Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach with Enhanced Error Resilience and Biological Constraint Optimization

In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content

Long-term (2 years) drying shrinkage evaluation of alkali-activated slag mortar: Experiments and partial factor analysis

Alkali-activated slag with many excellent properties was regarded as a novel low carbon building material, has received more and more attention. This research aims to study the impacts of alkali solution (alkali content and modulus), cement and gypsum contents on compressive strength, weight loss and drying shrinkage for alkali-activated slag mortar. Gypsum used as expanding source could compensate the drying shrinkage caused by silica and alkali components in the first three months, but it has a negative impact on the strength. The mainly results can be concluded that the alkali-activated slag blended with a cement content up to 20 wt% could effectively reduce the shrinkage and weight loss and increase the strength. Furthermore, the alkali content was below 3 wt%, the specimens possess relatively lower drying shrinkage. Based on the results of the test and analysis, the partial factors of combined activation on compressive strength and drying shrinkage of alkali-activated slag mortar were put forward. In the meantime, the relationships between compressive strength and combined activation factor are liner at 28, 90 and 365 days. Compressive strength and drying shrinkage could be estimated according to the combined activation and partial factor analysis. This work could provide a reasonable method for preparing the alkali-activated slag mortar and predict the shrinkage at different periods

Progressive Target-Styled Feature Augmentation for Unsupervised Domain Adaptation on Point Clouds

Unsupervised domain adaptation is a critical challenge in the field of point cloud analysis, as models trained on one set of data often struggle to perform well in new scenarios due to domain shifts. Previous works tackle the problem by using adversarial training or self-supervised learning for feature extractor adaptation, but ensuring that features extracted from the target domain can be distinguished by the source-supervised classifier remains challenging. In this work, we propose a novel approach called progressive target-styled feature augmentation (PTSFA). Unlike previous works that focus on feature extractor adaptation, our PTSFA approach focuses on classifier adaptation. It aims to empower the classifier to recognize target-styled source features and progressively adapt to the target domain. To enhance the reliability of predictions within the PTSFA framework and encourage discriminative feature extraction, we further introduce a new intermediate domain approaching (IDA) strategy. We validate our method on the benchmark datasets, where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/PTSFA.Comment: 14 pages, 6 figures, 8 table

A Tutorial on Coding Methods for DNA-based Molecular Communications and Storage

Exponential increase of data has motivated advances of data storage technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage provides a much higher data density and superior durability, compared with state-of-the-art media. In this paper, we provide a tutorial on DNA storage and its role in molecular communications. Firstly, we introduce fundamentals of DNA-based molecular communications and storage (MCS), discussing the basic process of performing DNA storage in MCS. Furthermore, we provide tutorials on how conventional coding schemes that are used in wireless communications can be applied to DNA-based MCS, along with numerical results. Finally, promising research directions on DNA-based data storage in molecular communications are introduced and discussed in this paper