Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined analysis of enamelled and gilded glassware from Frederick II Castle at Melfi (Italy) to identify technology and raw materials

A group of enamelled and gilded glass objects, coming from Melfi Castle (PZ - Italy) from an area dated to the period between the end of the 12th and the last quarter of the 13th century, offered the opportunity to closely investigate this technology with the aim of understanding the raw materials and the procedures employed to realize the objects and their precious decorations. Optical microscopy, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, Raman spectroscopy, laser ablation-inductively coupled plasma-mass spectrometry and X-Ray photoelectron spectroscopy were used to observe and analyze the glass, the enamels, the gildings and their mutual relations. The bulk of the objects resulted a soda-lime glass, while the enamels are lead-based ‘soft’ enamels or soda-lime glass; the palette of pigments employed to obtain their colours included iron III oxide and minium for red, lazurite and/or cobalt for blue, lead–tin–antimony pyrochlore solid solution oxide (yellow) plus cobalt for green, manganese oxides for black and calcium phosphate for white. Results obtained for gilding, in particular stratigraphy and morphology, suggest the use of the so called ‘liquid gold’. Copyright © 2015 John Wiley & Sons, Ltd.
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations

Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.
Sodium Pyruvate Ameliorates Influenza A Virus Infection In Vivo

Influenza A virus (IAV) causes seasonal epidemics annually and pandemics every few decades. Most antiviral treatments used for IAV are only effective if administered during the first 48 hours of infection and antiviral resistance is possible. Therapies that can be initiated later during IAV infection and that are less likely to elicit resistance will significantly improve treatment options. Pyruvate, a key metabolite, and end product of glycolysis, has been studied for many uses, including its anti-inflammatory capabilities. Sodium pyruvate was recently shown by us to decrease inflammasome activation during IAV infection. Here, we investigated sodium pyruvate’s effects on IAV in vivo. We found that nebulizing mice with sodium pyruvate decreased morbidity and weight loss during infection. Additionally, treated mice consumed more chow during infection indicating improved symptoms. There were notable improvements in pro-inflammatory cytokine production (IL-1β) and lower virus titers on days 7 post-infection in mice treated with sodium pyruvate compared to control animals. As pyruvate acts on the host immune response and metabolic pathways and not directly on the virus, our data demonstrate that sodium pyruvate is a promising treatment option that is safe, effective, and unlikely to elicit antiviral resistance.
AlexU-AUX-BERT at SemEval-2020 Task 3: Improving BERT Contextual Similarity Using Multiple Auxiliary Contexts

This paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is based on both BERT embeddings and WordNet. We simply use cosine similarity to find the closest synset of the target words. Our results show that using this simple approach greatly improves the system behavior. Our model is ranked 3rd in subtask-2 for SemEval-2020 task 3.
ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media

This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.
Towards automatically generating Questions under Discussion to link information and discourse structure

Questions under Discussion (QUD; Roberts, 2012) are emerging as a conceptually fruitful approach to spelling out the connection between the information structure of a sentence and the nature of the discourse in which the sentence can function. To make this approach useful for analyzing authentic data, Riester, Brunetti & De Kuthy (2018) presented a discourse annotation framework based on explicit pragmatic principles for determining a QUD for every assertion in a text. De Kuthy et al. (2018) demonstrate that this supports more reliable discourse structure annotation, and Ziai and Meurers (2018) show that based on explicit questions, automatic focus annotation becomes feasible. But both approaches are based on manually specified questions. In this paper, we present an automatic question generation approach to partially automate QUD annotation by generating all potentially relevant questions for a given sentence. While transformation rules can concisely capture the typical question formation process, a rule-based approach is not sufficiently robust for authentic data. We therefore employ the transformation rules to generate a large set of sentence-question-answer triples and train a neural question generation model on them to obtain both systematic question type coverage and robustness.
UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation

SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two subtasks, A and B, are focused in this work, i.e., detecting against-common-sense statements and selecting explanations of why they are false from the given options. Intuitively, commonsense validation requires additional knowledge beyond the given statements. Therefore, we propose a system utilising pre-trained sentence transformer models based on BERT, RoBERTa and DistillBERT architectures to embed the statements before classification. According to the results, these embeddings can improve the performance of the typical MLP and LSTM classifiers as downstream models of both subtasks compared to regular tokenised statements. These embedded statements are shown to comprise additional information from external resources which help validate common sense in natural language.
Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models

While internalized “implicit knowledge” in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question. Based on the text-to-text transfer transformer (T5) model, this work explores a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice (MC) question answering tasks. Experiments on three representative MC datasets show the surprisingly good performance of our simple template, coupled with a logit normalization technique for disambiguation. Furthermore, we verify that our proposed template can be easily extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. Starting from the MC task, this work initiates further research to find generic natural language templates that can effectively leverage stored knowledge in pretrained models.
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning

Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks. However, only a little research has explored the suitability of this approach in low resource settings with less than 1,000 training data points. In this work, we explore fine-tuning methods of BERT - a pre-trained Transformer based language model - by utilizing pool-based active learning to speed up training while keeping the cost of labeling new data constant. Our experimental results on the GLUE data set show an advantage in model performance by maximizing the approximate knowledge gain of the model when querying from the pool of unlabeled data. Finally, we demonstrate and analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters, making it more suitable for low-resource settings.
Histone H1 protects telomeric repeats from H3K27me3 invasion in Arabidopsis

While the pivotal role of linker histone H1 in shaping nucleosome organization is well established, its functional interplays with chromatin factors along the epigenome are just starting to emerge. Here we first report that in Arabidopsis, as in mammals, H1 occupies Polycomb Repressive Complex 2 (PRC2) target genes where it favors chromatin condensation and H3K27me3 deposition. We further show that, contrasting with its conserved function in PRC2 activation at genes, H1 selectively prevents H3K27me3 accumulation at telomeres and large pericentromeric interstitial telomeric repeat (ITR) domains by restricting DNA accessibility to Telomere Repeat Binding (TRB) proteins, a group of H1-related Myb factors mediating PRC2 cis recruitment. This study unveils a mechanistic framework by which H1 avoids the formation of gigantic H3K27me3-rich domains at telomeric sequences and contributes to safeguard nucleus architecture. Teano et al. report that that linker histone H1 and a group of H1-related telomeric proteins interplay to selectively influence the Polycomb repressive landscape at genes and telomeric repeats in Arabidopsis. These findings provide a mechanistic framework by which H1 influences the epigenome and nuclear organization in a sequence-specific manner. Highlights H1 promotes PRC2 activity and limits accessibility at a majority of genes H1 prevents PRC2 activity at telomeric DNA sequences PRC2 repression is achieved by restricting accessibility to TRB proteins H1 orchestrates the spatial organization of telomeres and interstitial telomeres (ITRs)
JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model

This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.
Diversity patterns and selective sweeps in a Southeast European panel of maize inbred lines as combined with two West European panels

More than one third of European grain maize is produced in South Eastearn Europe (SEE) and utilization of historical maize material developed in SEE for its favorable alleles and diversity has long been speculated. However, molecular diversity of the SEE maize genetic material is scarce. The objectives of this study were i) to analyze diversity patterns in a large panel of densely genotyped historical accessions from SEE, ii) to compare the data with those obtained from other two European panels, and iii) to identify genomic regions that have undergone selection (selective sweeps) in response to adaptation to SEE conditions. 572 accessions of the historical inbred lines from Maize Research Institute Zemun Polje representing the SEE material were genotyped using the 600k maize genotyping Axiom array. The genotyping results were merged with two European panels DROPS and TUM. Genetic structure and diversity were analyzed using neighbor-joining cladogram, PcoA, Admixture, Structure and sNMF. To detect the selective sweep signals, Tajima’s D statistic and RAiSD were employed. The best number of ancestral populations was K=7, whereby one of them is a subpopulation containing inbreds belong exclusively to the SEE panel. The prevalence of inbreds linked to historical US inbred lines Wf9, Oh43, Pa91 and A374 was detected in SEE. Possible soft selective sweep was detected in chromosome 2 in region harboring a gene linked to promotion of flowering FPF1. Additional scan for selective sweeps using the RAiSD methodology yielded four signals in chromosomes 5 and 6, all in gene-rich regions. Several candidates of selection were identified, influencing the plant morphology and adaptation. Our study provides the first step towards the utilization of the SEE genetic materials for use in maize breeding. Phenotypic analysis is needed for assessment of SEE accessions for favorable alleles, and identification of breeding targets.
Domain Transfer based Data Augmentation for Neural Query Translation

Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries. Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model. In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples. Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model. In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking. Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy.
Sentence Analogies: Linguistic Regularities in Sentence Embeddings

While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.
Revitalization of Indigenous Languages through Pre-processing and Neural Machine Translation: The case of Inuktitut

Indigenous languages have been very challenging when dealing with NLP tasks and applications because of multiple reasons. These languages, in linguistic typology, are polysynthetic and highly inflected with rich morphophonemics and variable dialectal-dependent spellings; which affected studies on any NLP task in the recent years. Moreover, Indigenous languages have been considered as low-resource and/or endangered; which poses a great challenge for research related to Artificial Intelligence and its fields, such as NLP and machine learning. In this paper, we propose a study on the Inuktitut language through pre-processing and neural machine translation, in order to revitalize the language which belongs to the Inuit family, a type of polysynthetic languages spoken in Northern Canada. Our focus is concentrated on: (1) the preprocessing phase, and (2) applications on specific NLP tasks such as morphological analysis and neural machine translation, both for Indigenous languages of Canada. Our evaluations in the context of lowresource Inuktitut-English Neural Machine Translation, showed significant improvements of the proposed approach compared to the state-of-the-art.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification

The classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies the problem of learning a sequence of tasks (sentiment classification tasks in our case). After each sentiment classification task is learned, its knowledge is retained to help future task learning. Following this setting, we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Network (BLAN). The key idea is to exploit the generative parameters of naive Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge base and later used to build lifelong attentions. The constructed lifelong attentions are then used to enhance the attention of the network to help new task learning. Experimental results on product reviews from Amazon.com show the effectiveness of the proposed model.
Red Is Open-Minded, Blue Is Conscientious: Predicting User Traits From Instagram Image Data

Various studies have addressed the connection between a user’s traits and their social media content. This paper explores the relationship between gender, age and Big Five personality traits of 179 university students from Germany and their Instagram images. With regards to both image features and image content, significant differences between genders as well as preferences related to age and personality traits emerged. Gender, age and personality traits are predicted using machine learning classification and regression methods. This work is the first of its kind to focus on data from European Instagram users, as well as to predict age from Instagram image features and content on a fine-grained level.
Prefrontal cortical activity predicts the extra-place field spiking of hippocampal place cells

The receptive field of a neuron describes the regions of a stimulus space where the neuron is consistently active. Sparse spiking outside of the receptive field is often considered to be noise, rather than a reflection of information processing. Whether this characterization is accurate remains unclear. We therefore contrasted the sparse, temporally isolated spiking of hippocampal CA1 place cells to the consistent, temporally adjacent spiking seen within their spatial receptive fields (“place fields”). We found that isolated spikes, which occur during locomotion, are more strongly phase coupled to hippocampal theta oscillations than adjacent spikes and, surprisingly, transiently express coherent representations of non-local spatial representations. Further, prefrontal cortical activity is coordinated with, and can predict the occurrence of future isolated spiking events. Rather than local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical-hippocampal process consistent with the generation of non-local scenario representations during active navigation.
Pfcerli2, a duplicated gene in the malaria parasite Plasmodium falciparum essential for invasion of erythrocytes as revealed by phylogenetic and cell biological analysis

Merozoite invasion of host red blood cells (RBCs) is essential for survival of the human malaria parasite Plasmodium falciparum. Proteins involved with RBC binding and invasion are secreted from dual-club shaped organelles at the apical tip of the merozoite called the rhoptries. Here we characterise P. falciparum Cytosolically Exposed Rhoptry Leaflet Interacting protein 2 (PfCERLI2), as a rhoptry bulb protein that is essential for merozoite invasion. Phylogenetic analyses show that cerli2 arose through an ancestral gene duplication of cerli1, a related cytosolically exposed rhoptry bulb protein. We show that PfCERLI2 is essential for blood-stage growth and localises to the cytosolic face of the rhoptry bulb. Inducible knockdown of PfCERLI2 led to an inhibition of merozoite invasion after tight junction formation. PfCERLI2 knockdown was associated with inhibition of rhoptry antigen processing and a significant elongation of the rhoptries, suggesting that the inability of merozoites to invade is caused by aberrant rhoptry function due to PfCERLI2 deficiency. These findings identify PfCERLI2 as a protein that has key roles in rhoptry biology during merozoite invasion.
A neural-network framework for modelling auditory sensory cells and synapses

In classical computational neuroscience, transfer functions are derived from neuronal recordings to derive analytical model descriptions of neuronal processes. This approach has resulted in a variety of Hodgkin-Huxley-type neuronal models, or multi-compartment synapse models, that accurately mimic neuronal recordings and have transformed the neuroscience field. However, these analytical models are typically slow to compute due to their inclusion of dynamic and nonlinear properties of the underlying biological system. This drawback limits the extent to which these models can be integrated within large-scale neuronal simulation frameworks and hinders an uptake by the neuro-engineering field which requires fast and efficient model descriptions. To bridge this translational gap, we present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical sensory neuron and synapse models into artificial-neural-network (ANN) neuronal units with the same biophysical properties. Our ANN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory sensory neurons and synapses, and show that our ANN-model architecture generalizes well to a variety of existing analytical models of different complexity. The model training and development approach we present can easily be modified for other neuron and synapse types to accelerate the development of large-scale brain networks and to open up avenues for ANN-based treatments of the pathological system.