Pitoyo A, Prameta AA, Marsusi, Suratman, Suranto. 2018. Morphological, anatomical and isozyme variability among taro (Colocasia esculenta) accessions from southeastern part of Central Java, Indonesia. Biodiversitas 19: 1811-1819. The objective of this study was to evaluate morphological, anatomical and isozyme variability among taro accessions from southeastern part of Central Java (Indonesia). A total of 20 taro accessions were collected from a wide range of sites during field surveys. Morphological characters measurements were taken on vegetative structures such as roots, stems, leaves, and corms. Anatomical characters were observed from both paradermal and transverse sections of leaf. Identification of biochemical markers was done by using peroxidase and esterase isozyme system. A UPGMA dendrogram among accessions was constructed based on the genetic similarity matrix by applying a cluster analysis using a computer programme, NTSYS Version 2.00. The analysis of variance for morphological and anatomical characters revealed that there was significant difference for majority of the tested traits indicating that there was a variability among the taro accessions. Polymorphism was observed using isozymes of esterase (12 banding pattern) and peroxidase (8 banding pattern). Based on the dendrogram at a level of 62 % similarity, taro accessions were segregated into two major clusters. In Cluster I, the closest relationship was shown between SKH and SKA accessions that had 96 % coefficient of similarity. The ten accessions from Klaten, Sragen, and Karanganyar were then clustered separately as Cluster II with coefficient of similarity 73.52 %.
This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, and incorporation of a feature scoring the alignment of function words in the hypothesized translation. We also explored the use of monolingual paraphrases to improve coverage, as well as co-training to improve the quality of the segmentation lattices used, but these did not lead to improvements.
We propose a phrase-based context-dependent joint probability model for Named Entity (NE) translation. Our proposed model consists of a lexical mapping model and a permutation model. Target phrases are generated by the context-dependent lexical mapping model, and word reordering is performed by the permutation model at the phrase level. We also present a twostep search to decode the best result from the models. Our proposed model is evaluated on the LDC Chinese-English NE translation corpus. The experiment results show that our proposed model is high effective for NE translation.
Background: On September 28th, 2018, at 18:02 local time (10:02 UTC), a strong earthquake of magnitude Mw = 7. 5 struck Central Sulawesi Province, Indonesia. The epicenter was located at 0.256 o south latitude and 119.846 o east longitude, around 77 km from Palu city, and 20 km below the ground surface. To understand the damage caused by the earthquake, and find a solution to mitigate the geo-disasters in Indonesia, a preliminary investigation on the 2018 Sulawesi earthquake was conducted from 16 to 20 November 2018. This quick report focuses on ground displacements induced by fault movement and large-scale ground flow. Results: During the survey, there is some geotechnical damage were found, such as ground displacement induced by fault movement, liquefaction, landslides, and large-scale ground flow in some certain areas. Large ground displacement was found in some particular areas, such as Kedondong, Pipa Air, Pangeran Diponegoro and Cemara streets in Palu city. The earthquake also triggered large-scale ground flow in some different sites, such as Balaroa and Petobo districts in Palu city and Jono Oge and Sibalaya Villages. Conclusions: The locations of large ground displacements appeared at surface coincide well with the estimated fault line. Therefore the large ground displacements were seems to be induced by the fault movement. Large ground flow caused severe damage to not only human but also houses and buildings. The mechanism of the large ground flow should be clarified in near future.
We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various nonlocal translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and ChineseEnglish translation over a state-of-the-art system that already includes neural network features.
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