This paper describes Uppsala University’s submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine regression, using a number of explicitly defined features extracted from the Machine Translation input, output and models in combination with tree kernels over constituency and dependency parse trees for the input and output sentenc...

Quercetin and resveratrol are known to have beneficial effects on the diabetes and diabetic complication, however, the effects of combined treatment of these compounds on diabetes are not fully revealed. Therefore, the present study was designed to investigate the combined antidiabetic action of quercetin (QE) and resveratrol (RS) in streptozotocin (STZ)-induced diabetic rats. To test the effec...

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in this studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potenti...

A self-calibrating method is described for measuring the radiative quantum efficiency (QE) in doped optical fibers. The method uses an integrating sphere to collect the fluorescence from the fiber, with pump light transmitted through the fiber end serving as a reference. QE measurements for a 780 or 808 nm pump were made on bismuth-doped AlGeP-silica fibers prepared by aerosol deposition. For b...

Quantifier elimination (QE) over real closed fields has found numerous applications. Cylindrical algebraic decomposition (CAD) is one of the main tools for handling quantifier elimination of nonlinear input formulas. Despite of its worst case doubly exponential complexity, CAD-based quantifier elimination remains interesting for handling general quantified formulas and producing simple quantifi...

We present Marmot — a new toolkit for quality estimation (QE) of machine translation output. Marmot contains utilities targeted at quality estimation at the word and phrase level. However, due to its flexibility and modularity, it can also be extended to work at the sentence level. In addition, it can be used as a framework for extracting features and learning models for many common natural lan...

In this paper we address the question of the reliability of the predictions made by MT Quality Estimation (QE) systems. In particular, we show that standard supervised QE systems, usually trained to minimize MAE, make serious mistakes at predicting the quality of the sentences in the tails of the quality range. We describe the problem and propose several experiments to clarify their causes and ...

BACKGROUND The quality of behaviour support plans (BSPs) can be an important influence on the quality of the support provided to people with disability who show challenging behaviours. The Behavior Support Plan Quality Evaluation Guide II (BSP-QE II) is one tool that may be useful in assessing the quality of behaviour support plans. It has previously been used to assess BSPs to support children...

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 m...

A connected graph is called of non-QE class if it does not admit a quadratic embedding in Euclidean space. primary contain as an isometrically embedded proper subgraph. The graphs on six vertices are completely classified into the classes QE graphs, and graphs.