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

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner

Comparison of Bacterial Communities in Two Partial Nitrification Systems for High-ammonia Wastewater and Sewage Treatment

ABSTRACT Partial nitritation is an important part of the biological nitrogen removal processes; it saves half of the aeration energy, since only half of NH 4 + -N need to be oxidized to nitrite. The performance of the process was determined by the microbial community structure. In this study, we measured the microbial diversity in terms of the quantity of ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) that were present. The results revealed that the amount of aerobic bacteria did not change significantly in high-ammonia wastewater, but decreased significantly with running time in sewage. The abundance of AOB and NOB in high-ammonia wastewater ranged from 1.23 × 10 7 to 8.95 × 10

NEAR-AMBIENT-PRESSURE X-RAY PHOTOELECTRON SPECTROSCOPY INVESTIGATION OF SMALL MOLECULES ACTIVATION MECHANISM ON ZnO SURFACES

Ph.DDOCTOR OF PHILOSOPHY (FOS

Preparation and Characterization of High Temperature Resistant Polyimide Films

To prepare high temperature resistant polyimide thin films, we have discussed the synthesis of high-purity diamine precursors in this study. We have prepared polyamide acid solutions first via two-step solution-based polymerization of heat-resisting diamine and dianhydride, and then obtain polyimides after dehydration during a high temperature curing process. The thermal, mechanical and hygroscopic properties of four polyimides thin films were tested and compared

Synthesis and Raman Performance Enhancement of Multilayer AuAg Heterostructures with Magnetic Resonance

Significant amplification of surface enhanced Raman scattering (SERS) signals can be achieved mainly by the electric field enhancement in metal core-shell nanostructures, and the enhanced magnetic field is rarely studied. In this study, we prepared multi-gap Au/AgAu core-shell hybrid nanostructures by using gold nanocup as the core. The overgrowth processes to grow one, two, and three layers of AgAu hybrid nanoshells can produce Au/AgAu1, Au/AgAu2, and Au/AgAu3 heteronanostructures. The strong plasmon coupling between the core and shell leads to significant electromagnetic field enhancement. Under the synergistic effect of electromagnetic plasmon resonance and plasmon coupling, Au/AgAu core-shell hybrid nanostructures exhibit excellent SERS signals. We also investigate the effect of the interstitial position of the rhodamine B (RhB) molecule on Raman enhancement in Au/AgAu3 heteronanostructures. This study can provide new ideas for the synthesis of multi-gap Raman signal amplifiers based on magnetic plasmon coupling

Analysis of Hydrologic Drought Frequency Using Multivariate Copulas in Shaying River Basin

Droughts, considered one of the most dangerous and costly water cycle expressions, always occurs over a certain region, lasting several weeks or months, and involving multiple variables. In this work, a multivariate approach was used for the statistical characterization of hydrological droughts in Shaying River Basin with data from 1959–2008. The standard runoff index (SRI) and the run theory were employed to defined hydrological drought character variables (duration, severity, and intensity peak). Then, a multivariate joint probability analysis with four symmetric and corresponding asymmetric Archimedean Copulas was presented; and the multivariate frequency analysis with the joint return periods (Tand and Tor) were estimated. The results showed that the hydrological droughts have a severity of 4.79 and 5.09, and the drought intensity peak is of 1.35 and 1.50 in Zhoukou station and Luohe station, respectively; the rank correlation coefficients τ are more than 0.5, which means multivariate copulas can effectively describe the joint frequency distributions among multivariate variables. Drought risk shows a spatial variation: the downstream observed at Zhoukou station is characterized by a higher multivariate drought risk. In general, multivariate copulas provide a reliable method when constructing a comprehensive drought index and evaluating multivariate drought characteristics. Thus, this paper can provide useful indications for the multi-dimensional droughts’ risks assessment in Shaying River Basin

In situ spectroscopy-guided engineering of rhodium single-atom catalysts for CO oxidation

Single-atom catalysts have recently been applied in many applications such as CO oxidation. Experimental in situ investigations into this reaction, however, are limited. Hereby, we present a suite of operando/in situ spectroscopic experiments for structurally well-defined atomically dispersed Rh on phosphotungstic acid during CO oxidation. The identification of several key intermediates and the steady-state catalyst structure indicate that the reactions follow an unconventional Mars-van Krevelen mechanism and that the activation of O₂ is rate-limiting. In situ XPS confirms the contribution of the heteropoly acid support while in situ DRIFT spectroscopy consolidates the oxidation state and CO adsorption of Rh. As such, direct observation of three key components, i.e., metal center, support and substrate, is achieved, providing a clearer picture on CO oxidation on atomically dispersed Rh sites. The obtained information are used to engineer structurally similar catalysts that exhibit T₂₀ values up to 130 °C below the previously reported Rh₁/NPTA