269 research outputs found
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
BLEU scores on WMT14 English-German task and 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 BLEU score) over previous NAT baselines in terms of
translation accuracy, and greatly speed up (more than 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
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