With recent advancements in next-generation data storage, especially in biological molecule-based storage, insertion, deletion, and substitution (IDS) error-correcting codes have garnered increased attention. However, a universal method for designing tailored IDS-correcting codes across varying channel settings remains underexplored. We present an autoencoder-based approach, THEA-code, aimed at efficiently generating IDS-correcting codes for complex IDS channels. In the work, a disturbance-based discretization is proposed to discretize the features of the autoencoder, and a simulated differentiable IDS channel is developed as a differentiable alternative for IDS operations. These innovations facilitate the successful convergence of the autoencoder, producing channel-customized IDS-correcting codes that demonstrate commendable performance across complex IDS channels, particularly in realistic DNA-based storage channels.

Horizontal annotation-based weakly supervised rotating object detection is a research field that has just been explored. This concept is expected to have a transformative effect on the advancement of data-driven rotating object detection methods. Existing pioneering researches directly regress the rotating rectangle based on angle description, which mainly face two limitations under the weakly supervised framework: 1) the regression form of weakly supervised learning is redundant relative to the training objective, thereby increasing the difficulty of model training and 2) the training objective of self-supervised (SS) learning does not have a close logical relationship with the test metrics, which may result in loss of accuracy. Addressing the above issues, this article proposes an angle-free weakly supervised rotating object detection framework, whose salient points mainly include the following: 1) by improving an angle-free rotating object representation, the decoupling between horizontal and rotating regression parameters in describing rotating objects is achieved; 2) a weakly supervised learning pipeline that is completely equivalent to the common horizontal object detection is designed to effectively relieve the difficulty of model training; and 3) a geometrically intuitive SS learning loss function is introduced to bridge the gap between the training objective and testing metrics. Experimental results on multiple large-scale remote sensing datasets confirm that the accuracy of this method is superior to the state-of-the-art (SOTA) weakly supervised rotating object detection methods, and is competitive even with SOTA fully supervised-related works.