Accurate spatio-temporal traffic prediction is essential for optimizing urban traffic management and resource allocation. To reduce the cost and complexity of cross-city deployment, recent studies have explored spatio-temporal foundation models capable of accurate zero-shot prediction. However, these models are limited to single-modal data, which restricts their capacity to capture the complexity of real-world traffic dynamics. The increasing availability of multi-modality data—such as satellite imagery and points of interest (POI)—offers a promising avenue for enhancing cross-city traffic prediction by providing richer background contexts. Despite this potential, developing foundational models for multi-modality spatio-temporal prediction presents two challenges: the availability and quality of multi-modality data vary significantly across cities, with some cities lacking certain modalities or containing noisy information; and spatial patterns are highly localized and specific to individual regions, which hinders generalization. To address these challenges, we propose MoST, a foundation model for multi-modality spatio-temporal traffic prediction. We introduce a Multi-modality Refinement Module that encodes available modality data and adaptively selects task-relevant modalities while suppressing noisy modalities. Furthermore, we design a Spatio-Temporal Prediction Module that incorporates a spatial expert selection mechanism guided by multi-modality cues. This mechanism dynamically identifies region-specific spatial patterns and assigns appropriate spatial experts to model local dependencies. Finally, we conduct extensive experiments on real-world datasets to validate the superior performance and strong generalization capability of MoST.

The financial market plays a crucial role in the modern economy by influencing capital allocation, corporate valuation, and investor behavior. However, its complex dependencies and non-stationary dynamics present significant challenges for financial stock prediction. Previous predictive approaches are typically categorized into Univariate Time Series (UTS) and Multivariate Time Series (MTS) paradigms. UTS methods overlook both cross-feature and cross-stock influences, while MTS methods can only capture one of these simultaneously. Although some recent approaches claim to model 3D Multivariate Time Series (3D-MTS) dependencies, they often discard substantial information and fail to capture the dynamics of the stock market. To address these limitations, we propose FinD3, a Financial 3D model using Dual cubic state spaces and Dynamic hypergraphs. To extract the inherent complex relationships in 3D-MTS, we propose a novel Dual Cubic State Space Model (DCSSM) to capture both cross-feature and cross-stock patterns. Furthermore, to more accurately reflect the dynamics of the stock market, we present an Evolving Hypergraph Attention (EHA) module, which captures dynamic changes in financial markets and updates the hypergraph based on a priori hypergraph. Experimental results demonstrate that FinD3 achieves state-of-the-art performance in quantitative trading performance on two real-world stock market datasets, offering a promising solution to practical quantitative trading challenges. The code is available at: https://github.com/decisionintelligence/FinD3.

Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-based models usually struggle with high computational demands and closed-system assumptions, while data-driven models may overlook essential physical dynamics, confusing the capturing of spatiotemporal correlations. Although some physics-guided approaches combine the strengths of both models, they often face a mismatch between explicit physical equations and implicit learned representations. To address these challenges, we propose Air-DualODE, a novel physics-guided approach that integrates dual branches of Neural ODEs for air quality prediction. The first branch applies open-system physical equations to capture spatiotemporal dependencies for learning physics dynamics, while the second branch identifies the dependencies not addressed by the first in a fully data-driven way. These dual representations are temporally aligned and fused to enhance prediction accuracy. Our experimental results demonstrate that Air-DualODE achieves state-of-the-art performance in predicting pollutant concentrations across various spatial scales, thereby offering a promising solution for real-world air quality challenges.

Owing to their compact structure, high stability, and low cost, Indirect Time-of-Fligh (IToF) cameras have gained increasing attention in the fields of robotics and automation. However, in real-world scenarios, IToF cameras are affected by multipath interference, which severely degrades imaging quality. Existing learning-based methods for multipath interference correction are all based on CNN architectures and rely on synthetic datasets, leading to poor generalization in real-world scenarios. We proposed an efficient and accurate real data collection scheme and explored the application of Transformer and Mamba in multipath interference correction tasks. Additionally, we introduced a cross-propagation network that integrates Mamba and CNN modules, reducing system complexity to linear levels while achieving superior multipath interference correction compared to state-of-the-art methods.

Vegetation plays an extremely important role in maintaining the quality and stability of the ecological environment and can be monitored using remote sensing, which is interfered by shadows, resulting in reduced accuracy of chlorophyll content retrieval, and eliminating this shadow interference is challenging. In this paper, a ground-based polarized-multispectral imaging system is built and a polarized-spectrum-based shadow removal method is proposed to eliminate the interference of vegetation shadows and improve the accuracy of vegetation health status detection. Two shadow removal vegetation indices, Polarization-spectrum-based Shadow Removal-Simple Ratio Index (PSR-SR) and Polarization-spectrum-based Shadow Removal-Normalized Difference Vegetation Index (PSR-NDVI), were developed based on SR and NDVI, to evaluate the impact of shadows on vegetation health detection. Compared with the typical un-polarized vegetation indices, PSR-SR and PSR-NDVI can effectively eliminate the vegetation shadow effects with the retrieval accuracy of chlorophyll content (PSR-SR: R2=0.89, RMSE=0.033 and PSR-NDVI: R2=0.95, RMSE=0.053). Different illumination analysis shows that this method is suitable for various illumination environments, and has excellent shadow elimination performance in a low illumination environment of 1.13 lx (PSR-SR: R2= 0.77, RMSE=0.028) (PSR-NDVI: R2=0.91, RMSE=0.053). Moreover, it shows that this method still has excellent shadow elimination performance for real outdoor applications (PSR-SR: R2=0.85, RMSE=0.044) (PSR-NDVI: R2=0.92, RMSE=0.05). In summary, our method shows the potential of utilizing polarization and spectrum to remove vegetation shadows and provides a strategy for eliminating vegetation shadows with different health states to achieve accurate and efficient vegetation health detection.

This study proposes a distinctive and robust spatial and geometric histograms (SGHs) feature descriptor for three‐dimensional (3D) local surface description. The authors also introduce a new local reference frame for the generation of their SGH descriptor. To fully describe a local surface, the SGH descriptor considers both spatial distribution and geometrical characteristics in its underlying support region. To encode neighbourhood information, the SGH descriptor is constructed using histogram statistics with spatial partition and interpolation strategies. The performance of the SGH descriptor was rigorously tested on six public datasets for applications of both 3D object recognition and registration. Compared to eight state‐of‐the‐art descriptors, experimental results show that SGH achieves the best performance on noise‐free data. It also produces the best results even under different nuisances. The promising descriptiveness and robustness of their SGH descriptor have been fully demonstrated.