DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy constraint layers atop raw binary data-workable but primitive. Recent neural codecs compress data into learned latent representations with rich statistical structure, yet still convert these latents to DNA via naive binary-to-quaternary transcoding, discarding the entropy model's optimization. This mismatch undermines compression efficiency and complicates the encoding stack. A plug-in module that collapses latent compression and DNA encoding into a single step. SCONE performs quaternary arithmetic coding directly on the latent space in DNA bases. Its Constraint-Aware Adaptive Coding module dynamically steers the entropy encoder's learned probability distribution to enforce biochemical constraints-GC balance and homopolymer suppression-deterministically during encoding, eliminating post-hoc correction. The design preserves full reversibility and exploits the hyperprior model's learned priors without modification. Experiments show SCONE achieves near-perfect constraint satisfaction with negligible computational overhead (< 2% latency), establishing a latent-agnostic interface for end-to-end DNA-compatible learned codecs.

We investigate what structure emerges in 3D Gaussian Splatting (3DGS) solutions from standard multi-view optimization. We term these Rendering-Optimal References (RORs) and analyze their statistical properties, revealing stable patterns-log-normal scales and bimodal radiance-across diverse scenes. To understand what determines these parameters, we apply learnability probes: training predictors to reconstruct RORs from point clouds without rendering supervision. Our analysis uncovers fundamental density-stratification: dense regions exhibit geometry-correlated parameters amenable to render-free prediction, while sparse regions show systematic failure across architectures. We formalize this through variance decomposition, demonstrating that visibility heterogeneity creates covariance-dominated coupling between geometric and appearance parameters in sparse regions. This reveals RORs' dual character-geometric primitives where point clouds suffice, view synthesis primitives where multi-view constraints are essential. We provide density-aware strategies that improve training robustness and discuss architectural implications for systems that adaptively balance feed-forward prediction and rendering-based refinement.

Tactile information plays a vital role in human perception, robotics, and human-computer interaction, encapsulating the richness of physical sensations. Storing tactile information enables the preservation and precise reproduction of these sensory experiences, which can be used to enhance robotic manipulation with realistic feedback and preserve cultural artifacts for future study and interaction. With unparalleled density, stability, and longevity, DNA emerges as an ideal solution for the long-term preservation of such valuable data. However, the high cost of DNA synthesis and sequencing poses significant challenges for scalable storage. To address this, we propose an end-to-end neural network compression framework based on RVQGAN, MP-SENet, and robust DNA encoding strategies, enabling efficient compression of complex tactile data. Experimental results demonstrate that our method achieves high compression rates, maintains data integrity, and exhibits exceptional robustness against DNA-specific noise. This work also lays the foundation for the future development of multimodal perception AI by enabling the long-term storage of tactile information, ensuring its availability for future applications and advancements.

DNA data storage has unparalleled advantages in density, stability, and longevity, making it a promising solution to meet the exponentially growing demand for digital data preservation. As image compression has significantly improved the storage efficiency of DNA-based systems, recent progress has turned to learned image compression (LIC) methods applying deep neural networks. However, the application of generative deep learning to DNA-based image storage remains unexplored. This paper introduces HDCompression-DNA, a novel two-stream neural compression framework adapted for DNA storage based on the diffusion module, which enables efficient semantic compression and robust reconstruction at extreme-low bitrates. Experimental results show that HDCompression-DNA not only achieves excellent compression rates while maintaining high reconstruction fidelity, but also successfully satisfies key biological constraints, providing an efficient and scalable solution for long-term image preservation using neural network-based DNA encoding.

DNA storage has emerged as a promising solution to address the exponentially growing demand for storage capacity, offering advantages in density, stability, and long-term preservation potential. Currently, image compression for DNA storage has evolved into learned image compression (LIC), particularly through the application of deep learning methods based on artificial neural networks. The present study proposes a novel image compression framework for DNA Storage, named HybridFlow-DNA. HybridFlow-DNA is established by integration of VQGAN and MLIC with the adaptive dynamic DNA fountain encoding scheme. Experimental results demonstrate that HybridFlow-DNA achieves a high virtual information capacity while effectively maintaining the fidelity of the reconstruction of images.

DNA storage is a promising data storage method with high density, durability, and easy maintenance, ideal for data archiving. However, wide-scale adoption is hindered by challenges like high synthesis costs, data loss, and I/O complexities. Addressing robustness is a primary concern in advancing DNA storage. Traditional strategies for robustness rely on increasing redundancy, replicas, and error-correction codes (ECC) for each DNA sequence strand. Given the unpredictable errors in DNA storage and the associated costs of absolute accuracy, we’ve embraced an error-tolerance approach. This paper introduces an innovative method utilizing the residual Convolutional Neural Network (CNN) during image decoding in DNA storage to combat noise and enhance robustness. We simulated compressed images in DNA sequences and restored them using our network, achieving commendable peak signal-to-noise ratios (PSNR) even with lower-quality images. Our method offers a balance between redundancy and image quality in DNA storage.

As global data volume explodes, traditional storage systems face multiple challenges, including the lack of resources and low cost-efficient. Deoxyribonucleic acid (DNA) has attracted researchers' attention as a novel storage medium to address these issues with its high storage density, low maintenance cost, and extremely long shelf life. Specifically, DNA is also environmentally friendly compared to traditional disk storage because the disk is non-degradable. In this paper, we propose a strategy for image coding and storage based on compressing intra-predicted images from Versatile Video Coding (VVC) using synthetic genomics theories that enable the high throughput storage of images on DNA. We first define the length and format of the DNA oligo for the implementation of a storage system. Then we improve the LT codes to become a feasible DNA coding scheme. Last but not least, we design a voting mechanism to achieve the error correction function for robustness. The experimental results show high compression efficiency while achieving several strict biological constraints, such as GC-content balance and homopolymer control.

Chinese Character Recognition(CCR) is a critical application of Optical Character Recognition(OCR), a vital area of pattern recognition. Research on CCR in the past decades mainly focused on the modern Chinese characters, but not on the ancient ones. Compared to modern Chinese characters, ancient characters are more diverse and multiple ancient characters can correspond to one modern character. When doing recognition, the unique features of ancient Chinese characters cause a significant amount of time on manual labeling. This paper proposes an automatic labeling algorithm based on a semi-supervised dictionary training neural network that drastically decreases human effort. We first created an offline training set as a dictionary including 8,226 Chinese characters from ancient documents in modern fonts. And put the set into the network. Then we recursively retrained the network on an unlabeled data set of about 1.3 million characters images segmented from ancient documents resulting in a very high accuracy rate of 98.96 %. This work is one part of our wide recognition of ancient documents with handwritten Chinese characters project.