Search Results for author: Jiajie Xu

Found 13 papers, 2 papers with code

Generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation approach for next-generation green communication systems

no code implementations16 Aug 2024 Bang Huang, Jiajie Xu, Mohamed-Slim Alouini

Additionally, a closed-form expression for the upper bound of the average bit error probability (ABEP) of the DBLC algorithm has been derived.

Improved Model and Analysis for RIS-Assisted Indoor Terahertz Wireless Networks

no code implementations11 Jul 2024 Zhi Chai, Jiajie Xu, Mohamed-Slim Alouini, Justin P. Coon

We calculate the coverage probability (CP) as a function of RIS number, obstacle density, room size, and the transmitter's location.

Experimental Validation of Cooperative RSS-based Localization with Unknown Transmit Power, Path Loss Exponent, and Precise Anchor Location

no code implementations20 Jun 2024 Yingquan Li, Bodhibrata Mukhopadhyay, Jiajie Xu, Mohamed-Slim Alouini

In this paper, we propose Cooperative Localization Techniques (with Unknown Parameters), referred to as CTUP(s), which consider uncertainty in anchor nodes' locations and assume the transmit power and \textcolor{blue}{path loss exponent (PLE)} to be unknown.

A Crosstalk-Aware Timing Prediction Method in Routing

no code implementations7 Mar 2024 Leilei Jin, Jiajie Xu, Wenjie Fu, Hao Yan, Longxing Shi

With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay.

CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction

no code implementations journal 2024 Jing Zhao, Jiajie Xu, Yuan Xu, Junhua Fang, Pingfu Chao and Xiaofang Zhou

Moreover, these methods do not explicitly consider the diversity of moving patterns among users and trajectories, i. e., the learning difficulty of different user and trajectory samples, thus hindering the improvement of prediction accuracy.

Contrastive Learning Meta-Learning +1

NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

1 code implementation24 Aug 2023 Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela, Ioannis Gkioulekas, Ashish Shrivastava, Yuning Chai

We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.

Optical Flow Estimation

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

2 code implementations21 Mar 2022 Li Chen, Chonghao Sima, Yang Li, Zehan Zheng, Jiajie Xu, Xiangwei Geng, Hongyang Li, Conghui He, Jianping Shi, Yu Qiao, Junchi Yan

Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).

3D Lane Detection Autonomous Driving +1

Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation

no code implementations20 Nov 2021 Yunyi Li, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Xiaofang Zhou

In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.

Graph Neural Network Sequential Recommendation

Self-Guided Instance-Aware Network for Depth Completion and Enhancement

no code implementations25 May 2021 Zhongzhen Luo, Fengjia Zhang, Guoyi Fu, Jiajie Xu

Depth completion aims at inferring a dense depth image from sparse depth measurement since glossy, transparent or distant surface cannot be scanned properly by the sensor.

Depth Completion Depth Estimation

Index-based Solutions for Efficient Density Peak Clustering

no code implementations8 Feb 2020 Zafaryab Rasool, Rui Zhou, Lu Chen, Chengfei Liu, Jiajie Xu

Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space.

Clustering

Predicting Destinations by a Deep Learning based Approach

no code implementations IEEE Transactions on Knowledge and Data Engineering 2019 Jiajie Xu, Jing Zhao, Rui Zhou, Chengfei Liu

However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations.

Deep Learning

Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

no code implementations29 May 2019 Jian Liu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Fuzheng Zhuang, Jiajie Xu, Xiaofang Zhou, Hui Xiong

Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.

Transfer Learning

Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

no code implementations18 Jun 2018 Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Zhixu Li, Jiajie Xu, Victor S. Sheng

Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model.

Sequential Recommendation

Cannot find the paper you are looking for? You can Submit a new open access paper.