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The development of long-term data storage technology is one of the urging problems of our time. This paper presents the results of implementation of technical solution for long-term data storage technology proposed a few years ago on the basis of single crystal sapphire. It is shown that the problem of reading data through a substrate of negative single crystal sapphire can be solved by using for reading a special optical system with a plate of positive single crystal quartz. The experimental results confirm the efficiency of the proposed method of compensation.
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As the global need for large-scale data storage is rising exponentially, existing storage technologies are approaching their theoretical and functional limits in terms of density and energy consumption, making DNA based storage a potential solution for the future of data storage. Several studies introduced DNA based storage systems with high information density (petabytes/gram). However, DNA synthesis and sequencing technologies yield erroneous outputs. Algorithmic approaches for correcting these errors depend on reading multiple copies of each sequence and result in excessive reading costs. The unprecedented success of Transformers as a deep learning architecture for language modeling has led to its repurposing for solving a variety of tasks across various domains. In this work, we propose a novel approach for single-read reconstruction using an encoder-decoder Transformer architecture for DNA based data storage. We address the error correction process as a self-supervised sequence-to-sequence task and use synthetic noise injection to train the model using only the decoded reads. Our approach exploits the inherent redundancy of each decoded file to learn its underlying structure. To demonstrate our proposed approach, we encode text, image and code-script files to DNA, produce errors with high-fidelity error simulator, and reconstruct the original files from the noisy reads. Our model achieves lower error rates when reconstructing the original data from a single read of each DNA strand compared to state-of-the-art algorithms using 2-3 copies. This is the first demonstration of using deep learning models for single-read reconstruction in DNA based storage which allows for the reduction of the overall cost of the process. We show that this approach is applicable for various domains and can be generalized to new domains as well.
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of parameters, however, have a bottleneck in computing power resulting from limited memory capacity and data communication bandwidth. Here we demonstrate experimentally that LSTM can be implemented with a memristor crossbar, which has a small circuit footprint to store a large number of parameters and in-memory computing capability that circumvents the von Neumann bottleneck. We illustrate the capability of our system by solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.
Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been implemented in nanoelectronic synapses for neuromorphic applications, recent studies in neuroscience have revealed that biological synapses undergo meta-stable volatile strengthening followed by a long-term strengthening provided that the frequency of the input stimulus is sufficiently high. Such memory strengthening and memory decay functionalities can potentially lead to adaptive neuromorphic architectures. In this paper, we demonstrate the close resemblance of the magnetization dynamics of a Magnetic Tunnel Junction (MTJ) to short-term plasticity and long-term potentiation observed in biological synapses. We illustrate that, in addition to the magnitude and duration of the input stimulus, frequency of the stimulus plays a critical role in determining long-term potentiation of the MTJ. Such MTJ synaptic memory arrays can be utilized to create compact, ultra-fast and low power intelligent neural systems.
Description of the TAU-4 installation intended for long-term monitoring of the half-life value $T_{1/2}$ of the $^{212}$Po is presented. Natural thorium used as a source of the mothers chain. The methods of measurement and data processing are described. The comparative results of short test measurements carried out in the ground (680 h) and underground (564 h) laboratories are given. Averaged value $T_{1/2}$ =$294.09pm 0.07$ ns of the $^{212}$Po half-life has been found for the ground level data set similar one for the underground data set. The solar-daily variations with amplitudes $A_{So}=(11.7pm 5.2)times10^{-4}$ for the ground data and $A_{So}=(7.5pm 4.1)times10^{-4}$ for the underground one were found in a series of $tau$ values.
We perform calculations of our one-dimensional, two-zone disk model to study the long-term evolution of the circumstellar disk. In particular, we adopt published photoevaporation prescriptions and examine whether the photoevaporative loss alone, coupled with a range of initial angular momenta of the protostellar cloud, can explain the observed decline of the frequency of optically-thick dusty disks with increasing age. In the parameter space we explore, disks have accreting and/or non-accreting transitional phases lasting of $lesssim20 %$ of their lifetime, which is in reasonable agreement with observed statistics. Assuming that photoevaporation controls disk clearing, we find that initial angular momentum distribution of clouds needs to be weighted in favor of slowly rotating protostellar cloud cores. Again, assuming inner disk dispersal by photoevaporation, we conjecture that this skewed angular momentum distribution is a result of fragmentation into binary or multiple stellar systems in rapidly-rotating cores. Accreting and non-accreting transitional disks show different evolutionary paths on the $dot{M}-R_{rm wall}$ plane, which possibly explains the different observed properties between the two populations. However, we further find that scaling the photoevaporation rates downward by a factor of 10 makes it difficult to clear the disks on the observed timescales, showing that the precise value of the photoevaporative loss is crucial to setting the clearing times. While our results apply only to pure photoevaporative loss (plus disk accretion), there may be implications for models in which planets clear disks preferentially at radii of order 10 AU.
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