Effective fusion of multi-source remote sensing data remains a fundamental challenge for Earth observation, as CNN and Transformer models suffer from limited receptive fields and high computational complexity. While State Space Models (SSM) like Mamba show promise in sequence modeling, they face three critical challenges in multi-source remote sensing: insufficient spatial-spectral coordination, cross-modal heterogeneity, and inadequate multi-scale feature integration. To address these limitations, this paper proposes MCAMamba: a Multi-Level Cross-Modal Attention-Guided Mamba framework for joint classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR)/Synthetic Aperture Radar (SAR) data. MCAMamba introduces a novel three-stage feature fusion pipeline: 1) The FExt-Attention module enhances spatial structure and spectral information through parallel spatial-channel attention mechanisms. 2) The SSM-Attention module achieves deep cross-modal fusion by combining attention mechanisms with SSM for parametric interaction. 3) The FFus-Attention module performs adaptive multi-scale feature integration through global context modeling and cascaded attention. This hierarchical design enables superior feature representation with enhanced computational efficiency. Experiments on four public benchmark datasets (Houston2013, Houston2018, Augsburg, and Berlin) show that MCAMamba achieves Overall Accuracy (OA) of 94.75%, 93.35%, 92.46%, and 79.18%. The code will be available at https://github.com/Dmygithub/MCAMamba.

In this article, we propose a novel strategy leveraging multiresolution topological maps to guide ground robots in exploring complex 3-D environments. Our approach extracts traversable areas and local frontiers from detailed elevation maps and integrates them into a global multiresolution topological map with global frontiers and viewpoints. GPU-accelerated computation enhances efficiency, while a viewpoint-guided adaptive sampling mechanism generates a sparse global map for rapid path-length estimation to viewpoints. Collision-free global paths to target areas are computed efficiently using the multiresolution topological map. Extensive simulations with wheeled and legged robots demonstrate superior task completion rates and exploration efficiency compared to state-of-the-art methods, with further validation in real-world experiments. The code will be made publicly available to support the robotics community.

In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR) that embeds Self-Attention and our novel Double Branch Cross Attention (DBC-Attention) to enhance scatter suppression. Under controlled water tank validation conditions, StreakNet-Arch with Self-Attention or DBC-Attention outperforms traditional bandpass filtering and achieves higher $F_{1}$ scores than learning-based MP networks and CNNs at comparable model size and complexity. Real-time benchmarks on an NVIDIA RTX 3060 show a constant Average Imaging Time (54 to 84 ms) regardless of frame count, versus a linear increase (58 to 1,257 ms) for conventional methods. To facilitate further research, we contribute a publicly available streak-tube camera image dataset contains 2,695,168 real-world underwater 3D point cloud data. More importantly, we validate our UCLR system in a South China Sea trial, reaching an error of 46mm for 3D target at 1,000 m depth and 20 m range. Source code and data are available at https://github.com/BestAnHongjun/StreakNet.

Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. Results: We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. Availability and implementation: DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM".