Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input

Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline~\citep{gu2017non} by $5.11$ BLEU scores on WMT14 English-German task and $4.72$ BLEU scores on WMT16 English-Romanian task.Comment: AAAI 201

Deep Denitrification of Domestic Sewage by Sulfur-based Mixotrophic Denitrification Filter

As a result of relevant policies and regulations, most wastewater treatment plants are faced with upgrading to further improve the level of effluent targets. To this end, this paper conducts an experimental study on deep denitrification in the sulfur Oyster shells mixotrophic nitrification filter process using sulfur as filler. During the experiments, when the water temperature in the mixotrophic pool was 15 °C, the nitrogen load of the inflow was 7.3 × 10−3kg/m3·d and HRT equaled to 3.5 h, the average TN concentration in the effluent is 3.42 mg/L, and the TN removal rate reaches 54.49%, which can stably meet the core control area standard in the "Discharge Standard of Water Pollutants in Daqing River Basin" (DB13/2795-2018) and are the best operating parameters during the experimental period. The test results show that oyster shells can provide a large amount of alkalinity, alleviating the pH drop in the water column and effectively mitigating the acidification of the water column. Based on experimental calculations, without considering the loss of packing material, the operating cost of the sulphur-mixed denitrification filter process is reduced by $ 0.191 per tonne of water compared with the existing deep treatment unit in the WWTP. The above results show that the sulphur mixer denitrification filter has the ability to degrade the secondary effluent TN in depth, which provides some experimental basis for the sulphur mixer denitrification filter to be used as a deep treatment unit

Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than $1$ BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than $10$ times) the inference process over AT baselines.Comment: AAAI 202

Signature of Ericson Fluctuations in Helium Inelastic Scattering Cross Sections Near the Double Ionization Threshold

We calculated the inelastic electron impact excitation cross sections of He⁺ by electrons for a model helium atom to examine the onset of the signature of quantum chaotic scattering in this simple system. We find Ericson fluctuations (EF) in the calculated inelastic scattering cross sections only when the impact energies lie within about 0.21 eV below the double ionization threshold. We also discuss the stringent requirements and the proper methods for analyzing the inelastic scattering cross sections in order to observe EF experimentally

Self-Imaging of Molecules from Diffraction Spectra by Laser-Induced Rescattering Electrons

We study high-energy angle-resolved photoelectron spectra of molecules in strong fields. In an oscillating laser electric field, electrons released earlier in the pulse may return to recollide with the target ion, in a process similar to scattering by laboratory prepared electrons. If midinfrared lasers are used, we show that the images generated by the returning electrons are similar to images observed in typical gas-phase electron diffraction (GED). These spectra can be used to retrieve the positions of atoms in a molecule as in GED. Since infrared laser pulses of durations of a few femtoseconds are already available today, the study of these high-energy photoelectrons offers the opportunity of imaging the structure of transient molecules with temporal resolution of a few femtoseconds

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns