2 citations · 4 across the 7 of their papers we have counts for
7 papers
Exploiting the Prior of Generative Time Series Imputation
YuYang Miao, Chang Li, Zehua Chen
Time series imputation, i.e., filling the missing values of a time recording, finds various applications in electricity, finance, and weather modelling. Previous methods have intro…
Short Ticketing Detection Framework Analysis Report
Yuyang Miao, Huijun Xing, Danilo P. Mandic +1
This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduce…
Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer
Zehua Chen, Yuyang Miao, Liyuan Wang +3
Cardiovascular signals such as photoplethysmography (PPG), electrocardiography (ECG), and blood pressure (BP) are inherently correlated and complementary, together reflecting the h…
RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals
Yuyang Miao, Zehua Chen, Chang Li +1
Respiratory rate (RR) is a critical health indicator often monitored under inconvenient scenarios, limiting its practicality for continuous monitoring. Photoplethysmography (PPG) s…
GPINN: Physics-informed Neural Network with Graph Embedding
Yuyang Miao, Haolin Li
This work proposes a Physics-informed Neural Network framework with Graph Embedding (GPINN) to perform PINN in graph, i.e. topological space instead of traditional Euclidean space,…
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning
Yuyang Miao, Harry J. Davies, Danilo P. Mandic
Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG signals provide insight into the b…
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Yuyang Miao, Yao Xu, Danilo Mandic
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in…