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

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted

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

Self-consistent estimations of heating in stacks of intrinsic Josephson junctions

We use the temperature dependent c axis critical current and characteristic gap voltage as temperature gauges to assess the self-heating in stacks of intrinsic Josephson junctions etched out from the ultrathin Bi2Sr2CaCu2O8+ δ(BSCCO) single crystals. The thermal resistance which characterizes the overall cooling efficiency is found to be ∼ 120–160 K mW-1 for the two stacks studied. This value is in agreement with earlier reports for stacks of similar geometry, while it is about two to three times higher than for simple mesas made on the surfaces of mm-size BSCCO single crystals. The suggested method can guarantee the validity of the temperature estimations inside the stacks of various geometries and different cooling efficiencies without the need for attaching additional thermometers

Self-consistent estimations of heating in stacks of intrinsic Josephson junctions

We use the temperature dependent c axis critical current and characteristic gap voltage as temperature gauges to assess the self-heating in stacks of intrinsic Josephson junctions etched out from the ultrathin Bi2Sr2CaCu2O8+ δ(BSCCO) single crystals. The thermal resistance which characterizes the overall cooling efficiency is found to be ∼ 120–160 K mW-1 for the two stacks studied. This value is in agreement with earlier reports for stacks of similar geometry, while it is about two to three times higher than for simple mesas made on the surfaces of mm-size BSCCO single crystals. The suggested method can guarantee the validity of the temperature estimations inside the stacks of various geometries and different cooling efficiencies without the need for attaching additional thermometers

Influence of cathode oxidation via the hole extraction layer in polymer: fullerene solar cells

In this paper, we elucidate the role of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) in the degradation of polymer:PCBM ((6,6)-phenyl C61-butyric acid methyl ester) solar cells. The study is done on unencapsulated cells exposed to ambient conditions in dark. The cell degradation results from reduced carrier extraction, and an investigation of the various interfaces within the cell allows us to correlate this to oxidation of the low work function metal cathode. We further show that this oxidation is caused by water vapor diffusion from the edges through the hygroscopic PEDOT:PSS layer. We demonstrate that only the hygroscopic nature of PEDOT:PSS, and not its acidity, has a detrimental impact. The oxidation of the cathode progresses in synchrony with the water ingress into the PEDOT:PSS layer from the edges of the device towards the central part, and results in a progressive constriction of the active area. When the PEDOT:PSS layer is replaced by an evaporated layer of MoO3, the device lifetime is improved considerably even with highly reactive metal cathodes. Finally, we provide a quantitative relationship between device lifetime and the level of humidity in the ambient, thus establishing a suitable accelerated shelf-life test for organic solar cells and their encapsulation

A 4% efficient organic solar cell using a fluorinated fused subphthalocyanine dimer as an electron acceptor

status: publishe

Decreased recombination through the use of a non-fullerene acceptor in a 6.4% efficient organic planar heterojunction solar cell

An optimization of several aspects of planar heterojunction solar cells based on boron subnaphthalocyanine chloride (SubNc) as a donor material is presented. The use of hexachlorinated boron subphthalocyanine chloride (Cl-6 SubPc) as an alternative acceptor to C-60 allows for the simultaneous increase of the short-circuit current, fill factor, and open-circuit voltage compared to cells with fullerene acceptors. This is due to the complementary absorption of Cl-6 SubPc versus SubNc, reduced recombination at the hetero-interface, and improved energetic alignment. Furthermore, insertion of a thin diindeno[1,2,3-cd:1', 2', 3'-lm] perylene (DIP) layer at the anode results in a very significant 60% increase in photocurrent owing to reduced exciton quenching at the anode. The simultaneous improvement of all three solar cell parameters results in a power conversion efficiency of 6.4% for a non-fullerene planar heterojunction cell.status: publishe