ABSNFT: Securitization and Repurchase Scheme for Non-Fungible Tokens Based on Game Theoretical Analysis

The Non-Fungible Token (NFT) is viewed as one of the important applications of blockchain technology. Although NFT has a large market scale and multiple practical standards, several limitations of the existing mechanism in NFT markets exist. This work proposes a novel securitization and repurchase scheme for NFT to overcome these limitations. We first provide an Asset-Backed Securities (ABS) solution to settle the limitations of non-fungibility of NFT. Our securitization design aims to enhance the liquidity of NFTs and enable Oracles and Automatic Market Makers (AMMs) for NFTs. Then we propose a novel repurchase protocol for a participant owing a portion of NFT to repurchase other shares to obtain the complete ownership. As participants may strategically bid during the acquisition process, our repurchase process is formulated as a Stackelberg game to explore the equilibrium prices. We also provide solutions to handle difficulties at market such as budget constraints and lazy bidders.Comment: To appear in Financial Cryptography and Data Security 202

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

© 2020 Binbin Huang et al. Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme

Target of Rapamycin Signaling Involved in the Regulation of Photosynthesis and Cellular Metabolism in <i>Chlorella sorokiniana</i>

Target of rapamycin (TOR) is a serine/threonine protein kinase that plays a central regulating role in cell proliferation, growth, and metabolism, but little is known about the TOR signaling pathway in Chlorella sorokiniana. In this study, a Chlorella sorokiniana DP-1 strain was isolated and identified, and its nutritional compositions were analyzed. Based on homologous sequence analysis, the conserved CsTOR protein was found in the genome of Chlorella sorokiniana. In addition, the key components of TOR complex 1 (TORC1) were present, but the components of TORC2 (RICTOR and SIN1) were absent in Chlorella sorokiniana. Pharmacological assays showed that Chlorella sorokiniana DP-1 was insensitive to rapamycin, Torin1 and KU0063794, whereas AZD8055 could significantly inhibit the growth of Chlorella sorokiniana. RNA-seq analysis showed that CsTOR regulated various metabolic processes and signal transduction pathways in AZD8055-treated Chlorella sorokiniana DP-1. Most genes involved in photosynthesis and carbon fixation in Chlorella sorokiniana DP-1 were significantly downregulated under CsTOR inhibition, indicating that CsTOR positively regulated the photosynthesis in Chlorella sorokiniana. Furthermore, CsTOR controlled protein synthesis and degradation by positively regulating ribosome synthesis and negatively regulating autophagy. These observations suggested that CsTOR plays an important role in photosynthesis and cellular metabolism, and provide new insights into the function of CsTOR in Chlorella sorokiniana

An Energy Efficient Technique Using Electric Active Shielding for Capacitive Coupling Intra-Body Communication

Capacitive coupling intra-body communication (CC-IBC) has become one of the candidates for healthcare sensor networks due to its positive prevailing features of energy efficiency, transmission rate and security. Under the CC-IBC scheme, some of the electric field emitted from signal (SIG) electrode of the transmitter will couple directly to the ground (GND) electrode, acting equivalently as an internal impedance of the signal source and inducing considerable energy losses. However, none of the previous works have fully studied the problem. In this paper, the underlying theory of such energy loss is investigated and quantitatively evaluated using conventional parameters. Accordingly, a method of electric active shielding is proposed to reduce the displacement current across the SIG-GND electrodes, leading to less power loss. In addition, the variation of such loss in regard to frequency range and positions on human body was also considered. The theory was validated by finite element method simulation and experimental measurement. The prototype result shows that the receiving power has been improved by approximate 5.5 dBm while the total power consumption is maximally 9 mW less using the proposed technique, providing an energy efficient option in physical layer for wearable and implantable healthcare sensor networks

Scalable TCAM-based regular expression matching with compressed finite automata

International audienceRegular expression (RegEx) matching is a core function of deep packet inspection in modern network devices. Previous TCAM-based RegEx matching algorithms a priori assume that a deterministic finite automaton (DFA) can be built for a given set of RegEx patterns. However, practical RegEx patterns contain complex terms like wildcard closure and repeat character, and it may be impossible to build a DFA with a reasonable number of states. This results in prior work to being infeasible in practice. Moreover, TCAM-based RegEx matching is required to scale to a large-scale set of RegEx patterns. In this paper, we propose a compressed finite automaton implementation called (CFA) for scalable TCAM-based RegEx matching. CFA is designed to reduce TCAM space by using three compression techniques: transition, character, and state compressions. Experiments on realistic RegEx pattern sets show CFA highly outperforms previous solutions in terms of TCAM space, matching throughput, and TCAM power consumption

Comparison of DNA targeting CRISPR editors in human cells

Abstract Background Profiling and comparing the performance of current widely used DNA targeting CRISPR systems provide the basic information for the gene-editing toolkit and can be a useful resource for this field. In the current study, we made a parallel comparison between the recently reported miniature Cas12f1 (Un1Cas12f1 and AsCas12f1) and the widely used Cas12a and Cas9 nucleases in mammalian cells. Results We found that as a CRISPRa activator, Un1Cas12f1 could induce gene expression with a comparable level to that of Cas12a and Cas9, while as a DNA cleavage editor, Cas12f1 exhibited similar properties to Cas12a, like high specificity and dominantly induced deletions over insertions, but with less activity. In contrast, wild-type SpCas9 showed the highest activity, lowest specificity, and induced balanced deletions and insertions. Thus, Cas12f1 is recommended for gene-activation-based applications, Cas12a is for therapy applications, and wild-type Cas9 is for in vitro and animal investigations. Conclusion The comparison provided the editing properties of the widely used DNA-targeting CRISPR systems in the gene-editing field

RNA-Seq and Genome-Wide Association Studies Reveal Potential Genes for Rice Seed Shattering

The loss of the shattering ability is one of the key events in rice domestication. The strength of the seed shattering ability is closely related to the harvest yield and the adaptability of modern mechanical harvesting methods. In this study, using a population of 587 natural rice cultivars, quantitative trait loci associated with seed shattering were detected by genome-wide association studies (GWASs). We consider the quantitative trait loci (QTLs) qBTS1 and qBTS3 to be the key loci for seed shattering in rice. Additionally, the abscission zone (AZ) and nonabscission zone (NAZ) of materials with a loss of shattering (DZ129) and easy shattering (W517) were subjected to RNA-Seq, and high-quality differential expression profiles were obtained. The AZ-specific differentially expressed genes (DEGs) of W517 were significantly enriched in plant hormone signal transduction, while the AZ-specific DEGs of DZ129 were enriched in phenylpropanoid biosynthesis. We identified candidate genes for the lignin-associated laccase precursor protein (LOC_Os01g63180) and the glycoside hydrolase family (LOC_Os03g14210) in the QTLs qBTS1 (chromosome 1) and qBTS3 (chromosome 3), respectively. In summary, our findings lay the foundation for the further cloning of qBTS1 and qBTS3, which would provide new insights into seed shattering in rice

Mining trading patterns of pyramid schemes from financial time series data

The current studies relating to pyramid schemes are mostly about qualitative analysis, whereas the quantitative analysis is still rare owing to the insufficiency in knowledge of their specific trading modes. Often, the trading modes of pyramid schemes are inconspicuous in financial data, making it difficult to be identified in the data. In this study, we propose a quantitative framework for mining trading patterns of pyramid schemes from financial time series data. The framework includes two parts: Long Range Sequence De-noising (LoRSD) algorithm and Contrast Trading Pattern Mining (Contrast TPM) algorithm. LoRSD distinguishes noise items by folding the statistical frequent items and removes the infrequent items recursively. In Contrast TPM, we first identify the frequent one-itemset by comparing the pyramid-related samples with the general samples. Subsequently, a random model is added in the comparative analysis to generate the frequency conditions for mining pyramid scheme patterns. Instead of setting user-defined support thresholds, we adopt contrastive samples as benchmarks in determining the frequency conditions. Our extensive experiments on the financial data set including behaviour of a real-world pyramid scheme demonstrate the effectiveness of our framework in sequence de-noising and mining trading patterns of pyramid schemes from financial time series data