dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This research was performed using funding received from the  DOE Office of Nuclear Energy's Nuclear Energy University Programs  (Award # 18-15020 ) for the modeling and analysis, and from the  Center for Performance and Design of Nuclear Waste Forms  and Containers, an  Energy Frontier Research Center  funded by the  US DOE, Office of Science, Basic Energy Sciences  under Award # DESC0016584  for subsequent data analysis and preparation of this manuscript. We also acknowledge some financial support provided by the  National Science Foundation  (Grant No. 1562066 ).
Functional and Technical Architecture for Producing VRLE Among the studies that addressed the question of the functional and technical architecture of VRLE, we have identified those of  (Lanquepin et al. , 2013) , which propose a platform called HUMANS (Human Models based Artificial eNvironments Software). This platform offers a generic framework designed to produce personalized virtual environments. It allows modelling mainly the human system via the virtual human "AVATAR" which makes it difficult to adapt it to other environments or pedagogical situations. The objective of this project is to set up generic and independent scenario systems, able to create environments that can be adapted to different situations in a virtual environment. HUMANS offers an adaptive scenario approach via its SELDON module.
The SEDLON model is extrinsic, which means that the scenario is considered as an additional step in the framing of an existing virtual environment, and not as an integral part of the design process for that environment. The GVT (Generic Virtual Training) project  (Gerbaud et al. , 2008)  aims at developing a platform for producing pedagogical activities such as maintenance procedures. This platform is based on visual metaphors. This concept is important because it focuses on interactions with objects using a menu of icons representing the possible interactions between the object and the user. The generation of scenarios describing the sequence of these activities in a well-defined order is a complicated task in GVT. For this reason,  (Mollet and Arnaldi, 2006)  used the LORA language. This language allows the creation and edition of scenarios.
Datasets We perform our experiments on two widely used, reasonably sized datasets: the Penn Treebank (PTB;  Mikolov et al. 2011a ) and WikiText-2 (WT2;  Merity et al. 2017 ). The PTB, heavily preprocessed, retains the 10k most frequent words in its vocabulary, while the others tokens are replaced by a common <unk> token. The WT2, about two times larger, retains words that appear at least three times in the training data in its vocabulary, which makes it 33, 278 words. To study the impact of our various objective functions on a vocabulary containing very rare words, we also experiment with a version on the PTB to which we only apply limited preprocessing, allowing us to keep its full training vocabulary, which contains 39030 words.
Experimental setup We based our setup on the ASGD weight-dropped LSTM (AWD-LSTM) models of  Merity et al. (2018b) , since to the best of our knowledge they give state-of-the-art results on the PTB and WT2 for models that are build with a softmax output layer and do not use any adaptative method (as the pointer sentinel LSTM  (Merity et al. , 2017) , the neural cache  (Grave et al. , 2017b)  and the dynamic evaluation  (Krause et al. , 2018) ). Our models 11  , replications of their 3-layer LSTMs with tied input and output representations, were built using their implementation. 12 For each dataset, we follow their choice of hyperparameters, only modifying the objective functions. For our sampling-based objectives, we use k = 1024 samples, and the unigram distribution as p n. In the case of objectives derived from binary NCE, to avoid issues with the phenomenon of self-normalization and consistency issues  (Ma and Collins, 2018)  we chose to use blackout  (Ji et al.
99 1. 0 1. 01 1. 1 AS 0. 5 0. 9 0. 99 1. 0 1. 01 1. 1 1. 5 NCE 0. 5 0. 9 0. 99 1. 0 1. 01 1. 1 1. 5 Obj α Obj β Obj γ 65. 8 63. 3 62. 3 60. 7 61. 0 60. 6 60. 1 60. 8 61. 1 61. 0 63. 0 63. 9 63. 3 147. 0 87. 1 113. 8 61. 3 62. 8 61. 0 61. 7 61. 7 61. 6 61. 7 61. 8 61. 6 62. 0 65. 6 61. 6 64. 1 97. 7 127. 6 81. 7 1407. 5 88. 0 117. 2 61. 4 62. 6 61. 8 61. 1 61. 4 61. 7 61. 2 61. 3 61. 1 61. 8 64. 4 61. 0 64. 4 97. 9 123. 7 81. 2 Objective Obj α Obj β Obj γ Dataset PTB WT2 PTB WT2 PTB WT2 65. 8 75. 8 63. 3 68. 3 63. 2 70. 1 0. 9 39. 7 42. 4 107. 9 130. 0 62. 4 69. 0 60. 7 66. 5 61. 0 65. 6 60. 6 65. 9 0. 99 57. 9 63. 0 64. 0 69. 3 60. 6 65. 9 1. 0 60. 1 64. 9 60. 1 64. 9 60. 1 64. 9 60. 8 65. 6 61. 1 65. 4 61. 0 66. 0 1. 01 63. 8 69. 3 58. 3 61. 8 61. 0 66. 0 63. 0 66. 7 63. 9 68. 3 63. 3 68.
This allow to classify them into three families based on the relative order of these steps. The first type is the SKP-type (SKINNY  [6] ), for which S is followed by K, itself followed by the P-layer. The second type is the SPK-type (MIDORI  [3] , GIFT  [4] , PRESENT  [8] ), for which S is followed by the P-layer, itself followed by K. On the one hand, each family can be implemented without changing the order, simply by changing the first step of the algorithm. On the other hand, the two families are not coherent and require extra modules to be handled by the same architecture. Note that it is also possible to handle both these families by adding computation to the key since the P and K are both linear layers. The last family is that of the α -ref lective algorithms (PRINCE  [9] , MANTIS  [6] ) which change the order of the steps during the encryption.
Algorithms GIFT PRESENT SKINNY MANTIS PRINCE MIDORI SKP X SPK X X X Step order α reflection X X bit-level X X Permutation nibble-level X X X X Matrix None X X 4 × 4 matrix X X X P-Layer Multiplication 64 × 64 matrix X Sub key extraction A B C C C Rotation X X X Sbox X Key Scheduling Round constant X X X X X X The P-layer P has two main types of permutations, either at the bit-level (PRESENT  [8] , GIFT  [4] ) or at the nibble-level (SKINNY  [6] , PRINCE  [9] ). These two different approaches require more or less complex permutation modules and implementing one but not the other has an influence over the cost of the permutation. An additional matrix multiplication can be used. It can be a 4 × 4 multiplication of the nibbles (SKINNY  [6] , MANTIS  [6] ) or a 64 × 64 multiplication on the whole word (PRINCE  [9] ).
For example, GIFT  [4]  divides the entire key in 16-bit words and extracts two of them (A type), PRESENT  [8]  extracts two nibbles (B type) and use the Sbox. In other algorithms such as PRINCE  [9]  and MANTIS  [6]  the key is divided into two parts (C type) used separately. For these reasons, and because making the key scheduling generic in hardware would be complex and costly, we have decided to use a buffer that will contain pre-computed round keys. The round keys will be generated by software in the configuration phase.
Minimizing the Cost of the Permutation Permutation was a key issue as it is usually achieved by just reordering the wires. The overhead of making this bloc configurable could thus be significant. The simplest way to go is to consider a 64 × 64 crossbar which would allow any permutation but would be incredibly costly both in terms of area and in terms of the size of configuration memory. It was therefore essential to identify similarities between the different permutations in order to limit the area of this module. The result was two levels of permutations, as explained beforehand, the bitlevel permutation and the nibble-level permutation. The bit-level permutation (PRESENT  [8] , GIFT  [4] ) allows any layer-input bit to end up as any layeroutput bit within the same 16-bit word, the restriction being that two bits from the same input nibble may not end up in the same output nibble.
Acknowledgments This work was supported in part by the  Labex CEMPI  ( ANR-11-LABX-0007-01 ) and by the  Nord-Pas de Calais Regional Council and FEDER  through the  Contrat de Projets État-Région (CPER) , and in part by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  665148  (QCUMbER). EPJ Web of Conferences 198, 00013 (2019) https://doi. org/10. 1051/epjconf/201919800013QTech 2018.
In our simulations, the cardinality of the FOD H is 4. In each random generation, there are 2 4 À 1 ¼ 15 focal elements in the original BBA. The number of remaining focal elements for all the approaches used here is set to from 14 down to 2. We randomly generate BBA using Algorithm 1  37  in Table  19  below. The average (over 200 runs) combination time and average (over 200 runs) distance values (d J ) between the original BBA and the approximated BBA's obtained using different approaches given different remaining focal elements' numbers are shown in Figs. 4  and 5 , respectively. The average (over all runs and all numbers of remaining focal elements) computation time and distance values are shown in Table  20. Note that the computer for the experiments is with i7-8550CPU, 16 GB LPDDRIII RAM, WINDOWS 10 OS and MATLAB 2013B.
Acknowledgments The authors would like to thank the support on this research by the  National Natural Science Foundation of China  (Nos. 61671370 ,  61573275 ),  Postdoctoral Science Foundation of China  (No. 2016M592790 ),  Postdoctoral Science Research Foundation of Shaanxi Province, China  (No. 2016BSHEDZZ46 ) and  Fundamental Research Funds for the Central Universities, China  (No. xjj201066 ).
, Tokyo, Japan) and CYP2B6 (anti-human CYP2B6 rabbit polyclonal antibody, MBS9409762, Daiichi Pure Chemicals Co. , Tokyo, Japan), CYP2E1 (anti-human CYP2B6 rabbit polyclonal antibody, PA26, Oxford Biomedical, MI, USA), GSTA1 and Mu (Biotrin, Dublin, Ireland), albumin and transferrin (Kent laboratories, WA, USA), HSC70 (B-6, sc-7298, Santa Cruz Biotechnology), GFP (mAb 8758, Abnova), and cyclin A (anti-human cyclin A rabbit polyclonal antibody, H-432, sc-751, Santa Cruz Biotechnology). Primary antibodies were detected using secondary goat or rabbit antibodies coupled to horseradish peroxidase (Dako, Denmark), and detection of the immune complex was performed by chemiluminescent detection (Pierce™ ECL Substrate). The signals were quantified with scanning densitometry using the Quantity One software program (Bio-Rad Laboratories, Hercules, CA, USA).
The DNA content was analyzed for 5000 cells. Percentages of cells in G0/G1, S and G2/M phases was established using the ModFit LT™ software (Supplementary Materials). The GFP was detected in HepaRG cells by fluorescence microscopy using Zeiss Inverted Microscope, and photographs were analyzed with the AxioVision Software. The quantification of GFP was carried out by flow cytometry: The cells were detached with trypsin and resuspended in William's E medium containing FCS. The fluorescence intensity of 10,000 cells was analyzed with a Becton Dickinson le LSRFortessa™ X-20 (cytometry core facility of the Biology and Health Federative research structure Biosit, Rennes, France) to measure the fluorescence of the GFP positive cells. Flow cytometry data were analyzed using DIVA software (Becton Dikinson).
Statistical Analyses The Results were expressed as mean ± SD (the standard deviation) of two or three independent experiments including three to six independent culture wells. Statistically significant variations after treatment were compared with controls using Student's test with Excel software; * p value < 0. 05, ** p < 0. 01 and *** p < 0. 001. The most appropriate procedure to expand HepaRG cells is to culture them over 2 weeks between two passages (Figure  1A ). As previously reported  [4, [12] [13] [14] , HepaRG cells actively proliferate during the first 8 to 10 days after seeding as confirmed by the increasing cell counts and the high numbers of cells in S and G2/M phases during this period (Figure  1B, C ). Two weeks after cell seeding, the proliferation activity strongly decreased and over 95% of cells became quiescent (G0/G1 phase) while nearly 50% of quiescent HepaRG cells committed toward the hepatocyte-like cell lineage as demonstrated by the appearance of well-defined colonies of hepatocytes and the high expression of albumin detected by immunoblotting (Figure  1D ).
Establishment of Biosensor HepaRG Cells In order to establish stable CYP1A1/2, CYP2B6 and CYP3A4 biosensor HepaRG cells, we subcloned the partial promoter sequences of the CYP1A1/2, CYP2B6 and CYP3A4 (Supplementary Materials) in the lentiviral pLV-GFP backbone expression vector (Figure  2  These data of in situ GFP visualization were confirmed by the GFP protein detection by Western blot (Figure  3A ). A time-course analysis of the GFP expression in the CYP1A1/2-, CYP2B6-and CYP3A4-GFP HepaRG biosensor cells was performed and compared to the expression of the corresponding endogenous CYP proteins. Immunoblotting showed very similar expression patterns for the GFP and the corresponding endogenous CYP (Figure  3A ), demonstrating that the endogenous CYP gene and the corresponding transgene containing the CYP promoter driving the GFP expression After lentiviral transduction and selection of recombinant cells stably transduced, we studied the expression of the GFP in these derived HepaRG cell lines in order to evaluate the patterns of transcription of the different CYP promoter-GFP transgenes during the proliferation and differentiation processes taking place over 30 days after cell splitting (protocol described in Figure  1 ).
The GFP expression was detected in situ by fluorescent microscopy and quantified by flow cytometry (Figure  2 ). Visualization of the GFP by fluorescent microscopy demonstrated the low transcriptional activities of the CYP1A1/2, CYP2B6 and CYP3A4 promoters within the integrated lentiviral transgene at day 2 after HepaRG cell seeding. For all the transgenes, the GFP expression increased at days 14 and 28 after stimulation by DMSO and was much stronger in colonies of hepatocyte-like cells compared to the very low intensity of fluorescence detected in cholangiocyte-like cells. These data of in situ GFP visualization were confirmed by the GFP protein detection by Western blot (Figure  3A ). A time-course analysis of the GFP expression in the CYP1A1/2-, CYP2B6-and CYP3A4-GFP HepaRG biosensor cells was performed and compared to the expression of the corresponding endogenous CYP proteins.
Acknowledgments: Nicolas Quesnot received a fellowship from the  Région Bretagne and Ligue contre le Cancer (Comité Départemental des Côtes d'Armor). We would like to thank  Jean-Marc Pascussi  ( INSERM U1191 ,  CNRS UMR 5203 ,  Institut de Génomique Fonctionnelle, Montpellier, France)  for providing us the CYP3A4 promoter sequence. We also thank the  Vectalys Company (Toulouse, France)  for the subcloning in lentiviral expression vectors and the production of lentivirus infectious particles and the cytometry core facility of the  Biology and Health Federative research structure Biosit (Rennes, France ) for technical support. The authors would like to dedicate this article to  Marie-Anne Robin  and  Fabrice Morel  who passed away during the preparation of the manuscript. Our colleagues were exceptional scientists and friends whose enthusiasm for life and belief in science will continue to inspire all of us who were privileged to know them.
g. , nickel or zinc). In this opinion presented to the International Journal of Environmental Research and Public Health, we look at progress in environmental sciences and medicine within a restricted sample population. Among the 167 articles with the "heavy metal" term in the title and the 996 with it in the subject, which we identified from a total of 12,700 articles published in International Journal of Environmental Research and Public Health in the widely used databases of Scopus and the Web of Science using the search term "heavy metal" (data accessed 10 October 2019), we found lead (Pb), cadmium (Cd) and zinc (Zn) to be the three most commonly studied elements (69%, 67% and 62%, respectively, when considering the term "heavy metal" in the title (n = 167), and 32%, 30% and 23%, respectively, when considering the term in the subject (n = 996); Table  1  and Figure  4 ).
The RDF format offers powerful possibilities for machines, such as reasoning or federated queries over interlinked datasets. However, presenting RDF data to humans is very challenging: its very structure defeats traditionnal approaches, as it separates information into small pieces, making it difficult for users to make sense of it. My PhD work proposes an approach that presents RDF data in a context, to make them understandable by humans. We first describe S-Paths, a system to support set-based exploration of a dataset's content. We show that it works well on simple models, but that its efficiency is limited by performance issues on very abstract models. Then we lay the basis for a second project, whose aim is to take one more step back and put these sets of entities in a broader context, to give a structural overview of Linked Datasets.
Another approach, used by most Linked Data browsers, consists in displaying one page per resource, with all statements directly related to it, as clickable links when they consist of URIs. While this allows users to hop from one resource to another across graphs and datasets, whatever their size, the first drawback is that relevant properties to describe a resource might be several hops away: when the user reaches a piece of information after several clicks, this information is displayed separately from the resource that was originally of interest. Keeping the chain of previous pages in mind requires a significant memorization effort. It is similar to following series of sentences Peter has a son. The latter has a nephew. The latter has a wife. The latter is a pharmacist. instead of the single sentence The nephew of Peter's son is married to a pharmacist.
Clicking a URI would attempt to dereference it, and a new HTML page with the properties of the corresponding resource would be displayed. Some browsers featured support for Fresnel  [11] , a language giving high-level indications about how to display RDF data, which can be used to generate more human-friendly representations of the data. Such languages, however, require that declarative presentation rules be available to the browsers for the different RDF vocabularies involved. Tabulator  [1]  was one of the first linked data browsers to provide users with more relevant visual representations of RDF data without resorting to such vocabulary-specific presentation languages. However, since Tabulator only features a limited set of views (map, timeline, calendar), it frequently displays data using a generic, more tripleoriented, tree or tabular view on data which are neither spatial nor temporal.
Another significant limitation of many linked data browsers is their strong focus on the follow-your-nose browsing strategy. Users explore individual paths from resource to resource over the Web of Data, incrementally fetching the properties of the new resource of interest, without much context (if any) about similar resources. Context information, however, is essential to answering questions related to the distribution of values, to the identification of potential correlations, and to the direct comparison of resources forming a coherent set such as, e. g. , all resources of a given type in a particular dataset. Answering questions such as these, and more generally gaining insights about collections of resources, requires generating multivariate data visualizations (e. g. , scatterplots  [8] ) of a set of properties associated with resources in the set.
The Linked Data Visualization Model  [4]  defines a transformation workflow to dynamically associate one or more datasets with multiple visualizations. Implementations of this workflow typically support a range of data visualization techniques. The methodology defined in  [3]  focuses on statistical linked data as found in, e. g. , RDF data cubes, making it possible to combine data from different linked sources and display them in visualization dashboards. Multiple other projects have been attempting at making it easier for users to generate data visualizations from linked data. Of particular interest here are Visualbox  [6]  and LinkDaVis  [14]. Visualbox enables its users to create different visualizations by writing SPARQL queries and populating predefined visualization templates with the queries' results.
The produced visualizations can then be embedded in classic Web pages. LinkDaVis provides users with a hierarchical representation of properties, from which users can choose the ones to visualize. It then performs a heuristic analysis of the data to suggest a ranked list of visualization configurations for these properties, based on different bindings to visual encoding channels. Once a particular visualization is selected, it can further be customized, as in other mixed-initiative approaches such as that adopted in Voyager  [15] , a tool for the creation of multi-variate data visualizations. Such tools are very powerful, and enable the creation of a wide variety of visualizations from linked data: maps, timelines, and a range of statistical charts (scatterplot, line plot, density plot, etc.
S-Paths: Browsing the Content of a Dataset, from Overview to Detail The first project of this PhD is S-Paths. Its model is based on a mixed-initiative approach  [14, 15]  where the system automatically suggests interesting views for a set of resources, but lets users reconfigure them at will. Exploring a set of resources is an iterative process: whenever users make a selection, S-Paths defaults to what it considers to be the most relevant view on that selection, which users can, again, reconfigure at will. A demo instance is available 1.
METHODOLOGY Persona. We started by organizing two workshops, one with 9 Linked Data experts, and another with 7 lay users interested in Linked Data. From these workshop, combined with informal observations during French National Library's hackathon and other Linked Data community events in Paris, we derived 3 persona characters: a Linked Data publisher, a Linked Data reuser, and a lay user interested in the content of Linked Datasets. We used these persona as a leading thread through the development of proofs of concept, to keep in mind relevant user tasks and concerns. We also rely on them to define use cases, and identify relevant users for surveys and studies. Interviews and Participatory Design. For our second project, we are conducting a series of interviews with experts to better characterize what they need to know about their datasets.
We will follow a participatory design approach together with a few of them, who agreed to test our tools and give feedback all along the design and development process. The code will be partly distributed, and run on the endpoints of participants. Proofs of Concept. We develop high-fidelity prototypes to give proofs of concept of our approach, and evaluate them. Their code is open source. For S-Paths we first made an API to identify and characterize sets of entities and the corresponding semantic paths. Then we started a light version of the visualisation system with two view components, to set up the generic query and transition mechanisms, and then we progressively added other views. The dataset used for development was the Nobel Prize Linked Dataset, which contain 85 797 triple statements, representing 15 classes of ressources.