Automatic Translating Between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora

The Chinese language has evolved a lot during the long-term development. Therefore, native speakers now have trouble in reading sentences written in ancient Chinese. In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese. However, the existing ancient-contemporary Chinese parallel corpora are not aligned at the sentence level and sentence-aligned corpora are limited, which makes it difficult to train the model. To build the sentence level parallel training data for the model, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs by using the fact that the aligned sentence pair shares many of the tokens. Based on the aligned corpus, we propose an end-to-end neural model with copying mechanism and local attention to translate between ancient and contemporary Chinese. Experiments show that the proposed unsupervised algorithm achieves 99.4% F1 score for sentence alignment, and the translation model achieves 26.95 BLEU from ancient to contemporary, and 36.34 BLEU from contemporary to ancient.Comment: Acceptted by NLPCC 201

Study of intermetallic nanoparticles and MOF-derived nanostructures in electrocatalysis

Electrocatalysis plays a critical role in the clean energy conversions nowadays, such as fuel cells and batteries. With the increasing population and energy demands, people are aiming to build a global-scale sustainable energy system in the future, which could convert the abundant N2, O2, water into more useful chemicals and fuels via electrocatalysis and electricity from renewable energies (e.g., wind, solar energy). Therefore, there is an urgent need to design electrocatalysts with higher activity, better stability, and higher selectivity. Two strategies are commonly applied to enhance the activity: 1) increase the number of active sites and 2) increase the intrinsic activity of each active site. By using support and alloying in our research, we could achieve both strategies simultaneously. In this dissertation, I present several examples of confined intermetallic nanoparticles and MOF-derived nanomaterials for enhanced electrocatalysis. In chapter 2, I synthesized sub-4 nm monodispersed PtZn intermetallic nanoparticles supported on carbon nanotubes with the protection of mesoporous silica shell via high temperature annealing. Both specific activity and mass activity towards methanol oxidation reaction of smaller PtZn nanoparticles are greatly enhanced, revealing that the smaller particles not only increase the number of active sites but also increase the intrinsic activity of each site. Moreover, both DFT calculation and experimental results indicating PtZn systems go through a “non-CO pathway”, due to the stabilization of the *OH species by Zn atoms. Chapter 3 shows a facial synthesis of intermetallic nanoparticles as electrocatalysts via one-pot pyrolysis of ZIF-8 encapsulated metal nanoparticles. ZIF-8 works as both carbon source, zinc precursors, encapsulation shell in the formation of zinc-containing intermetallic nanoparticles supported by nitrogen doped carbon. This method allows the fine tuning of particle size and composition, and more importantly, provides high thermal stabilities up to 1000 ˚C. In chapter 4, I showed the enhanced hydrogen evolution activity of ordered Pt3Ti intermetallic nanoparticles supported by Ti3C2 Mxene sheet. We further demonstrated the enhanced activity is due to the strong synergistic effect between Pt3Ti and Mxene support. Chapter 5 shows the morphology inherence from hollow ZIFs to hollow carbons with superior activities of oxygen reduction reaction. The designed hollow carbons are not only having better mass transfer but also able to introduce the heteroatoms such as Fe onto the inner wall, which could promote the activity of ORR

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.Comment: 12 page