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

Joint Training for Neural Machine Translation Models with Monolingual Data

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.Comment: Accepted by AAAI 201

Power allocation for coordinated multi-cell systems with imperfect channel and battery-capacity-limited receivers

This letter studies the transmit power allocation in downlink coordinated multi-cell systems with the batterycapacity-limited receivers, where the battery level of receivers is considered. The power allocation is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of users under the per-base station power constraints and the feasible maximum received data rate constraints determined by the receiver battery level. The optimal solutions are derived by the proposed monotonic optimization technique based algorithm. The proposed algorithm can extend the battery lifetime of the receivers with lower battery level. Simulation results illustrate the performance of the proposed algorithm

Regularizing Neural Machine Translation by Target-bidirectional Agreement

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.Comment: Accepted by AAAI 201

Power allocation for coordinated multi-cell systems with imperfect channel and battery-capacity-limited receivers

This letter studies the transmit power allocation in downlink coordinated multi-cell systems with the batterycapacity-limited receivers, where the battery level of receivers is considered. The power allocation is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of users under the per-base station power constraints and the feasible maximum received data rate constraints determined by the receiver battery level. The optimal solutions are derived by the proposed monotonic optimization technique based algorithm. The proposed algorithm can extend the battery lifetime of the receivers with lower battery level. Simulation results illustrate the performance of the proposed algorithm

Combined administration of nicorandil and atorvastatin in patients with acute myocardial infarction after coronary intervention, and its effect on postoperative cardiac systolic function

Purpose: To study the effect of a combination of nicorandil and atorvastatin calcium in patients with acute myocardial infarction after coronary intervention, and its effect on postoperative cardiac systolic function of patients.Methods: Retrospective analysis was performed on 100 patients with acute myocardial infarctiontreated with coronary interventional therapy in The Third Affiliated Hospital of Qiqihaer MedicalUniversity from April 2019 to August 2020. The patients were randomised into control and study groups, with 50 patients in each group. The control group was treated with nicorandil, while the study group was treated with a combination of nicorandil and atorvastatin. Treatment response, cardiac structural indices, cardiac systolic function, blood lipid profiles, quality of life (QLI) score, Barthel Index (BI), Fugl- Meyer assessment (FMA), motor function score, incidence of adverse reactions, and blood pressure changes on days 1, 2, 3 and 4 after surgery, were compared between the two groups.Results: Treatment effectiveness, cardiac systolic function, QLI score, BI index and FMA motor function score in the study group were higher than the corresponding control values (p < 0.05). However, lower cardiac structure indices, blood lipid profiles and incidence of adverse reactions were greater in the study group than in the control group (p < 0.05). No significant disparity in blood pressure was found between the two groups on post-surgery days 1, 2, 3 and 4.Conclusion: The combination of nicorandil and atorvastatin calcium tablets produced better outcomes in patients with acute myocardial infarction after coronary intervention therapy; furthermore, the combination therapy significantly improved the cardiac systolic function of patients

Novel Markov model of induced pluripotency predicts gene expression changes in reprogramming

<p>Abstract</p> <p>Background</p> <p>Somatic cells can be reprogrammed to induced-pluripotent stem cells (iPSCs) by introducing few reprogramming factors, which challenges the long held view that cell differentiation is irreversible. However, the mechanism of induced pluripotency is still unknown.</p> <p>Methods</p> <p>Inspired by the phenomenological reprogramming model of Artyomov et al (2010), we proposed a novel Markov model, stepwise reprogramming Markov (SRM) model, with simpler gene regulation rules and explored various properties of the model with Monte Carlo simulation. We calculated the reprogramming rate and showed that it would increase in the condition of knockdown of somatic transcription factors or inhibition of DNA methylation globally, consistent with the real reprogramming experiments. Furthermore, we demonstrated the utility of our model by testing it with the real dynamic gene expression data spanning across different intermediate stages in the iPS reprogramming process.</p> <p>Results</p> <p>The gene expression data at several stages in reprogramming and the reprogramming rate under several typically experiment conditions coincided with our simulation results. The function of reprogramming factors and gene expression change during reprogramming could be partly explained by our model reasonably well.</p> <p>Conclusions</p> <p>This lands further support on our general rules of gene regulation network in iPSC reprogramming. This model may help uncover the basic mechanism of reprogramming and improve the efficiency of converting somatic cells to iPSCs.</p

Quantum interference and controllable magic cavity QED via giant atom in coupled resonator waveguide

We study the Markovian and Non-Markovian dynamics in a giant atom system which couples to a coupled resonator waveguide (CRW) via two distant sites. Under certain conditions, we find that the giant atom population can exhibit an oscillating behavior and the photon can be trapped in the giant atom regime. These phenomena are induced by the interference effect among the bound states both in and outside the continuum. As an application of the photon trapping, we theoretically propose a magic cavity model where the giant atom serve as either a perfect or leaky cavity, depending on the distance between the coupling sites. The controllability of the magic cavity from perfect to leaky one can not be realized in the traditional cavity or circuit QED setup. The predicted effects can be probed in state-of-the-art waveguide QED experiments and provide a striking example of how the different kinds of bound states modify the dynamics of quantum open system in a structured environment.Comment: 11 pages, 7 figures, comments are welcome