Chinese-Catalan: A neural machine translation approach based on pivoting and attention mechanisms

This article innovatively addresses machine translation from Chinese to Catalan using neural pivot strategies trained without any direct parallel data. The Catalan language is very similar to Spanish from a linguistic point of view, which motivates the use of Spanish as pivot language. Regarding neural architecture, we are using the latest state-of-the-art, which is the Transformer model, only based on attention mechanisms. Additionally, this work provides new resources to the community, which consists of a human-developed gold standard of 4,000 sentences between Catalan and Chinese and all the others United Nations official languages (Arabic, English, French, Russian, and Spanish). Results show that the standard pseudo-corpus or synthetic pivot approach performs better than cascade.Peer ReviewedPostprint (author's final draft

Production of hot Jupiter candidates from high-eccentricity mechanisms for different initial planetary mass configurations

Hot Jupiters (HJs) are giant planets with orbital periods of the order of a few days with semimajor axis within ∼0.1 au. Several theories have been invoked in order to explain the origin of this type of planets, one of them being the high-eccentricity migration. This migration can occur through different high-eccentricity mechanisms. Our investigation focused on six different kinds of high-eccentricity mechanisms, namely, direct dispersion, coplanar, Kozai-Lidov, secular chaos, E1 and E2 mechanisms. We investigated the efficiency of these mechanisms for the production of HJ candidates in multiplanet systems initially tightly-packed in the semimajor axis, considering a large set of numerical simulations of the exact equations of motion in the context of the N-body problem. In particular, we analyzed the sensitivity of our results to the initial number of planets, the initial semimajor axis of the innermost planetary orbit, the initial configuration of planetary masses, and to the inclusion of general relativity (GR) effects. We found that the E1 mechanism is the most efficient in producing HJ candidates both in simulations with and without the contribution of GR, followed by the Kozai-Lidov and E2 mechanisms. Our results also revealed that, except for the initial equal planetary mass configuration, the E1 mechanism was notably efficient in the other initial planetary mass configurations considered in this work. Finally, we investigated the production of HJ candidates with prograde, retrograde, and alternating orbits. According to our statistical analysis, the Kozai-Lidov mechanism has the highest probability of significantly exciting the orbital inclinations of the HJ candidates.Fil: Garzón, H.. Universidade Federal do Rio de Janeiro; BrasilFil: Rodríguez, Adrián. Universidade Federal do Rio de Janeiro; BrasilFil: de Elia, Gonzalo Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentin

Low Voltage Ride-through in DFIG Wind Generators by Controlling the Rotor Current without Crowbars

Among all the different types of electric wind generators, those that are based on doubly fed induction generators, or DFIG technology, are the most vulnerable to grid faults such as voltage sags. This paper proposes a new control strategy for this type of wind generator, that allows these devices to withstand the effects of a voltage sag while following the new requirements imposed by grid operators. This new control strategy makes the use of complementary devices such as crowbars unnecessary, as it greatly reduces the value of currents originated by the fault. This ensures less costly designs for the rotor systems as well as a more economic sizing of the necessary power electronics. The strategy described here uses an electric generator model based on space-phasor theory that provides a direct control over the position of the rotor magnetic flux. Controlling the rotor magnetic flux has a direct influence on the rest of the electrical variables enabling the machine to evolve to a desired work point during the transient imposed by the grid disturbance. Simulation studies have been carried out, as well as test bench trials, in order to prove the viability and functionality of the proposed control strategy

The TALP–UPC Spanish–English WMT biomedical task: bilingual embeddings and char-based neural language model rescoring in a phrase-based system

This paper describes the TALP–UPC system in the Spanish–English WMT 2016 biomedical shared task. Our system is a standard phrase-based system enhanced with vocabulary expansion using bilingual word embeddings and a characterbased neural language model with rescoring. The former focuses on resolving outof- vocabulary words, while the latter enhances the fluency of the system. The two modules progressively improve the final translation as measured by a combination of several lexical metrics.Postprint (published version

A mucin-like peptide from Fasciola hepatica instructs dendritic cells with parasite specific Th1-polarizing activity

Fasciolosis is a trematode zoonosis of interest in public health and cattle production. We report here the immunostimulatory effect of a 66 mer mucin-like peptide from Fasciola hepatica (Fhmuc), which synergizes with lipopolysaccharide (LPS) to promote dendritic cell (DC) maturation, endowing these cells with Th1-polarizing capacity. Exposure of DCs to Fhmuc in presence of LPS induced enhanced secretion of pro-inflammatory cytokines and expression of co-stimulatory molecules by DCs, promoting their T cell stimulatory capacity and selectively augmenting IFN- secretion by allogeneic T cells. Furthermore, exposure of DCs to Fhmuc augmented LPS-induced Toll-like receptor (TLR) 4 expression on the cell surface. Finally, Fhmuc-conditioned DCs induced parasite specific-adaptive immunity with increased levels of IFN-gamma secreted by splenocytes from vaccinated animals, and higher parasite-specific IgG antibodies. However, DC-treated Fhmuc conferred modest protection against F. hepatica infection highlighting the potent immuno-regulatory capacity of the parasite. In summary, this work highlights the capacity of a mucin-derived peptide from F. hepatica to enhance LPS-maturation of DCs and induce parasite-specific immune responses with potential implications in vaccination and therapeutic strategies.Fil: Noya, Verónica. Universidad de la República; UruguayFil: Brossard, Natalie. Universidad de la República; UruguayFil: Rodríguez, Ernesto. Universidad de la República; UruguayFil: Dergan Dylon, Leonardo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Carmona, Carlos. Universidad de la República; UruguayFil: Rabinovich, Gabriel Adrián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Freire, Teresa. Universidad de la República; Urugua

Byte-based neural machine translation

This paper presents experiments compar- ing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural ma- chine translation system is to build multi- lingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.Postprint (author's final draft

From bilingual to multilingual neural machine translation by incremental training

Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task.This work is supported in part by a Google Faculty Research Award. This work is also supported in part by the Spanish Ministerio de Economa y Competitividad, the European Regional Development Fund and the Agencia Estatal de Investigacin, through the postdoctoral senior grant Ramon y Cajal, contract TEC2015-69266-P (MINECO/FEDER,EU) and contract PCIN-2017- 079 (AEI/MINECO).Peer ReviewedPostprint (published version

A Convolutional Approach to Quality Monitoring for Laser Manufacturing

[Abstract] The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing

Multilingual machine translation: Deep analysis of language-specific encoder-decoders

State-of-the-art multilingual machine translation relies on a shared encoder-decoder. In this paper, we propose an alternative approach based on language-specific encoder-decoders, which can be easily extended to new languages by learning their corresponding modules. To establish a common interlingua representation, we simultaneously train N initial languages. Our experiments show that the proposed approach improves over the shared encoder-decoder for the initial languages and when adding new languages, without the need to retrain the remaining modules. All in all, our work closes the gap between shared and language-specific encoder-decoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.This work is supported by the European Research Council (ERC) under the European Union’sHorizon 2020 research and innovation programme (grant agreement No. 947657).Peer ReviewedPostprint (published version