Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. Along such measurements the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite of the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities, and then the network captures the features of these characteristic traces, and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight to the decision making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4' bipyridine-gold single molecule break junction data. arXiv:2001.03006v1 [cond-mat.mes-hall] 9 Jan 2020

Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.

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We present PADAPT 1.0, the Pannonian Database of Plant Traits which relies on regional data sources and integrates existing data and new measurements on a wide range of traits and attributes of the plant species of the Pannonian Biogeographical Region and makes it freely accessible at www.padapt.eu. The current version covers the species of the region occurring in Hungary (cc. 90% of the region's flora) and consists of 126,337 records on 2745 taxa. There are 53 plant attributes in PADAPT 1.0 organised in six major groups: (i) Habitus and strategy, (ii) Reproduction, (iii) Kariology, (iv) Distribution and conservation, (v) Ecological indicator values, and (vi) Leaf traits. By including species of the eastern part of Europe not covered by other databases, PADAPT can facilitate studying the flora and vegetation of the eastern part of the continent. Data collection will continue in the future and the PADAPT team welcomes researchers interested in contributing with data. The main task before an updated version of the database is to include species of the Pannonian region not covered by the current version. In conclusion, although data coverage is far from complete, PADAPT meets the longstanding need for a regional database of the Pannonian flora.

Questions Plant invasions are considered among the biggest threats to biodiversity worldwide. In a full‐factorial greenhouse experiment we analysed the effect of soil burial depth and litter cover on the germination of invasive plants. We hypothesised that: (a) burial depth and litter cover affect the germination of the studied species; (b) the effects of burial and litter cover interact with each other, and (c) the effects are species‐specific, but dependent on seed size. Methods We tested the germination and seedling growth of 11 herbaceous invasive species in a full‐factorial experiment using four levels of seed burial depths and litter cover. We analysed the effect of burial, litter cover, and their interactions on germination, seedling length and biomass across species and at the species level. Results Soil burial depth and litter cover had a significant effect on the germination of the studied species, but there were considerable differences between species. We observed a general trend of species with bigger seeds being not or less seriously affected by soil burial and litter cover than smaller‐seeded species. Correlations between seed weight and effect sizes mostly confirmed this general trend, but not in the case of soil burial. Conclusions Our findings confirmed that seed size is a major driver of species’ response to litter cover and to the combined effects of litter cover and soil burial, but there is no general trend regarding the response to soil burial depth. Despite its very small seeds, the germination of Cynodon dactylon was not affected by soil burial. The germination of Ambrosia artemisiifolia was hampered by both soil burial and litter cover despite its relatively large seeds. Thus, specific information on species’ response to burial depth and litter accumulation is crucial when planning management or restoration in areas threatened by plant invasions.

AbstractQuestionsPlant invasions are considered among the biggest threats to biodiversity worldwide. In a full-factorial greenhouse experiment we analysed the effect of soil burial depth and litter cover on the germination of invasive plants. We hypothesised that (i) burial depth and litter cover affect the germination of the studied species, (ii) the effects of burial and litter cover interact with each other, and (iii) the effects are species-specific, but dependent on seed size.MethodsWe tested the germination and seedling growth of 11 herbaceous invasive species in a full-factorial experiment using four levels of seed burial depths and litter cover. We analysed the effect of burial, litter cover, and their interactions on germination, seedling length and biomass across species and at the species level.ResultsSoil burial depth and litter cover had a significant effect on the germination of the studied species, but there were considerable differences between species. We observed a general trend of species with bigger seeds being not or less seriously affected by soil burial and litter cover than smaller-seeded species. Correlations between seed weight and effect sizes mostly confirmed this general trend, but not in the case of soil burial.ConclusionsOur findings confirmed that seed size is a major driver of species’ response to litter cover and to the combined effects of litter cover and soil burial, but there is no general trend regarding the response to soil burial depth. Despite its very small seeds the germination of Cynodon dactylon was not affected by soil burial. The germination of Ambrosia artemisiifolia was hampered by both soil burial and litter cover despite its relatively large seeds. Thus, specific information on species’ response to burial depth and litter accumulation is crucial when planning management or restoration in areas threatened by plant invasions.

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