Is there any correlation between digital currency price fluctuation? Based on the DCC-GARCH and wavelet coherence analysis

The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a ‘contagious’ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main ‘contagious source’ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new ‘contagious source’. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the ‘currency’ attribute and serve the real econom

Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information

Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the enormous decoding space. To address this problem, we propose a novel NAT framework named ReorderNAT which explicitly models the reordering information in the decoding procedure. We further introduce deterministic and non-deterministic decoding strategies that utilize reordering information to narrow the decoding search space in our proposed ReorderNAT. Experimental results on various widely-used datasets show that our proposed model achieves better performance compared to existing NAT models, and even achieves comparable translation quality as autoregressive translation models with a significant speedup.Comment: Accepted by AAAI 202

Stability Analysis for Markovian Jump Neutral Systems with Mixed Delays and Partially Known Transition Rates

The delay-dependent stability problem is studied for Markovian jump neutral systems with partial information on transition probabilities, and the considered delays are mixed and model dependent. By constructing the new stochastic Lyapunov-Krasovskii functional, which combined the introduced free matrices with the analysis technique of matrix inequalities, a sufficient condition for the systems with fully known transition rates is firstly established. Then, making full use of the transition rate matrix, the results are obtained for the other case, and the uncertain neutral Markovian jump system with incomplete transition rates is also considered. Finally, to show the validity of the obtained results, three numerical examples are provided

Synthesis of Porous NiO and ZnO Submicro- and Nanofibers from Electrospun Polymer Fiber Templates

Porous nickel oxide (NiO) and zinc oxide (ZnO) submicro- and nanofibers were synthesized by impregnating electrospun polyacrylonitrile (PAN) fiber templates with corresponding metal nitrate aqueous solutions and subsequent calcination. The diameter of the NiO and ZnO fibers was closely related to that of the template fibers and larger diameters were obtained when using the template fibers with larger diameter. SEM results showed that the NiO and ZnO fibers have a large amount of pores with diameters ranging from 5 nm to 20 nm and 50 nm to 100 nm, respectively. Energy dispersive X-ray (EDX) spectra and X-ray diffraction (XRD) patterns testified that the obtained materials were NiO and ZnO with high purity

NumNet: Machine Reading Comprehension with Numerical Reasoning

Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56% on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.Comment: Accepted to EMNLP2019; 11 pages, 2 figures, 6 table

Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning

Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (ASR). However, the robustness impact of combining the two pre-training tasks and constructing different negative samples for contrastive learning still remains unclear. In this paper, we propose a noise-robust data2vec for self-supervised speech representation learning by jointly optimizing the contrastive learning and regression tasks in the pre-training stage. Furthermore, we present two improved methods to facilitate contrastive learning. More specifically, we first propose to construct patch-based non-semantic negative samples to boost the noise robustness of the pre-training model, which is achieved by dividing the features into patches at different sizes (i.e., so-called negative samples). Second, by analyzing the distribution of positive and negative samples, we propose to remove the easily distinguishable negative samples to improve the discriminative capacity for pre-training models. Experimental results on the CHiME-4 dataset show that our method is able to improve the performance of the pre-trained model in noisy scenarios. We find that joint training of the contrastive learning and regression tasks can avoid the model collapse to some extent compared to only training the regression task.Comment: Submitted to ICASSP 202

A new species of Fordiophyton (Sonerileae, Melastomataceae) from Yunnan, China

Fordiophyton jinpingense (Melastomataceae; Sonerileae), a species occurring in south-eastern Yunnan, China, is described as new, based on morphological and molecular data. Phylogenetic analyses, based on nrITS sequence data, showed that, except F. breviscapum, all species sampled in Fordiophyton formed a strongly supported clade in which two geographical lineages were recovered. The generic placement of F. jinpingense is well supported by phylogenetic analyses and a character combination of 4-merous flowers, distinctly dimorphic stamens and the connectives basally not calcarate. Molecular divergence and morphological evidence indicate that F. jinpingense is well separated from other members of the genus, thus justifying its recognition as a distinct species. Fordiophyton jinpingense is phylogenetically closest to F. repens, but differs markedly from the latter in stem morphology (short, obtusely 4-sided vs. long, 4-angular), habit (erect vs. creeping), leaf size (6–16.5 × 4.5–13 cm vs. 4–7.5 × 4–6.5 cm) and flower number per inflorescence (5–13 vs. 3–6)