Wood Quality in Pine Stands Damaged by Industrial Pollutants in Poland

Information on the impact of industrial air pollutants on forest stands aid consequences on wood quality are a little mixed up and misleading. Some experiments made on pines 130-230 years old did not reveal serious changes of wood quality. In this work, we present results of investigations on wood from 60-year-old pine trees which were under the influence of air pollutants for about 40 years. Such pine stands, of an age near the mean for the present forests in Poland, were the basis for some evaluations of financial losses in Poland due to the effects of air pollution on forests. Detailed data on the properties of the pine wood, which were the basis of the above-mentioned calculations, are also presented. The factors influencing economic losses are given based on the findings presented

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Pseudosymmetry, polymorphism and weak interactions: 4,4′′-difluoro-5′-hydroxy-1,1′:3′,1′′-terphenyl-4′-carboxylic acid and its derivatives

The crystal structures of three derivatives of 4,4′′-difluoro-5′-hydroxy-1,1′:3′,1′′-terphenyl-4′-carboxylic acid are discussed. The acid itself (1), its ethyl ester (2)and hydrazide (3) have been chosen to study the influence of the hydrogen bonding potential on the crystal packing. In 1 and 2 short intramolecular O–H⋯O hydrogen bonds between the hydroxyl and carbonyl groups engage the strong hydrogen bond donors and acceptors, and both these compounds show the effects of packing conflicts. In 1 almost centrosymmetric, stable hydrogen-bonded dimers form between symmetry independent molecules, but the crystal structure is non-centrosymmetric and contains altogether four symmetry-independent molecules (two independent dimers), which show different pseudosymmetries. In 2 dimer formation is impossible but two different crystal forms of this compound have been found. Both polymorphs crystallize in the P1̄space group and differ mainly in the orientation of the OEt group. In turn in 3 there are no intramolecular hydrogen bonds and the crystal structure is determined mainly by the open motifs created by classical hydrogen bonds and by the complementarity of the respective hydrophilic and hydrophobic parts of the molecule

Leveraging contextual sentence relations for extractive summarization using a neural attention model

As a framework for extractive summarization, sentence regression has achieved state-of-The-Art performance in several widely-used practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to encode a sentence into a feature vector. So far, sentence regression approaches have neglected to use features that capture contextual relations among sentences. We propose a neural network model, Contextual Relation-based Summarization (CRSum), to take advantage of contextual relations among sentences so as to improve the performance of sentence regression. Specifically, we first use sentence relations with a wordlevel attentive pooling convolutional neural network to construct sentence representations. Then, we use contextual relations with a sentence-level attentive pooling recurrent neural network to construct context representations. Finally, CRSum automatically learns useful contextual features by jointly learning representations of sentences and similarity scores between a sentence and sentences in its context. Using a two-level attention mechanism, CRSum is able to pay attention to important content, i.e., words and sentences, in the surrounding context of a given sentence. We carry out extensive experiments on six benchmark datasets. CRSum alone can achieve comparable performance with state-ofthe-Art approaches; when combined with a few basic surface features, it significantly outperforms the state-of-The-Art in terms of multiple ROUGE metrics