SCONE：一种实用的、具有约束意识的插件，用于学习DNA存储中的潜在编码
SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
AI总结：
SCONE通过直接在潜在空间上进行四进制算术编码，结合约束意识自适应编码模块，实现了高效且可靠的DNA存储编码，满足生物化学约束并提升压缩效率。
摘要：
DNA存储已从概念阶段发展到实用阶段，但其与神经压缩管道的整合仍效率低下。早期的DNA编码器在原始二进制数据上应用了冗余较高的约束层——可行但原始。最近的神经编码器将数据压缩成具有丰富统计结构的学得潜在表示，但仍然通过简单的二进制到四进制转换将这些潜在表示转换为DNA，丢弃了熵模型的优化。这种不匹配破坏了压缩效率，并使编码栈复杂化。一种将潜在压缩和DNA编码合并为单一步骤的插件模块。SCONE在DNA碱基上直接进行四进制算术编码。其约束意识自适应编码模块动态地引导熵编码器的学得概率分布，以强制生物化学约束——鸟嘌呤-胞嘧啶（GC）平衡和同多聚物抑制——在编码过程中确定性地执行，消除了事后修正。该设计保持了完整的可逆性，并利用超先验模型的学得先验，而无需修改。实验显示SCONE在几乎完美的约束满足下具有极小的计算开销（<2%延迟），建立了一个无潜在依赖的接口，用于端到端的DNA兼容的学得编码器。
英文摘要：
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 - Guanine-Cytosine (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
