Ferulic acid promoted in-situ generation of AgNPs@silk as functional colorants

A rapid, green and simple procedure for the in-situ generation of AgNPs@silk as functional colorant is described herein. Silver (Ag+) ions were first diffused into the silk fabric matrix by soaking into aqueous AgNO3 solution, subsequently, alcoholic solution of ferulic acid, a natural polyphenol, was added as an eco-friendly reductant for the generation of AgNPs@silk. The formation of AgNPs was confirmed by visible color changes and UV–visible absorption spectra. The residual AgNPs solution was characterized via UV–visible spectroscopy, TEM and DLS. The UV–visible spectra and TEM analyses confirmed the formation of more or less spherical well-dispersed AgNPs. The AgNPs@silk was characterized by SEM, EDS, XRD, XPS and FTIR. The Ag content of AgNPs@silk was determined by nitric acid digestion followed by ICP-OES. The color, antibacterial and UV protection characteristics of AgNPs@silk were also evaluated. AgNPs@silk produced a beautiful color pallete ranging from light creamish brown to dark golden brown. The AgNPs treated silk exhibited outstanding antibacterial activity (>99% bacterial reduction) and excellent laundering durability, where it inhibited >94% of E. coli even after 10 washing cycles. Moreover, AgNPs@silk was highly effective blocking of UV radiation in both UVA and UVB regions, and thus offered excellent UV protection

HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of distribution matching, most existing discrepancy-based methods are designed to match the second-order or lower statistics, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we explore the benefits of using higher-order statistics (mainly refer to third-order and fourth-order statistics) for domain matching. We propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, the third-order and the fourth-order moment tensor matching are expected to perform comprehensive domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at \url{https://github.com/chenchao666/HoMM-Master}Comment: Accept by AAAI-2020, codes are available at https://github.com/chenchao666/HoMM-Maste

THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: AN INTEGRATED LANGUAGE MODEL

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements

Hybridized surface plasmon polaritons at an interface between a metal and a uniaxial crystal

The surface plasmonpolariton (SPP) at an interface between a metal and a uniaxial crystal is studied. A new class of hybridized SPP found in this work is quite different from the traditional SPP at the interface between a metal and an isotropic dielectric. In contrast to the two evanescent fields for the traditional SPP, the hybridized SPP involves four evanescent fields: transverse-electric-like and transverse-magnetic-like waves in the metal, and ordinary-light-like and extraordinary-light-like waves in the uniaxial crystal. The necessary conditions and the regimes for the existence of the hybridized SPP are presented. Some potential applications are also discussed.This work is supported in part by NSFC under Grant No. 10325417, by the State Key Program for Basic Research of China under Grant No. 2006CB921805, and by the 111 Project under Grant No. B07026