Feature drift is caused by the dynamic coupling of target features and degradation factors, which reduce underwater detector performance. We redefine feature drift as the instability of target features within boundary constraints while solving partial differential equations (PDEs). From this insight, we propose the Spatial Residual (SR) block, which uses SkipCut to establish effective constraints across the network width for solving PDEs and optimizes the solution space. It is implemented as a general-purpose backbone with 5 Spatial Residuals (BSR5) for complex feature scenarios. Specifically, BSR5 extracts discrete channel slices through SkipCut, where each sliced feature is parsed within the appropriate data capacity. In gradient backpropagation, SkipCut functions as a ShortCut, optimizing information flow and gradient allocation to enhance performance and accelerate training. Experiments on the RUOD dataset show that BSR5-integrated DETRs and YOLOs achieve state-of-the-art results for conventional and end-to-end detectors. Specifically, our BSR5-DETR improves 1.3% and 2.7% AP than RT-DETR with ResNet-101, while reducing parameters by 41.6% and 6.6%, respectively. Further validation highlights BSR5's strong convergence and robustness, especially in training from scratch scenarios, making it well suited for data-scarce, resource-constrained, and real-time tasks.

Retrospective artefact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine-learning-driven techniques for RAC are predominantly based on supervised learning, so practical utility can be limited as data with paired artefact-free and artefact-corrupted images are typically insufficient or even non-existent. Here we show that unwanted image artefacts can be disentangled and removed from an image via an RAC neural network learned with unpaired data. This implies that our method does not require matching artefact-corrupted data to be either collected via acquisition or generated via simulation. Experimental results demonstrate that our method is remarkably effective in removing artefacts and retaining anatomical details in images with different contrasts.

Harmonization improves Magn. Reson. Imaging (MRI) data consistency and is central to effective integration of diverse imaging data acquired across multiple sites. Recent deep learning techniques for harmonization are predominantly supervised in nature and hence require imaging data of the same human subjects to be acquired at multiple sites. Data collection as such requires the human subjects to travel across sites and is hence challenging, costly, and impractical, more so when sufficient sample size is needed for reliable network training. Here we show how harmonization can be achieved with a deep neural network that does not rely on traveling human phantom data. Our method disentangles site-specific appearance information and site-invariant anatomical information from images acquired at multiple sites and then employs the disentangled information to generate the image of each subject for any target site. We demonstrate with more than 6,000 multi-site T1- and T2-weighted images that our method is remarkably effective in generating images with realistic site-specific appearances without altering anatomical details. Our method allows retrospective harmonization of data in a wide range of existing modern large-scale imaging studies, conducted via different scanners and protocols, without additional data collection.

Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface–volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

Typically, the secondary controllers for distributed battery storage units (BSUs) have been designed using asymptotic or finite-time consensus protocols, which may not be suitable for DC microgrids with intermittent renewable energy operations and frequent load power fluctuations. This article presents a novel fixed-time secondary controller, which designed to achieve both rapid State-of-Charge (SoC) balancing and flexible bus voltage regulation for parallel BSUs in DC microgrids. The proposed fixed-time secondary controller provides a bounded settling time independent of initial conditions, whereas also offering a rapid SoC balancing speed. Pinning control is combined with the proposed fixed-time secondary controller to achieve flexible regulation and accurate restoration of the average bus voltage. This scheme saves computing resources and releases communication pressures. Rigorous Lyapunov stability proofs, along with experimental results using StarSim, demonstrate the effectiveness and superiority of the proposed fixed-time secondary controller under various testing scenarios.