dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018)  or ARC  (Clark et al. , 2018). However, even if Uni-fiedQA achieves good results on previously unseen datasets, it often fails to achieve optimal performances on these datasets until it is further finetuned on dedicated human annotated data. This tendency is increased when the target dataset deals with questions about a very specific domain. One solution to this problem would be to finetune or retrain these models with additionnal human annotated data. However, this is expensive both in time and resources. Instead, a lot of work has been done lately on automatically generating training data for fine-tuning or even training completely unsupervised models for question answering. One commonly used dataset for unsupervised question answering is the extractive dataset SQUAD  (Rajpurkar et al.
This method, being unsupervised, needs no additional annotated domain specific data requiring only a set of unannotated sentences of the domain of interest from which the questions are created. Contrarily to most of the aforementioned works, our aim is not to train a new completely unsupervised model but rather to incorporate new information into an existing stateof-the-art model and thus to take advantage of the question-answering knowledge already learned. We conduct our experiments on the SciQ dataset  (Welbl et al. , 2017)   choice questions (4 choices) featuring subjects centered around physics, biology and chemistry. An example of question can be found in Figure  1. We focus on the SciQ dataset because it has not yet been used for training UnifiedQA and it requires precise scientific knowledge.
Furthermore, our experiments reveal that the direct application of UnifiedQA on the SciQ benchmark leads to a much lower performance than when fine-tuning it on the SciQ training set (see Section 4). Our objective in this work is to solve this gap between UnifiedQA and UnifiedQA fine-tuned on supervised data with the unsupervised question generation approach described in Section 2. We additionally test our method on two commonly used multiple choice question answering datasets: Common-senseQA  (Talmor et al. , 2019)  and QASC  (Khot et al. , 2020). These datasets contain questions with similar domains to SciQ even though the questions are slightly less specific. Furthermore, neither of them has been used during the initial training of UnifiedQA.
We only keep articles with more than 800 average visitors per day for the last ten days (on April 27, 2021), resulting in 12 656 pages. The two other selection methods extract sentences from SciQ itself and therefore are not entirely unsupervised but rather simulate a situation where we have access to unannotated texts that precisely describe the domains of interest such as a school book for example. The SciQ dataset includes a support paragraph for each question (see Figure  1 ). Pooled together, these support paragraphs provide us with a large dataset of texts about the domains of interest. We gather the paragraphs corresponding to all questions and split them into sentences to produce a large set of sentences that are no longer associated with any particular question but cover all the topics found in the questions.
We compare two different setups. In the first one, we include all the sentences extracted from the train, validation and test sets thus simulating a perfect selection of sentences that cover all the knowledge expressed in the questions. Still, we only use the support paragraphs and not the annotated questions themselves. As compared to the classical supervised paradigm, this setting removes all annotation costs for the application developer, but it still requires to gather sentences that are deemed useful for the test set of interest. We then compare this setup with another one, where only the sentences from the train set are included. This scenario arguably meets more practical needs since it would suffice to gather sentences close to the domain of interest. The number of sentences for each dataset is presented in Table  1.
Questions Generation The generation of questions from a sentence relies on the jsRealB text realizer  (Lapalme, 2021)  which generates an affirmative sentence from a constituent structure. It can also be parameterized to generate variations of the original sentence such as its negation, its passive form and different types of questions such as who, what, when, etc. The constituency structure of a sentence is most often created by a user or by a program from data. In this work, it is instead built from a Universal Dependency (UD) structure using a technique developed for SR'19  (Lapalme, 2019). The UD structure of a  sentence is the result of a dependency parse with Stanza  (Qi et al. , 2020). We thus have a pipeline composed of a neural dependency parser, followed by a program to create a constituency structure used as input for a text realizer, both in JavaScript.
Used without modification, this would create a complex echo program for the original affirmative sentence, but by changing parameters, its output can vary. In order to create questions from a single constituency structure, jsRealB uses the classical grammar transformations: for a who question, it removes the subject (i. e. the first noun phrase before the verb phrase), for a what question, it removes the subject or the direct object (i. e. the first noun phrase within the verb phrase); for other types of questions (when,where) it removes the first prepositional phrase within the verb phrase. Depending on the preposition, the question will be a when or a where. Note that the removed part becomes the answer to the question. In order to determine which questions are appropriate for a given sentence, we examine the dependency structure of the original sentence and check if it contains the required part to be removed before parameterizing the realization.
Distractors Selection Since SciQ is a multiple-choice dataset, we must add distractors to each question we generate, to match the format of SciQ. A simple solution to this problem is to select random distractors among answers to other similar questions generated from the dataset of sentences we gathered. Obviously, selecting random distractors may lead to a fine-tuning dataset that is too easy to solve. Therefore, we propose another strategy that selects hard distractors for each question. To do so, starting from our synthetic dataset with random distractors, we finetune RoBERTa  (Liu et al. , 2019)  using the standard method of training for multiple choices question answering. Each pair question/choice is fed to RoBERTa and the embedding corresponding to the first token ("[CLS]") is given to a linear layer to produce a single scalar score for each choice.
Furthermore, a lot distractors have an incorrect grammar (eg: a distractor might be plural when the question expects a singular). Therefore, in this initial training, RoBERTa might learn to isolate the answer with a corresponding semantic field or the one with correct grammar. The re-selection then minimizes the amount of trivial distractors and models trained on this new refined dataset will have to focus on deeper and more meaningful relations between the questions and the answers. The process is better shown in Figure  4 , and an example of refined distractors can be found in Figure  3. The number of scored candidate distractors is an hyper-parameter. A small number of candidates may result in a situation where none of the candidates are credible enough, while a large number requires more computation time, since the score of  each candidate for every question needs to be computed, and has a higher risk of proposing multiple valid answers.
Training and Implementation Details To refine distractors, we use the "Large" version of RoBERTa and all models are trained for 4 epochs and a learning rate of 1 × 10 -5. These hyperparameters are chosen based on previous experiments with RoBERTa on other multiple-choice datasets. The final UnifiedQA fine-tuning is done using the same multiple choices question answering setup as the one used in the original UnifiedQA paper  (Khashabi et al. , 2020). We use the "Large" version of UnifiedQA and all the models are trained for 4 epochs using Adafactor and a learning rate of 1 × 10 -5. The learning rate is loosely tuned to get the best performance on the validation set during the supervised training of UnifiedQA. We use the Hugging Face pytorch-transformers  (Wolf et al. , 2020)  library for model implementation.
Model CQA QASC UnifiedQA (no fine-tuning) 60. 9 44. 5 UnifiedQA (supervised) 74. 3 61. 0 Wikipedia data (Random) 64. 9 57. 2 Wikipedia data (Refined) 65. 1 59. 4 Table  3 : Accuracy results obtained on the dev set of CommonsenseQA and QASC when fine-tuning Uni-fiedQA using data from Wikipedia. the original QASC and CommonsenseQA datasets are overall easier and therefore it is less advantageous for a model to be trained on harder questions.
Conclusion In this work, we proposed a multiple-choice question generation method that can be used to fine-tune the state-of-the-art UnifiedQA model and improve its performance on an unseen and out of domain dataset. Our contributions are: • We have shown that simple unsupervised methods could be used to finetune existing multipurpose question answering models (in our case UnifiedQA) to new datasets or domains. • We propose a novel distractor refining method able to select harder distractors for a given generated question and show its superiority compared to a random selection. Future work includes comparing our method to other question generation methods (including supervised methods:  Liu et al. (2020) ,  Puri et al. (2020) ) in order to assess the effect of both the generation method and the questions quality on the final performances of our models.
Experimental Setup We evolved 30 populations of 100 individuals, each starting from clones of a random individual with 60 genes (20 of each type), for 1,000,000 generations. The parameter values that we used are given in The simulation was implemented in Python, with computationally heavy parts optimized using the numba package  (Lam et al. , 2015). The source code for the simulation, as well as the data analysis code, are available online at https://gitlab. inria. fr/tgrohens/evotsc. Running the complete simulation took around 36 hours of computation on a server using a 24-core Intel Xeon E5-2620 v3 @ 2. 40GHz CPU, with each replicate running on a single core and using approximately 300 MB of RAM. Evolution of Class-Specific Gene Expression Levels These results are however not specific to this particular individual.
Acknowledgments The authors would like to thank  Dr. Amira Klip  ( SickKids, Toronto, ON, Canada ) for her generous gift of L6 myoblasts and  Dr. Mary-Ellen Harper  ( University of Ottawa, ON, Canada ) for the use of the XF-96 analyzer ( Seahorse Bioscience ) and the provision of her lab for radiation experiments. We also thank  Caroline Cachero  and  Cailin Outhwaite  for their technical support.
It induced a significant increase in terms of NP-specific interferon-gamma (IFNg) spot forming cells (SFCs), NP-specific CD4+ T-cells (essentially polyfunctional cells) and anti-NP IgG responses. OVX836 was superior to Influvac Tetra for all immunological parameters related to NP, and the 180µg dose was significantly superior to the 90µg dose for SFCs and CD4+ T-cells expressing IFNg. Both the CD4+ T-cell and the anti-NP IgG responses persisted up to Day 180. An efficacy signal was observed with OVX836 at 180µg through reduction of ILI episodes occurring during the flu season as of 14 days post-vaccination. In conclusion, these results encourage further clinical evaluation of OVX836 in order to confirm the signal of efficacy on ILIs and/or laboratory-confirmed influenza cases. NCT04192500 (https://clinicaltrials.
However, a dose of 30μg was considered potentially insufficient to induce a strong and persistent immune response, especially in populations with weakened immunity, such as older adults. Therefore, OVX836 dose levels of 90μg and 180μg were selected to be further evaluated in a Phase 2a study (ClinicalTrials. gov Identifier: NCT04192500; EudraCT number 2019-002939-28; IND number 19276), the object of the current paper.
Due to unavailability of NPh swab samples in some subjects as a result of containment measures for the COVID-19 pandemic, hemagglutination inhibition assays (HAI specific of A/H1N1/Brisbane/02/2018 and A/H3N2/ Kansas/14/2017 strains, two strains representative of those that circulated most during 2019-20 influenza season) were performed on Day 1 and Day 180 samples of OVX836 groups as additional means to identify influenza infection as causal agent of the ILI episode. A four-fold increase in HAI titers between Day 1 and Day 180 was considered indicative of an influenza infection. In the Influvac Tetra cohort, where the vaccine induced a rise in HAI titers, a four-fold increase between Day 1 and Day 180 in anti-NP IgG was considered as a serological marker of influenza infection  (22, 23). The epidemiological data from Sciensano (Belgian Institute for Public Health), namely the percentage of laboratory-confirmed flu positive samples (Supplementary Figure  3 ), were used to define the flu season as the period between 02 December 2019 and 09 March 2020 (weeks during which percentage of lab-confirmed influenza cases was above 20%)  (24).
The study was designed to detect a difference of 2. 3 between mean ratios (Day 8/Day 1; primary endpoint) for Influvac Tetra (1. 0) and for OVX836  (3. 3)  in the NP-specific IFNg SFC counts measured by ELISPOT, assuming that the common standard deviation was 4. 0, using a two group t-test with a 0. 017 (Bonferroni's correction for three comparisons) two-sided alpha significance level. A sample size of 83 subjects in each group would have 90% power to detect such a difference. To account for potential dropouts, a total of 100 subjects per group were included in the study. SAS (Version 9. 4) was used for the statistical analyses. Descriptive statistics were used to summarize all relevant parameters and the balance between the three treatment groups at baseline was confirmed. Continuous variables were analyzed using analyses of variance for repeated measures (PROC MIXED) with the factor 'time', 'treatment group or age strata', and interaction of both factors as fixed factors.
Demographic and Baseline Characteristics Three hundred subjects (208 aged 18-49 years and 92 aged 50-65 years) were vaccinated between 11 December 2019 and 09 March 2020 (100, 101 and 99 with OVX836 90μg, OVX836 180μg and Influvac Tetra, respectively); all were included in the modified intention-to-treat (mITT) cohort (Supplementary Figure  4 ). All subjects completed the study and had samples taken on Day 8 (primary endpoint). Due to the COVID-19 containment measures (replacement of visits by phone contacts), samples for the immunogenicity analyses on Day 29 could only be taken in 227 participants (per protocol [PP] cohort: 78, 73 and 76 in the OVX836 90μg, OVX836 180μg and Influvac Tetra group, respectively). Demographic data and baseline characteristics of the 300 study participants are shown in Supplementary Table  1.
Other approaches targeting T-cell responses against NP using different technologies have failed to demonstrate efficacy in clinical trials (NCT03450915 and NCT03883113). The first limitation of this study is the fact that vaccination occurred during the influenza season, which had an impact on several immune parameters (e. g. CD8+ T-cells). A second limitation is the interference of the COVID-19 pandemic which prevented taking 20% of the Day 29 samples and performing PCRs after 17 th March 2020, even though the primary endpoint was secured as all subjects were recruited before the COVID-19 epidemic peak. A third limitation is the fact that the study was not designed and powered to demonstrate efficacy, so that the efficacy signal observed cannot be conclusive. In conclusion, these results warrant further evaluation of the safety and immunogenicity of higher doses of OVX836 (300μg and 480μg in addition to 180μg) in order to select the best dose for a Phase 2b trial designed and powered to confirm the signal of efficacy on laboratory-confirmed flu cases.
ACKNOWLEDGMENTS The authors are grateful to the healthy volunteers who took part in this study. They acknowledge the contributions of the staff of the  Center for Vaccinology (Ghent, Belgium)  and  ECSOR (Expert Clinical Services Organization). They would also like to thank  Florian Sourd  for helping in the preparation of figures, and  Marion Chevandier  and  Manon Lambiel  for performing immunological analysis on some of the clinical samples.
Periodic FEA comparisons While the accuracy and robustness of the mechanical analysis homogenization scheme has been extensively validated in previous studies, we perform additional validation tasks of the mechanical properties obtained through comparisons with periodic finite element models (P-FEA) created with the commercial software Abaqus. To that scope, we create periodic beam-element based models (Fig. 5b ), employing the unit-cell architecture identified at the end of the optimization process (Fig. 5a ). We employ a   times   periodic arrangement (Fig. 5b ) to avoid edge or size effects  (Li et al. , 2020; Tekoglu et al. , 2011). Thereafter, uniaxial and shear loading tests are carried out (Fig. 5c ) for all components of the compliance tensor to be characterized  (Gornet, 2008). For each loading scenario in Fig.
32 -0. 0085∕  -0. 32 -8. 7∕  ) to well describe the modulus evolution, forming a plato region for ∕  values of approximately 0. 5. The identified unit-cell patterns are schematically presented for the target cases of Table  1  and for the highest moduli case of Table  2  in Fig. 7  for direct comparison and visualization purposes. The exact element thickness unit-cell distribution and therefore the required inner material stiffness distribution is provided at the last iteration step of the algorithm following the numbering convention defined in Appendix A. A Matlab reference code is provided in the form of complementary material in GADIHOM-example-code for the constant density case formulation. 1  and 2. Fading line colors represent reduced element thickness values, while white inner parts signify removed inner elements in the corresponding positions.
Single crystal X-ray data collection and processing Blue plate-like crystals suitable for diffraction were coated with Paratone oil (Hampton Research) and mounted on MiTeGen loops and flash frozen in liquid nitrogen. All X-ray diffraction measurements were done at 100K. Diffraction measurements for F-Cu crystals were done at the ESRF synchrotron, station ID23-1. Data were collected and processed using MXCube and the automated XDS pipeline. The structure was solved by direct methods using SHELXT-2013 or SHELXT 2016/4. The structure was refined by fullmatrix least-squares against F2 with SHELXL 2016/4. The crystallographic data are given in Table  S1. The structure was illustrated using Mercury 3. 9 (Cambridge Crystallographic Data Centre, Cambridge, UK). 1  Powder X-ray diffraction The F-Cu nanosheets were deposited on a quartz zero-background sample holder, and powder XRD was performed on Bruker D8 Advance X-ray powder diffractometer (Bruker) at room temperature between 5 and 45° 2θ.
Atomic force microscopy (AFM) The F-Cu crystals before and after ultrasonication treatment (for 10 min) were deposited onto a clean microscope glass coverslip. The samples were characterized using the AIST-NT Smart AFM system in non-contact (tapping) mode, 100-mm-long silicon nitride cantilevers (OMCL-RC800PSA-W, Olympus) with a resonance frequency of 70 kHz. The images were analyzed and visualized using WSxM imaging software (Nanotec Electronica S. L).
Acknowledgments This work was partially supported by a grant from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  ( BISON, Advanced ERC  grant, no. 694426 ) (to E. G. ). P. M. gratefully acknowledges the  Center for Nanoscience and Nanotechnology of Tel Aviv University  for a postdoctoral fellowship, S. S. R. K. C. Y. and B. M. W. acknowledges the support of the  National Science Foundation  under Grant No. CBET-1833218  and the  Extreme Science and Engineering Discovery Environment (XSEDE) Comet computing cluster at the  University of California, San Diego , through allocation TG-ENG160024. We also thank  S. Rencus-Lazar  for linguistic editing and the members of the  Gazit laboratory  for helpful discussions.
Introduction Pediatric systemic lupus erythematosus (pSLE) is more frequent in the Afro-Caribbean population. It has been less widely studied in this population in France, due to ethical issues in recruiting patients based on ethnic origin, and in North America because these populations have less access to care  [1]. The French national epidemiological databases compiling national health data (namely the SNIIRAM and PMSI) have estimated the incidence and prevalence of systemic lupus in France  [2]. In 2010, it was 41/100,000, and there was an excess prevalence of Lupus in the French overseas departments of America (FOAD) (94/100 k in Guadeloupe and 127/100 k in Martinique  [2] ). The FOAD (Martinique, Guadeloupe, French Guyana, Saint-Martin, Saint-Barthelemy) have a combined population of approximately 330,000 children.
Subsequently, the lists of patients were analyzed according to the relevance of the diagnoses and inclusion criteria to eliminate duplicates. Data collection was completed by consulting follow-up and hospitalization reports available in the patients' medical files. The duration of the follow-up was determined as the time from the date of diagnosis to the date of last contact. Patients were categorized into groups according to their department of origin, and not according to the place where they were treated. The variables collected are shown in Table  1  supplementary. The diagnostic criteria for pSLE were defined according to the SLICC and the EULAR/ACR2019 classification  [9, 10]. We also calculated damage index based on the SLICC/ACR Damage Index (SDI)  [11]. Anti-nuclear antibodies (ANA) were considered positive if greater than or equal to 1/320, for both girls and boys.
Discussion This retrospective study from the FOAD between 2000 and 2021 describes a cohort of 54 patients suffering from pSLE. This is one of the largest cohorts of pediatric lupus patients of Afro-Caribbean origin. One of the strengths of this work is the multicenter inclusions, with the participation of all the reference pediatricians in the FOAD. Our methodology allowed us to analyze patient therapies by consulting the registries of referring physicians and examining computerized hospital archives in each center using broad inclusion criteria. This explains why, from the original, wide selection of 2148 patients identified, only 54 patients with a diagnosis of pSLE were finally included. This methodology exhaustive identification of patients, with cross-checking of multiple data sources to minimize the loss of patients and data.
The industrial world has begun to enter the 5. 0 era which marked by increasingly advanced technological developments. One of the them is the development of digital wallets that make people to pay online or offline easier. People do not need to carry cash, they can buy whatever they want only through online applications, one of which is ShopeePay. This research is a type of quantitative research with a descriptive research design using SPSS 25 and path analysis techniques. ShopeePay users who use more than three times of use amounted to 200 respondents by using the Lemeshow formula aid. The results of this study indicate that (1) e-service quality have a positive and significant direct effect on e-loyalty, (2) e-trust have a positive and significant direct effect on e-loyalty, (3) e-satisfaction has a positive and significant direct effect on e-loyalty, (4) e-service quality has a positive and significant direct effect on e-satisfaction, (5) e-Trust has a positive and significant direct effect on e-satisfaction, (6) e-service quality have a positive and significant indirect effect on e-loyalty through e-satisfaction, dan.