Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources

The impact of COVID-19 shocks on the volatility of stock markets in technologically advanced countries

This study examines the volatility of China and the most advanced countries of the world stock markets due to the pandemic of COVID-19 using the TGARCH model. This research study presents empirical support for the TGARCH specification for explaining the daily time dependence in the rate of information arrival to the market for stocks traded on China stock market. Using the sample containing closing stock market returns from 05 January 2015 to 04 April 2020 of sample countries, we found that through the COVID-19, there is no significant impact of returns volatility coming from advanced countries towards the China stock market. Further, results state that China has a significant impact on explaining the volatility of the most advanced countries of the world (Switzerland, Sweden, Netherlands, and the UK) except the U.S.A. during COVID-19. We found no significant impact of China stock market returns on the U.S.A.’s volatility, but there is a presence of leverage effect during COVID 19

Improving the wind‐induced human comfort of the Beijing Olympic Tower by a double‐stage pendulum tuned mass damper

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/1/tal1704_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/2/tal1704.pd

Jasmonate mediates salt-induced nicotine biosynthesis in tobacco (Nicotiana tabacum L.)

AbstractJasmonate (JA), as an important signal, plays a key role in multiple processes of plant growth, development and stress response. Nicotine and related pyridine alkaloids in tobacco (Nicotiana tabacum L.) are essential secondary metabolites. Whether environmental factors control nicotine biosynthesis and the underlying mechanism remains previously unreported. Here, we applied physiological and biochemical approaches to investigate how salt stress affects nicotine biosynthesis in tobacco. We found that salt stress induced the biosynthesis of JA, which subsequently triggered the activation of JA-responsive gene expression and, ultimately, nicotine synthesis. Bioinformatics analysis revealed the existence of many NtMYC2a-recognized G-box motifs in the promoter regions of NtLOX, NtAOS, NtAOC and NtOPR genes. Applying exogenous JA increased nicotine content, while suppressing JA biosynthesis reduced nicotine biosynthesis. Salt treatment could not efficiently induce nicotine biosynthesis in transgenic anti-COI1 tobacco plants. These results demonstrate that JA acts as the essential signal which triggers nicotine biosynthesis in tobacco after salt stress

Radio frequency fingerprint identification for Internet of Things: A survey

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field

Pharmacological changes in cellular Ca2+ homeostasis parallel initiation of atrial arrhythmogenesis in murine langendorff-perfused hearts

Intracellular Ca2+ overload has been associated with established atrial arrhythmogenesis. The present experiments went on to correlate acute initiation of atrial arrhythmogenesis in Langendorff-perfused mouse hearts with changes in Ca2+ homeostasis in isolated atrial myocytes following pharmacological procedures that modified the storage or release of sarcoplasmic reticular (SR) Ca2+ or inhibited entry of extracellular Ca2+.Caffeine (1mmol/L) elicited diastolic Ca2+ waves in regularly stimulated atrial myocytes immediately following addition. This was followed by a decline in the amplitude of the evoked transients and the disappearance of such diastolic events, suggesting partial SR Ca2+ depletion.Cyclopiazonic acid (CPA; 0.15µmol/L) produced more gradual reductions in evoked Ca2+ transients and abolished diastolic Ca2+ events produced by the further addition of caffeine.Nifedipine (0.5µmol/L) produced immediate reductions in evoked Ca2+ transients. Further addition of caffeine produced an immediate increase followed by a decline in the amplitude of the evoked Ca2+ transients, without eliciting diastolic Ca2+ events.These findings correlated with changes in spontaneous and provoked atrial arrhythmogenecity in mouse isolated Langendorf-perfused hearts. Thus, caffeine was pro-arrhythmogenic immediately following but not >5min after application and both CPA and nifedipine pretreatment inhibited such arrhythmogenesis.Together, these findings relate acute atrial arrhythmogenesis in intact hearts to diastolic Ca2+ events in atrial myocytes that, in turn, depend upon a finite SR Ca2+ store and diastolic Ca2+ release following Ca2+-induced Ca2+ release initiated by the entry of extracellular Ca2+