What Motivates People to Purchase NFTs? A Self-Discrepancy Perspective

Non-fungible token (NFT) sales change unpredictably and trend downward, which motivates us to explore the determinants of why people purchase NFTs. To answer this question, we develop a model based on the self-discrepancy theory and symbolic self-completion theory. The model proposes that the desire for self-completion is a key driver for such purchases, and functional properties of attractiveness, price utility, emotional properties of aesthetics and playfulness, and social properties of parasocial interaction and social relationship support are antecedents of the desire for self-completion. We also hypothesize that psychological ownership moderates the relationship between the desire for symbolic self-completion and purchase intention. The model will be validated using survey data collected from some popular NFT platforms. The results are expected to support our hypothesis, which contributes to the understanding of the purchase of NFTs by extending the theory of self-discrepancy and adds a new perspective to research on NFTs

Stability studies of ZnO and AlN thin film acoustic wave devices in acid and alkali harsh environments

Surface acoustic wave (SAW) devices based on piezoelectric thin-films such as ZnO and AlN are widely used in sensing, microfluidics and lab-on-a-chip applications. However, for many of these applications, the SAW devices will inevitably be used in acid or alkali harsh environments, which may cause their early failures. In this work, we investigated the behavior and degradation mechanisms of thin film based SAW devices in acid and alkali harsh environments. Results show that under the acid and alkali attacks, chemical reaction and corrosion of ZnO devices are very fast (usually within 45 s). During the corrosion, the crystalline orientation of the ZnO film is not changed, but its grain defects are significantly increased and the grain sizes are decreased. The velocity of ZnO-based SAW devices is decreased due to the formation of porous structures induced by the chemical reactions. Whereas an AlN thin-film based SAW device does not perform well in acid–alkali conditions, it might be able to maintain a normal performance without obvious degradation for more than ten hours in acid or alkali solutions. This work could provide guidance for the applications of both ZnO or AlN-based SAW devices in acid/alkali harsh environments

Mis-classified Vector Guided Softmax Loss for Face Recognition

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination. To this end, several margin-based (\textit{e.g.}, angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from three issues: 1) Obviously, they ignore the importance of informative features mining for discriminative learning; 2) They encourage the feature margin only from the ground truth class, without realizing the discriminability from other non-ground truth classes; 3) The feature margin between different classes is set to be same and fixed, which may not adapt the situations very well. To cope with these issues, this paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative face features. To the best of our knowledge, this is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function. Experimental results on several benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives.Comment: Accepted by AAAI2020 as Oral presentation. arXiv admin note: substantial text overlap with arXiv:1812.1131

Trefoil Factor 3, Cholinesterase and Homocysteine: Potential Predictors for Parkinson\u27s Disease Dementia and Vascular Parkinsonism Dementia in Advanced Stage

Trefoil factor 3 (TFF3), cholinesterase activity (ChE activity) and homocysteine (Hcy) play critical roles in modulating recognition, learning and memory in neurodegenerative diseases, such as Parkinson\u27s disease dementia (PDD) and vascular parkinsonism with dementia (VPD). However, whether they can be used as reliable predictors to evaluate the severity and progression of PDD and VPD remains largely unknown. METHODS: We performed a cross-sectional study that included 92 patients with PDD, 82 patients with VPD and 80 healthy controls. Serum levels of TFF3, ChE activity and Hcy were measured. Several scales were used to rate the severity of PDD and VPD. Receivers operating characteristic (ROC) curves were applied to map the diagnostic accuracy of PDD and VPD patients compared to healthy subjects. RESULTS: Compared with healthy subjects, the serum levels of TFF3 and ChE activity were lower, while Hcy was higher in the PDD and VPD patients. These findings were especially prominent in male patients. The three biomarkers displayed differences between PDD and VPD sub-groups based on genders and UPDRS (III) scores\u27 distribution. Interestingly, these increased serum Hcy levels were significantly and inversely correlated with decreased TFF3/ChE activity levels. There were significant correlations between TFF3/ChE activity/Hcy levels and PDD/VPD severities, including motor dysfunction, declining cognition and mood/gastrointestinal symptoms. Additionally, ROC curves for the combination of TFF3, ChE activity and Hcy showed potential diagnostic value in discriminating PDD and VPD patients from healthy controls. CONCLUSIONS: Our findings suggest that serum TFF3, ChE activity and Hcy levels may underlie the pathophysiological mechanisms of PDD and VPD. As the race to find biomarkers or predictors for these diseases intensifies, a better understanding of the roles of TFF3, ChE activity and Hcy may yield insights into the pathogenesis of PDD and VPD

Integrated mRNA Sequence Optimization Using Deep Learning

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5\u27 untranslated region (UTR) and 3\u27UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics

Multicarrier Modulation-Based Digital Radio-over-Fibre System Achieving Unequal Bit Protection with Over 10 dB SNR Gain

We propose a multicarrier modulation-based digital radio-over-fibre system achieving unequal bit protection by bit and power allocation for subcarriers. A theoretical SNR gain of 16.1 dB is obtained in the AWGN channel and the simulation results show a 13.5 dB gain in the bandwidth-limited case