Identification and characterization of the chemical constituents in the roots of Ilex asprella by high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry.

The roots of Ilex asprella (Rla) are a well-known traditional Chinese medicine for the treatment of viral and bacterial infectious diseases, such as influenza, tonsillitis, sphagitis, and trachitis. However, due to the complexity of the chemical constituents in Rla, few investigations have acquired a comprehensive understanding of material basis. High-performance liquid chromatography coupled to electrospray ionisation and quadrupole time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS/MS) was used for the identification of chemical constituents from the extract of Rla in negative ion mode. Their chemical structures were tentatively elucidated based on exact formulas, fragmentation patterns and literature data. A total of 32 compounds were discovered and tentatively characterised in Rla, including 4 phenolic glycosides and 28 triterpenoid glycosides. 10 compounds have not been previously reported in Rla and 8 of them have not been previously reported in the literature. The chemical composition of Rla was identified and summarised, providing a basis for further study on Rla.

ExS-GAN: Synthesizing Anti-Forensics Images via Extra Supervised GAN.

So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.

Carry-free vector-matrix multiplication on a dynamically reconfigurable optical platform.

Applying the parallelism of optical computing, we present a novel method of vector-matrix multiplication (VMM) based on a new optical computing platform, the ternary optical computer, which can reconfigure any two-input trivalued logic optical processor at runtime, according to the decrease-radix design principle. In this work, we investigate a novel optical VMM (OVMM) using five logic operations with the modified signed-digit (MSD) number system. To simplify the computation process, we realize a carry-free optical addition in three steps, which is independent of the length of the operands. And a new implementation method is proposed that can be used to realize the MSD multiplication in parallel. Based on the generation of partial products in parallel and the binary-addition-tree algorithm, the multiplication can be implemented with the MSD addition. Our initial experiments have been performed to verify the proposed OVMM method. The results show that the proposed method of OVMM is feasible and correct.