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Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map. Results of the accuracy and replicability on both synthetic and real network data show that the proposed manifold learning approach outperforms all hub identification methods employed in this evaluation.

The third-order simplified harmonic spherical approximation (SP3) and diffusion approximation (DA) equations have been widely used in the three-dimensional (3D) whole-body optical imaging of small animals. With different types of tissues, which were classified by the ratio of µ s'/µ ɑ, the two equations have their own application scopes. However, the classification criterion was blurring and unreasonable, and the scope has not been systematically investigated until now. In this study, a new criterion for classifying tissues was established based on the absolute value of absorption and reduced scattering coefficients. Using the newly defined classification criterion, the performance and applicability of the SP3 and DA equations were evaluated with a series of investigation experiments. Extensive investigation results showed that the SP3 equation exhibited a better performance and wider applicability than the DA one in most of the observed cases, especially in tissues of low-scattering-low-absorption and low-scattering-high-absorption range. For the case of tissues with the high-scattering-low-absorption properties, a similar performance was observed for both the SP3 and the DA equations, in which case the DA was the preferred option for 3D whole-body optical imaging. Results of this study would provide significant reference for the study of hybrid light transport models.

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models. [ABSTRACT FROM AUTHOR]