BACKGROUND: Multi-omics integration may provide additional information about the development of tumors and improve the performance of predictive models. The key challenge lies in integrating several omics sources, especially to capture their biological relationships. Previous studies proposed a structural equation model framework to combine two data platforms for predicting survival; however, several limitations remain. RESULTS: In this study, we introduce an extended Bayesian survival model combined with a structural equation model for adaptation to broader applications. The No U-turn Sampling (NUTS) algorithm was utilized to efficiently sample the posterior distribution of model parameters. Through a series of simulation studies, our model showed excellent goodness-of-fit and predictive performance. To validate the efficiency of our model, we utilized a gastric cancer dataset with three omics types (mRNA, microRNA, and methylation) obtained from The Cancer Genome Atlas. After bioinformatic processing, we included six mRNA, microRNA, and methylation loci datasets into the framework and discovered that our model exhibited greater predictive performance compared to non-integrated and Integrative Bayesian Analysis of Genomics (iBAG) models. CONCLUSIONS: In conclusion, our extended Bayesian structural equation model for multi-omics survival analysis provides a robust framework that significantly enhances predictive accuracy by effectively capturing complex biological relationships across diverse omics data sources, demonstrating clear advantages over both non-integrated approaches and existing integrative methods like iBAG.

Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce \textbf{Test3R}, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets ($I_1,I_2,I_3$), Test3R generates reconstructions from pairs ($I_1,I_2$) and ($I_1,I_3$). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image $I_1$. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at https://github.com/nopQAQ/Test3R.

4D Gaussian Splatting (4DGS) has recently gained considerable attention as a method for reconstructing dynamic scenes. Despite achieving superior quality, 4DGS typically requires substantial storage and suffers from slow rendering speed. In this work, we delve into these issues and identify two key sources of temporal redundancy. (Q1) \textbf{Short-Lifespan Gaussians}: 4DGS uses a large portion of Gaussians with short temporal span to represent scene dynamics, leading to an excessive number of Gaussians. (Q2) \textbf{Inactive Gaussians}: When rendering, only a small subset of Gaussians contributes to each frame. Despite this, all Gaussians are processed during rasterization, resulting in redundant computation overhead. To address these redundancies, we present \textbf{4DGS-1K}, which runs at over 1000 FPS on modern GPUs. For Q1, we introduce the Spatial-Temporal Variation Score, a new pruning criterion that effectively removes short-lifespan Gaussians while encouraging 4DGS to capture scene dynamics using Gaussians with longer temporal spans. For Q2, we store a mask for active Gaussians across consecutive frames, significantly reducing redundant computations. Compared to vanilla 4DGS, our method achieves a $41\times$ reduction in storage and $9\times$ faster rasterization on complex dynamic scenes, while maintaining comparable visual quality.

Unsupervised change detection in season-varying images remains challenging due to the pseudo-changes induced by the seasonal variation of the images acquired at different times. Although various transformations [e.g., multivariate alteration detection (MAD), slow feature analysis (SFAs), etc.] have been developed to alleviate this issue, the complicated seasonal variation in a heterogenous landscape cannot be well addressed. This study developed a novel “difference-in-differences”-based change detection (DIDCD) method, which calculates the change magnitude by performing two difference operations between the changes observed in the target pixel and those in the control group. The control group is established by a combined method of k-means clustering and minimum covariance determinant (MCD), which identifies similar pixels that undergo parallel seasonal changes compared to the target pixels. Through DID operation, DIDCD eliminates seasonal variation and accurately identifies land cover change. DIDCD is evaluated using four pairs of images under both season-consistent and season-varying conditions across two scenarios (urban expansion and forest fire disturbance). The results demonstrate that DIDCD effectively suppresses the pseudo-changes induced by the seasonal variation and outperforms the existing unsupervised change detection algorithms.