Addressing the Rare Word Problem in Neural Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT14 contest task.Comment: ACL 2015 camera-ready versio

GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation

Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. In this work, we build a new dataset dubbed "Aquatic Animal Species." We also devise a novel GUided mixup augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL) that leverages the advantages of multiple view segmentation models to effectively segment aquatic animals and improves the training performance by synthesizing hard samples. Extensive experiments demonstrated the superiority of our proposed framework over existing state-of-the-art instance segmentation methods

Combined effects of a simulated marine heatwave and an algal toxin on a tropical marine aquaculture fish cobia (Rachycentron canadum)

Ongoing global warming is one of the major challenges for the development of aquaculture in the tropical regions where species are already cultured in the water temperature close to their upper physiological thresholds. Furthermore, warming can trigger blooms of toxic algae, yet we do not know how extreme warming such as a marine heatwave (MHW) and algal toxins may affect marine aquaculture species. To address this issue, we investigated the effects of a simulated MHW in combination with exposure to trans-4-trans-decadienal (PUA), a diatom-derived toxin, on survival, growth, development and biochemical composition of cobia larvae and juveniles. Cobia larvae were exposed for 48 hr to one of two temperatures (29 vs. 34°C) and two PUA treatments (0 vs. 0.5 µM). Surviving larvae from each treatment were divided into two subsets: three replicates were used for the feeding test and five replicates were used for the recovery test in a non-contaminated environment at the respective temperatures of 29 or 34°C. Survival of cobia larvae was reduced by 16% in either MHW or PUA, but it dropped by 60% when both stressors were present, indicating a synergistic effect. MHW, but not PUA, reduced the feeding of cobia larvae. PUA had no delayed effects on growth rate and biochemical composition of the fish. MHW strongly reduced specific growth rate, body protein and lipid contents in cobia. Our results provide the first empirical evidence of how MHW and toxic algae may interact and challenge cobia and marine aquaculture production in tropical countries.publishedVersio

On the study of the FSO link performance under controlled turbulence and fog atmospheric conditions

In this paper, the effect of turbulence and fog on the free space optical (FSO) communication systems for on off keying (OOK), pulse amplitude modulation (PAM) and subcarrier intensity modulation (SIM) based on binary phase shift keying (BPSK) is experimentally investigated. The experiment is carried out in a controlled laboratory environment where turbulence and fog could be generated in a dedicated FSO chamber. In comparison to 4 PAM signal, the BPSK and OOK NRZ modulation signalling format are more robust against the fog and turbulence effects. In addition BPSK system is much less susceptible to the signal amplitude fluctuation due to turbulence compared to the other two modulation formats

Seeing the world from its words: All-embracing Transformers for fingerprint-based indoor localization

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy