No abstract

In this paper, we present a two-stage neural quality estimation model that uses multilevel task learning for translation quality estimation (QE) at the sentence, word, and phrase levels. Our approach is based on an end-to-end stacked neural model named Predictor-Estimator, which has two stages consisting of a neural word prediction model and neural QE model. To efficiently train the two-stage model, a stack propagation method is applied, thereby enabling us to jointly learn the word prediction model and QE model in a single learning mode. In addition, we deploy multilevel task learning with stack propagation, where the training examples available for all QE subtasks (i.e., sentence/word/phrase levels) are used to train a Predictor-Estimator for a specific subtask. All of our submissions to the QE task of WMT17 are ensembles that combine a set of neural models trained under different settings of varying dimensionalities and shuffling training examples, eventually achieving the best performances for all subtasks at the sentence, word, and phrase levels.

Many heterogeneous catalytic reactions occur at high temperatures, which may cause large energy costs, poor safety, and thermal degradation of catalysts. Here, we propose a light-assisted surface reaction, which catalyze the surface reaction using both light and heat as an energy source. Conventional metal catalysts such as ruthenium, rhodium, platinum, nickel, and copper were tested for CO2 hydrogenation, and ruthenium showed the most distinct change upon light irradiation. CO2 was strongly adsorbed onto ruthenium surface, forming hybrid orbitals. The band gap energy was reduced significantly upon hybridization, enhancing CO2 dissociation. The light-assisted CO2 hydrogenation used only 37% of the total energy with which the CO2 hydrogenation occurred using only thermal energy. The CO2 conversion could be turned on and off completely with a response time of only 3 min, whereas conventional thermal reaction required hours. These unique features can be potentially used for on-demand fuel production with minimal energy input.

No abstract

No abstract