Accuracy and timeliness are indeed often conflicting goals in prediction tasks.Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful.In applications such as wildfires, crimes, and traffic jams, timely forecasting are vital for safeguarding human life and property.Consequently, finding a balance between accuracy and timeliness is crucial.In this paper, we propose an early spatio-temporal forecasting model based on Multi-Objective reinforcement learning that can either implement an optimal policy given a preference or infer the preference based on a small number of samples.The model addresses two primary challenges: 1) enhancing the accuracy of early forecasting and 2) providing the optimal policy for determining the most suitable prediction time for each area.Our method demonstrates superior performance on three large-scale real-world datasets, surpassing existing methods in early spatio-temporal forecasting tasks.

MOTIVATION: Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships. RESULTS: To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI). AVAILABILITY AND IMPLEMENTATION: Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel method for dense matching for DSM generation is presented in this paper. First, a global energy function that contains matching cost and smoothness constraint is constructed, and then multiple candidates for disparity of each pixel are obtained by solving the global energy function, where multipath dynamic programming (MDP) is employed. Subsequently, a reliability measure is proposed, which evaluates the reliability of a given candidate and is calculated by Parzen estimation. The candidate with the highest reliability is selected as the final disparity for a given pixel. Moreover, postprocessing steps for removing outliers and the interpolation of gaps are presented to refine the disparity result. Consequently, forward intersection is employed to generate DSM with the disparity result. The complexity of this method is linear to the number of pixels and independent of the disparityrange, which is the reason for generating disparity result in a short time. The experiment conducted on ZY-3 imagery demonstrates the efficiency and accuracy of the proposed method.