Bowen Liao

Zepu Wang, Bowen Liao, Jeff et al.

Standard direct forecasting models typically rely on point-wise objectives such as Mean Squared Error, which fail to capture the complex spatio-temporal dependencies inherent in graph-structured signals. While recent frequency-domain approaches such as FreDF mitigate temporal autocorrelation, they often overlook spatial and cross spatio-temporal interactions. To address this limitation, we propose FreST Loss, a frequency-enhanced spatio-temporal training objective that extends supervision to the joint spatio-temporal spectrum. By leveraging the Joint Fourier Transform (JFT), FreST Loss aligns model predictions with ground truth in a unified spectral domain, effectively decorrelating complex dependencies across both space and time. Theoretical analysis shows that this formulation reduces estimation bias associated with time-domain training objectives. Extensive experiments on six real-world datasets demonstrate that FreST Loss is model-agnostic and consistently improves state-of-the-art baselines by better capturing holistic spatio-temporal dynamics.

Junbo Wang, Haofeng Tan, Bowen Liao et al.

We present a novel and practically significant problem-Geo-Contextual Soundscape-to-Landscape (GeoS2L) generation-which aims to synthesize geographically realistic landscape images from environmental soundscapes. Prior audio-to-image generation methods typically rely on general-purpose datasets and overlook geographic and environmental contexts, resulting in unrealistic images that are misaligned with real-world environmental settings. To address this limitation, we introduce a novel geo-contextual computational framework that explicitly integrates geographic knowledge into multimodal generative modeling. We construct two large-scale geo-contextual multimodal datasets, SoundingSVI and SonicUrban, pairing diverse soundscapes with real-world landscape images. We propose SounDiT, a novel Diffusion Transformer (DiT)-based model that incorporates geo-contextual scene conditioning to synthesize geographically coherent landscape images. Furthermore, we propose a practically-informed geo-contextual evaluation framework, the Place Similarity Score (PSS), across element-, scene-, and human perception-levels to measure consistency between input soundscapes and generated landscape images. Extensive experiments demonstrate that SounDiT outperforms existing baselines in both visual fidelity and geographic settings. Our work not only establishes foundational benchmarks for GeoS2L generation but also highlights the importance of incorporating geographic domain knowledge in advancing multimodal generative models, opening new directions at the intersection of generative AI, geography, urban planning, and environmental sciences.