dataesr/scientific-paragraphs-categorization huggingface dataset:
While all the data is from the CNN-DM train dataset in the supervised setups, for the unsupervised setups, we set the proportion of unsupervised data to 50% (either CNN-DM VAL, CNN-DM TEST for in-domain or TL;DR for out-of-domain data). Thus, for 50% of the data, the model has access only to the QA conf and QA f score reward signals, since the ROUGE-L reward can only be computed on supervised batches. Therefore, for all the unsupervised setups, in order to keep consistency in the reward signal throughout the training, we multiply by a factor of 2 the weight associated with ROUGE-L when this reward is computable, and set it to 0 otherwise.
This can be explained by the aforementioned drawbacks of ROUGE, which are likely amplified when used both as evaluation and reward: the system might be optimizing for ROUGE, at the price of losing the information captured with the NLL loss by its language model. We hence conducted a human evaluation for the different setups, reported in Table  3 , assessing their outputs for readability and relevance in line with  Paulus et al. (2017). We randomly sampled 50 articles from the CNN-DM test set; since the learned metric used in our experiments is derived from the subset manually evaluated in  Paulus et al. (2017)  we ensured that there was no overlap with it. For each of those 50 articles, three English speakers evaluated the summaries generated by the 7 different systems reported in Table  2.
This can be explained by the stronger weight of ROUGE L for this setup, a fact which might lead to a degradation of the quality of the output consistently with the observations reported in  (Paulus et al. , 2017)  as well as in our ROUGE experiment. Another observation from Tables  3  and 4  is that while QA learned performs significantly better in term of readability than QA learned + CNN-DM (VAL), the opposite holds for relevance. This could be explained by the setup difference during training: as detailed in section 4. 2. 2, for unsupervised setups (i. e. QA learned + CNN-DM (VAL)) only the QA-based metrics are computed for the portion of data for which no reference is available. While testing (TEST) and validation (VAL) splits come the same dataset (CNN-DM), we observe that using the samples from TEST in an unsupervised fashion allows for maintaining comparably high relevance compared to QA learned + CNN-DM (VAL), while also obtaining similar readability to QA learned.
This shows the possible benefits that can be obtained by exposing the model to the evaluation data in unsupervised setups. To further study our unsupervised metrics, we performed additional experiments on the TL;DR corpus. We observed more than one absolute point of improvement w. r. t CNN-DM TEST in terms of ROUGE-L, QA f score (unsup) and QA conf (unsup). This indicates that the proposed unsupervised metrics allow the model to better transfer to new domains such as TL;DR. These results pave the way for leveraging large numbers of texts, in a self-supervised manner, to train automatic summarization models.
In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F 1 score of 63. 02% on the subtask evaluation dataset.
, 2013; Azab et al. , 2013; Attia et al. , 2012). Most recently,  (Bouamor et al. , 2018; Salameh et al. , 2018; AL-Walaie and Khan, 2017)  started to investigate the problem of the Arabic Dialect Identification with different classification methods. In this paper, we are describing our work in the same research direction using the MADAR shared task corpus described in  (Bouamor et al. , 2019). The goal of this task is to classify a given text into one of 26 classes, corresponding to various dialects of Arabic language. The remainder of this paper is organized as follows. In section 2, we describe the different techniques used in this work. In Section 3, we present our experimental setup and discuss the models and features used as well as our results. Finally, in Section 4 we conclude and give our future directions.
Vote Based Probabilistic Classifier The low size of our data set made statistical models perform much better than the deep learning methods. Our proposed method will take into account the large number of classes by creating two different pipelines. The first one uses a Multinomial Naive Bayes. The second model uses a Random Forest Classifier. These models were implemented using the package scikit-learn  (Pedregosa et al. , 2011). The pipelines are pre-trained before they are given to the voting classifier. Then, the whole system is trained again to maximize the model performance for the dialects classification task. The data is first given into a count vectorizer then into a TF-IDF tranformer to extract meaningful information on word level. The voting classifier uses a hard voting method to select the model with the correct prediction.
Data We used the data set provided by the MADAR Shared Task. The corpus covers the dialects of 25 Arab cities and the MSA. It is the same data set described in  Bouamor et al. (2019)  and  Salameh et al. (2018). This corpus is composed of 2000 sentences translated to each dialect, with a total of 52000 sentences. We refer to this set as the MADAR corpus. We split this data set evenly between dialects in three parts: 80% constitutes the Train set, 10% the Dev and the last 10% the Test set. In our experiment, we limit the length of the sequences to 40 words and pad the sequences with zeros. For preprocessing we remove all non Arabic characters with the exception of Arabic numbers. To maximize the precision of the hierarchical deep learning system the input of the models is produced by a word2vec.
As Xpert MTB/RIF can detect only RIF resistance, it is unable to identify MDR-TB in its earliest form (i. e. , INH mono-resistance). In addition, it must always be used together with other tests, such as DST, to confirm and identify the whole resistance phenotype of each MTB isolate. Another remarkable limitation is its high cost due to the use of complex GeneXpert system and disposable cartridges (the GeneXpert apparatus costs between US$12 000 and $71 000, depending on the number of test modules, and the price of each single-use test cartridge is $9•98)  (WHO, 2017b; Walzl et al. , 2018). Therefore, its use as initial diagnostic test for all suspected TB cases is unaffordable in many high TB-burden countries. According to a WHO report (2016), only 15 of the 48 high TB-burden countries have used the Xpert tests for all suspected TB cases (i.
Therefore, the use of WGS as a DR-TB detection tool could be potentially used in clinical settings in the future. The large-scale application of sequencing, especially in middle-and low-income countries is still difficult for the following reasons: (i) robust software and database tools need to be developed for the full exploitation of this technology in this specialized area of medicine; (ii) specialized personnel and bioinformatics facilities are required for the experiments, data acquisition and data analysis; (iii) the high cost of NGS platforms; (iv) need to determine whether new mutations confer anti-TB drug resistance; and (v) high amounts of high quality DNA are required for sequencing  (Phelan et al. , 2016; Dheda et al. , 2017; Zignol et al. , 2018). Moreover, the sample preparation procedure and DNA extraction method should be standardized for consistency  (Zignol et al.
This work investigates solute transport due to self-interstitial defects and radiation induced segregation tendencies in dilute ferritic alloys, by computing the transport coefficients of each system based on ab initio calculations of binding energies, migration rates, as well as formation and migration vibrational entropies. The implementation of the self-consistent mean field method in the KineCluE code allows for the calculation of transport coefficients extended to arbitrary interaction ranges, crystal structures, and diffusion mechanisms. In addition, the code gives access to the diffusion and dissociation rates of small solute-defect clusters -in this case, vacancy-and dumbbell-solute pairs. The results show that the diffusivity of P, Mn, and Cr solute atoms is dominated by the dumbbell mechanism, that of Cu by vacancies, while the two mechanisms might be in competition for Ni and Si, despite the fact that the corresponding mixed dumbbells are not stable.
It is unclear whether this is due to vacancy drag, SIA transport, or a combination of both. The objective of this study is to perform an accurate analysis of SIA-driven solute transport in a set of dilute Fe-X alloys (X=Cr, Cu, Mn, Ni, P, Si), and provide a comprehensive overview of the expected intrinsic RIS behavior of said chemical species. In addition, this work establishes a general modeling framework aimed at accurate RIS predictions by vacancies and interstitials in dilute alloys, providing in this way a sound knowledge of the kinetic properties necessary to the interpretation of diffusion-driven microstructure phenomena. This is achieved by means of the recently published KineCluE code  [56]  that implements the SCMF method in its cluster-expansion form  [57]. For SIA diffusion, KineCluE allows for a considerable advancement with respect to the available analytical models  [58, 59]  developed in the SCMF framework and featuring limited solute-SIA interaction ranges.
The latter has been applied multiple times to vacancy diffusion  [36, 40, 42, 37, 57, 60] , but the only model available for SIA-assisted solute transport is that by  Barbe et al. [58, 59, 61, 62]. The latter is here substantially improved with the KineCluE code  [56] , which implements a recent development of SCMF where the Onsager matrix is expanded in terms of cluster contributions L (c) ij  [57] : L ij = C c f c L (c) ij. (2) C is the total concentration, while f c is the concentration fraction of cluster c proportional to the cluster partition function (cf. Appendix B). In the dilute limit, the matrix is split into a "monomer" (an isolated defect) and a (solute-defect) "pair" contribution, while immobile species such as substitutional solutes do not contribute to the total transport matrix.
Ab initio methodology The binding and migration energies are calculated in the framework of Density Functional Theory (DFT) with the Vienna ab initio Simulation Package (vasp)  [68, 69, 70]. The vasp full-core pseudopotentials developed within the projector augmented wave (PAW) method  [71, 72]  are employed for all elements. The Perdew-Burke-Ernzerhof (PBE) parameterization  [73]  of the generalized gradient approximation (GGA) is used to describe the exchange-correlation function. Calculations are spin-polarized, and make use of the Vosko-Wilk-Nusair (VWN) interpolation scheme of the correlation potential. Particular attention has been paid to achieve the correct antiferromagnetic state of Mn and avoid local minima  [74] , by providing a precise initial guess of the magnetic moment on Fe (2.
445 THz Dumbbell diffusion prefactor D0 3. 745 • 10 -7 m 2 /s 2. 268 • 10 -6 m 2 /s [86]   3. 2. Solute-dumbbell interactions and jump frequencies Table  2  reports the relevant binding energies and migration barriers of dumbbell-solute pairs, following the nomenclature of Figs. 1  and 2. In addition to RT jumps, the table lists the results obtained by previous studies  [44, 30, 45]  for 2nn jumps leading to a MD displacement (ω 1,2nn ) or dissociation (ω M2b ). The complete set of DFT results, including those related to vacancy diffusion, is available in the associated supplementary database  [90]. In a few cases, no barrier between the initial and final state is found, namely for athermal transitions where the end-point energy difference is large. In FeP, since configuration '2b' is unstable and relaxes spontaneously to a MD, the related barriers have not been computed.
Solute transport The DFT data presented in the previous section is used as input in the numerical part of KineCluE to obtain the vacancy-and dumbbell transport coefficients for each Fe-X binary alloy, applying the kinetic cluster-expansion approach of Eq. 3. The complete set of results is available in the associated database  [90]. Solute-defect flux coupling and transport is then analyzed based on the following quantities: -The flux-coupling ratios involving the off-diagonal coefficients: L The mathematical framework used to derive these quantities from the KineCluE output is presented in Appendix B and summarized in Table  B.
Acknowledgments This work was financially supported by  Vattenfall AB ,  Göran Gustafsson Stiftelse , and the  Euratom  research and training programme 2014-2018 under the grant agreement No  661913  (SOTERIA). The paper reflects only the authors' view and the  European Commission  is not responsible for any use that may be made of the information it contains. It contributes to the  Joint Program on Nuclear Materials (JPNM)  of the  European Energy Research Alliance (EERA). The high-performance computing resources were provided by the  Swedish National Infrastructure for Computing (SNIC)  and the  GENCI (CINES/CCRT ) computer center under Grant No. A0070906973. Moreover, the authors acknowledge  T. Garnier  for the valuable contributions to the SCMF method.
A large number of genetic studies in yeast rely on the use of expression vectors. To facilitate the experimental approach of these studies, several collections of expression vectors have been generated (YXplac, pRS series, etc. ). Subsequently, these collections have been expanded by adding more diversity to many of the plasmid features, including new selection markers and new promoter sequences. However, the ever growing number of plasmid features makes it unrealistic for research labs to maintain an up-to-date collection of plasmids. Here, we developed the COSPLAY toolbox: a Golden Gate approach based on the scheme of a simple modular plasmid that recapitulates and completes all the properties of the pRS plasmids. The COSPLAY toolbox contains a basal collection of individual functional modules.
Using an appropriate design of flanking sequences of the DNA fragments to be integrated, one can assemble a virtually unlimited number of modules in the right order and in one reaction. This technique has been further developed to allow large-scale construction of multi-genic vectors using a multi-level assembly scheme  [23, 24]. The Golden Gate system has recently been adapted to yeast  [25]  with the generation of a collection of modules (i. e plasmid type, selection markers, promoters, etc. ) that can be assembled in different combinations to produce expression vectors (up to 11 modules) as well as complex multi-gene assemblies for synthetic biology approaches. Here, we report the development of a simpler alternative plasmid architecture based on the Golden Gate system that allows both efficient assembly of individual modules into a destination expression vector and rapid generation of new custom modules using the MEGAWHOP technique.
The resulting COSPLAY (COmbinatorial Swift PLasmid Assembly in Yeast) toolbox includes a collection of 26 modules that can be integrated into 6-module expression vectors that recapitulates and completes the properties of the pRS plasmid series. We have paid particular attention to make this toolbox straightforward to use and to expand it by developing custom Matlab software that automates the design of the tailed primers used to generate individual modules. In addition, unlike standard yeast integrating plasmids, our toolbox is designed to offer the possibility to ensure single copy integration of the expression vector to a target locus in the genome, independently of auxotrophic markers. The COSPLAY toolbox is available on Addgene as a package of 28 plasmids and the software can be downloaded from github: https://github.
COSPLAY primers design software The COSPLAY primer design software was developed in MatLab to simplify and to automate the addition of new custom modules to the COSPLAY modules collection. The software can be downloaded from github: https://github. com/gcharvin/cosplay (as a Matlab application, requires the version 2017b or higher of Matlab). The main functions of the COSPLAY software are: 1. Automated design of optimized primers for amplifying a specific target sequence. Several criteria (Primer Tm, length, self complementarity, 3' complementarity, matrix complementarity, 3' matrix complementarity, GC %, GC clamps, 3' stability, 3' GC%, primers pair Tm difference, cross complementarity, cross 3' complementarity) are used for this optimisation. The basic rules of optimisation are similar to those of the Primer3 software  [28].
COSPLAY, a toolbox for rapid generation of yeast expression vectors The object of the COSPLAY toolbox is to provide the possibility of easily, quickly and efficiently generate new expression vectors containing different combinations of functional elements or modules. Each of these modules can be selected from a collection of ready-to-use plasmids, or can be easily generated from cDNA, as described below. The organization of a yeast expression vector generated with the COSPLAY toolbox is based on six functional modules assembled in a specific order into a destination vector (pUC57;  which could be either a fluorescent protein or a degron to decrease the stability of the resulting protein. The fifth position consist of a transcriptional terminator sequence followed by a selection marker (Position 6) used upon yeast transformation.
The COSPLAY toolbox uses the Golden Gate cloning technology  [21, 22] , which relies on the usage of Type IIS restriction enzymes (i. e. BsaI, BpiI, etc. ). These enzymes cleave outside of their recognition site (Fig 1B ), leaving a 4 bp 3' overhang that can be manipulated to generate DNA ends (Fig  1B ), which will dictate the compatibility between individual DNA fragments and the cloning order and orientation (Fig  1C ). In addition, by placing the recognition sites at each extremity of the DNA fragment of interest (in opposing orientations), the restriction sites are lost upon ligation. Thus allowing to simultaneously carry out the restriction digestion and ligation reactions in a single tube (Fig  1C ). The destination vector (pDV) has been engineered to contain a chloramphenicol resistance (CmR) and carries only 2 BsaI restriction sites (compatible with the module library) and flanking a LacZ cassette and located upstream of an SV40 polyA sequence (Fig  1C ).
This cassette is lost upon successful assembly of individual modules into the destination vector and allows performing a Blue/White screening to increase the cloning efficiency. The product of the restriction/ligation reaction is transformed into E. coli and grown on CmR plates. Since the destination vector and the backbone vector for the module library contain different antibiotic selection markers (Table  3 ), one can easily select only for the destination vector only. Individual white colonies are grown in liquid culture, plasmid DNA is prepared and vector integrity is verified by restriction digest using appropriate restriction enzymes and sent for sequencing. With the COSPLAY toolbox, any given construct can be assembled in 4 days with 100% cloning efficiency (see cloning note on methods section).
An efficient cloning method to generate new modules In addition to the modules available in the COSPLAY library it is possible to easily generate new modules to meet user-specific needs (e. g. a specific cDNA, promoter, fluorescent protein, etc. ). To this end, we have standardized a cloning-free two-step PCR process  [37]  whereby the sequence of interest is amplified by PCR using appropriate primers containing BsaI sites with appropriate overhangs and 25 bp of the pUC57 multiple cloning site (Fig  2A ). The PCR product of this first reaction is then used as a mega-primer to amplify the pUC57 destination vector, leading to the direct integration of the target sequence into pUC57 (Fig  2A ). The pUC57 plasmid contains a LacZ cassette, which is rendered nonfunctional when a DNA sequence is cloned in its multiple cloning site, thus allowing Blue/White screening after transformation in E.
coli (Fig 2C). In order to facilitate primer design to generate new modules, we have developed a custom program with a graphical user interface in Matlab (Fig  2B ) that provides assistance in this process: the user either enters the sequence of primers homologous to the sequence to be integrated in the module or the complete sequence of the module. For each module position (1 to 6), the program adds the primer tails and runs a battery of tests to evaluate the quality of the primers. In addition, the program identifies potentially conflicting BsaI restriction sites in the module sequence and proposes strategies to mutate them through fusion PCR. A comprehensive documentation of the program is provided as supplementary material, and the program can be downloaded from GitHub: https://github.
Therefore, the number of integrated clones must be assessed quantitatively either by directly scoring integration events (e. g. using southern blot) or by measuring the expression level of the gene of interest (e. g. using QPCR, western blot, cytometry, etc. ). To overcome this limitation, we developed a module in position 1 that ensures a single copy integration of the expression vector to a specific target region in the genome. This module is based on two inverted sequences (separated by the rare AscI and FseI restriction sites) which are homologous to a small genomic region (see Fig  4A  and Methods ). To facilitate module variant design, we have added an option in the software that takes this additional constraint into account for the generation of primers. After AscI-driven linearization (or FseI if AscI is present in the inverted sequences), an assembled plasmid containing this module can be integrated at the homologous genomic locus of interest (Fig  4B ).
Discussion In this study, we have developed a new toolbox called COSPLAY that provides fast and easy access to a high number of plasmid variants by handling the combinatorial part of the expression vector design. Our toolbox is based on a ready-to-use collection of individual functional modules and a one-step protocol to rapidly assemble a combination of modules into a functional expression vector. Each module can be assembled with any of the others modules leading to a large number of potential expression vectors. A particular feature of the COSPLAY toolbox is that the collection of modules can be easily expanded using the MEGAWHOP protocol  [37]. Here, specific sequences of interest (promoters, cDNAs, fluorescent reporters, epitope tags, etc. ) are seamlessly cloned by PCR, thus producing the possibility to expand the flexibility of the COSPLAY toolbox at will.
To streamline this process, we have developed a standardized protocol that makes the generation of new modules very straightforward. A potential future expansion of the COSPLAY toolbox would incorporate some of the recent advances in the field of synthetic transcriptional regulation in Saccharomyces cerevisiae. While the COSPLAY toolbox already includes some widely used promoters/terminators, new sequences have been developed to improve gene expression robustness  [39] [40], inducibility  [41] [42]  [43] , as well as mRNA stability  [44]. These elements could be easily integrated within the COSPLAY toolbox to further increase the number of potential expression vectors. While other combinatorial strategies for plasmid generation based on the Golden Gate system already exist for yeast  [25] , we made the choice to develop a toolbox that is simpler, that yet recapitulates all the features of the pRSII series plasmids.