Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the Covid-19 pandemic on media consumption in Spain

Introduction: This study aims to explore the impact of the Covid-19 pandemic on media habits and consumption in Spain, one of the most severely affected countries Methodology: A representative online survey of the Spanish population (N = 1,500 participants) was conducted from March 13 to 30, 2020, coinciding with the first weeks of home confinement The sample was studied according to two variables, gender (Women N = 750;Men N = 750) and age (18-39 years individuals, N = 720;over 40 years individuals N = 780) to detect the most relevant specificities of each group The sampling error is +/- 4 38% at a 95% confidence Results: Results highlight that television is the most used media when it comes to finding out about the virus, before digital media It should be noted that infoshow-type television programs manage to accumulate more audience than the traditional news programs The least used media to get information about the evolution of the Covid-19 are the Internet, social networks, radio, information by relatives or acquaintances, and traditional print press Although radio consumption seems not to be significant to stay informed, this medium is the most credible, in addition to television Finally, the consumption of over-the-top (OTT) platforms has grown considerably during this period Discussion: Television recovers the audience of the youngest targets that it seemed to have lost in in favor of digital media Conclusions: An increasingly complex and competitive media landscape is consolidating
How much pretraining data do language models need to learn syntax?

Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls for a study of the impact of pretraining data size on the knowledge of the models. We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data. First, we use syntactic structural probes to determine whether models pretrained on more data encode a higher amount of syntactic information. Second, we perform a targeted syntactic evaluation to analyze the impact of pretraining data size on the syntactic generalization performance of the models. Third, we compare the performance of the different models on three downstream applications: part-of-speech tagging, dependency parsing and paraphrase identification. We complement our study with an analysis of the cost-benefit trade-off of training such models. Our experiments show that while models pretrained on more data encode more syntactic knowledge and perform better on downstream applications, they do not always offer a better performance across the different syntactic phenomena and come at a higher financial and environmental cost.
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression

Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.
INOCULATION WITH N2-FIXING PLANT GROWTH PROMOTING RHIZOBACTERIA TO REDUCE NITROGEN FERTILIZER REQUIREMENT OF LETTUCE

This study was undertaken to determine the effect of three different mixtures of some N2-fixing plant growth-promoting rhizobacteria (PGPR) on growth, yield, element content and nitrate accumulation as well as the effect on the reduction of nitrogen fertilization of lettuce (Lactuca sativa L.). The measurements were made in two separate experiments in 2015 between 6 June – 5 August (Experiment 1) and 2 July – 3 September (Experiment 2) under the field conditions. Butterhead form and heat tolerant summer cultivar ‘Luna’ was used as a plant material. Agrobacterium rubi RK-34, Pantoea agglomerans RK-79 and RK-92, Pseudomonas putida RK-142 and TV-42A, Bacillus megaterium TV-6D, TV-60D and TV-91C, Pseudomonas flourescens TV-11D and Paenibacillus polymyxa TV-12E were used as N2-fixing plant growth promoting rhizobacteria. The treatments were 150 kg N/ha (available dose of AS) as ammonium sulphate (AS) [(NH4)2SO4), (21% N)] and three different mixtures of PGPR. Further, combined uses of decreasing doses of AS (50%, 75 kg ha–1AS and 75%, 112.5 kg ha–1 AS) and PGPR mixtures (M) such as M-1 + 75 AS, M-1 + 112.5 AS, M-2 + 75 AS, M-2 + 112.5 AS, M-3 + 75 AS and M-3 + 112.5 AS were used as additional treatments. All treatments increased the yield and the growth of lettuce according to the control. While inoculation with PGPR mixtures decreased the accumulation of heavy metals such as Cd, Ni, and Pb in lettuce, increased nutrient uptake of lettuce. It was determined that the nitrate accumulation of lettuce (cv. ‘Luna’) in PGPR mixtures were lower than the available dose of AS but higher than control. The yield in M-3 + 112.5 AS (48431 kg ha–1) was similar and in the same statistical group with the available dose of AS (48225 kg ha–1) in both experiments. Furthermore, according to the results of cost analyses, using 25% less of AS (112.5 kg ha–1) with M-3 will supply the same income instead of using AS (150 kg ha–1). It can be clearly said that the mixtures with some N2-fixing plant growth promoting rhizobacteria (PGPR), especially M-3 (P. putida RK-142 + P. flourescens TV-11D + B. megaterium TV-91C), have a great potential to decrease the nitrogen use (25%) for environmentally friendly crop production of lettuce.
An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models

Prompt-based knowledge probing for 1-hop relations has been used to measure how much world knowledge is stored in pretrained language models. Existing work uses considerable amounts of data to tune the prompts for better performance. In this work, we compare a variety of approaches under a few-shot knowledge probing setting, where only a small number (e.g., 10 or 20) of example triples are available. In addition, we create a new dataset named TREx-2p, which contains 2-hop relations. We report that few-shot examples can strongly boost the probing performance for both 1-hop and 2-hop relations. In particular, we find that a simple-yet-effective approach of finetuning the bias vectors in the model outperforms existing prompt-engineering methods. Our dataset and code are available at \url{https://github.com/cloudygoose/fewshot_lama}.
Immune challenges increase network centrality in a queenless ant

Social animals display a wide range of behavioural defences against infectious diseases, some of which increase social contacts with infectious individuals (e.g. mutual grooming), while others decrease them (e.g. social exclusion). These defences often rely on the detection of infectious individuals, but this can be achieved in several ways that are difficult to differentiate. Here, we combine non-pathogenic immune challenges with automated tracking in colonies of the clonal raider ant to ask whether ants can detect the immune status of their social partners and to quantify their behavioural responses to this perceived infection risk. We first show that a key behavioural response elicited by live pathogens (allogrooming) can be qualitatively recapitulated by immune challenges alone. Automated scoring of interactions between all colony members reveals that this behavioural response increases the network centrality of immune-challenged individuals through a general increase in physical contacts. These results show that ants can detect the immune status of their nest-mates and respond with a general ‘caring’ strategy, rather than avoidance, towards social partners that are perceived to be infectious. Finally, we find no evidence that changes in cuticular hydrocarbon profiles drive these behavioural effects.
The role of ADAMTS-1, -4 and -5 in multiple sclerosis.

ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs)-1, -4 and -5 are secreted enzymes which are members of the glutamyl endopeptidases (GEPs) group of ADAMTSs. These enzymes break down chondroitin sulphate proteoglycans (CSPGs) which are key components of brain extracellular matrix (ECM). In multiple sclerosis (MS), CSPG breakdown by ADAMTSs may enable axonal regeneration or conversely it may lead to alterations of the ECM, allowing influx of inflammatory cells promoting tissue damage.ADAMTS-1, -4 and -5 mRNA expression was studied by quantitative real-time PCR (qRT-PCR) using the Taqman method in SHSY-5Y and SK-N-DZ human neuroblastoma cells, undifferentiated or differentiated to a more neuronal phenotype using retinoic acid (RetA). Modulation by pro-inflammatory cytokines ((interleukin-1 IL-1) or tumour necrosis factor (TNF)), which are involved in the pathogenesis of MS, was also studied. As ADAMTS-1 was the most abundant ADAMTS in the neuronal cell lines, it was investigated at its protein level in both cell lines by Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) with western blotting. Furthermore, the presence of ADAMTS-1 at its mRNA and protein levels was confirmed by the small interfering RNA (siRNA) technique in SHSY-5Y cells. Cryostat sections of normal and MS central nervous system (CNS) tissue white matter, obtained from the UK Multiple Sclerosis Tissue Bank, were used to determine the localisation of V0/V2 neoepitopes of versican, derived by ADAMTS cleavage, using immunohistochemistry.SHSY-5Y and SK-N-DZ cells expressed mRNA for ADAMTS-1, -4 and -5. ADAMTS-1 expression was significantly increased on cellular differentiation with Ret A in SHSY-5Y cells. Its expression was confirmed at the mRNA and protein level. IL-1 B and TNF had no effect on ADAMTS mRNA expression in SHSY-5 Y cells. However, ADAMTS-1 mRNA expression was upregulated by IL-1 B in differentiated SK-N-DZ and there was also a significant increase in ADAMTS-4 mRNA expression with TNF treatment. ADAMTS-mediated versican breakdown, as determined immuohistochemically by versican (V0/V2) neoepitopes expression, was increased in MS brain tissue compared to normal brain tissue.In conclusion, ADAMTS-1, -4 and -5 were constitutively expressed in SHSY-5Y and SK-N-DZ neuronal cells. Modulation by the cytokines tested was seen in the SK-N-DZ cells. From these in vitro studies, neuronal ADAMTSs in the CNS may have a potential role in MS pathogenesis. However further investigation is needed on primary neuronal cells and CNS to elucidate the role of neuronal ADAMTSs and their contribution in MS.ADAMTSs do appear to be involved in increased proteolysis of versican at the known cleavage site in human brain tissue as indicated by (V0/V2) versican neoepitopes expression. Upregulation of versican (V0/V2) neoepitopes was observed in lesional MS sections on immunohistochemistry. These enzymes require further investigation, by immunohistochemical methods for co-localisation with versican (V0/V2) neoepitopes in MS, to determine which of the ADAMTSs generates these neoepitopes.
How can Germany reduce production-based and consumption-based carbon emissions? A decomposition analysis

Abstract Developing more effective mitigation strategy to achieve the carbon reduction targets set by the Paris Climate Agreement requires to more comprehension of the driving factors of the decline in carbon emissions. Production-based and consumption-based carbon emission in Germany declined by 2.8% and 8.5% from 2000 to 2015, respectively. In this work, the key drivers behind decline in carbon emission in Germany is explored by combing the multi-regional input-output and structural decomposition analysis. The results show German carbon emissions has been undergoing structural changes. Although its production-based and consumption-based emission declined, German carbon footprint embodied in exports increased, and its share in the production-based carbon footprint also increased. Germany's embodied emission exports to emerging countries increased, although Germany's embodied carbon exports remained mainly concentrate in developed countries, especially in EU (over 40%) and USA (∼10%). The decomposition results revealed that economic scale effect was the leading contributor to increase in carbon footprint in Germany's trade, followed by the industrial structure effect of final demand. The effects of production structure on German's exports was less than its imports. The carbon intensity coefficient was the most significant factor of decreased carbon emission. Generally, the decreasing effect of carbon intensity coefficient exceeded the increasing effect of economic scale, which lead to carbon reduction. The example of Germany shows that improving carbon emission efficiency and optimizing production structure can reduce carbon emission without lowering economic growth. Graphical Abstract
IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages

In this paper we present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. Different from existing pre-trained models, In-dicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT for 12 language pairs and extreme summarization for 7 languages using multilingual ﬁne-tuning show that IndicBART is competitive with or better than mBART50 despite containing sig-niﬁcantly fewer parameters. Our analyses focus on identifying the impact of script uniﬁ-cation (to Devanagari), corpora size as well as multilingualism on the ﬁnal performance. The IndicBART model is available under the MIT license at https://indicnlp. ai4bharat.org/indic-bart .
Sea ice reduction drives genetic differentiation among Barents Sea polar bears

Loss of Arctic sea ice owing to climate change is predicted to reduce both genetic diversity and gene flow in ice-dependent species, with potentially negative consequences for their long-term viability. Here, we tested for the population-genetic impacts of reduced sea ice cover on the polar bear (Ursus maritimus) sampled across two decades (1995–2016) from the Svalbard Archipelago, Norway, an area that is affected by rapid sea ice loss in the Arctic Barents Sea. We analysed genetic variation at 22 microsatellite loci for 626 polar bears from four sampling areas within the archipelago. Our results revealed a 3–10% loss of genetic diversity across the study period, accompanied by a near 200% increase in genetic differentiation across regions. These effects may best be explained by a decrease in gene flow caused by habitat fragmentation owing to the loss of sea ice coverage, resulting in increased inbreeding of local polar bears within the focal sampling areas in the Svalbard Archipelago. This study illustrates the importance of genetic monitoring for developing adaptive management strategies for polar bears and other ice-dependent species.
Combustion characteristics of coal, biomass and their chars in air and oxy-fuel environments

Carbon capture and storage (CCS) is expected to play an important role in mitigating the effects of climate change. The focus of this work is to determine how the change of combustion environment in an oxy-fuel CCS plant affects the combustion behaviour of coal, biomass and a torrefied biomass. The industrially relevant fuels selected were analysed to determine their fundamental composition and combusted in air and a range of oxy-fuel environments (5-30% O2/CO2) using a thermogravimetric analyser (TGA). The key temperatures and kinetic parameters of both the devolatilisation and char combustion stages were investigated to determine how the shift to an oxy-fuel combustion environment effects overall combustion behaviour. The changes in devolatilisation behaviour were determined through the derivation of apparent first order kinetics and no noticeable difference between combustion in air and 21% O2/CO2 atmospheres were observed. The increase in oxygen concentration in the oxy-fuel environments resulted in linear increases in kinetic parameters which were then used to develop fuel specific empirical equations that relate the devolatilisation rate to the oxygen concentration. The devolatilisation of the biomass fuels were shown to be more sensitive to the change in combustion atmosphere than the coals. Chars were produced using ballistic heating rates in a TGA (1000 K min-1) and it was found that the coals exhibited similar mass loss behaviour in N2 and CO2 environments during char production. The biomass and torrefied biomass samples showed enhanced devolatilisation in CO2 atmospheres which leads to differences in the char combustion behaviour between the coal and biomass fuels. The char combustion behaviour was determined through the determination of apparent mth order kinetics, from which, fuel specific nth order kinetic models were derived to describe char combustion accurately over the full range of oxy-fuel combustion atmospheres. The kinetic parameters determined highlighted the similarity between the N2 and CO2 produced coal chars and the difference between the biomass chars. The coal chars were found to be more sensitive to the change in combustion atmosphere. The work in this thesis gives a good understanding of the differences between conventional air and oxy-fuel combustion atmospheres using industrially relevant fuels. Several useful kinetic models have been derived for both the devolatilisation and char combustion stages that lend themselves to computational fluid dynamics and process optimisation while the fundamental characterisation lends itself to life cycle analysis of CCS systems.
Atypical bacterial co‐infections among patients with COVID‐19: A study from India

Emerging evidence shows co‐infection with atypical bacteria in coronavirus disease 2019 (COVID‐19) patients. Respiratory illness caused by atypical bacteria such as Mycoplasma pneumoniae, Chlamydia pneumoniae, and Legionella pneumophila may show overlapping manifestations and imaging features with COVID‐19 causing clinical and laboratory diagnostic issues. We conducted a prospective study to identify co‐infections with SARS‐CoV‐2 and atypical bacteria in an Indian tertiary hospital. From June 2020 to January 2021, a total of 194 patients with laboratory‐confirmed COVID‐19 were also tested for atypical bacterial pathogens. For diagnosing M. pneumoniae, a real‐time polymerase chain reaction (PCR) assay and serology (IgM ELISA) were performed. C. pneumoniae diagnosis was made based on IgM serology. L. pneumophila diagnosis was based on PCR or urinary antigen testing. Clinical and epidemiological features of SARS‐CoV‐2 and atypical bacteria‐positive and ‐negative patient groups were compared. Of the 194 patients admitted with COVID‐19, 17 (8.8%) were also diagnosed with M. pneumoniae (n = 10) or C. pneumoniae infection (n = 7). Confusion, headache, and bilateral infiltrate were found more frequently in the SARS CoV‐2 and atypical bacteria co‐infection group. Patients in the M. pneumoniae or C. pneumoniae co‐infection group were more likely to develop ARDS, required ventilatory support, had a longer hospital length of stay, and higher fatality rate compared to patients with only SARS‐CoV‐2. Our report highlights co‐infection with bacteria causing atypical pneumonia should be considered in patients with SARS‐CoV‐2 depending on the clinical context. Timely identification of co‐existing pathogens can provide pathogen‐targeted treatment and prevent fatal outcomes of patients infected with SARS‐CoV‐2 during the current pandemic.
Failing forward and EU foreign policy: the dynamics of ‘integration without membership’ in the Eastern Neighbourhood

ABSTRACT The article aims to investigate the applicability of the ‘failing forward’ framework to the case of the European Neighbourhood Policy (ENP). The ‘failing forward’ framework explains how the liberal intergovernmentalist and neo-functionalist developments shape policy dynamics in the ENP. It is, however, proven to be challenging to determine the initial policy incompleteness and, due to the influence of exogenous factors, also explain the origins of the Ukraine crisis using the framework. Although the ‘failing forward’ framework responds to the intellectual demand for stronger exchange between the theories of EU integration and the EU Foreign Policy Studies, it is not able to fully explain complex crises at the ‘high politics’ level.
Towards a continuous forecasting mechanism of parking occupancy in urban environments

Searching for an available parking space is a stressful and time-consuming task, which leads to increasing traffic and environmental pollution due to the emission of gases. To solve these issues, various solutions relying on information technologies (e.g., wireless networks, sensors, etc.) have been deployed over the last years to help drivers identify available parking spaces. Several recent works have also considered the use of historical data about parking availability and applied learning techniques (e.g., machine learning, deep learning) to estimate the occupancy rates in the near future. In this paper, we not only focus on training forecasting models for different types of parking lots to provide the best accuracy, but also consider the deployment of such a service in real conditions, to solve actual parking occupancy problems. It is therefore needed to continuously provide accurate information to the drivers but also to handle the frequent updates of parking occupancy data. The underlying challenges addressed in the present work so concern (1) the self-tuning of the forecasting model hyper-parameters according to the characteristics of the considered parking lots and (2) the need to maintain the performance of the forecasting model over time. To demonstrate the effectiveness of our approach, we present in the paper several evaluations using real data provided for different parking lots by the city of Lille in France. The results of these evaluations highlight the accuracy of the forecasts and the ability of our solution to maintain model performance over time.
A Random Restart Local Search Matheuristic for the Flying Sidekick Traveling Salesman Problem

Drones and unmanned vehicles in general are gaining more and more interest in the logistic sector, due to the potential economic advantages they can provide. In this paper we focus on optimizing the use of a drone in conjunction with a truck for urban deliveries, dealing with what is called the flying sidekick traveling salesman problem. There is a set of customers that it is possible to serve either by a truck or by a drone. The target is to minimize the total time required to complete deliveries to all the customers. In this paper we show how an effective and simple random restart local search heuristic algorithm can be derived from a known mixed integer programming model for the problem.