This article investigates the relationship between the exchange rate and non-oil trade balance in Iran between 1981 and 2014. A structural vector auto-regression model is built. The results indicated that the increasing effect of the real effective exchange rate worsens the non-oil trade balance in the short term. In contrast, the increasing effect of the real effective exchange rate improves t...

Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence. We find that, in fact, an n-gram language model with a simple heuristic gives strong results on this task. Furthermore, we show that a long short-term memory (LSTM) language model is even more effective at recovering order, with our basic model out...

Soil moisture exhibits spatial and temporal variability. Spatial variability is mostly associated to climatology patterns, topography, soil texture, and vegetation index; where as, temporal variability is primarily associated to rainfall and air temperature events. The spatial variability is modeled by a long-term memory model whereas the temporal variability by a short-term memory model. The l...

The language model is an important component of any speech recogn ition system. In this paper, we present a lexical enrichment methodology of corpora focused on the construction of statistical language models. This methodology considers, on one hand, the identification of the set of poor represented words of a given training corpus, and on the other hand, the enrichment of the given corpus by t...

Attention mechanisms have attracted considerable interest in image captioning due to its powerful performance. However, existing methods use only visual content as attention and whether textual context can improve attention in image captioning remains unsolved. To explore this problem, we propose a novel attention mechanism, called textconditional attention, which allows the caption generator t...

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreeme...

We perform a quantitative, comparative study of the spin pumping, spin Seebeck, and spin Hall magnetoresistance effects, all detected via the inverse spin Hall effect in a series of over 20 yttrium iron garnet/Pt samples. Our experimental results fully support present, exclusively spin current-based, theoretical models using a single set of plausible parameters for spin mixing conductance, spin...

Outliers and multi-collinearity often have large influence in the model/variable selection process in linear regression analysis. To investigate this combined problem of multi-collinearity and outliers, we studied and compared Liu-type S (liuS-estimators) and Liu-type Least Trimmed Squares (liuLTS) estimators as robust model selection criteria. Therefore, the main goal this study is to select s...