Learning unsupervised multilingual word embeddings with incremental multilingual hubs

Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces. In this work, we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues. We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space. Through the gradual language addition our method can leverage the interdependencies between the new language and all other languages in the current multilingual hub/space. We find that it is beneficial to project more distant languages later in the iterative process. Our fully unsupervised multilingual embedding spaces yield results that are on par with the state-of-the-art methods in the bilingual lexicon induction (BLI) task, and simultaneously obtain state-of-the-art scores on two downstream tasks: multilingual document classification and multilingual dependency parsing, outperforming even supervised baselines. This finding also accentuates the need to establish evaluation protocols for cross-lingual word embeddings beyond the omnipresent intrinsic BLI task in future work

Lightweight ropes for lifting applications

Summary The level of wire rope technology used in lifting or hoisting applications has a significant effect on the overall efficiency of the system. This paper presents various examples which show the different benefits which may be accrued from the use of lightweight ropes. Most hoisting operations by their very nature involve long lengths of rope in the system. In the deep mining application, these lengths may easily be 3000 m or more. At this length the rope self weight becomes a significant component of the total payload which it has to support. A 20% reduction in the rope mass per metre for a typical hoist rope (of the same strength) will allow an increase in the skip payload of 30% at 3000 m. In the case of a crane, the weight of the rope can form a large part of the whole machine. Here, reduction in rope weight can allow benefits in terms of stability and in the case of mobile cranes, significant savings in axle loads. Another area where composite ropes may provide advantages is in the offshore environment where the lightweight benefits of fibre may be combined with the ruggedness of steel wire. The paper closes by making a brief discussion of the issues of NDT inspection of such ropes

Detection of Mycosphaerella graminicola in Wheat Leaves by a Microsatellite Dinucleotide Specific-Primer

Early detection of infection is very important for efficient management of Mycosphaerella graminicola leaf blotch. To monitor and quantify the occurrence of this fungus during the growing season, a diagnostic method based on real-time PCR was developed. Standard and real-time PCR assays were developed using SYBR Green chemistry to quantify M. graminicola in vitro or in wheat samples. Microsatellite dinucleotide specific-primers were designed based on microsatellite repeats of sequences present in the genome of M. graminicola. Specificity was checked by analyzing DNA of 55 M. graminicola isolates obtained from different geographical origins. The method appears to be highly specific for detecting M. graminicola; no fluorescent signals were observed from 14 other closely related taxa. Primer (CT) 7 G amplified a specific amplicon of 570 bp from all M. graminicola isolates. The primers did not amplify DNA extracted from 14 other fungal species. The approximate melting temperature (Tm) of the (CT) 7 G primer was 84.2 °C. The detection limit of the real-time PCR assay with the primer sets (CT) 7 G is 10 fg/25 μL, as compared to 10 pg/25 μL using conventional PCR technology. From symptomless leaves, a PCR fragment could be generated two days after inoculation. Both conventional and real-time PCR could successfully detect the fungus from artificially inoculated wheat leaves. However, real-time PCR appeared much more sensitive than conventional PCR. The developed quantitative real-time PCR method proved to be rapid, sensitive, specific, cost-effective and reliable for the identification and quantification of M. graminicola in wheat

Learning unsupervised multilingual word embeddings with incremental multilingual hubs

Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces. In this work, we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues. We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space. Through the gradual language addition our method can leverage the interdependencies between the new language and all other languages in the current multilingual hub/space. We find that it is beneficial to project more distant languages later in the iterative process. Our fully unsupervised multilingual embedding spaces yield results that are on par with the state-of-the-art methods in the bilingual lexicon induction (BLI) task, and simultaneously obtain state-of-the-art scores on two downstream tasks: multilingual document classification and multilingual dependency parsing, outperforming even supervised baselines. This finding also accentuates the need to establish evaluation protocols for cross-lingual word embeddings beyond the omnipresent intrinsic BLI task in future work