A Bird-Eye view on DNA Storage Simulators

• URL: http://arxiv.org/abs/2404.04877v1
• Date: Sun, 7 Apr 2024 08:46:42 GMT
• Title: A Bird-Eye view on DNA Storage Simulators
• Authors: Sanket Doshi, Mihir Gohel, Manish K. Gupta,
• Abstract summary: This paper aims to review some of the software that performs DNA storage simulations in different domains. We present 3 different softwares on the basis of domain, implementation techniques, and customer/commercial usability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the current world due to the huge demand for storage, DNA-based storage solution sounds quite promising because of their longevity, low power consumption, and high capacity. However in real life storing data in the form of DNA is quite expensive, and challenging. Therefore researchers and developers develop such kind of software that helps simulate real-life DNA storage without worrying about the cost. This paper aims to review some of the software that performs DNA storage simulations in different domains. The paper also explains the core concepts such as synthesis, sequencing, clustering, reconstruction, GC window, K-mer window, etc and some overview on existing algorithms. Further, we present 3 different softwares on the basis of domain, implementation techniques, and customer/commercial usability.

Related papers

• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering [126.00165445599764]
  We present DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering. Our dataset contains over 1500 human subjects, 5000 motion sequences, and 67.5M frames' data volume. We construct a professional multi-view system to capture data, which contains 60 synchronous cameras with max 4096 x 3000 resolution, 15 fps speed, and stern camera calibration steps.
  arXiv  Detail & Related papers  (2023-07-19T17:58:03Z)
• Online Continual Learning Without the Storage Constraint [67.66235695269839]
  We contribute a simple algorithm, which updates a kNN classifier continually along with a fixed, pretrained feature extractor. It can adapt to rapidly changing streams, has zero stability gap, operates within tiny computational budgets, has low storage requirements by only storing features. It can outperform existing methods by over 20% in accuracy on two large-scale online continual learning datasets.
  arXiv  Detail & Related papers  (2023-05-16T08:03:07Z)
• Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search [70.31239620427526]
  Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods. In this comprehensive survey, we focus specifically on DNAS and review recent approaches in this field.
  arXiv  Detail & Related papers  (2023-04-11T13:15:29Z)
• DNA data storage, sequencing data-carrying DNA [2.4493299476776778]
  We study accuracy trade-offs between deep model size and error correcting codes. We show that, starting with a model size of 107MB, the reduced accuracy from model compression can be compensated by using simple error correcting codes.
  arXiv  Detail & Related papers  (2022-05-11T13:31:57Z)
• Image Storage on Synthetic DNA Using Autoencoders [6.096779295981377]
  This paper presents some results on lossy image compression methods based on convolutional autoencoders adapted to DNA data storage. The model architectures presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules.
  arXiv  Detail & Related papers  (2022-03-18T14:17:48Z)
• Single-Read Reconstruction for DNA Data Storage Using Transformers [0.0]
  We propose a novel approach for single-read reconstruction using an encoder-decoder Transformer architecture for DNA based data storage. Our model achieves lower error rates when reconstructing the original data from a single read of each DNA strand. This is the first demonstration of using deep learning models for single-read reconstruction in DNA based storage.
  arXiv  Detail & Related papers  (2021-09-12T10:01:59Z)
• Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning [49.3231734733112]
  We show a modular and holistic approach that combines Deep Neural Networks (DNN) trained on simulated data, Product (TP) based Error-Correcting Codes (ECC) and a safety margin into a single coherent pipeline. Our work improves upon the current leading solutions by up to x3200 increase in speed, 40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a high noise regime.
  arXiv  Detail & Related papers  (2021-08-31T18:21:20Z)
• Efficient approximation of DNA hybridisation using deep learning [0.0]
  We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. We introduce a synthetic hybridisation dataset of over 2.5 million data points, enabling the use of a wide range of machine learning algorithms.
  arXiv  Detail & Related papers  (2021-02-19T19:23:49Z)
• Neural Network Compression for Noisy Storage Devices [71.4102472611862]
  Conventionally, model compression and physical storage are decoupled. This approach forces the storage to treat each bit of the compressed model equally, and to dedicate the same amount of resources to each bit. We propose a radically different approach that: (i) employs analog memories to maximize the capacity of each memory cell, and (ii) jointly optimize model compression and physical storage to maximize memory utility.
  arXiv  Detail & Related papers  (2021-02-15T18:19:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.