Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation of the positive streamer propagation and chemical reactions in SF6/N2 mixtures under non-uniform field

SF<inf>6</inf>/N<inf>2</inf> mixtures is considered as a practical alternative proposal of SF<inf>6</inf> in GIS and GIL. Simulating particle transportations and chemical reactions during the positive streamer propagation in SF<inf>6</inf>/N<inf>2</inf> mixtures assists in the insulation performance optimization of SF<inf>6</inf>/N<inf>2</inf> mixtures. This paper presents a fluid dynamic model coupled with the chemical reaction system for the positive streamer propagation in SF<inf>6</inf>/N<inf>2</inf> mixtures. The Townsend coefficients of the 20%SF<inf>6</inf>/80%N<inf>2</inf> mixture derived from the proposed chemical reaction system are in accordance with the measurement results. Based on this model, the propagation of the positive streamer in a 1 mm needle-plate gap in the 20%SF<inf>6</inf>/80%N<inf>2</inf> mixture is simulated. Simulated results indicate that there exists a negative space charge region in the streamer channel. It can be attributed to the increasing attachment rates of SF<inf>6</inf> yielding SF<inf>5</inf><sup>−</sup> and SF<inf>6</inf><sup>−</sup>, and the reduction of the ionization rates of N<inf>2</inf> and SF<inf>6</inf> in the streamer channel. Besides, it is noted that the ionization activities of N<inf>2</inf> and SF<inf>6</inf> at the streamer head are equally important to support the continuous propagation of the positive streamer. The ionization of N<inf>2</inf> in the streamer channel serves as an important factor to supply electrons and avoid the accumulation of the negative space charge.
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach

Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ. If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC.
Are juvenile forms of New Zealand heteroblastic trees more resistant to water loss than their mature counterparts?

Abstract One of the theories for the evolution of divaricating shrubs and juvenile forms of heteroblastic woody plants suggests that these forms were selected for resistance to water loss. We examined the water relations and carbon isotope ratios (δ13C) of excised shoots from juvenile and mature forms of one homoblastic species, seven heteroblastic species, and the divaricating shrub Coprosma rotundifolia. Shoots from the juvenile divaricate forms and C. rotundifolia showed responses (relatively high rates of water loss, high osmotic water contents, more negative δ13C, elastic cell walls and low water contents at turgor loss) characteristic of water spenders. Mature and juvenile forms of Pittosporum eugenioides and Pseudopanax crassifolius and mature Carpodetus serratus showed the opposite characteristics. The ordination of these characteristics suggested that juvenile forms of P. eugenioides, Streblus microphylllus, Sophora microphylla, and Carpodetus serratus were more water‐spending than their mature forms, whilst juvenile forms of Pseudopanax crassifolius, Hoheria angustifolia, and Plagianthus regius tended to be somewhat less water‐spending than their mature forms. Our results do not support the hypothesis that juvenile forms of heteroblastic trees are more resistant to water loss than their mature counterparts. We suggest that forms with a divaricating habit produce a micro‐environment within the shrub that favours small leaves, with relatively high leaf conductance, water loss, and CO2 assimilation rates, that also may be partially adapted to shade.
Multi-Scale Decision Network With Feature Fusion and Weighting for Few-Shot Learning

Learning from limited labelled examples is key a research hotspot with excellent scenarios and potential applications. Currently, most of metric learning-based few-shot models still have the problem of low recognition accuracy. This is mainly because that they only use the top-layer abstract feature with semantic information, which ignores the low-layer features that are also critical for the few-shot recognition. Therefore, the extracted features do not have abundant representation ability, and it is difficult to recognize easily confusing objects. Moreover, they usually adopt a fixed distance function or train a comparable network to measure features. These methods lack adaptability, cannot sufficiently fuse features, which leads to weaken the fitting ability of the metric function. And the same or different classes of images are treated equally, which makes the metric function have no emphasis point during training. To address these issues, we propose an end-to-end, metric learning-based model in this paper, called multi-scale decision network with feature fusion and weighting for few-shot learning (MSDN). Considering the importance of the low-layer features, we exploit a convolutional network to extract each layer feature. Then, we exploit a relation network to learn a non-linear metric between the support set and the query set features of each layer and classify the test images via a voting decision. During feature concatenation, we design a non-linear feature fusion item to improve the way of concatenation, so that the relation network can have a stronger function fitting ability to learn the relation score. Meanwhile, we introduce the attention mechanism by calculating the cosine similarity between the support set and the query set features as their weight, which makes the relation network pay more attention to the same class of images. Our model achieves the state-of-the-art accuracy result on Omniglot and miniImageNet datasets compared with popular few-shot recognition models.
Chromosomes and Nuclear DNA in Regenerants of Scilla indica (Roxb.) Baker Derived from Two Explant Sources

Chromosome analysis of regenerants of Scilla indica (Roxb.) Baker obtained through organogenesis from leaf callus and somatic embryogenesis from anther callus was carried out. The plants regenerating from the leaf callus were all diploids whereas those regenerating through somatic embryos from anther cellus showed both diploid and mixoploid plantlets. The karyotype of the regenerants did reveal major structural changes in the diploid plantlets derived from both callus lines. In situ nuclear DNA estimation of the regenerants indicated incerase in mean DNA value only in case of a few mixoploid plantlets regenerated from anther callus via somatic embryogenesis. The nuclear state of the explant thus has an important role to play in determining the cytological status of regenerants.
Yield and water use efficiency of pulses seeded directly into standing stubble in the semiarid Canadian Prairie

In semiarid climates, appropriate management of the previous crop stubble in combination with seeding method is important to improve growing conditions for the subsequent crop. To determine the effects of standing stubble of various heights on the microclimate and on the growth and yield of pulse crops, we seeded desi chickpea (Cicer arietinum L. “Cheston”), field pea (Pisum sativum L. “Grande”), and lentil (Lens culinaris L. “Laird”) directly into cultivated, short (15 to 18 cm), and tall (25 to 36 cm) spring wheat (Triticum aestivum L.) stubble. Standing stubble changed the microclimate near the soil surface by reducing soil temperatures, solar radiation, wind speed, and potential evapotranspiration throughout the life cycle of these crops. Microclimate effects were much more pronounced for tall versus short stubble. The three pulses responded similarly to increasing stubble height. Vine length increased as stubble height increased, but the plants did not stand more erect. However, there was a tendency .
AutoSegNet: An Automated Neural Network for Image Segmentation

Neural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks. The generated neural networks are mainly applied for image classification, and natural language processing. However, there are increasing demands for image segmentation in various areas, such as medical image processing, satellite image object location, and autopilot technology. We propose a NAS method called Automated Segmentation Network (AutoSegNet), targeting industrial and medical image segmentation. The search architectures are constructed by stacking the downsampling layer, the bridge layer, and the upsampling layer, which are explored by a recurrent neural network. Compared with other related methods for image segmentation, the proposed method has a small search space but can explore most of the-state-of-the-art supervised image segmentation models. We perform verification on two datasets, and the results show that AutoSegNet achieves superior segmentation results with clear and continuous segmented edges, as well as better image details.
Creating Contexts in Engineering Research Writing Using a Problem-Solution-Based Writing Model: Experience of Ph.D. Students

Background: The ability to create a context is essential in writing the introduction of a research article (RA). This study explores the experience of engineering Ph.D. students in Australia, for whom English is an Additional Language (EAL), in using a problem-solution-based writing model to develop context-creating skills in writing RA introductions. Research question: What is the experience of engineering Ph.D. students in creating contexts through explicit learning of a problem-solution-based model for writing RA introductions? Literature review: Genre-based teaching is a common approach in the second language classroom. Recently, a genre-based approach for writing the introduction of engineering RAs has been proposed. The descriptive values of the model, PSP-CaRS, have been shown in corpus studies of published engineering RAs. However, its applicability has not been explored pedagogically. Methodology: Twenty-nine Ph.D. students were asked to respond to a questionnaire nine months after learning the model and reflect on their experience using it. The findings were then corroborated with data obtained from interviews, researcher observation, and writing samples. Results: The findings showed that the participants perceived PSP-CaRS to be useful and they continued using it after nine months despite some difficulties encountered in the writing process. Participants’ responses showed that explicit teaching of PSP-CaRS formed the foundation upon which more competent skills to create contexts were developed through practice and integration of subject knowledge. Discussion: Explicit teaching using a model can impart the basics of genre awareness to students. Once students gained an in-depth understanding of the model by working through their difficulties, they developed better genre awareness, and used the model adaptively to visualize and write their RA introductions. Conclusion: The results confirm the usefulness of the proposed model and reveal how a continuing process of learning and practicing using the model helps students develop their skills to create contexts and enhance their genre awareness.
Possible chemotypes from cell wall polysaccharides, as an aid in the systematics of Penicillium and its teleomorphic states Eupenicillium and Talaromyces

From the alkali-extractable material obtained from cell walls of 44 species (49 strains) of Penicillium a water-soluble fraction (F1S) was isolated. The main component (F1S-S) of fraction F1S was purified by gel permeation chromatography through Sepharose CL6B, analysed chemically and its structure determined by 1 H- and 13 C-NMR. Galactose was the predominant neutral sugar in this fraction. Six different polysaccharidic structures were found and the species investigated were arranged into six groups according to these structures. These results illustrate the complexity of the cell wall components of Penicillium and add to existing information on the relatedness of species of the genus with Eupenicillium and Talaromyces . The results show that there are several types of cell wall in Penicillium .
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task

Word embeddings that consider context have attracted great attention for various natural language processing tasks in recent years. In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task. We present two different approaches (feature-based and fine-tuning-based) for answer selection. In the feature-based approach, we utilize two types of contextualized embeddings, namely the Embeddings from Language Models (ELMo) and the Bidirectional Encoder Representations from Transformers (BERT) and integrate each of them with the transformer encoder. We find that integrating these contextual embeddings with the transformer encoder is effective to improve the performance of sentence similarity modeling. In the second approach, we fine-tune two pre-trained transformer encoder models for the answer selection task. Based on our experiments on six datasets, we find that the fine-tuning approach outperforms the feature-based approach on all of them. Among our fine-tuning-based models, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model results in new state-of-the-art performance across five datasets.
Floral scent in bat‐pollinated plants: a case of convergent evolution

The chemical composition of floral scent in eight bat-pollinated species belonging to six different plant families was investigated. Floral scent was collected by headspace trapping using porous adsorbents and the chemical composition determined by coupled gas chromatography-mass spectrometry. In all species except one the floral scent was found to include sulphur-containing compounds, of which several are reported for the first time in floral scents. Three species contained mushroom-like smelling fatty acid derivatives with a C8-skeleton. Such flowers may be recognized by pollinators as humid environments in otherwise dry surroundings. The presence of similar or chemically closely related sulphur containing compounds in floral scent of bat-pollinated plant species from differing families may represent a case of convergent evolution in scent composition and an adaptation to attract this specific group of pollinators with similar sensory preferences.
Lenition and Fortition of Stop Codas in Romanian

The present paper aims at providing a first study of lenition- and fortition-type phenomena in coda position in Romanian, a language that can be considered as less-resourced. Our data show that there are two contexts for devoicing in Romanian: before a voiceless obstruent, which means that there is regressive voicelessness assimilation in the language, and before pause, which means that there is a tendency towards final devoicing proper. The data also show that non-canonical voicing is an instance of voicing assimilation, as it is observed mainly before voiced consonants (voiced obstruents and sonorants alike). Two conclusions can be drawn from our analyses. First, from a phonetic point of view, the two devoicing phenomena exhibit the same behavior regarding place of articulation of the coda, while voicing assimilation displays the reverse tendency. In particular, alveolars, which tend to devoice the most, also voice the least. Second, the two assimilation processes have similarities that could distinguish them from final devoicing as such. Final devoicing seems to be sensitive to speech style and gender of the speaker, while assimilation processes do not. This may indicate that the two kinds of processes are phonologized at two different degrees in the language, assimilation being more accepted and generalized than final devoicing.
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT

In this paper, we reproduce some of the experiments related to neural network training for Machine Translation as reported in (Vanmassenhove and Way, 2018). They annotated a sample from the EN-FR and EN-DE Europarl aligned corpora with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation (NMT) toolkit. Following the original publication, we obtained lower BLEU scores than the authors of the original paper, but on a more limited set of annotations. In the second half of the paper, we try to analyze the difference in the results obtained and suggest some methods to improve the results. We discuss the Byte Pair Encoding (BPE) used in the pre-processing phase and suggest feature ablation in relation to the granularity of syntactic and semantic annotations. The learnability of the annotated input is discussed in relation to existing resources for the target languages. We also discuss the feature representation likely to have been adopted for combining features.
IMPACT OF SELENIUM FERTILIZATION ON GLUCOSINOLATE CONCENTRATION IN ARABIDOPSIS THALIANA AND RAPID CYCLING BRASSICA OLERACEA

Brassica vegetables are a significant source of glucosinolates (GSs), which are sulfur (S)-containing phytonutrients exhibiting, upon hydrolysis by endogenous myrosinase, antioxidant and anti-carcinogenic activity. Selenium (Se) is an essential micronutrient in mammalian health exhibiting antioxidant activity by inhibiting experimental carcinogenesis in animal models and reducing cancer incidence in human clinical trials. Selenium is readily accumulated in both Brassica species and Arabidopsis thaliana, a model species with similar S-metabolism. The research objectives for this project were to: 1) compare the impact of Se fertilization on GS concentrations between Arabidopsis thaliana and rapid cycling Brassica oleracea; and 2) determine the level of Se fertilization needed to optimize plant tissue Se concentration to maximize potential human health benefits. Both S and Se concentrations increased in A. thaliana and B. oleracea tissues in response to increasing Se treatments. Concentrations of glucoiberin, glucoraphanin, aliphatic, and total GS differed significantly between A. thaliana and B. oleracea. Data demonstrates that anti-carcinogenic GSs can be modified through changes in Se treatment concentrations, yet high levels of anti-carcinogenic GSs can be maintained while increasing Se concentration to 0.8 mg Se L-1. Thus, it is feasible to increase Se to beneficial dietary levels without compromising GS concentrations.
A new DNA extraction method for high‐throughput marker analysis in a large‐genome species such as Triticum aestivum

Gene mapping and marker-assisted selection in complex, polyploid genomes still relies strongly on restriction fragment length polymorphism (RFLP) analysis, as conversion of RFLP to polymerase chain reaction (PCR) markers can be very difficult. DNA extraction in amounts suitable for RFLP analysis represents the most time-consuming and labour-intensive step in molecular marker analysis of plant populations. In this paper, a new flexible method for plant DNA extraction is presented. It allows a high-throughput of samples in a short time without the need for freezing or lyophilizing the plant material. The method allows the isolation of genomic DNA with a yield of 100 μg for a minimal amount of 200 mg of leaf material. This is sufficient for work with large-genome plant species such as hexaploid wheat, where 20 μg of genomic DNA are required for a single RFLP analysis.
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?

We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references. The data was collected as part of the EU APE-QUEST project and comprises crawled content from EU websites with translation from English into three European languages: Dutch, French and Portuguese. Altogether, the data consists of around 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, a post-edited version of such translation by a professional translator, and - where available - the original reference translation crawled from parallel language websites. We describe the data collection process, provide an analysis of the resulting post-edits and benchmark the data using state-of-the-art quality estimation and automatic post-editing models. One interesting by-product of our post-editing analysis suggests that neural systems built with publicly available general domain data can provide high-quality translations, even though comparison to human references suggests that this quality is quite low. This makes our dataset a suitable candidate to test evaluation metrics. The data is freely available as an ELRC-SHARE resource.