue Wang, Qijin Li
The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.
1 code implementation • 25 Sep 2023 • Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
no code implementations • 13 Jul 2023 • Michael James Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Jing Zhu, Prabal Datta Barua, U. Rajendra Acharya, Fang Chen, Jianlong Zhou
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
no code implementations • 1 Jun 2023 • Jing Zhu, YuHang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
no code implementations • 22 Nov 2022 • Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
no code implementations • 13 Sep 2022 • Huayu Chen, Huanhuan He, Jing Zhu, Shuting Sun, Jianxiu Li, Xuexiao Shao, Junxiang Li, Xiaowei Li, Bin Hu
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.
1 code implementation • 23 Aug 2022 • Jing Zhu, Danai Koutra, Mark Heimann
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains.
no code implementations • 24 Jan 2022 • Michael Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Jing Zhu, Hui Wen Loh, Prabal Datta Barua, U. Rajendra Arharya
In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
no code implementations • 7 Aug 2021 • HaoYu Fang, Jing Zhu, Yi Fang
Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects.
1 code implementation • 26 Feb 2021 • Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.
1 code implementation • EMNLP 2021 • Tara Safavi, Jing Zhu, Danai Koutra
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence.
no code implementations • 27 Jun 2020 • Jing Zhu, Yanan Xu, Yanmin Zhu
First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively.
no code implementations • 20 Feb 2020 • Hanshu Cai, Yiwen Gao, Shuting Sun, Na Li, Fuze Tian, Han Xiao, Jianxiu Li, Zhengwu Yang, Xiaowei Li, Qinglin Zhao, Zhenyu Liu, Zhijun Yao, Minqiang Yang, Hong Peng, Jing Zhu, Xiaowei Zhang, Guoping Gao, Fang Zheng, Rui Li, Zhihua Guo, Rong Ma, Jing Yang, Lan Zhang, Xiping Hu, Yumin Li, Bin Hu
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
no code implementations • 28 Sep 2019 • Yunxiao Shi, Jing Zhu, Yi Fang, Kuochin Lien, Junli Gu
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
no code implementations • 10 Sep 2019 • Jing Zhu, Yunxiao Shi, Mengwei Ren, Yi Fang, Kuo-Chin Lien, Junli Gu
To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.
Ranked #57 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • ICCV 2019 • Jing Zhu, Yi Fang, Husam Abu-Haimed, Kuo-Chin Lien, Dongdong Fu, Junli Gu
Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving.