Semi-Supervised Learning for Neural Machine Translation

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.Comment: Corrected a typ

Application and measurement of underwater acoustic reciprocity transfer functions with impulse sound sources

The underwater acoustic reciprocity transfer function measuring method with impulse underwater sound source is studied, and time gates are utilized to eliminate the reverberant field influences on the sound pressures which are used to compute the source volume velocity. The method is validated in a lake experiment. It is showed that, the reciprocity measurement results based on the impulse source is similar to the reciprocal measurements results based on the traditional electromagnetic source, meanwhile the time cost of the impulse-based measurement is less. Thereby the measuring efficiency is boosted with an impulse source. Moreover, the reverberant field influences on volume velocity obtainments can be eliminated with suitable time gates in impulse-based measurements. Aiming at representative problems of the experiments, some suggestions about impulse-based reciprocity measurements are provided. This work may be valuable for the study of underwater sound sources and reciprocal measuring techniques

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

Tunable Unidirectional Sound Propagation through a Sonic-Crystal-Based Acoustic Diode

Nonreciprocal wave propagation typically requires strong nonlinear materials to break time reversal symmetry. Here, we utilized a sonic-crystal-based acoustic diode that had broken spatial inversion symmetry and experimentally realized sound unidirectional transmission in this acoustic diode. These novel phenomena are attributed to different mode transitions as well as their associated different energy conversion efficiencies among different diffraction orders at two sides of the diode. This nonreciprocal sound transmission could be systematically controlled by simply mechanically rotating the square rods of the sonic crystal. Different from nonreciprocity due to the nonlinear acoustic effect and broken time reversal symmetry, this new model leads to a one-way effect with higher efficiency, broader bandwidth, and much less power consumption, showing promising applications in various sound devices

Iodine oxoacids in atmospheric aerosol formation : from chamber simulations to field observations

New particle formation is estimated to contribute to around half of the cloud condensation nuclei (CCN) in the atmosphere which in turn have a cooling effect on Earth’s surface. Only a few gas species, including sulfuric acid, oxidized organic vapors and iodine species, are confirmed to contribute to new particle formation, which converts gases to aerosol particles. While new particle formation from sulfuric acid (with water or bases, such as ammonia and amines) is recognized globally, new particle formation solely induced by pure organic vapors only occurs under special conditions. Least is known about the coverage of iodine particle formation processes in the atmosphere. Iodine species have widely been measured in marine and polar environments. However, most ambient measurements concentrated on molecular iodine (I2 ), iodine monoxide (IO), iodine dioxide (OIO) and organic iodine precursors. These measurements constrained the effect of iodine species in catalytic ozone loss processes, but far from enough to understand the particle formation processes. In this thesis, I utilized a bromide chemical ionization method to nearly comprehensively measure inorganic iodine species, including I2 , iodine oxides and oxoacids. An unprecedented performance both in coverage and sensitivity is achieved. We further deployed the bromide chemical ionization method for ambient observations at the Mace Head observatory on the Atlantic coast of Ireland. First successful online measurements of hypoiodous acid (HOI), bromoiodide (IBr) and chloroiodide (ICl) confirmed the heterogeneous uptake of HOI at ambient conditions which enhanced iodine atom production rate by 32% and accelerated ozone (O3 ) loss by 12%. Comprehensive experiments were further carried out at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber to understand the iodine particle formation mechanisms. We found that the ion-induced (charged) and neutral nucleation proceed via distinct mechanisms. The ion-induced nucleation proceeds primarily by sequential addition of iodic acid (HIO3 ) which was measured to proceed at the kinetic limit. However, in contrast to earlier expectations, neutral nucleation additionally involves iodous acid (HIO2 ) to stabilize HIO3 , replacing the role of the negative charge in the ion-induced nucleation. After passing the critical size of nucleation, the growth of iodine particles is essentially sustained by HIO3 , with minor contributions from other species, which are present at much lower concentrations. Additionally, iodine oxoacids have much faster particle formation rates than the sulfuric acid – ammonia mixture at the same acid concentrations (when the ammonia mixing ratio is 100 parts per trillion by volume). While sulfuric acid – ammonia new particle formation has been confirmed to be an important mechanism in polar regions, the role of iodine new particle formation is usually considered to have a limited global reach. We carried out iodic acid measurements at ten boundary layer sites, ranging from the cleanest polar regions to polluted urban environments. The existence of iodic acid is ubiquitously confirmed, with concentrations comparable to sulfuric acid. This indicates a greater importance of iodine oxoacid particle formation processes than just a coastal phenomenon

MODEL OF WORKING SHIP CROSSING CHANNEL

An application method for working ship crossing safely is proposed to determine how to make navigation scheme at a certain time. This method makes it possible for decision makers to make reasonable judgments at different times. In this paper, the position relationship between working ship and navigation vessel in waterway is analysed by considering the ship size, hydrological conditions of waterway, ship arrival model and ship navigation trajectory. Using genetic algorithm, the operation scheme of keeping a safe distance between the working ship and the vessel in the channel is solved by taking the speed and direction of the working ship as genetic factors. By analysing the crossing scheme at each starting time in a given time range, the optimal crossing scheme with the farthest distance between the working ship and the vessels in the channel is obtained. According to the measured data, the simulation is carried out with MATLAB to verify the model of working ship crossing channel. The results show that it is safe and reliable to choose the navigation scheme proposed in this paper, which has strong application value

Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations

Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data. Although many approaches can achieve good performance on anomalous sequence detection, how to identify the anomalous entries in sequences is still challenging due to a lack of information at the entry-level. In this work, we propose a novel framework called CFDet for fine-grained anomalous entry detection. CFDet leverages the idea of interpretable machine learning. Given a sequence that is detected as anomalous, we can consider anomalous entry detection as an interpretable machine learning task because identifying anomalous entries in the sequence is to provide an interpretation to the detection result. We make use of the deep support vector data description (Deep SVDD) approach to detect anomalous sequences and propose a novel counterfactual interpretation-based approach to identify anomalous entries in the sequences. Experimental results on three datasets show that CFDet can correctly detect anomalous entries