14 papers
Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching
Chyong Yi Poh, Hwa Hui Tew, Junn Yong Loo +4
Structural connectivity (SC) and functional connectivity (FC) provide complementary information on interactions between brain regions and are widely used in neuroimaging studies of…
On the Robustness of Audio Deepfake Detection under Audio Watermarking
Zi Qian Yong, Ajinkya Kulkarni, Julia Lau +4
Recent advances in generative audio models have enabled highly realistic synthetic speech, increasing the importance of reliable audio deepfake detection (ADD) systems. While prior…
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
Hwa Hui Tew, Junn Yong Loo, Fang Yu Leong +6
Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the…
KnowDiffuser: A Knowledge-Guided Diffusion Planner with LLM Reasoning
Fan Ding, Xuewen Luo, Fengze Yang +4
Recent advancements in Language Models (LMs) have demonstrated strong semantic reasoning capabilities, enabling their application in high-level decision-making for autonomous drivi…
T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Models
Hwa Hui Tew, Junn Yong Loo, Yee-Fan Tan +5
Functional Magnetic Resonance Imaging (fMRI) is an advanced neuroimaging method that enables in-depth analysis of brain activity by measuring dynamic changes in the blood oxygenati…
GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction
Fan Ding, Hwa Hui Tew, Junn Yong Loo +6
Accurate trajectory prediction for buses is crucial in intelligent transportation systems, particularly within urban environments. In developing regions where access to multimodal…
Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving
Fan Ding, Ding Fan, Xuewen Luo +6
Personalized autonomous driving requires understanding the driving needs implicit in user requests. This is challenging when driving demonstrations lack style annotations and behav…
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching
Junn Yong Loo, Michelle Adeline, Julia Kaiwen Lau +6
Energy-based models (EBMs) are a powerful class of probabilistic generative models due to their flexibility and interpretability. However, relationships between potential flows and…
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
Xuewen Luo, Fan Ding, Fengze Yang +4
This study addresses the critical need for enhanced situational awareness in autonomous driving (AD) by leveraging the contextual reasoning capabilities of large language models (L…
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew, Fan Ding, Gaoxuan Li +4
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections…
KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Hwa Hui Tew, Gaoxuan Li, Fan Ding +5
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improvin…
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Jia Quan Loh, Xuewen Luo, Fan Ding +5
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligen…
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
Fan Ding, Xuewen Luo, Gaoxuan Li +6
To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive P…
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights
Gaoxuan Li, Chern Hong Lim, Qiyao Ma +4
Recent research in the field of Human Activity Recognition has shown that an improvement in prediction performance can be achieved by reducing the number of LSTM layers. However, t…