Miracle-dz/UniFAR-FTUs huggingface dataset:
Refroidissement Raman et vols de Lévy: Atomes de césium au nanoKelvin

Les temperatures que l'on peut atteindre par refroidissement laser sub-recul d'atomes libres ne sont limitees que par le temps d'interaction. Cette these presente dans sa premiere partie une theorie du refroidissement sub-recul qui utilise les vols de Levy, qui est confirmee par simulations Monte-Carlo et appliquee a la methode du refroidissement Raman. Ce travail montre l'importance de la forme des impulsions Raman et donne la premiere expression quantitative de la temperature la plus basse que l'on peut atteindre avec une bonne efficacite dans un temps donne. En particulier, de simples impulsions carrees sont plus efficaces pour le refroidissement unidimensionnel que les impulsions Blackman utilisees auparavant. De plus, nous montrons comment la theorie et les simulations permettent de determiner les valeurs optimales des parametres tels que duree et desaccord des impulsions. Ces resultats nouveaux sont appliques dans une experience de refroidissement Raman unidimensionnel utilisant des atomes de cesium. Le dispositif, qui utilise uniquement des diodes laser, comprend un piege magneto-optique en cellule et deux lasers verrouilles en phase avec une difference de frequence de 9.19 GHz pour exciter la transition Raman. En utilisant des impulsions carrees, on obtient une temperature unidimensionnelle de 2.8 nK avec une hauteur du pic a vitesse nulle qui est 10 fois celle de la distribution initiale. Nous avons atteint des temperatures encore plus basses, jusqu'a 0.8 nK, avec un gain legerement inferieur. Ces resultats representent a notre connaissance les plus basses temperatures atteintes par refroidissement laser. A ces temperatures, la fonction d'onde atomique est delocalisee sur plus de 10 longueurs d'onde optiques. Les applications d'un tel ensemble atomique ultrafroid vont de l'observation d'oscillations de Bloch d'atomes dans un potentiel periodique lumineux jusqu'a l'amelioration des horloges atomiques. Le refroidissement sub-recul d'atomes pieges dans un potentiel lumineux constitue une voie prometteuse pour la production d'un gaz quantique degenere avec des techniques purement optiques.
Amperometric urea biosensor based on platinum electrode modified with a nanocomposite film

A novel amperometric biosensor for the detection of urea was prepared by immobilizing urease on a modified electrode of nanocomposite film/platinum substrate. The nanocomposite film was obtained by electrochemical co‐polymerization of the corresponding monomers in the presence of functionalized carbon nanotubes and different additives. These nanocomposites were used as conducting films for amperometric urea sensing after covalent immobilization of urease onto nanocomposite‐coated electrodes. The electrochemical performance of the modified electrode was studied by cyclic voltammetry, chronoamperometry and scanning electron microscopy. The immobilized urease on the nanocomposite film surface exhibited an excellent electrocatalytic response toward the reduction of urea. Amperometric response was measured as a function of urea concentration at a fixed potential, in this case, the open circuit potential. The obtained sensor showed rapid response and good reversibility. The response time was less than 10 s, whereas the reversible time was approximately 5 to 15 s. The enzymatic nanostructured electrode exhibited high sensitivity, a wide linear range and low detection limit for the electrochemical detection of urea. Copyright © 2012 John Wiley & Sons, Ltd.
Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning

Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available. These methods typically use a heuristic strategy to construct the graph based on some fixed data representation, independently of the available labels. In this pa- per, we propose to jointly learn a data representation and a graph from both labeled and unlabeled data such that (i) the learned representation indirectly encodes the label information injected into the graph, and (ii) the graph provides a smooth topology with respect to the transformed data. Plugging the resulting graph and representation into existing graph-based semi-supervised learn- ing algorithms like label spreading and graph convolutional networks, we show that our approach outperforms standard graph construction methods on both synthetic data and real datasets.
JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity

There is a growing research interest in studying word similarity. Without a doubt, two similar words in a context may considered different in another context. Therefore, this paper investigates the effect of the context in word similarity. The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity). In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian. Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pretrained embeddings techniques for each of the four languages. Our proposed model outperformed the baseline models in both subtasks and acheived the best result for subtask 2 in English and Finnish, with score 0.725 and 0.68 respectively. We have been ranked the sixth for subtask 1, with scores for English, Croatian, Finnish, and Slovenian as follows: 0.738, 0.44, 0.546, 0.512.
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities

Named entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a lot of named entities — viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while most occur rarely. In this paper, we build upon the previous work done on language models developed for text with named entities by introducing a Hierarchically Disentangled Model. Training is divided into multiple branches with each branch producing a model with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the performance of the model. Hence, we have curated 158,473 cooking recipes from several publicly available online sources. To reliably derive the entities within this corpus, we employ a combination of Named Entity Recognition (NER) as well as an unsupervised method of interpretation using dependency parsing and POS tagging, followed by a further cleaning of the dataset. This unsupervised interpretation models instructions as action graphs and is specific to the corpus of cooking recipes, unlike NER which is a general method applicable to all corpora. To delve into the utility of our language model, we apply it to tasks such as graph-to-text generation and ingredients-to-recipe generation, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations and processed action graphs) available for use, considering their potential use cases for language modeling and text generation research.
Possible biased virulence attenuation in the Senegal strain of Ehrlichia ruminantium by ntrX gene conversion from an inverted segmental duplication

Ehrlichia ruminantium is a tick-borne intracellular pathogen of ruminants that causes heartwater, a disease present in Sub-saharan Africa, islands in the Indian Ocean and the Caribbean, inducing significant economic losses. At present, three avirulent strains of E. ruminantium (Gardel, Welgevonden and Senegal isolates) have been produced by a process of serial passaging in mammalian cells in vitro, but unfortunately their use as vaccines do not offer a large range of protection against other strains, possibly due to the genetic diversity present within the species (Cangi et al. 2016). So far no genetic basis for virulence attenuation has been identified in any E. ruminantium strain that could offer targets to facilitate vaccine production. Virulence attenuated Senegal strains have been produced twice independently, and require many fewer passages to attenuate than the other strains. We compared the genomes of a virulent and attenuated Senegal strain and identified a likely attenuator gene, ntrX, a global transcription regulator and member of a two-component system that is linked to environmental sensing. This gene has an inverted partial duplicate close to the parental gene that shows evidence of gene conversion in different E. ruminantium strains. The pseudogenisation of the gene in the avirulent Senegal strain occurred by gene conversion from the duplicate to the parent, transferring a 4bp deletion which is unique to the Senegal strain partial duplicate amongst the wild isolates. We confirmed that the ntrX gene is not expressed in the avirulent Senegal strain by RT-PCR. The inverted duplicate structure combined with the 4bp deletion in the Senegal strain can explain both the attenuation and the faster speed of attenuation in the Senegal strain relative to other strains of E. ruminantium. Our results identify nrtX as a promising target for the generation of attenuated strains of E. ruminantium by random or directed mutagenesis that could be used for vaccine production.
Informative Manual Evaluation of Machine Translation Output

This work proposes a new method for manual evaluation of Machine Translation (MT) output based on marking actual issues in the translated text. The novelty is that the evaluators are not assigning any scores, nor classifying errors, but marking all problematic parts (words, phrases, sentences) of the translation. The main advantage of this method is that the resulting annotations do not only provide overall scores by counting words with assigned tags, but can be further used for analysis of errors and challenging linguistic phenomena, as well as inter-annotator disagreements. Detailed analysis and understanding of actual problems are not enabled by typical manual evaluations where the annotators are asked to assign overall scores or to rank two or more translations. The proposed method is very general: it can be applied on any genre/domain and language pair, and it can be guided by various types of quality criteria. Also, it is not restricted to MT output, but can be used for other types of generated text.
A comprehensive analysis of vomeronasal organ transcriptome reveals variable gene expression depending on age and function in rabbits

The vomeronasal organ (VNO) is a chemosensory organ specialized in the detection of pheromones and consequently the regulation of behavioural responses mostly related to reproduction. VNO shows a broad variation on its organization, functionality and gene expression in vertebrates, and although the species analyzed to date have shown very specific features, its expression patterns have only been well-characterized in mice. Despite rabbits represent a model of chemocommunication, unfortunately no genomic studies have been performed on VNO of this species to date. The capacity of VNO to detect a great variety of different stimuli suggests a large number of genes with complex organization to support this function. Here we provide the first comprehensive gene expression analysis of the rabbit VNO through RNA-seq across different sexual maturation stages. We characterized the VNO transcriptome, updating the number of the two main vomeronasal receptor (VR) families, 129 V1R and 70 V2R. Among others, the expression of transient receptor potential channel 2 (TRPC2), a crucial cation channel generating electrical responses to sensory stimulation in vomeronasal neurons, along with the specific expression of some fomyl-peptide receptors and H2-Mv genes, both known to have specific roles in the VNO, revealed a the particular gene expression repertoire of this organ, but also its singularity in rabbits. Moreover, juvenile and adult VNO transcriptome showed consistent differences, which may indicate that these receptors are tuned to fulfill specific functions depending on maturation age. We also identified VNO-specific genes, including most VR and TRPC2, thus confirming their functional association with the VNO. Overall, these results represent the genomic baseline for future investigations which seek to understand the genetic basis of behavioural responses canalized through the VNO. HIGHLIGHTS First description of the rabbit vomeronasal organ (VNO) transcriptome VNO contains a unique gene repertoire depending on the species High fluctuation of the VNO gene expression reveals changes dependent on age and specific functions Most vomeronasal-receptors (VR) and transient receptor potential channel 2 (TRPC2) genes are VNO-specific Reproduction-related genes shows a wide expression pattern
Varying trypsin inhibitor activity in differently processed soybean expellers linearly reduces prececal amino acid digestibility in broilers

The present study investigated the effect of varying trypsin inhibitor activity (TIA) in differently processed soybean expellers on apparent prececal amino acid (AA) digestibility in male broiler chickens. Two different raw soybean batches were treated using four different processing techniques (thermal, hydrothermal, pressure, kilning) at varying intensities. In this way, 45 expeller extracted soybean meal (ESBM) variants were created. The processed soybean variants were then merged into a basal diet (160 g/kg crude protein (CP)) at two inclusion levels (15%, 30%) resulting in 91 different diets (1 basal diet plus 90 experimental diets) with TIA ranging from 0.4 mg/g to 8.5 mg/g. All diets contained 0.5% of titanium dioxide (TiO2). During four experimental runs, a total of 5,460 1-day old male broiler chickens (Ross 308) were fed a commercial starter diet (CP 215 g/kg, 14 g/kg Lysine, 12.5 MJ ME/kg) ad libitum from day 1 to day 14. Subsequently, birds were allocated to a total of 546 pens with 10 birds per pen and were fed the 91 experimental diets ad libitum. At day 22, birds were sacrificed and digesta of the terminal ileum was collected for determination of AA digestibility. TIA depressed the prececal digestibility of every single AA significantly in a straight linear fashion (p < 0.001). cystine and methionine expressed the strongest suppression by TIA with cystine showing the lowest apparent prececal digestibility measured (4.94% at 23.6 mg/g TIA in raw ESBM). Correspondingly, live weight (LW) (p < 0.001) and total weight gain (TWG) (p < 0.001) declined in a linear manner with increasing TIA in feed. The present data demonstrate that TIA severely depresses digestibility of essential and non-essential AA and thus growth performance in a straight linear fashion. On the one hand, this questions the usefulness of defined upper limits of TIA in soy products whereas on the other hand, TIA must be considered when testing raw components for their feed protein value in vivo.
Crystal structure of chloroplastic thioredoxin z defines a novel type-specific target recognition

Thioredoxins (TRXs) are ubiquitous disulfide oxidoreductases structured according to a highly conserved fold. TRXs are involved in a myriad of different processes through a common chemical mechanism. Plant thioredoxins evolved into seven types with diverse subcellular localization and distinct protein targets selectivity. Five TRX types coexist in the chloroplast, with yet scarcely described specificities. We solved the first crystal structure of a chloroplastic z-type TRX, revealing a conserved TRX fold with an original electrostatic surface potential surrounding the redox site. This recognition surface is distinct from all other known TRX types from plant and non-plant sources and is exclusively conserved in plant z-type TRXs. We show that this electronegative surface endows TRXz with a capacity to activate the photosynthetic Calvin-Benson cycle enzyme phosphoribulokinase. TRXz distinct electronegative surface thereby extends the repertoire of TRX-target recognitions.
The anti‐inflammatory peptide Catestatin blocks chemotaxis

Increased levels of the anti-inflammatory peptide catestatin (CST), a cleavage product of the pro-hormone chromogranin A, correlates with less severe outcomes in hypertension, colitis and diabetes. However, it is unknown how CST reduces the infiltration of monocytes and macrophages in inflamed tissues. Here, we report that CST blocks leukocyte migration towards inflammatory chemokines. By in vitro and in vivo migration assays, we show that although CST itself is weakly chemotactic, it blocks migration of monocytes and granulocytes to inflammatory attracting factor CC-chemokine ligand 2 (CCL2) and macrophage inflammatory protein 2 (MIP-2). Moreover, it directs CX3CR1+ macrophages away from pancreatic islets. These findings support the emerging notion that CST is a key anti-inflammatory modulator.
BullStop: A Mobile App for Cyberbullying Prevention

Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.
DNA polymerase theta suppresses mitotic crossing over

Polymerase theta-mediated end joining (TMEJ) is a chromosome break repair pathway that is able to rescue the lethality associated with the loss of proteins involved in early steps in homologous recombination (e.g., BRCA1/2). This is due to the ability of polymerase theta (Pol θ) to use resected, 3’ single stranded DNA tails to repair chromosome breaks. These resected DNA tails are also the starting substrate for homologous recombination. However, it remains unknown if TMEJ can compensate for the loss of proteins involved in more downstream steps during homologous recombination. Here we expand the number of homologous recombination proteins synthetic lethal with Pol θ to the Holliday junction resolvases SLX4 and GEN1. SLX4 and GEN1 are required for viability in the absence of Pol θ in Drosophila melanogaster, and lack of all three proteins results in very high levels of apoptosis. We observe that flies deficient in Pol θ and SLX4 are extremely sensitive to DNA damaging agents, and mammalian cells require either Pol θ or SLX4 to survive. Our results suggest that TMEJ and Holliday junction formation/resolution share a common DNA substrate, likely a homologous recombination intermediate, that when left unrepaired leads to cell death. One major consequence of Holliday junction resolution by SLX4 and GEN1 is cancer-causing loss of heterozygosity due to mitotic crossing over. We measured mitotic crossovers in flies after a Cas9-induced chromosome break, and observed that this mutagenic form of repair is increased in the absence of Pol θ. This demonstrates that TMEJ can function upstream of the Holiday junction resolvases to protect cells from loss of heterozygosity. Our work argues that Pol θ can thus compensate for the loss of the Holliday junction resolvases by utilizing homologous recombination intermediates, suppressing mitotic crossing over and preserving the genomic stability of cells. Author summary Chromosome breaks are a common threat to the stability of DNA. Mutations in genes involved in the early steps of homologous recombination (BRCA1 and BRCA2), a mostly error-free chromosome break repair pathway, lead to hereditary breast cancer. Cells lacking BRCA1 and BRCA2 rely on DNA polymerase theta, a key protein for a more error-prone pathway, for survival. Using fruit flies and mammalian cells, we have shown that mutations in genes involved in later steps of homologous recombination (SLX4 and GEN1) also make cells reliant on polymerase theta. Moreover, we have shown that polymerase theta acts upstream of a type of homologous recombination that is error-prone and depends on SLX4 and GEN1. This form of homologous recombination, termed Holliday junction resolution, creates mitotic crossovers, which can lead to loss of heterozygosity and cancer. Our results expand the cellular contexts that make cells depend on polymerase theta for survival, and the substrates that this protein can use to repair chromosome breaks.
TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles

In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.
TrainX – Named Entity Linking with Active Sampling and Bi-Encoders

We demonstrate TrainX, a system for Named Entity Linking for medical experts. It combines state-of-the-art entity recognition and linking architectures, such as Flair and fine-tuned Bi-Encoders based on BERT, with an easy-to-use interface for healthcare professionals. We support medical experts in annotating training data by using active sampling strategies to forward informative samples to the annotator. We demonstrate that our model is capable of linking against large knowledge bases, such as UMLS (3.6 million entities), and supporting zero-shot cases, where the linker has never seen the entity before. Those zero-shot capabilities help to mitigate the problem of rare and expensive training data that is a common issue in the medical domain.
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection

This paper presents a solution for the Span Identification (SI) task in the “Detection of Propaganda Techniques in News Articles” competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.