Quantitative Investigation of Residual Cementite on the Performance of an Imidazolinium-type Corrosion Inhibitor with Mild Steel

In the present work, a ca. 40 µm residual cementite layer was developed on a 1018 carbon steel surface in a 2-day pre-corrosion step. After adding the imidazolinium-type inhibitor, its inhibition efficiency (IE) significantly decreased in the presence of the cementite layer when compared with what was observed for the bare steel surface. With a high enough concentration of inhibitor, the minimum inhibited corrosion rate (CR) obtained is still higher than that for the bare steel surface. The anodic reaction was retarded to the same extent as that on the bare surface. Retardation of the cathodic reaction was evaluated after normalizing cathodic reaction area, relating to the area increase due to residual cementite. After area normalization, retardation of the cathodic reaction was essentially the same as for the bare surface. This implied that the larger minimum inhibited CR of the specimen with residual cementite was only because of the increased cathodic reaction area

Time-Dependent Density Matrix Renormalization Group Algorithms for Nearly Exact Absorption and Fluorescence Spectra of Molecular Aggregates at Both Zero and Finite Temperature

We implement and apply time-dependent density matrix renormalization group (TD-DMRG) algorithms at zero and finite temperature to compute the linear absorption and fluorescence spectra of molecular aggregates. Our implementation is within a matrix product state/operator framework with an explicit treatment of the excitonic and vibrational degrees of freedom, and uses the locality of the Hamiltonian in the zero-exciton space to improve the efficiency and accuracy of the calculations. We demonstrate the power of the method by calculations on several molecular aggregate models, comparing our results against those from multi-layer multiconfiguration time- dependent Hartree and n-particle approximations. We find that TD-DMRG provides an accurate and efficient route to calculate the spectrum of molecular aggregates.Comment: 10 figure

Unsupervised Neural Machine Translation with SMT as Posterior Regularization

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure